FDI promotion through bilateral investment treaties: more than a bit?


Policy makers in developing countries have increasingly pinned their hopes on bilateral investment treaties (BITs) in order to improve their chances in the worldwide competition for foreign direct investment (FDI). However, the effectiveness of BITs in inducing higher FDI inflows is still open to debate. It is in several ways that we attempt to clarify the inconclusive empirical findings of earlier studies. We cover a much larger sample of host and source countries by drawing on an extensive data set on bilateral FDI flows. Furthermore, we account for unilateral FDI liberalization, in order not to overestimate the effect of BITs, as well as for the potential endogeneity of BITs. Employing a gravity-type model and various model specifications, including an instrumental variable approach, we find that BITs do promote FDI flows to developing countries. BITs may even substitute for weak domestic institutions, though probably not for unilateral capital account liberalization.

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  1. 1.

    These authors use OECD data on product market regulations in OECD countries as well as the World Bank’s Doing Business database.

  2. 2.

    This is even though it is sometimes discussed whether BITs may substitute for weak local (political and economic) conditions; see below.

  3. 3.

    Note, however, that “another—relatively small—category of BITs imposes a higher degree of discipline on the contracting parties” (UNCTAD 2007, p. 155). We return to this issue in Sect. 4 and discuss the resulting limitations of the dummy variable on BITs used here and in previous literature.

  4. 4.

    As discussed in more detail in Sect. 4, this argument leads us to consider the share of host country j in total FDI flows from source country i to be our preferred FDI measure when specifying the empirical model.

  5. 5.

    The short review of previous empirical literature is restricted to studies that focus on the effects of BITs on FDI flows to developing countries, where this issue appears to be most relevant. Some other studies concentrate on FDI relations within the OECD, or between OECD countries and a small number of East and Central European transition countries; see Egger and Merlo (2007) for a recent example. Arguably, these studies offer limited insights for policy makers in developing countries. They exclude “the very set of poor to lower middle-income and small to medium-sized developing countries, for which the conclusion of a DTT (or BIT, for that matter) can be an important instrument to woo foreign investors” (Neumayer 2007, p. 1506). See also Sect. 4 below.

  6. 6.

    Blonigen and Davies (2005) use bilateral FDI data to evaluate the effects of double taxation treaties.

  7. 7.

    Similar to Tobin and Rose-Ackerman (2005), Hallward-Driemeier (2003, p. 22) concludes: “A BIT has not acted as a substitute for broader domestic reform.” Note, however, that none of the three studies employs FDI-specific regulations as a control variable which with the BITs variable is interacted, as we do in the following.

  8. 8.

    See Sect. 4 on how we deal with endogeneity.

  9. 9.

    By contrast, Tobin and Rose-Ackerman (2005) do not find that US FDI is directed to host countries that concluded BITs with the United States. Gallagher and Birch (2006) focus on Latin America. They show that the total number of BITs had a positive effect on aggregate FDI flows to South America, whereas having a BIT with the United States did not attract US FDI.

  10. 10.

    The number of observations varies considerably depending on the specification of the model, i.e., the use of alternative indicators on information frictions.

  11. 11.

    Major developing source countries include Brazil, China, Hong Kong, Rep. of Korea, Singapore, and Taiwan.

  12. 12.

    We would like to thank Hiro Ito for providing access to these data. See Sect. 4 for a short discussion of alternative indicators of unilateral capital account liberalization.

  13. 13.

    Blonigen et al. (2007, 1309) note that the gravity model “is arguably the most widely used empirical specification of FDI”.

  14. 14.

    As noted by Shatz (2003, p. 118), “national statistical agencies publish bilateral data about the investment activities of their multinationals only for host countries that have sizeable inflows of FDI. This means that nearly all research on foreign direct investment focuses on the winners, countries that have achieved at least some success in attracting FDI. This is a significant problem since policy advice is most often sought by the countries that are excluded from analysis”.

  15. 15.

    We are particularly grateful to an anonymous referee for alerting us to this point.

  16. 16.

    The fixed-effect approach might result in less efficient coefficient estimates, compared to using the explicit multilateral resistance terms. Yet, Feenstra (2004, p. 161–162) considers the fixed-effect approach to be the preferred empirical method due to its computational simplicity.

  17. 17.

    See also Egger (2000, p. 29) who argued in a panel setting of trade flows that “the proper econometric specification of the gravity equation in most applications would be one of fixed country and time effects”.

  18. 18.

    In contrast to Baier and Bergstrand (2007), multilateral resistance terms are assumed to be “slow moving” by Bergstrand and Egger (2007, p. 284) so that pair fixed effects are supposed to capture also “the (most important) cross-sectional influence of these terms”.

  19. 19.

    The results hardly change if we exclude negative values.

  20. 20.

    See Table 6 in Appendix 1 for exact definitions and data sources for all variables.

  21. 21.

    Descriptive statistics can be found in Table 7 in Appendix 1.

  22. 22.

    See Quinn and Toyoda (2008) for a comparison of different measures on capital account openness.

  23. 23.

    We would like to thank Dennis Quinn for providing access to these data, derived from the coding of de jure measures published in the IMF’s Annual Report on Exchange Arrangements and Restrictions. The scoring takes into account the severity of restrictions in various categories of financial transactions.

