FDI in Cultural Space:
The Role of Bilateral Cultural Distance and Culture
Clusters in FDI Expansion
Short (Running) Title: Cultural Distance and FDI
Suparna Chakraborty
Associate Professor of Economics, University of San Francisco
2130 Fulton Street, San Francisco, CA 94117
Email: [email protected]; Phone: (415) 422-4715
Miao Wang
Associate Professor of Economics, Marquette University
606 N. 13th St., Milwaukee, WI 53233
Email: [email protected]; Phone: (414) 288-7310
M.C. Sunny Wong
Professor of Economics, University of San Francisco
2130 Fulton Street, San Francisco, CA 94117
Email: [email protected]; Phone: (415) 422-6194
Abstract:
Using tools of spatial econometrics to study patterns in U.S. FDI to 74 host countries over 1984-
2011, this paper focuses on the role of distance as a determinant of FDI by redefining the concept of distance to
encompass cultural similarities and differences and by exploring interdependence among culturally similar hosts when
receiving U.S. FDI. We find that bilateral cultural distance deters U.S. FDI to another country much like geographical
distance would do. However, there exists significantly positive spatial interdependence of U.S. FDI in host countries
that have a similar culture, or hosts in a culture cluster. In other words, U.S. FDI in a particular host country increases
when U.S. investment in other hosts that share a similar culture with the country under consideration rises. Further,
there is a time component to this relationship – the positive association of U.S. FDI in host countries in a culture
cluster is stronger in the 1980s and 1990s, but tends to lack significance in the 2000s.
Keywords: cultural distance, foreign direct investment, spatial analysis
JEL Codes: F21, F23, Z13
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1.
Introduction
Foreign direct investment (FDI) by multinational corporations (MNCs) has risen significantly over the past
few decades. The value of worldwide FDI inflows was $1.47 trillion in 2013, growing at an annual average rate of
10.4% since 1983 (United Nations, various issues). In comparison, the world GDP and export of goods and services
grew at an annual average rate of 2.9% and 5.8%, respectively, during the same period. Not surprisingly, drivers of
FDI have attracted considerable attention from both researchers and policy makers and there is a large number of
studies exploring potential factors affecting MNC investment decisions (see for example, Dunning, 1993; Carr,
Markusen, and Maskus, 2001; Charkrabarti, 2001; Lavellée and Vicard, 2013, Blonigen and Piger, 2014).
Most empirical research on FDI determinants focus on geographical distance as a key gravity variable leaving
one to wonder whether distance is just geographical in scope. Questioning this focus on interpreting distance as
geography alone, Head and Mayer (2013:1196) argue that “cultural difference and inadequate information manifest
themselves most strongly at national borders and over distance”. The role of culture in management strategy has also
been recognized in international business research (Kogut and Singh, 1988) and alternative distance concepts have
since gained a broad interest in strategy, management, and organizational behavior research (Shenkar, 2001; Stahl and
Voigt, 2005; Reus and Lamont, 2009).
The goal of our paper is to explore how cultural distance between home and host countries – that is,
differences between national cultures – affect FDI magnitude and patterns, drawing on a sample of FDI from the U.S.
to 74 countries over the time period of 1984 through 2011. Our paper adds to the FDI literature in two broad areas.
First, we reinterpret distance as both a geographical and a cultural concept, and focus on the role of cultural
distance as a determinant of FDI. Understanding cultural differences is of critical importance to any organization’s
success in international business since such understanding is vital to effective communication. Firm-level studies in
international management point out that cultural differences can negatively affect cross-border acquisition
performance (Stahl and Voigt, 2005), and decrease MNCs’ commitment to invest (Shenkar, 2001; Siegel, Licht and
Schwartz, 2013). These findings are consistent with arguments that cultural differences create a number of challenges
for the home country such as increased investment costs associated with different organizational practices and
managerial styles (Child, Faulkner, and Pitkethly, 2001).
Existing economics literature on FDI determinants mainly consider language as the most important cultural
factor since a common language reduces cultural barriers and eases communication. For example, Bergstrand and
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Egger (2007), Stein and Daude (2007) and Petri (2012) include a binary common-language variable in their models
and find that home and host countries sharing a common language contributes to a larger value of bilateral FDI.
Language indeed shapes how individuals think and represents a very important aspect of culture. However, culture,
conceptualized in social science as “a society’s system of shared values, beliefs, norms, and symbols”, can go beyond
a common language (Siegel, Licht, and Schwartz, 2013: 1175). Corporate management styles can be influenced by
social norms and vary significantly even in countries sharing a common official language.
On the other hand, countries that do not share a common language might have similar profiles in terms of
social norms and values, and hence a similar culture. For instance, the U.S. does not share a common official language
with either Albania or Italy, but social values or norms in the U.S. are more closely aligned with those in Italy than in
Albania. In such a situation, focusing solely on common language would not capture the smaller cultural differences
between the U.S. and Italy as compared to that between the U.S. and Albania, but would instead erroneously treat both
Italy and Albania as culturally equidistant from the U.S.
A few recent economic studies on FDI decisions try to look at the link between home and host countries with
a more comprehensive measure of cultural proximity in a gravity model. For instance, Davies, Ionascu and
Kristjansdottir (2008) use various cultural indicators constructed by Hofstede (1980, 2001) and find that more FDI
originates from and goes to countries that value competitiveness and community, and countries where individuals
handle uncertainty easily. Also using cultural values developed by Hofstede and studying FDI from six home countries
to mainland China, Du, Lu and Tao (2012) find that home countries that are more culturally distant from mainland
China show a stronger aversion to weaker local economic institutions when choosing to invest in different regions in
China.
In this paper, we take a broader approach to the concept of culture and use the influential Hofstede and
GLOBE cultural values to construct cultural distances (Hofstede, 2001; Grove, 2005).1 Both cultural values consider
differences in a wide set of well-defined cultural dimensions, better representing cultural similarity/dissimilarity
encompassing both verbal and non-verbal communication strategies.
The second contribution of our paper is to explore patterns of FDI in culturally similar host countries and we
also add to the emerging research on the analysis of FDI dependence across multiple hosts.
1
We use GLOBE cultural distances for robustness checks.
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Much of the previous research on determinants of FDI adopts a bilateral home-host framework that controls
for only home and host characteristics. This framework assumes that FDI inflows in a host country are not affected
by FDI inflows in other hosts. However, modern foreign investment decisions have evidently gone beyond the
traditional bilateral framework as MNCs investing in a particular host also pays close attention to conditions of other
potential hosts nearby. In addition, ignoring multi-host linkages when they exist can give rise to econometric problems
in a model, such as biased and inconsistent coefficient estimates (Anselin, 1988).
Given these concerns, a small but growing strand of research in recent years has extended the home-host
framework using spatial models to allow for multilateral dependence among FDI hosts. These studies primarily
consider correlation of inward FDI among hosts that are geographical neighbors and in general find significant spatial
interdependence among them (Coughlin and Segev, 2000; Baltagi, Egger and Pfaffermayr, 2007, 2008; Blonigen,
Davies, Waddell and Naughton, 2007; Bode, Nunnenkamp and Waldkiruch, 2012; Sharma, Wang and Wong, 2014)
(expounded in Section 2).
Complementing the existing literature, we adopt spatial analysis to explore the correlation of inward FDI
among hosts that are culturally similar, which we refer to as hosts in a culture cluster. As countries with similar cultural
values are not necessarily geographical neighbors in our study, patterns of spatial interdependence in our paper can
throw new light on present day MNC strategies. Our motivation for this spatial framework is embedded in the concept
of “learning by doing”. MNCs can obtain cost advantages and enhanced efficiency by performing an action repeatedly
through experience gained in culturally similar environments. In other words, an MNC’s knowledge about the local
culture of a particular host country can benefit its investment and operation in other culturally similar hosts. Although
it might be costly for an investor to learn about and invest in a new culture, this learning may reap rich dividends by
making it easier for the investor to explore other culturally similar markets.
Our empirical results show that a host country that is culturally distant from the U.S. receives less FDI from
the U.S, even after controlling for geographical distance in the model. However, we also find a significant and positive
spatial correlation of FDI across hosts in a culture cluster. That is, the amount of U.S. FDI in a host country increases
with the amount of FDI that other culturally similar hosts receive from the U.S. More interestingly, although such
positive spatial interdependence is significant over the full sample period of 1984-2011, the strength of the correlation
of U.S. FDI among culturally similar hosts is lower during the latter sample period of 1999-2011 as compared to the
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earlier sample period of 1984-1998. This likely indicates a weakening of the beneficial effect of learning from
investing in culturally proximate hosts over time.
The remainder of our paper proceeds as follows. In section 2, we summarize studies on FDI determinants
with spatial econometrics techniques and discuss our expectation concerning spatial relationship of FDI among
culturally similar hosts. Section 3 presents our model specification and data, followed by a discussion of the empirical
results in section 4. Robustness checks are provided in section 5 and we conclude in section 6.
