Download - PPT Banking and Regulations

Does Regulation Matter for Banks in the New EU Member States?

A. Kalyvas (Bournemouth) E. Mamatzakis (Sussex) and J.Piesse (King’s

College, London)

OverviewAim: 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

Bank Efficiency and RegulationsFinancial 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.

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.

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.

DEA Methodology

DEA Methodology (2)

Econometric Specification

Data Description (DEA scores)Unbalanced panel: 350 banksAll 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 revenueCommon cost-efficiency frontier

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.

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

Data Description (Credit Regulations)- Fraser Index of Economic Freedom

CR-Own: ownership of banks CR-Comp: foreign competitionCR-PrS: crowding-out of private sector

creditCR-NiR: negative real interest rates Values in a 0-10 scale. Higher values

denote a more liberal regulatory environment

Cross-time Evolution of the cost efficiency in the Banking sector of the new EU member states

1996 1998 2000 2002 2004 2006 2008 20100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

BulgariaCzech RepublicEstoniaHungaryLatviaLithuaniaPolandRomaniaSlovakiaSlovenia

Year

Cost Efficiency

Annual Cost Efficiency in the Banking Sector (1996-2009)

Year Obs Mean Std. Dev

1996 86 0.30 0.18

1997 102 0.23 0.22

1998 88 0.26 0.24

1999 100 0.39 0.25

2000 103 0.42 0.25

2001 102 0.30 0.24

2002 102 0.51 0.24

2003 90 0.41 0.24

2004 141 0.33 0.27

2005 162 0.26 0.23

2006 163 0.19 0.21

2007 176 0.19 0.20

2008 204 0.15 0.18

2009 193 0.21 0.24

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

Model 1: Bank-Specific Model 2: Financial Structure Model 3: Level of Economic Development

VARIABLES

lnTA -0.0722*** -0.0607*** -0.0616***

(0.00960) (0.0136) (0.0194)

E/A 0.682*** 0.748*** 0.747***

(0.136) (0.138) (0.135)

LLP/L -0.00305 -0.00360 -0.00367

(0.0133) (0.0104) (0.0103)

L/A -0.0641 -0.0187 -0.0202

(0.0694) (0.0767) (0.0760)

DCPS/GDP -0.0429 -0.0439

(0.0441) (0.0421)

SPR 0.0901* 0.0903*

(0.0495) (0.0495)

HHI 0.103 0.103

(0.0645) (0.0631)

lnGDPcppp 0.00645

(0.0830)

Constant 1.204*** 0.962*** 0.914

(0.135) (0.194) (0.618)

Observations 1,792 1,627 1,627

R-squared 0.226 0.230 0.230

Number of banks 350 331 331

Model 1: CR Model 2: CR and LR Model 3: CR, LR and BR

VARIABLES

lnTA -0.0311 -0.0493* -0.0493*(0.0283) (0.0277) (0.0274)

E/A 0.865*** 0.826*** 0.817***(0.164) (0.154) (0.152)

LLP/L 0.263 0.213 0.221(0.207) (0.191) (0.194)

L/A 0.0126 0.00588 0.00300(0.0973) (0.0961) (0.0956)

DCPS/GDP 0.0824* 0.0872* 0.0812*(0.0430) (0.0454) (0.0451)

SPR 0.107 -0.207 -0.128(0.481) (0.484) (0.479)

HHI -0.0680 -0.0738 -0.0405(0.0881) (0.0885) (0.0885)

lnGDPcppp -0.425*** -0.552*** -0.545***(0.163) (0.146) (0.144)

CR 0.00697 -0.189 -0.175(0.177) (0.184) (0.183)

LR 0.612*** 0.589***(0.117) (0.116)

BR 0.232**(0.105)

Constant 4.636*** 5.911*** 5.698***(1.208) (1.084) (1.084)

Observations 1,140 1,140 1,140R-squared 0.333 0.358 0.361Number of banks 266 266 266

Model 1:CR-Own Model 2: CR-Comp Model 3: CR-PrS Model 4: CR-NiR Model 5: Overall CR

