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Cultural Explanation of the Foreign Bias in International Investing


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Paper by Beugelsdijk and Frijns about the Foreign Bias in International Investing and how it is directly related to Hofstede's Cultural Factors with Uncertainty Avoidance being the most important one. Result: when dealing with international markets investors normally penalize against those markets that are culturally most different from their own. Even to such an extant that they double count specific risk factors.

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Cultural Explanation of the Foreign Bias in International Investing

  1. 1. 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: BART FRIJNS* Department of Finance Auckland University of Technology, Auckland, New Zealand e-mail:* 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.AcknowledgementsWe would like to thank Aaron Gilbert, Dimitri Margaritis and Alireza Tourani-Rad andparticipants of the Australasian Banking and Finance Conference (2008) for their usefulcomments and suggestions. Part of this paper was written when the first author was visitingthe Vienna University of Economics and Business (WU Wien) and we thank the WU for theirhospitality. The first author also thanks the Netherlands Organization for Scientific Research(NWO) for their financial support.
  2. 2. A Cultural Explanation of the Foreign Bias in International Asset AllocationAbstractIn this paper we examine the foreign bias in international asset allocation. Following extantliterature in behavioral finance, we argue that a society’s culture and the cultural distancebetween two markets play an important role in explaining the foreign bias. In particular, wehypothesize that the degree of a nation’s uncertainty avoidance affects the foreign bias (moreuncertainty-avoiding countries allocate less to foreign markets), as does the degree of acountry’s individualism (in individualistic countries performance is more directly attributedto a person and less to teams, causing these individuals to be more aggressive in their foreignasset allocations). We further expect that the degree of cultural distance between twocountries affects the amount of money allocated to that market. Based on extensiverobustness analyses, we find support for our hypotheses on the role of culture in internationalasset allocation.JEL Codes: C24, G15, G23.Keywords: Foreign Bias, Home Bias, Culture, International Asset Allocation. 1
  3. 3. 1. IntroductionOne of the major puzzles in financial economics is the fact that investors do not allocate theirmoney optimally across international markets, but instead systematically overweigh thesecurities of their home country. This phenomenon, referred to as the home bias or domesticbias,1 is inconsistent with standard asset pricing theory where all investors have identicalinformation sets and markets function perfectly (Lewis, 1999; Karolyi and Stulz, 2003) and avast amount of research has been dedicated to explaining the domestic bias (see Sercu andVanpée, 2007 for a recent review of the literature). The presence of a domestic bias alsoimplies that there is a relative underweighting of foreign markets. However, when investingin foreign markets, investors can allocate money to each market according to theirpreferences, i.e. the weights on each foreign country may differ and this issue has becomeknown as the foreign bias (see e.g. Chan et al., 2005) and this issue has received much lessattention. In this paper we extend the literature on the foreign bias by providing a culturalexplanation for the foreign bias, answering Ivkovic and Weisbenner’s recent call forexploration of ‘the role of societal characteristics in portfolio decisions’ (Ivkovic andWeisbenner, 2007: 1356). In doing so, we complement existing literature in several importantways. First, following the research on the home bias, we fill a gap by studying the allocationacross foreign countries given the preference for domestic stocks. Second, building on studiesthat investigate the role of familiarity by incorporating language differences and geographicdistance, we explicitly measure differences in culture. We do so in two ways. First, we showthat a country’s cultural characteristics explain investors’ absolute preferences for foreignstocks, 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 morerisk averse and have a lower preference for foreign stocks, ii) investors from moreindividualistic societies are more driven by individual performance and tend to be less riskaverse, and therefore display a higher preference for foreign equity. Second, based on thefamiliarity argument we argue that higher cultural differences between countries lead to alower preference for foreign stocks.1 The 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 andforeign bias to refer to any over- or underweighting of the portfolio in foreign markets. 2
  4. 4. Our empirical results are broadly in line with our expectations. Overall, we find thatmore uncertainty avoiding nations allocate less money abroad and more individualisticnations invest more abroad. Although having the expected sign, the cultural distance variableis insignificant in the full sample, but when we split the sample into developed and emergingmarkets we find that cultural distance is significant for developed markets and insignificantfor emerging ones. This finding is expected, because when investing in an emerging marketinvestors are typically seeking for differences. These findings are robust for different controlvariables and different model specifications. We also find that uncertainty avoidance plays agreater role in emerging markets. Our findings have important implications, as they question the assumptions ofstandard asset pricing theory. First, controlling for economic, political and legal differencessuch as corporate governance regime and stock market development, investors do not treatnon-domestic markets as one category, but rather make choices that are partly driven bybehavioral scripts originating from their