# Functional distance and US global banks' foreign branch lending

Dieter Vanwalleghem, Carmela D'avino

► To cite this version:

Dieter Vanwalleghem, Carmela D'avino. Functional distance and US global banks' foreign branch lending. Journal of International Financial Markets, Institutions and Money, 2025, 103, pp.102180. 10.1016/j.intfin.2025.102180. hal-05147313

HAL Id: hal-05147313

https://hal.science/hal-05147313v1

Submitted on 7 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ELSEVIER

Contents lists available at ScienceDirect

Journal of International Financial Markets,
Institutions & Money

journal homepage: www.elsevier.com/locate/intfin

<figure><img src="image_3.png" /></figure>

# Functional distance and US global banks' foreign branch lending

Dieter Vanwalleghem<sup>a,*</sup>, Carmela D'Avino<sup>b,†</sup>

## ARTICLE INFO

JEL classification:

G21

Keywords:

Global banks

Foreign branches

Bank lending

Functional distance

## ABSTRACT

This paper examines the significance of functional distance in explaining the lending behavior of foreign branches of global banks. We operationalize functional distance, or the distance between the global bank's headquarters and the host country of the foreign branch, along a geographic, linguistic, and cultural dimension. Analyzing the lending activities of US global banks' foreign branches in 38 countries from 2001 to 2020, we find that geographic and linguistic functional distance has an adverse effect on local lending. We further find that a host country's institutional quality can moderate the effect of functional distance on local lending.

## 1. Introduction

In the wake of the 2007–2009 financial crisis, global banking has witnessed a consolidation of physical banking operations through foreign offices. Brick-and-mortar banking, conducted through foreign branches and subsidiaries, has become the primary approach for global banks to deliver financial services in third countries, gradually replacing direct cross-border transactions (Claessens, 2017). Having a physical presence in host markets helps reduce the operational distance between banks and local borrowers by mitigating informational asymmetries and lowering monitoring costs, enabling closer oversight of foreign borrowers (Petersen and Rajan, 1994). However, conducting foreign operations through affiliates can increase functional distance, or the distance between the global bank's headquarters (HQ) and the host country of the foreign branch, leading to informational frictions between HQ and local branch managers due to communication barriers and disruptions in information flow (Alessandrini et al., 2009). This paper aims at uncovering whether the functional distance between the global banks' HQ and local loan managers affects the lending extended in third countries. To this extent, we consider three dimensions of functional distance: geographical, linguistic, and cultural.

Our empirical analysis examines the lending activities of US global banks' foreign branches in 38 countries over the 2001–2020 period. Focusing exclusively on the lending operations of foreign branches allows us to better address the identification challenge posed by functional distance. Unlike subsidiaries, foreign branches operate under a centralized decision-making model, where lending decisions are controlled by the parent bank (Fiechter et al., 2011). As a result, foreign branches may face significant challenges in transmitting locally gathered information to the HQ for final loan-granting decision-making (Alessandrini et al., 2009; Claessens and Van Horen, 2014). The potential impact of functional distance on lending decisions may be particularly relevant for the transmission of “soft” information. While “hard” information can be derived from objective financial data, “soft” information involves more subjective insights, such as perceived borrower trustworthiness and character, which are central to relationship banking (Petersen and Rajan, 2002).

Our paper relates to the research stream in the international banking literature focusing on the effect of distance on banking flows. Related research to date has primarily focused on the concept of operational distance, exploring how various distance metrics, such as geographical, linguistic, and cultural factors, impact cross-border lending by foreign banks. Overall, these studies provide

<footnote><sup>a</sup> Rennes School of Business, 2 Rue Robert D'Arbrissel, 365065, Rennes, France</footnote>

<footnote><sup>b</sup> IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM- Lille Economie Management, F-59000 Lille, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: dieter.vanwalleghem@rennes-sb.com (D. Vanwalleghem), c.davino@ieseg.fr (C. D'Avino).</footnote>
compelling evidence that operational distance impedes the free allocation of financial assets across national borders by global financial institutions (Buch, 2005; Cerutti et al., 2007; Claessens, 2017; Brei and von Peter, 2018).

Our baseline empirical strategy employs a panel framework inspired by the gravity literature (Anderson, 2011), allowing us to analyze how functional distance, as captured by geographical, linguistic, and cultural factors, affect local lending. The findings point to a negative relationship between geographical and linguistic functional distance and the total amount of loans extended by US global banks through their foreign branches. Notably, a 10% increase in geographical distance is associated with 1.5 to 1.8% fewer local loans, while a one standard deviation increase in linguistic distance is associated with 47.1 to 51% fewer loans. This result is robust to controlling for various macro economic, financial development, and institutional factors. We find mixed evidence regarding the effect of cultural distance as measured using the Hofstede (2001) framework and operationalized along the lines of Kogut and Singh (1988). However, when exploring in detail the sub-components of the Hofstede framework, our results suggest that some dimensions of cultural distance, such as masculinity and uncertainty avoidance, appear to deter foreign branch lending while others, such as power distance, enhance it. Further results suggest that the level of institutional or economic development moderates the impact of linguistic and cultural distance. In particular, linguistic similarity becomes more important to promote foreign branch lending in institutionally strong host nations. However, strong local institutions reduce the negative impact of geographic distance while cultural distance impedes lending only in developing host countries.

From a normative perspective, our results suggest that informational frictions between headquarters and foreign branches can have an adverse effect on financial flows. To address this, policymakers and senior management at headquarters could implement measures to strengthen cross-country communication, including cultural training and language development programs for international banking staff.

The paper is organized as follows. Section 2 presents the literature and testable hypotheses. Section 3 presents the data and the empirical strategy results. Section 4 presents our empirical results and Section 5 concludes.

## 2. Literature and testable hypotheses

### 2.1. Background: theory and empirical evidence

#### 2.1.1. Geographical distance and foreign lending of global banks

The drivers of global banks' international lending decisions have been extensively investigated in the literature, which highlights the role of local and external shocks as well as economic and financial integration with a third country (Galindo et al., 2003; Temesvary, 2014; Avdjiev et al., 2019a; Papaioannou, 2009). A few contributions have further analyzed the impact of geographic distance, and the informational frictions or costs it might entail, on international lending decisions by global banks (Buch, 2005; Mian, 2006; Brei and von Peter, 2018; Zhao et al., 2021). These studies, which align with earlier findings on the inverse relationship between geographical distance and cross-border financial flows (Portes et al., 2001), suggest that the geographic aspect of bank lending is largely influenced by frictions arising from asymmetric information and agency costs. Indeed, banks play a unique role in utilizing private information to screen and monitor loans, a process that heavily relies on the closeness between banks and potential borrowers (Diamond, 1991). The proximity between banks and borrowers enhances monitoring and screening abilities, primarily due to the ease of gathering soft information, which is crucial for assessing credit risk (Agarwal and Hauswald, 2010).

Furthermore, geographical distance may amplify the principal-agent problem between the decision-making headquarter and the local loan officer (Stein, 2002). This occurs because, when loan decisions rely more on soft information rather than on hard or codified data, local loan officers have fewer incentives to collect and communicate this information to headquarters. As a result, the probability of securing funding for local loan projects decreases. Although Petersen and Rajan (2002) have argued that advances in information and credit scoring technologies have allowed banks to increasingly codify information, empirical evidence suggests geographical distance inside banking groups matters. For instance, Mian (2006) finds that greater distance between a bank's headquarters and its foreign branch reduces lending to borrowers when information in the host country is limited. Alessandrini et al. (2009) term this distance as “functional” distinguishing it from “operational distance” between the lending office and the borrower. They find that greater functional distance increased financing constraints for Italian SMEs, whereas reduced operational distance did not always improve credit access. We illustrate the distinction between operational and functional distance for the case of a US global bank in Fig. 1.

#### 2.1.2. Culture and foreign lending of global banks

Geographical distance alone cannot fully account for the extent to which information costs affect global banking activities. Several contributions have indeed highlighted the key role of cultural similarities in explaining global banking flows. Cultural similarities are often identified through linguistic and institutional factors, such as a shared legal origin (Herrero and Peria, 2007) and a common language (Buch, 2003).<fnref n="1" /> Recently, the finance literature has begun to examine alternative definitions of cultural similarities to explain variations in cross-border portfolio allocation.<fnref n="2" /> These constructs are primarily derived from international business studies

<footnote n="1">¹ Other types of distance may subsist, however not necessarily shown to be relevant in global banking. Notably, Yu and Wahid (2014) provide empirical backing to the relevance of an accounting distance, intended as the relative adoption of international accounting standards, in investment decisions of mutual funds.</footnote>

<footnote n="2">² Culture has been found to influence various corporate decisions, including entry mode choices in foreign markets (Kogut and Singh, 1988), corporate leverage (Chui et al., 2002), dividend policy (Shao et al., 2010), and corporate investment (Shao et al., 2013).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. This diagram depicts the difference between functional and operational distance in an international bank's branch network.</figcaption></figure>

and are based on Geert Hofstede's framework, which defines culture as a collective mental programming that differentiates members of one group or category from another (Hofstede, 1984, 2001; Karolyi, 2016). This mental framework manifests in people's values, which reflect their preferences for certain outcomes and guide their behavior in determining what they consider good or bad, rational or irrational, and moral or immoral. This is particularly significant in situations where markets or contracts are incomplete, as not all future contingencies in a trading relationship can be specified. In these contexts, the behavior of economic agents can be influenced by their values and cultural background (Aggarwal et al., 2012).

As detailed below, the Hofstede framework identifies six dimensions of culture which each represent a distinct aspect of cultural values and behaviors, namely (1) power distance, (2) individualism, (3) masculinity, (4) uncertainty avoidance, (5) long term orientation and (6) indulgence. Several contributions have investigated Hofstede's cultural dimensions in the context of international portfolio selection, revealing that cultural factors significantly impact investment behaviors (Beugelsdijk and Frijns, 2010; Anderson et al., 2011; Aggarwal et al., 2012; Karolyi, 2016). Overall, these papers suggest that greater cultural distance between home and host countries intensifies the negative effects of geographic distance on investment decisions.

To date, the Hofstede cultural dimensions have not been examined in relation to global bank lending patterns. Instead, they have been utilized in the literature to explain various cross-country banking behaviors, such as lending corruption (Zheng et al., 2013), bank misconduct (Conlon et al., 2024), and bank risk-taking and volatility (Jin et al., 2022; Berger et al., 2021; Galil and Varon, 2024). Nevertheless, cultural differences can be expected to affect foreign bank lending due to the challenges in transmitting “soft” information across cultural divides both in direct cross-border transactions and inside a banking group hierarchy. Du et al. (2017), for instance, provide evidence that cultural familiarity, beyond linguistic similarities, improves analyst forecasts. They find that US based analysts of Chinese origin or ascendance forecast more accurately firms headquartered in regions sharing the Chinese culture. Moreover, linguistic familiarity does not fully capture their findings so that the higher forecasting accuracy is likely due to the ability to better read between the lines or better understand the impact of Chinese norms and institutions on firm performance.

## 2.2. Hypothesis development

### 2.2.1. Distance and lending through branches

Various forms of distance between a bank and its customers can create information costs which impede the cross-border provision of financial services in general and lending in particular. In order to reduce this negative impact of geographic, linguistic and cultural distance, global banks have set-up foreign affiliates, such as branches or subsidiaries managed largely by local staff and managers, to reduce the effective operational distance between the bank and the customer (Alessandrini et al., 2009). Whereas foreign subsidiaries tend to operate more or less as stand-alone entities within global banking groups, lending through foreign branches brings its own set of distance-related challenges. Indeed, as pointed out by Stein (2002), Mian (2006) and Alessandrini et al. (2009), foreign branches rely on their global bank headquarters for key managerial decision-making, such as, credit policy implementation, risk management, large loan approval and overall strategy. This dependency of the foreign branch on the HQ, requires relatively frequent collaboration and communication which may be impeded by functional distance along at least three dimensions. First, increased geographical distance creates time zone differences and less opportunity for face-to-face interactions which may be critical for building inter-personal trust and relationships, understanding local contexts or the quick resolution of complex managerial challenges. Secondly, linguistic differences create communication barriers due to misunderstandings or misinterpretations and can impede the transmission of soft information which is highly context-dependent and sensitive to nuances (Liberti and Petersen, 2019). Finally, with cultural distance often come differences in negotiation styles, expectations regarding standard business practices and local norms, all of which can delay decision making, cause differences of opinion on the interpretation of context dependent soft information and prevent the reaching of consensus on loan portfolio decisions. In sum, this would suggest that, after controlling for the economic and institutional factors which drive foreign branch lending, foreign branches in geographically, linguistic or culturally distant host nations might be able to secure fewer resources from the group HQ and/or get approval for fewer loans than foreign branches in closer host nations.

