cross-border bank lending: empirical evidence on new determinants from oecd banking markets

Int. Fin. Markets, Inst. and Money 23 (2013) 136– 162

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Journal of International FinancialMarkets, Institutions & Money

journal homepage: www.elsevier.com/locate/ intf in

Cross-border bank lending: Empirical evidence on newdeterminants from OECD banking markets�

Oliver Müller ∗, André UhdeUniversity of Bochum, Department of Economics, 44780 Bochum, Germany

a r t i c l e i n f o

Article history:Received 12 July 2012Accepted 11 September 2012

Available online 26 September 2012

JEL classification:F 21F 34G 15G 21

Keywords:Foreign bank claimsGravity measuresOECD banking markets’ characteristicsLending banks’ characteristics

a b s t r a c t

Employing data on foreign bank claims from 13 OECD countrieson 51 emerging markets between 1993 and 2007, this studyinvestigates specific characteristics of OECD banking markets andlending banks as new important determinants of cross-border lend-ing. We initially provide empirical evidence that in addition towell-accepted “gravity measures”, characteristics of OECD bankingmarkets as well as lending banks’ attributes may describe furtherimportant determinants of cross-border bank lending with regardto our sample. Building subsamples of more-developed emergingmarkets vs. frontier markets, addressing (non) common lender rela-tionships and analyzing cross border lending flows during differenttime periods, our analysis additionally reveals that both the deter-minants’ explanatory power and their direction of impact notablyvary with respective subsamples.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

The incentive for OECD country banks to increasingly engage in cross-border lending to more-developed emerging markets and frontier markets primarily results from different effects of anongoing process of financial globalization since the beginning of the 1990s. Stronger financial global-ization and integration in mature industrialized countries have led to a more vehement competition

� We thank anonymous referees as well as participants and discussants of the 3rd EMG Conference on Emerging MarketsFinance, May 5–6, 2011, London, UK, the 8th Annual Meeting of the Irish Society of New Economists (ISNE), August 18–19,2011, Dublin, Ireland, and of the 26th Congress of the European Economic Association, August 25–29, 2011, Oslo, Norway, forhelpful comments and suggestions. We also thank Carina Trimborn for outstanding research assistance.

∗ Corresponding author.E-mail addresses: [email protected] (O. Müller), [email protected] (A. Uhde).

1042-4431/$ – see front matter © 2012 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.intfin.2012.09.004

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O. Müller, A. Uhde / Int. Fin. Markets, Inst. and Money 23 (2013) 136– 162 137

and growing financial disintermediation seriously threatening the domestic banks’ profit margins(Claessens, 2006; Claessens et al., 2001). However, as many emerging countries have started liberaliz-ing and deregulating their financial markets at the same time by abandoning controls on cross-bordercapital flows and suppressing market entry barriers, banks from mature countries have recognizednew investment opportunities in these countries allowing them to sidestep their own local markets(Arestis et al., 2002; Eichengreen, 2001).

As a consequence, cross-border bank lending to emerging markets by OECD country banks hasincreased vigorously since the beginning of the 1990s. While foreign claims on more-developed emerg-ing and frontier markets added up to USD 464bn in 1993, their amount grew roughly sevenfold toUSD 2958bn in 2007, but has decreased strikingly since 2008 due to the global financial crisis thathas emerged in late-2007 (BIS, 2008a; Fig. 1). Regardless of the recent financial crisis, the integrationof worldwide financial markets has reached a new peak with OECD banking markets and emergingmarket economies being as interconnected as never before in the modern history.

The empirical study at hand investigates new determinants of cross-border bank lending between13 OECD and 51 emerging markets for the period from 1993 to 2007 and contributes to the strand ofliterature analyzing external (push) factors and domestic (pull) factors that may impact bank lendingto emerging markets. Thereby, our work complements and extends previous studies for the followingtwo aspects. First, while existing studies primarily consider the macroeconomic and political endow-ment as important push and pull factors, this is the first study that additionally analyzes specificfeatures of different OECD banking markets (market structures and the regulatory environment) aswell as characteristics of lending banks as new important determinants of cross-border bank claims.Second, we explicitly account for the high degree of heterogeneity among emerging markets and thelong period of time of our analysis. Building subsamples of cross-border lending to more-developedemerging markets vs. frontier markets, differentiating between common-lender relationships andnon-common lender commitments and investigating cross border lending flows during different timeperiods, our study provides further important insight into the underlying mechanisms of cross-borderlending between OECD countries and emerging markets.

Overall, we provide empirical evidence that in addition to well-accepted “gravity measures”, char-acteristics of OECD banking markets and lending banks describe further important determinants ofcross-border bank lending to emerging economies. Our baseline analysis reveals that banks operatingin highly concentrated OECD banking markets may less extensively engage in lending to emergingmarkets, whereas fiercer competition in their home markets and the possibility to arbitrage on costsarising from different regulatory requirements may encourage domestic banks to foster cross-borderlending. In addition, we find that better capitalized banking systems may hold smaller amounts offoreign claims towards emerging markets. In contrast, banks suffering from either low loan portfolioquality or cost inefficiency may be more prone to engage in cross-border bank lending with emergingmarkets supporting predictions from the “search for yield” and “gambling for resurrection approach”.Finally, our analysis reveals that both the determinants’ explanatory power and their direction ofimpact notably vary as regards different subsamples.

The remainder of this paper is organized as follows. While Section 2 reviews related literature,determinants of cross-border bank lending employed in this study are discussed in Section 3. Section4 introduces our empirical model and strategy and describes data and sources. Empirical results frombaseline regressions and further regressions based on subsamples are presented and discussed inSection 5. Finally, Section 6 concludes.

2. Literature review

Although international capital flows have obviously become an interesting field of research foreconomists, existing empirical research either does not exclusively focus on banking flows or primarilyattempts to explain the source and recipient countries’ macroeconomic and institutional determinantsas major push and pull factors (e.g. Ferrucci et al., 2004; Kim, 2000; Bohn and Tesar, 1996; Fernandez-Arias, 1996; Hernandez and Rudolph, 1995). With regard to our research question, the number ofcomprehensive empirical studies explicitly evaluating the determinants of international bank lending

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mil. USD

0

500,00 0

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

Amount of foreign claims

Fig. 1. Amount of foreign claims on emerging markets by year.


to emerging markets is still small, and we identify five comprehensive, recently undertaken empiricalstudies on this issue.

To begin with Jeanneau and Micu (2002), they analyze cross-border bank lending to large Asianand Latin-American countries. Focusing on the macroeconomic endowment between 1985 and 2000,their panel data analysis reveals that economic cycles in lending countries may have a procyclicalimpact on international bank claims. Moreover, they find that fixed and intermediate exchange ratearrangements may encourage foreign bank lending flows while floating rate agreements may inhibitthem.

More recently, Herrero and Pería (2007) study a mixture of Italian, Spanish and US foreign bankclaims on more than 100 recipient countries worldwide for the period from 1997 to 2002. They provideevidence that regulatory barriers to banking as well as restricted business opportunities in borrowingcountries may have a significant negative impact on the share of a lending bank’s local claims in favorof cross-border claims.

In an influential study, Papaioannou (2009) employs data on 40 lending and 140 recipient countriesfor the period from 1984 to 2002 to further explore the nexus between institutions in borrow-ing countries and capital inflows. The study reveals that under-performing institutions in recipientcountries, i.e. weak property rights, legal inefficiencies or a high risk of expropriation, may be majorobstacles for foreign bank lending to emerging markets. In contrast to this, the author suggests thatpolitical liberalization, privatization and other structural policies may enable local economies to attractsubstantially more foreign bank capital.

Herrmann and Mihaljek (2010) examine the impact of financial distress in source and recipientcountries on international bank lending based on cross-border bank flows between 17 advanced and28 emerging markets for the period from 1993 to 2008 and find that country specific risk factors aresignificant determinants of cross-border bank flows. In this context, they identify increasing expectedglobal financial market volatility, higher fiscal deficits and deteriorating banking sector performancein emerging markets as well as loose financial and monetary linkages between the source and therecipient country to reduce cross-border banking flows.

Finally, Houston et al. (2012) employ data on international bank flows from 26 lending countriesto 120 borrowing countries for the period from 1996 to 2007. They provide evidence that the abilityof banks to avoid regulations by shifting parts of their business to less regulated markets may posi-tively affect international bank flows between mature and developing countries. Moreover, while laxregulations may not be a significant determinant of international bank flows, they find that recipi-ent countries may encourage the inflow of capital by imposing stronger property rights and creditorrights.

3. Determinants of cross-border bank lending

3.1. Gravity measures

Following relevant previous empirical studies, we employ well-accepted standard variables of the“gravity model” to explain differences in the volume of financial claims between source and recipientcountries. The gravity model is derived from trade theory in which it is commonly used to analyzebilateral trade flows (Deardorff, 1998; Bergstrand, 1985; Anderson, 1979; Tinbergen, 1962). In recentyears, however, the gravity approach has also become popular in empirical studies on internationalbanking and finance (e.g. Herrmann and Mihaljek, 2010; Papaioannou, 2009; Portes and Rey, 2005;Rose and Spiegel, 2004; Jeanneau and Micu, 2002).