VARIABLES

lnTA -0.0326 -0.0333 -0.0318 -0.0289 -0.0328(0.0282) (0.0270) (0.0281) (0.0294) (0.0286)

E/A 0.862*** 0.872*** 0.848*** 0.866*** 0.854***(0.164) (0.162) (0.164) (0.165) (0.164)

LLP/L 0.276 0.265 0.211 0.266 0.233(0.200) (0.192) (0.209) (0.210) (0.192)

L/A 0.00854 0.0251 0.00302 0.0141 0.0135(0.0971) (0.0963) (0.0977) (0.0978) (0.0970)

DCPS/GDP 0.0945** 0.0924** 0.0626 0.0827* 0.0855**(0.0385) (0.0466) (0.0463) (0.0462) (0.0397)

SPR 0.117 -0.429 0.0771 0.0593 -0.456(0.506) (0.476) (0.488) (0.499) (0.462)

HHI -0.0743 0.0375 -0.0291 -0.0806 0.0426(0.0799) (0.0871) (0.0826) (0.0879) (0.0943)

lnGDPcppp -0.449*** -0.619*** -0.383** -0.440** -0.603***(0.162) (0.160) (0.162) (0.173) (0.171)

CR-Own 0.0484 0.0396(0.0865) (0.0861)

CR-Comp 0.247*** 0.219***(0.0643) (0.0618)

CR-PrS -0.239*** -0.211**(0.0858) (0.0847)

CR-NiR -0.0731 -0.0843(0.196) (0.189)

Constant 4.851*** 6.274*** 4.453*** 4.835*** 6.387***(1.207) (1.206) (1.228) (1.401) (1.357)

Observations 1,140 1,140 1,140 1,140 1,140R-squared 0.334 0.350 0.342 0.333 0.357Number of banks 266 266 266 266 266

Overview of Fixed-Effects ResultsSome 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 CRCR-Comp and CR-PrS most significant CR

Panel VAR Analysis-AdvantagesStandard OLS - endogeneity bias. All variables in the panel-VAR are

entering as endogenousExamination of the underlying dynamic

relationships

Panel VAR Analysis-Methodology

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We specify a first order 4x4 panel-VAR model as follows:

tiitiit e ,1 XX , 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, Φ is an 4x4 matrix of coefficients, μi is a vector of m individual effects and ei,t are iid residuals.

The panel-VAR takes the following form:

Using the panel-VAR individual heterogeneity in the levels is ensured by:

Introducing fixed effects in the model, denoted μi, 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.

Impulse Response Function (IRF) for Ef, CR, LR and BRImpulse-responses for 1 lag VAR of Ef CR LR BR

