## _wp15100

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---

### I. Introduction — purpose and theoretical framing
- Objective: Assess whether compliance with international regulatory standards and protocols (focus: adoption of international capital standards and the Basel Core Principles for Effective Bank Supervision (BCP)) affects bank performance, with emphasis on operating (input) efficiency.
- Theoretical perspectives:
  - Public interest view: regulation promotes better functioning and more efficient banks (Feldstein 1972).
  - Private interest view: regulation may reflect rent-seeking and distort efficiency (Stigler 1971; Kane 1977).
- Empirical motivation: Conflicting evidence in prior studies (e.g., Barth and others 2008, 2013; Demirgüç-Kunt and Detragiache 2011).

### II. Data, sample, and scope
- Data sources: IMF and World Bank FSAP BCP assessments (1999–2010); Barth and others (2004, 2006, 2008) regulatory surveys; World Bank WDI; Bankscope (Bureau van Dijk and Fitch).
- Sample composition:
  - Cross-sectional final sample: 863 publicly listed banks across 63 countries over the period 2001–10.
  - U.S. banks: approximately 35 percent (304 banks) of the sample.
  - Country groupings: Major Advanced (G7), Advanced, Emerging and Developing, Transitional; 10 geographic regions (EEU, LAM, MEA, FSU, NAM, PAS, PAO, SAS, AFR, WEU).
- Data handling:
  - Matching of regulatory survey waves: 1999→2000–01; 2002→2002–04; 2005/2006→2005–10.
  - Exclusions: banks missing key accounting variables; drop top and bottom 1 percent by total assets (10 observations dropped); outliers removed via data cloud methods.

### III. Methodology — efficiency measurement and econometric approach
- Efficiency estimation:
  - Nonparametric input-oriented Data Envelopment Analysis (DEA) under Variable Returns to Scale (VRS) with three inputs and three outputs.
  - Inputs: (i) customer deposits and short-term funding; (ii) total costs (interest + noninterest expenses); (iii) equity capital (quasi-fixed input).
  - Outputs: (i) loans; (ii) other earning assets; (iii) noninterest income.
- Two-stage double bootstrapping (Simar and Wilson 2007):
  - Stage 1: raw DEA.
  - Stage 2: truncated regression of DEA scores on environmental variables with bootstrap to bias-correct efficiency estimates.
  - Stage 3: truncated regression on bias-corrected scores with parametric bootstrap for final inference.
  - Bootstrap details: Algorithm 2 of Simar and Wilson (2007) with 30,000 replications for bootstrapped confidence intervals.
- Baseline truncated regression specification:
  - Dependent variable: EFFbc,i,j (bias-corrected input technical efficiency).
  - Key regressors: BCPIndexj (overall compliance index) and BCPch1–BCPch7 (disaggregated chapters).
  - Controls: bank-specific Bi,j (logta, lta, eqta, roe); country regulatory Rj (RESTR, COMP, CAPRQ, PRMON, DEPSEC, CORPGOV); macro/financial MFj (gdpg, inf, PrCrGDP, conc); regional/development dummies.

### IV. Measuring BCP compliance and regulatory variables
- BCP scoring:
  - Source: IMF and World Bank FSAP assessments of BCP (1999–2010).
  - Each of 25 principles scored on four-point scale: noncompliant=0; materially noncompliant=1; largely compliant=3; compliant=4.
  - Seven chapter indices: BCPch1–BCPch7 (normalized to [0, 1]); overall BCPscore = mean of seven chapter indices (normalized to [0, 1]).
- Chapter definitions:
  - BCPch1: Preconditions for effective banking supervision (6 subprinciples).
  - BCPch2: Licensing and structure (principles 2–5).
  - BCPch3: Prudential regulations and requirements (principles 6–15).
  - BCPch4: Methods of ongoing supervision (principles 16–20).
  - BCPch5: Information requirements (principle 21).
  - BCPch6: Formal powers of supervisors (principle 22).
  - BCPch7: Cross-border banking (principles 23–25).
- Regulatory condition indices (normalized to [0, 1]): RESTR, COMP, CAPRQ, PRMON, DEPSEC, CORPGOV.

