Outside Options and IMF Conditionality∗

Bargain Down or Shop Around? Outside Options and IMF Conditionality∗

Richard Clark†

June 23, 2020

Abstract

When do overlapping international organizations (IOs) serve as credible outside options to one another? Utilizing an original dataset on cooperation among IOs in the emergency lending space, I find that exit options are only credible when IOs compete as opposed to cooperate with one another. While the literature frames the International Monetary Fund (IMF) as a monopoly organization, I show that it increasingly competes with Regional Financing Arrangements (RFAs). When RFAs compete with the IMF, they become credible forum shopping destinations that member states can leverage in negotiations over conditional lending at the Fund. I first offer original descriptive analysis of patterns of cooperation among these IOs. I then hypothesize that as states become members of competitive IOs, but not cooperative institutions, they ought to receive less intrusive conditionality from the IMF. A series of regressions lend support for my theory, as do supplemental interviews and text analysis.

Keywords: outside options, forum shopping, international organizations, IMF, conditionality

Word Count: 10,808

∗I thank Allison Carnegie, Don Casler, Lindsay Dolan, Nikhar Gaikwad, Randall Henning, Felicity Vabulas, and Noah Zucker for helpful comments on previous drafts. Participants at the Barcelona Workshop on Global Governance and several workshops at Columbia University also provided valuable feedback. All remaining errors are my own. †Richard Clark (Email: [email protected]) is a Ph.D. Candidate, Department of Political Science, Columbia University, New York, NY. In April 2013, the International Monetary Fund (IMF) and Tunisia agreed to a two- year, $1.75 billion Stand-By Arrangement (SBA).1 Disbursement began in June 2013, with Tunisia taking on 20 loan conditions mandating various economic and institutional reforms. Meanwhile, Tunisia was party to a second emergency lending institution – the Arab Monetary Fund (AMF) – which Tunisia co-founded in 1976. The AMF remained on the sidelines as Tunisia negotiated its IMF deal. However, in February 2014, the AMF agreed to join the IMF program as a co-financier.2 That same month, the IMF notified Tunisia that it was increasing the number of conditions that the country had to meet under the loan program from 20 to 35 – an increase of 75 percent.3 Insofar as the number of conditions attached to a loan proxies for its stringency, Tunisia’s obligations suddenly became much more burdensome. I argue that the simultaneous nature of (1) the AMF’s decision to co-finance the loan program and (2) the addition of new conditions to the program is far from coincidental. Rather, when the AMF signed onto the loan, it eliminated Tunisia’s only outside option to IMF lending. Without the credible threat to forum shop at the AMF, Tunisia lost a great deal of bargaining leverage at the IMF, allowing the Fund to mandate additional reforms. This story places a spotlight on the agency of international organizations (IOs) under regime complexity – a situation where multiple, partially overlapping IOs govern a single issue space.4 It therefore contrasts much of the literature, which emphasizes the agency of member states of multiple IOs, as they can secure bargaining leverage by threatening to forum shop or simply select the forum offering the least stringent set of rules.5 Critically, I suggest that the credibility of outside options under regime complexity hinges on whether IOs cooperate or compete with one another. When do overlapping IOs become credible outside options for states seeking forum shopping opportunities? I contend that the credibility of outside options under regime complexity

1https://www.imf.org/external/pubs/ft/scr/2013/cr13161.pdf 2https://www.imf.org/external/pubs/ft/scr/2014/cr1450.pdf 3Conditionality figures come from Kentikelenis, Stubbs and King(2016). 4This is close to the definition offered by Henning(2017, 19). 5See Busch(2007); Davis(2009); Alter and Meunier(2009).

