Abstract
Conditional lending by the IMF is predicated, in part, on the belief that IMF programs are associated with increased capital inflows to participating countries. This belief is generally consistent with theoretical arguments in the academic literature (e.g., Bird and Rowlands 1997; Bordo et al. 2004) but the empirical literature often finds otherwise (e.g., Jensen 2004). This paper argues that the effect of IMF agreements on a country’s access to foreign direct investment (FDI) depends on its domestic institutions. Access to FDI depends on a country’s ability to credibly commit to implementation, and this ability varies systematically across regime type. The theory is empirically tested using a treatment effects model with a Markov transition in the treatment equation in a dataset covering 142 countries from 1976 to 2006. We find that in democracies IMF program participation has a strong positive effect on FDI inflows and in autocracies participation has a weak negative effect.
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Notes
Throughout the paper we refer to the IMF as “lending” and to countries as “borrowing,” however, technically the IMF does not “lend” money and countries do not “borrow” money. Officially, the countries can make purchases by exchanging their currency for the equivalent of another members’ currency or Special Drawing Rights (SDR), and then the country repurchases its own currency over time. The IMF places charges on these purchases and thus the purchase and repurchase is equivalent to making a loan with interest.
The model we use is a treatment effects model with a built in first order Markov process in the treatment equation. Throughout the rest of the paper we refer to the model as a “treatment effects Markov transition model.”
For example, according to the former Managing Director of the IMF, the Fund’s “prime objective is growth…there is no longer any ambiguity about this. It is towards growth that our programs and their conditionality are aimed” (Polak 1991, 19).
In a review of IMF compliance Vreeland (2006) discusses how consensus has emerged that IMF agreements tend to have unfavorable macroeconomic effects, but it is unclear if this is the result of poor policy choices or policy compliance. We believe that it is a result of lack of compliance and not poor policy prescriptions, though the empirics here are difficult to untangle (see, for example, Mercer-Blackman and Unigovskaya 2004). Although some attempts have been made at disentangling implementation and participation (e.g., Arpac et al. 2008), we argue these attempts have been modest at best. Including a measure of implementation in the econometric analysis limits the dataset, and all of the measures of implementation are indirect and tangential to the concept.
There are anecdotal examples of countries requiring approval from other branches of government. For example, Brazil needs approval of the Parliament to get a new loan, but not to continue or rollover an existing one (http://www.brettonwoodsproject.org/art-27514). However, examples such as these are exceptions rather than the rule.
This argument is not necessarily incompatible with that made by Fang and Owen (2011), who argue that non-democratic states have a greater need to employ IMF agreements as a means to make their promises of reform more credible. It is possible both for participation in an IMF agreement to be the only option available to autocracies to make their commitments to reform more credible, and for the effectiveness of this device to be weak.
Pooling across different types of IMF agreements, as is standard in the literature, is potentially problematic, as conditionality varies by agreement type. Empirical differentiation by agreement type could potentially produce further insight into the causal mechanism behind the catalytic affect of IMF participation. However, this differentiation is beyond the scope of this paper.
All independent variables are lagged 1 year unless otherwise noted. The dependent variables are measured in time t; so to obviate simultaneity, all the independent variables are measured at time t−1.
The net measure of investment varies for each model, depending on which specification of the dependent variable is used.
We do not include time dummies because WorldFDI captures the relevant time trends. However, the results are robust to the inclusion of time dummies. The Hausman test and the test statistic are significant, indicating that it is necessary to include country fixed effects.
Beck and Katz suggest that LSDV is strictly preferable to alternative estimators when T is greater than 20, and that it likely remains preferable for values of T somewhat less than 20 as well (2004, p. 15). Our substantive results do not require direct interpretation of the coefficient on the lagged dependent variable, and results are robust to omission of the country fixed effects.
The descriptive statistics for both the Democracy and LIEC measures can be found in the Appendix.
Calculating the Durbin Watson statistic indicated the presence of correlated errors, and that Beck and Katz’s suggestions apply to the data used in this analysis.
If there is reverse causality between an independent variable and a dependent variable, and the dependent variable is determined simultaneously with at least one of the regressors, endogeneity bias may also be a problem. For endogeneity bias, the dependent variable is observed for all observations of the data. In contrast, sample selection bias arises when the dependent variable is observed only for a restricted, nonrandom sample of observations.
For a thorough discussion of this problem see Steinwand and Stone (2008). However, while most of the recent literature addresses selection into IMF agreements, it does so without addressing the dynamic nature of IMF lending, which the selection equation by itself does not correct for. For example, see Abouharb and Cingranelli (2009).
Including a lagged dependent variable corrects for serial correlation, but also soaks up much of the variation. This is well known in the literature (see Achen 2000). Running the models without the lagged dependent variable should produce results with asymptotically correct coefficients, but with larger statistical significance.
Recidivism is common among participants in IMF arrangements. Countries which participate in an IMF agreement in time t−1 are far more likely to be under an agreement in time t than countries not under an agreement in time t−1.
As Ai and Norton (2003) point out, the magnitude of the interaction effect in non-linear models does not equal the marginal effect of the interaction term. In the Appendix, we plot both the Stata-estimated (incorrect) marginal effect and the correct interaction effect for the interaction of LIEC and Under.
The full tables are displayed in the Appendix.
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Appendix
Appendix
1.1 Estimation of Interaction Effects
The following plots give both the simple probit-estimated (incorrect) marginal effect and the correct interaction effect for the interaction between LIEC and Under (Ai and Norton 2003). Despite the large differences in the estimated effects, the z-scores from both estimation techniques are nearly identical across all three models. We omit plots of the z-scores from this appendix, but these can be created from the replication do file available at the Review of International Organization’s webpage.
Pooled
Democracies only
Autocracies only
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Bauer, M.E., Cruz, C. & Graham, B.A.T. Democracies only: When do IMF agreements serve as a seal of approval?. Rev Int Organ 7, 33–58 (2012). https://doi.org/10.1007/s11558-011-9122-9
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DOI: https://doi.org/10.1007/s11558-011-9122-9