Abstract
The dominant approach to studying the effects of IMF programs has emphasized moral hazard, but we find that adverse selection has more impressive effects. We propose a novel strategic selection model to study the growth effects of IMF programs, which allows for the possibility of adverse selection. We find that adverse selection occurs: the countries that are most interested in participating in IMF programs are the least likely to have favorable growth outcomes. Controlling for this selection effect, we find that countries benefit from IMF programs on average in terms of higher growth rates, but that some countries benefit from participation, while others are harmed. Moral hazard predicts that long-term users of Fund resources benefit least from participating in programs, while adverse selection predicts the opposite. Contrary to previous findings, we find that IMF programs have more successful growth performance among long-term users than among short-term users.
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Notes
Przeworski and Vreeland (2000) made an important advance by pointing out that initiating an IMF program requires the consent of two agents, a government and the IMF. This implies that two selection equations are needed to model the process of program approval. Przeworski and Vreeland (2000) and Vreeland (2003) use a bivariate probit model with partial observability to account for these separate decisions, and find that IMF program participation is harmful to growth when correcting for selection effects. We introduce an alternative approach, which also incorporates partial observability, but unlike Przeworski and Vreeland (2000) and Vreeland (2003), our model incorporates strategic interaction.
Strategic interaction, in effect, introduces a series of interaction terms into the government decision equation between variables that affect government utilities and variables that affect IMF utilities.
Moser and Sturm (2011, p. 317) find a different effect for the post-Cold War period. In a pooled analysis, they find a robust relationship between prior participation and continuing participation; however, in a conditional fixed-effects analysis, they find that prior participation reduces the probability of participating. This indicates that results claiming an effect of recidivism were instead capturing the underlying propensity to participate.
The assumption of our econometric model is that there are unobservable variables that affect both government decisions to participate in IMF programs and subsequent growth performance under those programs. It is not necessary to this argument that these variables be unobservable to the IMF. The IMF might, for example, have good intelligence that the government plans to renege on its commitments, but be willing to offer support nevertheless. What is necessary to our argument is only that these variables are not observable to us as analysts, so that their effects can only be estimated, rather than controlled for. However, we argue that some of these variables are in fact unobservable to the IMF, and this accounts for the pattern of adverse selection that we identify below.
Matching techniques rely on the assumption of strong ignorability, which means that any factors that distinguish the treatment and non-treatment groups after matching have no effect on the probability of receiving the treatment. This cannot be the case if there is adverse selection.
In practice, rejection takes the form of insisting on the adoption of performance criteria or prior actions that the borrower is unwilling to fulfill, but in that case the analyst observes only non-participation.
We use the agent error specification of Signorino’s (1999) strategic probit. To make estimated coefficients comparable to the bivariate probit specification that has been used in the literature (Vreeland 2003), one needs to either assume that the stochastic components associated with IMF and Government’s expected utilities have standard errors equal to \(1/\sqrt {2}\), or be aware that the estimated coefficients represent an estimate for the actual coefficients scaled by \(\sqrt {2}\sigma \). This is akin to the problem of unidentified error variance in a probit model, where scholars either assume that \(\sigma =1\) or estimate \(\beta \)s scaled by \(\sigma \)s.
This approach is superior, for example, to assuming that none of the countries that are not participating in programs applied for support, or that all applied but were rejected. Assigning countries to the most likely case takes advantage of the information we have about country choices from the strategic selection model, and allows us to estimate the differences between these two theoretically distinct groups of countries, which would otherwise bias our results.
The Vreeland (2003) data and the extended data begin in 1951, but the estimation sample begins in 1970 because missing data cause the earlier decades to be eliminated by listwise deletion.
References
Aggarwal, V. K. (1996). Debt games: strategic interaction in international debt rescheduling. Cambridge: Cambridge University Press.
Akerlof, G. A. (1970). The market for lemons. Quarterly Journal of Economics, 84(3), 488–500.
Atoian, R., & Patrick, C. (2006). Evaluating the impact of IMF programs: a comparison of matching and instrumental-variable estimators. Review of International Organizations, 1, 99–124.
Bauer, M. E., Cruz, C., Graham, A. T. (2012). Democracies only: when do IMF agreements serve as a seal of approval? Review of International Organizations, 7, 33–58.
Bird, G., & Rowlands, D. (2002). Do IMF programmes have a catalytic effect on other international capital flows? Oxford Development Studies, 30, 229–49.
