Abstract
This paper empirically investigates the economic and political factors that affect a country’s likelihood to sign an arrangement with the IMF and the determinants of the financial size of such a program. Arguably the world and the global financial architecture underwent structural changes after the ending of Cold War and so did the role of the IMF. Hence, we update and extend the work of Sturm et al. (Economics and Politics 17: 177–213, 2005) by employing a panel model for 165 countries that focuses on the post-Cold War era, i.e., 1990–2009. Our results, based on extreme bounds analysis, suggest that some economic and political variables are robustly related to these two dimensions of IMF program decisions. Furthermore, we show that it is important to distinguish between concessional and non-concessional IMF loans.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
It is worth noting that our conclusions about the robustness of determinants of IMF participation are conditional on the empirical models chosen. While our extreme bounds analyses are based on estimators that are commonly used in the literature, it is beyond the scope of this paper to test for the robustness of these results to for instance other estimators or functional forms. We leave this for future research.
Beyond these two common definitions of IMF participation, Vreeland (2003) proposes to distinguish the initiation of an IMF participation spell from the continuation of such a spell.
As an alternative we also experimented with total reserves scaled by total external debt. Both variables are highly correlated. To circumvent multicollinearity problems, we do not include both variables, but opt for RESIMP because it increases our sample. However, the qualitative conclusions are not affected by this choice.
We have also experimented with total debt services scaled by gross national income. Given its high correlation with DEBTSERVEXP, we do not include this variable in our analysis. The qualitative conclusions are, however, not affected by this.
As alternative we have also used the current account as percentage of GDP. The trade balance is available for a larger set of countries and is highly correlated with the current account balance. We opt for the trade balance in the results presented. The qualitative results are, however, not affected by this.
The KOF globalization index can be downloaded from http://www.kof.ethz.ch/globalisation (cf. Dreher 2006b).
Note that the inflation rate is highly correlated with the lending interest rate in a particular country (ρ = 0.77). For that reason, we opt to keep country-specific interest rate variables out of the analysis. To reduce the influence of potentially outlying observations this variable is rescaled using the formula \( {{ x } \left/ {{\left( {1 + x } \right)}} \right.} \). For instance, Dreher et al. (2010) use this data transformation.
Trudel (2005) finds no direct effect of a fixed exchange rate on the likelihood of entering into an IMF program, but documents an indirect effect by showing that the dwindling of international reserves only in combination with a fixed exchange rate increases IMF participation.
On the homepage of Menzie Chinn (http://web.pdx.edu/~ito/Chinn-Ito_website.htm), an updated version of the data set of Chinn and Ito (2006) is available.
To cope with multicollinearity, for four groups of political variables, we only incorporate the first principal component or averages in the EBA. Hence, we implicitly cover 24 political variables in our analyses.
Given the conceptual similarity and to circumvent multicollinearity problems we use principal components analysis here. This is a statistical technique used for data reduction. The leading eigenvectors from the eigen decomposition of the correlation matrix of the variables describe a series of uncorrelated linear combinations of the variables that contain most of the variance. The first principal component accounts for as much of the variability in the data as possible and is therefore taken to represent political instability.
Dreher and Gassebner (2008) provide evidence that IMF and World Bank involvement increases the likelihood of a government crisis.
We have also experimented with the index of executive competitiveness. This variable is, however, highly correlated with our democracy measure and therefore omitted from the analysis.
See footnote 7.
For a more sophisticated approach to geopolitical importance, see for instance Reynaud and Vauday (2009).
Another potential variable that proxies for political interest is the bank exposure of the United States or the G5 in a given country as recorded by the Bank of International Settlements (see for instance Oatley and Yackee 2004; Broz and Hawes 2006; Copelovitch 2010). The results from Breen (2010) indicate that the effects of bilateral trade and bank exposure on IMF program participation are very similar.
Sala-i-Martin (1997) proposes using the (integrated) likelihood to construct a weighted CDF(0). However, the varying number of observations in the regressions due to missing observations in some of the variables poses a problem. Sturm and de Haan (2001) show that this goodness of fit measure may not be a good indicator of the probability that a model is the true model, and the weights constructed in this way are not equivariant to linear transformations in the dependent variable. Hence, changing scales result in rather different outcomes and conclusions. We thus restrict our attention to the unweighted version. Furthermore, for technical reasons—in particular our unbalanced panel setup—we are unable to use extensions of this approach, like Bayesian Averaging of Classical Estimates (BACE), as introduced by Sala-i-Martin et al. (2004), or Bayesian Modeling Averaging (BMA).
