The Payouts Choice for Deposit Insurance System

Entropy balancing is introduced to assess the deposit insurance design characteristics in this paper. Applying an extensive duration data including 141 countries from 1960 to 2015, the authors employ the entropy balancing method to simulate the data structure under the implicit deposit insurance system. Then the paper adopts an endogenous treatment effects model and a Heckman two-step selection model to examine payouts choice of the deposit insurance. It is found that entropy balancing can calibrate unit weights and reweight treatment and control groups by a maximum entropy scheme. Thus, a possibly given conditions will be satisfied and information concerning sample moments will be integrated. The results show clearly that different payouts choice and the corresponding coverage setting can effectively reduce the moral hazards that may result from the introduction of a deposit insurance scheme. When the Payouts is Per Depositor Account or Per Depositor, the banks’ moral hazard is higher. However, the payment method of Per Depositor Per Institution can effectively restrain the banks’ risk-taking activities.

[1]  Mark Elliot,et al.  Entropy balancing: a maximum-entropy reweighting scheme to adjust for coverage error , 2016 .

[2]  R. Jagannathan,et al.  Uninsured Idiosyncratic Risk and Aggregate Saving , 1994 .

[3]  Jasjeet S. Sekhon,et al.  Opiates for the Matches: Matching Methods for Causal Inference , 2009 .

[4]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2000 .

[5]  Asli Demirgüç-Kunt,et al.  Deposit Insurance Design and Implementation: Policy Lessons from Research and Practice , 2006 .

[6]  Philip H. Dybvig,et al.  Bank Runs, Deposit Insurance, and Liquidity , 1983, Journal of Political Economy.

[7]  Luc Laeven,et al.  Determinants of deposit-insurance adoption and design , 2008 .

[8]  A. Saunders,et al.  The Determinants of Bank Interest Rate Margins: An International Study , 2000 .

[9]  Clas Wihlborg,et al.  Deposit Insurance Coverage, Ownership, and Banks' Risk-Taking in Emerging Markets , 2010 .

[10]  Jens Hainmueller,et al.  Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies , 2012, Political Analysis.

[11]  James R. Barth,et al.  Bank Regulation and Supervision in 180 Countries from 1999 to 2011 , 2013 .

[12]  Philip E. Strahan,et al.  The Finance-Growth Nexus: Evidence from Bank Branch Deregulation , 1996 .

[13]  Asli Demirgüç-Kunt,et al.  Deposit Insurance Around the World: A Comprehensive Database , 2005 .

[14]  Asli Demirgüç-Kunt,et al.  Deposit Insurance Database , 2014 .

[15]  Luc Laeven,et al.  The Political Economy of Deposit Insurance , 2004 .

[16]  Oz Shy,et al.  Limited Deposit Insurance Coverage and Bank Competition , 2014 .

[17]  Wei Yu,et al.  An introduction to convex optimization for communications and signal processing , 2006, IEEE Journal on Selected Areas in Communications.

[18]  R. C. Merton,et al.  An analytic derivation of the cost of deposit insurance and loan guarantees An application of modern option pricing theory , 1977 .

[19]  Gary King,et al.  Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.

[20]  W. Greene Sample Selection Bias as a Specification Error: Comment , 1981 .

[21]  G. King,et al.  Causal Inference without Balance Checking: Coarsened Exact Matching , 2012, Political Analysis.

[22]  G. Imbens,et al.  Bias-Corrected Matching Estimators for Average Treatment Effects , 2011 .

[23]  Wei Zhang,et al.  Credit rationing and the simulation of bank-small and medium sized firm artificial credit market , 2016, J. Syst. Sci. Complex..

[24]  Jianping Li,et al.  Risk Contagion in Chinese Banking Industry: A Transfer Entropy-Based Analysis , 2013, Entropy.

[25]  J. Heckman Sample selection bias as a specification error , 1979 .

[26]  J. Sekhon,et al.  Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies , 2006, Review of Economics and Statistics.

[27]  Hedley Rees,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1985 .

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

[29]  Partha Deb,et al.  Maximum Simulated Likelihood Estimation of a Negative Binomial Regression Model with Multinomial Endogenous Treatment , 2006 .

[30]  Philip E. Strahan,et al.  What Drives Deregulation? Economics and Politics of the Relaxation of Bank Branching Restrictions , 1997 .

[31]  Marta Curto-Grau,et al.  Voters’ responsiveness to public employment policies , 2014 .

[32]  David A. Belsley A Guide to using the collinearity diagnostics , 1991, Computer Science in Economics and Management.