We therefore formulate the following testable hypothesis:
**Hypothesis 1.** Geographic, linguistic and cultural functional distance negatively affects US banks' foreign branch lending.

### 2.2.2. Moderation effects

We next posit that the impact of functional distance on foreign branch lending is moderated by the level of institutional development of the host country. Indeed, as suggested by the related literature, strong institutions are associated with increased foreign lending and cross-border financial transactions (Focarelli and Pozzolo, 2005; Papaioannou, 2009; Qian and Strahan, 2007). This effect may extend to functional distance: even when there is significant (geographical, linguistic or cultural) distance between the decision-making HQ and the host country, a well-developed institutional framework can facilitate the effective transfer of critical information. Strong institutions, for example, may improve the likelihood that information is codified or can be codified, thereby supporting foreign lending activities. This effect is due to several reasons. First, countries with a strong regulatory framework impose clear and transparent set of rules along with a strong emphasis on corporate transparency to ensure the enforceability of those rules. This transparency facilitates the task by loan officers to obtain reliable data on business operations, market status and compliance. Second, countries in which the rule of law is upheld and corruption is minimal, regulatory enforcement is more effective, and information is generally reliable. Such a robust legal system ensures accountability for both individuals and corporations, enhancing the credibility of available information. Third, countries with stronger institutions tend to have a civil society and media which promotes public scrutiny and promotes the dissemination of business information. The quality of a host markets' institutions is therefore a critical determinant of the ease with which business transactions can be based on hard information. Lastly, host countries with better protection of property rights should allow global banks to recover more of their investment in case of default and counteract the negative impact of functional distance. Overall, the above arguments suggest that the host nation's institutional quality will reduce the negative impact of geographical, linguistic and cultural distance on foreign branch lending. We therefore hypothesize:

**Hypothesis 2.** Better institutional quality in the foreign branch host market reduces the negative effect of geographic, linguistic and cultural functional distance on US banks' foreign branch lending.

## 3. Data and empirical strategy

### 3.1. International banking data

A major challenge encountered when using international banking statistics is the lack of granularity, which limits the ability to measure functional and operational distances accurately. Notably, the country-level bilateral banking statistics published by the Bank for International Settlements do not allow for the identification of the legal status of the foreign office, branches vs. subsidiaries, preventing the measurement of functional distance. Indeed, as the functional distance concept refers to the distance between the decision-making and the decision-implementing units, we must ascertain that the headquarters of the global bank based in the US decide on the extent of the credit supply by their offices in third countries.<fnref n="3" />

We therefore use data on the local positions of foreign branches of US banks as available on demand from the Federal Financial Institution Examination Council (FFIEC). The FFIEC 030 report contains balance sheet information of all branches of US banks by country of establishment with total assets exceeding $250 million. We restrict our sample to countries for which loan data is available for the period 2001 to 2020.<fnref n="4" /> In addition, we remove countries for which we do not have sufficient data on key economic, institutional or distance related variables.<fnref n="5" /> Our final annual and balanced dataset covers 38 jurisdictions where foreign branches of US banks operate, as detailed in Table 1.

Fig. 2 provides an overview of the lending by the branches of US global banks in the host countries comprising our sample. It reveals that important branch lending takes place in Canada, South America, Europe and Asia Pacific.

To provide further insight on the economic importance of lending by US banks' foreign branches, Fig. 3 provides insights on the evolution over time of the lending positions disaggregated by geographical region, respectively: Africa and the Middle East (AFME), the Americas (AMER), Asia and the Pacific (ASPA) and Europe (EURO). The left panel reveals that aggregate lending has increased over time for all regions except for Africa and the Middle East, which saw a small decline. The share of loans to total assets, shown in the right panel, indicates that despite an initial decline at the beginning of our sample period, loans continue to represent a significant portion of the overall asset portfolio, generally ranging between 10% and 40%, though with considerable fluctuations.

<footnote n="3">³ Our results on the impact of functional distance are conditional on the branch legal status of the foreign office. We do not seek to make claims for foreign offices in general, and focusing on branches-only data therefore does not introduce a selection bias in our data.</footnote>

<footnote n="4">⁴ For the following countries, loan data for the year 2013 was not reported: Algeria, Bangladesh, Dominican Republic, Ecuador, Egypt, Jordan, Kenya, Pakistan, Panama, Tunisia, Uruguay & Vietnam. In addition data was missing for Brazil (2010,2011,2012), Ecuador (2006,2007) and Paraguay (2006). Since the missing data consisted of observations in the middle of the sample period and the number of missing observations per country never exceeded 3 we used linear interpolation to balance our panel and reduce the risk of introducing sample selection bias. Our main results are robust to the removal of the interpolated loan data and are available upon request.</footnote>

<footnote n="5">⁵ In our empirical strategy we control for a host of financial development variables provided by the World Bank. Though data is widely available for most countries in our sample, several years are missing for key countries in our loan dataset such as Argentina, Australia, Bahrain, Canada or New Zealand. The removal of these countries from our dataset however renders our findings on geographical distance insignificant because branches of US global banks are particularly active in those countries. Our findings on language distance are robust to the removal of these countries. Because the unavailability of the control variables appears to introduce a sample selection bias we again used linear interpolation to fill in missing data points. Our results using the raw control data are available upon request.</footnote>
<table><caption>Table 1</caption><caption>Country sample.<br>Country list (n = 38)</caption><thead><tr><th>Europe (n = 6)</th><th>Africa & Middle East (n = 9)</th><th>Asia-Pacific (n = 14)</th><th>Americas (n = 9)</th></tr></thead><tbody><tr><td>France</td><td>Algeria</td><td>Australia</td><td>Argentina</td></tr><tr><td>Germany</td><td>Bahrain</td><td>Bangladesh</td><td>Brazil</td></tr><tr><td>Italy</td><td>Egypt, Arab Rep.</td><td>China</td><td>Canada</td></tr><tr><td>Spain</td><td>Israel</td><td>Hong Kong (China)</td><td>Chile</td></tr><tr><td>Switzerland</td><td>Jordan</td><td>India</td><td>Dominican Rep.</td></tr><tr><td rowspan="5">United Kingdom</td><td>Kenya</td><td>Indonesia</td><td>Ecuador</td></tr><tr><td>South Africa</td><td>Japan</td><td>Panama</td></tr><tr><td>Tunisia</td><td>Korea Rep.</td><td>Paraguay</td></tr><tr><td rowspan="4">United Arab Emir.</td><td>New Zealand</td><td rowspan="4">Uruguay</td></tr><tr><td>Pakistan</td></tr><tr><td>Philippines</td></tr><tr><td>Singapore</td></tr><tr><td></td><td>Thailand</td><td></td></tr><tr><td></td><td>Vietnam</td><td></td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 2. Heatmap of the world representing average annual lending positions of foreign branches of US Global Banks for each country in our sample over the period 2001–2020.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Left hand graph shows aggregate lending positions of foreign branches of US Global Banks over time and by region for the full sample of countries over the period 2011–2020. Right hand graph shows aggregate lending positions as a percentage of total assets of foreign branches of US Global Banks over time and by region for the full sample of countries over the period 2011–2020.</figcaption></figure>
<table><caption>Fig. 4. Aggregate Balance sheet of foreign branches of US Global Banks by region. Numbers represent average annual position over the period 2001–2020 (in 000 000 USD).</caption><thead><tr><th>ASSETS</th><th>AFME</th><th>AMER</th><th>ASPA</th><th>EURO</th><th>TOTAL</th></tr></thead><tbody><tr><td>CASH &amp; BANK BAL</td><td>$ 3 243</td><td>13%</td><td>$ 5 572</td><td>12%</td><td>$ 54 401</td><td>14%</td><td>$ 84 270</td><td>8%</td><td>$ 147 486</td><td>10%</td></tr><tr><td>SECURITIES &amp; TRAD ASSETS</td><td>$ 4 257</td><td>16%</td><td>$ 7 015</td><td>15%</td><td>$ 66 854</td><td>18%</td><td>$ 235 517</td><td>23%</td><td>$ 313 642</td><td>21%</td></tr><tr><td>LOAN &amp; LEASE</td><td>$ 6 733</td><td>26%</td><td>$ 18 490</td><td>39%</td><td>$ 79 725</td><td>21%</td><td>$ 79 973</td><td>8%</td><td>$ 184 921</td><td>13%</td></tr><tr><td>INTER OFF (DUE FROM)</td><td>$ 10 853</td><td>42%</td><td>$ 11 770</td><td>25%</td><td>$ 161 689</td><td>43%</td><td>$ 554 396</td><td>55%</td><td>$ 738 709</td><td>51%</td></tr><tr><td>OTHER ASSETS</td><td>$ 820</td><td>3%</td><td>$ 4 520</td><td>10%</td><td>$ 17 394</td><td>5%</td><td>$ 54 996</td><td>5%</td><td>$ 77 729</td><td>5%</td></tr><tr><td>TOTAL ASSETS</td><td>$ 25 905</td><td>100%</td><td>$ 47 366</td><td>100%</td><td>$ 380 063</td><td>100%</td><td>$ 1 009 152</td><td>100%</td><td>$ 1 462 486</td><td>100%</td></tr></tbody></table><table><thead><tr><th>LIABILITIES</th><th>AFME</th><th>AMER</th><th>ASPA</th><th>EURO</th><th>TOTAL</th></tr></thead><tbody><tr><td>DEPOSITS</td><td>$ 10 845</td><td>42%</td><td>$ 22 630</td><td>48%</td><td>$ 165 547</td><td>43%</td><td>$ 326 428</td><td>32%</td><td>$ 525 450</td><td>36%</td></tr><tr><td>INTER OFF (DUE TO)</td><td>$ 12 919</td><td>50%</td><td>$ 19 967</td><td>42%</td><td>$ 193 521</td><td>50%</td><td>$ 527 185</td><td>52%</td><td>$ 753 592</td><td>51%</td></tr><tr><td>OTHER LIABILITIES</td><td>$ 2 170</td><td>8%</td><td>$ 4 811</td><td>10%</td><td>$ 27 711</td><td>7%</td><td>$ 153 481</td><td>15%</td><td>$ 188 173</td><td>13%</td></tr><tr><td>TOTAL LIABILITIES</td><td>$ 25 934</td><td>100%</td><td>$ 47 408</td><td>100%</td><td>$ 386 779</td><td>100%</td><td>$ 1 007 093</td><td>100%</td><td>$ 1 467 214</td><td>100%</td></tr></tbody></table>

Fig. 4 further corroborates these findings by providing a snapshot of the average aggregate balance sheet of US foreign branches by region. This figure shows that branches of US global banks serve an important role as conduits of liquidity within global banking groups, as evidenced by the substantial inter office assets and liabilities. Nevertheless, loans & leases, the focus of this paper, are economically important across regions ranging from around USD 7 trillion for Africa and the Middle East to around USD 79 trillion for Europe.