cultural backgrounds. Thus, in addition to existingattempts to adapt portfolio theory by incorporating the home bias phenomenon, our resultssuggest that we need to go beyond this domestic-foreign distinction and include culturalguidelines to explain asset allocation across foreign markets. Second, to the extent assetallocation 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 allocationare becomes important. Finally, while the law and finance literature has concentrated onformal institutions (La Porta et al, 1998, 2008; Djankov et al., 2008), our analysis implies thatthis 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 literatureon the home bias, including the recent behavioral turn in finance. We then introduce cultureas an explanation for the domestic and foreign bias and develop hypotheses on the wayculture affects global asset allocation in Section 3. In Section 4 we describe our data, and inSection 5 we discuss the results of our main tests and several robustness tests. Finally, inSection 6 we conclude.2. BackgroundTraditional explanations of the home bias have mostly focused on rational arguments as towhy optimal diversification is impossible. These arguments include (tax) restrictions on 3
  5. 5. international capital flows, non-tradability of goods, inflation hedging motives, institutionalbarriers or more general transaction costs. In addition, the home bias has been attributed toinformation (cost) arguments, familiarity arguments or a combination of the two.2 However,more recent research has proposed several irrational arguments based on behavioral financetheory. These behavioral arguments assume that investors are boundedly rational andconsider foreign markets more risky than they truly are just because they are foreign(Huberman 2001; Solnik 2008). These behavioral arguments build on informationadvantages, perceived familiarity, overconfidence and a range of associated personalitycharacteristics leading to underdiversification and the home bias phenomenon (Kilka andWeber, 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 distanceas behavioral proxies. For example, using data on US equity holdings of more than 3,000mutual funds from 22 developed and developing countries, Ke et al. (2006) show that fundswith a local presence in the US invest a greater proportion in US firms than those that haveno such presence. This effect becomes even stronger for managers from non-Englishspeaking countries and from countries further away from the US, showing that greaterfamiliarity reduces the home bias. In a similar vein, both American professional moneymanagers (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 headquarteredin nearby locations. In addition, Grinblatt and Keloharju (2001) use language to proxy forfamiliarity in their study. Taking into account Finland’s two official languages, Finnish andSwedish, they find that Finnish (Swedish) native speakers prefer stocks of firms that publishtheir annual reports in Finnish (Swedish). A similar phenomenon has been found for sharednationality between an investor and the firm’s CEO (Grinblatt and Keloharju, 2001), and fora county’s level of patriotism (Morse and Shiva, 2008). The familiarity argument specifically applies to the foreign bias. For example, Kangand Stulz (1997) analyze foreign stock ownership in Japanese firms and find that foreigninvestors prefer larger firms, firms in manufacturing industries and firms with goodaccounting 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-level2 For a recent review of the literature see Sercu and Vanpée (2007). 4
  6. 6. 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 familiaritybetween home and host as proxied by a shared official language, lower geographic distanceand more bilateral trade flows between two countries increases the amount of equity held byone 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 isobserved for country pairs such as Germany-Austria and Austria-Germany, and persistentover the years. In sum, these studies show that the extent of underinvestment in foreign markets isnot just a simple choice between home and foreign markets, but also between country- (andfirm-) specific aspects on which foreign markets differ. While cultural differences arementioned as one of the familiarity variables affecting the foreign bias, our theoretical andempirical understanding on the role of culture remains incomplete. Culture is generally nottheorized upon, but rather part of some kind of residual explanation. Further, culture istypically not measured in terms of shared values, but by proxies like shared nationality(Grinblatt and Keloharju, 2001), a geographical classification of Asian versus Europeancountries (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 biasOur 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 socializationand from parents to children (cf., Tabellini, 2008a). In cross-cultural psychology, culture isdefined as the collective programming of the mind that distinguishes the members of onegroup from those of another (Hofstede, 2001, 1980). Although the field of cross-culturalstudies is characterized by multiple approaches towards culture (Adler, 1983), comparativeempirical work in economics and international business has been dominated by Hofstede’sseminal study Culture’s Consequences, where, based on a cross-cultural survey of IBMemployees, Hofstede distinguishes between four dimensions that are assumed to capturecross-cultural differences: power distance, masculinity-femininity, individualism-collectivismand uncertainty avoidance. ‘Power distance’ refers to the extent to which people believe thatpower and status are distributed unequally and the extent to which they accept an unequal 5