The only exception is the conditional fixed effects logit model for which we do not report clustered standard errors. Note that the time fixed effects control for variables like the LIBOR (London Interbank Offered Rate) or the number of countries that are under an IMF program in a given year.
Note that the conditional fixed effects logit estimator as proposed by Chamberlain (1980) is a conditional maximum likelihood estimator based on a log density for country i that conditions on the total number of signed IMF programs equal to 1 for a given country i over time. Since it is not possible to condition on those countries that either never signed an IMF program or those that signed one in every single year, these observations are lost. The sum of outcomes varies between 0 and 8 in our sample, whereby 63 countries never signed an IMF program during the sample period.
An alternative measure for past IMF participation is a dummy variable that takes the value of one if a country has been under an IMF arrangement in the past and zero otherwise.
Note that the observations on non-concessional (3) and concessional loans (4) for the conditional fixed effects logit estimator do not add up to the overall number of observations (2), because some countries receive a non-concessional and a concessional loan during our sample period.
South Korea and Turkey have signed the biggest IMF arrangements in our sample period.
References
Andersen, T. B., Harr, T., & Tarp, F. (2006). On US politics and IMF lending. European Economic Review, 50, 1843–1862.
Bal-Gunduz, Y. (2009). Estimating Demand for IMF Financing by Low-Income Countries in Response to Shocks. IMF Working Paper No. 09/263.
Barro, R., & Lee, J.-W. (2005). IMF programs: Who is chosen and what are the effects? Journal of Monetary Economics, 52, 1245–1269.
Biglaiser, G., & DeRouen, K. (2010). The effects of IMF programs on U.S. Foreign direct investment in the developing world. Review of International Organizations, 5, 73–95.
Binder, M., & Blum M. (2010). On the Conditional Effect of IMF Program Participation on Economic Growth. CESifo Working Paper No. 3161.
Bird, G. (1995). IMF Lending to Developing Countries. Issues and Evidence. Routledge, London.
Bird, G. (2007). The IMF: A bird’s eye view of its role and operations. Journal of Economic Surveys, 21, 683–745.
Bird, G., Hussain, M., & Joyce, J. (2004). Many happy returns? Recidivism and the IMF. Journal of International Money and Finance, 23, 231–251.
Bird, G., & Orme, T. (1981). An analysis of drawings on the international monetary fund by developing countries. World Development, 9, 563–568.
Bird, G., & Rowlands, D. (2009a). Exchange rate regimes in developing and emerging economies and the incidence of IMF programs. World Development, 37, 1839–1848.
Bird, G., & Rowlands, D. (2009b). A disaggregated empirical analysis of the determinants of imf arrangements: Does one model fit all? Journal of International Development, 21, 915–931.
Boughton, J. (2004). The IMF and the Force of History: Ten Events and Ten Ideas That Have Shaped the Institution. IMF Working Paper No. 04/75.
Breen, M. (2010). Domestic Interests, International Bargaining, and IMF Lending. Working Paper No. 07/2010, Centre for International Studies, Dublin City University.
Broz, L., & Hawes M. B. (2006). U.S. Domestic Policies and International Monetary Fund Policy. In: Hawkins, D., Lake, D. A., Nielson, D., & Tierney, M. J. (Ed.,) Delegation and Agency in International Organizations. Cambridge University Press.
Cerutti, E. (2007). IMF Drawing Programs: Participation Determinants and Forecasting. IMF Working Paper No. 07/152.
Chamberlain, G. (1980). Analysis of covariance with qualitative data. Review of Economic Studies, 47, 225–238.
Chinn, M., & Ito, H. (2006). What matters for financial development? capital controls, institutions, and interactions. Journal of Development Economics, 61, 163–192.
Copelovitch, M. (2010). Master or servant? Common agency and the political economy of IMF lending. CInternational Studies Quarterly, 54, 49–77.
Conway, P. (2006). The international monetary fund in a time of crisis: A review of stanley fischer’s IMF essays from a time of crisis: The international financial system. stabilization, and development. Journal of Economic Literature, 44, 115–144.
Conway, P. (2007). The revolving door: Duration and recidivism in IMF programs. Review of Economics and Statistics, 89, 205–220.
Cornelius, P. (1987). The demand for IMF credits by sub-Saharan African countries. Economics Letters, 23, 99–102.
Dreher, A. (2006a). IMF and economic growth: The effects of programs. loans, and compliance with conditionality. World Development, 34, 769–788.
Dreher, A. (2006b). Does globalization affect growth? Evidence from a new Index of globalization. Applied Economics, 38, 1091–1110.