## 3.2. Distance measures and data

We conceptualize functional distance along three dimensions: geographic, linguistic and cultural distance.<fnref n="6" />

Geographical distance is measured as the geodesic distance, using the great-circle formula,<fnref n="7" /> between the most populated cities in the US and a particular host nation (Mayer and Zignago, 2011). This metric is widely used in the literature and often preferred over the distance between capital cities since, for instance, two neighboring countries with a large landmass can have a considerable distance between their capitals yet much smaller distance between major cities for trade and commerce close to their common border.<fnref n="8" />

We capture linguistic distance between the US and a host nation using a measure provided in Dow and Karunaratna (2006), which is based on three different sub components: (1) a 5 point scale which quantifies the difference between the dominant languages of the United States and a particular host nation,<fnref n="9" /> (2) a 5 point scale based on the incidence of the United States' dominant language(s) in a particular host nation, and (3) a 5 point scale based on the incidence of a particular host nation's dominant language(s) in the United States. The linguistic distance measure is the single factor solution to a principle component analysis of the above three components, which is rescaled to have values between 1, low linguistic distance, and 10, high linguistic distance. We further consider two binary linguistic distance measures using CEPII data: (1) a binary variable equal to 1 if the United States and a particular host nation do not share a common primary or official language, (2) a binary variable equal to 1 if the United States and a particular host nation do not share a common language spoken by at least 9% of the population.<fnref n="10" />

Turning to cultural differences, these have been captured in various ways in the literature, ranging from equating them to geographical, linguistic and religious differences (Stulz and Williamson, 2003; Mian, 2006) to using specific constructs which attempt to measure culture along a set of distinct dimensions (Aggarwal et al., 2012; Karolyi, 2016). The construct proposed by Geert Hofstede (Hofstede, 1984, 2001), as discussed in Section 2.1.2, has been applied extensively in international business and finance research. Although the Hofstede framework has had several extensions and updates since its initial inception, we will define a host nation's culture along the original four dimensions of power distance, individualism, masculinity and uncertainty avoidance.

Power distance (PDI) is defined as the extent to which less powerful members of organizations and institutions (like the family) accept and expect that power is distributed unequally. In high power distant countries, hierarchy is established and executed in society, without questioning from the latter. In low power distant countries, instead, people question authority and attempt to distribute power.

Individualism (IDV), as opposed to collectivism, captures the degree to which a society is integrated in groups. Individualistic societies have loose ties that often only relate an individual to his/her immediate family. They emphasize the “I” versus the “we”.

<footnote n="6">⁶ See for instance (Buch, 2005; Mian, 2006; Anderson et al., 2011).</footnote>

<footnote n="7">⁷ Geodesic distance is the shortest path between two points on a curved surface, measured along the surface itself, rather than through the surrounding space.</footnote>

<footnote n="8">⁸ Alternative measures of distance are the population weighted harmonic or arithmetic distance measures between the most populated cities for a given country pair. These distance measures vary over time with the most populated cities' population numbers for each country pair but overall are very highly correlated (correlations range from 0.98 to 0.99 for the different distance measures). Our results when using alternative distance measures to the simple distance are very similar.</footnote>

<footnote n="9">⁹ The difference between dominant languages is based on a family language file provided on the website of Dow.</footnote>

<footnote n="10">¹⁰ These measures are the reverse coded versions of the *comlang_off* and *comlang_ethno* variables provided by the CEPII in their gravity datasets.</footnote>
Collectivist societies have tightly integrated relationships that tie extended families and others into in-groups. These in-groups are laced with undoubted loyalty and support each other when a conflict arises with another in-group.

Masculinity (MAS) indicates societal preferences for the expression of masculine vs. feminine traits and distinct emotional gender roles. In highly masculine societies there is a preference for expressing and rewarding achievement, heroism, assertiveness, and material rewards for success and work tends to dominate family life. In more feminine societies there is a general preference for cooperation, modesty, sympathy for the weak, and quality of life.

Finally, uncertainty avoidance (UAV) shows a society's tolerance for ambiguity", or the extent to which people embrace or avert an event of something unexpected, unknown, or away from the status quo. Uncertainty-avoiding countries tend to opt for stiff codes of behavior, guidelines, laws, and generally rely on absolute truth, or the belief in the existence of one universal truth. In societies that embrace uncertainty, there is a broad acceptance of diverse ideas and perspectives, along with a higher degree of flexibility and adaptability to ambiguity, which often results in fewer regulations.

Following standard practice in cultural distance research, we use the Hofstede framework to calculate a Euclidean distance measure between the US and the various host nations in our dataset (Kogut and Singh, 1988; Konara and Mohr, 2019). In particular, cultural distance in our paper is defined as:

$$CULTDIST(US,HN) = \sqrt{\sum_{i=1}^{4} \frac{(D_{i}^{HN} - D_{i}^{US})^2}{VAR_{i}}}$$

and where $D_{i}^{HN}$, $D_{i}^{US}$ stands for power distance, individualism, masculinity or uncertainty avoidance of the US or host nation. $VAR_{i}$ is the variance of each cultural dimension.<fnref n="11" />

### 3.3. Empirical strategy

#### 3.3.1. Regression model

The first hypothesis is tested by means of a panel regression model, aligned with the gravity literature in international trade (Anderson, 2011), as follows:

$$LEND_{i,t} = \alpha + \beta DIST_{i} + \gamma CONTROLS_{i,t} + \delta_{t} + \epsilon_{i,t} \quad (1)$$

where $LEND_{i,t}$ is the aggregate amount of dollar lending extended in host country i in year t by foreign branches of US commercial banks (log-transformed). $DIST_{i}$ captures several measures of functional distance between the US and the host country i, accounting for the ease with which information can be transmitted between headquarters and foreign branches. In particular, in our baseline regressions, we consider geographical (GEODIST), linguistic (LANGDIST), and cultural (CULTDIST) distance, respectively.<fnref n="12" /> GEODIST is the natural log of the geodesic distance between the largest cities (by population) of the US and host nation i. LANGDIST is a measure of linguistic difference between a host nation and the United States. Our primary measure is the composite measure from Dow and Karunaratna (2006); As detailed above, we also consider the three sub-components of linguistic differences on which it is based: (1) distance between the two closest major languages in respectively the US and a particular host nation (DIFFMAJL); (2) the incidence of the US' major language(s) in a particular host nation (HOML-HOST); (3) the incidence of a host nation's major language(s) in the US (HOSTL-HOM). Furthermore, we consider the two binary variables CoOFFL and CoETHL using CEPII data. Finally, CULTDIST is the Kogut and Singh (1988) and Konara and Mohr (2019) euclidean distance between the US and the host country comprising the four dimensions of cultural differences, power distance (PDI), individualism (IDV), masculinity (MAS), and uncertainty avoidance (UAV) as reported by Hofstede (2001).

In line with the gravity literature, we first control for the economic mass of the host nation through the natural log of GDP in dollars, LN(GDP). We expect that, all else equal, host countries with a larger economy will attract more lending. We further control for inflation, INFL, and host country exchange rate volatility against the dollar, FXVOL, to capture local economic stability (Buch, 2005; Avdjiev et al., 2019b).

A second set of local controls captures several dimensions of the development of the local banking sector. Specifically, we consider local banking sector's:

1. Depth, proxied by the ratio of local banks' liquid liabilities to GDP (LL/GDP)

2. Efficiency, proxied by local banks' average cost to income ratio (BNKCINC)

3. Concentration, measured as the assets of the three largest commercial banks as a share of total commercial banking assets (BNKCON)

4. Financial stability, measured as the average z-score of the host nation's banking sector (BNKZSCOR)

<footnote n="11">¹¹ We adopt the Euclidean distance measure proposed by Konara and Mohr (2019), who introduced a minor methodological enhancement to the original measure developed by Kogut and Singh (1988), demonstrating improved performance.</footnote>

<footnote n="12">¹² Details on the construction of the distance measures are provided in Section 3.2.</footnote>
In line with Bilir et al. (2019), we expect that the financial development of host countries has a positive impact on the local activities of foreign banks.

A third set of control variables captures institutional and legal features. COMLEG is a binary variable equal to 1 if host nation i shares a common legal origin after 1991. Since the US has a common law legal origin, this variable captures host nation common law legal traditions and as such host nations with superior creditor rights (La Porta et al., 1998). All else equal, we expect that US banks will have higher branch lending exposures in host nations with stronger creditor rights protection. Secondly, as argued by the gravity literature (Zwinkels and Beugelsdijk, 2010) host nations which are politically more stable should be more favorable investment environments. We control for political stability through the political stability measure provided by the World Governance Indicators (PV).

To test Hypothesis 2 we estimate the following specification:

$$ \begin{aligned} \text{LEND}_{i,t} = & \alpha + \beta_1 \text{DIST}_i + \beta_2 \text{INSTIT}_i + \beta_3 \text{DIST}_i \times \text{INSTIT}_i \\ & + \gamma \text{CONTROLS}_{i,t} + \delta_i + \epsilon_{i,t} \end{aligned} \quad (2) $$

We use two proxies for institutional quality, INSTITI. First, we take a host nation's average institutional quality using the six governance dimensions provided by the WGI: voice and accountability (VA), political stability (PV), government effectiveness (GE), regulatory quality (RQ), rule of law (RL), and corruption control (CC).<fnref n="13" /> Secondly, since institutional quality is highly correlated with broad economic development, we use a binary indicator ADVECON which is equal to 1 if the host nation is an advanced economy, according to the International Monetary Fund (IMF) classification.

The interaction variable is expected to have a positive sign, implying that institutional quality reduces the negative impact of functional distance on foreign lending. In addition to the hypothesized signs of the interaction variable, we expect the sign on the main effect of institutional quality to be positive in line with Papaioannou (2009), Focarelli and Pozzolo (2005) and Ghosh (2016).

Tables 2a–2c give an overview of the variable definitions used in our empirical analysis, while Tables 3 and 4 respectively report the summary statistics and correlation matrix.

### 3.3.2. Estimation method

Following the gravity literature on single country of origin foreign investment studies (Zwinkels and Beugelsdijk, 2010), we estimate our panel regression models using pooled OLS with Huber-White standard errors. This panel model setup does not include country-level fixed effects in order to preserve the estimation of time-invariant distance variables.<fnref n="14" /> We tested the suitability of random effects using Hausman tests, which consistently rejected their use at the 5% level (or at best 10%). This suggests that unobserved country-level effects are correlated with our control variables. To address this, we control for a broad set of host country-level variables that capture key economic and institutional drivers of foreign branch lending (detailed above), and include year fixed effects to account for time trends.

As pointed out by Zwinkels and Beugelsdijk (2010), a potential issue in estimating the distance coefficients arises from the tendency of the investment variable, in our case branch lending, and several control variables, particularly GDP, to exhibit non-stationarity. This non-stationarity can lead to spurious regression problems, potentially biasing the estimation of all other coefficients in the model. Using Breitung panel data unit root tests, we indeed confirm the issue of non-stationarity in both the dependent and independent variables. To address this, in the robustness Section, we employ a 2-stage estimation procedure, as suggested by Zwinkels and Beugelsdijk (2010). To this end, we first model (panel regression) lending considering only the non-stationary controls, except the distance metrics. In the second stage, we regress the residuals of the first stage estimation on the distance variables and the stationary controls.

## 4. Results

### 4.1. Base results

In Table 5 we report the results of model (1) for several specifications. In column (1) we present the estimates without distance measures, while in specifications (2)–(5) we consider the distance measures both individually and jointly.