1 is shaped by whether the overlapping IOs in question cooperate or compete with one another. When IOs compete, member states have credible exit options, and IOs grant them concessions through more lenient policies to prevent forum shopping. By contrast, when IOs cooperate, the credibility of outside options is diminished, and IOs can better constrain state behavior. This paper therefore refines the typical narrative about when states can forum shop. Existing theories suggest that member states can forum shop any time they belong to more than one institution performing similar functions.6 Because cooperation undercuts the credibility of outside options, I argue that forum shopping is only possible for member states when the overlapping IOs in question compete as opposed to cooperate with one another. Therefore, the interactions among institutions under regime complexity are critical for determining whether states can credibly threaten to forum shop. Importantly, cooperation is then distinct from the negotiation of a division of labor or hierarchy, through which IOs seek to minimize substantive overlap in organizational mandates and activities.7 My theory applies to IOs whose operations substantively overlap, which might imply low levels of hierarchy or specialization. In particular, I consider two common types of cooperation: information sharing and co- financing.8 Both are formal, meaning that they are publicly announced – typically through policy papers, project documents, and press releases – and accompanied by changes to IOs’ policies. They can have finite end points, as when development IOs co-finance a single infrastructure project, or they can persist indefinitely, as when IOs sign a memorandum agreeing to share information. Formal cooperation stands in contrast to ad hoc or informal arrangements, such as handshake agreements among bureaucrats.9 I focus on formal cooperation for feasibility and generalizability reasons – because informal cooperation is often

6See Alter and Raustiala(2018, 341) for a review, though also see Busch(2007); Helfer(2009); Morse and Keohane(2014) for speciﬁc examples. 7See Gehring and Faude(2014); Pratt(2018) for examples of such coordination. 8See Keohane and Victor(2011); Abbott et al.(2015) for more on the beneﬁts of cooperation. 9See Henning(2017, 9) for examples of informal governance arrangements in Europe.

2 unobservable, it is difficult to study in a systematic way. While scholars have had some success qualitatively probing single instances of informal cooperation, my goal in this paper is to make generalizable predictions that can be tested across organizations and over time; I further discuss the situations to which my theory applies in the conclusion. My definition of competition, meanwhile, is simply the absence of cooperation among IOs whose activities overlap. This competition is therefore quite passive – if two IOs perform equivalent tasks and serve the same countries but fail to cooperate by sharing information or co-financing, member states can exercise their exit option by threatening to pursue support from the other organization unless more lenient terms are awarded. Competition then implies nothing more than the potential for mutual member states to forum shop. I argue that they can do so when (1) they belong to two or more IOs that perform very similar functions; (2) the outside IO can plausibly offer them more lenient terms; and (3) the IOs are not engaged in cooperation. Existing accounts of when states can forum shop focus only on the first condition.10 Cooperation undercuts the credibility of outside options by either synchronizing the information upon which IOs make decisions (information sharing) or by forcing IOs to explicitly agree to a common regulatory framework (co-financing). When IOs share private information, it harmonizes the information environment in which the organizations make decisions. Therefore, IOs that share information should design more similar policies for member states than competing IOs, which limits the extent to which members of these organizations can forum shop for more lenient conditions. Co-financing, meanwhile, occurs when two IOs mutually agree to fund a project governed by a single policy framework instead of offering competing terms to member states, thereby eliminating the exit option. To probe the validity of this theory, I study the emergency lending regime complex, which includes IOs that engage primarily in balance of payments lending. I do so for several reasons. First, scholars have characterized the IMF as possessing a pseudo-monopoly over

10See Alter and Raustiala(2018) for a review.

3 emergency lending (Lipscy 2015, 2017). I contradict this literature by showing that regional financing arrangements (RFAs) like the AMF, whose activities are substantively similar to those performed by the IMF, can serve as credible exit options to the Fund in the absence of cooperation. Second, emergency lending is an area where the stringency of the policies administered by IOs to member states is of the utmost importance. The policy terms attached to loans in this area are called conditionality. Given that conditions from the IMF often incite protests and political upheaval,11 one could argue that the burdensomeness of conditionality is even more important to recipient countries than the dollar amount of the loan. Therefore, countries have strong incentives to bargain down their conditionality burdens when they have the leverage to do so, as when they are members of the UN Security Council (Dreher 2009b). Insofar as competitive RFAs grant their members similar bargaining leverage at the IMF, it should manifest in the stringency of their conditionality programs. To show that IO cooperation and competition mediate the extent to which overlapping IOs serve as credible outside options for members, I utilize regression analyses performed on original data and supplement the results with interviews and text analysis. First, I introduce an original dataset of dyadic cooperation among IOs in the emergency lending space – the first such dataset on cooperation among IOs. I code the incidence of information sharing and co-financing on the basis of the contents of thousands of policy papers, project documents, and news releases from the IMF and eight RFAs active in the space. I then utilize this data in two ways. I begin with original descriptive analysis of the patterns of cooperation and competition in the emergency lending space, showing that IOs controlled by more politically similar countries are more likely to cooperate with one another. Second, I convert this data into country-year variables that measure whether IMF member states are party to cooperative or competitive outside IOs and whether they have received any funding from these organizations in a given year. I then pair this original data with IMF conditionality

11See e.g. https://reut.rs/2xGtVbg.