Dreher, A., & Gassebner M. (2008). Do IMF and World Bank Programs Induce Government Crises? An Empirical Analysis. KOF Working Papers No. 200.
Dreher, A., Sturm, J.-E., & de Haan, J. (2010). When a central bank governor replaced? Evidence based on a new data set. Journal of Macroeconomics, 32, 766–781.
Dreher, A., Sturm, J.-E., & Vreeland, J. (2009). Global horse Trading: IMF loans for votes in the United Nations. European Economic Review, 53, 742–757.
Dreher, A., & Vaubel, R. (2004). Do IMF and IBRD cause moral hazard and political business cycles? Evidence from panel data. Open Economies Review, 15, 5–22.
Eichengreen, B., Gupta P., & Mody A. (2006). Sudden Stops and IMF-supported Programs. NBER Working Paper No. 12235.
Elekdag, S. (2008). How does the global economic environment influence the demand for IMF resources? IMF Staff Papers, 55, 624–653.
Frankel, J., & Rose, A. (1996). Currency crashes in emerging markets: An empirical treatment. Journal of International Economics, 41, 351–366.
Harrigan, J., Wang, C., & El-Said, H. (2006). The economic and political determinants of imf and world bank lending in the Middle East and North Africa. World Development, 34, 247–270.
Joyce, J. (2005). Time past and time present: A duration analysis of IMF program spells. Review of International Economics, 13, 283–297.
Knight, M., & Santaella, J. A. (1997). Economic determinants of IMF financial arrangements. Journal of Development Economics, 54, 405–436.
Laeven, L., & Valencia F. (2008). Systematic Banking Crises: A New Database. IMF Working Paper No. 08/224.
Leamer, E. (1983). Let’s take the con out of econometrics. American Economic Review, 73, 31–43.
Levine, R., & Renelt, D. (1992). A sensitivity analysis of cross-country growth regressions. American Economic Review, 82, 942–963.
Oatley, T., & Yackee, J. (2004). American interests and IMF lending. International Politics, 41, 415–429.
Nooruddin, I., & Simmons, J. W. (2006). The politics of hard choices: IMF programs and government spending. International Organization, 60, 1001–1033.
Pop-Eleches, G. (2009). Public goods or political pandering: Evidence from IMF programs in Latin America and Eastern Europe. International Studies Quarterly, 53, 787–816.
Przeworski, A., & Vreeland, J. R. (2000). The effect of IMF programs on economic growth. Journal of Development Economics, 62, 385–421.
Reynaud, J., & Vauday, J. (2009). Geopolitics and international organizations: An empirical study on IMF facilities. Journal of Development Economics, 89, 139–162.
Sala-i-Martin, X. (1997). I just ran two millions regressions. American Economic Review, 87, 178–183.
Sala-i-Martin, X., Doppelhofer, G. and Miller, R. I. (2004). Determinants of long-term growth - a Bayesian Averaging of Classical Estimates (BACE) Approach. American Economic Review, 94, 813–835
Steinwand, M., & Stone, R. (2008). The fund: A review of the recent evidence. Review of International Organization, 3, 123–149.
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.
Sturm, J.-E., & de Haan J. (2001). How Robust is Sala-i-Martin’s Robustness Analysis. University of Groningen, mimeo.
Temple, J. (2000). Growth regressions and what the textbooks don’t tell you. Bulletin of Economic Research, 52(3), 181–205.
Thacker, S. (1999). The high politics of IMF lending. World Politics, 52, 38–75.
Trudel, R. (2005). Effects of exchange rate regime on IMF program participation. Review of Policy Research, 22, 919–936.
Veiga, F. J. (2005). Does IMF support accelerate inflation stabilization? Open Economies Review, 16, 321–340.
Vreeland, J. R. (2003). The IMF and Economic Development. New York: Cambridge University Press.