Overall, the signs of the control variables are in line with expectations drawn from the related literature, as detailed above. Specifically, we find that lending extended by foreign branches of US banks is positively associated with the GDP of the host country. This captures both the effect of within country improvements in macro economic conditions over time and the relative local economic attractiveness. This evidence is in line with the theoretical and empirical literature suggesting that stronger local macro fundamentals are positively related to banking inflows (Morgan et al., 2004; De Haas and Van Lelyveld, 2010). The positive and significant effect of the FXVOL across specifications suggests that host markets with more volatile local currencies attract higher levels of US branch bank lending. Although we cannot observe the currency decomposition of local lending, this may be due to the fact that branches of US banks are in a stronger position to provide loans dominated in foreign currencies, and specifically in US dollars, allowing borrowers to hedge against local currency risk. The variable capturing the liquidity available in the local banking system, i.e. LL/GDP, indicates that looser local funding conditions are significantly and positively associated with the

<footnote n="13">¹³ When using this metric to proxy for institutional quality, we exclude political stability from our set of control variables to avoid multicollinearity.</footnote>

<footnote n="14">¹⁴ In single country of origin studies, the country level fixed effects would absorb the explanatory variables of the time invariant distance variables.</footnote>
<table><caption>**Table 2a**
Variable definitions: Dependent & Distance variables.</caption><thead><tr><th>Variable</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td colspan="3"><strong>Dependent variables</strong></td></tr><tr><td>TOTLOANS</td><td>All loans (i.e., extensions of credit resulting either from direct negotiation between lender and borrower or from the purchase of loan assets from another lender) and all lease financing receivables held on the balance sheets of branches of US banks, in ln(000 USD) (time varying).</td><td>FFIEC030</td></tr><tr><td>COMINDL</td><td>All loans for commercial and industrial purposes to business enterprises (proprietorships, partnerships, and corporations), whether secured or unsecured, single payment or installment. Also includes loans to individuals for commercial, industrial, and professional purposes but not for investment or personal expenditure purposes, in ln(000 USD). (time varying)</td><td>FFIEC030</td></tr><tr><td>REALDEP</td><td>All loans, whatever the purpose, secured primarily by real estate as evidenced by mortgages, deeds of trust, land contracts, or other instruments, whether first or junior liens (e.g., equity loans, second mortgages) on real estate AND all loans, discounts, overdrafts, and leases to depository institutions in ln(000 USD). (time varying)</td><td>FFIEC030</td></tr><tr><td colspan="3"><strong>Distance variables</strong></td></tr><tr><td>GEODIST</td><td>Geodesic distance between the largest cities (by population) of two countries. The distance is calculated using the great-circle formula, which accounts for the curvature of the Earth, in ln(km). (constant over time)</td><td>CEPII gravity database</td></tr><tr><td>LANGDIST</td><td>Measure of linguistic difference between a host nation and the United States based on three sub-components of linguistic differences: (1) DIFF MAJL; (2) HOML-HOST and (3) HOSTL-HOM. (constant over time)</td><td>Dow and Karunaratna (2006)</td></tr><tr><td>DIFF MAJL</td><td>Distance between the two closest major languages in respectively the US and a particular host nation. This distance is based on the 'Family of Languages' provided on the website of Douglas Dow. (constant over time)</td><td>Dow and Karunaratna (2006)</td></tr><tr><td>HOML-HOST</td><td>Incidence of the US' major language(s) in a particular host nation. (constant over time)</td><td>Dow and Karunaratna (2006)</td></tr><tr><td>HOSTL-HOM</td><td>Incidence of a host nation's major language(s) in the US. (constant over time)</td><td>Dow and Karunaratna (2006)</td></tr></tbody></table>

<table><caption>**Table 2b**
Variable definitions: Distance variables (cont.).</caption><thead><tr><th>Variable</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td colspan="3"><strong>Distance variables (continued)</strong></td></tr><tr><td>CoOFFL</td><td>A binary variable equal to 1 if the United States and a particular host nation do not share a common primary or official language. (constant over time)</td><td>CEPII</td></tr><tr><td>CoETHL</td><td>A binary variable equal to 1 if the United States and a particular host nation do not share a common language spoken by at least 9% of the population. (constant over time)</td><td>CEPII</td></tr><tr><td>CULTDIST</td><td>Euclidean distance measure between US and host country based on 4 Hofstede dimensions of culture (PDI, IDV, MAS, UAI). (constant over time)</td><td>Hofstede (2001)</td></tr><tr><td>PDI</td><td>Power distance or the extent to which less powerful members of a society accept and expect that power is distributed unequally. (constant over time)</td><td>Hofstede (2001)</td></tr><tr><td>IDV</td><td>Individualism or the degree to which individuals in a society are integrated into groups. (constant over time)</td><td>Hofstede (2001)</td></tr><tr><td>MAS</td><td>Masculinity or the extent to which a culture is more masculine, prioritizing achievement, competition, and material success, as opposed to more feminine, emphasizing relationships, care for others, and quality of life. (constant over time)</td><td>Hofstede (2001)</td></tr><tr><td>UAI</td><td>extent to which a society feels uncomfortable with ambiguity and uncertainty. (constant over time)</td><td>Hofstede (2001)</td></tr><tr><td>PDI D</td><td>Absolute value difference between PDI of the United States and a particular host nation. (constant over time)</td><td>OC, Hofstede (2001)</td></tr><tr><td>IDV D</td><td>Absolute value difference between IDV of the United States and a particular host nation. (constant over time)</td><td>OC, Hofstede (2001)</td></tr><tr><td>MAS D</td><td>Absolute value difference between MAS of the United States and a particular host nation. (constant over time)</td><td>OC, Hofstede (2001)</td></tr><tr><td>UAI D</td><td>Absolute value difference between UAI of the United States and a particular host nation. (constant over time)</td><td>OC, Hofstede (2001)</td></tr></tbody></table>
<table><caption>**Table 2c**
Variable definitions: Interaction & Control variables.</caption><thead><tr><th>Variable</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td colspan="3"><strong>Interaction variables</strong></td></tr><tr><td>WGI</td><td>Weighted average of the six world governance indicators for a given Host Country: (1) Voice and accountability, (2) Political stability & absence of violence or terrorism, (3) Government Effectiveness, (4) Regulatory Quality, (5) Rule of Law, (6) Corruption Control. (time varying)</td><td>Kaufmann & Kraay (2023)<br>World Governance Indicators</td></tr><tr><td>ADV ECON</td><td>Dummy variable equal to 1 if the host nation is considered to be an advanced economy by the IMF. (constant over time)</td><td>IMF</td></tr><tr><td colspan="3"><strong>Macro control variables</strong></td></tr><tr><td>LN(GDP)</td><td>GDP host country, in ln(000 USD). (time varying)</td><td>CEPII gravity database</td></tr><tr><td>INFL</td><td>Host country inflation (%, GDP Deflator). (time varying)</td><td>World Bank</td></tr><tr><td>FXVOL</td><td>Volatility of the yearly host country exchange rate calculated over the sample period (2001–2020). (constant over time)</td><td>IMF, IFS</td></tr><tr><td>LL/GDP</td><td>Ratio of liquid liabilities to GDP (%), liquid liabilities are also known as broad money, or M3. (time varying)</td><td>World Bank</td></tr><tr><td>BNK CINC</td><td>Bank cost to income ratio (%, country level). (time varying)</td><td>World Bank</td></tr><tr><td>BNK ZSCOR</td><td>Bank Z-score (country level). (time varying)</td><td>World Bank</td></tr><tr><td>BNK CON</td><td>Bank concentration, assets of three largest commercial banks as a share of total commercial banking assets. (time varying)</td><td>World Bank</td></tr><tr><td>COMLEG</td><td>Dummy variable equal to 1 if countries share common legal origins after 1991. (constant over time)</td><td>CEPII gravity database</td></tr><tr><td>POLSTAB</td><td>Index measuring perceptions of the likelihood of political instability and/or politicallymotivated violence, including terrorism. (time varying)</td><td>(Kaufmann and Kraay, 2023)<br>World governance indicators</td></tr></tbody></table>

lending extended by foreign branches in host countries. Further, we find a negative association between local loan positions and host country banking concentration *BNKCON*. This can be accounted for in at least two ways: first, in markets with a higher degree of bank concentration, banks might exploit their market power by restricting loan provision (Carbó-Valverde et al., 2009); secondly, concentrated markets are also often more heavily regulated, limiting the ability of foreign branches to extend credit (Cerutti et al., 2007). As expected, we further find that local lending tends to be higher in host countries featuring common law legal origins. This trend can be attributed to the reduced uncertainty these banks experience when operating within a familiar legal framework, which minimizes cost of legal disputes following default or contractual violations. In addition, as indicated by La Porta et al. (1998), common law jurisdictions are characterized by stronger creditor rights, including increased access to collateral in case of default, facilitating loan recovery processes and reducing overall credit risk. Qian and Strahan (2007) indicated that foreign banks are particularly sensitive to creditor rights protection and supporting why US foreign branches tend to lend more in common law jurisdictions. Lastly, we find that those host countries that depict a higher degree of political stability are associated with more loans granted by foreign branches of US banks. This evidence is in line with Papaioannou (2009) and the general gravity literature on foreign investment (Zwinkels and Beugelsdijk, 2010).

In columns (2)–(5), we consider our distance variables of interest, geographical (*GEODIST*), linguistic (*LANGDIST*) and cultural (*CULTDIST*), with the aim of investigating the empirical backing to Hypothesis 1. In specifications (2)–(4) the distance metrics enter the regression separately, while in specification (5) we consider them jointly.

In line with Hypothesis 1, geographical distance depicts a negative and significant coefficient. Our results indicate that a 10% increase in geographical distance results in a 1.5 to 1.8% lower level of lending in a given host country.<fnref n="15" /> These findings are in line with Buch (2005) and Mian (2006); the latter contribution, in articlar, relates this effect to the greater hierarchical distance rendering soft information transmission more difficult in geographically distant host countries. In specifications (3) and (5), we find that branches in host countries with a larger linguistic difference with respect to the US are associated with lower lending. Notably, we find that a one standard deviation increase in linguistic distance results in a 47.1 to 51.7% lower level of host market lending on average<fnref n="16" />

We find no significant evidence that the composite measure of cultural distance, *CULTDIST*, affects US foreign branch lending (specifications (4) and (5)). Since cultural distance is a complex and multi-dimensional construct, we explore further below whether the sub-components of culture can help explain the cross-country variation in US foreign branch lending.

Table 6 builds on our first set of main results by investigating language distance more in-depth. In particular, as detailed in Section 3.3.1 above, we consider the sub-components of *LANGDIST* (that is, *DIFFMAJL*, *HOML-HOST* and *HOSTL-HOM*) in columns (1)–(3) as well as alternative measures of linguistic distance (that is, *CoOFFL* and *CoETHL*) in (4)–(5)

<footnote n="15">¹⁵ Since both our dependent lending and independent geographical distance variables are log transformed, the estimated coefficient takes the interpretation of an elasticity.</footnote>

<footnote n="16">¹⁶ Since our language distance measure is not ln transformed we calculate the economic impact as $(e^{\beta_{LANGDIST}} - 1) \times \sigma_{LANGDIST}$ for a 1 standard deviation increase in linguistic distance.</footnote>
<table><caption>**Table 3**
Summary statistics.</caption><thead><tr><th></th><th>Mean</th><th>Stdev</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>TOTLOANS</td><td>13.62</td><td>2.20</td><td>0.00</td><td>18.51</td></tr><tr><td>COMINDL</td><td>12.72</td><td>2.98</td><td>0.00</td><td>17.99</td></tr><tr><td>REALDEP</td><td>8.89</td><td>4.94</td><td>0.00</td><td>16.77</td></tr><tr><td>GEODIST</td><td>9.05</td><td>0.61</td><td>6.32</td><td>9.72</td></tr><tr><td>LANGDIST</td><td>6.89</td><td>2.79</td><td>1.04</td><td>10.00</td></tr><tr><td>DIFF MAJL</td><td>2.97</td><td>1.69</td><td>1.00</td><td>5.00</td></tr><tr><td>HOML-HOST</td><td>4.08</td><td>1.16</td><td>1.00</td><td>5.00</td></tr><tr><td>HOSTL-HOM</td><td>3.76</td><td>1.07</td><td>2.00</td><td>5.00</td></tr><tr><td>CoOFFL</td><td>0.69</td><td>0.46</td><td>0.00</td><td>1.00</td></tr><tr><td>CoETHL</td><td>0.41</td><td>0.49</td><td>0.00</td><td>1.00</td></tr><tr><td>CULTDIST (HOF)</td><td>3.32</td><td>1.27</td><td>0.29</td><td>5.46</td></tr><tr><td>PDIDIST</td><td>0.07</td><td>0.04</td><td>0.00</td><td>0.16</td></tr><tr><td>IDVDIST</td><td>0.12</td><td>0.06</td><td>0.00</td><td>0.20</td></tr><tr><td>MASDIST</td><td>0.05</td><td>0.04</td><td>0.00</td><td>0.14</td></tr><tr><td>UAIDIST</td><td>0.06</td><td>0.04</td><td>0.01</td><td>0.15</td></tr><tr><td>PDI</td><td>62.46</td><td>19.30</td><td>13.00</td><td>95.00</td></tr><tr><td>IDV</td><td>41.11</td><td>23.18</td><td>8.00</td><td>90.00</td></tr><tr><td>MAS</td><td>53.24</td><td>12.22</td><td>28.00</td><td>95.00</td></tr><tr><td>UAI</td><td>62.51</td><td>20.41</td><td>8.00</td><td>100.00</td></tr><tr><td>LN(GDP)</td><td>19.47</td><td>1.64</td><td>15.66</td><td>23.41</td></tr><tr><td>INFL</td><td>4.75</td><td>6.49</td><td>-16.27</td><td>49.20</td></tr><tr><td>FXVOL</td><td>168.33</td><td>608.68</td><td>0.00</td><td>3149.00</td></tr><tr><td>LL/GDP</td><td>88.27</td><td>59.34</td><td>16.47</td><td>452.41</td></tr><tr><td>BNKCINC</td><td>56.03</td><td>15.83</td><td>18.60</td><td>237.05</td></tr><tr><td>BNKCON</td><td>64.51</td><td>19.27</td><td>22.76</td><td>138.59</td></tr><tr><td>BNKZSCOR</td><td>17.87</td><td>11.12</td><td>-1.84</td><td>70.97</td></tr><tr><td>COMLEG</td><td>0.38</td><td>0.49</td><td>0.00</td><td>1.00</td></tr><tr><td>POLSTAB</td><td>-0.04</td><td>0.93</td><td>-2.81</td><td>1.62</td></tr><tr><td>WGI</td><td>0.34</td><td>0.87</td><td>-1.18</td><td>1.86</td></tr><tr><td>ADV ECON</td><td>0.38</td><td>0.49</td><td>0.00</td><td>1.00</td></tr></tbody></table>

Note: The table reports the summary statistics of the dependent and independent variables used in our empirical analysis. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.

