Cultural Explanation of the Foreign Bias in International Investing

A Cultural Explanation of the Foreign Bias in International Asset

Allocation

SJOERD BEUGELSDIJK

Faculty of Economics and Business

University of Groningen

Groningen, The Netherlands

e-mail: [email protected]

BART FRIJNS*

Department of Finance

Auckland University of Technology,

Auckland, New Zealand

e-mail: [email protected]

* Corresponding Author: Bart Frijns, Department of Finance, Faculty of Business and Law,

Auckland University of Technology, Private Bag 92006, 1142 Auckland, New Zealand, Ph:

+64 9 921 9999 (ext. 5706), F: +64 9 921 9940.

Acknowledgements

We would like to thank Aaron Gilbert, Dimitri Margaritis and Alireza Tourani-Rad and

participants of the Australasian Banking and Finance Conference (2008) for their useful

comments and suggestions. Part of this paper was written when the first author was visiting

the Vienna University of Economics and Business (WU Wien) and we thank the WU for their

hospitality. The first author also thanks the Netherlands Organization for Scientific Research

(NWO) for their financial support.

1

A Cultural Explanation of the Foreign Bias in International Asset

Allocation

Abstract

In this paper we examine the foreign bias in international asset allocation. Following extant

literature in behavioral finance, we argue that a society’s culture and the cultural distance

between two markets play an important role in explaining the foreign bias. In particular, we

hypothesize that the degree of a nation’s uncertainty avoidance affects the foreign bias (more

uncertainty-avoiding countries allocate less to foreign markets), as does the degree of a

country’s individualism (in individualistic countries performance is more directly attributed

to a person and less to teams, causing these individuals to be more aggressive in their foreign

asset allocations). We further expect that the degree of cultural distance between two

countries affects the amount of money allocated to that market. Based on extensive

robustness analyses, we find support for our hypotheses on the role of culture in international

asset allocation.

JEL Codes: C24, G15, G23.

Keywords: Foreign Bias, Home Bias, Culture, International Asset Allocation.

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1. Introduction

One of the major puzzles in financial economics is the fact that investors do not allocate their

money optimally across international markets, but instead systematically overweigh the

securities of their home country. This phenomenon, referred to as the home bias or domestic

bias,1 is inconsistent with standard asset pricing theory where all investors have identical

information sets and markets function perfectly (Lewis, 1999; Karolyi and Stulz, 2003) and a

vast amount of research has been dedicated to explaining the domestic bias (see Sercu and

Vanpée, 2007 for a recent review of the literature). The presence of a domestic bias also

implies that there is a relative underweighting of foreign markets. However, when investing

in foreign markets, investors can allocate money to each market according to their

preferences, i.e. the weights on each foreign country may differ and this issue has become

known as the foreign bias (see e.g. Chan et al., 2005) and this issue has received much less

attention.

In this paper we extend the literature on the foreign bias by providing a cultural

explanation for the foreign bias, answering Ivkovic and Weisbenner’s recent call for

exploration of ‘the role of societal characteristics in portfolio decisions’ (Ivkovic and

Weisbenner, 2007: 1356). In doing so, we complement existing literature in several important

ways. First, following the research on the home bias, we fill a gap by studying the allocation

across foreign countries given the preference for domestic stocks. Second, building on studies

that investigate the role of familiarity by incorporating language differences and geographic

distance, we explicitly measure differences in culture. We do so in two ways. First, we show

that a country’s cultural characteristics explain investors’ absolute preferences for foreign

stocks, and in turn the domestic bias. More specifically, building on insights from cross-

cultural psychology, we argue that i) investors from uncertainty-avoiding societies are more

risk averse and have a lower preference for foreign stocks, ii) investors from more

individualistic societies are more driven by individual performance and tend to be less risk

averse, and therefore display a higher preference for foreign equity. Second, based on the

familiarity argument we argue that higher cultural differences between countries lead to a

lower preference for foreign stocks.

1The term home bias is generally used to describe any (unexplainable) deviation from optimal portfolio weights.

In this paper we use the term domestic bias to refer to (typical) overweighting of the domestic market and

foreign bias to refer to any over- or underweighting of the portfolio in foreign markets.

3

Our empirical results are broadly in line with our expectations. Overall, we find that

more uncertainty avoiding nations allocate less money abroad and more individualistic

nations invest more abroad. Although having the expected sign, the cultural distance variable

is insignificant in the full sample, but when we split the sample into developed and emerging

markets we find that cultural distance is significant for developed markets and insignificant

for emerging ones. This finding is expected, because when investing in an emerging market

investors are typically seeking for differences. These findings are robust for different control

variables and different model specifications. We also find that uncertainty avoidance plays a

greater role in emerging markets.

Our findings have important implications, as they question the assumptions of

standard asset pricing theory. First, controlling for economic, political and legal differences

such as corporate governance regime and stock market development, investors do not treat

non-domestic markets as one category, but rather make choices that are partly driven by

behavioral scripts originating from their cultural backgrounds. Thus, in addition to existing

attempts to adapt portfolio theory by incorporating the home bias phenomenon, our results

suggest that we need to go beyond this domestic-foreign distinction and include cultural

guidelines to explain asset allocation across foreign markets. Second, to the extent asset

allocation is not in line with asset pricing theory, but is driven in part by (differences in)

value patterns, the question of how large the costs of these cultural drivers of asset allocation

are becomes important. Finally, while the law and finance literature has concentrated on

formal institutions (La Porta et al, 1998, 2008; Djankov et al., 2008), our analysis implies that

this literature should be extended to allow for a more elaborate view on culture and finance

(cf. Stulz and Williamson, 2003).

The paper is organized as follows. In Section 2 we provide a review of the literature

on the home bias, including the recent behavioral turn in finance. We then introduce culture

as an explanation for the domestic and foreign bias and develop hypotheses on the way

culture affects global asset allocation in Section 3. In Section 4 we describe our data, and in

Section 5 we discuss the results of our main tests and several robustness tests. Finally, in

Section 6 we conclude.

2. Background

Traditional explanations of the home bias have mostly focused on rational arguments as to

why optimal diversification is impossible. These arguments include (tax) restrictions on

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international capital flows, non-tradability of goods, inflation hedging motives, institutional

barriers or more general transaction costs. In addition, the home bias has been attributed to

information (cost) arguments, familiarity arguments or a combination of the two.2 However,

more recent research has proposed several irrational arguments based on behavioral finance

theory. These behavioral arguments assume that investors are boundedly rational and

consider foreign markets more risky than they truly are just because they are foreign

(Huberman 2001; Solnik 2008). These behavioral arguments build on information

advantages, perceived familiarity, overconfidence and a range of associated personality

characteristics leading to underdiversification and the home bias phenomenon (Kilka and

Weber, 2000).