Acknowledgements
We would like to thank two anonymous referees, Axel Dreher, Martin Gassebner, Michael Lamla, and James Vreeland for fruitful discussions and participants of the conference on “The Political Economy of International Financial Institutions” in Tübingen (10–13 June 2010) for helpful comments. All remaining errors are ours.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix A: Summary of studies on the determinants of IMF program participation since 2005
Study | Type of model | Definition of dependent variable | Economic variables included | Effect | Political variables included | Effect |
|---|---|---|---|---|---|---|
Sturm et al. (2005) | Extreme Bounds Analysis on Probit model for IMF programs; 118 countries; 1971–2000 using yearly data. | Binary variable of one, if a country signs an IMF agreement in a given year. | International reserves | − | Past IMF program (5 years) | + |
GDP growth (real, p.c.) | − | Number of countries currently under IMF program | 0 | |||
Debt service to exports | + | Election year executive | 0 | |||
Current account to GDP | 0 | Election year legislative | 0 | |||
External debt to GDP | 0 | Election year executive (lagged) | − | |||
GDP per capita | 0 | Election year legislative (lagged) | − | |||
Inflation (in logarithm) | 0 | Election year executive (lead) | 0 | |||
Nominal exchange rate growth | 0 | Election year legislative (lead) | 0 | |||
Budget deficit to GDP | 0 | Assassinations | 0 | |||
Terms of trade growth rate | 0 | Revolutions | 0 | |||
Investment to GDP | − | Guerrilla problems | 0 | |||
LIBOR | 0 | Government crisis | 0 | |||
Government expenditure to GDP | 0 | Government instability | − | |||
Demonstrations | 0 | |||||
Strikes | 0 | |||||
Riots | 0 | |||||
Competitiveness, index | 0 | |||||
Bank exposure (U.S.) | 0 | |||||
Intensity of trade (U.S.) | 0 | |||||
Asia dummy variable | 0 | |||||
OECD dummy variable | 0 | |||||
Africa dummy variable | 0 | |||||
Liberal, index | 0 | |||||
Corruption | 0 | |||||
Rule of law | 0 | |||||
Repudiation | 0 | |||||
Quality of bureaucracy | 0 | |||||
Relative size | 0 | |||||
IMF quota | 0 | |||||
Ethnic fractionalization | − | |||||
Special interests | 0 | |||||
Political cohesion | 0 | |||||
Barro and Lee (2005) | Probit model for short-term IMF programs (SBA and EFF); 130 countries; 1975–1999 using five-year intervals. | Binary variable of one, if a new IMF loan program approved in any year of the 5-year period | GDP growth (p.c.) | − | IMF quota (in logarithm) | 0 |
International reserves | − | IMF staff (in logarithm) | + | |||
GDP per capita | + | Political proximity (U.S.) | + | |||
GDP per capita squared | − | Political proximity (EU) | 0 | |||
GDP (in logarithm) | + | Intensity of trade (U.S.) | + | |||
GDP (in logarithm) squared | − | Intensity of trade (EU) | 0 | |||
OECD dummy | 0 | |||||
Veiga (2005) | Logit model for IMF programs (SBA, EFF, SAF, ESAF, PRGF); 10 countries with high inflation; 1957–1999 using quarterly data. | Binary variable of one, if a country participates in an IMF arrangement in the present or previous quarter of a current inflation spell. | Amount drawn to amount agreed | 0 | IMF program (lagged) | 0 |
Disbursements to reserves | 0 | Fragmentation of political system (lower level dummies) | + | |||
Inflation (in logarithm) | + | |||||
Fiscal balance to GDP | 0 | |||||
GDP growth (real, yoy) | + | |||||
International reserves | 0 | |||||
Andersen et al. (2006) | Logit model for short-term IMF programs (SBA and EFF); 102 countries; 1995–2000 using yearly data. | Binary variable of one, if a new IMF program is signed in a given year. | GNP per capita | 0 | Political proximity to U.S. (key votes in UN General | + |
Balance of payments | − | Bliss point | − | |||
Δ Balance of payments | − | |||||
BoP per capita | 0 | |||||
Δ BoP per capita | 0 | |||||
Current account | 0 | |||||
Δ Current account | + | |||||
Current account to GNP | 0 | |||||
Δ Current account to GNP | − | |||||
Debt | + | |||||
Δ Debt | 0 | |||||
