Geographical and Cultural Patterns in Cross-Border Mergers and Acquisitions: The Role of Experience Massimo Del Gatto 1 ‘G.d’Annunzio’ University, LUISS Guido Carli, CRENoS Carlo S. Mastinu University of Cagliari Abstract We exploit temporal and spatial correlation in a cross-border merger and acquisition (M&A) database with worldwide coverage to show that firms’ M&A choices are significantly influenced by experience effects related to knowledge accumulation. Our empirical strategy rests on the assumption that experience effects are expected to be at play in M&A directed toward culturally contiguous countries (arguably driven by knowledge accumulation) and not in M&A deals in geographically contiguous countries (arguably driven by transport costs). The analysis is conducted on a bilateral measure of cultural distance (encompassing linguistic, religious, and genetic distance indicators), which is meant to capture the idea of the cultural heritage originated by historic linkages. Keywords: Cross-border M&A, Cultural Distance, Contiguity, Experience, Knowledge. JEL Codes: R12, F23, L22, D23 1 Corresponding author. Contact details: Massimo Del Gatto; ‘G.d’Annunzio’ University, Department of Economics (DEc); Viale Pindaro, 42; 65127 - PESCARA (ITALY); [email protected]1
Transcript
Geographical and Cultural Patterns in Cross-Border
We exploit temporal and spatial correlation in a cross-border merger and acquisition (M&A) database withworldwide coverage to show that firms’ M&A choices are significantly influenced by experience effects related toknowledge accumulation. Our empirical strategy rests on the assumption that experience effects are expected tobe at play in M&A directed toward culturally contiguous countries (arguably driven by knowledge accumulation)and not in M&A deals in geographically contiguous countries (arguably driven by transport costs). The analysisis conducted on a bilateral measure of cultural distance (encompassing linguistic, religious, and genetic distanceindicators), which is meant to capture the idea of the cultural heritage originated by historic linkages.
1Corresponding author. Contact details: Massimo Del Gatto; ‘G.d’Annunzio’ University, Department of Economics (DEc); VialePindaro, 42; 65127 - PESCARA (ITALY); [email protected]
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1 Introduction
A number of studies have analysed the extent to which the reasons behind firms’ mergers and acquisitions (M&A)
choices (e.g. strategic competition and acquisition of markets, technologies and intangible assets in general, skills
and managerial practices, etc.) are captured by country-level measures of ‘distance’. While geographical and
cultural distance has received significant attention, the empirical evidence is not univocal. For example, Di Giovanni
(2005), Hijzen et al. (2008), and Hyun and Kim (2010) document negative effects for geographic distance (Hijzen
et al. (2008) also show that the relationship is less negative for horizontal mergers). Di Giovanni (2005) also
report positive effects associated with sharing a common language. Boschma et al. (2016) report positive effects
for geographical, industrial, organisational, and institutional proximity on domestic M&As in Italy. Coeurdacier et
al. (2009) find that geographical distance is a non-significant determinant of M&A flows in developed economies.
Blonigen et al. (2007) report strong evidence of significant and positive contiguity effects in US outbound foreign
direct investment (FDI). Di Guardo et al. (2015) reveal significant negative effects of geographical and cultural
distance on cross-border M&As. Ahern et al. (2015) find M&A flows are negatively correlated with cultural
distance.
By highlighting the shortcomings of such country-specific measures of distance (see Ambos and Hakanson, 2014;
Yildiz, 2014), a recent vein of literature provides a possible explanation for the inconclusiveness of results. The idea
is that the experience gained by the firm through its prior cross-border M&A deals can actually soften the effect
of cross-country diversity (Shenkar, 2012) on its future M&A choices. This places emphasis on distance between
entities, rather than between countries (Gaur, et al., 2007; Chapman, et al., 2008).
The relevance of the experience dimension as a determinant of firms’ international investment has been cited in
different theoretical contests (mainly at the intersection between international business, international economics,
and regional studies) since the beginning of the eighties, when the upsurge of cross-border M&As flows began.
A first vein of literature sees transaction costs as the main determinant of firms’ international activities. Due to
imperfections in knowledge markets, investing abroad entails search and information costs, as well as costs related
to bargaining and contract enforcement (Kogut and Zander, 1993). These costs grow with the extent of cultural
differences between the entities involved (e.g., Kogut and Singh, 1988). Multinational enterprises (MNEs) eliminate
transaction costs by absorbing them inside the hierarchy of the firm (Buckley and Casson, 1976). Together with
ownership and location, international business theory regards these internalisation advantages as one of the most
crucial perceived advantages of MNEs (Dunning, 1977).
A second line of research recognises that investing abroad always entails an evaluation of whether the firm is able
to establish and manage its operations (Guillen, 2003) based on its capabilities and knowledge of the host country
environment. On the one hand, knowledge enables firms to compete in both domestic and international markets (i.e.
knowledge-based theories). On the other hand, firms can acquire market knowledge through increasing involvement
in the foreign country, thereby improving their organisational capabilities. Such a process of organisational learning
can be related to the observation of the experiences of other organisations located in the same geographic region
(Baum et al., 2000) or to a process of experiential learning in which relevant knowledge is derived from past
experience (Levitt and March, 1988).
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Hence, past M&A deals can foster a learning process (Cyert and March, 1963; Leung et al., 2005; Shenkar,
2012; Hutzschenreuter et al., 2011) leading to improvements in terms of technological routines and know-how,
intangible assets, marketing intensity, product portfolio management, and plant/equipment planning (Delios and
Beamish, 2001; Perkins, 2014). According to Chao and Kumar (2010), the obstacles faced by a firm entering a
new location can be understood in terms of its ‘marginal distance’ (encompassing all sorts of learning accumulated
through its prior international pursuits) more than in terms of the institutional or cultural distance between the
host and the home country. Other contributions (Barkema et al., 1996; Haleblian and Finkelstein, 1999) highlight
the positive effect of firm experience in the post M&A phase by, for example, increasing the firm’s capability to deal
with uncertainty in deal negotiations, resolve deadlocks, and reduce the risk of deal abandonment in subsequent
deals (Very et al., 1997). Popli et al. (2016) demonstrate how previous experience in culturally similar countries
generates a ‘cultural experience reserve’ (Luo and Shenkar, 2011), which mitigates the positive impact of cultural
differences on cross-border deal abandonment.
The Uppsala model (Carlson, 1974; Johanson and Vahlne, 1977) also provides a framework in which firm
internationalisation is a learning process wherein the firm enlarges its involvement in international activities at the
pace of its learning capacity. After investing in a given country, it acquires new information about that market
and its surroundings, and so its next investment is likely to be in that or a ‘similar’ country. Hence, firms would
choose their target countries according to their perceived distance from those markets (familiar markets are ‘close’,
whereas unknown markets are ‘distant’); such distance decreases over time as new information is acquired. There
is mixed empirical evidence regarding the effectiveness of this idea (Yildiz and Fey, 2016). Davidson (1980) shows
that US investment in Canada and the UK are well beyond what their market size, growth, tariffs, and geographical
proximity would predict, arguably due to cultural similarity, and firms prefer countries in which they are active to
those in which they are not. Benito and Gripsrud (1992) find no evidence of cultural and experience effects. Mitra
and Golder (2002) find that cultural distance is an insignificant factor, unlike knowledge. Chapman et al. (2008)
argue that cultural distance measures should take managers’ perceptions into account by considering previous
interactions with the host country.