### V. Key descriptive statistics and efficiency estimates
- BCP and regulatory descriptive statistics:
  - Full-sample mean of BCPscore: 0.24; median 0.18; SD 0.22.
  - Emerging and Developing median BCPscore: 0.45.
  - Regulatory medians for Emerging and Developing: RESTR = 0.67; COMP = 0.67; DEPSEC = 0.
- DEA efficiency estimates (bias-corrected EFFbc):
  - Raw (uncorrected) overall mean: 0.537 (All, n=863).
  - Bias-corrected mean EFFbc (All, n=863): 0.419; SD = 0.134; CV = 0.320.
  - Bias-corrected mean EFFbc (Non-U.S. sample, n=559): 0.584; SD = 0.109; CV = 0.187.
  - Bias-corrected mean EFFbc (Emerging and Developing sample, All (n=235)*): 0.803; SD = 0.096; CV = 0.120.
  - Comparative average bias: full sample = 0.12; non-U.S. = 0.07; emerging market & developing = 0.03.
- Interpretation:
  - Mean EFFbc = 0.419 implies typical bank could reduce inputs to achieve frontier performance (interpretation in source: "typical bank could improve input efficiency by 58 percent").

### VI. Empirical findings — truncated regressions and robustness
- Main aggregate finding:
  - No robust statistical evidence that overall BCP compliance (BCPscore) or compliance with any individual BCP chapter has a positive association with bias-corrected bank input efficiency in the full sample, after controlling for bank-specific characteristics, macroeconomic environment, institutional quality, and regulatory framework.
- Heterogeneity — Emerging and Developing subsample:
  - Aggregate BCPscore: no robust positive association with efficiency.
  - Disaggregated chapter effects:
    - BCP Chapter 4 (Methods of Ongoing Supervision): negatively and significantly associated with bias-corrected efficiency for banks in emerging countries.
    - BCP Chapter 7 (Cross-Border / application of international global standards): positively associated with bank input efficiency in emerging markets.
  - Interpretation: ongoing supervisory scrutiny may raise compliance costs and lower input efficiency in emerging markets, while application of international consolidated supervision standards may improve input efficiency.
- Bank-specific characteristics:
  - Larger banks (logta) and higher loan-to-asset ratios (lta) are typically more efficient across subsamples (statistically significant).
  - Mixed evidence on eqta and roe across models.
- Macro/financial and regulatory controls:
  - Positive associations: gdpg and PrCrGDP often positively associated with bank efficiency in full sample models.
  - Negative associations: conc often negatively associated with efficiency in some specifications.
  - Regulatory variables: greater private monitoring (PRMON) associated with higher efficiency; activity restrictions (COMP/RESTR) and depositor protection (DEPSEC) can be negatively associated with efficiency in some specifications.
- Robustness checks:
  - Fractional logit (Papke and Wooldridge 1996) on raw DEA estimates corroborates key findings: no evidence of beneficial relationship between BCP compliance and bank efficiency.

### VII. Policy-relevant implications and interpretive points
- Primary implication: simply achieving higher compliance scores with the Basel Core Principles does not, on average, translate into higher bank operating (input) efficiency across a broad cross-section of publicly listed banks.
- Regulatory design nuance:
  - Checklist-style compliance and increased ongoing supervisory procedures may impose material compliance costs that can harm banks’ input efficiency, particularly in emerging markets (BCPch4 effect).
  - Adherence to international consolidated supervision standards (BCPch7) can be associated with improved input efficiency in emerging markets.
- Considerations for policymakers:
  - Effects of regulatory compliance are context-dependent; country institutional settings and development level matter.
  - Policymakers should weigh compliance costs (investment in systems, risk management, training) against potential stability/monitoring benefits and consider calibration to local institutional capacity.
  - Improved assessment consistency across jurisdictions may be necessary to accurately evaluate regulatory effectiveness.
- Limitation noted: BCP compliance measured at a single point in time does not capture dynamic evolution of compliance; few countries have multiple assessments within the sample period, limiting longitudinal inference.

