Abstract
This paper looks at the effects of International Monetary Fund (IMF) lending programs on banking crises in a large sample of developing countries, over the period 1970–2010. The endogeneity of the IMF intervention is addressed by adopting an instrumental variable strategy and a propensity score matching estimator. Controlling for the standard determinants of banking crises, the results indicate that countries participating in IMF-supported lending programs are significantly less likely to experience a future banking crisis than nonborrowing countries. The paper also provides evidence suggesting that compliance with conditionality and loan size matter, corroborating the importance of IMF-supported reform and liquidity provision for banking sector stability.
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Notes
A different strand of literature has investigated whether the existence of an IMF-supported program modifies interest rate spreads, both on commercial bank loans and on international bonds, and countries’ debt maturity (Mody and Saravia, 2006; Saravia, 2010). Chapman and others (2015) find that increasing the scope of conditionality attached to IMF programs reduces the yield on government bonds.
In addition to what may be envisaged in the attached conditionality to a specific program, the IMF might facilitate the national authorities’ effort to promote special financial reforms which, in the absence of IMF support, could be politically too difficult to implement due to opposition at home. Consequently, governments of member countries, by using the international financial institution as a scapegoat (Vreeland, 1999), may want to delegate responsibility for carrying out domestic unpopular reforms to the politically unaccountable IMF, deflecting toward the latter the possible blame for the resulting social and political costs (Haggard and Kaufman, 1995; Vreeland, 2003).
See Laeven and Valencia (2013, section I) for more details on the actual definition of banking crisis episodes. Their extensive data set dates 147 systemic banking crises over the period 1970–2011, and also lists 211 currency crises and 61 sovereign crises over the same period.
Giustiniani and Kronenberg (2005, p. 11) note that “comparing the periods before (1995–96) and after (1997–2003) the Asian crisis, the share of banking sector conditionality has expanded from 65 percent to 80 percent of total financial sector measures [ and that this] is indicative of a growing and more comprehensive attention of IMF programs, and hence of IMF conditionality, to the functioning of the banking industry.”
For a recent review of this strand of literature, see Demirguc-Kunt and Detragiache (2005) and Kauko (2014).
In addition, the inclusion of country fixed effects weakens our instrumental variable strategy (see the section “The identification strategy”).
We also used the ratio of the total amount of loan arrangements agreed with the IMF in previous five-year period to the average value of GDP in the period, finding almost identical results. In addition, unreported regressions also show that results are unaffected measuring loan size as the amount of actual disbursement rather than the agreed quantity. For robustness, we have also measured the ratio between IMF loan and country quota, as published in the IMF’s historical data set. In all cases we take the logarithm of one plus the respective ratio. Results are not reported for the sake of brevity but they are available on request.
Admittedly, however, undrawn loans could also be a sign of success associated with a faster recovery of the country’s economy and balance-of-payments problems. In addition, when IMF programs are agreed precautionarily, undrawn loans would only indicate that realized economic conditions in the country have not required a full use of the loan (Bird and Willettt, 2004). That said, the share of undrawn loans at expiration is the most widely used indicator of compliance with conditionalities in the literature, as direct information on the implementation of program conditions provided by the IMF’s database on Monitoring Fund Arrangements (MONA) is largely incomplete in terms of covered programs and number of years.
We are helped in this task by the fact that Laeven and Valencia (2013) indicate in their data set the starting and ending year of each crisis. For the episodes which started in 2008, we assume that they are still ongoing in 2010, if no ending year is specified.
See the IMF website at: www.imf.org/external/about/lending.htm.
In unreported regressions we observe that adding country fixed effects significantly weakens our identification. However, results are robust to the inclusion of region dummies; see the section “Other potential triggers of banking crises.”
For a detailed description of the data set, see Kilby (2009b).The data set also includes identification of important votes as declared by the U.S. State Department. However, since this information is not available for the whole time span, we cannot construct the alignment scores based on important UNGA votes. Therefore, we cannot use the difference between the alignment score in important votes in the UNGA and the same score in all other UNGA votes, a measure introduced by Barnebeck Andersen, Harr, and Tarp (2006). As in Thacker (1999) and Dreher and Jensen (2007), the alignment score of country Y with country X is measured considering, for each vote, that country Y scores 1 if it follows X, 0.5 if it abstains or is absent when X votes (or vice versa), and 0 if it opposes X. Political similarity with the G7 is built by averaging the pairwise annual alignment scores.