In summary, while the occurrence of knowledge-related experience effects fostered by a firm’s foreign investment
history is recognised in several (but somewhat distant) contexts, empirical evidence is still scant. First, to the best
of our knowledge, the evidence is limited to investment originated in a single country, which prevents general
conclusions being drawn. In fact, we are still unable to verify whether having already invested in a given country
increases the probability of investing there again. Second, it is still unclear whether and how past investment
choices interact, through affecting firm knowledge, and the cultural sphere or experience is also likely to reduce the
attrition of (physical) geography. In both cases, the eventual attrition of distance on firms’ international investment
choices cannot be based on country-level measures only; firm experience must be taken into account. Third, if the
experience effects occur through the cultural sphere, the probability of engaging in foreign investment in a given
country should also depend on whether there has been investment in countries that are culturally similar to that
country.
To address these issues, we exploit cross-time and cross-space correlation in cross-border M&A data (drawn
from the Thomson Financial Security Database), with worldwide coverage, to investigate the eventual presence of
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knowledge-related experience effects and uncover cross-patterns of (country-specific) cultural distance and (firm-
specific) experience.
Our empirical strategy rests on isolating two different types of M&A deals, depending on the type of costs they
are mostly driven by: spatial and knowledge costs. The first type concerns activities that are more intensive in
transportation (e.g. international importing and exporting along the supply chain). In these cases, the costs related
to geographical distance are particularly binding, so that firms tend to choose countries that are (geographically)
closest. Conversely, M&As related to activities that are particularly sensitive to knowledge costs will tend to be
oriented to culturally similar countries.
Disentangling these two types of deals helps us identify experience effects. In fact, while knowledge can be
accumulated, the burden of transport costs related to economic activity in a country can be compressed only by a
negligible fraction (e.g. changing storage and logistics service providers) through experience accumulated via past
investment. As a consequence, with experience effects at play, the occurrence of M&A deals in culturally contiguous
countries (arguably driven by knowledge costs) is expected to positively and significantly depend on the number of
past M&As operated in those countries and those that are culturally contiguous. On the other hand, the number
of past M&As operated in the same country or in its neighbour countries is not expected to influence the choice
to engage in an M&A deal in a geographically country contiguous (arguably driven by spatial costs). If this is the
case in the data, we can conclude that the experience effects are related to a process of knowledge accumulation.
Following Guiso et al. (2006) and Spolaore and Wacziarg (2016), we build a measure of bilateral cultural
distance (encompassing linguistic, religious, and genetic differences across populations) to capture the idea of the
cultural heritage originated by historic linkages.
The econometric analysis strongly supports the presence of experience effects associated with knowledge accu-
mulation. These effects are strong and persist after controlling for key country-level characteristics. In particular,
M&As in culturally contiguous countries occur despite higher tax and risk rates. Moreover, the probability of
engaging in M&A deals in a culturally (geographically) contiguous country is positively (negatively) related to
labour costs.
Overall, our analysis supports the idea that: i) firms’ M&A choices are significantly influenced by experience
effects related to knowledge accumulation; ii) to uncover such experience effects, we need to look where they are
potentially at play, that is, in knowledge-intensive deals/activities.
From a policy perspective, these results have strong implications for the current protectionism wave in the
FDI area. We show that experience plays an important role in bringing foreign capital to geographically distant
markets, usually at rather different stages of economic development, and that knowledge accumulation requires
time. Hence, the social costs of compressing the outward flows of foreign investment is higher in the case of
knowledge-intensive deals. As long as they are not selective, re- and back-shoring policies bring about a high risk
of knowledge dissipation.
The exposition proceeds as follows. Section 2 sets out the empirical strategy. Section 3 describes the data and
defines the variables used. The empirical analysis is reported in Sections 4 (benchmark results) and 5 (robustness
checks). Concluding remarks are presented in Section 6. Appendix A outlines the data generating process. Ap-
pendix B provides an in-depth description of the M&A flows in our dataset, also addressing their relationship with
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geographical and cultural distance. Appendix C highlights the similarities and differences between our cultural
measures and comparable available measures.2
2 Empirical strategy
The literature discussed in the previous section recognises that a firm’s M&A history is likely to generate experience
effects insofar as it is related to knowledge accumulation along its various dimensions. In turn, the idea of knowledge
accumulation intersects the sphere of ‘cultural’ distance. By investing in a given country, the firm increases its
capacity to take advantage of further investment in that country or in those that are culturally ‘similar’. This
can modify, even substantially, the effect exerted by cultural distance on firms’ M&A choices. To operationalise
this idea, we follow an empirical strategy (Appendix A provides a description of a fully consistent data generating
process) grounded on disentangling between two types of M&A deals: those driven by spatial costs and those
driven by knowledge costs. The first type embraces activities that are intensive in transportation and, as such,
specifically affected by geographic distance. Indeed, while delivery costs have shrunk considerably over recent
decades, the frequency of shipments has also grown and the combination of frequency of transactions, storage,
inventory-handling, and other forms of logistics costs has determined an overall increase in the economic costs of
geographic distance in some industries (McCann, 1998; McCann and Shefer, 2004). In these cases, the costs related
to geographical distance are particularly binding, so that firms tend to choose countries that are (geographically)
as close as possible. The second type of deals refers to foreign investment in activities that are more sensitive to
knowledge costs. Consistent with the literature reviewed thus far, this type of M&A operations tends to be oriented
to culturally similar countries. Indeed, the literature has shown that the location of knowledge-intensive industries
is affected by the knowledge transmission mechanism. For example, Storper (1995, 1997) argues that innovative
industries and activities tend to concentrate when the innovation process spreads out through informal relations,
more than through traded and formalised interactions. Other contributions (e.g. Lundvall, 1992; Camagni, 1991;
Capello, 1999; Lawson, 2000) stress that knowledge transmission often involves interactive learning among actors
through cooperation or other joint activities, for example. Todtling et al (2006) highlight how high-tech firms tend
to combine knowledge sources from the region with those of national and international origin in their innovation
process. Even transferring ‘ready’ pieces of information or knowledge (i.e. licensing a specific technology, reading a
patent description, imitating other firms’ practices) is arguably affected by the extent of cultural distance between
actors. Disentangling these two types of deals helps identify experience effects under the two following hypotheses.
First, while knowledge can be accumulated, the burden of transport costs related to economic activity in a country
cannot be compressed, or only by a negligible fraction (e.g. by changing storage and logistics services provider),
through experience accumulated via past investment. Second, the cost-minimising location choice is a contiguous
country in both cases: culturally contiguous in one case, geographically contiguous in the other. Thus, as long
as firms’ realised deals in geographically (culturally) contiguous countries reveal spatial costs (knowledge costs)
minimising choices, M&As in geographically contiguous countries can be used as a sort of control group. On
2The full (195x195) matrix of bilateral distance measures (i.e., linguistic, religious and genetic distance, as well as the overall measure
of cultural distance) can be downloaded from http://docenti.unich.it/delgatto/delgatto_web/research.htm along with areplication package including original data and STATA files.
the one hand, with experience effects at play, the occurrence of M&A deals in culturally contiguous countries is
expected to positively and significantly depend on the number of past M&As operated in the those countries and/or
those that are culturally contiguous to those ones. On the other hand, the choice to engage in a M&A deal in a
geographically contiguous country is not expected to be influenced by the number of past M&As operated in that
country or in its neighbour countries. If this is the case in the data, we can argue that the experience effects are
related to a process of knowledge accumulation.
