Adjustment costs in the technical efficiency: An application to global banking

This paper proposes a new framework of measuring technical efficiency that takes into account adjustment costs in variable inputs associated with changes in efficiency. We look closely at the implicit assumption in any model of technical efficiency that inputs could freely adjust. Yet, the technical efficiency is determined from the allocation of inputs by the firm to production on the one hand and to efficiency on the other. We show that technical efficiency depends on adjustment costs in variable inputs. Estimating the proposed model has certain complexities that we overcome by employing a non-parametric Local Linear Maximum Likelihood (LLML). In the empirical section, we employ a comprehensive global banking sample and estimate bank alternative profit efficiency across a plethora of countries with strong variability in the underlying adjustment costs. Moreover, given the observed heterogeneity across countries evidence shows that adjustment costs due to personnel expenses are the highest among advanced countries. Emerging economies show strong potential in terms of efficiency post-financial crisis, mainly due to lower labor adjustment costs. Alas, our findings show some persistence in adjustment costs post the financial crisis.

[1]  J. Barth,et al.  Bank Regulation and Supervision: What Works Best? , 2001 .

[2]  Loretta J. Mester,et al.  Inside the Black Box: What Explains Differences in the Efficiencies of Financial Institutions? , 1997 .

[3]  Fotios Pasiouras,et al.  The Impact of Non-Traditional Activities on the Estimation of Bank Efficiency: International Evidence , 2008 .

[4]  Loretta J. Mester,et al.  Erratum to "Explaining the dramatic changes in performance of US banks: Technological change, deregulation, and dynamic changes in competition" [J. Fin. Intermed. 12 (2003) 57-95] , 2005 .

[5]  Nickolaos G. Tzeremes Efficiency dynamics in Indian banking: A conditional directional distance approach , 2015, Eur. J. Oper. Res..

[6]  Saul I. Gass,et al.  Managing the modeling process: a personal reflection , 1987 .

[7]  Fotios Pasiouras,et al.  Financial Supervision Regimes and Bank Efficiency: International Evidence , 2013 .

[8]  M. C. Jensen,et al.  Harvard Business School; SSRN; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI); Harvard University - Accounting & Control Unit , 1976 .

[9]  Efthymios G. Tsionas,et al.  Inference in dynamic stochastic frontier models , 2006 .

[10]  Mike G. Tsionas,et al.  Zero-inefficiency stochastic frontier models with varying mixing proportion: A semiparametric approach , 2016, Eur. J. Oper. Res..

[11]  P. Backé,et al.  Credit booms, monetary integration and the new neoclassical synthesis , 2008 .

[12]  S. Kumbhakar,et al.  Does labour regulation affect technical and allocative efficiency? Evidence from the banking industry , 2015 .

[13]  Sophocles N. Brissimis,et al.  Bank-Specific, Industry-Specific and Macroeconomic Determinants of Bank Profitability , 2008, SSRN Electronic Journal.

[14]  Léopold Simar,et al.  Nonparametric stochastic frontiers: A local maximum likelihood approach , 2007 .

[15]  Subal C. Kumbhakar,et al.  Estimation of production risk and risk preference function: a nonparametric approach , 2010, Ann. Oper. Res..

[16]  Loretta J. Mester,et al.  Explaining The Dramatic Changes In Performance Of U.S. Banks: Technological Change, Deregulation, And Dynamic Changes In Competition , 2003 .

[17]  A. Sentance,et al.  The global credit boom: Challenges for macroeconomics and policy , 2009 .

[18]  Emmanuel Mamatzakis,et al.  Efficiency under quantile regression: What is the relationship with risk in the EU banking industry? , 2011 .

[19]  Michael P. Wiper,et al.  Dynamic effects in inefficiency: Evidence from the Colombian banking sector , 2013, Eur. J. Oper. Res..

[20]  Mike G. Tsionas,et al.  Parameters measuring bank risk and their estimation , 2016, Eur. J. Oper. Res..

[21]  Christopher F. Parmeter,et al.  Imposing Economic Constraints in Nonparametric Regression: Survey, Implementation and Extension , 2009, SSRN Electronic Journal.

[22]  O. Linton,et al.  Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically , 1994 .

[23]  C. Lovell,et al.  Stochastic Frontier Analysis: Frontmatter , 2000 .

[24]  O. Linton,et al.  Local nonlinear least squares: Using parametric information in nonparametric regression , 2000 .

[25]  E. Mamatzakis,et al.  Performance and Merton-type default risk of listed banks in the EU: A panel VAR approach , 2009 .

[26]  Mike G. Tsionas,et al.  Notes on technical efficiency estimation with multiple inputs and outputs , 2016, Eur. J. Oper. Res..