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Dyadic military conflict, security alliances, and bilateral FDI flows

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Abstract

Although multinationals operate under cross-border jurisdictions, the relevance of interstate security relations to international business has received little attention. Despite the impressive accumulation of knowledge in international business and international relations, the two intellectual communities have largely ignored the insights from each other. In this article, we seek to bridge this gap. We argue that interstate military conflict and security alliances, as two central features of interstate security relations, often change both government policies toward international business and investor expectations of political risk. From the perspectives of both states and investors, military conflict should reduce bilateral investment whereas security alliances increase it. Our empirical analysis applies the system GMM estimator to a gravity model of bilateral investment flows for 1117 directed dyads among 58 countries from 1980 to 2000. Among 18 countries whose per capita real incomes remain consistently above 12,000 constant dollars, the security factors do not affect bilateral investment; in the high-income/low-income dyads, interstate military conflict and security alliances significantly influence bilateral investment as expected. The findings depict two separate realms in which international politics does and does not interfere with international business, helping us improve political risk assessments and understand the interactions between states and firms.

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Notes

  1. For a recent review of the research on the relationship between military conflict and FDI, see Li (2008).

  2. Almost all countries enact at times export control laws and capital control restrictions for national security reasons.

  3. As a recent example, in 2005 the US Congress prevented the acquisition by CNOOC (a Chinese state-owned enterprise) of Unocal (a US oil company) for national security reasons. Further, we note that this point is complemented by, but does not require the help of, the dependency theory. The latter argues that the advanced countries in the core use MNEs as a vehicle to increase the reliance of the developing countries in the periphery on foreign capital, perpetuate their low status in the world system, and undermine their national security and autonomy (e.g., Choucri & North, 1995; Moran, 1978).

  4. Such sentiments and behaviors are popular and easily observed in business news during episodes of interstate conflicts.

  5. Examples are abundant for the involvement in the US defense industry of foreign producers from Israel, Japan, and other US military allies.

  6. The failed CNOOC acquisition of Unocal in 2005 is a perfect counter-example. Had China been a formal security ally of the US, the acquisition might very well have been approved.

  7. The effects of defense pacts on international monetary cooperation and bilateral trade are stronger than those of other types of alliance agreements. See Li (2003) and Long (2003).

  8. We stop at year 2000 owing to data availability on our key independent variables.

  9. We employ the procedure constructed by Neumayer and Plümper (2010). And we lag the spatial autoregressive term to control for possible endogeneity. There may also be other forms of spatial dependence. For example, a shock in one home country affects investment outflows irrespective of the destination, or the investment climate in one host affects investments from many sources at once. First-differencing in the system GMM removes such dependence if it is time invariant and specific to either the host or home country. We leave the study of other time-varying type dependence for future research.

  10. For the effects of FDI on conflict, GDP per capita, BIT, and trade, see, for example, Li (2008), Gartzke, Li, and Boehmer (2001), Henisz (2008), Elkins et al. (2006), and Amiti and Wakelin (2003), respectively.

  11. We do not use the specification of the knowledge capital model developed theoretically by Markusen (2002), for two reasons. First, the empirical specification of the model remains controversial (see Blonigen, Davies, & Head, 2003; Carr, Markusen, & Maskus, 2001). Second, the empirical testing in previous studies has been limited to the US FDI flows: thus, for our much larger sample, the demands for data on skill endowments and investment barriers are too difficult to satisfy.

  12. First-differencing introduces regressor–error correlation (e.g., Δy it −1 = (y it −1−y it −2) is a function of ɛ it −1, an element in the first-differenced error term). The GMM estimator addresses this by applying y it −2 or Δy it −2 as an instrument that correlates with Δy it −1, but not ɛ it −ɛ it −1.

  13. These 18 high-income countries are the US, Canada, Germany, Netherlands, Belgium, Luxembourg, France, Switzerland, Austria, Australia, Italy, Sweden, Norway, Denmark, Iceland, Japan, and New Zealand.

  14. Some other scholars replace all negative and zero values with some arbitrary positive value before taking the log of the FDI variable. This practice is problematic, because it conflates net divestment with net investment, and distorts the distance between any replaced observation and any other observation in the dataset. Inferences based on the distorted data are questionable.