2.
Spatial Interdependence of FDI
The concept of correlation of observations in space and the necessity to take into account such a correlation in research
have gradually become a subject of study in many fields such as geography (Harris, 1954; Robinson, 1956; Cliff and
Ord, 1981; Getis and Griffith, 2002), political science (Mahler, 1980), sociology (McPherson, Smith-Lovin and Cook,
2001), and urban/regional economics (Anselin, 1988). For example, Harris (1954) argues that the demand for goods
produced in a location also depends on the purchasing power in other locations suggesting that the proximity of an
economy to its peers heavily influences its likelihood of integration into a bigger economic system.
In international economics, traditional literature on MNCs have proposed various motivations of FDI,
including horizontal FDI when MNCs are seeking a new market for their products or to avoid restrictive trade policies
(Markusen, 1984) and pure vertical FDI that is mainly driven by lower input prices such as lower labor costs (Helpman,
1984). These models have significantly advanced and formalized our understanding of MNCs’ investment decisions,
but they often rely on a two-country (home-host) framework that inevitably assumes that FDI in a host country is not
affected by home’s investment in other hosts (as no other hosts exist in the model). To relax this strict assumption and
to better capture complex expansion strategies of MNCs in practice, recent theoretical research extends the initial twocountry model and allows for the “third country” effects. For example, Ekholm, Forslid and Markusen (2007)
formalize an export-platform FDI model where MNCs choose a single host in a region as a platform to serve either
the home market or other countries in the same region where the host is located. Baltagi et al. (2007) develop a
complex vertical FDI model in which a home country can decide to segment the vertical chain of production in
multiple hosts according to each individual host’s comparative advantage.
Recently, some studies have adopted a spatial analysis approach to address empirically the fact that FDI in
the preferred host country might depend not only on this host’s characteristics, but also on the characteristics of its
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geographical neighbors. 2 Using a spatial lag model, Blonigen et al. (2007) synthesizes patterns of the spatial
interdependence among geographically closer host countries corresponding to various motivations of FDI. 3 For
example, for pure vertical FDI, different hosts tend to become competitors of FDI and a negative spatial
interdependence is observed. This is because MNCs choose only the lowest-cost country in a region and the decision
to invest in one host is at the expense of the decision to invest in its neighbors. On the other hand, horizontal FDI often
suggests that spatial interdependence is insignificant among geographical neighbors as MNCs only target a specific
host’s market and FDI in nearby hosts becomes irrelevant.
Blonigen et al. (2007), utilizing FDI inflows data of 40 nations over 1983-1998, find a strong correlation
between U.S. FDI in a host country and U.S. FDI in this host’s neighbors. Their results suggest a dominant form of
complex vertical FDI, but the estimates can be sensitive to different specifications. Other studies using similar spatial
econometrics techniques include Baltagi et al. (2007) on U.S. outbound FDI at the industry level over 1989-1999,
Baltagi et al. (2008) on bilateral FDI between 24 home and 28 host countries in Europe over 1989-2001, Garretsen
and Peeters (2009) on Dutch outward FDI over 1984-2004, Ledyaeva (2009) on inward FDI in Russia over 19952005, Bode, et al. (2012) on the role of Marshallian externality generated by FDI in the U.S. over 1997-2003, and
Sharma, et al. (2014) on FDI inflows in China at the provincial level over 1999-2007. In each case the researchers
find a significant spatial interdependence, all focusing on geography.
In this study we also look at the spatial interdependence of FDI across multiple hosts. Complementing
previous research focusing on geographical neighbors, we focus on hosts that are culturally proximate. At the outset,
we expect a positive spatial interdependence among culturally similar hosts and our expectation mainly relies on
concepts of learning-by-doing. Markusen (1984) points out that an MNC in the home country often owns (knowledgebased) assets such as blueprints, patents, brand name, and reputation. These intangible assets can be supplied as an
input to production facilities in multiple host countries without lowering their value and any duplication efforts are
wasteful. Being able to avail themselves of the intangible assets such as the knowledge of cultural norms in a host,
MNCs may choose to invest in multiple countries of similar cultures. In addition, learning-by-doing suggests that the
more an MNC invests in hosts that are culturally proximate, the better it understands their market conditions, business
2
There are a few earlier but notable empirical papers that look at the interdependence of FDI across hosts. For instance,
Head, Reis and Swenson (1995) and Head and Mayer (2004) look at the location decisions of Japanese FDI in the
United States (1995) and the European Union (2004) and highlight the role of market size in surrounding countries as
an important determinant of the probability of a region or a country being chosen as the host.
3
We refer interested readers to Blonigen et al. (2007) for a comprehensive discussion.
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practices, and political climates, which can further reduce its cost of expansion into new but culturally similar hosts.
In other words, MNCs can gain from the common governance of dispersed activities in multiple countries (Campos
and Kinoshita, 2002; Bevan and Estrin, 2000). While it is costly to invest in a host with a drastically different culture,
it can become easier for a home country to explore multiple host markets (with horizontal, vertical, or more complex
motivations), when these hosts have similar cultures.
3.
Methodology and Data
3.1.
Baseline gravity model
The gravity model has been the workhorse in empirical studies of international trade/capital flows (Helpman,
Melitz and Rubinstein, 2008; Head and Ries, 2008). In its simplest form, a gravity model for FDI is given by:
𝐹𝐷𝐼𝑖𝑗 = 𝐴𝑌𝑖 𝑌𝑗 /𝐷𝑖𝑗
(1)
where FDIij denotes foreign direct investment between country i and country j; Yi and Yj are sizes of country i and
country j, respectively; Dij is the geographical distance between the two countries, and A is a constant term.
Empirically, the gravity model in equation (1) is often augmented with other control variables that could
potentially affect investment between two countries. Our baseline augmented gravity regression with bilateral cultural
distance in the log-linear form is:
ln(𝐹𝐷𝐼𝑖𝑡 ) = 𝛽0 + 𝛽1 ln(𝑐𝑑𝑖𝑠𝑡𝑖 ) + 𝛾 ′ 𝑋 + 𝜀𝑖𝑡
(2)
where ln(FDIit) is the log of the real value of U.S. FDI in host country i in year t; ln(cdisti) represents the log of the
cultural distance between country i and the U.S.; X is a set of additional controls including the market size of country
i, the geographical distance between country i and the U.S. as well as other host characteristics determining magnitude
of FDI suggested by previous literature, namely population, trade costs, labor quality, and institutional quality
(Blonigen and Davies, 2004; Blonigen, et al, 2007; Mina, 2012).
3.2.
Variables and data sources
3.2.1.
Cultural distance
We construct our measure of cultural distance based on the widely used indices of Hofstede (1980, 2001) that comprise
four dimensions of national culture: 1. Power Distance index (PDI) - tolerance for unequal power distribution. The
less powerful individuals in a country that scores high on this dimension are more acceptable to an unequal distribution
of power in a society. 2. Individualism (IDV) – reliance on self or immediate family as opposed to societal networks.
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A high value on individualism suggests a preference toward a “loosely-knit” social framework where people stress on
privacy and have loose connection with those who are not their family members or relatives. 3. Masculinity (MAS) –
preference of heroism, achievement and material measures of success. A high score on the masculinity dimension
indicates an emphasis on competitiveness and assertiveness as opposed to, for example, cooperation. 4. Uncertainty
Avoidance index (UAI) – akin to risk aversion.4 A high sore on this dimension suggests a society’s strong preference
toward rules and structures, which help improve predictability of outcomes. Further explanations of these cultural
dimensions are provided in Table A in the Appendix.
Individual Hofstede cultural index on each dimension ranges between zero and 100. Countries with similar
indices are considered culturally similar along a certain dimension. To better put the Hofstede cultural values into
context, we do a quick comparison between the U.S. and Guatemala along two dimensions of culture: Power Distance
and Uncertainty Avoidance. The power distance index (between zero and 100) is 40 for the U.S. and 95 for Guatemala,
suggesting individuals in Guatemala are much more likely to expect and accept that power is distributed unequally in
the society than individuals in the U.S. Along the uncertainty avoidance dimension (between zero and 100), the U.S.
has an index of 46 while Guatemala has an index of 99. The interpretation of these scores is that in general individuals
in the U.S. are more willing to try new ideas and innovations and more comfortable with uncertainties and ambiguities
brought by changes than individuals in Guatemala. The average scores on the four Hofstede cultural dimensions for
individual host countries in our sample are reported in Table B in the Appendix.
Following Chakrabarti, Jayaraman and Mukherjee (2009), we construct cultural distance between two
countries as the average of the differences (in absolute value) in the Hofstede’s four dimensions of culture, which we
refer to as the Hofstede distance.5 Specifically, the Hofstede cultural distance between country i and country j is:
1
𝑐𝑑𝑖𝑠𝑡𝑖𝑗 = ∑𝑀|𝐼𝑛𝑑𝑒𝑥𝑖𝑀 − 𝐼𝑛𝑑𝑒𝑥𝑗𝑀 |,
4
where 𝑀 = {PDI, IDV, MAS, and UAI}.