Debt to GNP | − | |||||
Δ Debt to GNP | 0 | |||||
Debt per capita | 0 | |||||
Δ Debt per capita | 0 | |||||
Interest payments to GNP | + | |||||
Δ Interest payments to GNP | 0 | |||||
International reserves to debt | 0 | |||||
Δ International reserves to debt | 0 | |||||
Asian crisis dummy | − | |||||
Dreher (2006a) | OLS model for short-term IMF programs (SBA and EFF); 98 countries; 1970–2000 using five-year intervals. | 5-year average of yearly dummies that take the value of one if there has been an SBA or EFF facility drawn in a given year for at least 5 months. | Short-term debt to total debt | − | Democracy, index | − |
Total debt service to GDP | + | |||||
Nooruddin and Simmons (2006) | Linear Probability model for IMF programs (SBA, EFF, SAF, ESAF); 130 countries; 1980–2000 using yearly data. | Binary variable of one, if the country participates in an IMF program (Vreeland 2003). | GDP growth | − | Past IMF program | + |
Current account to GDP | − | Democracy, index | 0 | |||
Budget balance to GDP | 0 | |||||
GDP per capita | − | |||||
Eichengreen et al. (2006) | Probit model for IMF programs; 24 emerging countries with significant access to international capital markets; 1980–2003 using yearly data. | Binary variable of one for the first two years of a new IMF program. | GDP growth (real) | + | Political proximity to U.S. (UN General Assembly voting) | − |
Trade Balance to GDP | + | Bank exposure (U.S.) | 0 | |||
Debt servicing to exports | + | Exports (G3) | 0 | |||
Domestic credit to GDP | − | |||||
Δ Domestic credit to GDP | − | |||||
Foreign debt to GDP | + | |||||
Δ Foreign debt to GDP | 0 | |||||
International reserves | 0 | |||||
Short-term debt to reserves | − | |||||
Fixed exchange rate | + | |||||
Limited flexible exchange rate | 0 | |||||
Harrigan et al. (2006) | Probit model for IMF programs (SAF, ESAF and PRGF); 11 Middle East and North Africa (MENA) countries; 1975–2000 using yearly data. | Binary variable of one, if country signs an IMF agreement in a given year. | GDP per capita | 0 | Democracy | - |
GDP growth | 0 | Election year legislative | 0 | |||
Current account to GDP | 0 | Election year legislative (past) | 0 | |||
Debt service to exports | + | Election year legislative (future) | + | |||
Short-term debt to total debt | − | |||||
Δ Net reserves to GDP | 0 | Peace treaty with Israel | + | |||
Cerutti (2007) | Probit model for short-term IMF programs (SBA and EFF); 59 countries (non-PRGF-eligible developing countries); 1982–2005 using quarterly data. | Binary variable of one in the first quarter of a new IMF “drawing” program. | Net international reserves | − | Past IMF program (2 years) | + |
Current account | 0 | |||||
GDP growth (real) | − | |||||
Inflation | + | |||||
Real exchange rate | 0 | |||||
Government deficit | 0 | |||||
IMF quota | 0 | |||||
IMF liquidity | 0 | |||||
LIBOR (real) | + | |||||
World GDP growth (real) | − | |||||
Elekdag (2008) | Panel Probit model (random effects) for short-term IMF programs (SBA); 169 countries; 1970–2004 using yearly data. | Binary variable of one, if SBA facility is approved in a given year. | Oil price (real) | + | + | |
U.S. Short-term interest rate (real) | + | |||||
World GDP (real, dev. trend) | 0 | |||||
GDP growth (real) | − | |||||
International reserves | − | |||||
Exchange rate depreciation | − | |||||
Bal Gündüz (2009) | Panel Probit model (random effects) for IMF programs (SBA, SAF/ESAF/PRGF augmentations and CFF); 55 low-income countries; 1980–2004 using yearly data | Binary variable of one, if a new IMF program is approved. | Current account to GDP | − | Debt restructuring (Paris Club) | |
International reserves | − | |||||
Exchange rate depreciation | + | |||||
Government balance to GDP | 0 | |||||
GDP growth (real) | − | |||||
Δ Terms-of-Trade | − | |||||
Oil price (change, 2 years) | + | |||||
World trade (cyclical) | − | |||||
Bird and Rowlands (2009a) | Probit model for IMF programs (SBA and SAF/ESAF/PRGF); emerging and developing countries; 1974–2000 using yearly data. | Binary variable of one, if a new IMF program is signed in a given year. | GDP growth | - | Debt rescheduling | − |
