Michel Keoula, Researcher, HEC Montreal & IRCCF Discussion of the paper DETERMINANTS OF FAILURE OF CREDIT UNIONS AND COMMERCIAL BANKS: SIMILARITIES AND DIFFERENCES Luis Dopico & James WILcox Michel Keoula, Researcher, HEC Montreal & IRCCF 24 August 2017, IWFSAS, Montreal, QC

Research questions and policy implications “First, large scale, long term analysis of failure of credit unions” Determinants that have over time endured and other determinants. May have implications for analysis on efficiency analysis, pricing, portfolio, activities. Will contribute to the debate on the optimal size of credit unions and banks.

Research questions and policy implications Seminal studies by Meyer & Pifer (1970), Altmann (1977), Kharadia &Collins (1981) Risk-based capital requirements for credit unions and risk-based deposit insurance premium Taylor the analysis to the comparison of failure of credit unions and banks Use: Early Warning System, off-site surveillance

Interesting take-aways Stock ownership vs. mutual ownership Agency problems in mutuals: managers may not be maximizing value, non-monetary perks, excessively risk-averse in order to show something in their next position (from the lit review in the paper). Empirical evidence is mix “Link between smaller size and bank failure has broken down during the financial crisis”

Research design: sample Period: 1980-2016 (observations) 1980-1986 1987-1993 1994-2007 2008-2013 2014-2016 Whole period Credit unions 113,852 94,266 145,287 44,585 18,781 416, 771 Banks 100,733 88,197 121,740 40,391 16,940 368,001 Period: 1987-2016 (observations) Tiny Smallish Medium Large All <10 M 10<..<100M 100M<..<1B >1B Credit unions 156,384 112,028 31,417 3,090* 302,919 Banks 2,402 122,752 126,599 16,515 268,268

Research design: the regression model Dependent variable Explanatory variables (15) Comment Failure (0-1 dummy) Constant   Securities Other assets (N.E.C) Loans other than res. Mortg./Consumer loans Residential mortgages Commercial mortgages C&I loans Log real assets Noninterest expenses Provision on loan losses/Delinquent loans Capital ROA Unemployment rate Logit Chow tests Expected probability of failure

Ideas to improve of the paper Logit is a nonlinear model; precision needed for what effects are reported as coefficients («averages» to precise). Full disclosure on what counts as a failure Explanation of the puzzling result that unemployment rate does not significantly affect credit union failure may be appreciated.

Thank you for your attention