Detailed information on individual host’s cultural distance from the U.S. along with a ranking is summarized
in Table 1. Out of the 74 hosts in our sample, Australia is culturally closest to the U.S. with a Hofstede distance of
Hofstede (2001) indices were later expanded to include two more dimensions – long term orientation and indulgence,
which we include in robustness analysis rather than our main results due to coverage limitations.
5
Alternative measures of cultural distance based on Hofstede’s six cultural dimensions and GLOBE indices (both
practice and value scores from http://www.tlu.ee/~sirvir/IKM/Leadership%20Dimensions/globe_project.html) are
also adopted for robustness checks.
4
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2.75, followed by the U.K. (5.5), Canada (6), New Zealand (9.25), and Switzerland (12.25). 6 The five countries in our
sample that have the largest Hofstede cultural distance from the U.S. are Romania (43.75), Russian Federation (45),
Slovenia (45), Panama (48.25), and Guatemala (54.25).
“Table 1 goes about here”
3.2.2.
Other variables
The FDI variable in equation (2) is measured by total assets of U.S.-owned affiliates in host country i in constant 2005
U.S. dollars. Our FDI data are from different issues of the Survey of Current Business, a monthly publication of U.S.
national and international macroeconomic data by the Bureau of Economic Analysis (BEA). 7
In terms of additional controls, market size (measured by real GDP) in the host economy is used to capture
market demand. As a higher GDP is associated with larger demand for products and services and larger expected
revenue for foreign investment, we expect a positive association between host real GDP and inward FDI. Holding
GDP constant, a larger population in the host country indicates a lower GDP per capita, implying a lower standard of
living, with perhaps a negative impact on FDI. Following Blonigen et al. (2007), host trade costs are measured by the
inverse of the host’s imports and exports as a share of GDP. Depending on the nature of FDI, trade costs could have
either positive or negative impact on FDI. In general, higher trade costs lead to an increase in horizontal FDI (or
affiliates in host countries roughly duplicate the same activities) but a decrease in vertical FDI (different stages of
production are located in different countries), while the net effect can be ambiguous. Geographical distance is used as
a proxy for transportation costs, which leads to an ambiguous coefficient. The effect of higher transportation costs on
FDI depends on the nature of FDI. If the objective of FDI is to seek a new market, higher transportation cost will
encourage foreign investment since rising shipping cost makes exporting to the host less attractive. On the other hand,
if the objective is to take advantage of, for example, cheap labor costs in a host country and final products produced
in the host country need to be shipped back to the home country for consumption, then a higher transport cost will
deter FDI.
We also include average years of schooling attained for those over the age of 25 in the host country as a
proxy for labor quality. On one hand, the schooling measure captures the skill endowment of the host country, which
can attract greater FDI. On the other hand, high labor quality in a country can also lead to high labor costs, which may
6
Our sample is chosen to give us the maximum coverage across Hofstede cultural dimensions along with reliable data
on US FDI to these countries.
7
http://www.bea.gov/international/di1usdop.htm
10 | P a g e
deter FDI if MNCs are to take advantage of cheap input prices in the host country. As a result, the sign of the coefficient
on the schooling measure is ambiguous.
It is also widely acknowledged that institutional quality has non-trivial consequences on corporate
governance and investment (La Porta, Lopez-de-Silanes, Shleifer and Vishny, 1999, 2000 and and La Porta, Lopezde-Silanes, and Shleifer, 2002). In our paper, we control for institutional quality measured by a political risk index.
This index ranges from zero to 100 with zero indicating an extremely risky political environment and 100 indicating
an extremely stable political environment. 8 We expect that a stable host country with low political risk would
encourage larger FDI inflows.
We collect real GDP, population, and trade costs from Feenstra, Inklaar and Timmer (2013) and the Penn
World Table 8.0. Geographical distance data are from Centre D'Etudes Prospectives Et D'Informations
Internationales (CEPII). Schooling data are from Barro and Lee (2013). The political risk index is constructed by the
Political Risk Services Group, Inc. Definitions of the variables and data sources are summarized in Table C in the
Appendix. Descriptive statistics are provided in Table 2 and the correlation matrix is in Table 3.
The simple correlation between geographic distance and Hofstede distance in our sample (Table 3) is fairly
low at 0.05. This suggests that focusing on geography alone as a determinant of spatial FDI would limit the scope of
understanding linkages between nations, and further makes a case for including cultural dimensions in studies of FDI
determinants and spatial linkages.
3.3. Spatial regression
Spatial analysis recognizes the importance of locations in business, economics, and social activities, the essence of
which is reflected in the first law of geography, “Everything is related to everything else, but near things are more
related than distant things” (Tobler, 1970: 236).
To better understand the interdependence of FDI across hosts in the cultural space, we include in the model
a measure of FDI received by culturally similar hosts as an additional determinant of FDI. We refer to this measure
hereafter as the spatial FDI and modify our baseline regression (2) as follows:
ln(𝐹𝐷𝐼𝑖𝑡 ) = 𝛽0 + 𝛽1 ln(𝑐𝑑𝑖𝑠𝑡𝑖 ) + 𝜌𝐰𝒊 ln(𝐹𝐷𝐼𝑡 ) + 𝛾 ′ 𝑋 + 𝜀𝑖𝑡
8
(3)
These 12 components of political risk are Government Stability, Socioeconomic Conditions, Investment Profile,
Internal conflict, External Conflict, Corruption, Military in Politics, Religion in Politics, Law and Order, Ethnic
Tensions, Democratic Accountability, and Bureaucracy Quality.
11 | P a g e
Equation (3) is often referred to as a spatial lag model, which postulates the feedback among observations in a sample
of N countries. The new term 𝐰𝒊 ln(𝐹𝐷𝐼𝑡 ) is the spatial FDI (also called the spatial lag). For a host country i, the
spatial FDI term is a weighted average of U.S. FDI in all other hosts j with 𝐰𝒊 – the ith row of an 𝑁 × 𝑁 weight matrix
Wt – measuring cultural proximity between a host country i and all other hosts j in our sample, for 𝑗 ≠ 𝑖. The sign
and the magnitude of the coefficient on the spatial FDI, 𝜌 , shows the direction and the strength of inter-host
dependence. The range of the spatial FDI coefficient 𝜌 is between −1 and +1. A positive 𝜌 indicates that an increase
in U.S. FDI in a host i is positively associated with U.S. FDI in other culturally similar hosts while a negative 𝜌
suggests U.S. FDI in a host i is negatively associated with U.S. FDI in other culturally similar hosts. The larger 𝜌 is
in absolute value, the stronger is the correlation of U.S. FDI across various hosts in a culture cluster (or a group of
hosts with a similar culture).9
For panel data of N countries over T periods, W is a block diagonal matrix with a dimension of 𝑁𝑇 × 𝑁𝑇
with each block capturing a single year’s observation. The W matrix can be written as:10
where each block Wt is an 𝑁 × 𝑁 matrix in year t:
0
𝐖𝐭 = [ 𝑤𝑡 (𝑐𝑑𝑖𝑠𝑡𝑗𝑖 )
𝑤𝑡 (𝑐𝑑𝑖𝑠𝑡𝑘𝑖 )
𝑤𝑡 (𝑐𝑑𝑖𝑠𝑡𝑖𝑗 ) 𝑤𝑡 (𝑐𝑑𝑖𝑠𝑡𝑖𝑘 )
0
𝑤𝑡 (𝑐𝑑𝑖𝑠𝑡𝑗𝑘 )]
𝑤𝑡 (𝑐𝑑𝑖𝑠𝑡𝑘𝑗 )
0
where 1 ≤ 𝑡 ≤ 𝑇, and wt(cdistij) is an inverse function of cultural distance between any two host countries i and j, or,
wt(cdistij) = 1/cdistij. Often used in the spatial analysis literature, the inverse distance function assigns a larger weight
to countries that are more culturally similar. 11 Moreover, diagonal elements of the matrix Wt are set to zero so that no
9
Interested readers are referred to Anselin (1998, 2010) and Anselin, Florax, and Rey (2004) for a thorough discussion
of the development of the spatial lag model and technical details.
10
Cultural ties are assumed to be time-invariant such that W1 = W2 = … = WT. While one could argue that this is a
strong assumption as migration and modern communication technology have brought nations and cultures together
fostering the ties between some, we believe that cultural ties, if dynamic, are very slow to change, and as such would
not be very volatile over the two decades of our sample, since the cultural dimensions measured over four decades or
more in Hofstede (2001) that form the core of our cultural distance measure do not show much variability.
11
The weight cannot be constructed if the cultural distance between countries i and j equals zero. We therefore
substitute the zero distance value with the smallest positive distance value in the sample for the pair of countries with
the zero cultural distance. This ensures that all off-diagonal weight values between any two countries are finite in the
weight matrix.