Current account to GDP | 0 | Future debt rescheduling | + | |||
Δ International reserves | − | Past IMF program (2 years) | + | |||
Real depreciation (3 years) | 0 | |||||
Debt service to exports | + | |||||
Capital account restrictions | 0 | |||||
Fixed exchange rate | + | |||||
Freely floating exchange rate | 0 | |||||
Freely falling exchange rate | + | |||||
Bird and Rowlands (2009b) | Probit model for IMF programs (SBA, EFF, SAF, ESAF/PRGF); 88 low and middle-income countries; 1977–2000 using yearly data. | Binary variable of one, if a new IMF program is signed in a given year. | GNP per capita | 0 | Past IMF program (2 years) | + |
GDP growth | − | |||||
International reserves | 0 | |||||
Δ International reserves | 0 | |||||
Current account to GDP | 0 | |||||
Δ Current account to GDP | 0 | |||||
Real exchange rate depreciation | 0 | |||||
Debt service to exports | 0 | |||||
Δ Debt service to exports | 0 | |||||
Public external debt to GDP | 0 | |||||
Debt rescheduling | 0 | |||||
Past debt rescheduling (2 years) | 0 | |||||
Inflation | 0 | |||||
Fixed exchange rate | 0 | |||||
Flexible exchange rate | 0 | |||||
Market pressure index | + | |||||
Pop-Eleches (2009) | Panel Logit model (random effects) for IMF programs; 47 countries (three subsamples: Latin America 1982–1989; Latin America 1990–2001; Eastern Europe 1990–2001); 1982–2001 using quarterly data. | Binary variable of one, if a new IMF program is signed in a given quarter. | Foreign debt | 0/+/0 | Government orientation | -/0/0 |
International reserves | −/0/− | Quality of bureaucracy | 0/0/+ | |||
Interest payments to GNI | +/0/0 | Political regime | 0/0/0 | |||
Inflation | +/0/+ | Past IMF program (fraction last 5 year) | +/0/0 | |||
GDP per capita | 0/0/0 | |||||
Dreher et al. (2009) | Logit model for IMF programs (SBA, EFF, SAF, ESAF/PRGF); 197 countries; 1951–2004 using yearly data. | Binary variable of one, if a country participates in an IMF program during part of a given year. | GDP per capita (real) | − | UN Security Council membership (temporary) | + |
Investment to GDP | − | Past IMF program | ||||
Debt service to GDP | + | Checks and Balances | + | |||
Budget surplus to GDP | + | |||||
Copelovitch (2010) | Logit model for nonconcessional IMF programs (SBA, EFF and SRF); 47 countries; 1984–2003 using yearly data. | Binary variable of one, if a country receives an IMF loan in a given year. | GDP (in logarithm) | 0 | Bank exposure (G5) | 0 |
GDP per capita (in logarithm) | 0 | Variation bank exposure (G5) | 0 | |||
GDP growth | − | Bank exposure (U.S.) | 0 | |||
Current account to GDP | − | Bank exposure (UK) | 0 | |||
External debt to GDP | 0 | Bank exposure (Japan) | 0 | |||
Debt service to exports | 0 | Bank exposure (Germany) | 0 | |||
Short-term debt to reserves (in logarithm) | + | Bank exposure (France) | 0 | |||
Currency crisis dummy | 0 | S-score (G5) | 0 | |||
Number of currency crises | + | Variation S-score (G5) | 0 | |||
LIBOR | 0 | Years since last IMF loan | 0 | |||
Veto players (in logarithm) | 0 | |||||
IMF liquidity ratio | 0 | |||||
IMF quota review | 0 | |||||
Biglaiser and De Rouen (2010) | Selection equation of a treatment effects regression model for IMF programs (four subgroups: All, SBA, EFF and PRGF); 126 developing countries; 1980–2003 using yearly data. | Binary variable of one, of a country participates in an IMF program. | Inflation (in logarithm) | 0/-/0/+ | ||
GDP per capita (in logarithm) | −/+/0/+ | |||||
International reserves | −/−/0/0 | |||||
Budget balance | 0/0/0/0 | |||||
Breen (2010) | Probit model for IMF programs (SBA, EFF, SAF/ESAF/PRGF); 159 countries; 1983–2006 using yearly data. | Binary variable of one, if a new IMF program is approved in a given year. | International reserves | − | Bank exposure (G5) (in logarithm) | + |
Current account to GDP | 0 | IMF quota review | 0 | |||
External debt to GDP | 0 | IMF delegation index | 0 | |||