Several studies that focus on behavioral arguments for the existence of the home bias

(and in turn the foreign bias) have suggested using language, culture and geographic distance

as behavioral proxies. For example, using data on US equity holdings of more than 3,000

mutual funds from 22 developed and developing countries, Ke et al. (2006) show that funds

with a local presence in the US invest a greater proportion in US firms than those that have

no such presence. This effect becomes even stronger for managers from non-English

speaking countries and from countries further away from the US, showing that greater

familiarity reduces the home bias. In a similar vein, both American professional money

managers (Coval and Moskowitz, 1999) and individual investors (Ivkovic and Weisbenner,

2005) exhibit a strong bias towards locally headquartered firms. Grinblatt and Keloharju

(2001) find that Finish investors have a preference for stocks of firms that are headquartered

in nearby locations. In addition, Grinblatt and Keloharju (2001) use language to proxy for

familiarity in their study. Taking into account Finland’s two official languages, Finnish and

Swedish, they find that Finnish (Swedish) native speakers prefer stocks of firms that publish

their annual reports in Finnish (Swedish). A similar phenomenon has been found for shared

nationality between an investor and the firm’s CEO (Grinblatt and Keloharju, 2001), and for

a county’s level of patriotism (Morse and Shiva, 2008).

The familiarity argument specifically applies to the foreign bias. For example, Kang

and Stulz (1997) analyze foreign stock ownership in Japanese firms and find that foreign

investors prefer larger firms, firms in manufacturing industries and firms with good

accounting performance, low leverage, high market-to-book ratios and low unsystematic risk

(see also Dahlquist and Robertsson, 2001 and Aggarwal et al. 2005). In a country-level

2For a recent review of the literature see Sercu and Vanpée (2007).

5

analysis of mutual funds from 26 developed and developing countries, Chan et al. (2005)

investigate both the foreign and domestic bias. Their findings indicate that familiarity

between home and host as proxied by a shared official language, lower geographic distance

and more bilateral trade flows between two countries increases the amount of equity held by

one country in another, and thus decreases the foreign bias. Berkel (2007) reports a

‘friendship bias’ and observes that this friendship bias is reciprocal, meaning that it is

observed for country pairs such as Germany-Austria and Austria-Germany, and persistent

over the years.

In sum, these studies show that the extent of underinvestment in foreign markets is

not just a simple choice between home and foreign markets, but also between country- (and

firm-) specific aspects on which foreign markets differ. While cultural differences are

mentioned as one of the familiarity variables affecting the foreign bias, our theoretical and

empirical understanding on the role of culture remains incomplete. Culture is generally not

theorized upon, but rather part of some kind of residual explanation. Further, culture is

typically not measured in terms of shared values, but by proxies like shared nationality

(Grinblatt and Keloharju, 2001), a geographical classification of Asian versus European

countries (Ke et al., 2006), common language (Chan et al., 2005) or a fixed effect labeled as

‘friendship bias’ but derived from a shared legal origin dummy (Berkel, 2007).

3. Hypothesis Development: culture and the foreign bias

Our understanding of culture and its economic implications has advanced significantly (e.g.,

Tabellini, 2008a, 2008b; Guiso et al., 2006). Culture is often described as a system of values,

providing scripts for behavior and perceptions of the world transmitted through socialization

and from parents to children (cf., Tabellini, 2008a). In cross-cultural psychology, culture is

defined as the collective programming of the mind that distinguishes the members of one

group from those of another (Hofstede, 2001, 1980). Although the field of cross-cultural

studies is characterized by multiple approaches towards culture (Adler, 1983), comparative

empirical work in economics and international business has been dominated by Hofstede’s

seminal study Culture’s Consequences, where, based on a cross-cultural survey of IBM

employees, Hofstede distinguishes between four dimensions that are assumed to capture

cross-cultural differences: power distance, masculinity-femininity, individualism-collectivism

and uncertainty avoidance. ‘Power distance’ refers to the extent to which people believe that

power and status are distributed unequally and the extent to which they accept an unequal

6

distribution of power as the proper way of organizing social systems; ‘Masculinity-

femininity’ refers to the extent to which a society emphasizes traditional masculine values

such as competitiveness, assertiveness, achievement, ambition and the acquisition of money

and other material possessions, versus feminine values such as nurturing, helping others, not

showing off and caring for the quality of life; ‘Individualism-collectivism’ reflects the degree

to which a society emphasizes the role of the individual as opposed to that of the group; and

‘Uncertainty avoidance’ refers to the extent to which people are uncomfortable with

uncertain, unknown or unstructured situations. These four dimensions are assumed to reflect

key aspects of a society’s culture. Hofstede then assigns each country a score on each cultural

dimension to indicate how people from different cultures feel about the above societal

issues.3 Throughout the years, these scores have become available for an increasing number

of countries. Of the four dimensions, uncertainty avoidance and individualism in particular

have been related to economic phenomena (Kirkman et al., 2006).4

3.1 Hypothesis I: Uncertainty Avoidance

Uncertainty avoidance reflects the extent to which people feel (un)comfortable in situations

with uncertain outcomes and their willingness to deal with risk. Risk aversion plays a key

role in traditional models of investment behavior, but behavioral finance is especially focused

on the heterogeneity in risk attitude. Agent heterogeneity regarding risk attitude is explored

along multiple dimensions, mostly linking portfolio allocation characteristics to individual

characteristics like age, gender and level of education (Goetzmann and Kumar, 2008; Dorn

and Huberman, 2005; Barber and Odean, 2001; Karlsson and Norden, 2007). For example,

Dorn and Huberman (2005) find that the variation in self-reported risk aversion helps explain

the variation in actual risk taking measured by portfolio volatility and concentration. In a

similar vein, Dohmen et al. (2006) document a robust intergenerational correlation between

risk and risk attitudes. Using a sample of German families to investigate the origins of

3Hofstede and Bond (1988) later uncovered a fifth cultural dimension, called ‘long-term orientation’.

