Our sample consists of 120 low- and middle-income countries observed between 1971 and 2007.Footnote 10 Our main outcome of interest is the democracy score for each country in each given year of the sample. There is a longstanding debate in the social sciences over the best ways to conceptualize and measure democracy (Schmitter and Karl 1991). One strand of the literature offers a “procedural” understanding of democracy centered on the presence or absence of democratic electoral and legislative institutions (Przeworski 1999; Schumpeter 1950, 269). Other scholars of democracy emphasize the protection of individual civil and political liberties that extend beyond the institutions that govern the selection of the country’s leadership (Dahl 1971, 1–4). Alternative conceptualizations of democracy might also consider informal avenues by which officials are held accountable by citizens, the equitability of the distribution of income and assets, and the inclusivity of deliberative institutions and engagement of the citizenry.Footnote 11 In this article, however, our attention is focused on the empirical relationship between IMF program participation and the measurable levels of political democracy.

We rely on two different continuous indicators of the level of democracy in the analysis: the Polity2 and Freedom House scores. The Polity2 score is closer in spirit to the procedural conceptualization of democracy. The indicator combines information on the competitiveness of political participation, the extent of constraints on the power of the regime’s leader, and the openness and competitiveness of the process by which leaders are selected. The Polity2 measure is constructed by examining various attributes related to the competitiveness of participation and constraints on the executive to construct annual scores for countries that range from −10 (least democratic) to +10 (most democratic).

During “interregnum” and “transition” periods in which it was difficult for coders to measure the level of democracy in a country, the original Polity2 measure records a zero. Plümper and Neumayer (2010) show that this coding rule can produce misleading inferences; consequently, in the analysis below we use an amended version of the Polity2 variable that linearly interpolates values during the troublesome “interregnum” and “transition” periods.

The Freedom House score is a composite of two indexes, one that measures respect for civil liberties and the other that measures political rights. We transform the composite Freedom House score so that it runs from 0 (least democratic) to 12 (most democratic). The Freedom House score is available from 1972 onward.

Both the Polity2 and Freedom House scores have been criticized as measures of political democracy (e.g., Giannone 2010; Gleditsch and Ward 1997; Munck and Verkuilen 2002). However, they remain the two most widely used continuous indicators in quantitative studies of the determinants and consequences of democracy.Footnote 12 In our discussion of the possible pathways through which IMF program participation may affect democracy scores we do not distinguish between the procedural (better captured by the Polity2 variable) and “liberal” (reflected in the construction of the Freedom House score) elements of democracy; consequently we choose to examine both indicators separately. Further, despite the fact that the two indicators are highly correlated (r = 0.85), previous research has demonstrated the potential for instability of results across highly correlated continuous measures of democracy (Casper and Tufis 2003), which suggests that it is good practice to examine the covariates of the Polity and Freedom House scores separately.

Our key explanatory variable is the presence of a conditional IMF lending arrangement in country i in year t.Footnote 13 For the 1971 to 2000 period we drew the IMF program indicator from Vreeland (2003). To update the indicator to 2007 we used the IMF’s MONA database and the IMF’s online archives.Footnote 14 We provide a full list of countries and years under IMF lending arrangements in the supplementary appendices.Footnote 15

We do not distinguish between the types of programs in the baseline analysis presented in the next section.Footnote 16 We note that some scholars analyze the effects of concessional loans, which are multi-year programs offering a lower repayment cost to the least developed member countries, separately from non-concessional loans (Dreher and Gassebner 2012; Hutchison 2003; Joyce and Noy 2008). In the baseline models we ask whether there are conditional differences in the level of democracy in participating countries without distinguishing between the types of programs. Concessional and non-concessional IMF loans do not differ much in terms of their fundamental objectives (Oberdabernig 2013). Further, matching can only be performed when some units are identified as “treated” and other units are coded as control cases, along the dimension of a single binary treatment. As a robustness check, however, we exclude non-concessional loans from the analysis to see if there are differences in the estimated effect of concessional loans on the level of democracy in borrowing countries, and find that the effects remain substantially the same.

Our approach focuses on the short-term impact of the presence of lending arrangements on the level of democracy in borrowing countries. As Bas and Stone point out, “the point of IMF lending is to help in the very short run” (2014, 2). Conditional IMF programs enable member states to purchase currency, repaid over time at an interest rate that is usually well below what the country would pay to borrow from private lenders, in order to replenish its reserves, recapitalize its banking system, and remain solvent. The conditions attached to the purchase target the sources of the imbalance that forced the recipient country to seek the institution’s support. We want to know whether the tranches of emergency financing and attendant policy conditions are associated or not with the level of democracy. Estimating the effect of program participation on democracy is challenging. Determining the medium- to long-term consequences of program participation is even more daunting. Finding that there is no difference in democracy levels for countries in the years or decades after they graduated from the lending arrangement could indeed indicate that IMF loans do not matter for democracy scores; alternatively, the finding could also mean that after countries exit from lending arrangements they simply reverse IMF-imposed policy measures that mattered for democracy scores during the duration of the program. Further, identifying countries’ “graduation dates” from IMF lending is not straightforward: some developing countries enter one program after another for years on end.Footnote 17 For these reasons, the main quantity that we want to estimate is the conditional difference in participating and non-participating countries’ democracy scores.

