The Determinants of Cross-Border Lending in the Euro Zone

The Determinants of Cross-Border Lending in the Euro Zone

The 14th

Dubrovnik Economic ConferenceJune 26-27, 2008

Sylvia Heuchemer Cologne University of Applied Sciences

Stefanie Kleimeier (not present)Maastricht University and METEOR Fellow

Harald SanderCologne University of Applied Sciences, and METEOR Fellow

2 Sylvia Heuchemer, Stefanie Kleimeier & Harald Sander

Introduction (1)

European financial market integration has made a big leap forward.

However, retail banking is lagging far behind, though recently gaining some momentum.

We explore the geography of cross-border lending in the euro zone by means of a gravity approach in order to: identify trade-theoretic determinants of cross-border lending identify important drivers of and barriers to integration special focus on exploring the role of cultural and political

differences


Introduction (2) Gravity models have recently been used in explaining

cross-border finance…(e.g. Guiso,Sapienza & Zingales 2005, Blank & Buch 2006)

…and highlighted the limiting role of cultural and political factors in economic exchange

(Guiso, Sapienza & Zingales 2005, Heuchemer & Sander 2007, Kalemli-Ozcan & Sørensen 2007)

Our approach innovates on: using a non publicly available bilateral data on cross-border

loans for the euro zone adapting a trade-theoretically-augmented gravity model to

cross-border lending modeling cultural and political differences in a gravity approach


Data

Bilateral cross-border loans Annual outstanding volume from 1999-2006 Austria, Belgium, Finland, France, Germany,

Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain except loans to customers in Lux and Por

More than 800 countrypair-year specific observations


- Increase in banking market integration- Lending is right-skewed, indicating that lending

activities are concentrated in a few countries

Cross-border loansmore than doubled

Table 1: The growth of bilateral cross-border lending in the euro zone over time

total mean median

yearin millions of

euroin millions

of euro

in % of GDP of

bank country

in % of GDP of

customer country

in millions of euro

in % of GDP of

bank country

in % of GDP of

customer country

number of country-

pairs1999 151.976,3 1.746,9 5,1 10,6 854,0 1,5 2,2 872000 174.443,0 1.982,3 5,3 11,2 796,0 1,5 2,2 882001 205.113,3 1.991,4 4,8 11,1 647,0 1,2 2,3 1032002 212.000,5 2.058,3 4,4 10,6 761,3 1,2 2,4 1032003 228.407,3 2.175,3 4,3 11,8 850,0 1,3 2,1 1052004 251.659,8 2.352,0 4,7 11,7 861,8 1,2 2,1 1072005 302.286,3 2.851,8 5,3 15,0 1.165,0 1,7 2,0 1062006 360.896,0 3.341,6 5,7 16,6 1.304,7 1,8 2,4 108

1999 to 2006 2.338,0 4,9 12,4 920,5 1,4 2,2 807

bilateral cross-border loan volume

Data


Methodology (1) Gravity models have been successfully applied for explaining

transactions over space, mainly trade in goods The canonical gravity model explains trade flows between two

countries as a function of their respective economic masses (GDP) and the geographical distance separating them - as a proxy for all

information and transaction costs

2ij

jtitijt

DISTANCE

GDPGDPGX

ij3jt2it10ijt lnDISTANCEβlnGDPβlnGDPβα)ln(X


Methodology (2) Augmented gravity models

are micro-founded by adapting them to trade theories(e.g. Anderson 1979, Bergstrand 1985 & 1989 Deardorf, 1989, Anderson & van Wincoop 2003, Baltagi et al. 2003) Size (sum of GDPs) “new trade theory” Similarity (of GDP) “new trade theory” Relative per-capita income Ricardo-HOS-theory vs. Linder

include additional trade impeding or trade promoting factors as proxy for information and transaction cost distance, common border, language alternative measures of cultural distances (“Hofstede”, trust

to/trust from, differences in trust levels, legal system origin) Differences in governance quality