Debt service to GDP | 0 | U.S. military aid | 0 | |||
GDP growth | − | System transition (exclusion restriction) | + | |||
GDP per capita (in logarithm) | 0 | |||||
Financial crisis | + |
Appendix B: Summary of studies on the determinants of IMF loan size
Study | Type of model | Definition of dependent variable | Economic variables included | Effect | Political variables included | Effect |
|---|---|---|---|---|---|---|
Bird and Orme (1981) | OLS model for IMF programs; 31 countries in the year 1976. | Amount of IMF loan (in SDR) for a country in a given year. | Current account to trade | + | ||
Debt service ratio | 0 | |||||
Inflation | + | |||||
GNP per capita | − | |||||
Imports | + | |||||
International reserves | 0 | |||||
Eurocurrency credits to imports | + | |||||
Cornelius (1987) | OLS models for IMF programs; 11 sub-Saharan African countries; two subsamples: 1975–1977 and 1981–1983 using yearly data. | Amount of IMF loan (in SDR) for a country in a given year. | Current account | 0/0 | ||
Debt service ratio | +/+ | |||||
Inflation | 0/0 | |||||
GNP per capita | −/− | |||||
Imports | +/+ | |||||
International Reserves | −/0 | |||||
Foreign borrowing | −/0 | |||||
Oatley and Yackee (2004) | OLS model for IMF programs (SBA and EFF; only for positive values of loan size); 61 countries; 986–1998 using yearly data. | Amount of IMF loan (in SDR) in a given year. | GNP | + | Bank exposure (U.S.) | + |
Foreign debt | 0 | Bank exposure (Britain) | 0 | |||
Current account | 0 | Bank exposure (Japan) | 0 | |||
Debt service | 0 | Political regime | ||||
Dummy stand-by arrangement | − | Political proximity to US (UN GA voting) | + | |||
Military aid (U.S.) | + | |||||
Dummy Cold War | + | |||||
Cold War * UN vote | − | |||||
Barro and Lee (2005) | Tobit models for short-term IMF programs (SBA and EFF); 130 countries; 1975–1999 using five-year intervals. | Average IMF loan program to GDP over 5-year period. | GDP growth (p.c.) | − | IMF quota (in logarithm) | + |
International reserves | − | IMF staff (in logarithm) | + | |||
GDP per capita | + | Political proximity (U.S.) | 0 | |||
GDP per capita squared | − | Political proximity (EU) | + | |||
GDP (in logarithm) | + | Intensity of trade (U.S.) | + | |||
GDP (in logarithm) squared | − | Intensity of trade (EU) | 0 | |||
OECD dummy | 0 | |||||
Dreher (2006a) | OLS model for short-term IMF programs (SBA and EFF); 98 countries; 1970–2000 using five-year intervals. | 5-year average of disbursed loans to GDP. | LIBOR | + | Political stability | + |
Government special interest | − | |||||
Rule of law | + | |||||
Pop-Eleches (2009) | OLS model for IMF programs (only for positive values of loan size); 47 countries (three subsamples: Latin America 1982–1989; Latin America 1990–2001; Eastern Europe 1990–2001); 1982–2001 using quarterly data. | Amount of an IMF loan relative to a country’s IMF quota (annualized). | Foreign debt | +/+/+ | Government orientation | 0/+/0 |
International reserves | 0/0/− | Quality of bureaucracy | 0/−/0 | |||
Interest payments to GNI | 0/−/0 | Political regime | 0/0/+ | |||
Inflation | −/0/+ | Past IMF program (fraction last 5 year) | 0/0/0 | |||
GDP per capita | 0/0/0 | |||||
Reynaud and Vauday (2009) | Tobit models for IMF programs (SBA and EFF; PRGF); 107 developing and emerging countries; 1990–2003 using yearly data. | Amount of a new IMF loan relative to a country’s IMF quota. | GDP growth (real)—Geopolitical factors | + | ||
GDP per capita (in logarithm) | + | |||||
International reserves | − | |||||
Total debt service to exports | + | |||||
Copelovitch (2010) | OLS model for nonconcessional IMF programs (SBA, EFF and SRF); 47 countries; 1984–2003 using yearly data. | Amount of a new IMF loan relative to a country’s IMF quota. | GDP (in logarithm) | + | Bank exposure (G5) | 0 |
GDP per capita (in logarithm) | − | Variation bank exposure (G5) | − | |||
GDP growth | 0 | Bank exposure (U.S.) | + | |||
Current account to GDP | 0 | Bank exposure (UK) | 0 | |||