### VIII. Key numeric facts and statistics (preserved exactly)
- Sample size: 863 banks across 63 countries over the period 2001–10.
- U.S. banks in sample: approximately 35 percent (304 banks).
- DEA raw mean: 0.537 (All, n=863).
- Bias-corrected mean EFFbc (All, n=863): 0.419; SD = 0.134; CV = 0.320.
- Bias-corrected mean EFFbc (Non-U.S. sample, n=559): 0.584; SD = 0.109; CV = 0.187.
- Bias-corrected mean EFFbc (Emerging and Developing sample, All (n=235)*): 0.803; SD = 0.096; CV = 0.120.
- Comparative average bias: full sample = 0.12; non-U.S. = 0.07; emerging market & developing = 0.03.
- Full-sample mean of overall BCP compliance index (BCPscore): 0.24; median 0.18; SD 0.22.
- Emerging and Developing median BCPscore: 0.45.
- Notable regulatory medians for Emerging and Developing: RESTR = 0.67; COMP = 0.67; DEPSEC = 0.
- Bootstrapping details: Simar and Wilson (2007) Algorithm 2 with 30,000 replications for bootstrapped confidence intervals.

*Content derived from the IMF/World Bank FSAP–based study as presented in the provided PDF chapter (1999–2010 BCP assessments; cross-sectional analysis of 863 publicly listed banks).*

### 1. Descriptive Statistics ..............................................................................................

### 1. Descriptive Statistics

### Major sections and page references
- 1. Descriptive Statistics ................................................................................................24
- 2. Bank Efficiency Estimates .......................................................................................25
- 3a. Truncated regressions.............................................................................................26
- 3b. Truncated regressions ............................................................................................27
- 4a. Fractional logit regressions ....................................................................................28
- 4b. Truncated regressions ............................................................................................29
- VI. Appendix............................................................................................................................30

### Appendix subsections and page references
- A.1. Full sample ...........................................................................................................30
- A.2. Summary statistics for original DEA estimates ...................................................31
- A.3. Variable definitions ..............................................................................................32

*Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp15100.pdf*

### References .............................................................................................................

### _wp15100 - References .............................................................................................................

### I. Introduction — Purpose and theoretical framing
- Objective: Assess whether compliance with international regulatory standards and protocols (focus: adoption of international capital standards and the Basel Core Principles for Effective Bank Supervision (BCP)) affects bank performance, with emphasis on operating (input) efficiency.
- Theoretical perspectives:
  - Public interest view: regulation promotes better functioning and more efficient banks (Feldstein 1972).
  - Private interest view: regulation may reflect rent-seeking and distort efficiency (Stigler 1971; Kane 1977).
- Empirical motivation: Conflicting evidence on whether regulation and supervision improve bank performance; prior studies (e.g., Barth and others 2008, 2013; Demirgüç-Kunt and Detragiache 2011) provide mixed results.
- Channel(s) by which regulatory compliance may affect banks: (i) lending decisions; (ii) asset allocation decisions; (iii) funding decisions; compliance is costly and costs are ultimately borne by bank customers.

### II. Data, sample, and scope
- Data sources: (a) IMF and World Bank FSAP BCP assessments (1999–2010); (b) Barth and others (2004, 2006, 2008) regulatory surveys; (c) World Bank WDI; (d) Bankscope (Bureau van Dijk and Fitch).
- Sample composition:
  - Cross-sectional final sample: 863 publicly listed banks across 63 countries over the period 2001–10.
  - U.S. banks: approximately 35 percent (304 banks) of the sample.
  - Sample unbalanced; countries categorized by development: (i) Major Advanced (G7), (ii) Advanced, (iii) Emerging and Developing, (iv) Transitional; and into 10 geographic regions (EEU, LAM, MEA, FSU, NAM, PAS, PAO, SAS, AFR, WEU).
- Matching of regulatory survey waves:
  - 1999 survey used for 2000–01; 2002 survey used for 2002–04; 2005/2006 survey used for 2005–10.
- Sample exclusions and data cleaning:
  - Exclude banks missing key accounting variables; drop top and bottom 1 percent by bank size (total assets) — this resulted in 10 observations being dropped.
  - Data cloud methods used to identify and remove outliers in input/output mix.