In line with these last named studies, we include the source and the recipient country’s log of realGDP as a measure of national income and one period lagged GDP growth to measure the state of theeconomies within the business cycle. We lag both GDP growth measures to avoid simultaneity with theGDP measure. Additionally, we address endogeneity problems since it is suggested that cross-borderlending may positively affect the state of the economy in recipient countries while economic boomphases in the source countries may not only provoke better investment opportunities on a nationallevel, but may also favor international bank lending. Next to these well-accepted macroeconomicvariables, we further employ the geographical distance between the capitals of both countries as well

140 O. Müller, A. Uhde / Int. Fin. Markets, Inst. and Money 23 (2013) 136– 162

as a measure of linguistic ties, i.e. a dummy variable with the value of one if the source and recipientcountry share a common language suggesting cultural proximity.

From an economic point of view, international bank lending may be positively related to boththe source and recipient countries’ income whereas two ambiguous effects of the state of the sourcecountry’s economy within its business cycles are possible. On the one hand, economic booms in sourcecountries may favor cross-border bank lending since growth enables source country banks to evaluatenew investment opportunities which they may find in emerging markets. On the other hand, lendingbanks may ration credit among borrowers in emerging markets since local investment opportunitiesin OECD countries usually gain significance during periods of economic upswings.

Turning to GDP growth rates in borrowing countries, we clearly expect that booms in recipientmarkets may have a positive impact on the volume and number of cross-border bank claims due tonew local investment opportunities. Finally, whereas the existence of a common official language(linguistic ties) may be favorable for cross-border bank lending, increasing distance and hence greaterinformation asymmetries, transaction costs and investment risk (Ahearne et al., 2004) may impedebank lending to emerging markets as long as recent developments in communication technologies donot compensate for these disadvantages.

3.2. Characteristics of OECD banking markets

Next to determinants employed in the traditional gravity model, we further focus on characteristicsof OECD banking markets, i.e. market structures and the regulatory environment, that are likely to havean impact on the amount of cross-border loans to emerging market economies.

To begin with, we include the degree of OECD banking market concentration and expect anambiguous impact of increasing banking market concentration in lending countries on the volumeof cross-border claims. On the one hand, applying traditional industrial organization theory to bank-ing, it is argued that granting monopolistic interest rates to local customers prevents from operatingcross-border in order to increase profitability and hence shareholder value (Repullo, 2004). Moreover,it is suggested that financial institutions operating in highly concentrated banking markets are likelyto ration credit among borrowers by selecting debtors with the highest solvency (Cetorelli, 2004; Bootand Thakor, 2000). If this is true, larger (monopolistic) banks will not extend their cross-border creditbusiness towards riskier borrowers in emerging markets.

On the other hand, it is suggested that concentrated banking systems may have comparative advan-tages in providing credit monitoring services and may be able to diversify loan portfolio risks moreefficiently due to higher economies of scale and scope (Boyd and Prescott, 1986; Diamond, 1984;Ramakrishnan and Thakor, 1984). Apart from these functional diversification effects, it is assumedthat larger banks engaging in cross-border lending may additionally obtain economies of scale andscope by geographical risk diversification. Since Méon and Weill (2005) have shown that economiccycles of a large number of countries are not perfectly correlated, geographical diversification mayplay an important role in reducing the banks’ overall risk exposure.

We additionally control for the degree of disintermediation in source country banking markets.Accordingly, we employ a proxy for the development of the national capital market in order to mea-sure the competitive pressure induced by other (near- and non-) banks, capital market investors (e.g.insurance companies, pension funds and hedge funds) and stock markets. We expect a positive effectof increasing disintermediation and competition on the number and volume of cross-border lendingsince it is suggested that banks operating in a more competitive environment with higher pressureon profit margins (Claessens, 2006; Claessens et al., 2001) may have stronger incentives to operatecross-border and pursue riskier investment strategies in order to hedge potential losses expected innational markets (Arestis et al., 2002; Eichengreen, 2001).

Next to different OECD banking market structures we further investigate differences in the bankingregulatory framework implemented by source and recipient countries in our sample. In particular, wecontrol for the possibilities of de jure and de facto regulatory arbitrage resulting from a disharmonizedglobal transformation of the Basel II framework into national equity capital regulations on the onehand and supervisory practices on the other hand (FSB, 2009). We expect a positive impact of dejure and de facto regulatory arbitrage on international bank lending since it is well accepted that the


implementation of tight regulatory rules with regard to the credit business is associated with higherdirect (equity capital) and indirect (monitoring and reporting requirements) costs for the bankingindustry (Barth et al., 2008). As a consequence, cross-country differences in the regulatory frameworkmay foster the flow of credit from highly regulated OECD markets to borrowers in less regulatedemerging economies.

3.3. Characteristics of OECD lending banks

Next to differences in market structures and the regulatory framework, we additionally control forconsolidated and aggregated OECD lending banks’ characteristics that are further likely to determinecross-border lending activities.

We initially employ the banks’ capital ratio and expect an ambiguous effect. On the one hand, wellcapitalized banks may stronger engage in international bank lending to emerging economies due tohigher “capital buffers” that may protect them against credit shocks (Boyd et al., 2004; Matutes andVives, 2000). On the other hand, Keeley (1990) argues that a higher charter or franchise value of largerbanks may discourage the bank’s management to take excessive risks (“charter value hypothesis”). Ashigher franchise values would result in higher opportunity costs when a higher capitalized bank goesbankrupt, bank managers or, even more, the bank’s shareholders may not accept risky investmentsthat could jeopardize their future profits (Hellmann et al., 2000; Matutes and Vives, 2000; Besankoand Thakor, 1993).

Next to the capital ratio, the banks’ loan loss provisions and cost inefficiency are included toinvestigate if banks in our sample may follow a “gambling for resurrection strategy” when lendingcross-border (Maddaloni and Peydró, 2010; Dell’Ariccia and Marquez, 2006; Rochet, 1992) in orderto meet capital market expectations and avoid regulatory constraints. Accordingly, we suggest thatbanks suffering from a poor loan portfolio quality and cost inefficiency are more prone to engagein high-risk-return cross-border lending to emerging markets. These banks may undertake high-risklending hoping for recovery from financial distress if the strategy turns out to be successful.

Since we cannot rule out that the lending banks’ loan loss provisions and cost inefficiency impactthe banking system’s overall capital ratio, we lag this ratio by one period to avoid biased results fromlikely multicollinearity and endogeneity. Moreover, the lag by one period addresses relatively highcorrelations between these measures.

4. Empirical analysis

4.1. Empirical model

To study the impact of OECD banking markets’ and lending banks’ characteristics on the variationin cross-border lending to emerging markets, we estimate the following random-effects model onpanel data:

yijt = ˛ij +∑

ˇkgijt,k + ˇlxijt,l + �t + εijt

Yijt represents the log of total foreign claims by banks from OECD country i to all sectors of recipientemerging markets j in year t. The vector gijt,k describes the gravity model which includes the variables asdiscussed in Section 3.1. Xijt,l either describes OECD banking markets’ (Section 3.2) or characteristicsof lending banks’ (Section 3.3) being employed in separate regressions. The variable aij representscountry-pair random effects favoring or inhibiting cross-border lending between country-pairs; εijt isthe independent and identically distributed (iid) random error term.

The model further includes time dummies �t to control for unobserved time-variant measureslike expectations, trust and social attributes or common shocks, which are assumed to influencefinancial linkages between source and recipient countries over time. Moreover, to control for likelytwo-way error correlation across both the first and second country in our country-pairs, we employ the


multi-clustering approach proposed by Cameron et al. (2009) and Cameron and Golotvina (2005) thatenables us to include heteroskedastic-robust standard errors at the country-pair-level.1

Estimating the model with random effects is a consequent strategy for two reasons. First, we dis-criminate between random and fixed effects by defining the target of inference (Wooldridge, 2002;Egger, 2002, 2000). A fixed effects model is more suitable if the data at hand is not sampled, but almostcovers the full population whereas random effects are more appropriate if the interest of inferencerelates to a population mean, i.e. units are viewed as sampled from an overall population as is thecase for our sample of source and recipient country-pairs. Thus, we are interested in the estimation offinancial flows between a randomly drawn sample of countries rather than between an ex ante pre-determined selection of nations. Furthermore, employing the random-effects model is a consequentstrategy for the study at hand since most variation should be observed over time. Random effectsallow for the inclusion of time-invariant variables among regressors such as single gravity elementsand several measures of OECD banking markets’ characteristics.2

Second, from an econometric point of view, the issue of correlated errors is the key driver in discrim-inating between fixed and random effect models. The random effect assumption is that the individualspecific effect is uncorrelated with the independent variables whereas the fixed effect assumes cor-relation between the individual effect and the exogenous measures. Since we include cluster-robuststandard errors at the country-pair-level and the Hausman test (1978) is inappropriate under het-eroskedasticity, we employ a generalization of the Hausman approach proposed by Arellano (1993)to test for the appropriateness of our model specification. Adopting this approach, the null hypoth-esis of “no correlation between the individual specific effect and the independent variables” cannotbe rejected with � < 0.811 suggesting that applying the random-effects model is appropriate for ouranalysis.

4.2. Data and sources

A detailed exposition of all variables and data sources is presented in Table 2. While descriptivestatistics for the entire dataset is provided in Table 3, a correlation matrix is presented in Table 4.