12 | P a g e
observation predicts itself. As is common in the literature, the weight matrix is also row-standardized so that the
elements in each row sum up to one.
“Tables 2 and 3 go about here”
4.
Empirical Results
4.1. Baseline results
Figure 1 plots the log of annual average U.S. FDI in different host countries by their Hofstede cultural distance from
the U.S. For example, the solid line in Figure 1 denotes the average value of U.S. outbound FDI in host countries that
are most culturally similar to the U.S. compared to other hosts in our sample (within the 25th percentile of cultural
distance from the U.S.). In general, U.S. FDI in different hosts has been growing over the past few decades. Figure 1
also shows that, at any given time, countries that are culturally similar to the U.S. tend to receive more U.S. FDI than
countries that are culturally “far apart” from the U.S.
“Figure 1 goes about here”
Before presenting our empirical results, we discuss two estimation issues related to a spatial lag model. First,
we perform statistical tests to determine the relevance of spatial analysis or whether spatial interdependence is indeed
an issue in our sample. The Moran’s I and Geary’s c tests results are presented in Table 4 in the Appendix. These
results strongly support the existence of global spatial interdependence.12 We also find that the null hypothesis of no
spatial interdependence (H0: ρ = 0) is rejected at the 1% level by both Lagrange multiplier (LM) test and robust LM
tests. Second, if ρ is indeed not equal to zero as our LM test results indicate, OLS estimated coefficients for equation
(3) are biased and inconsistent, since by construction, the spatial FDI term 𝐰𝒊 ln(𝐹𝐷𝐼𝑡 ) on the right-hand side of the
equation is endogenous (Anselin, 1988). Consequently, a spatial lag model is routinely estimated with the Maximum
Likelihood Estimation (MLE) technique, which we use for our spatial regressions in this paper (Coughlin and Segev,
2000; Blonigen et al., 2005; Sharma et al., 2014).
“Table 4 goes about here”
Moran’s I and Geary’s c tests are tests of spatial autocorrelation of a variable. Moran (1950) computes a correlation
statistic for all pairs of observations, weighted by spatial proximity. Geary (1954) measures the correlation for the
squared differences of all pairs of observations weighted by spatial proximity. Both tests determine if there is any
systematic pattern in the spatial distribution of a variable.
12
13 | P a g e
We report our regression results based on equations (2) and (3) in Table 5. Since the U.S. is the only home
country in our sample, any common shocks due to U.S. macroeconomic or institutional volatility that can potentially
cause variations in aggregate U.S. outbound FDI (which will affect all host countries) are captured by a time trend
and its squared term. Country fixed effects are included in two of the four specifications to control for unobserved
heterogeneity across host countries. However, doing so would not allow us to estimate the coefficients on timeinvariant factors, including geographical distance and cultural distance between the host country and the U.S. So we
include regional dummies in the rest of specifications in Table 5 that control for overall macroeconomic variations in
a particular region while allowing us to estimate the effect of geographical and cultural distances.
There are three notable points in Table 5. First, cultural similarities matter. The coefficient on the Hofstede
cultural distance is negative and significant at the 0.1% level. This indicates that host countries that are culturally
distant to the U.S. receive less U.S. FDI, presumably because pronounced differences in social norms and shared
values make it difficult to understand others’ behaviors, which can in turn lead to a higher operational costs for U.S.
MNCs. For instance, regression 5.2 suggests that, keeping other things constant, a 1% increase in the Hofstede distance
between a host country and the U.S. is associated with a 0.74% decrease in U.S. FDI in that host.
Second, coefficients on other FDI determinants, in general, align with our expectations and the existing
literature. Larger economies (as measured by real GDP) are likely to receive more FDI from the U.S. while hosts with
a large population see a smaller amount of FDI from the U.S. The coefficient on trade costs is robustly negative. As
we do not have a prior expectation of its coefficient, the result suggests that FDI undertaken in our sample is more
likely to exploit vertical linkages as trade costs tend to have a negative impact on vertical FDI but a positive impact
on horizontal FDI. Geographical distance also has a negative and significant coefficient as found in standard gravity
literature. The estimated coefficient on schooling is negative and likely supports the argument that MNCs might be
largely seeking relatively unskilled cheap labor in host countries, again consistent with vertical motivation of FDI.
Third, there is strong evidence supporting interdependence among hosts in the cultural space when receiving
U.S. FDI. The coefficient on spatial FDI is positive and significant at the 0.1% level in regressions 5.3 and 5.4. These
results show that a host country i will receive more FDI from the U.S. if other hosts with a similar culture receive
more FDI from the U.S. In other words, host countries in a culture cluster complement each other when attracting
investment from the U.S. For example, the point estimate in regression 5.3 shows that U.S. FDI in a host country i
rises by 0.14% for every 1% increase in U.S. FDI received by other hosts that share a similar culture as host i. This
14 | P a g e
positive spatial linkage reflects the importance of greater familiarity of U.S. MNCs with host countries’ cultures. The
results align with our expectation that learning-by-doing likely helps reduce management and administrative costs of
MNCs when investing and operating in culturally similar hosts and may motivate MNCs to expand even further into
new hosts that have similar cultures as their existing hosts.
“Table 5 goes about here”
4.2. Spatial interdependence of FDI over time
Another interesting aspect to investigate is whether the pattern of the inter-host dependence in the cultural space might
change over time. Berry, Guillen, and Zhou (2010) argue that the “variance of variables” in a cultural gravity model
“differs massively” over time, and we can reasonably expect such variations to affect their impact on FDI over time.
There are indeed a few possible reasons for us to expect so. The first reason is technological improvement.
Technological improvement or breakthrough, such as the invention of the Internet, can greatly reduce barriers of crosscultural communication although those barriers are difficult to completely eliminate. As a result, the learning benefits
for MNCs from investing in countries with a similar culture to reduce operational costs in a potential new host might
become less important over time. In addition, the scale economies of investing in various hosts in the cultural space
are not necessarily inexhaustible. As economies of scale are exhausted due to difficulties in coordination and possible
distortion of information along the chain of command when an MNC grows larger with operations in multiple hosts,
the cost of investment in a foreign host can rise again. For example, it is possible that a manager may be more
concerned about operations of his/her own facility in a host country than the firm’s broader strategic vision. Equally
possible is headquarter intervention that can disrupt some affiliates’ activities and possibly harm motivation (Foss,
Foss and Nell, 2012). This suggests that, after a certain point, cost reduction might be less of a benefit for a home
country to invest in new host countries.
Given the two possible arguments presented above, we would expect to observe a decline in the strength of
spatial interdependence among culturally proximate hosts over time. To explore this, we divide our sample into two
subsamples periods: 1984-1998 and 1999-2011. The choice of 1998-1999 as the split is dictated to some extent by
broad liberalization measures in Asia and Russia post the East Asian and Russian financial crisis, though we do test
robustness by pushing the break point to as far as 2002, immediately after China’s accession to the World Trade
Organization. While we do not have mainland China in the sample, we do have Hong Kong in our sample and China’s
15 | P a g e
participation in WTO might have a non-trivial effect on Hong Kong’s ability to attract FDI. We also tried a few
alternative cutoffs with similar results.
“Table 6 goes about here”
Subsample results reported in Table 6 suggest a significant spatial interdependence of U.S. FDI in culturally
similar hosts during the earlier but not the later sample period. The coefficient on spatial FDI is positive and significant
at the 5% level or better over 1984-1998, ranging from 0.15 to 0.25. In contrast, the coefficient on spatial FDI over
1999-2011 ranges between 0.04 and 0.07 and is not significant at conventional levels. The changes in the magnitude
and the level of significance of the coefficient on spatial FDI are compatible with our expectations. The gains of
investing in culturally similar hosts might be large initially, but become smaller over time possibly due to MNCs’
ability to learn about a foreign host’s culture through other channels or the erosion of economies of scale.
5.
Robustness Checks and Extensions
5.1. OECD vs. non-OECD countries
While our baseline results indicate strong spatial interdependence of FDI, especially prior to the 2000s, Blonigen and
Wang (2005) point out that pooling diverse countries in one sample might mask the true effects of certain controls on
the dependent variable. The authors find that determinants of FDI in developed and developing countries are
significantly different. To examine this issue, we separate our sample into OECD and non-OECD hosts and report the
results in Table 7. For brevity, only the estimated coefficient on the spatial FDI is reported. Estimated coefficients on
other variables are available upon request.
“Table 7 goes about here”
Results in Table 7 are qualitatively similar to results in Tables 5 and 6 but there also exists a certain amount
of heterogeneity across the two groups of host countries. For example, Table 7 shows that the estimated coefficient
on spatial FDI is 0.14 and significant at the 0.1% level in the non-OECD subsample while not statistically significant
in the OECD subsample over the full sample span 1984-2011. However, for both OECD and non-OECD host countries,
the cross-host dependence in a culture cluster is positive and significant at the 1% level over 1984-1998 and this
weakens and loses significance over 1999-2011. These subsample regression results are consistent with our general
hypotheses that there tends to be a significant interdependence of U.S. FDI in culturally similar hosts, but the strength
of such interdependence declines over time.