Methodology (3)Augmented gravity model adjusted to banking market analysis:

ijt

K

6kijtkij5

ij4ijt3ijt2ijt10ijt

ulnYβBORDERβ

lnDISTANCEβlnSIMILARβlnRELβlnSIZEβαlnX

With• Xijt: cross-border loans from bank country to customer country at time t

• Size: sum of the GDPs of the trading partner (“new” trade theory)

)GDPGDPln(SIZE jtitijt


Methodology (3)


Augmented gravity model adjusted to banking market analysis:

ijt

K

6kijtkij5

ij4ijt3ijt2ijt10ijt

ulnYβBORDERβ



• Similar: similarity of the size of the financial sector (“new” trade theory)

22

1jtit

jt

jtit

itijt CreditCredit

Credit

CreditCredit

CreditlnSIMILAR


Methodology (3)


Augmented gravity model adjusted to banking market analysis:

ijt

K

6kijtkij5

ij4ijt3ijt2ijt10ijt

ulnYβBORDERβ




• REL: indicator for relative financial development (“old” or “new” trade theory)

jt

jt

it

itijt GDP

Creditln

GDP

CreditlnREL


Methodology (3)Augmented gravity model adjusted to banking market analysis:

ijt

K

6kijtkij5

ij4ijt3ijt2ijt10ijt

ulnYβBORDERβ


With• Xijt: cross-border loans from bank country to customer country at time t• Size: sum of the GDPs of the trading partner (“new” trade theory)

• REL: indicator for relative financial development (“old” or “new” trade theory)


• Distance: geographical distance between two countries

• Border: dummy variable that is 1 if two countries share a common border and 0 otherwise

• Yijt: additional factors possibly influencing cross-border lending


1. Static Fixed Vs Random Effects Estimation

2. Static Vs Dynamic Fixed Effects Estimation habit persistence may be likely in banking

(switching and information costs)

3. Least Square Dummy Variables (LSDV) Estimation

Fixed effects estimations eliminate all time-invariant country-pair specific variables we are interested in

LSDV can remedy this, but we have to model the country-pair specific effects

Estimation Procedure


Estimation Procedure 1: Fixed Vs Random Effects in Loan Markets

None of the explanatory variables are statistically significant.

Variation in cross-border loans can be explained by country-pair and time specific factors.

Credit market proxies for

SIMILAR and REL

SIZE 0,83 1,56 *1,18 6,43

SIMILAR -0,20 0,02-0,92 0,09

REL -0,07 -0,13-0,33 -0,63

countrybank dummies No

countrycustomer dummies Noyear dummies Yes

Hausman test statstics (HT) 48,60HT (p-value) 0,00

adjusted R2 0,938number of observations 807 807Note: In each regression, the dependent variable is the log of bilateralcross-border loan volume. The two-way fixed effects model is estimatedwith White-robust standard errors. For each coefficient, the first rowshows the estimated coefficient and the second row the t-statistic. *indicates significance at least at 10%.

One-way random effects with time

dummies

Table 2: Determinants of cross border lendingFixed effects

0,983


Hausman tests substantiate our choice of fixed effects model.

Strong evidence for country-pair fixed effects Fixed time effects, capturing economic

shocks and account for important regulatory and behavioral changes in the first years of the currency union

Estimation Procedure 1: Fixed Vs Random Effects Estimation


Estimation Procedure 2: Dynamic Fixed Effects

Habit persistence is likely in retail banking (information and transaction costs)

Arellano-Bond Estimator


Data and Estimation Procedure


SIMILAR and REL


SIMILAR and REL

SIZE 0,70 0,301,67 0,29

SIMILAR 0,52 * 0,062,20 0,18

REL -0,08 -0,10-0,24 -0,35

ln(BILOANSt-1) 0,62 * 0,40 *6,56 3,69

countrybank dummies No No

countrycustomer dummies No Noyear dummies No Yes

Sargan test 28,62 19,86p-value 0,10 0,47m2 -0,89 -0,83p-value 0,37 0,41

number of observations 603 603Note: In each regression, the dependent variable is the log of bilateral cross-border loan volume. The dynamic model uses the Arellano-Bond (1991) 2-step generalised method of moments (GMM) estimator. * indicatessignificance at least at 10%.