<table><caption>**Table 4**
Correlation table.</caption><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>17</th><th>18</th><th>19</th><th>20</th><th>21</th><th>22</th><th>23</th><th>24</th><th>25</th><th>26</th><th>27</th></tr></thead><tbody><tr><td>(1)GEODIST</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(2)LANGDIST</td><td>0.03</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(3)DIFF MAJL</td><td>-0.00</td><td>0.93</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(4)HOML-HOST</td><td>0.02</td><td>0.95</td><td>0.91</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(5)HOSTL-HOM</td><td>0.05</td><td>0.89</td><td>0.73</td><td>0.72</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(6)CoOFFL</td><td>-0.07</td><td>0.81</td><td>0.77</td><td>0.83</td><td>0.65</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(7)CoETHL</td><td>0.10</td><td>0.60</td><td>0.43</td><td>0.45</td><td>0.75</td><td>0.60</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(8)CULTDIST</td><td>0.26</td><td>0.48</td><td>0.49</td><td>0.57</td><td>0.27</td><td>0.42</td><td>0.07</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(9)PDIDIST</td><td>0.23</td><td>0.22</td><td>0.20</td><td>0.25</td><td>0.16</td><td>0.24</td><td>0.12</td><td>0.72</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(10)IDVDIST</td><td>0.31</td><td>0.39</td><td>0.39</td><td>0.46</td><td>0.24</td><td>0.31</td><td>0.04</td><td>0.94</td><td>0.67</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(11)MASDIST</td><td>0.03</td><td>0.41</td><td>0.47</td><td>0.43</td><td>0.27</td><td>0.34</td><td>0.10</td><td>0.50</td><td>-0.01</td><td>0.34</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(12)UAIDIST</td><td>-0.07</td><td>0.40</td><td>0.43</td><td>0.53</td><td>0.14</td><td>0.48</td><td>-0.01</td><td>0.47</td><td>0.03</td><td>0.23</td><td>0.59</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(13)PDI</td><td>0.16</td><td>0.35</td><td>0.33</td><td>0.38</td><td>0.25</td><td>0.37</td><td>0.18</td><td>0.72</td><td>0.84</td><td>0.70</td><td>0.06</td><td>0.06</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(14)IDV</td><td>-0.31</td><td>-0.39</td><td>-0.39</td><td>-0.46</td><td>-0.24</td><td>-0.31</td><td>-0.04</td><td>-0.94</td><td>-0.67</td><td>-1.00</td><td>-0.34</td><td>-0.23</td><td>-0.70</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(15)MAS</td><td>-0.01</td><td>-0.09</td><td>-0.16</td><td>-0.18</td><td>0.06</td><td>-0.10</td><td>0.20</td><td>-0.41</td><td>-0.23</td><td>-0.40</td><td>-0.46</td><td>-0.22</td><td>-0.20</td><td>0.40</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(16)UAI</td><td>-0.22</td><td>0.38</td><td>0.41</td><td>0.50</td><td>0.16</td><td>0.55</td><td>0.06</td><td>0.21</td><td>-0.07</td><td>0.01</td><td>0.45</td><td>0.67</td><td>-0.07</td><td>-0.01</td><td>-0.15</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(17)LN(GDP)</td><td>-0.12</td><td>0.15</td><td>0.12</td><td>0.05</td><td>0.26</td><td>-0.03</td><td>0.22</td><td>-0.40</td><td>-0.47</td><td>-0.43</td><td>-0.03</td><td>-0.02</td><td>-0.36</td><td>0.43</td><td>0.40</td><td>-0.08</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(18)INFL</td><td>0.09</td><td>0.10</td><td>0.15</td><td>0.17</td><td>-0.02</td><td>0.10</td><td>-0.11</td><td>0.12</td><td>0.02</td><td>0.16</td><td>-0.06</td><td>0.05</td><td>0.10</td><td>-0.16</td><td>-0.14</td><td>0.11</td><td>-0.19</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(19)FXVOL</td><td>0.22</td><td>0.28</td><td>0.31</td><td>0.21</td><td>0.28</td><td>0.17</td><td>0.28</td><td>0.19</td><td>0.11</td><td>0.26</td><td>0.22</td><td>-0.19</td><td>0.13</td><td>-0.26</td><td>-0.23</td><td>-0.28</td><td>-0.05</td><td>0.15</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(20)LL/GDP</td><td>0.00</td><td>-0.07</td><td>-0.10</td><td>-0.18</td><td>0.08</td><td>-0.14</td><td>0.07</td><td>-0.12</td><td>-0.14</td><td>-0.11</td><td>0.00</td><td>-0.01</td><td>-0.13</td><td>0.11</td><td>0.35</td><td>-0.33</td><td>0.35</td><td>-0.35</td><td>-0.05</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(21)BNKCINC</td><td>-0.24</td><td>0.04</td><td>0.00</td><td>0.07</td><td>0.02</td><td>0.01</td><td>-0.08</td><td>-0.24</td><td>-0.40</td><td>-0.28</td><td>0.02</td><td>0.15</td><td>-0.38</td><td>0.28</td><td>0.16</td><td>0.26</td><td>0.18</td><td>-0.08</td><td>-0.10</td><td>-0.01</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(23)BNKZSCOR</td><td>-0.01</td><td>-0.26</td><td>-0.30</td><td>-0.27</td><td>-0.17</td><td>-0.14</td><td>-0.14</td><td>-0.00</td><td>0.34</td><td>-0.04</td><td>-0.19</td><td>-0.08</td><td>0.08</td><td>0.04</td><td>0.07</td><td>-0.11</td><td>-0.25</td><td>-0.12</td><td>-0.15</td><td>0.13</td><td>-0.21</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(22)BNKCON</td><td>-0.03</td><td>-0.24</td><td>-0.30</td><td>-0.23</td><td>-0.16</td><td>-0.13</td><td>-0.15</td><td>-0.23</td><td>-0.18</td><td>-0.20</td><td>-0.17</td><td>0.02</td><td>-0.27</td><td>0.20</td><td>-0.06</td><td>-0.03</td><td>-0.14</td><td>-0.12</td><td>-0.23</td><td>0.03</td><td>0.05</td><td>0.13</td><td>1.00</td><td></td><td></td><td></td></tr><tr><td>(24)COMLEG</td><td>0.14</td><td>-0.70</td><td>-0.69</td><td>-0.74</td><td>-0.52</td><td>-0.72</td><td>-0.26</td><td>-0.33</td><td>-0.19</td><td>-0.20</td><td>-0.23</td><td>-0.43</td><td>-0.29</td><td>0.20</td><td>-0.04</td><td>-0.44</td><td>-0.09</td><td>-0.11</td><td>-0.21</td><td>0.10</td><td>-0.26</td><td>0.06</td><td>0.12</td><td>1.00</td><td></td></tr><tr><td>(25)POLSTAB</td><td>-0.22</td><td>-0.14</td><td>-0.08</td><td>-0.14</td><td>-0.14</td><td>0.03</td><td>0.00</td><td>-0.38</td><td>-0.45</td><td>-0.44</td><td>0.07</td><td>0.19</td><td>-0.37</td><td>0.44</td><td>0.19</td><td>-0.04</td><td>0.26</td><td>-0.26</td><td>-0.06</td><td>0.39</td><td>0.18</td><td>0.03</td><td>0.18</td><td>-0.05</td><td>1.00</td></tr><tr><td>(26)WGI</td><td>-0.24</td><td>-0.35</td><td>-0.32</td><td>-0.36</td><td>-0.29</td><td>-0.23</td><td>-0.15</td><td>-0.55</td><td>-0.58</td><td>-0.63</td><td>0.06</td><td>0.14</td><td>-0.61</td><td>0.63</td><td>0.19</td><td>-0.03</td><td>0.38</td><td>-0.38</td><td>-0.23</td><td>0.44</td><td>0.28</td><td>0.04</td><td>0.29</td><td>0.14</td><td>0.86</td><td>1.00</td></tr><tr><td>(27)ADV ECON</td><td>-0.22</td><td>-0.22</td><td>-0.24</td><td>-0.29</td><td>-0.10</td><td>-0.28</td><td>-0.10</td><td>-0.55</td><td>-0.60</td><td>-0.61</td><td>-0.01</td><td>0.10</td><td>-0.67</td><td>0.61</td><td>0.30</td><td>-0.07</td><td>0.52</td><td>-0.40</td><td>-0.19</td><td>0.50</td><td>0.32</td><td>0.01</td><td>0.27</td><td>0.13</td><td>0.60</td><td>0.83</td><td>1.00</td></tr></tbody></table>

Note: The table reports the pairwise correlation between the independent variables used in our empirical analysis. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.
<caption>Table 5</caption>

Total loans: Main results.

<table><thead><tr><th></th><th>(1)<br>BASE</th><th>(2)<br>GEO DIST</th><th>(3)<br>LANG DIST</th><th>(4)<br>CULT DIST</th><th>(5)<br>ALL DIST</th></tr></thead><tbody><tr><td>GEODIST</td><td></td><td>-0.175**<br>(-2.41)</td><td></td><td></td><td>-0.145**<br>(-2.11)</td></tr><tr><td>LANGDIST</td><td></td><td></td><td>-0.156***<br>(-4.89)</td><td></td><td>-0.170***<br>(-4.28)</td></tr><tr><td>CULTDIST (HOF)</td><td></td><td></td><td></td><td>-0.055<br>(-0.78)</td><td>0.078<br>(0.94)</td></tr><tr><td>LN(GDP)</td><td>0.534***<br>(13.90)</td><td>0.533***<br>(13.92)</td><td>0.555***<br>(15.31)</td><td>0.518***<br>(11.33)</td><td>0.594***<br>(13.69)</td></tr><tr><td>INFL</td><td>0.014*<br>(1.68)</td><td>0.015*<br>(1.70)</td><td>0.012<br>(1.43)</td><td>0.014<br>(1.50)</td><td>0.014<br>(1.48)</td></tr><tr><td>FXVOL</td><td>0.000*<br>(1.93)</td><td>0.000**<br>(2.25)</td><td>0.000***<br>(2.68)</td><td>0.000*<br>(1.92)</td><td>0.000***<br>(3.21)</td></tr><tr><td>LL/GDP</td><td>0.008***<br>(9.67)</td><td>0.009***<br>(9.66)</td><td>0.009***<br>(10.39)</td><td>0.009***<br>(8.15)</td><td>0.009***<br>(8.15)</td></tr><tr><td>BNKCINC</td><td>0.002<br>(0.56)</td><td>0.001<br>(0.21)</td><td>-0.002<br>(-0.58)</td><td>0.000<br>(0.10)</td><td>-0.003<br>(-0.84)</td></tr><tr><td>BNKZSCOR</td><td>-0.011<br>(-1.64)</td><td>-0.012*<br>(-1.80)</td><td>-0.019**<br>(-2.42)</td><td>-0.013**<br>(-1.96)</td><td>-0.020***<br>(-2.60)</td></tr><tr><td>BNKCON</td><td>-0.010***<br>(-2.98)</td><td>-0.010***<br>(-2.89)</td><td>-0.012***<br>(-3.31)</td><td>-0.011***<br>(-3.00)</td><td>-0.011***<br>(-3.00)</td></tr><tr><td>COMLEG</td><td>1.611***<br>(12.29)</td><td>1.642***<br>(12.37)</td><td>0.958***<br>(4.32)</td><td>1.559***<br>(11.51)</td><td>1.026***<br>(4.77)</td></tr><tr><td>POLSTAB</td><td>0.232***<br>(2.98)</td><td>0.208***<br>(2.59)</td><td>0.153**<br>(2.10)</td><td>0.211**<br>(2.19)</td><td>0.156*<br>(1.71)</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>N</td><td>760</td><td>760</td><td>760</td><td>720</td><td>720</td></tr><tr><td>adj. R<sup>2</sup></td><td>0.429</td><td>0.430</td><td>0.443</td><td>0.396</td><td>0.412</td></tr></tbody></table>

Note: The table reports the results of estimating a pooled OLS regression with ln(Total Loans) as the dependent variable and different measures of distance as focal independent variables. Huber/White standard errors are used for determining the z-statistics in parentheses and p-values, \* p < 0.1, \*\* p < 0.05, \*\*\* p < 0.01. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.