Unfortunately, the scores on this dimension are only available for a limited number of countries, thus reducing

its empirical applicability. Moreover, scholars have questioned its added value, as it has been argued to reflect

the same underlying cultural values as the individualism dimension (see Barkema and Vermeulen, 1997). 4The validity of Hofstede’s culture dimensions has been empirically confirmed in many studies (e.g., Van

Oudenhoven, 2001; for an overview of earlier replications, see Sφndergaard, 1994). The impact of Hofstede’s

seminal work on culture is clearly indicated in Kirkman et al.’s (2006) review of empirical research that has

used Hofstede’s four-dimensional culture framework. In this review, Kirkman et al. (2006) examine 180 articles

published in top-tier journals between 1980 and 2002 that empirically assess one or more dimensions of

Hofstede’s framework. Although they limit their search to major management and (cross-cultural) applied

psychology journals, their analysis shows that the Hofstede dimensions have been applied to a broad range of

topics, including entrepreneurship, entry modes of multinational firms, innovation and foreign direct investment.

7

economic decision making, they establish a strong positive association between the attitudes

of parents and children regarding the willingness to take risks and the willingness to trust

others. This finding is important because it implies that attitudes towards risk, including the

decision on how to allocate investments, are persistent over time and across individuals. At

the country level, Chui and Kwok (2008) show that uncertainty-avoiding nations have higher

levels of life insurance consumption. Taken together, the above arguments lead us to expect

that individuals from high uncertainty avoiding nations have lower preferences to invest

abroad. High levels of uncertainty avoidance are associated with a high preference for

domestic equity and a low (relative to the host country’s market weight) preference for

foreign equity.

3.2 Hypothesis II: Individualism

The second cultural dimension that can be argued to affect international asset allocation is the

country’s degree of individualism. More individualistic societies tend to develop rewards

systems that are more individually oriented (Schuler and Rogovsky, 1998). For example, both

theoretically (Kirkman and Shapiro, 1997) and empirically (Kirkman and Shapiro, 2000),

individualism is shown to be negatively related to receptivity to team-based rewards in a US

insurance company. One mediating link between individualism and rewards systems is the

increased competitiveness in more individualistic groups. Oetzel (1998) finds that

individualistic European-American groups are more competitive than collectivistic Japanese-

American groups. In a different context, Ali (1993) finds that individualism is positively

related to autocratic decision-making styles and attitudes towards risk among Saudi Arabian

managers. The finding on autocratic decision-making styles correlates with the finding by

Spector et al. (2001), who show in a sample of 5,000 managers from 24 countries that

individualism is positively associated with internal locus of control. Hence, these studies

suggest that high individualism is correlated with risk taking and individual rewards systems,

leading to a positive association between individualism and the preference for foreign

investments.

A long-standing literature in economics and social psychology has focused on the

distinction between group-based decision making and individual-based decision making

(Kerr et al., 1996). Recent work by Shupp and Williams (2008) tries to answer whether small

groups reveal systematically different risk preferences than individuals, and if so, how the

risk preferences of the individual group members aggregate into a group risk preference. This

is directly related to individualism because more individualistic societies are characterized by

8

more autocratic and individual decision making. Using experiments, Shupp and Williams

(2008) find that groups are more risk averse than individuals in high risk situations, and

group decisions exhibit a smaller variance than individual decisions. One reason for these

findings may be that those highest in individualism show the lowest levels of information

seeking in individual networks (Zaheer and Zaheer, 1997). What this means for the relation

between individualism and foreign asset allocation is that more individualistic societies can

be expected to invest more in foreign markets. We therefore hypothesize that the higher the

level of individualism of a home country, the more it will allocate to foreign assets.

3.3 Hypothesis III: Cultural Distance

A greater cultural distance is associated with unfamiliarity and leads to economic decisions in

which risk is reduced either by choosing a certain type of investment (Anderson and

Gatignon, 1986; Hill et al., 1990) or by not investing at all (Loree and Guisinger, 1995; Sethi

et al., 2003). Huberman (2001) shows that investors more familiar with domestic stocks feel a

sense of discomfort with foreign stocks. This not only suggests that countries that are more

risk averse have higher levels of discomfort, but also that the level of discomfort may

increase when the cultural distance between countries increases. Culturally distant countries

are not considered attractive investment opportunities, ceteris paribus, and this will lead to a

lower preference for investing in these countries.

The three hypotheses above are the focus of the remainder of this paper. Although we

have no a priori expectations about the relationship between the other two cultural variables

(power distance and masculinity-femininity) and the foreign bias, we do test these

relationships empirically.

4. Data and Summary Statistics

Our data on foreign asset allocation are based on the mutual fund holdings of 26 countries

investing in a broader sample of 48 countries. The country-level data are based on underlying

individual fund-level data obtained from Thomson Financial Services (TFS). Fund-level data

contain the holdings of 20,821 and 24,589 mutual funds across the 26 countries for the years

1999 and 2000, respectively. All types of mutual funds are included in this sample, i.e.,

closed- and open-end funds, and equity or balanced funds. However, the allocation of one

country into another only considers the equity part of the funds. Aggregating at the country

level therefore shows the percentage of money allocated by mutual funds from country i to

9

the share market of country j (wij). For clarity we label the 26 countries from where mutual

funds operate from as home countries and the 48 countries for which we have data on how

much investments they receive as host countries. The 26 home countries are relatively well-

developed nations, and the additional 22 host countries are mainly emerging market

economies.5

4.1 Dependent variable: foreign bias

To calculate our dependent variable measuring the foreign bias, we follow standard empirical

finance literature and calculate deviations from the optimal portfolio as described by asset

pricing theory (see also Chan et al., 2005; Ke et al., 2006). According to the CAPM, optimal

weights are given by the market value of a particular country relative to the global market

value (i.e., the sum of the value of all markets). The difference between the actual holdings in

a country and the optimal weight reflects the degree of bias towards a particular country. To

formalize the discussion, let wij be the percentage weight of the mutual fund holdings of

home country i in host country j, i.e.,

∑=

=48

1j

ij

ij

ij

MV

MVw , (1)

where MVij is the amount of money mutual funds from country i invest in country j and

∑=

48

1j

ijMV is the total amount of money country i allocates to all markets in the sample.

Likewise, we can define the optimal weight according to CAPM, wj*, as the market value of

country j relative to the market value of all markets:

∑=

=48

1i

*

i

*

j*

j

MV

MVw . (2)

5For a more detailed discussion on the data, see Chan et al. (2005).

10

From these weights we can compute the foreign bias score as the (log) ratio of the actual

allocation of country i in country j relative to the optimal portfolio allocation. We define the

foreign bias score as

=

*

j

ij

ijw

wlogFBIAS , for i ≠ j. (3)

Given the fact that most countries have a large and positive domestic bias, we expect in most

cases that *

jij ww < , i.e., investments in host countries are lower than the optimal investments.