Table 3: Determinants of cross-border lendingDynamic

0,70 0,30 Model performs better without time dummies capturing regulatory changes.

no yesYear dummies In order to capture regulatory changes


Data and Estimation Procedure


SIMILAR and REL


SIMILAR and REL

SIZE 0,70 0,301,67 0,29

SIMILAR 0,52 * 0,062,20 0,18

REL -0,08 -0,10-0,24 -0,35

ln(BILOANSt-1) 0,62 * 0,40 *6,56 3,69

countrybank dummies No No

countrycustomer dummies No Noyear dummies No Yes

Sargan test 28,62 19,86p-value 0,10 0,47m2 -0,89 -0,83p-value 0,37 0,41

number of observations 603 603Note: In each regression, the dependent variable is the log of bilateral cross-border loan volume. The dynamic model uses the Arellano-Bond (1991) 2-step generalised method of moments (GMM) estimator. * indicatessignificance at least at 10%.

Table 3: Determinants of cross-border lendingDynamic

no yesYear dummies

Cross-border lending is habit persistent.

0,62 0,40

0,52

Cross-border lending is promoted by similarity of financial systems.


Estimation Procedure 3: LSDV

Summary of the Fixed Effects Models Strong evidence for country-pair FE Habit persistence in loan markets

Least Square Dummy Variables (LSDV) Estimation

Fixed effects estimations eliminate all time-invariant country-pair specific variables which we are interested in

LSDV can remedy this, but we have to model the country-pair specific effects


Estimation Procedure 3: LSDV Country-pair fixed effects are replaced by country-pair specific

economic, cultural and political variables measured as Euclidean distance or as dummy variables

K

kjtkitkijt VVED

1

2

with V as possible influencing factors on cross-border lending

Country specific effects (country dummies) are included, capturing the effects of “multilateral resistance”

Time dummies to account (at least partly) for habit persistence


Cultural proxies Common language Legal family (La Porta et. al) Trust_from/Trust_to (Eurobarometer and Guiso et. al) Trust levels (World Value Survey) Overall cultural proxy based on Hofstede’s cultural

dimensions (power distance, individualism, masculinity and uncertainty avoidance)

Political proxies Dimension of governance as defined by the World Bank

(voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, control of corruption)

Overall political proxy based on all six dimensions

Data (extended): Cultural and Political Proxies


Table 4: Determinants of cross-border lendingconstant SIZE SIMILAR REL ln(DISTANCE) BORDERD ln(TRUSTED) LEGALFAMD ln(FRGBNKED) ln(VOICEED) adj. R2

obs.

LSDV 3,07 1,32* 0,54** 0,43** -0,59** 0,54* 0,830 8071,15 2,90 2,38 2,00 -6,44 5,79

LSDV 5,09** 0,84** 0,51** 0,54** -0,56* 0,48* -0,24* 0,55* 0,13* 0,14* 0,844 8072,00 1,97 2,31 2,45 -6,10 5,46 -5,11 5,37 3,40 3,73

Note: In each regression, the dependent variable is the log of bilateral cross-border loan volumne. LSDV estimation wie country and year dummies. White-robust t-values in second row. The subscript D indicates a dummy variable and the subscript ED a variable measured as an euclidian distance.*, **, and *** indicate significance at 1%, 5%, and 10% level, respectively.

LSDV Results: LoansBasic Version

Size1,32

SIM0,54

REL0,43

R2

0,830Dist.-0,59

Border0,54

Economic Geography

Common borders increase cross-border banking by 71,6%

Intra-industry trade- Product heterogeneity- Economies of scale

„New“ Trade Theory „Old“ Trade Theory

Comparative Advantages


Table 5: LSDV model selectionCross-border loans

Reg 1 Reg 2 Reg 3 Reg 4 Reg 5 Reg 6 Reg 7 Reg 8 Reg 9 Reg 10 Reg 11 Reg 12constant 3,07 3,46 2,88 3,47 2,38 3,00 2,99 3,27 2,63 1,95 4,27 3,08