### III. Methodology — Efficiency measurement and econometric approach
- Efficiency estimation:
  - Nonparametric input-oriented Data Envelopment Analysis (DEA) under Variable Returns to Scale (VRS) with three inputs and three outputs.
  - Inputs: (i) customer deposits and short-term funding; (ii) total costs (interest + noninterest expenses); (iii) equity capital (quasi-fixed input).
  - Outputs: (i) loans; (ii) other earning assets; (iii) noninterest income (proxy for off-balance-sheet activities).
- Heterogeneity and inference:
  - Two-stage double bootstrapping procedure (Simar and Wilson 2007): Stage 1 raw DEA; Stage 2 truncated regression of DEA scores on environmental variables with bootstrap to bias-correct efficiency estimates and obtain valid inference; Stage 3 truncated regression on bias-corrected scores with parametric bootstrap for final inference.
  - Rationale: account explicitly for cross-country heterogeneity and correct downward bias in raw DEA estimates.
- Baseline empirical specification (truncated regression):
  - Dependent variable: EFFbc,i,j (bias-corrected input technical efficiency).
  - Key regressor: BCPIndexj (overall compliance index) and disaggregated BCP chapter indices (BCPch1–BCPch7).
  - Controls: bank-specific B i,j (logta, lta, eqta, roe), country regulatory Rj (RESTR, COMP, CAPRQ, PRMON, DEPSEC, CORPGOV), macro/financial MFj (gdpg, inf, PrCrGDP, conc), and regional/development dummies.
  - Bootstrap details: Algorithm 2 of Simar and Wilson (2007) with 30,000 replications for bootstrapped confidence intervals.

### IV. Measuring BCP compliance and regulatory variables
- BCP compliance measurement:
  - Source: IMF and World Bank FSAP assessments of BCP (1999–2010).
  - Each of 25 principles scored on four-point scale: noncompliant=0; materially noncompliant=1; largely compliant=3; compliant=4.
  - Seven chapter indices: BCPch1–BCPch7 (normalized to [0, 1]); overall BCPscore = mean of seven chapter indices (normalized to [0, 1]).
  - Chapter definitions:
    - BCPch1: Preconditions for effective banking supervision (6 subprinciples).
    - BCPch2: Licensing and structure (principles 2–5).
    - BCPch3: Prudential regulations and requirements (principles 6–15).
    - BCPch4: Methods of ongoing supervision (principles 16–20).
    - BCPch5: Information requirements (principle 21).
    - BCPch6: Formal powers of supervisors (principle 22).
    - BCPch7: Cross-border banking (principles 23–25).
- Regulatory condition indices (normalized to [0, 1]): RESTR (activity restrictions), COMP (competition restrictions), CAPRQ (capital stringency), PRMON (private sector monitoring), DEPSEC (depositor security / deposit insurance), CORPGOV (external corporate governance).

### V. Key descriptive statistics and efficiency estimates
- BCP and regulatory descriptive statistics (full sample, Panel 1 summary):
  - Full-sample mean of BCPscore: 0.24 (mean); median 0.18; SD 0.22.
  - Notable medians by economic development (Panel 2):
    - Emerging and Developing median BCPscore: 0.45.
    - Major Advanced median BCPscore: 0.15.
  - Regulatory medians for Emerging and Developing: RESTR = 0.67; COMP = 0.67; DEPSEC = 0.
- DEA efficiency estimates:
  - Raw (uncorrected) overall mean: 0.537 (All, n=863).
  - Bias-corrected input technical efficiency (EFFbc) — key means:
    - Overall mean EFFbc (All, n=863): 0.419; SD = 0.134; CV = 0.320.
    - Non-U.S. sample (All, n=559): EFFbc = 0.584; SD = 0.109; CV = 0.187.
    - Emerging and Developing sample (All, n=235*): EFFbc = 0.803; SD = 0.096; CV = 0.120.
  - Comparative bias: average bias = Raw mean (0.537) − Bias-corrected mean (0.419) = 0.12 for full sample; comparative average bias for Non-U.S. = 0.07; for Emerging & Developing = 0.03.
  - Interpretation: typical bank could improve input efficiency by 58 percent (since mean EFFbc = 0.419 implies only 42 percent of inputs required if on frontier).
  - Cross-sample patterns:
    - Major advanced countries: higher/stabler efficiency (e.g., non-U.S. Major Advanced mean = 0.642, CV = 0.142).
    - Emerging market and developing countries: higher volatility in efficiency (e.g., CV up to 0.208 in non-U.S. sample).

