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Crisis and contract breach: The domestic and international determinants of expropriation

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Abstract

In this paper we address how external factors shape government decisions to break or uphold contracts, specifically focusing on how economic shocks and support from multilateral financial institutions shape leader decisions to expropriate from investors. Contrary to conventional wisdom and much of the existing scholarship, we argue that governments are less likely to expropriate from investors during periods of economic crisis since governments become more sensitive to the reputational costs of expropriating. We also argue that governments are sensitive to the levers other governments may use to punish for expropriation, such as withholding IMF and World Bank funding. We test these theories using a dataset of investment expropriations and case studies of thirty-four investment disputes that were resolved pre-claim. Our econometric analysis suggests that expropriations of foreign investment are less common during periods of crisis, and that countries under IMF agreements or borrowing from the World Bank are less likely to expropriate. Our thirty-four case studies, which substantiate the role of government reputation and multilateral pressure, support our statistical results.

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Notes

  1. For an overview see Jensen (2006).

  2. One strategy is to engage in joint ventures with local firms. See Henisz (2000).

  3. For an overview see Jensen et al. (2012).

  4. For an overview see Rudra (2008).

  5. Jensen (2003) and Li and Resnick (2003), for example, examine the impact of democratic institutions on FDI flows. While Jensen (2003) argues that democratic institutions increase FDI flows, Li and Resnick (2003) show that this increase is through democracies having a stronger rule of law. Other aspects of democracies, for example stronger anti-monopoly laws, reduce FDI flows.

  6. Kerner (2009) provides an excellent overview and empirical test of these signaling and constraining aspects of BITs. See Guzman (2006) on the diffusion of BITs. See Yackee (2008) and Allee and Peinhardt (2010) on the variation in dispute mechanisms across BITs. See UNCTAD (1998), Neumayer and Spess (2005), Salacuse and Sullivan (2005), and Kerner (2009), and Lee and Johnston (2016) for published studies of the impact of BITs on FDI.

  7. More recently, Tomz and Wright (2010) model both sovereign bond investment and FDI to examine governments’ incentives to expropriate/default foreign investment. Consistent with Cole and English (1991), they argue that governments are more likely to expropriate during good economic times if they are risk neutral, and during bad times if they are risk averse. On the other hand, leaders are more likely to default during bad economic times, independent of their risk acceptance level.

  8. If, for example, a government wishes to privatize after a crisis, they may be less likely to jeopardize future investment by expropriating.

  9. “Washington Ruling Makes NAIA 3 Use Illegal,” Inquirer.net 2011.

  10. Business scholars discuss numerous ways that avoiding expropriation can create financial market benefits. They find, for example, that protections against expropriations are associated with more investments (per investment opportunities) (Wurgler 2000), an expansion of the financial market (La et al. 2002), more valuable stock markets (La et al. 1997), and listed firms that are larger (Kumar et al. 1999), more numerous (La et al. 1999), more valuable (Claessens et al. 2002; La et al. 2002), pay greater dividend payments (La et al. 2000), and have lower private benefits of control (Zingales 1994; Nenova 1999). See Shleifer and Wolfenzon (2002) for a more comprehensive review. Given the capital scarcity during a crisis, expropriations may trigger multiple types of broader financial market costs.

  11. According to de Paoli et al. (2006), there are two costs of default: reduced access to future finance and output loss (because domestic firms are also unable to borrow). They argue that banking crises and currency crises exacerbate the output loss during default because domestic banks cannot function as intermediaries and provide credit as before and currency crises increase governments’ fixed debt. They call this output loss broader financial costs.

  12. 12 For interested readers, in the Online Appendix we formalize this logic with a game theoretical model. The model makes three assumptions: 1) that revenue is more valuable in a crisis than in normal times, 2) that there is more uncertainty in the expected expropriation payoff than simply keeping the investment as is, and 3) that a government may face a further drop if additional market costs accompany a crisis-time expropriation. As stated on page 9 of the theoretical Appendix , the comparative statics analysis finds that the incidence of expropriation will decrease during a crisis due to: long- and short-term costs (e.g. from currency and bond markets), the chance of spillover effects, the prospect of future investment loss, and the possibility (even remote) of a larger downturn.

  13. Wellhausen (2015b) also notes that home governments do not always follow through these threats because high FDI national diversity in the host country reduces the home government’s diplomatic leverage.

  14. 14 For interested readers, see the Online Appendix for a game theoretic derivation of this hypothesis.

  15. A list of these countries can be seen in the Online Appendix .

  16. Another often used database on financial crises is the Reinhart-Rogoff financial crises data. We choose the Laeven and Valencia data because the Reinhart-Rogoff data cover only 70 countries, including OECD countries which are not our focus.

  17. Thanks to an anonymous reviewer for this excellent suggestion. We also try numerous ways of calculating moving averages, including averaging from 3 to 10 years and using exponential weights, and the results remain largely unchanged.

  18. First, we operationalize crisis as a dichotomous indicator of financial crises. Second, as an alternative to a one-year lag, we test all models using a two-year lag. We also try another commonly used measure of economic crisis—an indicator of negative economic growth. The results remain robust when different measures or lags of crises are used.