5.2. Cultural distance based on alternative cultural values
16 | P a g e
In our analysis so far, we have used the Hofstede four dimensions of culture. In Table 8, we provide robustness checks
with alternative measures of cultural distance, namely cultural distance based on values of Hofstede’s six cultural
dimensions (extended Hofstede distance) and cultural distance based on the Global Leadership and Organizational
Behavior Effectiveness (GLOBE) research project, designed to expand Hofstede’s work (House, Hangs, Javidan,
Dorfman and Gupta, 2004). The traditional Hofstede four dimensions of culture have been extended to include two
more dimensions, Long Term Orientation and Indulgence versus Restraint, by Hofstede, Hofstede and Minkov (2010).
Long term orientation refers to a focus on the future while short term orientation refers to a focus on the present and
the past. Indulgent cultures emphasize freedom of speech and personal life control while restrained cultures pay more
attention to strict social norms. While using all six dimensions enhances our measurement of culture, it reduces our
sample size as the last two dimensions are not available for all countries in our sample. So we choose to use the six
dimensions in our robustness checks as opposed to benchmark results.
The GLOBE project measures culture in terms of practices (what it is in a society) and values (what people
think it should be) and summarizes them along nine dimensions with value of each dimension ranging between one
and seven: Uncertainty Avoidance, Power Distance, Institutional Collectivism, In-Group Collectivism, Gender
Egalitarianism, Assertiveness, Future Orientation, Performance Orientation and Humane Orientation. Detailed
definitions of the GLOBE cultural dimensions are provided in Table D in the Appendix. We also report average value
of cultural indices used for robustness checks in Table E and the correlation matrix for the four measures of cultural
distances in Table F in the Appendix. The simple correlation between the extended Hofstede distance and the original
Hofstede distance is 0.87. In general, the correlation between the Hofstede distance measures and the GLOBE distance
measures ranges between 0.45 – 0.52, suggesting a fairly strong association between Hofstede and GLOBE measures.
“Table 8 goes about here”
Table 8 shows that our results are quite robust to different measures of cultural distance. Results based on
all alternative measures of cultural distance show that the spatial FDI has a positive coefficient that is significant at
the 0.1% level over 1984-2011, again suggesting a strong interdependence of FDI in culturally similar hosts. All three
sets of regressions show that the estimated coefficient on spatial FDI becomes smaller over time (1984-1998 as
compared to 1999-2011), reflecting a weakening of the spatial interdependence among hosts in a culture cluster.
The estimated coefficient on spatial FDI is not statistically significant in the extended Hofstede distance
regression over 1999-2011. When cultural distance is measured with GLOBE cultural dimensions, although the
17 | P a g e
estimated coefficient on spatial FDI over 1999-2011 is still positive and significant, both the magnitude and the level
of significance of the coefficient decline significantly compared to the previous period of 1984-1999, consistent with
our baseline findings.
5.3. Industry FDI
Previous literature has shown that spatial interdependence (as measured by geographical distance) can vary
significantly across industries (Blonigen et al, 2007; Kucera and Principe, 2014; Sharma et al, 2014). In our final
robustness tests, we analyze this issue in the context of our cultural distance. In Table 9, we present the estimated
coefficient on the spatial FDI with country fixed effects at a more disaggregated industry level. 13 We have U.S.
outward FDI data in 13 industries and these represent the finest levels of disaggregation of data available to the public
on an annual basis from the Bureau of Economic Analysis. The industries are Food and Kindred Products (Food),
Chemical and Allied Products (Chemicals), Primary and Fabricated Metals (Metal), Machinery, except Electrical
(Machinery), Transportation Equipment (Transport), Wholesale Trade (Wholesale), Finance (except banking),
Insurance, and Real Estate (Finance), Computers and Electronic Products (Computers), Electrical Equipment,
Appliances, and Components (Electrical), Information (Information), Professional, Scientific and Technical Services
(Professional), Utilities (Utilities), and other industries (Other Industries).14
“Table 9 goes about here”
As shown in Table 9, there exists substantial heterogeneity in spatial interdependence of FDI in different
industries. Out of the 13 industries studied, the estimated coefficient on spatial FDI is non-negative in nine cases with
a statistically significant spatial interdependence of U.S. FDI in six of them - Food, Wholesale, Finance, Computers,
Professional, and Other Industries. For instance, in the Food industry a 1% increase in U.S. FDI to countries with a
similar culture as country i will increase U.S. FDI in the host country i by 0.38%, other things remaining constant.
Interestingly, the estimated coefficient on spatial FDI is negative in four industries and significant in the
Utilities industry. This may not be surprising as investment decisions in this industry are likely to be strongly
associated with the availability of natural resources (fossil fuel) or geographical location (for natural gas pipeline) than
with cultural dimensions of a host country. The findings in Table 9 seem to indicate that the inter-host dependence of
13
The industry classifications are adopted from the Bureau of Economic Analysis (www.bea.gov). The industries are
described in BEA's Guide to Industry and Foreign Trade Classifications for International Surveys.
14
Over the sample period of 1984-1998 we have data for 8 industries. For the latter period 1999-2011 we have data
for 5 additional industries.
18 | P a g e
FDI in the cultural space that we have observed so far is from a wide spectrum of industries and not necessarily the
artifact of one dominant industry.
6.
Conclusions and Future Research
Understanding cultural distance is essential to all organizations as globalization deepens. However, the effect of
cultural distance on the magnitude of FDI has not been systematically examined in previous economic studies. This
paper constructs and includes measures of cultural distance based on well-defined national cultural traits in FDI
models. With data on U.S. outbound FDI in 74 host countries, we find that while bilateral cultural distance has a
negative effect on U.S. FDI to a host country, there is a significantly positive correlation of U.S. FDI in culturally
similar hosts. That is, U.S. invests less in a culturally distant host, but U.S. FDI in this host country rises with U.S.
FDI in other hosts that share similar cultures with this host of interest. It is also worth noting that our subsample results
show a decline in the strength of such spatial interdependence of FDI over 1999-2011 compared to results from 19841998. These findings are robust to different specifications.
Our results suggest that in a multilateral framework, cultural distance can possibly become an opportunity
rather than a mere deterrent of FDI as the cost of integration and operation for MNCs in a foreign host can likely be
reduced when they also invest in other culturally proximate hosts. From a policy perspective, countries might find it
easier to target hosts with similar cultures in their initial bid for expansion, though with the advent of media capabilities,
distance itself (either geographical or cultural) is becoming less of a concern.
In our study, we have restricted the parent to one country – the U.S. There are a few reasons for us to choose
such a design. By using a single home country, we are able to hold parent characteristics constant and concentrate on
different aspects of host cultural distance. Second, the U.S. is one of the largest countries that engage in outward FDI
flows. United Nations records show that between 2000 and 2014, U.S. outward FDI alone accounts for approximately
25% of the total world outward FDI stock, making US one of the strongest parent nations to consider. While this is a
first step towards the study of culture clusters, it would be interesting for a future project to include multiple home
countries, studying dependence not only from the perspective of hosts, but from the perspective of home countries as
well. A potential direction might be the test of a “herd” behavior in FDI investment, where one could similarly examine
spatial linkages among home countries when deciding on MNC expansion.
19 | P a g e
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Figure 1. Cultural Distance and outbound FDI from the United States
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Table 1. Cultural Distance from the United States
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
21
Country
Australiab
United Kingdoma
Canadaa
New Zealandb
Switzerlanda
Irelanda
Germanya
South Africa
Italyb
Luxembourga
Netherlandsa
Estoniac
Hungaryb
Czech Republicb
Finlandb
Norwaya
India
Latvia
Trinidad and Tobago
Lithuania
Jamaica
Hofstede Distance
Rank
2.75
5.5
6
9.25
12.25
12.5
13
14.75
15.5
16.75
17
19.25
19.75
20.75
21
22.25
23
23.75
23.75
23.75
24
26
28
29
31
32
33
35
36
38
41
42
43
44
Country
Austriaa
Morocco
Francea
Denmarka
Polandb
Malta
Israelc
Uganda
Tanzania
Hong Kong
Spaina
Philippines
Saudi Arabia
Jordan
Iraq
Brazil
Pakistan
China
Bangladesh
Ghana
Mali
Hofstede Distance
Rank
26.5
26.5
26.75
27
27
28.25
28.5
28.75
28.75
29
29.25
29.25
31
31
31
31.25
32
32.75
33
33
33
51
52
53
55
56
57
58
60
62
63
64
65
66
67
68
69
70
71
Country
Japanb
Vietnam
Colombia
Thailand
Slovak Republicc
Ecuador
Mexicob
Croatia
Malaysia
Bulgaria
Uruguay
Koreab
Singapore
Peru
Venezuela
Costa Rica
Chilec
El Salvador
Portugala
Romania
Russian
Federation
Sloveniac
Panama
Guatemala
Hofstede
Distance
34.5
34.75
35.25
35.25
35.5
35.75
36.25
36.75
36.75
38
38
38.75
39.25
40
40.25
40.5
41.25
42
42.75
43.75
45
Belgiuma
24.25
Taiwan
33
45
Argentina
25
48
Indonesia
33.25
73
48.25
Iran
25
49
Greecea
33.75
74
54.25
a
25
Sweden
25.75
50
Turkeya
34
Notes: Rank is based on an individual host’s Hofstede cultural distance from the United States
a
Countries became OECD members in 1961.
b
Countries became OECD members between 1962 and 1999: Italy (1962); Japan (1964); Finland (1969); Australia (1971); New Zealand (1973); Mexico (1994);
Czech Republic (1995); Hungary (1996); Korea (1996); and Poland (1996).
c
Countries became OECD members in 2000 and after: Slovak Republic (2000); Chile (2010); Estonia (2010); Israel (2010); and Slovenia (2010).