  7. 7. distribution of power as the proper way of organizing social systems; ‘Masculinity-femininity’ refers to the extent to which a society emphasizes traditional masculine valuessuch as competitiveness, assertiveness, achievement, ambition and the acquisition of moneyand other material possessions, versus feminine values such as nurturing, helping others, notshowing off and caring for the quality of life; ‘Individualism-collectivism’ reflects the degreeto 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 withuncertain, unknown or unstructured situations. These four dimensions are assumed to reflectkey aspects of a society’s culture. Hofstede then assigns each country a score on each culturaldimension to indicate how people from different cultures feel about the above societalissues.3 Throughout the years, these scores have become available for an increasing numberof countries. Of the four dimensions, uncertainty avoidance and individualism in particularhave been related to economic phenomena (Kirkman et al., 2006).43.1 Hypothesis I: Uncertainty AvoidanceUncertainty avoidance reflects the extent to which people feel (un)comfortable in situationswith uncertain outcomes and their willingness to deal with risk. Risk aversion plays a keyrole in traditional models of investment behavior, but behavioral finance is especially focusedon the heterogeneity in risk attitude. Agent heterogeneity regarding risk attitude is exploredalong multiple dimensions, mostly linking portfolio allocation characteristics to individualcharacteristics like age, gender and level of education (Goetzmann and Kumar, 2008; Dornand 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 explainthe variation in actual risk taking measured by portfolio volatility and concentration. In asimilar vein, Dohmen et al. (2006) document a robust intergenerational correlation betweenrisk and risk attitudes. Using a sample of German families to investigate the origins of3 Hofstede 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 reducingits empirical applicability. Moreover, scholars have questioned its added value, as it has been argued to reflectthe same underlying cultural values as the individualism dimension (see Barkema and Vermeulen, 1997).4 The validity of Hofstede’s culture dimensions has been empirically confirmed in many studies (e.g., VanOudenhoven, 2001; for an overview of earlier replications, see Sφndergaard, 1994). The impact of Hofstede’sseminal work on culture is clearly indicated in Kirkman et al.’s (2006) review of empirical research that hasused Hofstede’s four-dimensional culture framework. In this review, Kirkman et al. (2006) examine 180 articlespublished in top-tier journals between 1980 and 2002 that empirically assess one or more dimensions ofHofstede’s framework. Although they limit their search to major management and (cross-cultural) appliedpsychology journals, their analysis shows that the Hofstede dimensions have been applied to a broad range oftopics, including entrepreneurship, entry modes of multinational firms, innovation and foreign direct investment. 6
  8. 8. economic decision making, they establish a strong positive association between the attitudesof parents and children regarding the willingness to take risks and the willingness to trustothers. This finding is important because it implies that attitudes towards risk, including thedecision on how to allocate investments, are persistent over time and across individuals. Atthe country level, Chui and Kwok (2008) show that uncertainty-avoiding nations have higherlevels of life insurance consumption. Taken together, the above arguments lead us to expectthat individuals from high uncertainty avoiding nations have lower preferences to investabroad. High levels of uncertainty avoidance are associated with a high preference fordomestic equity and a low (relative to the host country’s market weight) preference forforeign equity.3.2 Hypothesis II: IndividualismThe second cultural dimension that can be argued to affect international asset allocation is thecountry’s degree of individualism. More individualistic societies tend to develop rewardssystems that are more individually oriented (Schuler and Rogovsky, 1998). For example, boththeoretically (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 USinsurance company. One mediating link between individualism and rewards systems is theincreased competitiveness in more individualistic groups. Oetzel (1998) finds thatindividualistic European-American groups are more competitive than collectivistic Japanese-American groups. In a different context, Ali (1993) finds that individualism is positivelyrelated to autocratic decision-making styles and attitudes towards risk among Saudi Arabianmanagers. The finding on autocratic decision-making styles correlates with the finding bySpector et al. (2001), who show in a sample of 5,000 managers from 24 countries thatindividualism is positively associated with internal locus of control. Hence, these studiessuggest that high individualism is correlated with risk taking and individual rewards systems,leading to a positive association between individualism and the preference for foreigninvestments. A long-standing literature in economics and social psychology has focused on thedistinction 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 smallgroups reveal systematically different risk preferences than individuals, and if so, how therisk preferences of the individual group members aggregate into a group risk preference. Thisis directly related to individualism because more individualistic societies are characterized by 7