  24. 24.

    The sample of Quinn and Toyoda includes just 18 countries with a per capita income of less than US$ 875 (2005), compared to 47 low-income countries for which the Chinn–Ito index is available. Moreover, the Quinn–Toyoda data set covers just four annual observations for the period under consideration here, with 1997 being the most recent observation for most of the country sample. Hence, it is also with respect to the time dimension that the Chinn–Ito index is to be preferred for the present purpose.

  25. 25.

    A few countries signed BITs but never ratified them; for example, Brazil was signatory of 14 non-ratified BITs as of 1 June 2008. Any impact of the signed BITs is thus questionable.

  26. 26.

    As argued by Neumayer (2007) with respect to DTTs, this would require an enormous effort; various provisions may be next to impossible to quantify.

  27. 27.

    Hallward-Driemeier (2003) applies the number of BITs a host country has concluded with third countries as an instrument for the BITs concluded between particular pairs. This instrumentation is awkward if Neumayer and Spess (2005) are right in that BITs concluded with a particular source country have signaling effects and may, thus, be correlated with FDI from other sources, too. Tobin and Rose-Ackerman (2005) use a time variable and the level of democracy in the host country as instruments. The reason given for this instrumentation is that, observing that more and more countries conclude BITs, a particular host country may feel the need to join this trend in order not to be left out. However, this argument rather suggests employing the number of BITs concluded by other host countries, and in particular by neighboring host countries, as an instrument for pair-wise BITs concluded by the particular host country under consideration.

  28. 28.

    The FDI data for financial offshore centers are highly likely to be biased. We exclude all countries that are on the list of offshore financial centers as reported by Eurostat (2005).

  29. 29.

    See Appendices 2 and 3 for the source and host country sample.

  30. 30.

    The sample declines by 330 observations if CapOpen is included (Model II).

  31. 31.

    This point is stressed by Egger and Merlo (2007) in the context of BITs and FDI.

  32. 32.

    Overall, our sample consists of 14,077 observations and 2,313 country pairs, that is, more than four times as many country pairs as used by Hallward-Driemeier (2003), who employed 537 pairs.

  33. 33.

    Note the increase in the size of the coefficient for BIT from Models I and II to Model III. This is mainly due to the fact that we add the interaction term. To get the net impact of a ratification of a BIT, we would have to take the estimated coefficient for the interaction term into account too. The overall impact in this specification (and all other specifications in the following) is always positive and significant, which has been confirmed by an appropriate F-test.

  34. 34.

    Note that DTT turns out to be positive and highly significant when excluding the BIT variable in the regressions with FDI2. Obviously, a large number of countries ratified both BITs and DTTs more or less at the same time, thereby making it difficult to sort out the net impact of both variables on FDI flows.

  35. 35.

    We also employed a Tobit model to examine the robustness of the results. Importantly, the BIT variable remains positive and highly significant.

  36. 36.

    Again, DTT would be highly significant if we excluded the BIT variable.

  37. 37.

    Neumayer and Spess (2005) use several indicators for institutional quality and also find that the interaction terms are not always significant.

  38. 38.

    For the interaction terms, we obtain the same outcome as in the fixed-effects estimation, that is, a negative coefficient for PolCon × BIT and CapOpen × BIT, though only the former is statistically significant.

  39. 39.

    All GMM robustness checks reported in this section have also been performed for the OLS and PPML models as well as for FDI2. As the sign and significance levels of the coefficients are quite similar, we do not report them. Like all other non-reported results, they can be obtained from the first author upon request.

  40. 40.

    For reference, we show previous GMM estimates for the full sample in the first row of Table 4.

  41. 41.

    We classify a country as resource-intensive if its resource rents, that is, energy plus mineral depletion in percent of GNI, are higher than 15% in the first three-year period (1978–1980). See the notes below Table 4 for all resource-intensive countries that have been excluded in this set of regressions.

  42. 42.

    By contrast, Neumayer (2007) finds that double taxation treaties were effective in increasing FDI flows only to middle-income host countries.

  43. 43.

    In the context of tax treaties, Blonigen and Davies (2005) refer to concerns that such treaties arise due to lobbying efforts by profit-seeking investors. They conclude that treaties may then be geared towards maximizing investor profits rather than promoting FDI.

  44. 44.

    To save space, we continue to only report the results with zero observations for FDI flows included.


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We would like to thank Mariana Spatareanu and an anonymous referee for helpful comments and suggestions. Hiro Ito and Dennis Quinn generously shared their data on capital account restrictions with us. Signe Nelgen, Michaela Rank, and Wendy Soh provided excellent research assistance.

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Correspondence to Peter Nunnenkamp.


Appendix 1

See Tables 6 and 7.

Table 6 Definition of variables and data sources
Table 7 Descriptive statistics for the main variables

Appendix 2


Appendix 3


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Busse, M., Königer, J. & Nunnenkamp, P. FDI promotion through bilateral investment treaties: more than a bit?. Rev World Econ 146, 147–177 (2010). https://doi.org/10.1007/s10290-009-0046-x

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  • FDI
  • Multinational corporations
  • Bilateral investment treaties

JEL Classification

  • C33
  • F21
  • F23