This suggests bringing to the data the following
Testing Hypothesis. The probability that firm h, located in country H, engages in an M&A in culturally con-
tiguous country F is positively affected by experience (i.e., firm h’s number of past M&As) in country F and in
those that are culturally contiguous to F . The probability that firm h, located in country H, engages in a new
M&A in the geographically contiguous country F is not affected by experience in country F and in those that are
geographically contiguous to F .
Insofar as the Testing Hypothesis proves true in the data, we can conclude, consistently with the literature
presented in Section 1, that the experience effects are related to a process of knowledge accumulation.
3 Data and variables
Our main data source is the Thomson Financial Security Database, which is extensively described in Brakman et
al. (2006). For all the M&As worldwide, the database reports country of origin, country of destination, year, date
of announcement, value of acquisition and International Standard Industrial Classification (ISIC) sector code for
both the acquiring and the target firm. We focus on cross-border M&As and exclude tax havens. Regarding the
reference period, we concentrate on 1985-2007 to avoid the potentially distortive effects of the 2008 economic crisis
which, as highlighted in Appendix B (Figure B.1), led to stagnation in both FDI and M&A international flows,
eventually associated to diversion effects related to cross-country asymmetric protectionism.3
A known problem with M&A data is the presence of M&A sequences realized by the same firm in the same
destination and announced on the same date. Since these observations usually correspond to the acquisition of
different branches of the same firm, we consider only the first observation in such cases (we drop 1054 observations),
with a value given by the sum of the values of the single deals in the sequence. In addition, we exclude the M&As
of firms investing in a single country (2244 observations).
The final dataset consists of 24402 deals realized by 17457 firms distributed across 21 (OECD) countries and
directed to 143 different countries. Around 73% of the deals occur between firms operating in the same Standard
Industrial Classification (SIC) two-digit sector (horizontal M&As). Table 1 reports the descriptive statistics for the
21 countries of origin. The US has the highest number of M&As, followed by the UK. However, it should be noted
that the UK is more involved in so-called ‘mega deals’, especially in the banking sector. This is also the case for
3The crisis hit differently the various economies, even turning into a double dip recession in some cases (e.g., in several Europeaneconomies). Although this heterogeneity might be controlled for in the estimates, by keeping the post-2008 years out of the analysis weaim at focusing on a phase characterized by a clear-cut positive trend in international M&A and FDI flows associated with increasingtrade freeness, thereby providing with quite general results.
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France, which generates 4.52% of the total number of M&As but 10.23% of the total value. This phenomenon is
inverse in some countries, such as Ireland.
A more in-depth description of the dataset is reported in Appendix B.
Table 1 around here
Cultural distance (DcultH,F ) is a bilateral index obtained as a weighted average of three distance measures: linguistic,
religious, and genetic distance. The choice of these three dimensions is meant to capture the idea of the cultural
heritage originated by historic linkages and combines this idea with the genetic traits of populations. We follow
Guiso et al. (2006), who consider cultural distance as “those customary beliefs and values that ethnic, religious, and
social groups transmit fairly unchanged from generation to generation”, and Spolaore and Wacziarg (2016), who
posit that genetic distance is a summary statistic for a wide array of cultural traits transmitted inter-generationally.
Following Fearon’s (2003) approach, the three measures are constructed based on two building blocks: i)
the international distribution of languages/ethnic groups/religions (i.e. for each country, the percentage of the
population speaking each language/belonging to each ethnic group/professing each religion); ii) a distance matrix,
including all possible language/religion/ethnic group pairs, based on the dissimilarity between any two pairs in terms
of number of common branches in a ‘tree’. The term ‘branches’ describes the points where language/religion/ethnic
groups divide in the tree. The tree is a diagram showing the relationship between groups derived from a single
‘family’.
Following Fearon (2003), the index used to measure the distance between the two groups i and j is
τij = 1 −(l
m
)α(1)
where l is the number of shared branches between i and j; m is the maximum number of shared branches between
any two languages, religions, or ethnic groups; α is a parameter with an assigned value of 0.5.4
The distance between countries H and F is then calculated using the following formula:
DH,F =K∑k=1
(QHi Q
Fj τij
)k
(2)
where Qi and Qj denote the share of the population speaking languages i and j, belonging to ethnic groups i and
j, or professing religions i and j. K represents all possible combinations of languages, ethnic groups, or religions
in countries H and F . The index varies between 0 (minimal distance) and 1 (maximal distance).
We use Spolaore and Wacziarg’s (2009) approach to calculate genetic distance. Linguistic and religious distances
are our own calculation. For linguistic distance, the distance matrix is obtained by exploiting information in the
phylogenetic tree provided by ‘Ethnologue: Languages of the World’ ; also the international distribution of languages
is drawn from Ethnologue. For religious distance, we use the tree in Spolaore and Wacziarg (2009)5, while the
international distribution of religions is drawn from the CIA World Factbook.
4See Desmet et al. (2009, p.1301), for an explanation of the meaning and estimation of α.5We thank Roman Wacziarg and James Fearon for providing us with the tree
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The final index of cultural distance is obtained as a weighted average of the three measures, with the weights
(0.27%, 0.39% and 0.34% for religious, linguistic and genetic distance, respectively) obtained through principal
component analysis.6
The correlation between geographic and cultural distance is quite low: 0.0523.
It is worth noting how several measures of cultural similarity are used in the literature. Di Guardo et al.
(2015) use the composite index proposed by Kaasa (2013) and Kaasa et al. (2013). The index is computed by
applying principal component analysis to items provided by the World and European Value Surveys relating to four
dimensions suggested by Hofstede (1980): power distance, uncertainty avoidance, individualism versus collectivism,
and masculinity versus femininity. Ahern et al. (2015) use information on trust, hierarchy and individualism drawn
from the World Values Survey. A widely used measure is the cultural distance index developed by Kogut and Singh
(1988). While such indexes differ in nature with respect to ours, comparable measures of cultural distance are those
provided by Melitz and Toubal (2014) and Spolaore and Wacziarg (2016): a comparison is provided in Appendix
C.
To conduct the analysis in Section 4, we need to set a criterion of cultural contiguity. Given the measure
of bilateral measure of cultural distance described in Section 3, we consider country H culturally contiguous to
country F if the cultural distance to F is, according to the bilateral measure described in Section 3, lower than the
value corresponding to the second percentile of country H’s distribution of bilateral distances and, in addition, lies
within the second decile of the worldwide distribution.7 (a different specification is used for robustness in Section
5).
Overall, M&As directed toward geographically and culturally contiguous countries represent about 17.5% and
32.5% of the sample, respectively, with only 5% of observations related to M&As in countries that are both
geographically and culturally contiguous. In the regression analysis, these observations are dropped to separate
out the geographical and cultural contiguity dimensions).