Overall, we find across all specifications that linguistic distance has a strong deterring effect on branch lending. The impact of sharing a common official language is particularly strong with on average 66.7% lower lending on average in countries who do not have English as their official language. This evidence further highlights the critical role of a shared language in reducing lending frictions. Overall, the linguistic ties between a host country and the US play a crucial role in facilitating the operations of US banks, leading to a greater willingness to extend loans in a host country.

In Table 7, we look in more detail into the impact of culture on foreign branch lending and investigate the impact of the four Hofstede dimensions, that is, power distance, individualism, masculinity, and uncertainty in both distance terms and in levels.

In addition, in Fig. 5 we give an overview of the country ranking of the different Hofstede culture dimensions as well as the score for the United States to facilitate the interpretation of our results. In the cultural distance literature, it is widely recognized that identifying distance effects is more challenging in studies focused on a single country of origin. This is because the cultural reference point remains constant, making it difficult to distinguish between difference effects and level effects (Shenkar, 2001; van Hoorn and Maseland, 2016).

In columns (1)–(4) of Table 7, we first consider the absolute value of the difference for each of the culture dimensions between the host country and the US. This allows us to account for cultural dissimilarities or distance with the US along each specific dimension.

The estimates indicate that larger gaps in masculinity and uncertainty avoidance have significant negative impacts on lending. These dimensions reflect societal norms related to rewards for achievement, heroism, assertiveness or gender roles on the one hand and tolerance for ambiguity on the other which, when starkly different from those observed in the US, appear to make informational frictions more acute and deter foreign branch lending. As shown in Fig. 5, however, the United States scores close to the average on both the MASC and IDV dimensions; thus, this evidence could be due either to the deterring effect of cultural differences on these dimensions or to a level effect. The estimates in specification (7) where we replace masculinity distance with host country masculinity in levels provide further support that differences in masculinity between the US and a host country deter branch lending since the level is insignificant. However, when considering uncertainty avoidance level in specification (8) the negative and significant coefficient suggests that US banks are less likely to lend in countries where there is a high aversion to ambiguity and uncertainty.
<caption>**Table 6**</caption>

Total loans: Language detail.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr><tr><th></th><th>DIFF DOML</th><th>HOML-HOST</th><th>HOSTL-HOM</th><th>CoOFFL</th><th>CoETHL</th></tr></thead><tbody><tr><td>DIFF MAJL</td><td>-0.193<sup>***</sup><br>(-3.98)</td><td></td><td></td><td></td><td></td></tr><tr><td>HOML-HOST</td><td></td><td>-0.436<sup>***</sup><br>(-7.05)</td><td></td><td></td><td></td></tr><tr><td>HOSTL-HOM</td><td></td><td></td><td>-0.250<sup>***</sup><br>(-2.90)</td><td></td><td></td></tr><tr><td>CoOFFL</td><td></td><td></td><td></td><td>-1.147<sup>***</sup><br>(-6.83)</td><td></td></tr><tr><td>CoETHL</td><td></td><td></td><td></td><td></td><td>-0.595<sup>***</sup><br>(-3.81)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>N</td><td>760</td><td>760</td><td>760</td><td>760</td><td>760</td></tr><tr><td>adj. R<sup>2</sup></td><td>0.436</td><td>0.446</td><td>0.436</td><td>0.452</td><td>0.441</td></tr></tbody></table>

Note: The table reports the results of estimating a pooled OLS regression with ln(Total Loans) as the dependent variable and different measures of linguistic distance as focal independent variables. The same control variables are used as in Table 5 but are suppressed for presentational clarity. Huber/White standard errors are used for determining the z-statistics in parentheses and p-values, \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.

<caption>**Table 7**</caption>

Total loans: Culture detail.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr><tr><th></th><th>PDI D</th><th>IDV D</th><th>MAS D</th><th>UAI D</th><th>PDI</th><th>IDV</th><th>MAS</th><th>UAI</th></tr></thead><tbody><tr><td>PDIDIST</td><td>12.947<sup>***</sup><br>(5.53)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>IDVDIST</td><td></td><td>-1.963<br>(-1.22)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MASDIST</td><td></td><td></td><td>-12.878<sup>***</sup><br>(-4.54)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UAIDIST</td><td></td><td></td><td></td><td>-4.528<sup>**</sup><br>(-2.51)</td><td></td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td></td><td>0.032<sup>***</sup><br>(6.93)</td><td></td><td></td><td></td></tr><tr><td>IDV</td><td></td><td></td><td></td><td></td><td></td><td>0.005<br>(1.22)</td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.015<br>(1.60)</td><td></td></tr><tr><td>UAI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.030<sup>***</sup><br>(-9.85)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>N</td><td>720</td><td>720</td><td>720</td><td>720</td><td>720</td><td>720</td><td>720</td><td>720</td></tr><tr><td>adj. R<sup>2</sup></td><td>0.418</td><td>0.397</td><td>0.436</td><td>0.400</td><td>0.435</td><td>0.397</td><td>0.401</td><td>0.438</td></tr></tbody></table>

Note: The table reports the results of estimating a pooled OLS regression with ln(Total Loans) as the dependent variable and different sub-components of cultural distance and culture in levels as focal independent variables. The same control variables are used as in Table 5 but are suppressed for presentational clarity. Huber/White standard errors are used for determining the z-statistics in parentheses and p-values, \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.

This can be attributed to the fact that such jurisdictions may have less tolerance for flexible business practices, leading to higher operational costs and lower attractiveness of these markets for US banks.

Lastly, the results in column (1) suggest that cultural differences in terms of power distance stimulate branch bank lending. Though this could be driven by diversification benefits not captured by our control variables, it is more likely a level effect is at play. In particular, Fig. 5 shows that most of the variation in the *PDID* variable captures host countries having a higher power distance than the US since the latter has a relatively low *PDI* score. Replacing *PDID* with the level of *PDI*, we find significant
<figure><img src="image_7.png" /><figcaption>Fig. 5. Bar graph of Hofstede cultural values for all countries in the sample. Red lines represent US cultural values for each dimension. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

evidence for higher levels of branch lending in host nations with higher power distance. This result is intuitive in our context since power distance captures the extent to which people accept that power is distributed unequally in a hierarchy, with those higher up the chain having stronger decision making power. In our branch-bank setting, such cultural trait can reduce potential conflicts between the decision making HQ and foreign branches and facilitate resource allocation and lending decisions.

Overall, our first set of baseline results support **Hypothesis 1** that geographic and linguistic differences can hamper the transmission of subjective and context dependent information between the HQ and the local branch and negatively affect local branch lending. In so far, as geographical and linguistic differences exist between local loan officers and the US HQ, the encoding/decoding of local information may result in loss of information or lack of trust in the information resulting in fewer loans being approved by the HQ or fewer loan projects being proposed by the local branches. We do not find a significant impact of cultural distance overall but some evidence for the sub-components masculinity and uncertainty avoidance.

## 4.2. *Moderation effects*

In Table 8 (top panel) we next investigate whether host country institutional quality can moderate the impact of distance on US foreign branch lending. That is, we estimate model (2) with the aim of empirically verifying **Hypothesis 2**.<fnref n="17" />

To this extent, we interact our distance measures with *WGI*, which captures the weighted average of the six world governance indicators as detailed in Table 2c.<fnref n="18" /> The estimates presented in the top panel of Table 8 reveal that better host country institutions are not significantly associated with higher foreign branch lending. However, the positive and significant interaction coefficient with *GEODIST* indicate that the negative impact of geographic is lower in host countries with stronger institutions. This result provides direct support for **Hypothesis 2**. The results in column (2) instead suggest that linguistic distance remains a strong inhibitor for local lending as the interaction term is negative and significant. This suggests that linguistic similarity and institutional quality complement rather than substitute each other. This evidence can be possibly due to the fact that a certain level of linguistic similarity is conducive to laying the foundations for increased lending transactions and that greater institutional quality then further amplifies this impact. The bottom panel of Table 8 explores how the status of the host country as an advanced economy (*ADVECON*) interacts with distance to influence lending. Across the three specifications, we find that lending of US banks is significantly more pronounced in advanced economies, as reflected by the positive and significant *ADVECON* coefficient. The interaction terms reveal that advanced economy status strongly counters the negative effect of cultural distance, as seen by the positive and significant coefficient for *CULTDIST* × *ADV*. The magnitude of the interaction term suggests that cultural distance no longer has a negative impact for advanced host economies. This suggests that in advanced economies, cultural barriers are easier to navigate due to

<footnote n="17">¹⁷ In order to avoid problems of multicollinearity between the distance and institution variables on the one hand and the interaction variable on the other when estimating model (2), we standardized the continuous distance variables and the institutional quality variable by subtracting their means and dividing by their standard deviation.</footnote>

<footnote n="18">¹⁸ In the baseline specifications we included in all specifications one dimension of the formal institutions, i.e. political stability. In the top panel of Table 8 we exclude the latter from the controls as political stability is captured in the composite measure *WGI*.</footnote>
<table><caption>**Table 8**
Total loans: Interaction distance & Institutions.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>GEO D X (.)</th><th>LANG D X (.)</th><th>CULT D X (.)</th></tr></thead><tbody><tr><td colspan="4"><strong>PANEL A: Interaction WGI</strong></td></tr><tr><td>GEODIST</td><td>-0.246***<br>(-3.05)</td><td></td><td></td></tr><tr><td>LANGDIST</td><td></td><td>-0.492***<br>(-6.83)</td><td></td></tr><tr><td>CULTDIST (HOF)</td><td></td><td></td><td>-0.136<br>(-1.34)</td></tr><tr><td>WGI</td><td>0.099<br>(0.71)</td><td>-0.056<br>(-0.42)</td><td>0.130<br>(0.83)</td></tr><tr><td>GEOD X WGI</td><td>0.134**<br>(2.23)</td><td></td><td></td></tr><tr><td>LANGD X WGI</td><td></td><td>-0.183***<br>(-3.66)</td><td></td></tr><tr><td>CULTD X WGI</td><td></td><td></td><td>0.043<br>(0.76)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td><em>N</em></td><td>760</td><td>760</td><td>720</td></tr><tr><td>adj. <em>R</em><sup>2</sup></td><td>0.428</td><td>0.445</td><td>0.392</td></tr><tr><td colspan="4"><strong>PANEL B: Interaction ADVECON</strong></td></tr><tr><td>GEODIST</td><td>-0.147<br>(-1.36)</td><td></td><td></td></tr><tr><td>LANGDIST</td><td></td><td>-0.414***<br>(-4.35)</td><td></td></tr><tr><td>CULTDIST (HOF)</td><td></td><td></td><td>-0.298*<br>(-1.89)</td></tr><tr><td>ADV ECON</td><td>0.649***<br>(3.67)</td><td>0.629***<br>(3.51)</td><td>0.733***<br>(4.13)</td></tr><tr><td>GEOD X ADV</td><td>0.073<br>(0.61)</td><td></td><td></td></tr><tr><td>LANGD X ADV</td><td></td><td>-0.006<br>(-0.06)</td><td></td></tr><tr><td>CULTD X ADV</td><td></td><td></td><td>0.367**<br>(2.30)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td><em>N</em></td><td>760</td><td>760</td><td>720</td></tr><tr><td>adj. <em>R</em><sup>2</sup></td><td>0.435</td><td>0.447</td><td>0.406</td></tr></tbody></table>

Note: The table reports the results of estimating a pooled OLS regression with ln(Total Loans) as the dependent variable and the interaction effects between institutional quality and the different distance variables as focal independent variables. The same control variables are used as in Table 5 except that for Panel A POLSTAB has been removed because its a component of WGI and highly correlated with it. Huber/White standard errors are used for determining the z-statistics in parentheses and p-values, * p < 0.1, ** p < 0.05, *** p < 0.01. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.

stronger institutions or more standardized business practices. However, advanced economy status does not significantly reduce the challenges posed by geographic or linguistic distances, indicating that certain barriers, particularly language, remain even in highly developed countries.