Some authors have used U.S. military aid as a proxy of the country’s economic and strategic importance to the United States (Oatley and Yackee, 2004). We experiment with such indicator, but it does not prove to be a relevant instrument in the first-stage regression.
It should be noted that a similar identification strategy based on friendships with IMF major shareholders has been followed to assess the impact of IMF-supported programs on the occurrence of other possible episodes of financial crisis, like sudden stops of capital flows, currency and sovereign debt crises, for which the plausibility of the excluding restriction is equally questionable (Eichengreen, Gupta, and Mody, 2008; Dreher and Walter, 2010; Jorra, 2012).
In our sample, the average value of the variable U.S. AID is almost identical (about 21 percent) considering crisis and noncrisis years.
In our sample, the average value of the variable ELECTION is even smaller in crisis than in noncrisis years, although the difference is not statistically significant. The simple correlation between ELECTION and BANKING CRISIS is equal to −0.03 and the former does not have any significant impact on the unconditional and conditional probability of a banking crisis (using specification (1) in Table 4).
We choose such a threshold range since we are not interested in tails of the sample distribution of the loan size variable. A loan-to-GDP ratio of 0.5 percent (7.5 percent) roughly corresponds to the 5th (95th) percentile of the sample distribution of the IMF LOAN/GDP variable.
For instance, Dollar and Svensson (2000) consider 182 adjustment loans disbursed between 1980 and 1995 and show that the average loan size is about USD 160 million; over the same period, the average size of the IMF-supported program in our sample is USD 345 million.
A similar IV strategy is followed by Kilby (2009b).
Also in this case the F-statistics suggest the risk of weak identification, but the LIML estimates support the 2SLS ones.
Indeed, in unreported regressions we find a negative association between past banking crises which were not preceded by any IMF arrangement in the previous three years and the likelihood of a current systemic banking crisis.
The Composite Index is a risk rating based on a set of 22 components grouped into three major categories of risk: political, financial, and economic. The index ranges between 0 and 100, with higher values indicating lower levels of risk. For details, see www.prsgroup.com
See: www.freetheworld.com/index.php and Gwartney, Lawson, and Hall (2012) for details.
Abiad, Tressel, and Detragiache (2008) build a database of financial reforms which covers 91 economies over the period 1973–2005. Financial policy changes are recorded along seven different dimensions: credit controls and reserve requirements, interest rate controls, entry barriers, state ownership, policies on securities markets, banking regulations, and restrictions on the capital account. Liberalization scores for each category are combined in a graded index that is normalized between zero and one. This is the index used in the regressions and it has the advantage of being a continuous measure, rather than a 0/1 dummy for financially liberalized countries.
The subsample of low-income countries is too small and the estimates lose power and precision, but the sign on the IMF presence variable is still negative.
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Additional information
Supplementary information accompanies this article on the IMF Economic Review website (www.palgrave-journals.com/imfer)
*Luca Papi is professor of economics at the Università Politecnica delle Marche and research affiliate of the MoFiR; his email address is: l.papi@univpm.it. Andrea F. Presbitero is an economist at the International Monetary Fund and MoFiR; his email address is: apresbitero@imf.org. Alberto Zazzaro is professor of economics at the Università Politecnica delle Marche and research affiliate of the MoFiR and CSEF; his email address is: a.zazzaro@univpm.it. The authors thank Axel Dreher, Christopher Kilby, and Gian Maria Milesi-Ferretti for providing data on IMF compliance with conditionality, UN votes and foreign assets and liabilities, respectively. The project was started when Andrea F. Presbitero was visiting scholar at the IMF. The authors wish to thank IMF colleagues for providing historical data on the IMF lending arrangements and for useful guidance. They also thank Pierre-Olivier Gourinchas (the editor), two anonymous referees, Ruchir Agarwal, Michele Fratianni, Joseph Joyce, Jacques Mélitz, Camelia Minoiu, Alessandro Missale, Fabian Valencia, SébastienWalker and participants at the 2013 CSAE Conference (Oxford) and at seminars at the Graduate Institute (Geneva), Università Politecnica delle Marche (Ancona), Università di Genova, and Heriot-Watt University (Edinburgh) for thoughtful suggestions. This research is part of a project on Macroeconomic Research in Low-Income Countries (project id: 60925) supported by the U.K. Department for International Development (DfID).
