PPT Banking and Regulations

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Does Regulation Matter for Banks in the New EU Member States? A. Kalyvas (Bournemouth) E. Mamatzakis (Sussex) and J.Piesse (King’s College, London)

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Paper presented in the “European Conference on Banking and the Economy”

Transcript of PPT Banking and Regulations

  • 1. Does Regulation Matter for Banks in the New EU Member States?
    A. Kalyvas (Bournemouth)
    E. Mamatzakis (Sussex) and J.Piesse(Kings College, London)
  • 2. Overview
    Aim: shed new light into the relationship between bank efficiency and regulations in the new EU member states.
    Three different types of regulations: Credit, Labour and Business regulations.
    Measuring bank cost efficiency: DEA
    Panel fixed effects regression
    Panel-VAR analysis: to tackle endogeneity issues and to examine the dynamic interactions between regulations and efficiency
  • 3. Bank Efficiency and Regulations
    Financial regulations ensure a sound financial system.
    No consensus about good regulation, (see e.g. Demirguc-Kunt et al., 2008).
    Limited Literature: Barth et al., 2004; Beck et al., 2006; Berger et al., 2008; Pasiouras et al.,2009; Delis et al., 2011).
    Financial regulation and regulation in labour and other business related legislation.
  • 4. Paper Contributions
    Cost Efficiency for the new EU member states from 1996-2009.
    Determinants : Credit, Labour and Business regulations.
    Different types of credit regulations are examined.
    Bank-specific: financial structure and economic development variables.
    Institutions: the Fraser Index of Economic Freedom.
    Both static (panel fixed effects) and Dynamic (panel VAR) econometric methods.
  • 5. Data Envelopment Analysis (DEA)
    All banks share same technology.
    Each bank benchmarked against the most efficient (frontier) banks.
    Deterministic Nature.
    We opt for variable returns to scale (VRS) due to flexibility.
  • 6. DEA Methodology
  • 7. DEA Methodology (2)
  • 8. Econometric Specification
  • 9. Data Description (DEA scores)
    Unbalanced panel: 350 banks
    All listed banks over the 1996-2009 period in the 10 new EU member states: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia.
    Data source: Bankscope
    Inputs, input prices and outputs are chosen using an intermediation approach and follow Sturm and Williams (2004)
    Inputs: financial capital (deposits and short-term funding) and non-financial inputs (overheads).
    Input prices: interest paid on funds/total funds (for financial capital), operating expenses/assets (for non-financial inputs)
    Outputs: interest income, non-interest revenue
    Common cost-efficiency frontier
  • 10. Data Description (Control Variables)
    Bank Specific: Total Assets (TA) , Loan/Assets (L/A), Equity/Assets (E/A), Loan Loss Provision/ Loans (LLP/L).
    Financial Structure: Domestic Credit to the Private Sector as a share of GDP (DCPS/GDP), the Hirschman-Hernfidahl index (HHI) and the net interest spread (SPR)
    Economic Development: real GDP per capita in PPP (GDPcppp)
    Data Source: Bankscope for the bank-specific variables, the 2010 version of the "New Database on Financial Development and Structure" developed by Beck et al. (2000) for the HHI variable and the World Development Indicators of the World Bank for the GDPcppp, SPR) and the DCPS variables.
  • 11. Data Description (Labour and Business Regulations)-Fraser Index of Economic Freedom
    Labour Regulations (LR): Labour market rigidities.
    Business Regulations (BR): Regulations and bureaucratic procedures restrain entry and reduce competition.
    Values in a 0-10 scale. Higher values denote a more liberal regulatory environment
  • 12. Data Description (Credit Regulations)- Fraser Index of Economic Freedom
    CR-Own: ownership of banks
    CR-Comp: foreign competition
    CR-PrS: crowding-out of private sector credit
    CR-NiR: negative real interest rates
    Values in a 0-10 scale. Higher values denote a more liberal regulatory environment
  • 13. Cross-time Evolution of the cost efficiency in the Banking sector of the new EU member states
  • 14. Annual Cost Efficiency in the Banking Sector (1996-2009)
    Note: The table reports the mean efficiency scores by year over the 1996-2009 period. The cost efficiencies were calculated using linear programming assuming variable returns to scale (VRS) and an annual common frontier
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  • 18. Overview of Fixed-Effects Results
    Some evidence in support of the quite life hypothesis.
    Strong support for the moral hazard hypothesis.
    DCPS/GDP positively correlated with efficiency but not when CR-PrS is included.
    LR and BR dominant over CR
    CR-Comp and CR-PrS most significant CR
  • 19. Panel VAR Analysis-Advantages
    Standard OLS - endogeneity bias.
    All variables in the panel-VAR are entering as endogenous
    Examination of the underlying dynamic relationships
  • 20. Panel VAR Analysis-Methodology
    We specify a first order 4x4 panel-VAR model as follows:
    , i =1,, N, t=1,,T.
    where Xit is a vector of four random variables, that is, the bank specific cost efficiency (Efit) and the three measures of regulation, namely Credit Regulation (CRit), Labour Regulation (LRit) and Business Regulation (BRit). Thus, is an 4x4 matrix of coefficients, iis a vector of m individual effects and ei,tare iid residuals.
    The panel-VAR takes the following form:
  • 21. Using the panel-VAR individual heterogeneity in the levels is ensured by:
    Introducing fixed effects in the model, denoted i, following Love and Zicchino (2006)
    The data are forward mean-differenced using the Helmert procedure (Arellano and Bover, 1995).
    Additionally, standard errors of the impulse response functions are calculated and confidence intervals generated with Monte Carlo simulations.
  • 22. Impulse Response Function (IRF) for Ef, CR, LR and BR
  • 23. Impulse Response Function (IRF) for Ef, CR-Own, CR-Comp, CR-PrS and CR-NiR
  • 24. Variance Decompositions (VDCs) for cost efficiency, CR, LR and BR
    Notes: s defines the periods ahead of VDCs.
  • 25. Variance Decompositions (VDCs) for cost efficiency, CR-Own, CR-Comp, CR-PrS and CR-NiR
    Notes: s defines the periods ahead of VDCs
  • 26. Overview of the Panel-VAR Results
    CR and BR positive impact on efficiency
    LR negative (initially) on efficiency but positive on BR
    Magnitude: CR dominant
    CR-Own and CR-Comp positive impact
    CR-PrS and CR-NiR negative impact
    Magnitude: CR-Own dominant
  • 27. Conclusion and Policy Implications
    IRFs and VDCs confirm strong causality Credit Regulations to bank efficiency
    Most important Credit Regulations: privatization and openness to foreign bank competition.
    Is not only Credit Regulations that matter.
    The impact of the other than Credit-related Regulations on bank efficiency is both direct and indirect.
    The banking sector, in terms of regulations, is not an enclave.
    Credit Regulations dominant impact on efficiency but economy-wide reforms have the potential for significant positive (direct and indirect) spillovers to financial institutions.