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Minority owned banks and efficiency revisited

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Abstract

U.S. Government support for minority owned banks (MOBs) dates to the late 1960s. Evidence through the early 1990s suggested these banks are relatively inefficient. This study updates that research, using Stochastic Frontier Analysis (SFA) and panel data from 2003 to 2014 on minority owned banks and other banks. It is, as far as we know, the first such study to exclude outliers in SFA estimation, while recovering the outliers for efficiency estimation. Initial results identify a disruption in cost efficiency during 2008, with statistically distinct regimes for 2003–2007 and 2009–2014. Including recovered observations alters the patterns of MOB efficiency in significant ways, and leads us to conclude that current MOB inefficiency is mainly limited to Asian American owned and Multi-racial and minority serving banks. Tests for the effects of government deposits under a U.S. Treasury program suggest these did not adversely effect efficiency among covered MOBs, but may have improved survival rates for those MOBs subsequent to the financial collapse.

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Notes

  1. We do not address the related possibility that large banks increasingly penetrated markets previously dominated by MOBs, but are pursuing that issue in future research.

  2. The effective date of these rules is often a few months after finalization and, for a very few rules, years later.

  3. It would be simplest to perform this task by including an exclusionary “if” statement in the sfpanel command (discussed below). However, doing so yields different results than excluding the observations entirely prior to running sfpanel estimation. We presume the latter are more accurate, and run the SF analyses presented here after excluding outliers entirely, then rerun the estimation while constraining all coefficients to values initially found, and apply those coefficients to all observations. This approach yields identical cost efficiency estimates to the fifth digit beyond the decimal point for non-outliers.

  4. Heteroskedasticity may be related to bank scale, and normalization by either total earning assets (as in Berger et al. 2009) or equity (as in Berger and Mester 1997) may be used for this purpose. However, during this period, equity often fluctated due to forces outside of the banks direct control, including tightened capital requirements (Department of the Treasury, Federal Reserve Bank and Federal Deposit Insurance Corporation 2011), and injections of capital by the U.S. Treasury under the Troubled Asset Relief Program (Cornett et al. 2013).

  5. Wang and Schmidt (2002) recommend use of a single-step estimator to remove this source of bias. The conditional mean estimator of BC95 allows direct entry of the MOB dummies in such a single-step estimator, but the results are implausible. Instead of yielding absolutely larger, same-signed coefficients, as Wang and Schmidt predict, the coefficients are opposite-signed. More troubling, regressing measured efficiency from that single-step estimator against the MOB dummies absolutely increases the MOB coefficients while leaving the signs unchanged, recommending the two-step approach taken here.

  6. The Kumbhakar and Heshmati (1995) approach uses a standard random effects regression estimator. However, the random effects regression command (xtreg) in Stata does not permit the use of constraints, so outliers cannot be recovered.

  7. The joint effect of BlkBs, HispBs, NatBs, and MultBs is entirely captured by the four AfHispNat and poverty categorical variables for MOBs, requiring the exclusion of one of the four specific groups in the estimation.

  8. Banker et al. (2010a) and Hsiao et al. (2010) include the capital adequacy ratio and the nonperforming loan ratio as controls for the inverse of undercapitalization and bank risk. These variables are not used as controls in the second stage regressions because the Tier 1 capital/asset ratio is positively correlated with the poverty measure described below (r = .113), the nonperforming loan ratio is positively correlated with the proportion of African Americans, Hispanics and Native Americans in the markets, particularly after 2009 (r = .130) and, among the MOBs, the nonperforming loan ratio post-2009 is closely correlated with both the poverty measure (r = .148) and the proportion of African Americans, Hispanics and Native Americans in their markets (r = .235). Since it is unlikely that either capital adequacy or nonperforming loans cause the demographic composition of bank markets, the variables are not included.

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Authors and Affiliations

  1. University of Wisconsin/Whitewater, Whitewater, WI, USA

    Russell Kashian & Richard McGregory

  2. Precision Numerics, 464 Dickinson St., Suite 5, Springfield, MA, 01108, USA

    Robert Drago

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  1. Russell Kashian
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Correspondence to Russell Kashian.

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Kashian, R., McGregory, R. & Drago, R. Minority owned banks and efficiency revisited. J Prod Anal 48, 97–116 (2017). https://doi.org/10.1007/s11123-017-0510-x

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