1,15 1,32 1,07 1,32 0,90 1,09 1,11 1,20 0,95 0,71 1,60 1,15SIZE 1,32 1,18 1,32 1,34 1,20 1,32 1,32 1,31 1,39 1,49 1,15 1,32

2,90 2,66 2,90 3,00 2,71 2,87 2,90 2,87 2,95 3,17 2,52 2,91SIMILAR_CRE 0,54 0,56 0,55 0,62 0,52 0,54 0,54 0,54 0,57 0,61 0,46 0,54

2,38 2,46 2,42 2,76 2,32 2,36 2,38 2,37 2,43 2,60 2,05 2,38REL_CRE 0,43 0,54 0,43 0,39 0,43 0,43 0,43 0,44 0,44 0,43 0,45 0,43

2,00 2,40 2,01 1,85 2,00 2,00 2,01 2,05 2,02 2,01 2,08 1,98ln(DISTANCE) -0,59 -0,58 -0,56 -0,66 -0,47 -0,59 -0,58 -0,61 -0,59 -0,58 -0,60 -0,60

-6,44 -6,27 -5,44 -7,27 -4,90 -6,42 -6,45 -6,65 -6,41 -6,32 -6,74 -6,42BORDERD 0,54 0,53 0,52 0,48 0,52 0,54 0,54 0,53 0,53 0,53 0,56 0,54

5,79 5,63 5,53 5,24 5,95 5,82 5,75 5,68 5,61 5,62 5,95 5,75ln(FRGBNKED) 0,11

2,79LANGUAGED 0,11

0,71ln(TRUSTED) -0,24

-5,08LEGALFAMD 0,66

6,27ln(POLED) -0,01

-0,16ln(CORRUPED) -0,01

-0,47ln(GOVEFFED) 0,03

0,81ln(POLSTABED) -0,03

-0,88ln(REGQALED) -0,06

-1,75ln(VOICEED) 0,09

2,50ln(LAWED) 0,00

0,07

countrylender dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yescountryborrower dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yesyear dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

adjusted R2 0,830 0,831 0,830 0,835 0,836 0,830 0,830 0,830 0,830 0,830 0,831 0,830number of observations 807 807 807 807 807 807 807 807 807 807 807 807Note: In each regression, the dependent variable is the log of bilateral cross-border loan volume. The Least Square Dummy Variable (LSDV) model isestimated as OLS with White-robust standard errors. For each coefficient, the first row shows the estimated coefficient and the second row the t-statistic.The subscript D indicates a dummy variable and the subscript ED a variable measured as an Euclidean distance where larger values indicate largerdifferences between borrower and lender country.

LSDV Results: LoansImpact of Cultural and Political Variables

0,11Language

Corruption -0,01

Gov. Effectivness

Political Stability

0,03

-0,03

Reg. Quality

Rule of Law

-0,06

0,00

Statisticallyinsignificant


Table 5: LSDV model selectionCross-border loans

Reg 1 Reg 2 Reg 3 Reg 4 Reg 5 Reg 6 Reg 7 Reg 8 Reg 9 Reg 10 Reg 11 Reg 12constant 3,07 3,46 2,88 3,47 2,38 3,00 2,99 3,27 2,63 1,95 4,27 3,08

1,15 1,32 1,07 1,32 0,90 1,09 1,11 1,20 0,95 0,71 1,60 1,15SIZE 1,32 1,18 1,32 1,34 1,20 1,32 1,32 1,31 1,39 1,49 1,15 1,32

2,90 2,66 2,90 3,00 2,71 2,87 2,90 2,87 2,95 3,17 2,52 2,91SIMILAR_CRE 0,54 0,56 0,55 0,62 0,52 0,54 0,54 0,54 0,57 0,61 0,46 0,54

2,38 2,46 2,42 2,76 2,32 2,36 2,38 2,37 2,43 2,60 2,05 2,38REL_CRE 0,43 0,54 0,43 0,39 0,43 0,43 0,43 0,44 0,44 0,43 0,45 0,43

2,00 2,40 2,01 1,85 2,00 2,00 2,01 2,05 2,02 2,01 2,08 1,98ln(DISTANCE) -0,59 -0,58 -0,56 -0,66 -0,47 -0,59 -0,58 -0,61 -0,59 -0,58 -0,60 -0,60