Information on geographical contiguity (i.e., sharing a common border) and geographic distance is taken from
the GeoDist database maintained by the Centre d’Etudes Prospectives et d’Informations Internationales – Paris
(CEPII). For geographic distance (i.e., DgeoH,F ), the variable dist is used. This is based on the simple geodesic
distance between the most populated cities/agglomerations in the two countries.8
CEPII data (namely, the Trade and Prod databases) are also used to obtain information on unit labour costs
(ULC ), calculated by applying wages to inverse labour productivity9. Average years of schooling (Schooling) are
from Barro and Lee (2013).
Among the other variables used, total population (Population), real interest rate (Intrate), and profit tax rate
6The complete replication package for the cultural index is available on http://docenti.unich.it/delgatto/delgatto_web/
research.htm.7Because of the combination of these two requirements, there might be countries whose culturally closest countries are not ‘close
enough’ to identify a culturally contiguous country, given the global distribution of bilateral distances. This is the case in Japan, forexample. Apart for these cases, the number of culturally contiguous countries identified ranges from one (Greece) to four (AU, CA,FN, SW, UK, and US). For the US, for instance, the culturally contiguous countries are, in order, AU, UK, NZ, and IR; For the UK,they are AU, NZ, IR, and the US.
8Geodesic distance is calculated following the great circle formula, which uses latitudes and longitudes of the most importantcities/agglomerations in terms of population.
9Using the CEPII variables notation, the ULC is calculated as wageva/lab
, where wage is the wage per employee, va is the value added,
and lab is the number of employees. The variables are expressed in nominal dollars.
(Tax ) are drawn from the World Development Indicators database provided by the World Bank.
Risk is the global index of country risk provided by SACE, which ranks all countries ranging from 1 (lowest
risk) to 9 (highest risk).10
4 Analysis
To gain insight into whether firms tend to reinvest in the same country and the extent to which this tendency
eventually changes in accordance with being the destination country geographically or culturally contiguous to the
country of origin, we report (Table 2) the results of a t-test statistics for the frequency of M&As directed towards:
countries in which the firm has already invested at least once (row 1), geographically (row 2) or culturally (row 3)
contiguous countries in which the firm has already invested at least once, second-order geographically (row 4) or
culturally (row 5) contiguous countries (i.e., being contiguous to contiguous country) in which the firm has already
invested at least once. The t-test considers the difference between the frequency observed in the original sample and
that observed in a control sample obtained by clustering the observations of the original sample into 553 groups,
including only M&As in given time windows by firms belonging to the same country and the same sector.11 The
results suggest the presence of a statistically significant tendency to reinvest in the same country. This tendency is
statistically significant in general (first row) and when restricted to M&As in contiguous countries (second, third,
and fourth rows). The null hypothesis of equal frequencies in the two samples is not rejected only in the case
of second-order cultural distance. However, this may depend on the very low share that second-order contiguous
countries represent, which shrink to 2.9% and 5%, respectively.
Table 2 around here
Such persistency in the data can be exploited to uncover possible patterns in firms’ M&A choices by bringing the
Testing Hypothesis stated in Section 2 to the data. Hence, we estimate the following equation:
mth(F conti H) = βo + β1P
th + β2M
th(F ) + β3M
th(Z contgeo F ) + β4M
th(Z contcult F ) + β5Γ
tH,F + εt
with i, j = geo, cult.
in which the dependent variable mth(F conti H) is a dichotomic variable assuming the value of 1 if the destination
country F is contiguous (geographically or culturally, depending on the specification) to the country of origin H,
and 0 otherwise. mth(F contcult H) = 1 in 3834 cases and mt
h(F contgeo H) = 1 in 1753 cases; that is, around 27
10The final ranking considers the political risk (associated with internal policy and international relationships), economic risk (eco-nomic conditions, public accounts, inflation, current account, balance of payments, and exchange rate), financial risk (bank structureand financial stability), and operative risk (legal system, attitude towards foreign investors, infrastructure, and natural conditions)(http://www.sace.it/).
11The windows are identified considering the quartiles of the M&A distribution. The observations in the control sample are chosen byrandomly drawing from each group a number of observations equal to the number of M&As in the group, so as to obtain two samplesof identical size. The last step is repeated 1000 times in order to obtain confidence intervals for the t-test statistics. The t-test statistictakes the form
zs − zc√[zs(1 − zs) + zc(1 − zc)] /Z
under H0 : zs = zc H1 : zs > zc, (3)
where Z is the total number of observations and z is the frequency of M&As. Subscripts s and c are used to highlight whether z isobserved in the original or control sample. Only the deals by firms with at least one previous M&A are considered in each period.
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and 12.3 percent of the sample, respectively. See Table 3 for descriptive statistics.
Table 3 around here
Besides the value of the M&A (i.e., P th), the right-hand side of Equation (4) includes a vector of exogenous factors
specific to the country of origin, the target country and the country pair (i.e., ΓtH,F ) and the experience variables
as defined in Section 3. Namely, the share of firm h’s past M&As at time t in country F , third countries Z that are
geographically contiguous to F , third countries Z that are culturally contiguous to F (i.e., M th(F ), M t
h(Z contgeo F )
and M th(Z contcult F ), respectively. The shares are calculated with respect to the total number of firm h’s past
M&As at time t, since 1985. It is worth noting that our 1985-2007 data coverage yields a discrepancy between
observed and actual experience for the firms established before 1985, as we are not allowed to know whether they
already engaged in M&A operations before that date; this circumstance entails initial condition issues (see below).
The three variables are summarised in Table 3, also in terms of cross-correlation. While experience in contiguous
countries is always less than one, M th(F ) = 1 in some cases, meaning that the firm engaged in M%A deals in one
country only; these cases have been dropped in the estimation.
Table 4 reports our benchmark findings. Models (1) and (2) are used to carry out the estimation of (4) for
M&As in culturally contiguous countries; models (3) and (4) are used for M&A in geographically contiguous coun-
tries. As explained, our data do not reveal whether a firm had already engaged in M&As before 1985. We tackle
this initial condition problem by including the initial and the average value of all the experience variables (namely,
P th and the three experience variables) in a Probit random-effects estimation. Introduced by Wooldridge (2005),
this strategy enables us to deal with the fact that we have no ‘true’ values observed at the beginning of the time
window, and that we therefore cannot derive a reduced-form equation for the initial condition based on available
pre-sample information as suggested by Heckman (1981). This approach provides an alternative method to take
firm effects into account, but under the hypothesis that they are not correlated with the explanatory variables, as
seems reasonable. Estimation results are reported in Models (1) and (3)12
Table 4 around here
For vector ΓtH,F , we include the total population in the origin and the host countries. This aims to capture country
size, together with differential average years of schooling, intended as a broad measure of differences in economic
development (with positive values indicating higher economic development in the target country).
Investing in a culturally contiguous country (Models 1 and 2) is positively affected by prior experience in that
country, as measured by M th(F ). On the contrary, and notably, direct experience in the destination country is
found to be uninfluential when the target country is geographically contiguous (Models 3 and 4). This suggests
that only M&A driven by factors in the cultural sphere, as captured by our cultural distance measure, are subject
to experience effects.