### VI. Empirical findings — Truncated regressions and robustness
- Main finding on BCP compliance:
  - Overall result: No robust statistical evidence that overall BCP compliance (BCPscore) or compliance with any individual BCP chapter has a positive association with bias-corrected bank input efficiency in the full sample; this result holds after controlling for bank-specific characteristics, macroeconomic environment, institutional quality, and regulatory framework.
  - Interpretation: compliance per se has little effect on bank operating efficiency; conditional on being a “good bank” (complying with international standards), regulation has no impact on performance.
  - Caveat: inability of assessors to provide consistent cross-country evaluation may affect results.
- Heterogeneity — Emerging and Developing subsample:
  - Aggregate BCPscore: still no robust positive association with efficiency.
  - Disaggregated findings:
    - BCP Chapter 4 (Methods of Ongoing Supervision): negatively and significantly associated with bias-corrected efficiency for banks in emerging countries (suggesting higher ongoing supervisory scrutiny increases compliance costs and may lower input efficiency).
    - BCP Chapter 7 (Cross-Border / application of international global standards): positively associated with bank input efficiency in emerging markets (suggesting adherence to international best-practice standards may improve performance in these economies).
  - Interpretation: in emerging markets, extent of ongoing supervision may reduce input efficiency, while supervisors’ application of international consolidated supervision standards may improve input efficiency.
- Effects of bank-specific characteristics:
  - Larger banks (logta) and banks with higher loan-to-asset ratios (lta) are typically more efficient across subsamples (statistically significant).
  - Mixed evidence on eqta and roe across models.
- Effects of macro/financial and regulatory controls:
  - Positive associations: GDP growth (gdpg) and private credit to GDP (PrCrGDP) often positively associated with bank efficiency in full sample models.
  - Negative associations: banking sector concentration (conc) often negatively associated with efficiency in some specifications.
  - Regulatory variables: evidence that greater private monitoring (PRMON) is associated with higher efficiency; greater activity restrictions (COMP/RESTR) and greater depositor protection (DEPSEC) can be negatively associated with efficiency in some specifications.
- Robustness checks:
  - Fractional logit (Papke and Wooldridge 1996) on raw DEA estimates: corroborates key findings; no evidence of beneficial relationship between BCP compliance and bank efficiency.

### VII. Policy-relevant implications and interpretive points
- Primary implication: simply achieving higher compliance scores with the Basel Core Principles does not, on average, translate into higher bank operating (input) efficiency across a broad cross-section of publicly listed banks.
- Regulatory design nuance:
  - Overreliance on checklist-style compliance and increased ongoing supervisory procedures may impose material compliance costs that can harm banks’ input efficiency, particularly in emerging markets (BCPch4 effect).
  - Adherence to international consolidated supervision standards and application of global best practices (BCPch7) can be associated with improved input efficiency in emerging markets.
- Considerations for policymakers:
  - The effect of regulatory compliance appears context-dependent; country institutional settings and development level matter.
  - Policymakers should weigh compliance costs (investment in systems, risk management, training) against potential stability/monitoring benefits and consider calibration to local institutional capacity.
  - Improved assessment consistency across jurisdictions may be necessary to accurately evaluate regulatory effectiveness.
- Limitation noted by authors: BCP compliance measured at a single point in time does not capture dynamic evolution of compliance; only a small number of countries have multiple assessments within the sample period, limiting longitudinal inference.

### VIII. Key numeric facts and statistics (preserved exactly as in source)
- Sample size: 863 banks across 63 countries over the period 2001–10.
- U.S. banks in sample: approximately 35 percent (304 banks).
- DEA raw mean: 0.537 (All, n=863).
- Bias-corrected mean EFFbc (All, n=863): 0.419; SD = 0.134; CV = 0.320.
- Bias-corrected mean EFFbc (Non-U.S. sample, n=559): 0.584; SD = 0.109; CV = 0.187.
- Bias-corrected mean EFFbc (Emerging and Developing sample, All (n=235)*): 0.803; SD = 0.096; CV = 0.120.
- Comparative average bias: full sample = 0.12; non-U.S. = 0.07; emerging market & developing = 0.03.
- Full-sample mean of overall BCP compliance index (BCPscore): 0.24; median 0.18; SD 0.22.
- Emerging and Developing median BCPscore: 0.45.
- Notable regulatory medians for Emerging and Developing: RESTR = 0.67; COMP = 0.67; DEPSEC = 0.
- Bootstrapping details: Simar and Wilson (2007) Algorithm 2 with 30,000 replications for bootstrapped confidence intervals.