4.2.1. Foreign bank claimsWe retrieve our measure of OECD banking markets’ foreign bank claims on the public, banking and

non-banking private sector of recipient emerging countries from the Consolidated Banking Statisticsprovided by the Bank for International Settlements (BIS). Introduced in the late 1970s, the ConsolidatedBanking Statistics aims at providing detailed information on contractual claims of banks’ domesticoffices in reporting countries including their foreign affiliates on the rest of the world. Consolidateddata is originally collected by national central banks in an aggregate form and reported to the BIS usingthem as a basis for calculating global data. The BIS statistics covers nearly 100% of the domestic bankingsystems’ claims (Table 1) and thus provides a unique and comprehensive data source for time-seriesanalyses.

The BIS distinguishes between international and foreign claims, comprising different on-balancesheet exposures. While international claims cover cross-border claims of domestic banks in all cur-rencies plus local claims of foreign affiliates in foreign currency, foreign claims additionally includelocal claims of foreign subsidiaries in local currency. To avoid double-counting, inter-office positionsbetween reporting banks and their foreign affiliates and branches are netted out. Additionally, claimswhich have been written off or have been abated are excluded from the statistics since the revaluation

1 With regard to the subsample built on different lending relationships (Section 5.2.2), we do not cluster at the country-pair-level since the split of the sample is based on country-pair differences per se.

2 As Table 3 reports, the number of observations varies. Thus, in addition to random effects, we apply the consistent estimatorfor the variance components by Baltagi and Chang (1994, 2000) as a robustness check to avoid possible biases resulting fromour unbalanced panel. However, as baseline results and results from subsamples do not differ significantly from the ordinaryrandom effects estimations, we do not comment them in this paper.


Table 1Included source and recipient countries.

OECD source country Recipient country (emerging market)

Name Percentage coverage offoreign claimsa

Number of reportingbanks at end-2007a

More-developedemerging marketsb

Frontier marketsb

Austria Nearly 100% 57 Brazil ArgentinaBelgium 100% 102 Chile BahrainFinland Nearly 100% 6 China BotswanaFrance Nearly 100% 347 Colombia BulgariaGermany Nearly 100% 2000 Czech Republic CroatiaItaly 100% 806 Egypt EstoniaJapan Nearly 100% 158 Hungary GhanaNetherlands 100% 101 India JamaicaSpain Nearly 100% 177 Indonesia JordanSweden Nearly 100% 11 Israel KazakhstanSwitzerland Approx. 95% 60 Malaysia KenyaUnited Kingdom 98% 190 Mexico KuwaitUnited States Nearly 100% 150 Morocco Lebanon

Peru LithuaniaPhilippines MauritiusPoland NigeriaRussia OmanSouth Africa PakistanSouth Korea QatarTaiwan RomaniaThailand Trinidad and TobagoTurkey Saudi Arabia

SerbiaSloveniaSri LankaTunisiaUkraineUnited Arab EmiratesVietnam

a BIS (2008b, pp. 35–39).b Classification according to MSCI Barra as of April 2009.

indicates that the present or prospective value of the claim is expected to be zero. Our analysis focuseson foreign claims on an immediate borrower basis.3

Due to the hub-like pattern of international bank lending, we include 13 OECD lending countrieswhich provide continuous information on their banking systems’ financial claims on other countriesfor our period of interest from 1993 to 2007.4 Following related empirical studies, we restrict ouranalysis to emerging markets and do not include financial transactions between developed markets.Consequently, our analysis encompasses 51 emerging markets classified as more-developed emergingand frontier markets in line with MSCI Barra as of April 2009. A list of countries included as well as

3 Foreign claims on an immediate borrower basis allocate claims to the country where the original risk is resident. However,as a reaction to financial crises in emerging markets in the late 1990s, the BIS enhanced its statistics. Since the third quarterof 2005, data on an ultimate risk basis are published, i.e. claims are allocated to the country where the final risk remains (e.g.due to risk mitigation). Unfortunately, the time horizon of this data is too short for a comprehensive empirical study employingpanel analysis.

4 We exclude the years covering the 2008–2011 financial crisis from our analysis for two related reasons. First, it is not themain contribution of our paper to investigate the nexus between cross-border lending and the recent financial crisis. Sincespecific studies on this issue already exist, we avoid a mixing-up with this distinct strand of literature. Second, the analysisat hand rather focuses on the impact of OECD country banking market- and banking sector-specific characteristics on cross-border lending trying to evaluate new source country determinants of cross-border financial flows. Including the recent financialcrisis, which has affected all source but not all recipient countries in our sample, requires to additionally employ attributes fromaffected recipient countries in order to obtain a reliable statistical inference as regards the change in cross-border lending flowsduring the crisis years.


Table 2Notes on variables and data sources.

Variable Definition Source

Foreign claims(i, j, t) Log of the sum of the OECD source country banking systems’cross-border claims on recipient countries in all currencies aswell as local claims of their foreign affiliates in foreign andlocal currency

Consolidated BankingStatistics (BIS)

GDP(i, t) Log of the source country’s real GDP World DevelopmentIndicators (WDI)

GDP(j, t) Log of the recipient country’s real GDP World DevelopmentIndicators (WDI)

GDP growth(i, t−1) Lag (1) of the source country’s growth of real GDP World DevelopmentIndicators (WDI)

GDP growth(j, t−1) Lag (1) of the source country’s growth of real GDP World DevelopmentIndicators (WDI)

Distance(i, j, t) Log of the distance between the capitals of the source andrecipient country

CEPII DistanceDatabase

Linguistic ties(i, j, t) Dummy variable that takes on the value of one if the borrowingcountry shares a common language with the lending country

CIA World Factbook

Concentration(i, t) Fraction of assets of a source country’s total banking system’sassets held by the largest 5 domestic banks

BankScope, own calc.

Disintermediation(i, i) Proxy for the development of the source country’s capitalmarket. Proportion of the banking sector assets to stockmarket capitalization

Beck et al. (2000)

Regulatory arbitrage(de jure)(i, j, t)

Difference between the source and recipient country’s capitalregulatory index (absolute index values). The index is built ofinitial capital stringency and overall capital stringency andthus, captures whether the capital requirement reflect certainrisk elements and deducts certain market value losses fromcapital before minimum capital adequacy is determined andwhether certain funds may be used to initially capitalize abank and whether these funds are officially verified. The indexranges from 0 to 9 with higher index values indicating greatercapital stringency. Data combined from three World BankSurveys on Bank Regulation and Supervision conducted in1998/2000, 2003 and 2007

Barth et al. (2008)

Regulatory arbitrage(de facto)(i, j, t)

Difference between the source and recipient country’sstrength of external audit (absolute index values). The indexmeasures whether the supervisory authorities have theauthority to take specific actions to prevent and correctproblems arising from banking activity. The index ranges from0 to 14 with higher index values indicating greater strength ofexternal auditing. Data combined from three World BankSurveys on Bank Regulation and Supervision conducted in1998/2000, 2003 and 2007

Barth et al. (2008)

Capital ratio(i, t−1) Lag (1) of the ratio of the source country banking system’sequity capital to total assets

BankScope

Loan loss provisions(i, t) Log of the loan loss provisions of the source country’s bankingsystem

BankScope

Cost inefficiency(i, t) Cost to income ratio of the source country’s banking system BankScopeMore-developed

emerging markets(MDEM)

Dummy variable that takes on the value of one if the recipientcountry is classified as a more-developed emerging market;zero otherwise

MSCI Barra

Frontier markets (FM) Dummy variable that takes on the value of one if the recipientcountry is classified as a frontier market; zero otherwise

MSCI Barra

Common lenderrelationship (CL)(i, j, t)

Dummy variable that takes on the value of one if the sourcecountry is a common lender for the recipient country; zerootherwise

Own calc.

No common lenderrelationship(NCL)(i, j, t)

Dummy variable that takes on the value of one if the sourcecountry is not a common lender for the recipient country; zerootherwise

Own calc.


Table 3Descriptive statistics (baseline regressions).

Variable Expected sign N Mean SD Min Max

Foreign claims(i, j, t) 9158 5.3341 2.4010 0 11.8293GDP(i, t) + 9945 13.4593 1.2858 11.4040 16.2634GDP(j, t) + 9763 10.8008 1.4456 8.0593 14.6761GDP growth(i, t−1) +/− 9282 2.2802 1.4920 −2.0500 6.0900GDP growth(j, t−1) + 9139 4.3796 4.4144 −22.9300 33.9900Distance(i, j, t) − 9945 8.4370 0.8691 4.4214 9.8189Linguistic ties(i, j, t) + 9945 0.0664 0.2489 0 1Concentration(i, t) +/− 9945 69.2873 13.6003 40.8545 95.2256Disintermediation(i, t) + 9945 88.9373 59.7380 13.0379 303.4418Regulatory arbitrage (de

jure)(i, j, t)

+ 8280 −0.1812 2.6176 −7 7

Regulatory arbitrage (defacto)(i, j, t)

+ 9360 −0.1314 1.4968 −12 4

Capital ratio(i, t−1) +/− 9231 4.5565 1.4967 2.5100 9.7500Loan loss provisions(i, t) + 9690 7.8392 1.7055 2.8332 12.0792Cost inefficiency(i, t) + 9894 63.7884 8.4105 43.8300 95.4300More-developed emerging

markets (MDEM)9945 0.4314 0.4953 0 1

Frontier markets (FM) 9945 0.5686 0.4953 0 1Common lender relationship

(CL)(i, j, t)

9945 0.0587 0.2351 0 1

No common lenderrelationship (NCL)(i, j, t)

9945 0.9413 0.2351 0 1

information on the percentage coverage of foreign claims in the lending country’s banking system isprovided in Table 1.