The evidence presented in Table 8 should however be interpreted with caution as our dependent variable is log transformed. Indeed, the sign of the interaction variable does not necessarily identify the moderation effect of institutional quality and total loans for the full range of our independent variables. To corroborate the interpretation of our results above, we next present a series of plots in Fig. 6 which represent the marginal effects of the cultural distance variables, for different levels of institutional quality captured either through the WGI or the advanced economy status of a host nation. For simplicity we restrict ourselves to the interaction effects which were significantly different from zero.
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Lines in each graph are marginsplots for the interaction effects between distance variables and institutional variables. The graphs are based on the model estimates presented in Table 8 and only represent the specifications for which the interaction effect was significantly different from zero. Vertical lines represent 95% confidence intervals.</figcaption></figure>

These plots confirm the interpretation of our results presented in Table 8. Indeed, the marginal effects confirm the evidence that stronger institutions measured by the world governance indicators weaken the impact of geographic distance but strengthen that of linguistic distance. Interestingly, when considering the moderating impact of advanced country status, we see that cultural distance matters only in a statistically significant way for non advanced countries. Although the slope for the advanced country curve is slightly upward sloping, the confidence bands do not suggest a statistically significant impact of cultural distance.

### 4.3. Robustness results

To check the robustness of our results we run three additional tests, two of a methodological nature and one conceptual.

First, in Section 3.3.2 we introduced the possibility that the non-stationarity of our lending variable and some independent variables may bias the estimated effect of functional distance on branch bank lending. Following Zwinkels and Beugelsdijk (2010), we provide in Table 9 the results of a two-stage estimation method to see if results are robust to this issue. First, using a Breitung unit root test we confirmed that our lending variable, TOTLOANS, LN(GDP), LL/GDP and BNKCON are non stationary for the majority of country panels in our sample. The two-stage estimation procedure then consists in the first stage of regressing the dependent total loan variable on all non-stationary and time unvarying control variables except the distance variables of interest (Panel A of Table 9). In the second stage, we then regress the residuals of the first stage on the stationary controls and the distance variables of interest (Panel B of Table 9).

Our results show that the presence of non-stationarity might indeed have biased our results since the coefficients on GEODIST and LANGDIST in specifications (1) and (2) are smaller than for our base results in Table 5 while they are larger for GEODIST but smaller for LANGDIST in specification (4). The signs and significance levels however are largely unchanged so that only the economic impact of the distance variables is affected. The coefficient on CULTDIST remains insignificant.

Secondly, the key focus of our paper is on identifying the negative impact of functional distance on total loans extended through foreign branches of US global banks. We account for our findings by referring to theory (Stein, 2002; Mian, 2006) which suggests that geographical, linguistic and cultural differences can result in information costs when “soft” or context dependent information needs to be transferred from the local branch office to the HQ where ultimate decision making power resides. If this line of reasoning is correct, then our findings should be stronger (weaker) for loan categories which are likely to be more (less) dependent on context dependent, “soft” information.

To test this we split our dependent variable into two categories and consider separately on the one hand the commercial and industrial loans and on the other the real estate loans and loans to depository institutions. We expect real estate loan decisions to be less affected by information costs because they are more likely to be collateralized, allowing credit risk to be more easily codifiable or quantifiable in relation to the value of the underlying real estate asset. Similarly, loans to depository institutions are to a large part extended to meet recurring liquidity needs of borrowing banks. Such loans are often short term, standardized and collateralized, e.g. repo transactions, resulting in credit risk evaluation again to be less dependent on “soft” context dependent information. Commercial and industrial loans, on the other hand, are typically extended to businesses for operational purposes or
<table><caption>**Table 9**
Total loans: 2-stage estimation.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5">Panel A: First stage - dependent variable: Ln(Total Loans)</td></tr><tr><td>LN(GDP)</td><td>0.561<sup>***</sup><br>(18.56)</td><td></td><td></td><td></td></tr><tr><td>LL/GDP</td><td>0.008<sup>***</sup><br>(8.21)</td><td></td><td></td><td></td></tr><tr><td>BNKCON</td><td>-0.011<sup>***</sup><br>(-3.12)</td><td></td><td></td><td></td></tr><tr><td>COMLEG</td><td>1.587<sup>***</sup><br>(14.09)</td><td></td><td></td><td></td></tr><tr><td>FXVOL</td><td>0.000<sup>***</sup><br>(2.83)</td><td></td><td></td><td></td></tr><tr><td>POLSTAB</td><td>0.217<sup>***</sup><br>(3.02)</td><td></td><td></td><td></td></tr><tr><td>Year FE</td><td>Yes</td><td></td><td></td><td></td></tr><tr><td>N</td><td>760</td><td></td><td></td><td></td></tr><tr><td>adj. R<sup>2</sup></td><td>0.426</td><td></td><td></td><td></td></tr><tr><td colspan="5">Panel B: Second stage - dependent variable: Residuals first stage</td></tr><tr><td>GEODIST</td><td>-0.157<sup>**</sup><br>(-2.36)</td><td></td><td></td><td>-0.175<sup>**</sup><br>(-2.29)</td></tr><tr><td>LANGDIST</td><td></td><td>-0.071<sup>***</sup><br>(-4.34)</td><td></td><td>-0.092<sup>***</sup><br>(-4.05)</td></tr><tr><td>CULTDIST (HOF)</td><td></td><td></td><td>-0.037<br>(-0.73)</td><td>0.081<br>(1.22)</td></tr><tr><td>INFL</td><td>0.011<sup>*</sup><br>(1.67)</td><td>0.014<sup>**</sup><br>(2.02)</td><td>0.012<br>(1.50)</td><td>0.014<sup>*</sup><br>(1.73)</td></tr><tr><td>BNKCINC</td><td>-0.000<br>(-0.00)</td><td>0.002<br>(0.71)</td><td>0.000<br>(0.14)</td><td>0.001<br>(0.30)</td></tr><tr><td>BNKZSCOR</td><td>-0.010<sup>**</sup><br>(-2.03)</td><td>-0.013<sup>***</sup><br>(-2.61)</td><td>-0.010<sup>**</sup><br>(-2.01)</td><td>-0.016<sup>***</sup><br>(-2.80)</td></tr><tr><td>N</td><td>760</td><td>760</td><td>720</td><td>720</td></tr><tr><td>adj. R<sup>2</sup></td><td>0.005</td><td>0.015</td><td>0.003</td><td>0.018</td></tr></tbody></table>

Note: Panel A in the table reports the results of estimating a pooled OLS regression with ln(Total Loans) as the dependent variable and different non-stationary or time invariant control variables as independent variables. Panel B in the table reports the results of estimating a pooled OLS regression with the residuals from the regression in Panel A as the dependent variable and the focal distance variables and stationary control variables as independent variables. Huber/White standard errors are used for determining the z-statistics in parentheses and p-values, * p < 0.1, ** p < 0.05, *** p < 0.01. The time period is 2001–2020. Variable definitions can be found in Tables 2a, 2b & 2c.

to finance specific projects, and they often require a deep understanding of local market conditions and firm-specific information. This understanding and information might not be easily quantifiable or codifiable resulting in them being more “soft” information sensitive. In Figure A1 in the Appendix we first give an overview of the importance of these two loan sub-categories over time and by region. In the left hand side panel, we first see that commercial and industrial loans are by far the dominant loan category over time, ranging from around 70 to 80% of total loans each year. This dominance of commercial and industrial loans is largely maintained across the different regions though we note a higher importance of real estate and depository institution loans in the Asia Pacific.

In Table A1 in the appendix we then re-run our baseline model for the two loan subcategories. In line with our expectations, the results in panel A first show that geographical and linguistic distances continue to have negative and statistically significant impacts on commercial and industrial lending. Cultural distance remains insignificant. Turning to panel B however, we find that while linguistic distance has a strong negative impact on real estate and depository institution lending, geographic distance is no longer significant. Moreover, surprisingly cultural distance now has a significant and positive sign. Although the results for geographical distance are in line with our expectations those for cultural distance are puzzling but could be due to two factors: (1) First, as we also showed in our main tests above, the cultural distance variable can be picking up level and or difference effects of the sub-components of culture. What appears as a preference for cultural distance could therefore be a preference for going into host countries with favorable cultural characteristics, (2) in addition, it is possible that cultural distance is capturing loan portfolio diversification benefits not captured by our economic and institutional control variables. In particular, previous literature
has suggested that with greater distance may come increased potential for diversification which reduces the exposure of the global banking group to idiosyncratic shocks (Deng and Elyasiani, 2008; García-Herrero and Vázquez, 2013; Fang and van Lelyveld, 2014; Yildirim and Efthyvoulou, 2018). While in our empirical set-up we control for a host of economic and institutional variables that should capture the economic and institutional diversification benefits of more distant locations, it is possible that not all of these are amply controlled for and we recognize the possibility that cultural distance here may capture diversification benefits which dominate cultural distance related information costs. Disentangling these two explanations is outside of the scope of this paper however and we propose it as a follow up research question. Overall we therefore find some support that functional distance matters more for commercial and industrial loans, the dominant loan category in our sample.

Finally, Fig. 3 shows that the four different regions of our sample, and hence the countries these represent, exhibit significantly different time trends.<fnref n="19" /> This raises the concern that the different countries in our sample might not be subject to the same time related shocks. Using generic time fixed effects for all countries might then introduce systematic residual variation and potentially lead to biased estimates of the coefficients for our focal distance variables. We address this concern in two ways. First, in Table A2 in the Appendix we re-estimate our main model but this time without year fixed effects. The results show that dropping year fixed effects does not affect the sign nor significance of our main coefficients of interest. We only observe a minor impact on the magnitude of the estimated coefficients. Secondly, in Table A3 in the Appendix we re-estimate the impact of linguistic and cultural distance by controlling for region, year and region interacted with year fixed effects.<fnref n="20" /> Doing so indicates that again the sign and significance of our main results is not affected and we only observe a minor impact on the magnitude of our estimated coefficients.

## 5. Conclusion

This paper contributes to the international banking literature by shedding light on the extent to which the distance between the headquarters and foreign offices of global banks (functional distance) has shaped international lending behavior since 2001. As global banks increasingly rely on centralized business models, the exchange of soft information between the headquarters and foreign branches plays a crucial role in determining the extent of lending in foreign countries. By focusing on functional distance, we explore the impact of information frictions between a bank's headquarters and its foreign offices on local lending dynamics. Our analysis supports the importance of geographic and linguistic functional distance in impeding the lending by US global banks through their foreign branches but we find little evidence for the impact of cultural distance. Further explorations reveal that sub-dimensions of culture play a role although the evidence is mixed in terms of whether cultural characteristics or cultural differences explain our estimation results.