This finding is confirmed by the fact that the occurrence of M&A deals in a culturally contiguous country is
also positively affected by accumulated experience in countries that are culturally contiguous to the destination
12Fixed-effects panel logit regressions have also been run for consistency; results are broadly in line with those in Table 4.
10
country, that is by M th(Z contcult F ). Conversely, M t
h(Z contgeo F ) is found to exert a negative effect, which is
evidence against the presence of experience effects associated with geographical proximity.
Interestingly, these effects tend to persist (see Model 3) in M&A operations driven by geographical contiguity
(although they disappear in Model 4), that is, in deals that we associate with transaction costs.
Regarding the control variables, higher economic development (i.e. schooling) in the target country exerts the
same, positive effect in both types of M&A deals. While positively correlated with M&As in culturally contiguous
countries, the size of the destination country seems to push in the direction when M&A in geographically contiguous
countries is considered. The value of the deal is in general not significant (also in the robustness analysis). In
summary, the above results strongly point to the presence of experience effects associated with the cultural sphere
and, notably, the absence of similar effects in association with geographical proximity. Hence, we interpret the
overall evidence as being driven by a process of learning, arguably in terms of knowledge accumulation.
5 Robustness
The robustness of the results in Table 4 is checked in several ways.
For the estimating method, we retain the Probit random-effects estimation as our preferred specification and
perform the robustness checks only with respect to this. As well as imposing the firm effects to be correlated with
our experience indicators, which seems too restrictive an assumption, the fixed-effects logit specification incurs
issues of convergence achieving in most cases.
In Table 5 we use different specifications for the control vector ΓtH,F . In Models (1) and (3), we include cross-
country differences in unit labour costs, interest rate, profit tax rate, and country risk. Results are qualitatively the
same, with experience still playing a key role, irrespective of higher tax and risk rate and the occurrence of M&A
deals in a culturally (geographically) contiguous country positively (negatively) related to ULC, which is consistent
with previous results on schooling. In Models (2) and (4), considering the data generating process described in
Appendix A, we also control for geographical distance in the cultural contiguity regression (Model 2) and cultural
distance in the geographical contiguity regression (Model 4) regression. Given the positive and significant action
exerted by geographical distance, this analysis makes the experience effects even more evident, indicating that
M&As mostly reach geographically distant markets when they are driven by cultural relationships. This evidence
explains the M&A flows occurring, for example, between the US and the UK, the US (or UK) and Australia,
and Spain/Italy/Portugal and Latin America very well. In contrast, DcultH,F acts as a deterrent with respect to
the probability of M&As in geographically (but not culturally) contiguous countries.13 It is worth noting that,
somewhat counter-intuitively, we do not include both distance measures in the same model for two reasons. First,
our dependent variable in the cultural (geographical) contiguity regression takes the value of one when cultural
(geographical) distance to the target country is below a certain threshold. Hence, cultural (geographical) distance
would absorb most of the variability in the dependent variable if included in the cultural (geographical) regression.
Second, the data generating process described in Appendix A suggests that cultural (geographical) distance is of
13A straightforward interpretation of the positive effect of geographical distance on M&As in culturally contiguous countries, andthe positive effect documented for ULC and profit tax rates might lie in the colonial heritage of the major acquiring countries (the USand UK).
11
negligible relevance in M&A deals directed toward culturally (geographically) contiguous countries. To generally
assess the concurrent impact of geographical and cultural distance in the aggregate, we perform regression analysis
in terms of total number and total value of the M&A deals taking place from H to F during the period under
consideration. Results reported in Appendix B.1, point to both types of distance exerting a negative impact.
Table 5 around here
In Table 6, we run the benchmark regressions separately for horizontal (models 1 and 2) and vertical (models 3 and
4) M&As, using a 1-digit sectoral breakdown. The analysis reveals the absence of remarkable differences between
the two types of M&As, meaning that our benchmark results can be regarded as general. Second, to confirm that
the final messages are not driven by the main players, we repeat the estimation without the first three countries in
terms of both inward and outward flows of M&As: the US, the UK, and Canada (results reported in the last two
columns). Our main results prove robust also to this check.
Table 6 around here
Finally, in Table 7, we use a different contiguity criterion for the dependent variables. In Model (1) and (2) we
redefine cultural contiguity by considering country H culturally contiguous to country F if its cultural distance
to F is lower than the value corresponding to the fifth (instead of second) percentile of country H’s distribution
of bilateral distances and, in addition, lies within the second decile of the worldwide distribution. This brings
the number of culturally contiguous country-pairs from 64 to 136 (out of 1047 in the whole database). Results
are unaffected. In Model (3) and (4) we change the definition of geographical contiguity from sharing a common
border to a definition that is consistent with the definition of cultural contiguity used in benchmark regressions.
Country H is considered geographically contiguous to country F if its geographical distance to F is lower than the
value corresponding to the second percentile of country H’s distribution of bilateral distances and, in addition, lies
within the second decile of the worldwide distribution. Benchmark results are confirmed in this case.
Table 7 around here
6 Conclusions
We investigated the eventual presence of knowledge-related experience effects and revealed cross-patterns of (country-
specific) cultural distance and (firm-specific) experience.
In particular, we exploited the cross-time and cross-space correlation in a cross-border M&A database (drawn
from the Thomson Financial Security Database) with worldwide coverage. We obtained empirical evidence on
whether a firm’s history in terms of prior M&As contributes to generating experience effects, associated with
knowledge accumulation, exerting a positive impact on the probability to invest in the same country and/or in
culturally similar countries again.
12
Our empirical strategy is built on the hypothesis that, while knowledge can be accumulated, the burden of
transport costs related to economic activity in a country cannot. Accordingly, experience effects are expected to be
at play in M&A in culturally contiguous countries (arguably driven by knowledge costs) and not at play in M&A
deals in geographically contiguous countries (arguably driven by spatial costs).
The analysis is conducted on a bilateral measure of cultural distance obtained as a weighted average of linguistic,
religious, and genetic distance indicators. The measure is conceived to capture the idea of the cultural heritage
originated by historic linkages, following Guiso et al. (2006) and Spolaore and Wacziarg (2016). We calculate
linguistic and religious distances based on information drawn from Ethnologue and the CIA World Factbook,
respectively. The linguistic index is more articulated than comparable available measures (e.g. Spolaore and
Wacziarg, 2016; Melitz and Toubal, 2014): 195 countries are covered (against 156 in Spolaore and Wacziarg, 2016);
bilateral distance is always maximum or always minimum in no cases (against 35 out of 195 in Melitz and Toubal,
2014).
Econometric results strongly support the hypothesis that firms’ M&A choices are significantly influenced by
experience effects related to knowledge accumulation. However, our approach highlights that to uncover such
experience effects, we need to look where they potentially are at play, that is, in knowledge-intensive deals/activities.
From a policy perspective, our results highlight how, by compressing firms’ involvement in FDI activities,
back-shoring policies are likely to disrupt the process of knowledge accumulation. This introduces an additional
channel through which protectionism can reduce the welfare gains associated with market openness. While this is a
potential research line along which our results might be developed, it would also be interesting investigating on the
potential asymmetric experience effects of cultural and geographical contiguity, as long as distance is recognized to
be not reciprocal (that is, when distance from A to B is not equivalent to distance from B to A), as the analysis
by Boschma et al. (2016) seems to suggest.