4.2.2. Gravity measuresAs regards variables frequently used in the traditional “gravity model”, we retrieve our measures

for the source and recipient countries’ GDP and GDP growth from the World Development Indicatorsdatabase, provided by the World Bank. With regard to the OECD countries and years included in thisstudy, Japan shows the lowest GDP growth rate among all source countries in 1998 (−2.05%) whenthe country heavily suffered from the Japanese banking crisis, while the growth rate of the Finisheconomy peaks in 1997 (6.09%) after a protracted economic contraction at the beginning of the 1990s.Examining the emerging markets, GDP growth rates are much more volatile. After the collapse ofthe Soviet Union, the Ukrainian economy plunged into a deep recession which reached its low pointin 1994 with a GDP growth rate of −22.93%. In contrast, Kuwait achieved two-digit growth in GDP(33.99%) in 1993 when the country recovered from the Gulf War.

Turning to further gravity measures included, the variable distance is taken from the CEPII databasewhile the dummy variable for the existence of linguistic ties between the OECD country and theemerging market is derived from data published in the CIA World Factbook. Interestingly, only about6.64% of the country-pairs included in our sample share a common language.

4.2.3. Characteristics of OECD banking marketsAs regards different OECD banking markets’ characteristics, we initially control for the respective

banking market structure. The concentration measure is based on bank-specific data retrieved fromthe BankScope database provided by Fitch Ratings. Concentration ratios are calculated as the fractionof assets of the total banking system’s assets held by the five largest domestic banks per OECD country.

The disintermediation variable is built in line with the methodology proposed by Beck et al. (2000)and measures the proportion of the banking sector’s total assets to stock market capitalization. Sug-gesting that this variable proxies for the development of a source country’s capital market we are ableto control for the degree of competitive pressure on lending banks arising from other (near- and non-)banks, capital market investors (e.g. insurance companies, pension funds and hedge funds) and stockmarkets.

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Table 4Correlation matrix.

GDP(i, t) GDP(j, t) GDPgrowth(i, t−1)

GDPgrowth(j, t−1)

Distance(i, j) Linguisticties(i, j)

Concen-tration(i, t)

Disinter-mediation(i, t)

Regulatoryarbitrage (dejure)(i, j, t)

Regulatoryarbitrage (defacto)(i, j, t)

Capitalratio(i, t−1)

Loan lossprovisions(i, t)

Costinefficiency(i, t)

GDP(i, t) 1.00

GDP(j, t) 0.01 1.00

GDP growth(i, t−1) −0.08*** 0.01 1.00

GDP growth(j, t−1) 0.01 0.05*** 0.04*** 1.00

Distance(i, j) 0.24*** 0.20*** 0.02** 0.09*** 1.00

Linguistic ties(i, j) 0.17*** −0.08*** 0.08*** 0.00 0.10*** 1.00

Concentration(i, t) −0.39*** −0.00 0.06*** 0.00 0.03** −0.09*** 1.00

Disintermediation(i, t) −0.01 0.03*** 0.29*** 0.02** 0.05*** 0.13*** 0.09*** 1.00

Regulatory arbitrage (dejure)(i, j, t)

−0.09*** 0.08*** 0.05*** 0.03** −0.03*** −0.05*** 0.01 0.20*** 1.00

Regulatory arbitrage (defacto)(i, j, t)

−0.26*** −0.14*** −0.03*** −0.10*** −0.11*** −0.03*** 0.21*** 0.04*** 0.03*** 1.00

Capital ratio(i, t−1) 0.26*** 0.01 0.23*** 0.03*** 0.10*** 0.15*** −0.33*** 0.10*** 0.04*** −0.18*** 1.00

Loan loss provisions(i, t) 0.40*** −0.00 −0.33*** −0.00 0.04*** −0.05*** −0.31*** −0.30*** 0.08*** −0.22*** −0.28*** 1.00

Cost inefficiency(i, t) −0.17*** −0.03** −0.29*** −0.07*** −0.13*** −0.10*** −0.12*** −0.10*** 0.02* 0.22*** −0.44*** 0.14*** 1.00***

* Statistically significant at the 10% level.** Statistically significant at the 5% level.*** Statistically significant at the 1% level.


Next to market structure characteristics we additionally control for the regulatory frameworkimplemented in different OECD countries by building two indices from combined data retrieved fromthree World Bank Surveys on Bank Regulation and Supervision conducted in 1998/2000, 2003 and2007 (Barth et al., 2008). The construction of each index is explained in detail in Table 2. While dejure regulatory arbitrage is measured as the difference between the source and recipient country’sformal regulations on bank equity capital stringency by means of the capital regulatory index, de factoregulatory arbitrage is measured as the difference between both countries’ strength of external auditsof banks.

4.2.4. Characteristics of OECD lending banksWe finally control for OECD lending banks’ characteristics by employing the banks’ capital ratio,

loan loss provisions and cost inefficiency as reported in Table 2. We retrieve banking sector-specificvariables from consolidated and aggregated balance sheet data from the BankScope database per coun-try and year.5 We build averages over all banks per year and source country but adjust all BankScopedata with regard to the so-called “survivorship bias”, i.e. BankScope deletes historical information onbanks that no longer exist in the latest release of the database (e.g. due to M&A). We forestall thisbias by reassembling the panel data set from individual cross-sections using historical releases of thedatabase based on archived CD-ROMs.

As regards descriptive statistics, the average aggregated capital ratio is 4.58% with a minimum valueof 2.51% (Japanese banking system in 2002) and a maximum value of 9.75% (Finish banking system in2003). The cost inefficiency measure reveals an average value of 63.79% and ranges between 43.83%(US banking system in 2006) and 95.43% (German banking system in 2001).

5. Empirical results

Results from baseline regressions including gravity measures as well as different characteristicsof OECD banking markets (market structures and the regulatory environment) and lending bank-ing system-specific attributes are reported in Table 5. Descriptive statistics of subsamples and theresults of difference-in-means and difference-in-proportions tests are reported in Table 6. Resultsfrom subsample regressions are illustrated in Tables 7–9.

5.1. Baseline regressions

5.1.1. Gravity measuresAs reported in Table 5, both the source and recipient country’s GDP turn out to be significantly

positive throughout all regressions indicating that prospering economies in both countries benefitcross-border bank lending which is in line with previous empirical studies by Papaioannou (2009),Alfaro et al. (2008) and Tornell and Velasco (1992). Moreover, since coefficient values of the recipientcountries’ GDP measure are systematically higher compared to the source country counterpart amongall regressions,6 empirical results further suggest that the national income of borrowing countries maybe a stronger (pull) determinant in explaining cross-border lending from OECD countries to emergingmarkets.

Controlling for the state of the economies within the business cycle, lagged GDP growth of thesource country enters the regressions with different levels of significance and exhibits no significancein regression (4). A negative sign of all estimates suggests that lending banks may ration credit towardsborrowers in emerging markets in periods of domestic economic upswing which may be due to the

5 We are aware of the fact that employing characteristics for each lending bank may be more appropriate with regard to ouranalysis. Unfortunately, the BIS Consolidated Banking Statistics does not provide information on single lending banks, but ratherretrieve aggregate data on cross-border claims from respective national central banks. However, since we employ bank-specificcharacteristics on a consolidated and aggregated level for each OECD banking sector, data included almost exclusively compriseinternationally operating banks.

6 We control for the significance of net differences in estimated coefficients applying the difference-in-means t-test, but donot comment test results separately in each case for the baseline results and following subsample regressions in Section 5.2.


Table 5Baseline regressions.

(1) (2) (3) (4)

GDP(i, t) 0.8106*** 0.7682*** 0.9499*** 0.8189***

(0.0452) (0.0509) (0.0467) (0.0480)GDP(j, t) 1.0153*** 1.0133*** 1.0815*** 1.0190***

(0.0550) (0.0546) (0.0560) (0.0485)GDP growth(i, t−1) −0.0294** −0.0441*** −0.0370** −0.0040

(0.0147) (0.0142) (0.0165) (0.0145)GDP growth(j, t−1) 0.0198*** 0.0196*** 0.0182*** 0.0190***

(0.0048) (0.0047) (0.0055) (0.0047)Distance(i, j, t) −0.5642*** −0.5508*** −0.6799*** −0.5420***

(0.0748) (0.0779) (0.0790) (0.0736)Linguistic ties(i, j, t) 1.3254*** 1.2340*** 1.5868*** 1.4527***

(0.2442) (0.2453) (0.2848) (0.2259)Concentration(i, t) −0.0110**

(0.0048)Disintermediation(i, t) 0.0024***

(0.0007)Regulatory arbitrage (de jure)(i, j, t) 0.0703***

(0.0250)Regulatory arbitrage (de facto)(i, j, t) 0.1132***

(0.0243)Capital ratio(i, t−1) −0.0929***

(0.0237)Loan loss provisions(i, t) 0.0971***

(0.0248)Cost inefficiency(i, t) 0.0162

(0.0028)Time dummies Yes Yes Yes YesCluster country-pair Yes Yes Yes YesCountry-pair random effects Yes Yes Yes YesNo. of obs. 8432 8432 6990 8211No. of groups 654 654 545 654Adj. R2 0.49 0.50 0.53 0.54

Notes: The panel model estimated is Foreign claims(i = source country, j = recipient country, t = time) = ˛i,j + ˇ1 GDPi,t + ˇ2 GDPj,t + ˇ3 GDPgrowthi,t + ˇ4 GDP growthj,t + ˇ5 distancei,j,t + ˇ6 linguistic tiesi,j,t + �t + εi,j,t .Characteristics of OECD banking markets are included in regression (2) and (3); characteristics of lending bank are employedin regression (4).Constant term included but not reported. Country-pair heteroskedastic-robust standard errors are in parenthesis.