In addition, we find that institutions can play an important role in moderating the impact of functional distance on foreign branch lending although much depends on how institutional strength is captured. In particular, our findings suggest that stronger institutions, as measured by the world governance indicators, can mitigate the negative impact of geographic distance on branch lending. This is in line with our second hypothesis that in stronger institutional contexts, communication can be increasingly based on hard codifiable information, reducing the challenge of communicating context specific information between the local branch officer and the global bank's HQ. Similarly, we find that a host country's status as an advanced economy neutralizes the impact of cultural distance in general. In particular, we find that cultural distance only appears to be a limiting factor for foreign branch lending in emerging economies. However, we further find that institutions can also work in tandem with functional distance. In particular for host countries with a stronger institutional base, linguistic similarity provides a stronger boost to lending than for countries with a weaker base. This is suggestive of important complementarities between linguistic similarity and institutional strength.

Overall, our findings suggest that senior management at global banking groups may facilitate cross-border liquidity flows inside their branch network by reducing communication frictions through language development programs and cultural training. In turn, policymakers in host nations heavily reliant on liquidity provided by distant foreign banking groups can work to strengthen key aspects of the institutional framework. This improvement would facilitate the codification and transmission of context-specific information across geographic and cultural divides and ease these jurisdictions' access to liquidity provided by branches of foreign banking groups.

### CRediT authorship contribution statement

**Dieter Vanwalleghem:** Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization.
**Carmela D'Avino:** Writing – original draft, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

<footnote n="19">¹⁹ We thank an anonymous referee for pointing this out and suggesting two additional robustness tests.</footnote>

<footnote n="20">²⁰ Controlling for region fixed effects or the interaction between region and year fixed effects is unfortunately not possible for the specification which tests the impact of geographic distance on total lending. Including region fixed effects would absorb between region variation in geographical distance and the geographic distance variable would capture the effect of relative distance within the region or from the region center rather than the effect of geographic distance between the HQ and the host nation, the focus of our paper.</footnote>
# Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.intfin.2025.102180.

## Data availability

Data will be made available on request.

## References

Agarwal, S., Hauswald, R., 2010. Distance and private information in lending. Rev. Financ. Stud. 23 (7), 2757-2788.

Aggarwal, R., Kearney, C., Lucey, B., 2012. Gravity and culture in foreign portfolio investment. J. Bank. Financ. 36 (2), 525-538. http://dx.doi.org/10.1016/j.jbankfin.2011.08.007.

Alessandrini, P., Presbitero, A.F., Zazzaro, A., 2009. Banks, distances and firms' financing constraints. Rev. Financ. 13 (2), 261-307.

Anderson, J.E., 2011. The gravity model. Annu. Rev. Econ. 3 (1), 133-160.

Anderson, C.W., Fedenia, M., Hirschey, M., Skiba, H., 2011. Cultural influences on home bias and international diversification by institutional investors. J. Bank. Financ. 35 (4), 916-934. http://dx.doi.org/10.1016/j.jbankfin.2010.09.006.

Avdjiev, S., Aysun, U., Hepp, R., 2019a. What drives local lending by global banks? J. Int. Money Financ. 90, 54-75.

Avdjiev, S., Bruno, V., Koch, C., Shin, H.S., 2019b. The dollar exchange rate as a global risk factor: Evidence from investment. IMF Econ. Rev. 67, 151-173.

Berger, A.N., Li, X., Morris, C.S., Roman, R.A., 2021. The effects of cultural values on bank failures around the world. J. Financ. Quant. Anal. 56 (3), 945-993. http://dx.doi.org/10.1017/S0022109020000150.

Beugelsdijk, S., Frijns, B., 2010. A cultural explanation of the foreign bias in international asset allocation. J. Bank. Financ. 34 (9), 2121-2131. http://dx.doi.org/10.1016/j.jbankfin.2010.01.020.

Bilir, L.K., Chor, D., Manova, K., 2019. Host-country financial development and multinational activity. Eur. Econ. Rev. 115, 192-220.

Brei, M., von Peter, G., 2018. The distance effect in banking and trade. J. Int. Money Financ. 81, 116-137.

Buch, C.M., 2003. Information or regulation: What drives the international activities of commercial banks? J. Money, Credit. Bank. 851-869.

Buch, C.M., 2005. Distance and international banking. Rev. Int. Econ. 13 (4), 787-804.

Carbó-Valverde, S., Rodriguez-Fernandez, F., Udell, G.F., 2009. Bank market power and SME financing constraints. Rev. Financ. 13 (2), 309-340.

Cerutti, E., Dell'Ariccia, G., Peria, M.S.M., 2007. How banks go abroad: Branches or subsidiaries? J. Bank. Financ. 31 (6), 1669-1692.

Chui, A.C., Lloyd, A.E., Kwok, C.C., 2002. The determination of capital structure: Is national culture a missing piece to the puzzle? J. Int. Bus. Stud. 33, 99-127.

Claessens, S., 2017. Global banking: Recent developments and insights from research. Rev. Financ. 21 (4), 1513-1555.

Claessens, S., Van Horen, N., 2014. Location decisions of foreign banks and competitor remoteness. J. Money, Credit. Bank. 46 (1), 145-170.

Conlon, T., Huan, X., Muckley, C.B., 2024. Does national culture influence malfeasance in banks around the world? J. Int. Financ. Mark. Inst. Money 90, 101888. http://dx.doi.org/10.1016/j.intfin.2023.101888.

De Haas, R., Van Lelyveld, I., 2010. Internal capital markets and lending by multinational bank subsidiaries. J. Financ. Intermediation 19 (1), 1-25.

Deng, S., Elyasiani, E., 2008. Geographic diversification, bank holding company value, and risk. J. Money, Credit. Bank. 40 (6), 1217-1238.

Diamond, D.W., 1991. Monitoring and reputation: The choice between bank loans and directly placed debt. J. Political Econ. 99 (4), 689-721.

Dow, D., Karunaratna, A., 2006. Developing a multidimensional instrument to measure psychic distance stimuli. J. Int. Bus. Stud. 37, 578-602.

Du, Q., Yu, F., Yu, X., 2017. Cultural proximity and the processing of financial information. J. Financ. Quant. Anal. 52 (6), 2703-2726. http://dx.doi.org/10.1017/S0022109017001028.

Fang, Y., van Lelyveld, I., 2014. Geographic diversification in banking. J. Financ. Stab. 15, 172-181.

Fiechter, J., Ötker, M.I., Ilyina, A., Hsu, M., Santos, M.A., Surti, J., 2011. Subsidiaries or Branches: Does One Size Fit All? Technical Report, International Monetary Fund Washington, DC.

Focarelli, D., Pozzolo, A.F., 2005. Where do banks expand abroad? An empirical analysis. J. Bus. 78 (6), 2435-2464.

Galil, K., Varon, E., 2024. National culture and banks stock volatility. J. Int. Financ. Mark. Inst. Money 91, 101932. http://dx.doi.org/10.1016/j.intfin.2023.101932.

Galindo, A.J., Micco, A., Serra, C.M., 2003. Better the devil that you know: Evidence on entry costs faced by foreign banks. IDB Work. Pap. 4.

García-Herrero, A., Vázquez, F., 2013. International diversification gains and home bias in banking. J. Bank. Financ. 37 (7), 2560-2571.

Ghosh, A., 2016. How does banking sector globalization affect banking crisis? J. Financ. Stab. 25, 70-82.

Herrero, A.G., Peria, M.S.M., 2007. The mix of international banks' foreign claims: Determinants and implications. J. Bank. Financ. 31 (6), 1613-1631.

Hofstede, G., 1984. Culture's Consequences: International Differences in Work-Related Values, vol. 5, sage.

Hofstede, G., 2001. Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. Sage publications.

van Hoorn, A., Maseland, R., 2016. How institutions matter for international business: Institutional distance effects vs institutional profile effects. J. Int. Bus. Stud. 47, 374-381.

Jin, Y., Gao, X., Li, D., 2022. The effect of individualism on bank risk and bank performance: An international study. J. Int. Financ. Mark. Inst. Money 80, 101634. http://dx.doi.org/10.1016/j.intfin.2022.101634.

Karolyi, G.A., 2016. The gravity of culture for finance. J. Corp. Financ. 41, 610-625.

Kaufmann, D., Kraay, A., 2023. Worldwide governance indicators, 2023 update. October 19, 2023.

Kogut, B., Singh, H., 1988. The effect of national culture on the choice of entry mode. J. Int. Bus. Stud. 19, 411-432.

Konara, P., Mohr, A., 2019. Why we should stop using the Kogut and Singh index. Manag. Int. Rev. 59, 335-354.

La Porta, R., Lopez-de Silanes, F., Shleifer, A., Vishny, R.W., 1998. Law and finance. J. Political Econ. 106 (6), 1113-1155.

Liberti, J.M., Petersen, M.A., 2019. Information: Hard and soft. Rev. Corp. Financ. Stud. 8 (1), 1-41.

Mayer, T., Zignago, S., 2011. Notes on CEPI2019s distances measures: The GeoDist database. SSRN Electron. J. http://dx.doi.org/10.2139/ssrn.1994531, URL: http://www.ssrn.com/abstract=1994531.

Mian, A., 2006. Distance constraints: The limits of foreign lending in poor economies. J. Financ. 61 (3), 1465-1505.

Morgan, D.P., Rime, B., Strahan, P.E., 2004. Bank integration and state business cycles. Q. J. Econ. 119 (4), 1555-1581.

Papaioannou, E., 2009. What drives international financial flows? Politics, institutions and other determinants. J. Dev. Econ. 88 (2), 269-281.

Petersen, M.A., Rajan, R.G., 1994. The benefits of lending relationships: Evidence from small business data. J. Finance 49 (1), 3-37.

Petersen, M.A., Rajan, R.G., 2002. Does distance still matter? The information revolution in small business lending. J. Financ. 57 (6), 2533-2570.

Portes, R., Rey, H., Oh, Y., 2001. Information and capital flows: The determinants of transactions in financial assets. Eur. Econ. Rev. 45 (4-6), 783-796.

Qian, J., Strahan, P.E., 2007. How laws and institutions shape financial contracts: The case of bank loans. J. Financ. 62 (6), 2803-2834.

Shao, L., Kwok, C.C., Guedhami, O., 2010. National culture and dividend policy. J. Int. Bus. Stud. 41 (8), 1391-1414. http://dx.doi.org/10.1057/jibs.2009.74.
Shao, L., Kwok, C.C., Zhang, R., 2013. National culture and corporate investment. J. Int. Bus. Stud. 44, 745-763.

Shenkar, O., 2001. Cultural distance revisited: Towards a more rigorous conceptualization and measurement of cultural differences. J. Int. Bus. Stud. 32, 519-535.

Stein, J.C., 2002. Information production and capital allocation: Decentralized versus hierarchical firms. J. Financ. 57 (5), 1891-1921.

Stulz, R.M., Williamson, R., 2003. Culture, openness, and finance. J. Financ. Econ. 70 (3), 313-349.

Temesvary, J., 2014. The determinants of US banks' international activities. J. Bank. Financ. 44, 233-247.

Yildirim, C., Efthyvoulou, G., 2018. Bank value and geographic diversification: Regional vs global. J. Financ. Stab. 36, 225-245.

Yu, G., Wahid, A.S., 2014. Accounting standards and international portfolio holdings. Account. Rev. 89 (5), 1895-1930.

Zhao, T., Luintel, K.B., Matthews, K., 2021. Soft information and the geography of SME bank lending. Reg. Stud. 55 (4), 679-692.

Zheng, X., Ghoul, S.E., Guedhami, O., Kwok, C.C.Y., 2013. Collectivism and corruption in bank lending. J. Int. Bus. Stud. 44 (4), 363-390. http://dx.doi.org/10.1057/jib.s.2013.19.

Zwinkels, R.C., Beugelsdijk, S., 2010. Gravity equations: Workhorse or trojan horse in explaining trade and FDI patterns across time and space? Int. Bus. Rev. 19 (1), 102-115.