  9. 9. 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, andgroup decisions exhibit a smaller variance than individual decisions. One reason for thesefindings may be that those highest in individualism show the lowest levels of informationseeking in individual networks (Zaheer and Zaheer, 1997). What this means for the relationbetween individualism and foreign asset allocation is that more individualistic societies canbe expected to invest more in foreign markets. We therefore hypothesize that the higher thelevel of individualism of a home country, the more it will allocate to foreign assets.3.3 Hypothesis III: Cultural DistanceA greater cultural distance is associated with unfamiliarity and leads to economic decisions inwhich risk is reduced either by choosing a certain type of investment (Anderson andGatignon, 1986; Hill et al., 1990) or by not investing at all (Loree and Guisinger, 1995; Sethiet al., 2003). Huberman (2001) shows that investors more familiar with domestic stocks feel asense of discomfort with foreign stocks. This not only suggests that countries that are morerisk averse have higher levels of discomfort, but also that the level of discomfort mayincrease when the cultural distance between countries increases. Culturally distant countriesare not considered attractive investment opportunities, ceteris paribus, and this will lead to alower preference for investing in these countries. The three hypotheses above are the focus of the remainder of this paper. Although wehave 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 theserelationships empirically.4. Data and Summary StatisticsOur data on foreign asset allocation are based on the mutual fund holdings of 26 countriesinvesting in a broader sample of 48 countries. The country-level data are based on underlyingindividual fund-level data obtained from Thomson Financial Services (TFS). Fund-level datacontain the holdings of 20,821 and 24,589 mutual funds across the 26 countries for the years1999 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 onecountry into another only considers the equity part of the funds. Aggregating at the countrylevel therefore shows the percentage of money allocated by mutual funds from country i to 8
  10. 10. the share market of country j (wij). For clarity we label the 26 countries from where mutualfunds operate from as home countries and the 48 countries for which we have data on howmuch investments they receive as host countries. The 26 home countries are relatively well-developed nations, and the additional 22 host countries are mainly emerging marketeconomies.54.1 Dependent variable: foreign biasTo calculate our dependent variable measuring the foreign bias, we follow standard empiricalfinance literature and calculate deviations from the optimal portfolio as described by assetpricing theory (see also Chan et al., 2005; Ke et al., 2006). According to the CAPM, optimalweights are given by the market value of a particular country relative to the global marketvalue (i.e., the sum of the value of all markets). The difference between the actual holdings ina country and the optimal weight reflects the degree of bias towards a particular country. Toformalize the discussion, let wij be the percentage weight of the mutual fund holdings ofhome country i in host country j, i.e., MVij wij = 48 , (1) ∑ MV j =1 ijwhere MVij is the amount of money mutual funds from country i invest in country j and 48∑ MVj =1 ij 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 ofcountry j relative to the market value of all markets: MV j* w*j = 48 . (2) ∑ MV i =1 i *5 For a more detailed discussion on the data, see Chan et al. (2005). 9
  11. 11. From these weights we can compute the foreign bias score as the (log) ratio of the actualallocation of country i in country j relative to the optimal portfolio allocation. We define theforeign bias score as w  FBIASij = log  ij  , for i ≠ j. (3)  w*   jGiven the fact that most countries have a large and positive domestic bias, we expect in mostcases that wij < w* , i.e., investments in host countries are lower than the optimal investments. jThis would cause FBIASij to be negative in most cases and implies that lower values forFBIASij 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 the26 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 homecountries computed as in equation (3). For the home countries we find the smallest averageforeign bias for Hong Kong (-0.69). This country is closely followed by the UK (-0.85) andLuxembourg (-0.90). The largest foreign bias is observed for Greece (-5.39), followed byTaiwan (-4.25) and New Zealand (-4.04). The US (-1.78) has a slightly lower foreign biasthan the average of -2.25. In the first two columns of Table 2 we present summary statistics for the foreign biasmeasures of the host countries. The first column reports the optimal allocations to eachmarket 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 columnshows that the highest allocation of all foreign markets is to the US, which also has thehighest weight based on market capitalization. The lowest allocations are to several LatinAmerican countries, which conjointly receive low weights based on their marketcapitalizations. Comparison of the first two columns clearly reveals the presence of a foreignbias, 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 thatthe foreign bias score is highest towards Finland (0.09), implying a slight overinvestment into 10
  12. 12. 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 differencesWe 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 scoresare highest in Greece (most uncertainty avoiding), and lowest in Singapore. The US scoresrelatively low on the uncertainty avoidance index. IND is highest in the US (mostindividualistic), and lowest in Taiwan. As expected, many of the Anglo-Saxon countriesscore high on the individualism index (US, UK, Australia, Canada, New Zealand) and theAsian countries score relatively low. The overall cultural distance measure is based on the country scores of all fourHofstede culture dimensions to capture cultural distance as completely as possible. Tocalculate one overall distance measure, we follow extant literature and use the followingformula based on the Euclidean distance between the culture dimensions. It computes theirdistance in a four-dimensional space as the square root of the sum of the squared differencesin the scores on each cultural dimension. Formally: 4 CDij = ∑{( I k =1 kj − I ki ) 2 / Vk } , (6)where CDij is the cultural distance between home country i and host country j, Ikj is countryj’s score on the kth cultural dimension, Iki is the score of country i on this dimension, and Vkis the variance of the score of the dimension. The cultural distance measure was introducedby Kogut and Singh (1988) and is often used in international business research (e.g., Loreeand Guisinger, 1995; Barkema and Vermeulen, 1997; Brouthers and Brouthers, 2001). Unlikethe Kogut and Singh index, our measure does not give equal weights to the differences in thescores on each of Hofstede’s dimensions, and hence does not assume that each dimension isequally 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 hostsand 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 thatLuxembourg and Spain are on average most closely related to all other markets in the sample, 11