Errors are 5% on each side generated by Monte-Carlo with 100 reps

response of Ef to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 60.0000

0.1882

response of Ef to CR shocks

(p 5) CR CR (p 95) CR

0 6-0.0015

0.0828

response of Ef to LR shocks

(p 5) LR LR (p 95) LR

0 6-0.0488

0.0000

response of Ef to BR shocks

(p 5) BR BR (p 95) BR

0 6-0.0088

0.0360

response of CR to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0135

0.0003

response of CR to CR shocks

(p 5) CR CR (p 95) CR

0 6-0.0053

0.0395

response of CR to LR shocks

(p 5) LR LR (p 95) LR

0 6-0.0031

0.0038

response of CR to BR shocks

(p 5) BR BR (p 95) BR

0 6-0.0201

0.0034

response of LR to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0136

0.0050

response of LR to CR shocks

(p 5) CR CR (p 95) CR

0 6-0.0203

0.0204

response of LR to LR shocks

(p 5) LR LR (p 95) LR

0 60.0000

0.0450

response of LR to BR shocks

(p 5) BR BR (p 95) BR

0 6-0.0321

0.0051

response of BR to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0339

0.0126

response of BR to CR shocks

(p 5) CR CR (p 95) CR

0 6-0.0482

0.0305

response of BR to LR shocks

(p 5) LR LR (p 95) LR

0 6-0.0004

0.0271

response of BR to BR shocks

(p 5) BR BR (p 95) BR

0 6-0.0325

0.0427

Impulse Response Function (IRF) for Ef, CR-Own, CR-Comp, CR-PrS and CR-NiR

Impulse-responses for 1 lag VAR of Ef CROwn



(p 5) Ef Ef (p 95) Ef

0 6-0.0025

0.1897

response of Ef to CROwn shocks

(p 5) CROwn CROwn (p 95) CROwn

0 6-0.0104

0.1839

response of CROwn to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0277

0.0002

response of CROwn to CROwn shocks

(p 5) CROwn CROwn (p 95) CROwn

0 6-0.0455

0.0755

Impulse-responses for 1 lag VAR of Ef CRComp



(p 5) Ef Ef (p 95) Ef

0 60.0000

0.1656

response of Ef to CRComp shocks

(p 5) CRComp CRComp (p 95) CRComp

0 60.0000

0.2577

response of CRComp to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0026

0.0151

response of CRComp to CRComp shocks

(p 5) CRComp CRComp (p 95) CRComp

0 60.0000

0.0733

Impulse-responses for 1 lag VAR of Ef CRPrS



(p 5) Ef Ef (p 95) Ef

0 60.0000

0.1500

response of Ef to CRPrS shocks

(p 5) CRPrS CRPrS (p 95) CRPrS

0 6-0.0440

0.0258

response of CRPrS to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0212

0.0090

response of CRPrS to CRPrS shocks

(p 5) CRPrS CRPrS (p 95) CRPrS

0 6-0.0016

0.0606

Impulse-responses for 1 lag VAR of Ef CRNiR



(p 5) Ef Ef (p 95) Ef

0 60.0000

0.1532

response of Ef to CRNiR shocks

(p 5) CRNiR CRNiR (p 95) CRNiR

0 6-0.0325

0.0234

response of CRNiR to Ef shocks

(p 5) Ef Ef (p 95) Ef

0 6-0.0191

0.0179

response of CRNiR to CRNiR shocks

(p 5) CRNiR CRNiR (p 95) CRNiR

0 6-0.0007

0.0930

Variance Decompositions (VDCs) for cost efficiency, CR, LR and BR

s Ef CR LR BR

Ef 10 0.839969 0.089997 0.063883 0.006152

CR 10 0.140477 0.744695 0.007411 0.107417

LR 10 0.072052 0.146369 0.601793 0.179787

BR 10 0.15687 0.340959 0.116101 0.38607

s Ef CR LR BR

Ef 20 0.839944 0.090007 0.063897 0.006152

CR 20 0.14054 0.744598 0.007457 0.107405

LR 20 0.072128 0.146367 0.601741 0.179764

BR 20 0.156871 0.340958 0.116102 0.386069

Notes: s defines the periods ahead of VDCs.

Variance Decompositions (VDCs) for cost efficiency, CR-Own, CR-Comp, CR-PrS and CR-NiR

s Ef CRown CRcomp CRprs CRnir

Ef 10 0.685402 0.274395 0.017177 0.0142347 0.008791

CRown 10 0.029701 0.252045 0.002598 0.0119231 0.703733

CRcomp 10 0.008969 0.026772 0.031333 0.888267 0.044658

CRprs 10 0.00237 0.077098 0.899128 0.0173834 0.004021

CRnir 10 0.007811 0.926466 0.003459 0.0608292 0.001435

s Ef CRown CRcomp CRprs CRnir

Ef 20 0.655534 0.301631 0.016468 0.0178107 0.008556

CRown 20 0.029058 0.281824 0.002516 0.0158607 0.670741

CRcomp 20 0.009036 0.032522 0.03153 0.8824307 0.044482

CRprs 20 0.002531 0.088115 0.885784 0.0196048 0.003965

CRnir 20 0.008205 0.92049 0.003415 0.06648 0.001409

Notes: s defines the periods ahead of VDCs

Overview of the Panel-VAR ResultsCR and BR positive impact on efficiencyLR negative (initially) on efficiency but

positive on BRMagnitude: CR dominantCR-Own and CR-Comp positive impactCR-PrS and CR-NiR negative impactMagnitude: CR-Own dominant

Conclusion and Policy Implications IRFs and VDCs confirm strong causality Credit

Regulations to bank efficiencyMost 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.