13
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17
Table 1: M&A data: descriptive statistics by country of origin.
COUNTRY Abbr. N Value N (share of tot.) Value (share of tot.)United States US 6474 1112325 26.53% 20.40%United Kingdom UK 5869 1269393 24.05% 23.28%Canada CA 2409 314659 9.87% 5.77%France FR 1102 557689 4.52% 10.23%Germany WG 1060 485834 4.34% 8.91%Australia AU 1046 175792 4.29% 3.22%Netherlands NT 871 331818 3.57% 6.09%Sweden SW 830 133608 3.40% 2.45%Japan JP 751 110867 3.08% 2.03%Ireland IR 581 44217 2.38% 0.81%Italy IT 504 120538 2.07% 2.21%Spain SP 497 229883 2.04% 4.22%Switzerland SZ 493 249992 2.02% 4.59%Norway NO 405 50391 1.66% 0.92%Finland FN 342 54216 1.40% 0.99%Belgium BL 338 88680 1.39% 1.63%Denmark DN 324 49160 1.33% 0.90%Austria AS 201 29538 0.82% 0.54%New Zealand NZ 139 16527 0.57% 0.30%Portugal PO 88 14168 0.36% 0.26%Greece GR 78 12436 0.32% 0.23%TOTAL - 24402 5451731 100% 100%
Table 2: T-test statistics.
# obs in # obs in ratio* t-test* P5* P95*Sample Control*
(1) F 4862 3658 1.329 15.568 15.106 16.056
(2) F contgeo H 1073 894 1.200 4.170 3.841 4.498
(3) F contcult H 2221 1844 1.204 6.377 5.974 6.784
(4) F cont2geo H 681 564 1.208 3.385 2.337 4.380
(5) F cont2cult H 935 1003 0.933 -1.601 -2.670 -0.511
* Bootstrapped values (average values after 1000 replications)
T-test on the sample frequency of M&As towards:(1) countries in which the firm has already invested at least once;(2) geographically contiguous countries in which the firm has already invested at least once;(3) culturally contiguous countries in which the firm has already invested at least once;(4) second-order geographically contiguous countries in which the firm has already invested at least once;(5) second-order culturally contiguous countries in which the firm has already invested at least once.
H: acquisition country; F : target country; Z: third country.
1Corresponding author. Contact details: Massimo Del Gatto; ‘G.d’Annunzio’ University, Department of Economics (DEc); VialePindaro, 42; 65127 - PESCARA (ITALY); [email protected]
1
A Appendix: Data generating process (an empirical model)
According to the literature discussed in Section 1, past investment in a given location is recognized to generate
experience effects insofar it is related to knowledge, along its various dimensions. In turn, the idea of knowledge
interrelates the ‘cultural’ sphere in different forms and, as a results, the effect exerted by cultural differences is
likely to be different from what can be measured in terms of cultural distance scores between the home and host
countries. it is in fact argued that, by investing in a given country, the firm increases its capacity to take advantage
of further investment in that country or in a country which is in turn culturally “similar” to that one.
We hereby sketch out a very simple model which consistent with this idea. This enables us to highlight the
data generating process underlying our testing hypothesis.
Firms engage in M&As to pursue relationship-specific activities. Acquiring firms benefit from M&As if and
only if the target firm fulfills given requirements. The acquisition price P th is a sufficient statistic for the set of
requirements that firm h sets itself, at time t. Only potential acquisitions of price P th are considered by firm h; P t
h
is the same, irrespective of the country in which the target firm is located.
Using lowercase letters to refer to firms and uppercase letters to refer to countries, the expected net profit
Πth,f (·), associated with the acquisition of firm f (in country F ) by firm h (in country H) at time t, can be
expressed as
Et[Πt
h,f
]= Et
[Rt
h,f (Γ, ·)− Sth,f (Dcult
H,F ,Φth,F )
]− T t
h,f (DgeoH,F )− P t
h (1)
where Rth,f denotes revenues, T t
h,f refers to spatial (i.e, transport) costs and Sth,f to knowledge costs, with
∂Sth,f
∂DcultH,F
> 0
and∂St
h,f
∂Φth,F
> 0. DgeoH,F and Dcult
H,F indicate geographic and cultural distance between country H and country F ,
respectively.
While spatial costs are known at time t, knowledge costs, as they also depend on experience, through the term
Φth,F = Φ(M t
h(F )
, Mth(Z contcult F ))(2)is assumed to be a function of the cumulative distribution function (i.e., number of past
M&As), at time t, of firm h’s M&As in country F - i.e., M th(F ) - and in countries Z that are culturally contiguous
to F - i.e., Mth(Z contcult F ).
Γ is a vector of country characteristics, specific to H and F (e.g., factor costs, taxation, etc.), where the dot
highlights that the general formulation in (1) does not aim at specifying how firms’ revenues are influenced by other
firm-specific factors (e.g., presence of managers with a particular degree of knowledge of a given market and total
factor productivity).
To keep the model as simple as possible, we assume that the search activity of the firm always produces a match
with a potential target firm priced P th (hence, with a firm that fully meets firm h’s requirements). Only one match
is produced and evaluated at each period and the decision concerning whether to take (i.e., mth,f = 1) or leave
(i.e., mth,f = 0) the opportunity is made independently of the decision to invest in other countries in the same
period. Production adjusts freely to current market conditions, entailing the profit-maximizing production level to
be always chosen.
2
In each period t, firm h chooses the infinite sequence mt[+]h,F that maximizes the expected present value of net
profits. The maximized payoff takes the form
V th,f (Ωt
h) = maxm
t[+]h,F
Et
∞∑j=t
δj−tΠth,f (·) | Ωt
h
(3)
where δ is the one-period discount rate and Ωth is firm h’s information set at time t.
Using Bellman’s equation, firm h’s current decision is the value of mth,F that satisfies
V th,f (Ωt
h) = maxmt
h,F
Πth,f (·) + δEt
[V t+1h,f (Ωt
h) |M t+1h,F ,Mh,F t+1
]. (4)
The firm will decide to take the M&A opportunity (that is, mth,F = 1) if
δ(Et[V t+1h,f (Ωt
h) | mth,F = 1
]− Et
[V t+1h,f (Ωt
h) | mth,F = 0
])+ Et
[Rt
h,f − Sth,f
]− T t
h,f > P th (5)
As well as to the acquisition target that the firm sets itself (i.e., P th), Equation (5) relates a firm’s decision to
engage in an M&A to geographical distance, cultural distance and experience.2
Now, consider two activities i and j, identical in everything but the fact that the former is ‘intensive’ in spatial
costs, so that[∂Πt
h,f
∂T th,f
]i>[∂Πt
h,f
∂T th,f
]j
while the latter is intensive in knowledge costs, so that so that[∂Πt
h,f
∂Sth,f
]j>[
∂Πth,f
∂Sth,f
]i. As long as geographical (cultural) contiguity minimizes the cost of geographic (cultural) distance, the
probability to engage in transport intensive M&As is the highest in geographically contiguous countries (where T th,f
is minimum), while the probability of engaging in knowledge intensive M&As is highest in the countries featuring
the best combination between cultural distance and experience; that is, starting from the initial condition in which
both M th(Z) = 0 and M t
h(Z contcult F ) = 0 ∀Z, in culturally contiguous countries (such as Sth(Z) is minimized).