This would cause FBIASij to be negative in most cases and implies that lower values for

FBIASij imply less foreign investments and a greater foreign bias.

<Insert Table 1 and 2 about here>

In Tables 1 and 2 we provide summary statistics of the foreign bias variables for the

26 home markets in the sample (Table 1) and the 48 host countries in the sample (Table 2).

In the first column of Table 1 we report the average foreign bias score for the home

countries computed as in equation (3). For the home countries we find the smallest average

foreign bias for Hong Kong (-0.69). This country is closely followed by the UK (-0.85) and

Luxembourg (-0.90). The largest foreign bias is observed for Greece (-5.39), followed by

Taiwan (-4.25) and New Zealand (-4.04). The US (-1.78) has a slightly lower foreign bias

than the average of -2.25.

In the first two columns of Table 2 we present summary statistics for the foreign bias

measures of the host countries. The first column reports the optimal allocations to each

market based on the market value of each market relative to the world market capitalization.

In column 2 we report the average of the actual allocations to each market. This column

shows that the highest allocation of all foreign markets is to the US, which also has the

highest weight based on market capitalization. The lowest allocations are to several Latin

American countries, which conjointly receive low weights based on their market

capitalizations. Comparison of the first two columns clearly reveals the presence of a foreign

bias, where in most of the cases the average actual allocation is below the optimal allocation.

Column 3 reports the average foreign bias towards each of the markets, where we find that

the foreign bias score is highest towards Finland (0.09), implying a slight overinvestment into

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Finland, and lowest towards Latin American countries such as Venezuela (-6.67), Colombia

(-6.10) and Chile (-6.02), implying an underinvestment into these markets.

4.2 Culture and cultural differences

We measure uncertainty avoidance (UAV) and individualism (IND) using Hofstede’s scores.

The scores for the home countries in our sample can also be found in Table 1. UAV scores

are highest in Greece (most uncertainty avoiding), and lowest in Singapore. The US scores

relatively low on the uncertainty avoidance index. IND is highest in the US (most

individualistic), and lowest in Taiwan. As expected, many of the Anglo-Saxon countries

score high on the individualism index (US, UK, Australia, Canada, New Zealand) and the

Asian countries score relatively low.

The overall cultural distance measure is based on the country scores of all four

Hofstede culture dimensions to capture cultural distance as completely as possible. To

calculate one overall distance measure, we follow extant literature and use the following

formula based on the Euclidean distance between the culture dimensions. It computes their

distance in a four-dimensional space as the square root of the sum of the squared differences

in the scores on each cultural dimension. Formally:

}/)({ 24

1

kkikj

k

ij VIICD −= ∑=

, (6)

where CDij is the cultural distance between home country i and host country j, Ikj is country

j’s score on the kth cultural dimension, Iki is the score of country i on this dimension, and Vk

is the variance of the score of the dimension. The cultural distance measure was introduced

by Kogut and Singh (1988) and is often used in international business research (e.g., Loree

and Guisinger, 1995; Barkema and Vermeulen, 1997; Brouthers and Brouthers, 2001). Unlike

the Kogut and Singh index, our measure does not give equal weights to the differences in the

scores on each of Hofstede’s dimensions, and hence does not assume that each dimension is

equally important in determining the cultural distance between countries (Shenkar, 2001).

Table 1, column 4 reports the average cultural distance of the home market to all hosts

and Table 2, column 4 contains statistics on the average cultural distance towards each host.

The average cultural distance over the whole sample is 2.73. Table 1 reveals that

Luxembourg and Spain are on average most closely related to all other markets in the sample,

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while Denmark and Sweden are culturally most different from all hosts. Table 2 reveals the

cultural distance of each host to the different home markets. Again, Luxembourg is found to

be culturally most similar to the average home country, while Malaysia appears to be

culturally most distant. However, apart from these average statistics we find that the cultural

distance measure is widely dispersed for various home and host country pairs, with the

highest cultural distance found between Japan and Sweden (CD = 5.65) and the lowest

between the US and Australia (CD = 0.26).

Because equity investments in foreign markets not only depend on cultural

differences, but are affected by the target country’s overall economic and political/legal

environment, such as corporate governance structure, accounting standards, transaction costs

and stock market development, we need to control for these host country characteristics in

our regression analysis.

4.3 Control variables

To control for the alternative explanations of the foreign bias, we include the following sets

of variables.

Focal host country attractiveness: To capture the traditional economic explanation for

the home/foreign bias, we include proxies for transaction and capital costs, tax levels, host

country stock market development and host country economic growth rate. All measures are

derived from extant research. Stock market development is measured as stock market

capitalization over GDP and comes from the Standard and Poor’s Emerging Stock Markets

Factbook 2000 (see also Chan et al., 2005). Higher levels of stock market development are

associated with a higher foreign investment as investors tend to invest in more liquid markets.

Transaction costs are operationalized as trading costs for pension funds, investment managers

and brokerage houses, as introduced by Domowitz et al. (2001). Costs include commissions

and fees for the period 1996-1998. Tax levels are derived from information provided by Price

Waterhouse Coopers (PWC) Corporate Tax 1996. We expect a negative relation between

foreign investment and both tax levels and transaction costs, because increasing tax levels

and higher transaction costs reduce a host country’s attractiveness. Capital controls are

proxied by the Economic Freedom index ranking countries in terms of restrictions on foreign

capital transactions. Scores range between 10 (zero restrictions) to 1 (both domestic

investments by foreigners and foreign investments by locals require government approval).

Finally, we also include average host country GDP per capita growth in the five years

preceding our investment data. Growing economies experiencing high GDP per capita growth

13

rates are considered attractive investment opportunities (cf. Berkel, 2007). Data on growth

rates are derived from the Penn World Tables.

Regional trade regime dummies: We include regional dummies taking the value 1 if

countries are part of a regional trade agreement, i.e., NAFTA, ASEAN and the EU. These

regional dummies capture potential effects of trade agreements or other regional fixed effects

– such as the Asian crises, the European Monetary Union (EMU) and other omitted variables

possibly related to a regional friendship bias (cf. Berkel, 2007) – potentially obscuring the

effects of cultural distance. In the robustness analysis, we also test for home and host country

fixed effects, because some funds are located in financial centers such as Luxemburg and

Ireland, and the objectives of these funds’ location decisions is often related to tax incentives

offered by these countries (cf. Ke et al., 2006).

Risk and return profile: In addition to the controls mentioned above, we include

variables that capture the risk and return characteristics of a particular market. In the returns

domain we consider average one-year and five-year lagged market return computed using

monthly data (data is obtained from Datastream and is corrected for dividend payouts).