* Statistically significant at the 10% level.** Statistically significant at the 5% level.

*** Statistically significant at the 1% level.

fact that the number of local investment opportunities in OECD countries typically increases duringeconomic boom phases. In contrast and as expected, GDP growth of the recipient country enters allregressions significantly positive at the 1% level indicating that emerging markets with prosperingeconomies attract higher levels of bank lending flows.

Introducing distance, this variable turns out to be significantly negative at the 1% level acrossall regressions indicating a negative impact of an increase in geographical distance between twocountries on the volume of cross-border lending activities. This result corresponds with empiricalfindings provided by Degryse and Ongena (2005) suggesting that geographical distance may raise thedifficulty to monitor creditors in more distant emerging markets, resulting in increasing informationasymmetries, transaction costs and investment risk. Taking into account a significant development incommunication technologies, Buch (2005) additionally proposes that the negative effect of distanceon international bank lending activities may also be due to a home bias of lending banks.

Finally, linguistic ties enters each regression in Table 5 significantly positive at the 1% level suggest-ing that the presence of a common language in both countries favors bilateral bank lending betweenOECD countries and emerging markets. This result is in line with findings provided by previous


Table 6Descriptive statistics (subsample regressions).

Variable Mean SD Min Max Mean SD Min Max Test statistic

More-developed emerging markets Frontier marketsForeign claims(i, j, t) 6.48 2.10 0 11.83 4.36 2.20 0 10.59 ***

GDP(j, t) 11.98 0.96 8.69 14.8 9.88 1.03 8.06 12.82 ***

GDP growth(j, t) 4.43 3.80 −13.10 14.43 4.34 4.84 −22.93 33.99 n.s.Distance(i, j, t) 8.59 0.89 5.38 9.78 8.32 0.84 4.42 9.82 ***

Linguistic ties(i, j, t) 0.05 0.22 0 1 0.08 0.27 0 1 ***

Common lender relationship No common lender relationshipForeign claims(i, j, t) 8.26 1.91 0 11.83 5.14 2.30 0 11.34 ***

GDP(j, t) 11.62 1.37 8.32 14.68 10.75 1.43 8.06 14.68 ***

GDP growth(j, t) 3.89 4.54 −16.23 14.00 4.41 4.40 −22.93 33.99 ***

Distance(i, j, t) 7.29 1.21 4.42 9.28 8.51 0.79 5.62 9.82 ***

Linguistic ties(i, j, t) 0.08 0.27 0 1 0.07 0.25 0 1 n.s.

Pre dot-com bubble Post dot-com bubbleForeign claims(i, j, t) 5.14 2.32 0 11.35 5.70 2.52 0 11.83 ***

GDP(j, t) 10.69 1.44 8.06 14.16 11.02 1.44 8.5172 14.68 ***

GDP growth(j, t) 3.68 4.84 −22.93 33.99 5.65 3.14 −11.03 17.72 ***

Distance(i, j, t) 8.44 0.87 4.42 9.82 8.44 0.87 4.42 9.82 –Linguistic ties(i, j, t) 0.07 0.25 0 1 0.07 0.25 0 1 –

Test statistic: difference-in-means test (log of foreign claims, log of GDP, GDP growth, log of distance) or difference-in-proportions test (linguistic ties).n.s., statistically not significant; –, difference-in-means test or difference-in-proportions test not required.* Statistically significant at the 10% level.** Statistically significant at the 5% level.

*** Statistically significant at the 1% level.

empirical studies (e.g. Papaioannou, 2009; Buch, 2005; Herrero and Pería, 2007; Stulz and Williamson,2003) proposing that linguistic ties may diminish informational frictions for two reasons. First, theexistence of a common language may reduce uncertainty and costs of communication during creditnegotiations. Second, ethnological ties may serve as a proxy for cultural proximity between twocountries since sharing a common language usually coincides with a common history and culture.

5.1.2. Characteristics of OECD banking marketsAs shown in Table 5, concentration enters regression specification (2) significantly negative at the

5% level indicating that larger (monopolistic) banks operating in highly concentrated OECD bankingmarkets do only engage in cross-border lending to emerging countries to a limited extent. Applyingtraditional industrial organization theory to banking, we suggest that granting monopolistic interestrates to local customers may prevent from financial constraints to engage in international lendingactivities in order to increase profitability and hence shareholder value (Repullo, 2004). Moreover,cross-border lending to emerging markets may not only be associated with higher expectations oninvestment returns but may also be accompanied by a higher level of investment risk resulting inhigher “risk-return” patterns (Ongena et al., 2011; Buch et al., 2010a; Herrmann and Mihaljek, 2010).If this is true, our findings support theoretical assumptions on the “credit rationing” phenomenon(Cetorelli, 2004; Beck et al., 2000; Boot and Thakor, 2000) proposing that even monopolistic bankstend to limit credit risk by primarily selecting borrowers exhibiting the highest solvency (Boyd andPrescott, 1986; Ramakrishnan and Thakor, 1984).

Due to the fact that even highly concentrated banking markets may be competitive markets follow-ing the “contestability approach” (Baumol, 1982), we further include disintermediation as a proxy forthe level of competitive pressure in local mature banking markets induced by other (near- and non-)banks, institutional capital market investors (e.g. insurance companies, pension funds and hedgefunds) and stock markets (Claessens, 2006; Claessens et al., 2001). As shown, the disintermediationmeasure enters regression specification (2) significantly positive at the 1% level. Hence, empiricalevidence indicates that fiercer competition in OECD banking markets may encourage (or even force)domestic banks to stronger engage in cross-border lending in order to compensate declining domestic

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23 (2013) 136– 162Table 7More-developed emerging and frontier markets.

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b)MDEM FM MDEM FM MDEM FM MDEM FM

GDP(i, t) 0.9196*** 0.7413*** 0.9113*** 0.6649*** 1.0620*** 0.9187*** 0.9396*** 0.7326***(0.0630) (0.0617) (0.0701) (0.0701) (0.0610) (0.0736) (0.0652) (0.0656)

GDP(j, t) 0.8980*** 0.7699*** 0.9006*** 0.7637*** 1.0220*** 0.8981*** 0.8759*** 0.7750***(0.0992) (0.1155) (0.0967) (0.1146) (0.0865) (0.1182) (0.0841) (0.1049)

GDP growth(i, t−1) −0.0176 −0.0394* −0.0374** −0.0496** −0.0258 −0.0467* 0.0076 −0.0011(0.0187) (0.0226) (0.0184) (0.0215) (0.0205) (0.0261) (0.0196) (0.0214)

GDP growth(j, t−1) 0.0014 0.0268*** 0.0013 0.0266*** −0.0013 0.0258*** 0.0011 0.0264***(0.0048) (0.0063) (0.0048) (0.0062) (0.0054) (0.0074) (0.0047) (0.0062)

Distance(i, j, t) −0.4358*** −0.6749*** −0.4420*** −0.6300*** −0.5535*** −0.7924*** −0.4143*** −0.6401***(0.1001) (0.0993) (0.1028) (0.1075) (0.0971) (0.1160) (0.0956) (0.1011)

Linguistic ties(i, j, t) 1.4202*** 1.3218*** 1.3623*** 1.2145*** 1.7728*** 1.5672*** 1.5182*** 1.4746***(0.3753) (0.3054) (0.3994) (0.2950) (0.4207) (0.3668) (0.3576) (0.2786)

Concentration(i, t) −0.0025 −0.0174**(0.0063) (0.0070)

Disintermediation(i, t) 0.0032*** 0.0018*(0.0009) (0.0010)

Regulatory arbitrage (de jure)(i, j, t) 0.1134*** 0.0011(0.0362) (0.0329)

Regulatory arbitrage (de facto)(i, j, t) 0.0785*** 0.3651***(0.0257) (0.0803)

Capital ratio(i, t−1) −0.0910*** −0.0922**(0.0308) (0.0364)

Loan loss provisions(i, t) 0.0688** 0.1167***(0.0320) (0.0369)

Cost inefficiency(i, t) 0.0122 0.0203***(0.0034) (0.0044)

Time dummies Yes Yes Yes Yes Yes Yes Yes YesCluster country-pair Yes Yes Yes Yes Yes Yes Yes YesCountry-pair random effects Yes Yes Yes Yes Yes Yes Yes YesNo. of obs. 3933 4499 3933 4499 3457 3533 3824 4387No. of groups 286 368 286 368 252 293 286 368Adj. R2 0.45 0.34 0.46 0.35 0.53 0.39 0.50 0.39

Notes: The regression model and statistical parameters are described in Table 5. Abbreviations: MDEM, more-developed emerging markets; FM, frontier markets.* Statistically significant at the 10% level.** Statistically significant at the 5% level.*** Statistically significant at the 1% level.