  13. 13. while Denmark and Sweden are culturally most different from all hosts. Table 2 reveals thecultural distance of each host to the different home markets. Again, Luxembourg is found tobe culturally most similar to the average home country, while Malaysia appears to beculturally most distant. However, apart from these average statistics we find that the culturaldistance measure is widely dispersed for various home and host country pairs, with thehighest cultural distance found between Japan and Sweden (CD = 5.65) and the lowestbetween the US and Australia (CD = 0.26). Because equity investments in foreign markets not only depend on culturaldifferences, but are affected by the target country’s overall economic and political/legalenvironment, such as corporate governance structure, accounting standards, transaction costsand stock market development, we need to control for these host country characteristics inour regression analysis.4.3 Control variablesTo control for the alternative explanations of the foreign bias, we include the following setsof variables. Focal host country attractiveness: To capture the traditional economic explanation forthe home/foreign bias, we include proxies for transaction and capital costs, tax levels, hostcountry stock market development and host country economic growth rate. All measures arederived from extant research. Stock market development is measured as stock marketcapitalization over GDP and comes from the Standard and Poor’s Emerging Stock MarketsFactbook 2000 (see also Chan et al., 2005). Higher levels of stock market development areassociated 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 managersand brokerage houses, as introduced by Domowitz et al. (2001). Costs include commissionsand fees for the period 1996-1998. Tax levels are derived from information provided by PriceWaterhouse Coopers (PWC) Corporate Tax 1996. We expect a negative relation betweenforeign investment and both tax levels and transaction costs, because increasing tax levelsand higher transaction costs reduce a host country’s attractiveness. Capital controls areproxied by the Economic Freedom index ranking countries in terms of restrictions on foreigncapital transactions. Scores range between 10 (zero restrictions) to 1 (both domesticinvestments by foreigners and foreign investments by locals require government approval).Finally, we also include average host country GDP per capita growth in the five yearspreceding our investment data. Growing economies experiencing high GDP per capita growth 12
  14. 14. rates are considered attractive investment opportunities (cf. Berkel, 2007). Data on growthrates are derived from the Penn World Tables. Regional trade regime dummies: We include regional dummies taking the value 1 ifcountries are part of a regional trade agreement, i.e., NAFTA, ASEAN and the EU. Theseregional 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 variablespossibly related to a regional friendship bias (cf. Berkel, 2007) – potentially obscuring theeffects of cultural distance. In the robustness analysis, we also test for home and host countryfixed effects, because some funds are located in financial centers such as Luxemburg andIreland, and the objectives of these funds’ location decisions is often related to tax incentivesoffered by these countries (cf. Ke et al., 2006). Risk and return profile: In addition to the controls mentioned above, we includevariables that capture the risk and return characteristics of a particular market. In the returnsdomain we consider average one-year and five-year lagged market return computed usingmonthly data (data is obtained from Datastream and is corrected for dividend payouts).Lagged returns may affect portfolio allocation in several ways. A positive relationshipbetween past performance and foreign investment may be expected if managers exhibit returnchasing 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). Previousstudies have presented evidence for both strategies followed by mutual fund managers (Bohnand Tesar, 1996 for return chasing behavior and Grinblatt and Keloharju, 2000 for contrariantrading strategies), and we therefore have no a priori expectations about the sign of therelation between past returns and foreign asset allocation. To capture diversification benefits,we include the correlation between home and host country returns (computed using monthlydata over the past five years). From a diversification perspective, we expect a negativerelation between return correlation and the foreign investment. Finally, we control for hostmarket 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 culturevariables, we include familiarity measures that have been used before, i.e., language andgeographic distance. Common language is measured as a dummy taking the value of 1 whenthe home and host countries share the same official language. We expect a positive relationbetween common language and the foreign investment. In the robustness analysis we also testthe language effect using an alternative continuous measure of shared language. We also 13