We select a set of variables that are likely to be (positively or negatively) correlated with the probability that a country is under an IMF program and also likely to influence the level of democracy. One possible confounding factor is the nature of the regime. While political scientists have spilled much ink distinguishing between varieties of democratic regimes – presidential or parliamentary, for example – far less attention has been paid to differences between types of dictatorships (c.f., Geddes 1999; Weeks 2008). Not all dictatorships are alike, which has consequences for both foreign and domestic policies.

Cheibub et al. (2009) distinguish between three types of autocratic rule: monarchic, military, and civilian. If we think of regime type as a continuum spanning the most repressive dictatorship to the fullest democracy, monarchic and military autocracies are generally closer to the most repressive pole than civilian dictatorships.Footnote 18 Above, we discussed how IMF loans might erode the repressive capacity of autocrats. Given that autocratic regimes headed by monarchs or military leaders tend to be highly repressive, these regimes have the most to lose from going to the IMF should constraints on their coercive capabilities result from participation in the adjustment program. Our inferences about the association between IMF loans and democracy scores would be biased if the bulk of the autocratic regimes that signed IMF programs were less repressive, civilian-headed governments. We draw two indicators (military autocracy and monarchic autocracy) from Cheibub et al.’s six-fold classification of regime types.

We also include a variable that records the sum of previous transitions to autocracy from 1946 to year t (Cheibub et al. 2009). We add the previous transitions variable because countries with a track record of unsettled, volatile political systems may be forced to seek out a disproportionate number of IMF programs and may also have lower (or higher) democracy scores on average.

Oil-rich countries are prone to boom and bust cycles, but they are less likely to obtain IMF loans than countries with little exportable oil. For instance, the least frequent users of IMF resources in the developing world are countries in the Middle East and North Africa. Many scholars argue that reliance on oil is inimical to democracy (Ross 2001). We include a dichotomous indicator that takes a value of one if a country is a major oil exporter.Footnote 19

We use four variables to account for potentially confounding economic factors. The level of reserves is an indicator of a country’s economic health. It is well known that falling reserves increase the likelihood that a country will seek to borrow from the IMF (Sturm et al. 2005); in addition, the health of the economy is likely to have an impact on the level of democracy, particularly in fragile, “unconsolidated,” democratizing regimes (Przeworski et al. 1996). We measure the ratio of international reserves to gross national income (GNI) (reserves); the data are constructed from the World Bank’s International Debt Statistics and World Development Indicators databases. A country’s average income per person and the size (as well as the direction) of the annual change in per capita wealth are expected to influence both the probability that a country is under an IMF program (the relatively rich and fast-growing do not have much need to draw on the IMF’s resources) and the prospects for democracy (the relatively poor and slow-growing countries are less likely to see increases in their democracy scores). The measures of real GDP per capita and real per capita GDP growth come from the Penn World Tables version 6.3. All of the economic indicators in the pre-matched datasets are lagged by 1 year.

Countries that seek IMF funding are in many (if not most) cases experiencing severe economic distress. We have to account for the crisis conditions that brought the country to the IMF in the first place, since currency crises have been linked to the breakdown of both democratic and autocratic regimes (Pepinsky 2009). Consequently we use a measure of exchange market pressure (currency crash) which, following Frankel and Rose’s (1996) widely-used definition, takes a value of one for years in which a country experiences a nominal devaluation in its exchange rate of at least thirty percent that is also at least a ten percent hike in the rate of depreciation compared to the previous year.

Countries that are wracked by severe internal violence or engaged in intense cross-border conflicts are less likely to be able to muster the resources to formulate a credible reform program in consultation with the IMF; in the worst episodes, the state may wither to the point that key economic policy positions in the finance ministry and/or central bank are vacant. The IMF cannot send a mission to a country that does not have the basic infrastructure to support loan negotiations. We are unlikely to see many episodes of IMF loans signed by governments in failed or failing states, and democracy is similarly unlikely to flourish in these environments. We include an index of political violence that records the intensity of annual episodes of intra- and interstate conflict (Marshall 2010). The political violence indicator ranges from 0 to 13.

We account for neighborhood effects by including regional variables. Economic crises often spill across borders into neighboring countries; we speculate that the presence of an IMF program in country i raises the probability that country i’s neighbors will also end up with IMF programs, either as a precautionary measure to reassure market actors or as an attempt to restore stability once the crisis has spread. In addition, there is convincing evidence of regional dynamics in the spread of democracy (Brinks and Coppedge 2006; Gleditsch and Ward 2006). We place countries into one of six regional classifications: Middle East and North Africa, Latin America and the Caribbean, East Asia and Pacific, Post-Communist, sub-Saharan Africa, and South Asia.

We also match on the year in which a unit is observed to minimize the likelihood that any results are a function of differences in the temporal periods for matched versus unmatched units. If countries were – for perhaps unrelated reasons – more likely to be under IMF programs during waves of democratization, we might mistake a coincidental positive correlation between IMF programs and democracy levels for a causal relationship. By matching a unit under an IMF program to a control case in the same or a proximate year we are able to control for the secular upward global trend in democracy scores.