  19. Here we only use the original crisis variable, and not the moving average for simplicity.

  20. For instance, in Ethiopia 1975 alone, there were 25 expropriation cases. To reduce the potential bias driven by these extreme cases, we also use a dichotomous measure indicating whether there was at least one expropriation event, and perform a logit model. The results remain unchanged, which can be seen in the supplemental files.

  21. These four arrangements are IMF Standby Arrangement, IMF Extended Fund Facility Arrangement, IMF Structural Adjustment Facility Arrangement, and IMF Poverty Reduction and Growth Facility Arrangement.

  22. We add a value of one to this variable before taking logs to avoid negative values.

  23. It is well-known that unconditional MLE fixed-effects models may have the incidental parameter problem. Allison and Waterman (2002), however, show that negative binomial models with fixed effects (by including dummy variables) do not suffer from this bias. When we use a fixed-effects Poisson model (which is free from the incidental parameter problem) and correct the standard errors, the results remain unchanged.

  24. To make the interpretation easier, we rescale the moving average for the crisis variable such that the value equals 1 when there is one crisis in the previous year (and none in the seven years before last year). This is simply done by multiplying the moving average by 4.5.

  25. An alternative explanation is that countries that are more open to the global market may have more opportunities to expropriate foreign assets. This explanation, however, seems less plausible than the “opportunistic” explanation because long-term factors related to host-country property rights and trade policies, such as FDI and country fixed effects, are already controlled for in the model.

  26. In an earlier version of this paper, we also test the interactive effect between financial crisis and IMF support, but do not find any significant impact of their interaction.

  27. An earlier version of this paper included foreign aid as an independent variable to test the effect of another form of foreign support. Aid, however, is not a robust predictor of expropriation, probably because foreign aid is a more complex foreign policy tool that involves the donor country’s strategic considerations. Also, the collinearity of aid with other control variables and the inconsistent results across specifications suggest caution is required in interpreting these results.

  28. See Terza et al. (2008) and Wooldridge (2014) for a detailed discussion of the CF approach.

  29. The items included in the assets and liabilities categories change throughout this period. If the term of the borrowings was not defined, we took one tenth of the given amount to consider only short-term borrowings. We did the same for any charges accrued on debt. Starting in 1991 IDA did not provide a liabilities category. We construct it from payables and undistributed loans from statement of development resources tables. Dreher et al. (2017) use a similar strategy to create instruments for WB loans. They suggest using separate measurements for IBRD and IDA aid resources. In our main analysis we use a combined measurement of IBRD and IDA loans, and therefore we prefer to measure them similarly and create an aggregated WB measurement of aid resources.

  30. We did not include country and year fixed effects in the first stage logit model due to the possible incidental parameter problem (Greene 2004).

  31. The alternatives for count data are Poisson and negative binomial models. Wooldridge (1999), however, argues that the Poisson model is too restrictive and the negative binomial model can be complicated and may produce inconsistent results if some aspects of the distribution is misspecified. He argues that the quasi-Poisson model is the more robust. We use the quasi-Poisson model in the CF analysis, in particular, because it provided more consistent results across 1000 bootstrapped samples.

  32. All models, except for the logit model, include country fixed effects. The full table is presented in the Online Appendix .

  33. We corrected the standard errors using 1,000 bootstrap samples.

  34. The results of the logit model also offer strong support for the main hypothesis. We choose to report the results of the negative binomial model because the count variable offers more information than the binary variable.

  35. Interviews were facilitated by Daniel Villar, Former Lead Risk Management Specialist at MIGA and currently Principal Economist and Credit Risk Head at the World Bank. All interviews were conducted via phone in April and May 2013.

  36. We discuss these two cases later in this section.

  37. The election of Correa in Ecuador led to general calls for public ownership of water utilities. Although there was criticism of this operation by some local stakeholders, MIGA staff indicated that this dispute was less about the performance of the contract and more of an ideological commitment to public ownership in this sector.

  38. AES eventually agreed to renegotiate the power generation contract, but the highly favorable terms of the distribution contract are a potential future expropriation risk.

  39. The major issue with the cotton gin was that the company negotiated a contract that guaranteed minimum payments. Unfortunately, few farmers grew cotton (presumably growing opium instead) and the gin was largely unused. The government resisted providing payments to a company that was not producing cotton. Details on the project can be found here: http://www.miga.org/projects/index.cfm?pid=?661.

  40. Interviews with MIGA suggest that disputes in China and Kazakhstan are examples of this.

  41. Interviews with MIGA suggest Benin, Egypt, Kyrgyzstan, and Moldova fit this pattern.

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Authors and Affiliations

  1. University of Texas at Austin, Austin, TX, USA

    Nathan M. Jensen

  2. University of California at Riverside, Riverside, CA, USA

    Noel P. Johnston

  3. Nanyang Technological University, Singapore, Singapore

    Chia-yi Lee

  4. Bursa, Turkey

    Hadi Sahin

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  1. Nathan M. Jensen
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Jensen, N.M., Johnston, N.P., Lee, Cy. et al. Crisis and contract breach: The domestic and international determinants of expropriation. Rev Int Organ 15, 869–898 (2020). https://doi.org/10.1007/s11558-019-09363-z

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