Such a data generating process is consistent the Testable Hypothesis stated in Section 2. In fact, M&A occurring
in culturally contiguous are arguably associated to a knowledge cost-minimizing choice and, as such, are expected
to be positively associated to M th(Z) and M t
h(Z contcult F ) and to not depend on DcultH,F . There is no a priori
expectation on the effect of DgeoH,F , as long as (being the activity knowledge intensive), the impact of transport
costs is comparatively marginal and it might be reasonable to choose geographically distance but culturally similar
countries (think about former UK colonies for investment from the UK) in horizontal M&A a la Barba Navaretti and
Venables, 2004. Differently, M&As in geographically contiguous countries are expected to take place independently
of experience, to not depend on DgeoH,F (which is already minimized by contiguity) and to still depend on cultural
distance, eventually.
2Note that with Sthf (·) + T t
h,f (·) + P th > Et[Rt
h,f (·)], firm h still chooses mth,F = 1 if the expected contribution to future profits
exceeds the negative expectation for the current period. That might be the case for a firm that wants to invest in a given country inorder to improve its production abilities through learning.
3
B Appendix: M&A Data
We use M&A data drawn from the Thomson Financial Security Database. The database has world coverage and,
for each deal, reports information on country of origin and country of destination, year, data of announcement,
value of the acquisition, ISIC industry of the acquiring and the target firms.
While a detailed description of the database can be found in Brakman et al. (2006), we hereby report descriptive
statistics and visual inspection along some key dimensions for the analysis carried out in the paper.
As known, M&As, as FDI in general, come in ‘waves’. Such circumstance is highlighted by Figure B.1 using the
FDI Database provided by UNCTAD. The Figure also underlines the growing importance of the M&A flows, as a
subset of FDI, from the 90s to the 2008 economic crisis, since when the share of FDI occurring in form of M&As
progressively shrinks.
Figure B.2 highlights how the time variability in the Thomson Financial Security Database, in terms of number
and value of M&As deals is broadly consistent with the UNCTAD Data. Figure B.3 shows the country patterns
(in terms of number of M&As). There are no substantial differences across countries over time.
From a static perspective, Figure B.4 describes the country breakdown fo the data, highlighting how the biggest
players in terms of outward M&As tend also to be the biggest players in terms of inward flows.
M&As occur mostly within a narrow group of developed countries, among which the US is the undisputed leader
and, as highlighted by Figure B.5, the vast majority of M&As is directed toward OECD countries.
Figure B.6 shows that the industries more involved in M&As deals are those in the the manufacturing sector,
followed by finance, insurance, real estate, and services industries.
Figures B.7, B.8 and B.9 provide a geographical representation of the data for three key cases: US, Spain,
Japan. A considerable share of the M&A flows originated in US and Spain is directed toward South America. In
the case of the US, this circumstance can be usefully explained in terms of geographical proximity. In the case of
Spain, on the contrary, the large amount of M&A in such a geographically distant area can be explained by the
cultural proximity related to the Spanish colonial heritage. Indeed, Japan displays very limited interests in that
area. This offers a first intuition of the potential role of cultural distance.
B.1 Aggregate correlation with geographical and cultural distance.
In this Section we address the concurrent impact of geographical and cultural distance on aggregate M&A flows
from country H to country F , occurred in the period 1985-2007, in terms of: i) total number of deals (i.e., models
1 to 3 in Table A1); ii) total value of the deals (i.e., models 4 to 6 in Table A1).
In the first case, the estimated equation is
M tH,F = δDi
H,F + γΓH,F + ε with i = geo, cult. (6)
where DH,F is a vector encompassing geographic (DgeoH,F ) and cultural (Dcult
H,F ) distance; ΓH,F is a vector of controls
specific to the host country and to the country of origin; ε is the iid error.
Since we observe only the deals that effectively took place within the period, the analysis poses a zero inflation
4
problem.3 To deal with this issue, Equation (6) is estimated using a zero-inflated negative binomial regression.
In particular, a hurdle model is considered, in which the equation for the first step (the equation that determines
whether the observed count is zero) includes the same variables used in the second step.
The estimation results are reported in Table A1 (models 1 to 3). We use our benchmark (parsimonious)
regression in which only total population and differential schooling are as country controls. In model (1) the full
sampe is used, while models (2) and (3) restrict the analysis to vertical and horizontal M&As, respectively.
Both geographic and cultural distance from the target country are significant determinants of both the count
(i.e., first stage) and the zero inflation (i.e., second stage) process. The sign is as expected: higher distance is
associated with more zeros, in the first stage, and a lower number of deals, in the second stage. However, the
cultural distance effect is larger in magnitude.
Regarding the control variables, M&A flows are mostly directed toward large (Population) countries, which is
expected. As for differential Schooling (proxy for differences in terms of degree of economic development) between
the host and the origin country, while less developed countries tend to be more targeted in the first stage, economic
development exerts a positive impact on the number of deals in the second stage (count process). While leaving
the sign of the coefficients unaffected, disentangling between horizontal and vertical M&As only (slightly) affect
their magnitude.
Models (4), (5) and (6) perform a similar regressions but, instead of the total number of M&A deals, their
aggregate value is considered. The estimating equation is similar to 6; however, the regressors are now expressed in
logarithms, in order to address the zero-inflation issue through the Poisson pseudo-maximum likelihood estimator
(PPML) introduced by Silva and Tenreyro (2006):
PTH,F = δ ln DH,F + γ ln ΓF + ZH + εt. (7)
PTH,F is the aggregate value of the M&A deals from country H to country F .
Regression results are broadly consistent with those produced by the second stage (i.e., count process) of the
zero inflated binomial regression approach. Also in this case, disentangling between horzintal and vertical deals
does not affect the final message of the regressions.
3When the dataset is filled in considering all possible combinations of countries and sectors in each year, we end up with 254100observations, 250544 of which are zeros.
5
31
Fonte: Baldwin e Martin (1999) su dati Dunning (1983) e Unctad - Fdi database.
Figura 34: Distribuzione Geografica degli Fdi, 1914-1996.
Fonte: elaborazione su dati Unctad - Fdi database.
Figura 35: Flussi mondiali (in valore) di Fdi e M&A.
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
FDI M&A
Figure B.1: FDI and M&A trends (in value).
Source: our calculation on UNCTAD - FDI Database
020
0000
4000
0060
0000
8000
00Va
lue
050
010
0015
0020
00N
umbe
r
1985 2000 2007.
Number Value
Figure B.2: Patterns of M&A: number of deals by year.
6
123
415
4
154
822
2
132
158
512
0
110
174
367
811
1
145
594
120
112
266
397
151
2546
4
1762
0
413
2
1985 2007 1985 2007 1985 2007 1985 2007
1985 2007
AS AU BL CA DN
FN FR GR IR IT
JP NO NT NZ PO
SP SW SZ UK US
WG
M&A
(num
ber)
.
Figure B.3: Patterns of M&As: number of deals by acquiring country.