  15. 15. include the log of the great-circle geographic distance (in kilometers) between each home andhost 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. Geographicdistance has been used as a proxy for familiarity or the degree of asymmetry explaining, forexample, 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 andfinance literature and include a dummy for shared common law, because of the superiorinvestor protection regimes in common law countries (La Porta et al. 1998, 2008). Thisdummy takes the value of 1 when home and host country share a common law system. Weexpect a positive relation between shared common law and foreign investment.5. Methodology and Results5.1 Main analysesAs a first step in analyzing determinants of the foreign bias, we develop a base model usingall abovementioned controls that have been associated with the home or foreign bias. Themodel takes the form FBIAS ij = f ( HBIAS i , Attractiveness j , Regional trade regime j , , (7) Risk − return profile j , Familiarity ij )where HBIASi is the level of the home bias of country i; Attractivenessj refers to the controlvariables for the transaction costs, capital controls, market size, tax level and economicgrowth of host country j; Regional trade regimej refers to the trade regime under which thecountry operates, i.e., whether the host is part of the EU, NAFTA or ASEAN; Risk-returnprofilej describes the characteristics of the host market in terms of past returns, stock marketvolatility and diversification opportunities; and Familiarityij explores the degree of familiaritybetween home i and host j based on commonality of language, shared common law systemand geographic distance.6 Distances are calculated between the major financial centers of a country and are calculated “as the crow flies”using the distance calculator from 14
  16. 16. 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 theabsolute level of the home bias has a negative impact on the foreign bias score, i.e., if thedomestic bias is high for a particular country, that country has proportionally less funds thatcan 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 unexpectedpositive sign and is highly significant. This is puzzling as it implies that fund managers donot allocate money to foreign markets based on diversification arguments. However, thisresult is also observed by Chan et al. (2005). To evaluate the role of cultural variables in explaining the foreign bias we add theculture variables to our base model, i.e., FBIAS ij = f ( Base Variablesij ,Uncertainty Avoidancei , , (8) Individualismi , Cultural Distanceij )where Base Variablesij are the base variables included in equation (7), UncertaintyAvoidancei is the degree of uncertainty avoidance of home country i, Individualismi is theindividualism score of home country i, and Cultural Distanceij is the cultural distancebetween 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 modelincluding all of cultural variables together improves the base model significantly.8 We findthat the degree of uncertainty avoidance of the home country has a significantly negativeimpact on the foreign bias score, i.e., the higher the degree of uncertainty avoidance of thehome country, the lower its allocation to other markets. This finding confirms our firsthypothesis. The individualism score of the home market also has a significant impact on theforeign bias score, with a positive sign. This implies that the higher the degree of7 We estimate equation (7) as a linear model, but need to control for the fact that allocations to several hostcountries in the sample are zero. Since the foreign bias is defined as the log of the proportion invested in acountry over the optimal proportion according to portfolio theory, this implies that FBIASij would not beobserved for a substantial proportion of the data. Ignoring these non-allocations could lead to potential sampleselection bias (see Heckman, 1979). To circumvent such issues we replace all “zero”-observations with a valueof 0.001 and estimate (7) as a Tobit model, where all “zero”-observations are censored on the left.8 A Likelihood Ratio test rejects the null of the restricted model at the 1% significance level. 15
  17. 17. individualism in the home country, the higher the foreign asset allocation, supportinghypothesis 2. Lastly, we find that the impact of cultural distance has the expected sign, but isinsignificant. The results for the base variables remain mostly unchanged and significant,implying that the cultural variables measure other dimensions of the foreign bias notexplained by these base variables. In our data set we can make a distinction between hosts that are developed marketsand hosts that are emerging markets. This is important because the decision to invest inemerging markets may be based on different criteria than the decision to invest in developedmarkets. Since many mutual funds classify themselves specifically as an emerging marketsfund, investors make a specific choice to invest in these markets. When investing in anemerging markets fund, investors are generally seeking higher returns in markets that aremore risky than and different from developed markets. We would therefore expect theuncertainty avoidance and individualism variables to be more important and the culturaldistance measure to be less important. On the other hand, when investing in developedmarkets, investors may have higher preferences for markets that are culturally similar and wetherefore 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 hostcountries are considered. As for the cultural variables we find that uncertainty avoidance isagain highly significant and negative. However, the size of the coefficient has decreasedwhen compared with the coefficient for the full model. Individualism is again highlysignificant and positive indicating that the more individualistic a society is, the higher itsdegree 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 fullmodel. In the last column of Table 3 we show the results for the allocations to emergingmarkets. We find that the level of uncertainty avoidance has a significantly negative impacton the foreign bias score and the size of the coefficient has increased compared with the fullmodel. Also compared with the developed markets model, we find that the coefficient hasdoubled, indicating the greater importance of uncertainty avoidance for emerging markets.This increase is expected as emerging markets are generally more risky than developedmarkets, and implies that home countries with given levels of uncertainty avoidance allocateless to emerging markets. Individualism has a marginally significant positive effect and its 16