Lagged returns may affect portfolio allocation in several ways. A positive relationship

between past performance and foreign investment may be expected if managers exhibit return

chasing behavior or follow momentum strategies (e.g., Jegadeesh and Titman 2001).

Alternatively, we may find a negative relationship if managers follow a contrarian strategy,

trading on potential long-run mean reversion (see Poterba and Summers, 1988). Previous

studies have presented evidence for both strategies followed by mutual fund managers (Bohn

and Tesar, 1996 for return chasing behavior and Grinblatt and Keloharju, 2000 for contrarian

trading strategies), and we therefore have no a priori expectations about the sign of the

relation between past returns and foreign asset allocation. To capture diversification benefits,

we include the correlation between home and host country returns (computed using monthly

data over the past five years). From a diversification perspective, we expect a negative

relation between return correlation and the foreign investment. Finally, we control for host

market risk by including stock market return volatility (computed over the past five years).

Familiarity between home-host: To prevent an omitted variables bias of our culture

variables, we include familiarity measures that have been used before, i.e., language and

geographic distance. Common language is measured as a dummy taking the value of 1 when

the home and host countries share the same official language. We expect a positive relation

between common language and the foreign investment. In the robustness analysis we also test

the language effect using an alternative continuous measure of shared language. We also

14

include the log of the great-circle geographic distance (in kilometers) between each home and

host country.6 Geographic distance is expected to have a negative effect on the foreign bias,

since greater distance is associated with reduced familiarity of a foreign market. Geographic

distance has been used as a proxy for familiarity or the degree of asymmetry explaining, for

example, international equity flows (e.g., Chan et al., 2005) and cross-border M&A activity

(DiGiovanni, 2005). In addition to the language and distance variables we follow the law and

finance literature and include a dummy for shared common law, because of the superior

investor protection regimes in common law countries (La Porta et al. 1998, 2008). This

dummy takes the value of 1 when home and host country share a common law system. We

expect a positive relation between shared common law and foreign investment.

5. Methodology and Results

5.1 Main analyses

As a first step in analyzing determinants of the foreign bias, we develop a base model using

all abovementioned controls that have been associated with the home or foreign bias. The

model takes the form

)

(

ijj

jjiij

yFamiliarit ,profile returnRisk

,regime trade Regional ,nessAttractive ,HBIASfFBIAS

−

=, (7)

where HBIASi is the level of the home bias of country i; Attractivenessj refers to the control

variables for the transaction costs, capital controls, market size, tax level and economic

growth of host country j; Regional trade regimej refers to the trade regime under which the

country operates, i.e., whether the host is part of the EU, NAFTA or ASEAN; Risk-return

profilej describes the characteristics of the host market in terms of past returns, stock market

volatility and diversification opportunities; and Familiarityij explores the degree of familiarity

between home i and host j based on commonality of language, shared common law system

and geographic distance.

6Distances are calculated between the major financial centers of a country and are calculated “as the crow flies”

using the distance calculator from http://www.geobytes.com.

15

In Table 3 we report the findings of the Tobit base model in the first column.7 Overall,

the model is estimated for 963 observations, of which 152 are left-censored. As expected the

absolute level of the home bias has a negative impact on the foreign bias score, i.e., if the

domestic bias is high for a particular country, that country has proportionally less funds that

can be allocated to foreign markets. Our control variables behave according to expectation,

only the coefficient on the return correlation between home and host has an unexpected

positive sign and is highly significant. This is puzzling as it implies that fund managers do

not allocate money to foreign markets based on diversification arguments. However, this

result is also observed by Chan et al. (2005).

To evaluate the role of cultural variables in explaining the foreign bias we add the

culture variables to our base model, i.e.,

)

(

iji

iijij

Distance Cultural ,ismIndividual

,Avoidancey Uncertaint ,Variables BasefFBIAS =, (8)

where Base Variablesij are the base variables included in equation (7), Uncertainty

Avoidancei is the degree of uncertainty avoidance of home country i, Individualismi is the

individualism score of home country i, and Cultural Distanceij is the cultural distance

between home i and host j.

<Insert Table 3 about here>

In column 2 of Table 3 we report the results of our full model. We note that the model

including all of cultural variables together improves the base model significantly.8 We find

that the degree of uncertainty avoidance of the home country has a significantly negative

impact on the foreign bias score, i.e., the higher the degree of uncertainty avoidance of the

home country, the lower its allocation to other markets. This finding confirms our first

hypothesis. The individualism score of the home market also has a significant impact on the

foreign bias score, with a positive sign. This implies that the higher the degree of

7We estimate equation (7) as a linear model, but need to control for the fact that allocations to several host

countries in the sample are zero. Since the foreign bias is defined as the log of the proportion invested in a

country over the optimal proportion according to portfolio theory, this implies that FBIASij would not be

observed for a substantial proportion of the data. Ignoring these non-allocations could lead to potential sample

selection bias (see Heckman, 1979). To circumvent such issues we replace all “zero”-observations with a value

of 0.001 and estimate (7) as a Tobit model, where all “zero”-observations are censored on the left. 8A Likelihood Ratio test rejects the null of the restricted model at the 1% significance level.

16

individualism in the home country, the higher the foreign asset allocation, supporting

hypothesis 2. Lastly, we find that the impact of cultural distance has the expected sign, but is

insignificant. The results for the base variables remain mostly unchanged and significant,

implying that the cultural variables measure other dimensions of the foreign bias not

explained by these base variables.

In our data set we can make a distinction between hosts that are developed markets

and hosts that are emerging markets. This is important because the decision to invest in

emerging markets may be based on different criteria than the decision to invest in developed

markets. Since many mutual funds classify themselves specifically as an emerging markets

fund, investors make a specific choice to invest in these markets. When investing in an

emerging markets fund, investors are generally seeking higher returns in markets that are

more risky than and different from developed markets. We would therefore expect the

uncertainty avoidance and individualism variables to be more important and the cultural

distance measure to be less important. On the other hand, when investing in developed

markets, investors may have higher preferences for markets that are culturally similar and we

therefore expect an important role for the cultural distance variable.

In column 3 of Table 3 we show the results for the model where only developed host

countries are considered. As for the cultural variables we find that uncertainty avoidance is

again highly significant and negative. However, the size of the coefficient has decreased

when compared with the coefficient for the full model. Individualism is again highly

significant and positive indicating that the more individualistic a society is, the higher its

degree of foreign bias. Cultural distance has a negative and highly significant coefficient,

indicating that the degree of cultural distance is an important factor for developed markets.