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Table 8Common lender vs. no common lender relationship.

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b)CL NCL CL NCL CL NCL CL NCL

GDP(i, t) 0.6423*** 0.7754*** 0.6665*** 0.7164*** 0.8011*** 0.9226*** 0.5961*** 0.8002***(0.1130) (0.0467) (0.1174) (0.0524) (0.1399) (0.0488) (0.1458) (0.0485)

GDP(j, t) 0.7009*** 0.9611*** 0.7158*** 0.9540*** 0.7200*** 1.0214*** 0.7156*** 0.9712***(0.1460) (0.0584) (0.1527) (0.0576) (0.1163) (0.0604) (0.1526) (0.0498)

GDP growth(i, t−1) 0.0756 −0.0345** 0.0557 −0.0511*** 0.0981** −0.0465*** 0.0809** −0.0015(0.0515) (0.0149) (0.0538) (0.0143) (0.0494) (0.0168) (0.0412) (0.0145)

GDP growth(j, t−1) 0.0082 0.0197*** 0.0059 0.0195*** 0.0041 0.0183*** 0.0083 0.0191***(0.0235) (0.0049) (0.0233) (0.0049) (0.0244) (0.0056) (0.0239) (0.0048)

Distance(i, j, t) −0.4744*** −0.3410*** −0.5437*** −0.3118*** −0.5321*** −0.4434*** −0.5020*** −0.3226(0.1755) (0.0796) (0.1925) (0.0824) (0.1881) (0.0866) (0.1735) (0.0744)

Linguistic ties(i, j, t) 2.2934*** 1.1737 2.3393*** 1.0505*** 2.4376 1.3426*** 2.1291*** 1.3158***(0.5064) (0.2344) (0.5338) (0.2293) (0.6174) (0.2830) (0.5528) (0.2112)

Concentration(i, t) 0.0157* −0.0145***(0.0090) (0.0048)

Disintermediation(i, t) 0.0021 0.0027***(0.0016) (0.0007)

Regulatory arbitrage (de jure)(i, j, t) 0.0920 0.0450*(0.0638) (0.0253)


Capital ratio(i, t−1) 0.1281 −0.1136***(0.1109) (0.0220)

Loan loss provisions(i, t) 0.0516 0.0984***(0.1467) (0.0233)

Cost inefficiency(i, t) −0.0014 0.0171***(0.0101) (0.0029)

Time dummies Yes Yes Yes Yes Yes Yes Yes YesCluster country-pair No No No No No No No NoCountry-pair random effects Yes Yes Yes Yes Yes Yes Yes YesNo. of obs. 540 7892 540 7892 526 6464 533 7678No. of groups 39 616 39 616 38 508 39 616Adj. R2 0.59 0.47 0.60 0.48 0.62 0.50 0.58 0.52

Notes: The regression model and statistical parameters are described in Table 5. Abbreviations: CL, “common lender relationship”; NCL, “no common lender relationship”.* Statistically significant at the 10% level.** Statistically significant at the 5% level.*** Statistically significant at the 1% level.

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23 (2013) 136– 162Table 9Pre/during and post dot-com crisis.

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b)Pre Post Pre Post Pre Post Pre Post

GDP(i, t) 0.8102*** 0.8606*** 0.7571*** 0.8561*** 0.9342*** 1.0272*** 0.7474*** 0.9269***(0.0456) (0.0510) (0.0517) (0.0570) (0.0477) (0.0532) (0.0486) (0.0512)

GDP(j, t) 1.0007*** 1.0666*** 0.9988*** 1.0679*** 1.0501*** 1.1245*** 1.0080*** 1.0651***(0.0491) (0.0484) (0.0487) (0.0476) (0.0508) (0.0526) (0.0456) (0.0444)

GDP growth(i, t−1) −0.0277** −0.0961*** −0.0334** −0.1425*** −0.0207 −0.1347*** −0.0014 −0.0570*(0.0138) (0.0310) (0.0134) (0.0307) (0.0156) (0.0338) (0.0138) (0.0295)

GDP growth(j, t−1) 0.0155*** 0.0112 0.0154*** 0.0111 0.0145*** 0.0037 0.0151*** 0.0097(0.0040) (0.0085) (0.0040) (0.0084) (0.0047) (0.0095) (0.0041) (0.0084)

Distance(i, j, t) −0.3417 −0.9333*** −0.3217*** −0.9493*** −0.4201*** −1.0735*** −0.3239*** −0.9027***(0.0728) (0.0881) (0.0755) (0.0932) (0.0771) (0.0920) (0.0727) (0.0866)

Linguistic ties(i, j, t) 1.1577*** 1.5868*** 1.0923*** 1.3782*** 1.3544*** 1.9482*** 1.2347*** 1.7542***(0.2307) (0.2882) (0.2322) (0.3004) (0.2777) (0.3293) (0.2179) (0.2843)

Concentration(i, t) −0.0122** −0.0066(0.0049) (0.0057)

Disintermediation(i, t) 0.0015* 0.0068***(0.0008) (0.0011)

Regulatory arbitrage (de jure)(i, j, t) 0.0710*** 0.0728***(0.0250) (0.0271)


Capital ratio(i, t−1) 0.0345 −0.1547***(0.0291) (0.0249)

Loan loss provisions(i, t) 0.0939*** 0.0076(0.0318) (0.0188)

Cost inefficiency(i, t) 0.0129*** 0.0219***(0.0035) (0.0034)

Time dummies Yes Yes Yes Yes Yes Yes Yes YesCluster country-pair Yes Yes Yes Yes Yes Yes Yes YesCountry-pair random effects Yes Yes Yes Yes Yes Yes Yes YesNo. of obs. 5412 3020 5412 3020 4485 2505 5323 2888No. of groups 649 643 649 643 540 534 649 642Adj. R2 0.51 0.49 0.52 0.50 0.55 0.55 0.53 0.56

Notes: The regression model and statistical parameters are described in Table 5. Abbreviations: Pre, pre dot-com bubble; Post, post dot-com bubble.* Statistically significant at the 10% level.** Statistically significant at the 5% level.*** Statistically significant at the 1% level.


profit margins or diversify credit risk concentration. Thus, as many emerging countries have liberal-ized and deregulated their own financial markets at the same time, banks from OECD countries mayexplore these new market niches and take advantage of resulting investment opportunities (Arestiset al., 2002; Eichengreen, 2001).

Turning to banking regulatory arbitrage options, Table 5 reports that de jure and de facto regulatoryarbitrage enter regression specification (3) significantly positive at the 1% level. Results indicate thatarbitraging on costs arising from different regulatory requirements between source and recipientcountries may be a significant determinant of cross-border lending to emerging markets which isin line with empirical findings provided by Houston et al. (2012). Moreover, as coefficients of bothmeasures remarkably differ in value, evidence suggests that de facto differences in the regulatoryframework of lending and borrowing countries may be stronger determinants for OECD country banksin our sample to engage in providing credit to borrowers in emerging markets.

5.1.3. OECD lending banks’ characteristicsTurning to OECD lending banks’ characteristics in Table 5, one period lagged capital ratio enters

regression specification (4) significantly negative at the 1% level suggesting that better capitalizedbanking systems hold smaller amounts of foreign claims towards emerging markets. Although it isproposed that even banks exhibiting higher capital buffers are less constrained to bear more credit riskfrom lending to emerging markets (Bouvatier and Lepetit, 2008), results at hand rather correspondwith the “charter value hypothesis” (Keeley, 1990) suggesting that higher franchise values of largerbanks may deter excessive risk-taking behavior by the bank’s management since higher franchisevalues result in higher opportunity costs when going bankrupt (Matutes and Vives, 2000; Besankoand Thakor, 1993). This may be due to the fact that bank managers and shareholders will not acceptrisky investments towards less developed economies if failed foreign investment results in higheropportunity costs under higher capital buffers and hence jeopardize future profits (Hellmann et al.,2000).

Introducing loan loss provisions and cost inefficiency, both variables enter the regression signifi-cantly positive at the 1% level. In general, these empirical results indicate that banks suffering fromlower loan portfolio quality and higher levels of cost inefficiency are more prone to engage in high-risk-return cross-border lending to emerging markets. Thus, additionally taking the significantly positiveimpact of competitive pressure on international bank lending (Section 5.1.2) into account, we suggestthat “searching for yield by following a gambling for resurrection strategy” (Maddaloni and Peydró,2009; Dell’Ariccia and Marquez, 2006; Rochet, 1992) in order to meet capital market expectationsand avoid regulatory constraints may be a further significant determinant of cross-border lending toemerging markets by OECD banks.