  18. 18. coefficient is higher than for the full sample or the sample of developed hosts. As in the fullmodel cultural distance remains insignificant.5.2 Robustness analysisBroadly, the results presented above are in line with our expectations. However, to assesstheir robustness we perform several tests. First, we include additional variables that couldpotentially affect the results obtained in Table 3. Second, we use alternative estimationmethods.5.2.1 Additional variablesOur first robustness analysis relates to the inclusion of the remaining two culture dimensionsas developed by Hofstede, i.e., Power distance (PD) and Masculinity (MAS). The addition ofthese 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 regardingtheir sign, we obtain significantly positive effects for PD in both sub-samples. MAS ispositive and significant in the set of emerging markets. Despite the lack of a clear theoreticaldirection, 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 actuallyproxy for institutional distance between home and host countries, in which case not includinghost country institutional quality characteristics may lead to an omitted variable bias. Ourmeasure of institutional quality is based on a five-item principal component. These five itemsare rule of law, public enforcement index, insider trading prevalence, risk of expropriationand 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 isshown in Table 4, including host country institutional quality does not affect our resultsreported in Table 3. The institutional quality variable is significant for both developed andemerging markets, though the size of the coefficient is logically smaller in the set ofdeveloped host countries (Panel A) than in the set of emerging host markets (Panel B). <Insert Table 4 about here> 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 17
  19. 19. reason to include country dummies is to control for free floats (not all listed shares aretradable), which may affect the home bias. Prior work shows that correcting for free floatsreduces the home bias, but does not make it disappear (Dahlquist et al., 2003). We includeboth home and host country fixed effects separately and together (the US is the defaultcountry). Although the sizes of the coefficients change, the signs remain unaltered andsignificance obtains. Fourth, we include alternative measures for the common language dummy. Dow andKarunaratna (2006) develop a continuous language difference variable based on languagefamilies and the fraction of people speaking a certain language. Using this variable, theresults remain unchanged for the developed markets, but the significance of the IND scoredisappears in the emerging markets sub-sample. As a final change in our set of independent variables we investigate the culturaldistance on each of the four different dimensions instead of calculating one overall culturaldistance measure. The results indicate that the differences in power distance and uncertaintyavoidance drive the cultural distance effect in the developed countries sub-sample. This is notsurprising given the importance of uncertainty avoidance as such. In line with theinsignificant results obtained in most alternative specifications for the emerging markets, weonly 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 countriesand Panel B for emerging host countries. First we perform a two-stage instrumental variableregression in which we explain the home bias from the culture variables UAV and IND andsubsequently use this estimated value of the home bias in the second stage explaining theforeign bias. We employ this method because UAV and IND may affect the foreign bias notonly 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 withthe earlier negative effect on the foreign bias reported in Table 3. Surprisingly, we also find apositive and significant effect of IND on the home bias for the developed markets, where anegative sign was expected. The cultural distance variable included in the second stageexplaining the foreign bias shows up negative and significant for the developed markets andremain insignificant in emerging markets. 18
  20. 20. <Insert Table 5 about here> In the second change of our estimation procedure we return to the question of thezeros in our sample. As our data are based on percentages of foreign investments compared tooptimal percentages, a number of countries receive zero investment, leading to left-censoringin our data set. By using a Tobit procedure we are able to include the zeros, but this raises thequestion as to the extent to which the zeros drive our results. To allow for a more completetest, we perform regressions that i) exclude the zeros (OLS), ii) include the zeros but do anOLS (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 of525) but substantial in the set of emerging markets (133 out of 438), the zero issue ispotentially of greater importance for the emerging markets sub-sample. Results for thedeveloped sub-sample are largely in line with previous findings; UAV is negative andsignificant, IND is positive and significant (though not when excluding the zeros or using theHeckman sample selection control) and cultural distance is negative and significant. Resultsfor 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 (exceptwhen the Heckman correction is not used). The latter result is opposite the result on INDobtained so far.6. ConclusionThis paper contributes to the existing literature on international asset allocation in severalways. First, instead of measuring familiarity in terms of common language or geographicdistance (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 homebias, we turn our attention towards explaining the foreign bias. In particular, using a uniquedatabase, 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 onecountry do not underinvest in a set of host countries in the same way. We show that the firstphenomenon is caused by differences in levels of Hofstede’s uncertainty avoidance; societiesthat are more uncertainty avoidant invest less in foreign equity and societies that are moreindividualistic invest more in foreign equity. The second phenomenon is caused by thedifferences in cultural distance between country pairs; culturally distant country pairs invest 19