-6,44 -6,27 -5,44 -7,27 -4,90 -6,42 -6,45 -6,65 -6,41 -6,32 -6,74 -6,42BORDERD 0,54 0,53 0,52 0,48 0,52 0,54 0,54 0,53 0,53 0,53 0,56 0,54

5,79 5,63 5,53 5,24 5,95 5,82 5,75 5,68 5,61 5,62 5,95 5,75ln(FRGBNKED) 0,11

2,79LANGUAGED 0,11

0,71ln(TRUSTED) -0,24

-5,08LEGALFAMD 0,66

6,27ln(POLED) -0,01

-0,16ln(CORRUPED) -0,01

-0,47ln(GOVEFFED) 0,03

0,81ln(POLSTABED) -0,03

-0,88ln(REGQALED) -0,06

-1,75ln(VOICEED) 0,09

2,50ln(LAWED) 0,00

0,07

countrylender dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yescountryborrower dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yesyear dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

adjusted R2 0,830 0,831 0,830 0,835 0,836 0,830 0,830 0,830 0,830 0,830 0,831 0,830number of observations 807 807 807 807 807 807 807 807 807 807 807 807Note: In each regression, the dependent variable is the log of bilateral cross-border loan volume. The Least Square Dummy Variable (LSDV) model isestimated as OLS with White-robust standard errors. For each coefficient, the first row shows the estimated coefficient and the second row the t-statistic.The subscript D indicates a dummy variable and the subscript ED a variable measured as an Euclidean distance where larger values indicate largerdifferences between borrower and lender country.

LSDV Results: LoansImpact of Cultural and Political Variables

-0,24Trust

Legal Family

Foreign Bank 0,11

0,66

Voice + Account. 0,09

Culture -0,34

Statistically significant


Table 4: Determinants of cross-border lendingconstant SIZE SIMILAR REL ln(DISTANCE) BORDERD ln(TRUSTED) LEGALFAMD ln(FRGBNKED) ln(VOICEED) adj. R2

obs.

LSDV 3,07 1,32* 0,54** 0,43** -0,59** 0,54* 0,830 8071,15 2,90 2,38 2,00 -6,44 5,79

LSDV 5,09** 0,84** 0,51** 0,54** -0,56* 0,48* -0,24* 0,55* 0,13* 0,14* 0,844 8072,00 1,97 2,31 2,45 -6,10 5,46 -5,11 5,37 3,40 3,73

Note: In each regression, the dependent variable is the log of bilateral cross-border loan volumne. LSDV estimation wie country and year dummies. White-robust t-values in second row. The subscript D indicates a dummy variable and the subscript ED a variable measured as an euclidian distance.*, **, and *** indicate significance at 1%, 5%, and 10% level, respectively.

LSDV Results: LoansPreferred Estimation

R2

0,844Size0,84

SIM0,51

REL0,54

Dist.-0,56

Border0,48

Legal0,55

Bank0,13

Voice0,14

Trust-0,24

Same legal system increases cross-border

loans by 73,3%

Cultural (rather than governance) factors play an important limiting role in banking market integration

Influence of economic geography is slightly

smaller

Intra-industry trade Comparative advantages


Conclusion Cross-border lending is promoted by

similarity of financial systems but… …differences in factor endowments and

financial development still play a role. Cross-border lending is habit persistent. Strong geographic and cultural limits to

full(er) integration Distance and border effects Differences in legal system origin Cultural Differences (Trust)


Research Challenges Ahead

Towards a more theory-based measurement of cultural dimensions

Exploring the interaction of the various concepts and proxies for information and transaction costs (also for trade, FDI, international finance)

Efficient estimation of time invariant and rarely changing variables in gravity models with fixed effects (e.g. Hausman/Taylor, or more recently: Plümper/Troeger 2007)

Thank you for your attention!

Sylvia Heuchemer, Stefanie Kleimeier & Harald Sander

Cultural and geographical factors remain for quite some time a barrier to banking market integration in the euro zone.

The Determinants of Cross-Border Lending in the Euro Zone

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