France
Canada
Germany
Australia
Netherlands
Others
US (26.6%)UK (24%)
Canada
Germany
Australia
France
Nether lands
Others
UK (15.6) US (27.4)
Distribution by acquiring country Distribution by target country
Figure B.4: M&A data: number of M&As by acquiring and target country.
Figure B.5: M&A data: distribution by OECD membership of the target country.
0 5,000 10,000 15,000 20,000
Agriculture, Forestry & Fishing
Communications
Construction
Finance, Insurance & Real Estate
Manufacturing
Mining
Public Administration
Retail Trade
Services
Transportation & storage
Wholesale Trade
number value
Figure B.6: M&A data: distribution by sector.
8
Figure B.7: M&A data: world map of US outward MAs (number).
Figure B.8: M&A data: world map of Spanish outward MAs (number).
Figure B.9: M&A data: world map of Japanese outward MAs (number).
9
Tab
leA
1:C
oncu
rren
tim
pact
of
geo
gra
ph
ical
an
dcu
ltu
ral
dis
tan
ce:
aggr
egate
regre
ssio
ns.
(1)
(2)
(3)
(4)
(5)
(6)
Dgeo
H,F
-1.2
64***
-1.6
91***
-1.3
25***
-0.5
86***
-0.4
78***
-0.6
22***
(-0.1
22)
(-0.1
50)
(-0.1
64)
(-0.1
05)
(-0.1
06)
(-0.1
23)
Dcult
H,F
-3.7
64***
-3.8
18***
-4.2
69***
-1.3
15***
-1.5
16***
-1.2
37***
(-0.1
84)
(-0.2
12)
(-0.2
19)
(-0.1
49)
(-0.1
53)
(-0.1
88)
Population
H0.0
00***
0.0
00***
0.0
00***
1.0
65***
1.1
40***
1.0
44***
(0.0
00)
(0.0
00)
(0.0
00)
(-0.1
70)
(-0.1
31)
(-0.2
01)
Population
F0.0
00***
0.0
00***
0.0
00***
1.1
65***
1.0
40***
1.2
05***
(0.0
00)
(0.0
00)
(0.0
00)
(-0.0
99)
(-0.1
51)
(-0.1
08)
Schooling(F
-H)
0.2
93***
0.4
18***
0.2
53***
0.1
54
0.3
66**
0.1
17
(-0.0
13)
(-0.0
16)
(-0.0
16)
(-0.1
42)
(-0.1
29)
(-0.1
56)
Inflate
Dgeo
H,F
1.2
41***
0.5
96*
1.7
15***
(-0.1
70)
(-0.2
79)
(-0.2
79)
Dcult
H,F
4.5
29***
5.7
75***
3.9
03***
(-0.2
89)
(-0.4
37)
(-0.5
14)
Population
H-0
.000***
-0.0
00***
-0.0
00***
(0.0
00)
(0.0
00)
(0.0
00)
Population
F-0
.000***
-0.0
00***
-0.0
00***
(0.0
00)
(0.0
00)
(0.0
00)
Schooling(F
-H)
0.2
24***
0.6
55***
0.1
71***
(-0.0
27)
(-0.0
56)
(-0.0
48)
N218526
198660
19866
51667
46970
4697
r2p
--
-0.3
18
0.3
27
0.4
57
Est
imationmethod:
Zero
Inflate
dB
inom
ial
Regre
ssio
ns
inm
odels
(1)
to(3
);P
ois
son
pse
udo-m
axim
um
likelihood
est
imato
r(P
PM
L)
inm
odels
(4)
to(6
);
Dependentvariable:
tota
lnum
ber
of
M&
Adeals
from
Hto
Fin
models
(1)
to(3
)to
tal
valu
eof
M&
Adeals
from
Hto
Fin
models
(1)
to(3
)
All
regre
ssors
inm
odels
(4)
to(6
)are
inlo
gs.
t-st
ati
stic
sin
pare
nth
ese
s.*p<
0.0
5,
**p<
0.0
1,
***p<
0.0
01.
10
C Linguistic and religious distance: comparison with available mea-
sures
As explained in Section 3, our bilateral measure of cultural distance is an index obtained as a weighted average
of three distance measures: genetic, linguistic, and religious distances. While genetic distance is from Spolaore
and Wacziarg (2009), linguistic and religious distances are our own computation based on information drawn from
Ethnologue and the CIA World Factbook, respectively.
The linguistic distance matrix is fully derived by applying Eq. (1) to information drawn from the phylogenetic
linguistic tree provided by ‘Ethnologue: Languages of the World.’ The phylogenetic tree is a diagram reflecting the
tree model of language origination. The first level of the tree consists of a certain number of language families.4 A
family is a monophyletic unit in which all members derive from a common ancestor; all attested descendants of that
ancestor are included in the family. Language families can be divided into smaller phylogenetic units (‘branches’).
The position of each language in the tree is identified by a code from which a common number of branches can
be identified. The maximum number of branches in the Ethnologue tree is 15. As an example, since English and
Standard German share tree branches (3.5.2.1.1 is the code for English and 3.5.2.3.1.1.1.1 is the code for Standard
German), their distance, according to Eq. (1), amounts to 1−(
315
)0.5.
The international distribution of languages is obtained from Ethnologue.
The final index of linguistic distance is calculated based on 6855 languages distributed across 195 countries. The
index is available, together with the other measures of distance, on http://docenti.unich.it/delgatto/delgatto_
web/research.htm. A replication package with all the data and STATA codes can also be downloaded.
Recently, two linguistic distance measures, based on Fearon’s procedure and using the Ethnologue data, which are
thereby comparable with ours, have been made available for download. The first is used in Melitz and Toubal (2014) and is
distributed through the CEPII website (variable LP1). The second is described by Spolaore and Wacziarg (2016) and can
be downloaded from the authors’ website.
For the three measures, Table A3 reports the summary statistics obtained after averaging the values by country (for
each country, we consider the average bilateral distance to the other countries).
Spolaore and Wacziarg (2016) have information on 156 countries and for 7 of them, the linguistic distance is always 1
(the maximum value). Compared to our measure, the distribution looks more skewed toward high values.
As for the measure developed by Melitz and Toubal (2014), a key difference with respect to ours is that their measure
subsumes the differences between languages in four possible cases: 0 for two languages belonging to separate family trees,
0.25 for two languages belonging to different branches of the same family tree (English and French), 0.50 for two languages
belonging to the same branch (English and German), and 0.75 for two languages belonging to the same sub-branch (German
and Dutch). Consequently, the resulting index is less articulated than ours: in 35 of the 195 countries under consideration
(the index takes a value of zero whenever two languages belong to different families), the index of linguistic proximity takes
a value of zero (i.e., the maximum value) and the overall variability is lower than ours.
Our index covers 195 countries and there are no countries for which bilateral distances are always maximum or minimum.
A comprehensive list of the countries covered by the three datasets is reported in Table 20.
4Ethnologue identifies 141 different language families (i.e., top-level genetic groups). Six of these (namely, Afro-Asiatic, Austronesian,Indo-European, Niger-Congo, Sino-Tibetan, and Trans-New Guinea), each of which has at least 5% of the speakers of the world’slanguages, stand out as the major language families of the world. Together, they account for nearly two-thirds of all languages andfive-sixths of the world’s population.