*Italic source attribution:* _Content derived from the IMF/World Bank FSAP–based study as presented in the provided PDF chapter (1999–2010 BCP assessments; cross-sectional analysis of 863 publicly listed banks)._

### REFERENCES

### _wp15100 - REFERENCES

### Bank regulation, supervision, and governance
- Barth, James R, Gerard Caprio, and Ross Levine. 2008. “Bank Regulations Are Changing: For Better or Worse?” Comparative Economic Studies 50(4): 537–63.
- Barth, James R. et al. 2013. “Do Bank Regulation, Supervision and Monitoring Enhance or Impede Bank Efficiency?” Journal of Banking and Finance 37(8l): 2879–92.
- Barth, James R., Gerard Caprio, and Ross Levine. 2004. “Bank Regulation and Supervision: What Works Best?” Journal of Financial Intermediation 13(2): 205–48.
- Barth, James R., Gerard Caprio, and Ross Levine. 2006. Rethinking Bank Regulation - Cambridge University Press. Second edi. eds. Govern, Till, and Angels. New York: Cambridge University Press.
- Barth, James R., Daniel E. Nolle, Triphon Phumiwasana, and Glenn Yago. 2003. “A Cross-Country Analysis of the Bank Supervisory Framework and Bank Performance.” Financial Markets, Institutions & Instruments 12: 205–48.
- Demirgüç-Kunt, Asli, and Enrica Detragiache. 2011. “Basel Core Principles and Bank Soundness: Does Compliance Matter?” Journal of Financial Stability 7(4): 179–90.
- Demirgüç-Kunt, Asli, Enrica Detragiache, and Thierry Tressel. 2008. “Banking on the Principles: Compliance with Basel Core Principles and Bank Soundness.” Journal of Financial Intermediation 17(4): 511–42.
- Laeven, Luc, and Ross Levine. 2008. Bank Governance, Regulation, and Risk Taking.
- Podpiera, R. 2004. “Does Compliance with Basel Core Principles Bring Measurable Benefits?” IMF working paper 04/024 53(2): 306–26.
- Sundararajan, V., D. Marston, and R. Basu. 2001. The Case of Basel Core Principles. Washington.
- Haldane, Andrew. 2013. Constraining Discretion in Bank Regulation. London.

### Basel Committee on Banking Supervision — Core Principles and methodology
- Basel Committee on Banking Supervision. 1997. Core Principles for Effective Banking Supervision.
- Basel Committee on Banking Supervision. 1999. Core Principles Methodology.
- Basel Committee on Banking Supervision. 2006. Core Principles Methodology.
- Basel Committee on Banking Supervision. 2011a. Consultative Document on Core Principles of Effective Banking Supervision.
- Basel Committee on Banking Supervision. 2011b. Core Principles for Effective Banking Supervision.
- Basel Committee on Banking Supervision. 2012. Core Principles for Effective Banking Supervision.

