Abstract
Using a firm-level dataset of more than 500,000 domestic and cross-border mergers and acquisitions (M&As) for 95 countries during the period 1995–2015, we measure the level and evolution of the border effect on M&As. We find that the number and value of M&As between same-country firms are five times larger than between firms of different countries. We show that the border effect on the number and value of M&As remained constant during the period. The border effect on M&As in the European Union is substantially lower than elsewhere. We find that the border effect is lower for large investors and firms operating in the primary industry and utilities.
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Notes
According to Cadestin et al. (2018), relying on the OECD’s Activity of Multinational Enterprises (AMNE) and the Trade in Value Added (TiVA) databases, multinationals are responsible for 50% of global exports.
For instance, OECD (2020) offers a FDI regulatory restrictiveness index for 69 countries for 1997, 2003, 2006; and the period 2010–2018.
Calculated by the authors based on the available data on greenfield investment and M&As in UNCTAD (2019).
UNCTAD (2019) only refers to those M&As whose value was higher than $50 million. The share over total cross-border M&As is calculated based on all M&As transactions recorded in the Eikon Thomson Reuters database.
Notice that the acquirer firm \(\times \) year fixed effect also controls for the source country’s time-variant characteristics.
The countries included in the sample are reported in Table 10 in Appendix. For the firm-level analysis, we exclude any transaction for which the acquirer firm is unknown.
Nevertheless, our results are robust to removing from the sample all transactions where a tax haven was involved.
As shown later, results are robust to removing the observations with imputed data.
According to Park and Gould (2017), there are six M&A waves. Our data capture those between 1993 and 2000, and 2003–2008. The previous M&As waves occurred during the years 1885–1905; 1924–1928; 1961–1969, and 1981–1989.
For example, the $110 billion merger between Anheuser-Busch InBev and SABMiller represented 37% of the total M&As into the UK in 2016. Carril-Caccia and Pavlova (2018) reported that 215 over 21,000 M&A projects explained 55% of the FDI value in 2016.
We filled the zero M&A operations in the dataset using Stata’s _fillin command. We estimate the model with Stata’s ppmlhdfe command (Correia et al. 2019).
In order of importance these countries are the USA, the UK, Japan, Canada, Germany, France, China, Italy, Australia, Netherlands, Brazil, Spain, Switzerland, Russia, Hong Kong, Sweden, Korea, Belgium, Luxembourg, Norway, Mexico, Singapore, India, Malaysia, South Africa, Ireland, Denmark, Finland, Turkey, and Taiwan.
Our dataset does not provide information on the usual proxies for firm size, such as number of employees or revenue. Therefore, we proxy the size of an acquirer firm with the value of the M&A investment. We sort all M&A operations according to value and create a dummy variable that turns one if the acquirer firm has performed any of the operations located in the top tenth percentile. To compare the value of transactions over time, we transform current values into constant values using the US GDP deflator.
Intra-industry operations account for 68% and 73% of the total number and value of M&As, respectively.
For an overview of the literature, see Patala et al. (2021).
We use Eq. (2) to estimate the border coefficients based on country-level data. Due to limitations in computing capacity, we cannot estimate all the coefficients included in this specification when using firm-level data. To overcome this limitation, we estimate a separate regression for each year (20 regressions in total). Every regression is estimated pooling data from the analyzed year and the base year (1995).
The EU15 is: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the UK.
These estimates are smaller than reported by Umber et al. (2014) using EU regional data for the period 1991–2007.
According to the GESTHA, their lists are more complete than those elaborated by the OECD or the European Union, which are criticized for omitting some countries from the list due to political reasons.
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We gratefully acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities (RTI2018-100899-B-I00, co-financed with FEDER), the Basque Government Department of Education, Language Policy and Culture (IT885-16), Junta de Andalucía (SEJ 340), and Generalitat Valenciana (GV/2020/012).
Appendix
Appendix
See Tables 10, 11, 12, 13, 14, and 15.
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Carril-Caccia, F., Garmendia-Lazcano, A. & Minondo, A. The border effect on mergers and acquisitions. Empir Econ 62, 1267–1292 (2022). https://doi.org/10.1007/s00181-021-02050-2
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DOI: https://doi.org/10.1007/s00181-021-02050-2