Geographical and cultural patterns in cross-border mergers and acquisitions: the role of experience

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 mergers and acquisitions
• cultural distance
• contiguity
• experience
• knowledge

JEL:

• F23
• D23
• L22
• R12

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 For the full ($195$ × $195$) matrix of bilateral distance measures (i.e., linguistic, religious and genetic distance, as well as the overall measure of cultural distance), along with a replication package including original data and STATA files, see http://docenti.unich.it/delgatto/delgatto_web/research.htm.

2 The crisis hit the various economies differently, even turning into a double-dip recession in some cases (e.g., in several European economies). Although this heterogeneity might be controlled for in the estimates, by keeping the post-2008 years out of the analysis, we aim to focus on a phase characterized by a clear-cut positive trend in international M&A and FDI flows associated with increasing trade freeness, thereby providing with quite general results.

3 See Desmet et al. (2009, p. 1301) for an explanation of the meaning and estimation of $\alpha$.

4 We thank Roman Wacziarg and James Fearon for providing the tree.

5 For the complete replication package for the cultural index, see http://docenti.unich.it/delgatto/delgatto_web/research.htm.

6 Because 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, for example. Apart for these cases, the number of culturally contiguous countries identified ranges from one (GR) to four (AU, CA, FN, SW, UK and US). For the United States, for instance, the culturally contiguous countries are, in order, AU, UK, NZ and IR; and for the UK, they are AU, NZ, IR and US.

7 Geodesic distance is calculated following the great circle formula, which uses the latitudes and longitudes of the most important cities/agglomerations in terms of population.

8 Using the CEPII variables notation, the ULC is calculated as $\left(\mathrm{wage}/\mathrm{va}/\mathrm{lab}\right)$, where $\mathrm{wage}$ is the wage per employee; $\mathrm{va}$ is the value added; and $\mathrm{lab}$ is the number of employees. The variables are expressed in nominal dollars.

9 The final ranking considers the political risk (associated with internal policy and international relationships), economic risk (economic conditions, public accounts, inflation, current account, balance of payments and exchange rate), financial risk (bank structure and financial stability), and operative risk (legal system, attitude towards foreign investors, infrastructure and natural conditions) (http://www.sace.it/).

10 The windows are identified by considering the quartiles of the M&A distribution. The observations in the control sample are chosen by randomly drawing from each group several observations equal to the number of M&As in the group, so as to obtain two samples of identical size. The last step is repeated $1000$ times in order to obtain confidence intervals for the t-test statistics. The t-test statistic takes the form:

${\displaystyle \frac{z_s-z_c}{\sqrt[]{\left[z_s\left(1-z_s\right)+z_c\left(1-z_c\right)\right]/Z}}\mathrm{under}{H}_0:z_s=z_c{H}_1:z_s>z_c,}$ (3)

where Z is the total number of observations; $z$ is the frequency of M&As; and subscripts $s$ and $c$ are used to highlight whether$z$ is observed in the original or control sample. Only the deals by firms with at least one previous M&A are considered in each period.

11 Fixed effects panel logit regressions have also been run for consistency; the results are broadly in line with those shown in Table 4.

12 A straightforward interpretation of the positive effect of geographical distance on M&As in culturally contiguous countries, and the positive effect documented for ULC and profit tax rates, might lie in the colonial heritage of the major acquiring countries (the US and UK).