5.2. Results from subsample analyses

5.2.1. More-developed emerging markets and frontier marketsIn order to shed a brighter light on the impact and underlying mechanisms of cross-border

bank lending determinants employed, we initially split the entire sample into two subgroups offoreign claims on more-developed emerging markets (MDEM) and frontier markets (FM) respec-tively (Table 7). While more-developed emerging markets experience rapid economic growth andare becoming industrialized, frontier markets represent a subgroup of emerging markets which isinvestable but exhibits considerably higher risk-return patterns (Ongena et al., 2011; Buch et al.,2010b; Herrmann and Mihaljek, 2010). This distinction sheds a brighter light on the determinantsof cross-border bank lending to the heterogeneous group of emerging markets and gains in impor-tance against the background of the recent hype of emerging market investment opportunities andthe associated risks.7

As presented in Fig. 2, more-developed emerging markets attract higher volumes of foreignbank flows although 29 out of the 51 countries in our sample are classified as frontier markets. In

7 In a recent study, Ciarlone et al. (2009) show that market spreads in emerging economies have declined below levelswarranted by improved fundamentals and making these markets vulnerable to sudden shifts in financial market conditions.

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500,000

1,000,000

1,500,000

2,000,000

2,500,000

More-developed emerging markets Frontier markets

mil. USD

Fig. 2. Amount of foreign claims on more-developed emerging and frontier markets by year.


addition and as expected, Table 6 reports that more-developed emerging markets exhibit higher lev-els of national income both on average and in maximum values. In contrast, both groups of emergingmarkets do not differ remarkably as regards the average growth rate of GDP. However, a few frontiermarkets in our sample exhibit very high growth rates resulting in a significantly higher maximum ofGDP growth as compared to more-developed markets. Interestingly, neither distance nor linguisticties represent strong distinctive features for emerging countries in our sample.

Turning to the empirical results shown in Table 7, some of the gravity elements exert differenteffects in both subsample regressions as compared to the baseline regressions. First, both coefficientvalues of the source and recipient country’s GDP turn out to be higher in regressions on more-developed emerging markets as compared to frontier markets indicating that lending banks located inOECD countries with higher national incomes prefer conducting business with borrowers in wealth-ier more-developed emerging markets. Second, our results reveal that coefficient values of the sourcecountry’s GDP are systematically higher in subsample regressions on more-developed emerging mar-kets as compared to the recipient country’s GDP whereas the opposite is true for all regressions withinthe frontier markets subsamples. These findings suggest that bank lending to more-developed emerg-ing markets is more dependent on the level of national incomes in OECD countries (push factor) whilean increase in frontier markets’ GDPs more strongly fosters the provision of loans from OECD banks(pull factor).

Turning to the recipient country’s GDP growth, this measure now turns out to be signifi-cant in regressions on frontier markets only indicating that the development of the recipient’seconomy is a determinant of cross-border lending to frontier rather than to more-developed emerg-ing markets. While the negative coefficients of distance turn out to be higher in regressions onfrontier markets, linguistic ties exhibits higher values in regressions on more-developed emerg-ing markets. Thus, evidence from subsample regressions further reveals that the source country’sincome and cultural proximity tend to be more important determinants of cross-border lend-ing from OECD countries to more-developed emerging markets whereas the recipient’s state ofeconomy within the business cycle and geographical distance present stronger determinants ofinternational bank lending flows to frontier markets.

Turning to OECD banking markets’ characteristics included in regressions (2a) and (2b), concentra-tion enters the frontier markets subsample significantly negative at the 5% level whereas we do notfind any significant impact with regard to the subsample on more-developed emerging markets. Theseresults from subsample regressions concretize our baseline findings and reveal that larger monopolis-tic banks may primarily tend to ration credit supply for customers located in riskier frontier markets.In line with baseline results, disintermediation enters both regressions significantly positive at the oneand 10% level respectively while the coefficient of this variable is almost twice the size of the valuefor more-developed as compared to frontier markets. Results suggest that eroding local profit mar-gins due to increasing competitive pressure may rather be compensated by exploring market nichesin economically prospering more-developed emerging markets experiencing industrialization. Thismay be due to the fact that lending banks face a trade-off between high risk and high return especiallywhen lending to (riskier) frontier markets.

Addressing de jure and de facto regulatory arbitrage in specifications (3a) and (3b), both variablesenter the regression on more-developed emerging markets significantly positive at the 1% level andwith coefficient values of de jure regulatory arbitrage being higher compared to de facto regulatoryarbitrage. With regard to the frontier markets subsample, only de jure regulatory arbitrage enters therespective regression statistically significant. Against this background, we provide empirical evidencethat arbitraging on costs arising from de facto different local regulatory requirements in the sourceand recipient country turn out to be more distinguished drivers of cross-border lending from OECDto more-developed emerging markets whereas de facto differences favor bank lending to riskier andless developed frontier markets. In addition, empirical results confirm that even though Basel II wasincorporated in most of the frontier markets included in our sample, a fully fledged global harmoniza-tion of banking regulation rules and practices as well as trans-border coordination of national bankingsupervisors has not been achieved yet.

Turning to OECD lending banks’ characteristics, regression specifications (4a) and (4b) in Table 7report that the one period lagged capital ratio enters both subsample regressions significantly


negative at the one and 5% level with coefficient values being slightly higher for regressions on frontiermarkets. Thus, evidence from subsample regressions is in line with our baseline findings and addi-tionally confirms that banks exhibiting higher capital buffers are less prone to risk taking which inparticular holds for riskier credit investments towards frontier markets. Finally and compared to base-line results, lending banks’ loan loss provisions and the degree of cost inefficiency remain robust insigns and significances during subsample regressions while coefficient values turn out to be higher asregards the frontier market estimates. Against this background, evidence from subsample regressionsconfirms our hypothesis that a “search for yield by following a gambling for resurrection strategy” byOECD country banks may be a meaningful determinant of cross-border lending, in particular whenlending towards (riskier) frontier markets.

5.2.2. Common lender relationshipIn a next step, we build two further subsamples and distinguish between cross-border claims

towards countries that share a common lender (CL) relationship and lending to emerging marketeconomies with no common lender (NCL) relationship (Table 8 and Fig. 3).8 A “common lender” isdefined to be an OECD creditor with a high exposure of foreign claims on a group of (contiguous)recipient countries in our sample. Since it is assumed that a common lender may exhibit economiesof scale and scope in cross-border lending resulting in a decrease in information asymmetries, trans-action costs and hence investment risk, we expect to gain further important insight concerning theexplanatory power of the determinants employed in this study.

As regards the descriptive statistics of the two subsamples, Table 6 initially reveals that recipientcountries with a common lender relationship attract higher volumes of foreign bank lending flows andexhibit slightly higher levels of national income on average. In contrast, recipient countries withouta common lender relationship show considerably higher average GDP growth rates and their capitalsare located farther away from respective source countries. Interestingly, the measure of linguistic tiesdoes not represent a strong distinctive feature for common lender relationships in our sample.

Turning to results from subsample regressions as shown in Table 8, all gravity elements remainrobust in signs and significances for the most part compared to baseline regressions from Table 5. How-ever, empirical results further reveal that source and recipient countries’ GDP exhibit distinctly highercoefficient values with regard to the no common lender subsample indicating that national incomeis both a significant push and pull factor for cross-border lending to this group of recipient countries.As regards the state of the economy within the business cycle, source countries’ GDP growth entersthe regressions on countries without a common lender significantly negative, except for regression(4b), indicating that lending banks tend to ration credit to borrowers in countries with loose lendingrelationships during economic upswings in domestic markets. In contrast, recipient countries’ GDPgrowth enters the no common lender subsample significantly positive at the 1% level whereas we donot find any empirical evidence for an impact of this measure for the common lender regressions.These findings suggest that emerging countries without a common lender relationship benefit on alarger scale from an increase in local economic growth.

Coefficient values of linguistic ties and distance are observed to be considerably higher with regardto the common lender subsample throughout all regressions suggesting that lending banks maintainclose lending relationships with countries sharing a common language and being located close to theirdomestic markets.

Introducing our measures of OECD banking markets’ characteristics, concentration enters the com-mon lender subsample regression significantly positive at the weak 10% level whereas this measureturns out to be significantly negative at the 1% level with regard to the no common lender subsample.Hence, as compared to baseline results from Table 5, subsample regressions further reveal that largermonopolistic OECD banks may ration credit supply towards emerging markets that do not share acommon lender whereas they may actually do engage in cross-border lending if they act as a common

8 The correlation between more-developed emerging markets/frontier markets and lending relationships equals 14.43%.Hence, we can rule out that the majority of recipient countries sharing a common lender are only either more-developedemerging markets or frontier markets respectively.

O.

Müller,

A.

Uhde

/ Int.

Fin. M

arkets, Inst.

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oney 23 (2013) 136– 162

157

30%

32%

34%

36%

38%

40%

42%

44%

Share "common lender relationship"

Fig. 3. Share of foreign claims based on a “common lender relationship”.


lender for a group of recipient countries. The latter may be attributed to the fact that credit rationingbecomes less severe for emerging markets with a common lender since those lending activities ben-efit from a decrease in information asymmetries, transaction costs and hence investment risk. Fromthis point of view, results correspond with findings from previous subsample regressions (Table 7)suggesting that larger banks primarily tend to ration credit supply for customers located in riskierfrontier markets.