22
23
24 | P a g e
Table 2. Summary Statistics
Variables
N
Mean
Standard
Deviation
Minimum
Maximum
Real Total Assets (millions of 2005 dollars)
1786
86375.21
303397.30
0.93
4891665
Population (millions)
2222
53.25
161.69
0.24
1324.35
Real GDP (millions of 2005 dollars)
2247
93.01
8.89
77.54
107.50
Trade Cost
2222
51.22
1946.95
0.05
90295.61
School (Years of Education)
2097
7.81
2.69
0.50
13.09
Institutional Quality
2138
68.70
14.37
24.42
97
Hofstede Cultural Distance from the U.S.
1888
29.32
10.47
2.75
54.25
Geographical Distance from the U.S. (km)
2221
7906.51
3383.89
548.3946
16180.32
25 | P a g e
Table 3. Correlation Matrix
Variable
1 ln(Population)
1
1
2
2 ln(Real GDP)
0.7646
1
3 ln(Trade Cost)
0.5419
0.168
1
4 ln(School)
-0.3074
0.2288
-0.5021
1
5 ln(Institutional Quality)
-0.2751
0.2277
-0.5517
0.6737
1
6 ln(Hofstede Distance)
0.0242
-0.2469
0.1704
-0.3179
-0.3914
1
7 ln(Geographical Distance)
0.2551
0.1719
0.1255
-0.104
-0.1067
0.0541
26 | P a g e
3
4
5
6
7
1
Table 4. Tests on Spatial Effects of FDI
a. Moran's I test and Geary's c test on global spatial autocorrelation a
Moran's I Test
I
0.218
E(I)
-0.001
sd(I)
0.006
z
37.004
p-value
0.000
Geary's c Test
c
0.850
E(c)
1.000
sd(c)
0.011
z
-13.432
p-value
0.000
b. Diagnostic tests for spatial dependence in OLS regression b
b.1. The period of 1984-2011
Regression w/ regional dummiesc
Statistic
d.f.
p-value
Lagrange multiplier
8.228
1
0.004
Robust Lagrange
multiplier
8.202
1
0.004
Regression w/ country Fedd
Statistic
d.f.
p-value
8.228
1
0.004
8.202
1
0.004
b.2. The period of 1984-1998
Statistic
Lagrange multiplier
9.365
Robust Lagrange
multiplier
8.324
d.f.
1
p-value
0.002
Statistic
110.416
d.f.
1
p-value
0.000
1
0.004
65.086
1
0.000
b.2. The period of 1984-2011
Statistic
d.f.
p-value
Statistic
d.f.
p-value
Lagrange multiplier
0.398
1
0.528
1.286
1
0.257
Robust Lagrange
multiplier
1.881
1
0.17
0.28
1
0.597
Notes: The variable of interest is ln(FDI) which is measured as the log value of the real total assets of USowned foreign affiliates, which are obtained from the Bureau of Economic Analysis (BEA).
a
The null hypothesis for Moran's I and Geary's c tests is that there is no global spatial dependence.
b
The null hypothesis is H0: ρ=0.
c
The OLS specification with the following control variables: ln(Population); ln(Real GDP); ln(Trade Cost);
ln(School); ln(Institutional Quality); Trend; Trend Squared; and six regional dummies: East Asia and Pacific
(EAP); Europe and Central Asia (ECA); Latin America and Caribbean (LAC); Middle East and North Africa
(MENA); South Asia (SA); and Sub-Saharan Africa (SSA).
d
The OLS specification with country fixed effects, instead of regional dummies.
27 | P a g e
Table 5. Effects of Cultural Distance and Spatial FDI
Variables
1984-2011
5.1
ln(Hofstede Distance)a
5.2
-0.74***
[0.06]
Spatial FDI
ln(Geographical Distance)a
ln(Population)
ln(Real GDP)
ln(Trade Cost)
ln(School)
ln(Institutional Quality)
Trend
Trend Squared
Constant
Country FE
Regional Dummies
Number of Countries
Observations
R-Squared/Log Likelihood
-1.15***
[0.34]
0.98***
[0.16]
-0.42***
[0.07]
-1.56***
[0.37]
0.13
[0.16]
0.09***
[0.02]
0.0002
[0.00]
5.80*
[2.39]
YES
NO
74
1,643
0.96
-0.51***
[0.06]
-1.10***
[0.08]
2.13***
[0.06]
-0.93***
[0.09]
-0.48***
[0.14]
-0.28
[0.24]
0.04*
[0.02]
-0.0006
[0.00]
-5.23***
[1.03]
NO
YES
74
1,495
0.82
5.3
-0.68***
[0.06]
0.14***
[0.05]
-0.50***
[0.06]
-1.10***
[0.08]
2.13***
[0.06]
-0.93***
[0.09]
-0.51***
[0.14]
-0.29
[0.23]
0.02
[0.02]
-0.0004
[0.00]
-6.27***
[1.11]
NO
YES
74
1,495
-2185.31
5.4
0.16***
[0.03]
-1.49***
[0.34]
1.04***
[0.18]
-0.45***
[0.07]
-1.24***
[0.39]
-0.03
[0.16]
0.06***
[0.02]
0.0005
[0.00]
-1.20
[2.26]
YES
NO
74
1,495
-1215.15
Notes: Robust standard errors in brackets: *** p<0.001, ** p<0.01, * p<0.05,
† p<0.10.
The dependent variable, ln(FDI), is measured as the log value of the real total
assets of US-owned foreign affiliates, which are obtained from the Bureau of
Economic Analysis (BEA).
In specifications 5.2 and 5.3, six regional dummies are included: East Asia
and Pacific (EAP); Europe and Central Asia (ECA); Latin America and
Caribbean (LAC); Middle East and North Africa (MENA); South Asia (SA);
and Sub-Saharan Africa (SSA). ln(Geographical Distance) is the log value of
the host distance from US (in km), obtained from the Centre d'Etudes
Prospectives et d'Informations Internationales (CEPII).
a
The measures of cultural distance and geographical distance are time
invariant.
28 | P a g e
Table 6. Subsample Results
Variables
1984-1998
6.1
ln(Hofstede Distance)
a
6.2
6.3
-0.69***
[0.07]
-0.63***
[0.07]
0.15*
[0.07]
-0.52***
[0.07]
-0.95***
[0.09]
2.00***
[0.08]
-0.92***
[0.11]
-0.05
[0.14]
-0.98***
[0.26]
-0.06
[0.04]
0.002
[0.00]
-2.78*
[1.25]
NO
YES
69
858
-1111.56
Spatial FDI
ln(Geographical Distance)a
ln(Population)
ln(Real GDP)
ln(Trade Cost)
ln(School)
ln(Institutional Quality)
Trend
Trend Squared
Constant
Country FE
Regional Dummies
Number of Countries
Observations
R-Squared/Log Likelihood
-0.72
[0.47]
0.78***
[0.15]
-0.32***
[0.06]
-0.37
[0.37]
-0.29†
[0.17]
-0.04*
[0.02]
0.005***
[0.00]
6.82**
[2.45]
YES
NO
69
931
0.97
-0.53***
[0.07]
-0.96***
[0.09]
2.01***
[0.08]
-0.92***
[0.11]
-0.02
[0.14]
-0.95***
[0.27]
-0.03
[0.04]
0.002
[0.00]
-1.67
[1.13]
NO
YES
69
858
0.85
1999-2011
6.4
6.5
6.6
6.7
-0.74***
[0.10]
-0.71***
[0.11]
0.07
[0.09]
-0.37***
[0.09]
-1.13***
[0.13]
2.19***
[0.11]
-1.0***
[0.09]
-1.56***
[0.28]
2.27***
[0.63]
-0.33†
[0.18]
0.006†
[0.00]
-11.27**
[3.87]
NO
YES
72
637
-997.67
0.25***
[0.03]
-0.75
[0.46]
0.98***
[0.16]
-0.33***
[0.06]
-0.20
[0.37]
-0.33*
[0.16]
-0.09***
[0.02]
0.005***
[0.00]
-4.10*
[1.96]
YES
NO
69
858
-365.20
-0.50
[0.68]
0.89***
[0.19]
0.30*
[0.13]
-2.38***
[0.65]
-0.25
[0.37]
-0.19**
[0.07]
0.006***
[0.00]
11.13***
[3.55]
YES
NO
72
712
0.99
-0.37***
[0.09]
-1.13***
[0.13]
2.20***
[0.11]
-1.0***
[0.09]
-1.56***
[0.28]
2.25***
[0.64]
-0.35†
[0.18]
0.006†
[0.00]
-10.31**
[3.63]
NO
YES
72
637
0.77
6.8
0.04
[0.04]
-2.0***
[0.61]
0.93***
[0.16]
0.19
[0.13]
-1.94***
[0.60]
0.09
[0.30]
-0.18**
[0.07]
0.01***
[0.00]
7.46**
[2.58]
YES
NO
72
637
-133.27
Notes: Robust standard errors in brackets: *** p<0.001, ** p<0.01, * p<0.05, † p<0.10.