The size of the coefficient has almost tripled when compared with the coefficient in the full

model.

In the last column of Table 3 we show the results for the allocations to emerging

markets. We find that the level of uncertainty avoidance has a significantly negative impact

on the foreign bias score and the size of the coefficient has increased compared with the full

model. Also compared with the developed markets model, we find that the coefficient has

doubled, indicating the greater importance of uncertainty avoidance for emerging markets.

This increase is expected as emerging markets are generally more risky than developed

markets, and implies that home countries with given levels of uncertainty avoidance allocate

less to emerging markets. Individualism has a marginally significant positive effect and its

17

coefficient is higher than for the full sample or the sample of developed hosts. As in the full

model cultural distance remains insignificant.

5.2 Robustness analysis

Broadly, the results presented above are in line with our expectations. However, to assess

their robustness we perform several tests. First, we include additional variables that could

potentially affect the results obtained in Table 3. Second, we use alternative estimation

methods.

5.2.1 Additional variables

Our first robustness analysis relates to the inclusion of the remaining two culture dimensions

as developed by Hofstede, i.e., Power distance (PD) and Masculinity (MAS). The addition of

these two variables does not affect our results regarding UAV and IND and cultural distance

(in fact results tend to become stronger). Although we have no a priori expectations regarding

their sign, we obtain significantly positive effects for PD in both sub-samples. MAS is

positive and significant in the set of emerging markets. Despite the lack of a clear theoretical

direction, these findings may be worth further theoretical scrutiny in future research.

The second additional variable we include is host country institutional quality.

Because our home countries are mainly developed countries, cultural distance may actually

proxy for institutional distance between home and host countries, in which case not including

host country institutional quality characteristics may lead to an omitted variable bias. Our

measure of institutional quality is based on a five-item principal component. These five items

are rule of law, public enforcement index, insider trading prevalence, risk of expropriation

and system efficiency and are taken from La Porta et al. (1998) and Djankov et al (2008).

Cronbach’s alpha for this multi-item factor is 0.85, suggesting it is a reliable indicator. As is

shown in Table 4, including host country institutional quality does not affect our results

reported in Table 3. The institutional quality variable is significant for both developed and

emerging markets, though the size of the coefficient is logically smaller in the set of

developed host countries (Panel A) than in the set of emerging host markets (Panel B).


Third, we include home and host country fixed effects to control for all other country-

specific variation that may affect our results regarding culture and cultural differences. One

18

reason to include country dummies is to control for free floats (not all listed shares are

tradable), which may affect the home bias. Prior work shows that correcting for free floats

reduces the home bias, but does not make it disappear (Dahlquist et al., 2003). We include

both home and host country fixed effects separately and together (the US is the default

country). Although the sizes of the coefficients change, the signs remain unaltered and

significance obtains.

Fourth, we include alternative measures for the common language dummy. Dow and

Karunaratna (2006) develop a continuous language difference variable based on language

families and the fraction of people speaking a certain language. Using this variable, the

results remain unchanged for the developed markets, but the significance of the IND score

disappears in the emerging markets sub-sample.

As a final change in our set of independent variables we investigate the cultural

distance on each of the four different dimensions instead of calculating one overall cultural

distance measure. The results indicate that the differences in power distance and uncertainty

avoidance drive the cultural distance effect in the developed countries sub-sample. This is not

surprising given the importance of uncertainty avoidance as such. In line with the

insignificant results obtained in most alternative specifications for the emerging markets, we

only find a weak negative effect of power distance in Panel B.

5.2.2 Alternative estimation procedures

In our second set of robustness tests, we consider alternative estimation procedures.

The results are shown in Table 5, where Panel A reports results for developed host countries

and Panel B for emerging host countries. First we perform a two-stage instrumental variable

regression in which we explain the home bias from the culture variables UAV and IND and

subsequently use this estimated value of the home bias in the second stage explaining the

foreign bias. We employ this method because UAV and IND may affect the foreign bias not

only directly, but perhaps also indirectly through an effect on the preference for home equity.

For both sub-samples, we find a positive effect of UAV on the home bias. This is in line with

the earlier negative effect on the foreign bias reported in Table 3. Surprisingly, we also find a

positive and significant effect of IND on the home bias for the developed markets, where a

negative sign was expected. The cultural distance variable included in the second stage

explaining the foreign bias shows up negative and significant for the developed markets and

remain insignificant in emerging markets.

19


In the second change of our estimation procedure we return to the question of the

zeros in our sample. As our data are based on percentages of foreign investments compared to

optimal percentages, a number of countries receive zero investment, leading to left-censoring

in our data set. By using a Tobit procedure we are able to include the zeros, but this raises the

question as to the extent to which the zeros drive our results. To allow for a more complete

test, we perform regressions that i) exclude the zeros (OLS), ii) include the zeros but do an

OLS (instead of Tobit), and iii) include the zeros with OLS and a Heckman sample correction

(Heckman, 1979). As the number of zeros is limited in the developed sub-sample (19 out of

525) but substantial in the set of emerging markets (133 out of 438), the zero issue is

potentially of greater importance for the emerging markets sub-sample. Results for the

developed sub-sample are largely in line with previous findings; UAV is negative and

significant, IND is positive and significant (though not when excluding the zeros or using the

Heckman sample selection control) and cultural distance is negative and significant. Results

for the emerging market sub-sample confirm the negative and significant effect of UAV.

Cultural distance is insignificant, and the IND variable is negative and significant (except

when the Heckman correction is not used). The latter result is opposite the result on IND

obtained so far.

6. Conclusion

This paper contributes to the existing literature on international asset allocation in several

ways. First, instead of measuring familiarity in terms of common language or geographic

distance (we control for these effects), we build on cross-cultural psychology and measure

‘real’ values and differences in these values. Second, whereas most studies focus on the home

bias, we turn our attention towards explaining the foreign bias. In particular, using a unique

database, our results indicate that a society’s cultural characteristics help us understand why,

ceteris paribus, some countries underinvest more than others and why investors from one

country do not underinvest in a set of host countries in the same way. We show that the first

phenomenon is caused by differences in levels of Hofstede’s uncertainty avoidance; societies

that are more uncertainty avoidant invest less in foreign equity and societies that are more

individualistic invest more in foreign equity. The second phenomenon is caused by the

differences in cultural distance between country pairs; culturally distant country pairs invest

20

less in each other than countries that are culturally more close, a phenomenon that especially

holds for developed countries. Our main results are robust to a battery of alternative

variables, estimation procedures and specifications of the foreign bias.