### Banking efficiency, productivity, and market structure
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- Berger, Allen N. 2007. “International Comparisons of Banking Efficiency.” Financial Markets, Institutions & Instruments 16(3): 119–44.
- Berger, Allen N., and Timothy H. Hannan. 1998. “The Efficiency Cost Of Market Power In The Banking Industry: A Test Of The ‘Quiet Life’ And Related Hypotheses.” The Review of Economics and Statistics 80(3): 454–65.
- Bos, J.W.B., and H. Schmeidel. 2007. “Is There a Single Frontier in a Single European Banking Market ?” Journal of Banking & Finance 31: 2081–2012.
- Brissimis, Sophocles N., Manthos D. Delis, and Nikolaos I. Papanikolaou. 2008. “Exploring the Nexus between Banking Sector Reform and Performance: Evidence from Newly Acceded EU Countries.” Journal of Banking & Finance 32(12): 2674–83.
- Casu, Barbara, Alessandra Ferrari, and Tianshu Zhao. 2013. “Regulatory Reform and Productivity Change in Indian Banking.” The Review of Economics and Statistics 95(3): 1066–77.
- Casu, Barbara, and Philip Molyneux. 2003. “A Comparative Study of Efficiency in European Banking.” Applied Economics 35(17): 1865–76.
- Delis, Manthos D., and Nikolaos I. Papanikolaou. 2009. “Determinants of Bank Efficiency: Evidence from a Semi-Parametric Methodology.” Managerial Finance 35(3): 260–75.
- Hughes, Joseph P., and Loretta J. Mester. 2010. “Efficiency in Banking: Theory, Practice and Evidence.” In The Oxford Handbook of Banking, eds. Alan Berger, Phil Molyneux, and John Wilson. Oxford Handbooks.
- Hughes, Joseph P., and Loretta J. Mester. 2013. FRB of Philadelphia Working Paper No. 13-13 Who Said Large Banks Don’t Experience Scale Economies? Evidence from a Risk-Return-Driven Cost Function.
- Hughes, Joseph P., and Loretta J. Mester. 2014. “Measuring the Performance of Banks, Theory, Practice, Evidence, and Some Policy Implications.” In Handbook of Banking, eds. John O.S. Wilson, Philip Molyneux, and Allen N. Berger. Oxford University Press, 247–71.
- Kasman, Adnan, and Canan Yildirim. 2006. “Cost and Profit Efficiencies in Transition Banking: The Case of New EU Members.” Applied Economics 38(9): 1079–90.
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- Pasiouras, Fotios, Sailesh Tanna, and Constantin Zopounidis. 2009. “The Impact of Banking Regulations on Banks’ Cost and Profit Efficiency: Cross-Country Evidence.” International Review of Financial Analysis 18(5): 294–302.
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### Methodologies, econometrics, and efficiency measurement techniques
- Bogeoft, P., and L. Otto. 2011. Benchmarking with DEA, SFA, and R. New York: Springer.
- Coelli, T.J., D.S.P Rao, C.J. O’Donnell, and G.E. Battese. 2005. An Introduction to Efficiency and Productivity Analysis. 2nd ed. New: Springer.
- McDonald, John. 2009. “Using Least Squares and Tobit in Second Stage DEA Efficiency Analyses.” European Journal of Operational Research 197(2): 792–98.
- Papke, Leslie E, and Jeffrey M Wooldridge. 1996. “Econometric Methods for Fractional Response Variables with an Application to 401 ( K ) Plan Participation Rates.” Journal of Applied Econometrics 11(February): 619–32.
- Simar, Leopold, and Paul W. Wilson. 2007. “Estimation and Inference in Two-Stage, Semi-Parametric Models of Production Processes.” Journal of Econometrics 136(1): 31–64.
- Wilson, Paul W. 1995. “Detecting Influential Observations in Data Envelopment Analysis.” Journal of Productivity Analysis 6(1): 27–45.

### Financial policy, system stress, and sector assessments
- Das, U.S., P. Iossifov, R Podpiera, and D. Rozkhov. 2005. “Quality of Financial Policies and Financial System Stress.” IMF working paper 05/173.
- Demirgüç-Kunt, Asli, Luc Laeven, and Ross Levine. 2004. “Regulations, Market Structure, Institutions, and the Cost of Financial Intermediation.” Journal of Money, Credit and Banking 36: 593–622.
- Feldstein, Martin S. 1972. “Equity and Efficiency in Public Sector Pricing: The Optimal Two-Part Tariff.” The Quarterly Journal of Economics 86: pp. 175–187.
- Glass, J. Colin, Donal G. McKillop, and Syamarlah Rasaratnam. 2010. “Irish Credit Unions: Investigating Performance Determinants and the Opportunity Cost of Regulatory Compliance.” Journal of Banking & Finance 34(1): 67–76.
- Kane, Edward J. 1977. “Good Intentions and Unintended Evil: The Case Against Selective Credit Allocation.” Journal of Money, Credit and Banking 9: 55–69.
- IMF. 2014. Review Of The Financial Sector Assessment Program: Further Adaptation To The Post Crisis Era Review Of The Financial Sector Assessment Program — Further Adaptation To The Post-Crisis Era.

*Reference list content from _wp15100 - REFERENCES.*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp15100.pdf_