4 data from Kentikelenis, Stubbs and King(2016), which covers the period 1978-2014. Last, I offer supplemental evidence via semi-structured interviews with officials in the emergency lending space and text analysis of the actual text of loan conditions. I show that when countries belong to RFAs that compete with the IMF, they receive fewer and softer conditions covering fewer issue areas. This is not true of countries belonging to cooperative RFAs. I also show that this effect is driven by membership in as opposed to the receipt of funding from RFAs, which suggests that members of competing IOs leverage exit options to bargain down conditions as opposed to simply forum shopping away from the IMF.

The Credibility of Outside Options

When do overlapping IOs become credible outside options for states seeking forum shopping opportunities? I argue that only competitive IOs serve as credible outside options to one another, yielding bargaining power that member states can utilize in negotiations over policies. It is first important to understand what I mean when I discuss credibility. I characterize credibility in the context of a bargaining process between an IO and a member state that is also a member of an outside institution that performs very similar functions to the first IO. In order for an outside option to be credible, the IO with which the member state is negotiating must believe that the outside institution could plausibly offer the state less burdensome terms. This could happen because the IO in question knows that the outside institution is generally less stringent, or it could manifest when IO staff operate under high levels of uncertainty about the stringency of policies across IOs. In the next section, I discuss how the emergency lending space falls into the former camp, as RFAs are widely known to offer less stringent terms than the IMF. Cooperation between IOs – either in the form of information sharing or co-financing –

5 prevents member states from playing IOs off of one another to secure more favorable terms by making it clear to each IO in the partnership that the other will not offer member states more lenient terms. Information sharing involves opening a steady line of communication between IOs and harmonizing the information upon which these organizations make decisions. If IOs have the same information about members, they are more likely to come to similar conclusions about policy. This limits the extent to which states can credibly threaten to forum shop to secure more lenient terms. Meanwhile, co-financing means explicitly negotiating one set of agreeable lending terms, which prevents member states from selecting the least stringent offer among a plurality of arrangements. Notably, because cooperation is relevant only when IOs’ operations overlap, it is distinct from IOs’ efforts to reduce operational redundancies, such as through hierarchy or specialization.12 In the absence of cooperation, member states of overlapping IOs can threaten to forum shop unless they are granted more lenient policy terms. Notably, this competition is more latent than active on the part of the IOs. By choosing not to pursue cooperation despite overlap in their activities, competitive IOs leave open the opportunity for member states to forum shop among them, or at least to credibly threaten to do so.13 A former IMF senior economist offered preliminary support for this theory: “Alternative multilateral lenders might give IMF program recipients some amount of leverage.”14 A second former IMF economist concurred, saying, “You can shop around to minimize conditionality in some ways.”15 Under my framework, states use the credible threat to forum shop to strong-arm a given IO into awarding them more agreeable terms. My theory therefore suggests that states can credibly threaten to forum shop when (1) they belong to a plurality of IOs that perform very similar activities; (2) the outside IO

12See Keohane and Victor(2011); Gehring and Faude(2014); Pratt(2018). Cooperation might then imply low levels of specialization or hierarchy. 13Others have examined the eﬀects of competition between lenders (Humphrey and Michaelowa 2013; Hernandez 2017). 14Interview B. All referenced interviews were performed by author. For more information on the interview process, see Appendix A. 15Interview C.