The dependent variable, ln(FDI), is measured as the log value of the real total assets of US-owned foreign affiliates, which are
obtained from the Bureau of Economic Analysis (BEA). Six regional dummies are included: East Asia and Pacific (EAP); Europe
and Central Asia (ECA); Latin America and Caribbean (LAC); Middle East and North Africa (MENA); South Asia (SA); and
Sub-Saharan Africa (SSA). ln(Geographical Distance) is the log value of the host distance from US (in km), obtained from the
Centre d'Etudes Prospectives et d'Informations Internationales (CEPII).
a
The measures of cultural distance and geographical distance are time invariant.
29 | P a g e
Table 7. Spatial Dependence: OECD vs. non-OECD Countries
OECD
Non-OECD
1984-2011 1984-1998 1999-2011
1984-2011 1984-1998 1999-2011
7.1
7.2
7.3
7.4
7.5
7.6
Spatial FDI
-0.03
0.34***
0.09
0.14***
0.20***
-0.02
[0.08]
[0.09]
[0.06]
[0.03]
[0.03]
[0.04]
Country FE
YES
YES
YES
YES
YES
YES
Number of Countries 31
27
31
52
48
45
Observations
620
332
288
875
526
349
Log Likelihood
-271.69
9.71
29.39
-807.01
-279.96
-108.70
Notes: Robust standard errors in brackets: *** p<0.001, ** p<0.01, * p<0.05, † p<0.10.
The dependent variable, ln(FDI), is measured as the log value of the real total assets of US-owned foreign
affiliates, which are obtained from the Bureau of Economic Analysis (BEA). All specifications include the
following control variables: ln(Population); ln(Real GDP); ln(Trade Cost); ln(School); ln(Institutional
Quality); Trend; and Trend Squared.
Variables
30 | P a g e
Table 8. Alternative Measures of Cultural Distance
(a) The Extended Hofstede Distancea
1984-2011
8.1
Spatial FDI
0.17***
[0.03]
Country FE
YES
Number of Countries
67
Observations
1,333
Log Likelihood
-1122.56
1984-1998
8.2
0.25***
[0.03]
YES
62
757
-342.88
1999-2011
8.3
0.05
[0.04]
YES
66
576
-114.11
(b) The GLOBE Cultural Distance (Practices Score)b
1984-2011
8.4
Spatial FDI
0.39***
[0.04]
Country FE
YES
Number of Countries
45
Observations
1,044
Log Likelihood
-412.21
1984-1998
8.5
0.50***
[0.06]
YES
44
610
-151.97
1999-2011
8.6
0.16*
[0.06]
YES
45
434
20.49
(c) The GLOBE Cultural Distance (Values Score)b
1984-2011
1984-1998
1999-2011
8.7
8.8
8.9
Spatial FDI
0.42***
0.48***
0.18**
[0.05]
[0.06]
[0.06]
Country FE
YES
YES
YES
Number of Countries
45
44
45
Observations
1,044
610
434
Log Likelihood
-407.43
-154.13
21.10
Notes: Robust standard errors in brackets: *** p<0.001, ** p<0.01, * p<0.05, † p<0.10. The
dependent variable, ln(FDI), is measured as the log value of the real total assets of US-owned
foreign affiliates, which are obtained from the Bureau of Economic Analysis (BEA).
ln(Geographical Distance) is the log value of the host distance from US (in km), obtained from
the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII). All specifications
include the following control variables: ln(Population); ln(Real GDP); ln(Trade Cost); ln(School);
ln(Institutional Quality); Trend; and Trend Squared.
a
The extended Hofstede distance is computed as the average of the four original Hofstede cultural
indicators as well as two additional dimensions: (1) long-term orientation; and (2) indulgence
versus restraint (Hofstede, Hofstede, and Minkov, 2010).
b
The Globe cultural distance is an average of the following nine cultural dimensions: (1)
assertiveness; (2) institutional collectivism; (3) in-group collectivism; (4) future orientation; (5)
gender egalitarianism; (6) humane orientation; (7) power distance; and (9) uncertainty avoidance.
31 | P a g e
Table 9. Industry-level outward FDI from the United States - Spatial Dependence
Variables
Spatial FDI
Country FE
Number of Countries
Observations
Log Likelihood
Fooda
0.38***
[0.05]
YES
51
820
-711.94
Chemicalsa
0.07
[0.08]
YES
52
1,011
-559.80
Metala
0.02
[0.08]
YES
50
886
-923.18
Machinerya
-0.05
[0.09]
YES
50
852
-1053.82
Transporta
-0.07
[0.07]
YES
47
581
-570.81
Wholesalea
0.32***
[0.06]
YES
52
1,014
-627.46
Financea
0.18*
[0.08]
YES
52
824
-955.67
Utilitiesb
Computersb
Electricalb
Informationb
Professionalb
Other Industriesa
-0.39***
0.29***
-0.13
0.1
0.15†
0.30***
[0.12]
[0.08]
[0.10]
[0.10]
[0.09]
[0.07]
Country FE
YES
YES
YES
YES
YES
YES
Number of Countries
32
48
47
49
49
51
Observations
136
433
354
333
473
655
Log Likelihood
-157.63
-344.31
-355.85
-410.57
-225.67
-839.75
Notes: Robust standard errors in brackets: *** p<0.001, ** p<0.01, * p<0.05, † p<0.10.
The dependent variable, ln(FDI), is measured as the log value of the real total assets of US-owned foreign affiliates in different industries, which are obtained from
the Bureau of Economic Analysis (BEA). All specifications include the following control variables: ln(Population); ln(Real GDP); ln(Trade Cost); ln(School);
ln(Institutional Quality); Trend; Trend Square; and country fixed effects.
a
Sample of countries in the period of 1984-2011.
b
Sample of countries in the period of 1999-2011.
Variables
Spatial FDI
32 | P a g e
APPENDIX
Table A. Definitions of Hofstede National Cultural Dimensions
Dimensions
Power Distance Index (PDI)
Definitions by Hofstede (2001, 2010)*
The degree to which the less powerful members of a society accept and
expect that power is distributed unequally.
Individualism vs.
Individualism (the high side of this dimension) is defined as a preference
Collectivism
for a loosely-knit social framework in which individuals are expected to
(IDV)
take care of only themselves and their immediate families. Collectivism
is that individuals can expect their relatives or members of a particular
in-group to look after them in exchange for unquestioning loyalty.
Masculinity vs. Femininity
Masculinity represents a preference in society for achievement, heroism,
(MAS)
assertiveness and material rewards for success. Femininity is a preference
toward cooperation, modesty, caring for the weak and quality of life.
Uncertainty Avoidance
This dimension expresses the degree to which the members of a society
Index (UAI)
feel uncomfortable with uncertainty and ambiguity.
Long Term Orientation vs.
Societies with long term orientation encourage thrift and efforts in
Short Term Normative
modern education as a way to prepare for the future while societies with
Orientation (LTO)**
short term orientation focus more on the present and the past and view
societal change with suspicion.
Indulgence vs. Restraint
Indulgence stands for a society that allows for free gratification of basic
(IND)**
and natural human drives related to enjoying life and having fun.
Restrained society suppresses gratification of needs and regulates it by
means of strict social norms.