Bird, G., Hussain, M., Joyce, J. P. (2004). Many happy returns? Recidivism and the IMF. Journal of International Money and Finance, 23, 231–51.
Blustein, P. (2001). The chastening: inside the crisis that rocked the global financial system and humbled the IMF. New York: Public Affairs.
Cameron, C., & Trivedi, P. (2005). Microeconometrics: methods and applications. New York: Cambridge University Press.
Cooper, R. (1971). Currency devaluations in developing countries. Princeton: Princeton University Press.
Copelovitch, M. S. (2010). Banks, bonds, and bailouts: the International Monetary Fund in the global economy.
Eichengreen, B., Gupta, P., Mody, A. (2006). Sudden stops and IMF-supported programs. Cambridge: NBER Working Paper.
Frankel, J. (2005). Contractionary currency crashes in developing countries. NBER Working Paper No. W11508.
Goldstein, M. (1998). The asian financial crisis: causes, cures and systemic implications. Institute for International Economics.
Goldstein, M., & Montiel, P. (1986). Evaluating fund stabilization programs with multi-country data: some methodological pitfalls. IMF Staff Papers, 33, 304–44.
Gould, E. (2006). Money talks: the International Monetary Fund, conditionality, and supplementary financiers. Stanford: Stanford University Press.
Greene, W. H. (2003). Econometric analysis. Upper Saddle River: Prentice Hall.
Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–62.
Hills, C. A., Peterson, P. G., Goldstein, M. (1999). Safeguarding prosperity in a global financial system: the future international financial architecture. Washington, D.C.: Council on Foreign Relations and Institute for International Economics.
Independent Evaluation Office, & International Monetary Fund (IEO) (2002). Evaluation of Prolonged use of IMF resources. Washington, D.C.: International Monetary Fund.
Ivanova, A., Mayer, W., Mourmouras, A., Anayiotos, G. (2003). What determines the implementation of IMF-supported programs? IMF Working Papers 03/8.
Jensen, N. (2004). Crisis, conditions, and capital: the effect of International Monetary Fund agreements on foreign direct investment inflows. Journal of Conflict Resolution, 48, 194–210.
Leblang, D. (2005). Pegs and politics. Working Paper.
Lipson, C. (1985). Bankers’ dilemmas: private cooperation in rescheduling sovereign debts. World Politics, 38, 200–225.
Maddala, G. S. (1983). Limited dependent and qualitative variables in econometrics. New York: Cambridge University Press.
Meltzer, A. H. (2000). Final report. International financial institution advisory commission (IFIAC or Meltzer Commission). http://www.house.gov/jec/imf/meltzer.pdf.
Mody, A., & Saravia, D. (2006). Catalysing private capital flows: do IMF programmes work as commitment devices? The Economic Journal, 116, 843–67.
Moser, C., & Sturm, J.-E. (2011). Explaining IMF lending decisions after the cold war. Review of International Organizations, 6, 307–40.
Pop-Eleches, G. (2009). From economic crisis to reform: IMF programs in Latin America and Eastern Europe. Princeton: Princeton University Press.
Przeworski, A., & Vreeland, J. R. (2000). The effect of IMF programs on economic growth. The Journal of Development Economics, 62, 385–421.
Signorino, C. S. (1999). Strategic interaction and the statistical analysis of international conflict. American Political Science Review, 93(2), 279–97.
Signorino, C. S., & Yilmaz, K. (2003). Strategic misspecification in regression models. American Journal of Political Science, 47(3), 551–66.
Steinwand, M., & Stone, R.W. (2008). The International Monetary Fund: a review of the recent evidence. Review of International Organizations, 3, 123–49.
Stiglitz, J. E. (2002). Globalization and its discontents. New York: Norton.
Stone, R. W. (2002). Lending credibility: the International Monetary Fund and the post-communist transition. Princeton: Princeton University Press.
Stone, R. W. (2008). The scope of IMF conditionality. International Organization, 62, 589–620.
Stone, R. W. (2011). Controlling institutions: international organizations and the global economy. Cambridge: Cambridge University Press.
Sturm, J.-E., Berger, H., de Haan, J. (2005). Which variables explain decisions on IMF credit? An extreme bounds analysis. Economics and Politics, 17, 177–213.
Vreeland, J. R. (2003). The IMF and economic development. New York: Cambridge University Press.
Vreeland, J. R. (2006). IMF program compliance: aggregate index versus policy specific research strategies. Review of International Organizations, 1, 359–78.
Woods, N. (2006). The Globalizers: the IMF, the World Bank, and their borrowers. Cornell University Press.