6 can plausibly offer them more lenient terms; and (3) the IOs are not cooperating with one another. Existing approaches assume that all overlapping IOs are viable forum shopping destinations. For instance, in a recent review article, Alter and Raustiala(2018) contend that “strategies of forum choice exist wherever there is more than one institution that can make a policy decision or adjudicate a case.”16 In other words, the ability of IOs to constrain state behavior is said to be a function of the number of institutions present in the issue space.17 I refine this narrative by showing that the nature of the interactions between overlapping institutions, and not just their number, are critical for determining whether states can credibly threaten to forum shop. Importantly, by focusing on the autonomy of IOs in this way, my work builds on a substantial literature exploring institutional bureaucracies, culture, and drift.18 Interviews with a former IMF economist who implemented several co-financed programs confirmed that cooperation decisions are typically left to IO staff, though staff are expected to bear the interests of powerful stakeholders in mind when negotiating these arrangements: “The U.S. is the largest shareholder at the Fund, but the US is rarely hands-on in designing programs; they just expect that the IMF won’t clash with the Treasury.”19 A second former IMF economist offered a more detailed account of how co-financing materializes, again characterizing the process as staff-led: “IMF management would not put together a lending program if you could not look 12 months into future and say this program is fully financed. So before anything gets cleared and sent to Board for approval, you need these assurances. The country team putting together the package is looking at the current account deficit and payments coming due, so it assesses all inflows from private market as well as public and multilateral sources to see how financing can happen. IMF country teams at that point will talk to other institutions.”20 Last, while interstate relations and narrow political interests may drive the initial creation of

16Also see Busch(2007); Helfer(2009); Morse and Keohane(2014). 17See Lipscy(2015), for example. 18For example, see Barnett and Finnemore(1999); Johnson(2014). 19Interview C. 20Interview B.

7 IOs by member states,21 existing work shows that these bureaucracies tend to take on lives of their own once created (Barnett and Finnemore 1999). IO staﬀ therefore have substantial authority to negotiate cooperation frameworks and shape opportunity structures. Altogether, I theorize that whether overlapping IOs cooperate or compete with one another aﬀects the credibility of outside options for member states and, in turn, the content of the policies produced by IOs in the issue area. I suggest that member states of overlapping IOs can leverage exit options into more lenient bargains only when the IOs compete with one another. Cooperation undercuts the credibility of forum shopping opportunities by either synchronizing the information upon which IOs make decisions or by forcing IOs to explicitly agree to a common regulatory framework. As such, I hypothesize the following:

Hypothesis 1. Members of overlapping competitive IOs, but not cooperative IOs, should receive more lenient policy terms from these organizations.

Applying the Logic to Emergency Lending

I apply the general logic described above to the emergency lending issue area for three reasons. First, scholars have overlooked this area in extant work on regime complexity and forum shopping, focusing on the development (Pratt 2017), trade (Busch 2007; Davis 2009), and environmental complexes (Keohane and Victor 2011). Second, the IMF is the IO that is arguably most dependent on monopoly power for the pursuit of its goals. In fact, the IMF’s monopoly over emergency lending is what theoretically allows it to attach costly conditions to loans (Henning 2011). Therefore, the Fund has strong incentives to avoid competition with other IOs, and any competition is likely to manifest in the policies created by the Fund. Last, the IMF’s area exhibits substantial temporal and spatial variation in the incidence of cooperation between IOs, and it is relatively easy to identify its presence or absence based on the content of IOs’ publications.

21See e.g. Pratt(2017).

8 Understanding the Emergency Lending Regime

It is first important to understand which IOs operate in the emergency lending space and how their policies differ from those produced by the IMF. Regional financing arrangements (RFAs) play a crucial role in the global financial safety net (GFSN) as regionally-focused financial institutions that offer a range of credit facilities to assist with balance of payments deficits. In total, there exist eight RFAs; they are listed in Table1 alongside information about memberships and resources. As the table shows, six have disbursed funds to member states. I bound the emergency lending issue area by looking exclusively at interactions between the IMF and these RFAs. As emergency lending IOs, RFAs and the IMF share high levels of functional equivalence, which is important for the credibility of threats to forum shop. Importantly, restricting the sample in this way has precedent in the literature (Lipscy 2015). Additionally, the IMF identifies RFAs as its primary institutional competitors in its internal documentation.22 My analysis then excludes lending from entities like development banks, which sometimes engage in similar lending practices.23 It also excludes swap lines, which are another wrinkle in the GFSN. Importantly, because RFAs lack global coverage, my tests only apply to the subset of countries that belong to these institutions. Why might countries attempt to leverage their membership in RFAs to bargain down their conditionality burdens at the IMF? First, extant work suggests that IMF members hope to minimize the stringency of conditions whenever possible. We know that membership on the UN Security Council, for example, is associated with receiving fewer IMF conditions (Dreher 2009b). Moreover, the IMF often gives breaks on conditionality to friends of the U.S. (Stone 2008, 2011).24 which might lead countries that are not close to the U.S. to

22See Miyoshi et al.(2013); Porter, Moriyama, Deb, Eugster, Huang, Menkulasi, Nor and Tovar(2017); Porter, Moriyama, Deb, Eugster, Menkulasi, Nor and Tovar(2017). 23For example, the World Bank oﬀers conditional lending through the Development Policy Financing instrument. 24Also see Nelson(2017) on neoliberal ideology and Copelovitch(2010, 50) on Executive Board represen- tation.