External debt to GDP | 0 | Bank exposure (Japan) | 0 | |||
Debt service to exports | + | Bank exposure (Germany) | + | |||
Short-term debt to reserves (in logarithm) | 0 | Bank exposure (France) | + | |||
Currency crisis dummy S-score (G5) | + | |||||
Number of currency crises | + | Variation S-score (G5) | 0 | |||
LIBOR | 0 | Past IMF program | − | |||
0 | Veto players (in logarithm) | 0 | ||||
IMF liquidity ratio | 0 | |||||
IMF quota review | 0 | |||||
Propensity score | 0 | |||||
Breen (2010) | Heckman selection model for IMF programs (SBA, EFF, SAF/ESAF/PRGF); 159 countries; 1983–2006 using yearly data. | Amount of IMF loan relative to a country’s IMF quota. | International reserves | − | Bank exposure (G5) (in logarithm) | + |
Current account to GDP | 0 | IMF quota review | 0 | |||
External debt to GDP | 0 | IMF delegation index | 0 | |||
Debt service to GDP | 0 | U.S. military aid | 0 | |||
GDP growth | − | |||||
GDP per capita (in logarithm) | 0 | |||||
Financial crisis | + | |||||
Binder and Bluhm (2010) | Panel Tobit model (random effects augmented with Mundlak variables) for IMF programs (SBA, EFF, SAF, ESAF/PRGF); 68 countries; 1975–2005 using yearly data. | Amount of (all) IMF loan(s) relative to a country’s IMF quota. | Investment to GDP | − | Democracy, index | − |
Inflation | − | Years under IMF program | + | |||
International reserves | 0 | Fertility rate (mean) | + | |||
Government share to GDP | 0 | Intensity of trade (Europe) (mean) | − | |||
Current account to GDP | 0 | |||||
Openness | 0 |
Appendix C: Summary statistics
Variable | Obs. | Mean | Std. Dev. | Min. | Max. |
|---|---|---|---|---|---|
Dependent variables | |||||
Signing (all) | 3700 | 0.1122 | 0.3156 | 0.0000 | 1.0000 |
Signing (non-conc.) | 3700 | 0.0641 | 0.2449 | 0.0000 | 1.0000 |
Signing (conc.) | 3700 | 0.0481 | 0.2140 | 0.0000 | 1.0000 |
Size (all) | 3700 | 15.7624 | 97.3621 | 0.0000 | 3182.841 |
Size (non-conc.) | 3522 | 11.8763 | 96.9263 | 0.0000 | 3182.841 |
Size (conc.) | 3463 | 4.7626 | 25.9396 | 0.0000 | 700.0000 |
Base Model | |||||
underimf5ma | 3700 | 0.1161 | 0.1707 | 0.0000 | 1.0000 |
resimp1 | 2849 | 3.6050 | 3.1507 | 0.0022 | 43.6915 |
grgdp1 | 3498 | 3.5953 | 6.6509 | −51.0309 | 106.2798 |
lgdppc1 | 3443 | 7.5489 | 1.5646 | 4.1309 | 10.9365 |
Further economic variables | |||||
invgdp1 | 3184 | 21.9191 | 8.6505 | −23.7626 | 113.5779 |
debtservexp1 | 1984 | 15.1844 | 13.6285 | 0.0226 | 152.2670 |
xdebtgni1 | 2240 | 79.4170 | 89.6522 | 0.1437 | 1209.3030 |
xbalgdp1 | 3325 | −6.5101 | 16.9839 | −135.6007 | 53.6586 |
globec1 | 2479 | 55.8736 | 17.9050 | 10.4721 | 98.7163 |
totadjgdp1 | 2736 | 0.3818 | 9.2123 | −57.2151 | 148.1556 |
infl1sc | 3046 | 9.9287 | 15.1371 | −16.0704 | 99.5920 |
defgdp1 | 1336 | 1.5565 | 7.8145 | −40.4263 | 203.7191 |
stdebtxdebt1 | 2188 | 12.5903 | 12.4755 | 0.0000 | 85.0791 |
peg | 2816 | 0.3817 | 0.4859 | 0.0000 | 1.0000 |
curcris | 3275 | 0.0577 | 0.2332 | 0.0000 | 1.0000 |
finopen | 2950 | 0.2471 | 1.5673 | −1.8081 | 2.5408 |
Further political variables | |||||
leadexelec | 3190 | 0.1028 | 0.3038 | 0.0000 | 1.0000 |
lagexelec | 3313 | 0.1035 | 0.3047 | 0.0000 | 1.0000 |
leadlegelec | 3189 | 0.2114 | 0.4083 | 0.0000 | 1.0000 |
laglegelec | 3312 | 0.2147 | 0.4107 | 0.0000 | 1.0000 |
polinstab | 1701 | 0.0032 | 1.3632 | −0.6774 | 13.4223 |
socunrest1 | 2393 | −0.0481 | 1.1322 | −0.4916 | 16.4803 |
liberal | 3413 | 3.5387 | 1.9791 | 1.0000 | 7.0000 |
globpol | 3098 | 56.2411 | 23.2876 | 1.5588 | 98.7830 |
icrg | 2478 | 0.5588 | 0.2200 | 0.0417 | 1.0000 |
unsc | 3156 | 0.0567 | 0.2313 | 0.0000 | 1.0000 |
relsize1 | 3520 | 0.5572 | 2.4466 | 0.0001 | 31.7090 |
tradeus | 2994 | 0.0923 | 0.1186 | 0.1186 | 1.5142 |
voteinlineusa | 3377 | 0.3317 | 0.1629 | 0.0000 | 1.0000 |
Rights and permissions
About this article
Cite this article
Moser, C., Sturm, JE. Explaining IMF lending decisions after the Cold War. Rev Int Organ 6, 307–340 (2011). https://doi.org/10.1007/s11558-011-9120-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11558-011-9120-y