  15. In our estimation sample, the smallest negative value is −9,717.291. Thus we add a constant value of 9718 to all observations.

  16. The added constant value cancels out in the change between two values of the transformed dependent variable.

  17. For example, the USA and Venezuela are recorded to have had an MID in 2000. The Venezuelan government accused the US Coast Guard of violating its territorial waters in mid-October. At the end of October, a Venezuelan patrol boat intercepted a US Coast Guard boat. The Venezuelan government declared this was a second US violation of Venezuela's territorial waters. As a result, Venezuela issued a formal protest to the US government. The US government replied that there was no violation by the US Coast Guard, the incident had happened in international waters, and Venezuela responded with a disproportionate military power. For more details, see the Dispute Narratives – MID 3.0 dataset, Correlates of War 2 Project at http://www.correlatesofwar.org/.

  18. For example, during the 1980–2000 period, US had defense pact agreements with Canada, Mexico, Costa Rica, Panama, to name a few.

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Acknowledgements

We thank Lorraine Eden, Edward Mansfield, Pablo Pinto, and the anonymous referees for helpful comments and suggestions. The usual disclaimer applies.

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Authors and Affiliations

  1. Department of Political Science, Texas A&M University, College Station, USA

    Quan Li

  2. Department of Political Science, Pennsylvania State University, State College, USA

    Tatiana Vashchilko

  3. Department of Political Science, University of Rochester, Rochester, USA

    Tatiana Vashchilko

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  1. Quan Li
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Correspondence to Quan Li.

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Accepted by Edward Mansfield, Guest Editor, 27 July 2009. This paper has been with the authors for two revisions.

Appendix

Appendix

DESCRIPTION OF DATA ON BILATERAL INVESTMENT FLOWS

The dataset is based on the balance of payments published by central banks or statistical offices of the corresponding countries (OECD, 2004). It provides statistics on FDI inflows and outflows from 1980 to 2008. Our statistical analysis does not include 2001–2008 because data on military conflict and security alliances are not available for those years. According to OECD (2004: 10), FDI inflows are the direct investments from abroad in OECD countries, and FDI outflows are the direct investments abroad from OECD countries. The dataset covers all flows between OECD countries in both directions and between OECD and non-OECD in both directions, but there is no coverage of any FDI flow between non-OECD countries. There are in total 58 countries, of which 29 are OECD countries and 29 non-OECD ones. The total number of non-OECD countries for which data are available on the OECD website is 34, but we include only 29, excluding Chinese Taipei, Hong Kong, Netherlands Antilles, and treating Czech Republic and Russia as successors of Czechoslovakia and the USSR, respectively. Data for Czechoslovakia before 1992 and for Czech Republic from 1992 are both represented under the name Czech Republic for the whole period; data for the USSR before 1992 and for Russia from 1992 are under the name Russia for the whole period. The 58 countries in the dataset are as follows:

illustration

figure a

The OECD data have by far the most comprehensive coverage of bilateral FDI flows. Still, it has several notable shortcomings. Figure 1 describes clearly the pairs of countries for which FDI flows are covered in the dataset. If we denote countries A and B as two out of 29 OECD countries, and countries C and D as two out of 29 non-OECD countries, then we have data on FDI flows between A and B, A and C, A and D, B and C, as well as B and D. As noted, however, we do not have data on FDI flows between C and D.

Figure 1
figure 1

Scheme of possible dyads or country pairs.

Full size image

Second, if we had data on FDI flows between all possible pairs of countries in our dataset for the 1980–2000 period, we should have 79,344 observations: N × (N−1) × T=58 × 57 × 21=69,426 Excluding non-OECD to non-OECD observations, we should have 52,374 observations. The actual size for the all dyads sample in Table 1 is only 10,401, the result of a large number of missing values.

The final drawback of the data is some possible discrepancies because FDI definitions used in balances of payments of different countries sometimes vary. For example, OECD (2004) gives an example of reinvested earnings that are not included in FDI definition for some countries. This accounts for why FDI outflows from one country (country A) to another (country B) do not always match B's inflows from A.

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Li, Q., Vashchilko, T. Dyadic military conflict, security alliances, and bilateral FDI flows. J Int Bus Stud 41, 765–782 (2010). https://doi.org/10.1057/jibs.2009.91

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