Notes: *Definitions are obtained from http://geert-hofstede.com/national-culture.html
**Dimensions included only in robustness checks
33 | P a g e
Table B. Average of Hofstede Cultural Indices along Four Dimensions
Country
Hofstede
Index
Country
Hofstede
Index
Argentina
Australia
Austria
Bangladesh
Belgium
Brazil
Bulgaria
Canada
Chile
China
Colombia
Costa Rica
Croatia
Czech Republic
Denmark
Ecuador
El Salvador
Estonia
Finland
France
Germany
Ghana
Greece
Guatemala
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Korea
25
2.75
26.5
33
24.25
31.25
38
6
41.25
32.75
35.25
40.5
36.75
20.75
27
35.75
42
19.25
21
26.75
13
33
33.75
54.25
29
19.75
23
33.25
25
31
12.5
28.5
15.5
24
34.5
31
38.75
Latvia
Lithuania
Luxembourg
Malaysia
Mali
Malta
Mexico
Morocco
Netherlands
New Zealand
Norway
Pakistan
Panama
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sweden
Switzerland
Taiwan
Tanzania
Thailand
Trinidad and Tobago
Turkey
Uganda
United Kingdom
Uruguay
Venezuela
Vietnam
23.75
23.75
16.75
36.75
33
28.25
36.25
26.5
17
9.25
22.25
32
48.25
40
29.25
27
42.75
43.75
45
31
39.25
35.5
45
14.75
29.25
25.75
12.25
33
28.75
35.25
23.75
34
28.75
5.5
38
40.25
34.75
Notes: Hofstede index is an average of the following four cultural
dimensions: (1) power distance (PDI), (2) individualism (IDV), (3)
masculinity (MAS), and (4) uncertainty avoidance (UAI).
34 | P a g e
Table C. Definitions of Variables
Variables
FDI
Definition
Total assets of foreign affiliates (millions of
constant 2005 dollars).
Population
Population (millions).
Real GDP
Real GDP (millions of constant 2005 dollars).
Trade cost
Sources
Comprehensive Financial and
Operating Data, Bureau of
Economic Analysis, U.S.
Department of Commerce
Penn World Table 8.0, Feenstra,
Inklaar, and Timmer (2013)
Penn World Table 8.0, Feenstra,
Inklaar, and Timmer (2013)
Penn World Table 8.0, Feenstra,
Inklaar, and Timmer (2013)
Barro-Lee Educational Attainment
Dataset, Barro and Lee (2013)
International Country Risk Guide
(ICRG), The PRS Group, Inc.
The Centre d'Etudes Prospectives et
d'Informations Internationales
(CEPII)
The Hofstede Center, and Hofstede,
et al. (2010)
The inverse of the sum of imports and exports as
the share of GDP.
School
Average years of schooling attained for
population aged 25 and over.
Institutional quality
The sum of 12 risk components in political,
financial, and economic aspects.a
Geographic distance The measures of bilateral distances between the
US (Washington, D.C.) and the capital city in a
host country (kilometers).
Hofstede cultural
Cultural distance between the US and a host
distance
country, measured by the Cartesian distance
between Hofstede's four cultural dimensions:
PDI distance, IDV distance, MAS distance, and
UAI distance. Authors' calculation.
PDI distance
The absolute difference between the values
The Hofstede Center, and Hofstede,
assigned to power distance of the US and a host
et al. (2010)
country.
IDV distance
The absolute difference between the values
The Hofstede Center, and Hofstede,
assigned to individualism of the US and a host
et al. (2010)
country.
MAS distance
The absolute difference between the values
The Hofstede Center, and Hofstede,
assigned to masculinity of the US and a host
et al. (2010)
country.
UAI distance
The absolute difference between the values
The Hofstede Center, and Hofstede,
assigned to uncertainty avoidance of the US and et al. (2010)
a host country.
a
Notes: Institutional quality is measured based on the following 12 risk components: 1. government stability (min=0,
max=12); 2. socioeconomic conditions (min=0, max=12); 3. investment profile (min=0, max=12); 4. internal conflict
(min=0, max=12); 5. external conflict (min=0, max=12); 6. corruption (min=0, max=6); 7. military in politics (min=0,
max=6); 8. religion in politics (min=0, max=6); 9. law and order (min=0, max=6); 10. ethnic tensions (min=0, max=6);
11. democratic accountability (min=0, max=6); and 12. bureaucracy quality (min=0, max=4). The total score of
institutional quality is 100.
35 | P a g e
Table D. Definitions of GLOBE Cultural Dimensions
Dimensions
Performance
Orientation
Uncertainty Avoidance
Definitions by House et al. (2004)
It reflects the extent to which a community encourages and rewards
innovation, high standards, excellence, and performance improvement.
It is the extent to which a society, organization, or group relies on social
norms, rules, and procedures to alleviate the unpredictability of future events.
Humane Orientation
The degree to which an organization or society encourages and rewards
individuals for being fair, altruistic, friendly, generous, caring, and kind to
others.
Institutional
The degree to which organizational and societal institutional practices
Collectivism
encourage and reward collective distribution of resources and collective
action.
In-Group Collectivism
The degree to which individuals express pride, loyalty, and cohesiveness in
their organizations or families.
Assertiveness
The degree to which individuals are assertive, confrontational, and aggressive
in their relationships with others.
Gender Egalitarianism
The degree to which a collectivity minimizes gender inequality.
Future Orientation
The degree to which a collectivity encourages and rewards future-oriented
behaviors such as planning and delaying gratification.
Power Distance
The extent to which a community accepts and endorses authority, power
differences, and status privileges.
Notes: Definitions of Globe Cultural Indices are taken from House, R.J., Hanges, P.J., Javidan, M., Dorfman,
P.W., and Gupta, V. 2004. Culture, Leadership, and Organizations. The GLOBE Study of 62 Societies.
Thousand Oaks, C.A. Sage Publishers.
36 | P a g e
Table E. Extended Hofstede and GLOBE Cultural Indices
Country
Argentina
Australia
Austria
Bangladesh
Belgium
Brazil
Bulgaria
Canada
Chile
China
Colombia
Costa Rica
Croatia
Czech Republic
Denmark
Ecuador
El Salvador
Estonia
Finland
France
Germany
Ghana
Greece
Guatemala
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Extended
Hofstede
Index*
18.63
3.21
24.29
33.58
27.36
25.28
41.19
5.72
28.33
39.46
28.16
GLOBE
Index*
(Practices)
0.61
0.12
0.32
GLOBE
Index*
(Values)
0.49
0.15
0.43
Country
Extended
Hofstede
Index*
32.14
33.87
19.49
28.78
26.50
22.75
29.36
26.74
18.22
8.38
18.40
36.65
GLOBE
Index*
(Practices)
GLOBE
Index*
(Values)
Latvia
Lithuania
Luxembourg
Malaysia
0.45
0.44
Mali
0.36
0.57
Malta
Mexico
0.38
0.44
0.21
0.10
Morocco
0.58
0.72
Netherlands
0.35
0.42
0.51
0.68
New Zealand
0.54
0.51
0.57
0.47
Norway
0.42
0.41
Pakistan
35.70
Panama
27.59
Peru
30.43
19.73
0.60
0.29
Philippines
24.08
0.51
0.41
0.50
0.53
Poland
26.42
0.57
0.28
32.53
0.48
0.58
Portugal
34.66
0.54
0.41
30.80
Romania
41.50
17.82
0.34
0.25
Russia
47.25
0.80
0.52
27.45
0.36
0.38
Saudi Arabia
26.83
22.74
0.42
0.34
Singapore
37.50
0.62
0.23
26.50
Slovak
38.70
28.80
0.62
0.58
Slovenia
37.18
0.55
0.30
0.60
0.50
South Africa
0.22
0.39
33.67
0.27
0.38
Spain
27.18
0.54
0.43
24.62
0.74
0.39
Sweden
23.26
0.62
0.38
26.46
0.46
0.38
Switzerland
16.41
0.40
0.46
33.21
0.42
0.59
Taiwan
36.31
0.42
0.71
23.33
Tanzania
24.83
26.83
Thailand
28.27
0.67
0.71
9.10
0.38
0.16
Trinidad and
20.09
Tobago
Israel
0.25
0.25
Turkey
29.08
0.49
0.63
Italy
22.63
0.51
0.37
Uganda
24.83
Jamaica
United
8.09
0.29
0.18
Kingdom
Japan
37.69
0.38
0.45
Uruguay
27.81
Jordan
26.83
Venezuela
33.90
0.51
0.53
Korea
44.59
0.63
0.52
Vietnam
33.78
Notes: The extended Hofstede index in an average of values of the four original dimensions as well as two additional
dimensions: (1) long-term orientation (LTO); and (2) indulgence versus restraint (IVR).
The GLOBE index is an average of the following nine cultural dimensions: (1) assertiveness; (2) institutional collectivism;
(3) in-group collectivism; (4) future orientation; (5) gender egalitarianism; (6) humane orientation; (7) power distance; and
(9) uncertainty avoidance.
Hofstede cultural indices along each dimension range between 0 and 100.
GLOBE indices along each dimension range between 1 and 7.
37 | P a g e
Table F. Alternative Cultural Distance Correlation Matrix
Variables
1
1
Hofstede distance
1
2
Extended Hofstede distance
0.870
1
3
GLOBE cultural distance (Practices)
0.510
0.521
1
4
GLOBE cultural distance (Values)
0.445
0.516
0.491
38 | P a g e
2
3
4
1