Disintermediation enters the no common lender subsample regression significantly positive at the1% level whereas we do not provide any evidence as regards the common lender subsample. Comparedto our baseline findings, results from subsample regressions further indicate that cross-border lendingdue to increasing competitive pressure in domestic markets is more likely among countries without acommon lender relationship. This may be due the fact that investment opportunities may be generallyhigher in countries exhibiting no common lender relationships with source countries.

Addressing de jure and de facto regulatory arbitrage in regression specifications (3a) and (3b),coefficients turn out to be positive in both subsample regressions while we do not provide any statis-tical evidence of de jure regulatory arbitrage as regards the common lender subsample. Furthermore,coefficient values of de facto regulatory arbitrage are higher in regressions based on the commonlender subsample. Since common lender commitments are assumed to imply long-term lending rela-tionships between OECD and emerging recipient countries we suggest that the common lender maybe more able to identify and exploit de facto regulatory gaps in recipient countries’ regulatory andsupervisory frameworks.

Turning to OECD lending banks’ characteristics, one period lagged capital ratio enters the no com-mon lender subsample regression significantly negative at the 1% level whereas this measure turnsout to be insignificant with regard to the common lender subsample. Thus, in line with previous find-ings on more developed and frontier markets empirical results indicate that better capitalized OECDbanks avoid risky credit investments by channeling cross-border lending flows towards more familiarmarkets.

Finally, as compared to our baseline findings loan loss provisions and cost inefficiency remainrobust in signs and significances in the regressions on countries without a common lender while wedo not provide any empirical evidence for a statistical impact for the common lender subsample.Accordingly, empirical results clearly indicate that a “search for yield” by following a “gambling forresurrection strategy” may be a determinant for (riskier) bank lending to countries with no commonlender relationships only. Results additionally confirm findings from previous subsample regressionssuggesting that the “gambling strategy” may be a meaningful determinant of foreign bank claims onriskier frontier markets.

5.2.3. Dot-com crisisApplying the Chow-test (1960) to our time series of cross border lending, we finally control for

structural breaks and split the entire sample running from 1993 to 2007 into two different timeperiods in order to distinguish between cross-border lending before and during (1993–2002) andafter (2003–2007) the burst of the dot-com bubble. Controlling for this break in time is importantsince the burst of the dot-com bubble and the resulting dot-com crisis represents the most outstand-ing event in our sample period that affected all source countries in our sample and that is assumedto have similar significant effects on the source country banks’ international investment decisions.Accordingly, as shown in Table 6, descriptive statistics reveal that the average amount of foreignclaims between OECD countries and emerging markets is higher in the post-crisis period compared tothe period before and during the burst of the bubble. Moreover, on average, recipient countries exhibithigher levels of national GDP and GDP growth rates during the post-crisis.

Turning to our findings shown in Table 9, all gravity elements remain robust in signs and signif-icances for the most part compared to baseline regressions from Table 5. However, since especiallycoefficient values of distance and linguistic ties are remarkably higher during the period followingthe burst of the internet bubble, we suggest that OECD banks may have more strongly perceivedthe importance of geographical and cultural proximity to these markets in response to the dot-comcrisis. Similarly, as coefficients of source and recipient countries’ GDP are observed to be higher dur-ing the post-crisis period these push and pull factors have become more important determinants of


cross-border lending after the dot-com crisis. In addition, coefficients of recipient countries GDP arehigher across all regressions compared to the source countries counterpart suggesting that wealthierborrowing countries may find it easier to attract foreign bank lending from mature markets. Further-more, while recipient countries’ GDP growth enters the subsample covering the pre-recovery periodsignificantly positive, this measure becomes insignificant for the post-crisis subsample. Results indi-cate that source country lending banks relied to a minor degree on recipient countries’ economicgrowth rates during the post-crisis period. This may be due to the fact that the number of investmentopportunities in OECD markets may have sharply declined as a consequence of the crisis while alter-native investments opportunities in emerging market economies may have gained in importance atthe same time.

Introducing OECD banking markets’ characteristics, source country banking market concentra-tion enters the regression on the subsample covering the period 1993–2002 significantly negative atthe 5% level whereas it turns out to be insignificant for the post-crisis subsample. We suggest thatlending banks may have primarily restricted cross-border lending towards emerging markets duringthe growth stage of the speculative bubble in order to participate in the boom of the local dot-comindustry.

In contrast, disintermediation enters both regressions significantly positive at the ten and 1%level respectively while its coefficient value is distinctively higher with regard to the post-crisisperiod. Accordingly, results suggest that an increase in competitive pressure on OECD banks intheir home markets after the dot-com crisis may have further fostered cross-border bank lendingtowards emerging markets since these markets may have provided new investment opportunitiesand thus higher profit margins which have become even more important since the aftermath of thecrisis.

Corresponding to our baseline findings, measures of de jure and de facto regulatory arbitrage enterrespective regressions (3a) and (3b) significantly positive with higher coefficient values being reportedfor the post-crisis period. In addition, estimates of de facto regulatory arbitrage turn out to be generallyhigher as compared to estimates of de jure regulatory arbitrage. Against this background, empiricalresults reveal that arbitraging on regulatory gaps may have already been a significant determinant ofcross-border lending before and during the crisis, but regulatory arbitrage has gained in importancefor OECD lending banks after the burst of the dot-com bubble, presumably due to the fact that thesebanks have been faced with stronger regulatory requirements in their home markets in response to thecrisis.

Turning to OECD lending banks’ characteristics, capital ratio enters the post-crisis regressionsignificantly negative at the 1% level whereas we do not find any empirical evidence for animpact of the banks’ capitalization on cross-border lending before and during the crisis. Accord-ingly, results from subsample regressions further reveal that better capitalized banks are lessprone to risk taking in international bank lending, at least in the period after the dot-comcrisis.

Finally, while the measure of loan loss provisions turns out to be significantly positive dur-ing the pre-crisis period only, cost inefficiency enters both subsample regressions significantlypositive with a higher coefficient value during the post-crisis period. Thus, empirical evidenceis ambiguous. On the one hand findings suggest that lending banks with higher loan portfoliorisks may follow a “gambling for resurrection strategy” during the dot-com crisis. On the otherhand, results indicate that banks exhibiting higher cost inefficiencies may try to spur efficiency bymeans of cross-border lending during the post-crisis period. This result was expected if it is truethat especially cost inefficient banks have been faced with tremendous challenges right after thecrisis.

6. Conclusion

Employing data on foreign bank claims from 13 OECD countries on 51 emerging markets, this studyinvestigates determinants of cross-border lending for the period between 1993 and 2007. The anal-ysis at hand extends previous empirical studies for two aspects. First, next to well-accepted “gravitymeasures” we investigate specific characteristics of OECD banking markets (market structures and the


regulatory environment) and lending banks as new determinants of cross-border bank lending. Sec-ond, building subsamples of different recipient countries, lending relationships and time periods, thisstudy further enlightens the underlying mechanisms of cross-border lending between OECD countriesand emerging markets.

Baseline regressions including gravity measures reveal that traditional push and pull factors ofcross-border bank lending also represent strong determinants with regard to our sample. How-ever, empirical results from subsample regressions further indicate that the impact of these gravitymeasures significantly differs depending on cross-border lending (i) to more developed vs. frontiermarkets, (ii) under or beyond common-lender relationships and (iii) during the pre and post dot-comcrisis period. As regards banking markets’ characteristics, we find that larger (monopolistic) OECDbanks tend to ration credit to emerging markets unless they lend under a common lender relationshipor after the burst of the dot-com bubble. In contrast, we find that increasing competitive pressure insource country banking markets may spur cross-border capital flows, especially right after the burstof the dot-com bubble, but to a lesser extend when lending to riskier frontier markets or if the sourcecountry is a common lender for a group of recipient countries. Furthermore, arbitraging on regulatorydifferences and higher risk-taking may be strong determinants of cross-border lending expect for thecase of emerging markets sharing a common lender. Finally, investigating OECD lending banks’ char-acteristics, we provide evidence that higher capital buffers may act as an impediment of cross-borderlending, but not before the burst of the dot-com bubble and in case the borrowing countries share acommon lender. In contrast, “search for yield” by following a “gambling for resurrection” strategy mayspur cross-border capital flows in particular when lending (i) towards frontier markets, (ii) beyond acommon-lender relationship and (iii) during the post dot-com crisis period.

With regard to these results, our analysis complements and extends previous studies in providingnew empirical evidence on a variety of important banking market- and lending bank-specific deter-minants as well as their underlying mechanisms with regard to cross-border lending between OECDsource countries and emerging markets. Accordingly, the empirical results at hand convey impor-tant implications for politicians, banking regulators and bank managers, and we derive two majorpolicy implications. On the one hand, if national regulators are interested in promoting cross-borderbank flows and thus fostering the integration of international financial markets, we primarily suggestensuring the contestability of national banking markets within OECD countries. Parts of this policy aremeasures that spur competition among market participants and carefully assess the influence of theongoing consolidation in banking. On the other hand however, if regulatory and supervisory authori-ties regard an increasing interconnectedness of international banking markets as a threat, we suggest(1) creating a level playing field with regard to the regulation and supervision of banks worldwide and(2) curtailing the possibility of the bank’s management to gamble for resurrection.

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cross-border bank lending: empirical evidence on new determinants from oecd banking markets

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