  21. 21. less in each other than countries that are culturally more close, a phenomenon that especiallyholds for developed countries. Our main results are robust to a battery of alternativevariables, estimation procedures and specifications of the foreign bias. Our findings have important theoretical and practical implications. Apart fromhighlighting the need to include cultural aspects in studies on asset allocation, our study hasmore fundamental implications that can lead to several new research agendas in financialeconomics, including i) investigation of the relation between culture and finance and ii)analysis of the costs of underinvestment. 20
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  27. 27. Table 1: Summary statistics for home markets Average cultural Average foreign Uncertainty Individualism distance from Country bias avoidance score score 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 2.15Note: This table reports summary statistics for the 26 home countries. We report the average foreign bias foreach home country as described in equation (3); the uncertainty avoidance score of the home country; theindividualism score of the home country; and the average cultural distance of the home country to all hosts inthe sample. 26
  28. 28. 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 3.3Note: This table reports summary statistics for the 48 host countries. For each market we report the optimal allocation toeach market based on market capitalizations; the actual average allocation to each market; the average foreign bias towardseach market computed as in equation (3); and the average cultural distance from the host countries to all home markets in thesample. 27
  29. 29. Table 3: Explaining the foreign bias in international asset allocation Sub-sample: Sub-sample: Expected Including cultural developed market emerging market sign Base model variables host countries host countriesPreference 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 + .143 (.105) .197 (.103) * .341 (.114) *** -.343 (.398)hostTax level host - -0.113 *** -.127 (.036) *** -.001 (.056) -.074 (.129) (.036)Economic growth host last 5 years + .150 (.046) *** .151 (.045) *** .141 (.080) * .064 (.168)Regional trade regimedummies: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 - -.057 (.073) -.169 (.065) *** .038 (.201)and hostN (number of left censored 963 (152) 963 (152) 525 (19) 438 (133)observations)χ2 727.44 *** 761.46 *** 342.88 *** 356.45 ***Log likelihood -1855.62 -1838.61 -875.64 -801.06Note: The dependent variable is the foreign bias as defined in Equation (3) (the log ratio of the share of country jin mutual fund holdings of host country i to the world market capitalization weight of country j). The tablereports Left-censored Tobit regression results with robust standard errors in parentheses. In the case of zeromarket share, the left-censored observations are calculated as the log of 0.001. ***, **, and * indicatesignificance at the 1%, 5% and 10% level, respectively. 28
  30. 30. Table 4: Robustness tests on foreign bias: additional variables Uncertainty Individualism Cultural distance Additional avoidance home Home variable for robustness test Panel A: Developed MarketsMain Results Table 3 -.011 (.003) *** .009 (.003) *** -.169 (.065) ***Power Distance and -.015 (.003) *** .014 (.004) *** -.169 (.065) *** .013 (.004) ***Masculinity-Femininity .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 -.009 (.003) *** .012 (.004) ***components: -.008 (.005) *Abs. difference power distance * -.006 (.003)Abs. difference uncertainty avoidance .006 (.004)Abs. difference individualism -.001 (.003)Abs. difference masculinity Panel B: Emerging MarketsMain Results Table 3 -.023 (.007) *** .013 (.007) * .038 (.201)Power Distance and -.030 (.007) *** .022 (.007) *** .347 (.214) .031 (.010) ***Masculinity-Femininity .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 -.028 (.007) *** .011 (.011)components: -.021 (.011) *Abs. difference power distance -.010 (.008)Abs. difference uncertainty avoidance .007 (.013)Abs. difference individualism .010 (.009)Abs. difference masculinityNote: The dependent variable is the foreign bias as defined in Equation (3) (the log ratio of the share of country jin mutual fund holdings of host country i to the world market capitalization weight of country j). The tablereports Left-censored Tobit regression results with robust standard errors in parentheses. In the case of zeromarket share, the left-censored observations are calculated as the log of 0.001. ***, **, and * indicatesignificance at the 1%, 5% and 10% level, respectively. 29
  31. 31. 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)Note: The dependent variable is the foreign bias as defined in Equation (3) (the log ratio of the share of country jin mutual fund holdings of host country i to the world market capitalization weight of country j). In the case ofzero market share, the left-censored observations are calculated as the log of 0.001. ***, **, and * indicatesignificance at the 1%, 5% and 10% level, respectively.a The uncertainty avoidance and individualism variables refer to the first-stage regression explaining the homebias, of which the estimated value is subsequently used to explain the foreign bias. The cultural distance effectrelates to the 2nd regression explaining the foreign bias.b The sample includes 19 zeros. The likelihood ratio test yields an insignificant result, suggesting that the twoequations are not different, i.e., no sample selection bias exists. Calculation of the inverse Mills ratio shows theMills ratio is insignificant. Variables included in the choice (probit) regression are GDP per capita home, capitalcontrols home, differences in capital controls between home and host, size of the stock market home, differencein size of the stock market between home and host, transaction costs home, and difference in transaction costsbetween home and host.c The 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 theinverse 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 betweenhome 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. 30