Our findings have important theoretical and practical implications. Apart from

highlighting the need to include cultural aspects in studies on asset allocation, our study has

more fundamental implications that can lead to several new research agendas in financial

economics, including i) investigation of the relation between culture and finance and ii)

analysis of the costs of underinvestment.

21

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26

Table 1: Summary statistics for home markets

Country

Average foreign

bias

Uncertainty

avoidance score

Individualism

score

Average cultural

distance from

home to host

US -1.78 46 91 2.65

UK -0.85 35 89 2.87

Canada -3.25 48 80 2.38

Germany -2.15 65 67 2.24

Italy -1.79 75 76 2.31

Sweden -1.99 29 71 3.65

France -2.14 86 71 2.28

Switzerland -0.95 58 68 2.35

Austria -1.69 70 55 3.06

Belgium -1.57 94 75 2.39

Denmark -1.09 23 74 3.67

Ireland -1.07 35 70 2.74

Finland -3.40 86 71 2.28

Greece -5.39 112 35 2.63

Luxembourg -0.90 70 60 2.03

Norway -2.34 50 69 3.27

Portugal -2.94 104 27 2.72

Spain -2.56 86 51 2.05

Netherlands -1.11 53 80 3.09

Japan -2.98 92 46 3.06

Australia -2.34 51 90 2.61

Singapore -1.30 8 20 3.30

Hong Kong -0.69 29 25 2.62

New Zealand -4.04 49 79 2.68

Taiwan -4.25 69 17 2.23

South Africa -3.86 49 65 2.15

Note: This table reports summary statistics for the 26 home countries. We report the average foreign bias for

each home country as described in equation (3); the uncertainty avoidance score of the home country; the

individualism score of the home country; and the average cultural distance of the home country to all hosts in

the sample.

27

Table 2: Summary statistics for host markets

Country Optimal Market Weights Average allocation Average foreign bias Average cultural distance

US 46.85 18.24 -1.32 2.24

UK 8.13 7.10 -0.39 2.45

Canada 2.44 0.32 -2.33 2.01

Germany 3.99 4.05 -0.63 2.01

Italy 2.22 1.64 -1.11 2.22

Sweden 1.03 1.43 -0.41 3.37

France 4.32 4.67 -0.41 2.4

Switzerland 2.21 2.30 -0.46 2.09

Austria 0.09 0.07 -1.60 2.88

Belgium 0.55 0.18 -2.06 2.5

Denmark 0.31 0.32 -0.98 3.29

Ireland 0.19 0.27 -0.76 2.39

Finland 0.95 1.97 0.09 2.4

Greece 0.46 0.08 -2.92 3.09

Luxembourg 0.1 0.21 -1.09 1.95

Norway 0.19 0.18 -1.42 3.03

Portugal 0.19 0.16 -1.22 3.24

Spain 1.39 1.21 -0.72 2.3

Netherlands 1.97 2.49 -0.34 2.82

Japan 11.29 6.30 -0.88 3.33

Australia 1.18 1.33 -1.21 2.18

Singapore 0.51 0.61 -0.78 3.65

Hong Kong 1.82 1.31 -1.44 3

New Zealand 0.07 0.06 -2.03 2.24

Taiwan 0.91 0.61 -2.43 2.77

South Africa 0.69 0.19 -2.72 2.01

India 0.49 0.39 -2.66 2.6

Korea 0.66 0.84 -1.23 2.85

Malaysia 0.39 0.35 -1.89 3.73

Thailand 0.13 0.43 -1.00 2.79

Indonesia 0.13 0.18 -2.32 3.12

Phillipines 0.15 0.10 -2.37 3.3

Mexico 0.41 0.16 -2.50 3.03

Brazil 0.67 0.23 -2.63 2.41

Argentina 0.37 0.03 -4.62 2.19

Chile 0.19 0.01 -6.02 2.97

Colombia 0.03 0.01 -6.10 3.03

Peru 0.04 0.01 -4.52 2.94

Venezuela 0.02 0.00 -6.67 3.42

Russia 0.16 0.06 -3.37 3.37

Hungary 0.04 0.10 -1.91 2.8

Czech 0.03 0.05 -2.49 1.99

Poland 0.09 0.10 -2.48 2.5

Pakistan 0.02 0.01 -4.59 N/A

Turkey 0.26 0.07 -2.97 2.48

Israel 0.19 0.04 -3.29 2.55

Egypt 0.09 0.01 -5.60 2.72

China 1.37 0.12 -4.02 3.3

Note: This table reports summary statistics for the 48 host countries. For each market we report the optimal allocation to

each market based on market capitalizations; the actual average allocation to each market; the average foreign bias towards

each market computed as in equation (3); and the average cultural distance from the host countries to all home markets in the

sample.

28

Table 3: Explaining the foreign bias in international asset allocation

Expected

sign

Base model

Including cultural

variables

Sub-sample:

developed market

host countries

Sub-sample:

emerging market

host countries

Preference for home funds:

Absolute level of home bias - -.029 (.003) *** -.024 (.003) *** -.017 (.003) *** -.039 (.006) ***

Host country attractiveness:

Transaction costs host market - -.025 (.003) *** -.026 (.003) *** -.003 (.007) -.033 (.006) ***

Capital controls host market - -.073 (.032) ** -.077 (.032) ** -.060 (.057) -.129 (.062) **

Stock market capitalization/GDP

host

+ .143 (.105) .197 (.103) * .341 (.114) *** -.343 (.398)

Tax level host - -0.113

(.036)

*** -.127 (.036) *** -.001 (.056) -.074 (.129)

Economic growth host last 5 years + .150 (.046) *** .151 (.045) *** .141 (.080) * .064 (.168)

Regional trade regime

dummies:

Host is EU country +/- .446 (.226) ** .325 (.224) -.131 (.207) 1.72 (.766) **

Host is Nafta country +/- -.173 (.287) -.061 (.282) -.913 (.281) *** 1.05 (.771)

Host is Asean country + 1.85 (.292) *** 2.02 (.292) *** .545 (.399) 3.08 (1.00) ***

Risk-return profile:

Lag 1 year return +/- 2.25 (.632) *** 2.05 (.622) *** 2.57 (.695) *** 3.46 (1.60) **

Lag 5 year return +/- -1.84 (.714) *** -1.62 (.702) ** -1.22 (1.63) -3.10 (1.90)

Return correlation - 1.48 (.522) *** .283 (.567) 1.26 (.581) ** -.742 (1.28)

Stock market volatility - .505 (1.45) -.536 (1.441) -5.68 (2.30) -1.40 (5.08)

Familiarity between home-host:

Common language + 1.34 (.235) *** .870 (.257) *** -.384 (.229) * 3.40 (.689) ***

Shared Common Law + -.308 (.153) ** -.001 (.159) .746 (.162) *** -.470 (.302)

Geographic distance - -.685 (.083) *** -.773 (.083) *** -.478 (.071) *** -1.60 (.245) ***

Cultural variables:

Uncertainty avoidance home - -.016 (.003) *** -.011 (.003) *** -.023 (.007) ***

Individualism home + .008 (.003) ** .009 (.003) *** .013 (.007) *

Cultural distance between home

and host

- -.057 (.073) -.169 (.065) *** .038 (.201)

N (number of left censored

observations)

963 (152) 963 (152) 525 (19) 438 (133)

χ2 727.44 *** 761.46 *** 342.88 *** 356.45 ***

Log likelihood -1855.62 -1838.61 -875.64 -801.06

Note: The dependent variable is the foreign bias as defined in Equation (3) (the log ratio of the share of country j

in mutual fund holdings of host country i to the world market capitalization weight of country j). The table

reports Left-censored Tobit regression results with robust standard errors in parentheses. In the case of zero

market share, the left-censored observations are calculated as the log of 0.001. ***, **, and * indicate

significance at the 1%, 5% and 10% level, respectively.

29

Table 4: Robustness tests on foreign bias: additional variables

Uncertainty

avoidance home

Individualism

Home

Cultural distance Additional

variable for

robustness test

Panel A: Developed Markets

Main Results Table 3 -.011 (.003) *** .009 (.003) *** -.169 (.065) ***

Power Distance and

Masculinity-Femininity

-.015 (.003) *** .014 (.004) *** -.169 (.065) *** .013 (.004)

.004 (.003)

***

Institutional quality -.011 (.003) ** .008 (.003) ** -.160 (.065) ** -.796 (.323) **

Home country fixed effects -.030 (.004) *** .023 (.005) *** -.183 (.065) ***

Host country fixed effects -.011 (.003) *** .007 (.003) ** -.172 (.066) ***

Home and host country fixed effects -.028 (.004) *** .020 (.006) *** -.187 (.067) ***

Dow’s measure for common language -.010 (.003) *** .009 (.003) *** -.161 (.068) ** .072(.075)

Individual cultural distance

components:

Abs. difference power distance

Abs. difference uncertainty avoidance

Abs. difference individualism

Abs. difference masculinity

-.009 (.003)

***

.012 (.004) ***

-.008 (.005)

-.006 (.003)

.006 (.004)

-.001 (.003)

*

*

Panel B: Emerging Markets

Main Results Table 3 -.023 (.007) *** .013 (.007) * .038 (.201)

Power Distance and

Masculinity-Femininity

-.030 (.007) *** .022 (.007) *** .347 (.214) .031 (.010)

.023 (.006)

***

***

Institutional quality -.020 (.007) *** .011 (.007) * .218 (.203) -2.78 (.642) ***

Home country fixed effects -.048 (.009) *** .073 (.012) *** -.185 (.211)

Host country fixed effects -.021 (.007) *** .014 (.007) ** .090 (.198)

Home and host country fixed effects -.043 (.008) *** .069 (.010) *** -.184 (.210)

Dow’s measure for common language -.026 (.006) *** .009 (.007) .084 (.198) -1.00 (.172) ***

Individual cultural distance

components:

Abs. difference power distance

Abs. difference uncertainty avoidance

Abs. difference individualism

Abs. difference masculinity

-.028 (.007)

***

.011 (.011)

-.021 (.011)

-.010 (.008)

.007 (.013)

.010 (.009)

*


in mutual fund holdings of host country i to the world market capitalization weight of country j). The table

reports Left-censored Tobit regression results with robust standard errors in parentheses. In the case of zero

market share, the left-censored observations are calculated as the log of 0.001. ***, **, and * indicate


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Table 5: Robustness tests on foreign bias: alternative model specifications

N Uncertainty Avoidance home Individualism Home Cultural distance

Panel A: Developed Markets

Main Results Table 3 -.011 (.003) *** .009 (.003) *** -.169 (.065)

Instrumental Variable Tobita 525 .413 (.038) *** .160 (.054) *** -.202 (.067) ***

OLS zeros excluded 506 -.007 (.002) ** .002 (.003) -.172 (.048) ***

OLS without Heckman control 525 -.011 (.003) *** .008 (.003) ** -.169 (.064) ***

OLS with Heckman controlb 504 -.008 (.002) *** .001 (.003) -.159 (.048) ***

Panel B: Emerging Markets

Main Results Table 3 -.023 (.007) *** .013 (.007) * .038 (.201)

Instrumental Variable Tobita 438 .401 (.052) *** -.003 (.052) .237 (.195)

OLS zeros excluded 305 -.016 (.003) *** -.009 (.003) *** -.172 (.101) *

OLS without Heckman control 438 -.020 (.005) *** .004 (.005) -.001 (.147)

OLS with Heckman controlc 421 -.014 (.003) *** -.014 (.004) *** -.155 (.098)


in mutual fund holdings of host country i to the world market capitalization weight of country j). In the case of

zero market share, the left-censored observations are calculated as the log of 0.001. ***, **, and * indicate


aThe uncertainty avoidance and individualism variables refer to the first-stage regression explaining the home

bias, of which the estimated value is subsequently used to explain the foreign bias. The cultural distance effect

relates to the 2nd

regression explaining the foreign bias.

bThe sample includes 19 zeros. The likelihood ratio test yields an insignificant result, suggesting that the two

equations are not different, i.e., no sample selection bias exists. Calculation of the inverse Mills ratio shows the

Mills ratio is insignificant. Variables included in the choice (probit) regression are GDP per capita home, capital

controls home, differences in capital controls between home and host, size of the stock market home, difference

in size of the stock market between home and host, transaction costs home, and difference in transaction costs

between home and host.

cThe sample includes 130 zeros. The likelihood ratio test yields a significant result (prob>ch-square=0.0265)

suggesting that the two equations are not independent, i.e., sample selection bias may exist. Calculation of the

inverse Mills ratio also shows the Mills ratio is significant at p<0.10 (p=0.068). Variables included in the choice

(probit) regression are GDP per capita home, capital controls home, differences in capital controls between

home and host, size of the stock market home, difference in size of the stock market between home and host,

transaction costs home, and difference in transaction costs between home and host.

Cultural Explanation of the Foreign Bias in International Investing

Economy & Finance

Transcript of Cultural Explanation of the Foreign Bias in International Investing