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Determinants of cross-border merger premia

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Abstract

Firms have a broad range of rationales for engaging in cross-border mergers and other forms of foreign direct investment (FDI); while some companies are in search of the cost advantages provided by foreign resources, other firms are primarily interested in gaining access to new markets. Although a significant amount of research has explored the patterns of FDI, little work has been done to assess what influences the value of cross-border mergers and, in particular, what determines why some cross-border mergers are expected to result in higher synergies when compared to others. This paper explores what characteristics of a merger are expected to increase the synergies that a firm will accrue from a cross-border merger by testing how a variety of factors impact the premia paid to effectuate a cross-border merger. We find that firms are willing to pay a higher premium to obtain greater control over foreign firms, and that this control is even more important in mergers involving firms in emerging markets. We also find that the factors affecting deal premia in cross-border mergers differ based on whether the acquirer has a high or low intangible asset intensity level.

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Notes

  1. See Cebenoyan et al. (1992), Hymer (1960), Vernon (1966), Kindleberger (1984), Caves (1982), Buckley and Casson (1976), Magee (1976) and Dunning and Rugman (1985).

  2. A number of other papers have also emphasized the importance of intangible assets in determining the value of a cross-border merger. Vermuelen and Barkema (2001), for example, find in their examination of the mode of entry used for international expansion by Dutch firms that the firms most likely to engage cross-border mergers were those whose goal was to expand the knowledge base of the firm.

  3. See also Coase (1937), Klein et al. (1978), Grossman and Hart (1986) and Williamson (1979).

  4. Moran (2001) finds that the impact of control is minimal in labor-intensive industries with limited intangible asset.

  5. The $250 million transaction value is admittedly somewhat restrictive. For example, if the threshold was lowered to $100 million in deal value, then the data set would consist of 1,130 transactions, while if there were no deal threshold, the data set would consist of 2,154 transactions. Specifications that include only larger mergers (those with deal values above $300 and $500 million) were qualitatively similar to those presented here. However, our results may not hold for relatively small transactions.

  6. We use the World Bank’s definition of developing versus developed regions.

  7. One possible source of mis-measurement in this variable could be deal renegotiation. The data set only includes accepted mergers, but it is possible that a new merger price could occur at a later time. This could occur if issues occur during due diligence that detract or potentially add to the value of the deal. A renegotiated deal value could bias the deal premium up or down. However, we believe it is unlikely that deal renegotiations would have a significant effect on the data set, since the data set consists of consummated mergers.

  8. There are admittedly other firm and deal characteristics that could also be considered, such as the presence of multiple bidders or the method of financing (cash versus stock). These variables were not included due to data limitations.

  9. See Sonenshine (2010).

  10. Measured by the nominal effective exchange rate (the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs, in which 2005 is the base year. This variable was provided by the World Bank’s World Development Indicator. See http://data.worldbank.org/indicator/PX.REX.REER.

  11. The regressions exclude approximately 70 observations with missing explanatory variables, primarily exchange rate variables. Parameter estimates associated with our primary variables of interest were qualitatively the same when we run the regression on the full sample, excluding the exchange rate variables.

  12. This result is consistent with the results in Sonenshine (2010), which analyzed large mergers of U.S. based companies.

  13. As noted earlier, we also analyzed the results from sub-samples consisting of larger deals in order to evaluate how sensitive our results were to the $250 million dollar cutoff. In these samples, the coefficient on the acquirer’s real effective exchange rate was significant while the coefficient for the target’s real effective exchange rate was not. It seems that for larger deals, acquirers are willing to pay higher premia when they have a stronger domestic currency. The results from the larger deal sub-samples are more consistent with results from Harris and Ravenscraft (1991) and Swenson (1993), both of which only looked at deals involving U.S. targets.

  14. We also ran this model by interacting a continuous measure of intangible asset intensity with the key explanatory variables. None of the interaction terms proved to be significant, although the coefficients from a model interacting a dummy variable for high intangible asset firms with our key variables of interest were qualitatively identical to those presented here. This suggests that while ownership is more important to firms with large values of intangible assets, this value of ownership does not increase for marginal increases in intangible asset intensity. The results from these specifications are available from the authors upon request.

  15. The World Bank ranks countries each year based on how conducive the regulatory environment is to starting and operating a local firm. India, Brazil, and Egypt were ranked 132, 126, and 110 in the World Bank’s 2012 Ease of Doing Business ranking. India had the second worst ranking among the countries in our data set; Brazil the fourth worse, and Egypt the seventh worst.

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Authors and Affiliations

  1. Department of Economics, American University, 4400 Massachusetts Avenue, NW, Washington, DC, 20016, USA

    Ralph Sonenshine & Kara Reynolds

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  1. Ralph Sonenshine
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  2. Kara Reynolds
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Correspondence to Ralph Sonenshine.

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Sonenshine, R., Reynolds, K. Determinants of cross-border merger premia. Rev World Econ 150, 173–189 (2014). https://doi.org/10.1007/s10290-013-0164-3

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