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Credit unions and risk

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Abstract

The structure of the credit union industry has been transformed by regulatory changes and the subsequent switch by many credit unions to community and multiple-bond fields of membership. This study explores the impact of these trends by testing for differences in risk across credit unions with different field-of-membership types. In tests for differences in risk of bankruptcy and of breaching regulatory standards, risk is found to be greater for credit unions with broader field-of-membership types. These differences in risk appear to derive from greater earnings volatility and lower ROA and net-worth ratios at community and multiple-bond credit unions. These differences in risk decline with greater asset size. Evidence is also presented that credit unions that switched from single-bond institutions to broader field-of-membership types now operate with greater risk.

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Notes

  1. A single common-bond credit union has a field of membership (FOM) that consists of one group which has a common bond of occupation (employment by the same entity or related entities or a trade, industry, or profession) or association (members and employees of a recognized association). A multiple-common-bond credit union has a FOM that “consists of more than one group, each of which has a common bond of occupation or association” while a community credit union has a FOM that consists of persons who “live, work, worship, or attend school in the same well-defined local community, neighborhood, or rural district” (National Credit Union Administration 2003, Appendix A).

  2. Interpretive Ruling and Policy Statement 82-1, Membership in Federal Credit Unions, 47 Fed. Reg. 16775 (April 20, 1982).

  3. See D’amours (1998) and US Government Accountability Office (2006, pp. 13–14).

  4. Effective May 2003: (1) any city, county, or political equivalent in a single political jurisdiction, regardless of population size, automatically meets the definition of a local community (previously, this was subject to a limit of 300,000 residents); (2) metropolitan statistical areas (MSAs) may meet the definition of local community provided the population does not exceed 1 million (previously, MSAs could not define a local community); and (3) contiguous political jurisdictions qualify as a local community if they contain 500,000 or fewer residents (previously, subject to a cap of 200,000) (US General Accounting Office 2003).

  5. Innovations in the financial sector may have diminished the advantages of lending within groups that share a common bond. Walter (2006) argues that four developments that have done so: the emergence of credit-reporting agencies, the availability of home equity lines of credit, credit card lending, and the decreased social pressure to repay loans due to the advent of deposit insurance.

  6. See Frame et al. (2002) for a review of earlier literature. Esho et al. (2005) relate risk to revenue concentration, merger activity, and size to examine the impact of Australian credit unions diversifying into fee-generating activities. Credit unions that increased the revenue share of fees increased risk and reduced returns. However, both risk and returns are reduced at credit unions that increase residential lending revenue share.

  7. Much of the asset growth in the single-bond category is due to a single institution. Excluding Navy Federal Credit Union, assets for the category rose from $25 billion in 1996 to $52 billion in 2011.

  8. Based on author’s calculations using NCUA 5300 Call Report data.

  9. See Berger and Bouwman (2009), Cetorelli and Goldberg (2011), Lepetit et al. (2008), and Laeven and Levine (2009) for other recent studies employing the Z-SCORE measure.

  10. Esho et al. (2005) define this measure for Australian credit unions using an 8 % risk adjusted capital requirement.

  11. A variable representing real estate loans is not included since the sum of it, CCLOANS, and AUTOLOANS would equal unity for many credit unions.

  12. Up to 898 small credit unions were dropped as a result of applying the size filters. These credit unions collectively held less than 0.5 % of the assets of all federally-chartered credit unions.

  13. Given the model: \(\hbox {Z-SCORE} = {\beta } 0 + {\beta } \hbox {1COMMUNITY} + {\beta } \hbox {2MULTIPLE} + {\beta } \hbox {3SIZE} + {\ldots }\), the Z-SCORE for a single-bond and a community credit union will be equal when \(\hbox {e}^{-{\beta } 1/{\beta } 3} = \hbox {Assets}_\mathrm{community CU}/\hbox {Assets}_\mathrm{single bond CU}\) and all other regressors are equal and Ln Assets = SIZE. The Z-SCORE for a single-bond and a multiple-bond credit union will be equal when \(\hbox {e}^{-{\beta } 2/{\beta } 3} = \hbox {Assets}_\mathrm{multiple-bond CU}/\hbox {Assets}_\mathrm{single bond CU}\). The Z-SCORE for a community and a multiple-bond credit union will be equal when \(\hbox {e}^{({\beta } 2- \beta 1)/\beta 3} = \hbox {Assets}_\mathrm{community CU}/\hbox {Assets}_\mathrm{multiple-bond CU}\).

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Acknowledgments

I would like to thank two anonymous referees and Michael Crew, editor, for helpful comments and suggestions

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  1. College of Business Administration, San Diego State University, 5500 Campanile Drive, San Diego, CA , 92182, USA

    David Ely

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Ely, D. Credit unions and risk. J Regul Econ 46, 80–111 (2014). https://doi.org/10.1007/s11149-013-9241-8

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