9 bargain for comparable reductions. Second, countries might fight back against conditionality because its effectiveness is highly contested in the literature (Montinola 2010). A number of scholars have shown that conditionality commonly fails to promote and sustain economic recovery (Bulir and Moon 2004; Li, Sy and McMurray 2015). Feldstein(1998) suggests that the stringency of IMF conditions deters states from applying for IMF support in the first place. Others have criticized the Fund for prescribing conditions in policy areas in which it has insufficient expertise (Radelet et al. 1998; Goldstein 2003) and for a lack of responsiveness to local conditions (Li, Sy and McMurray 2015). Last, conditionality often generates political turmoil, threatening leaders’ reelection (Vreeland 2005). Insofar as IMF conditionality is sometimes perceived to be ineffective, cumbersome, and politically costly, states have incentives to forum shop. RFAs are a particularly attractive forum shopping destination because conditionality is uncommon at these IOs.25 Therefore, RFAs can plausibly offer member states more lenient terms than the Fund. The Latin American Reserve Fund (FLAR), for instance, does not attach any policy conditions to loans. Meanwhile, the Arab Monetary Fund (AMF) applies conditions only to some loans, and these conditions tend to be much softer than Fund conditions (Miyoshi et al. 2013, 11). This is not to say that RFA conditionality is always easy for member states to implement, as an official from the Eurasian Fund for Stabilization and Development (EFSD) cautioned in an interview.26 However, he also noted that reforms are focused “in the areas recognized as priority by both the authorities and development partners... for facility shopping considerations.” In other words, the EFSD values buy-in from government officials so as to limit forum shopping. The minimally invasive nature of most RFA lending is unsurprising, as loan terms are designed by a cohort of sympathetic regional peers that are vulnerable to spillovers from

25While European RFAs impose more stringent conditions than other RFAs (Miyoshi et al. 2013, 11), all European lending in my data involves IMF cooperation. Therefore, all instances of competition in the data come from IOs that oﬀer less stringent terms than the Fund. 26Interview A.

10 Name Year Founded Members Resources Algeria, Bahrain, Comoros, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Arab Monetary Fund (AMF) 1976 US$4.8 billion Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, United Arab Emirates, Yemen BRICS’ Contingent Reserve 2014 Brazil, China, India, Russia, South Africa US$100 billion Arrangement (CRA)* Brunei Darussalam, Cambodia, China, The Chang Mai Initiative Indonesia, Japan, Korea, Lao PDR, 2000 US$240 billion Multilaterization (CMIM)* Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam Armenia, Belarus, Kazakhstan, Eurasian Fund for Stabilization 2009 Kyrgyz Republic, Russian Federation, US$8.5billion and Development (EFSD) Tajikistan Bulgaria, Croatia, Czech Republic,

11 EU Balance of Payments (BoP) 2002 Denmark, Hungary, Poland, Romania, US$54.5 billion Assistance Facility Sweden, United Kingdom Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, European Stability Mechanism Ireland, Italy, Latvia, Lithuania, (ESM); formerly the European 2010 US$763.5 billion Luxembourg, Malta, Netherlands, Financial Stability Facility (EFSF) Portugal, Slovak Republic, Slovenia, Spain Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, European Financial Stabilisation 2010 Hungary, Ireland, Italy, Latvia, US$71.3 billion Mechanism (EFSM) Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom Latin American Reserve Fund Bolivia, Colombia, Ecuador, Costa (FLAR); formerly the Andean 1978 US$4.7 billion Rica, Peru, Uruguay, and Venezuela. Reserve Fund

Table 1: Regional Financing Arrangements (RFAs). * denotes RFAs that have not been used. Information comes from Porter, Moriyama, Deb, Eugster, Menkulasi, Nor and Tovar(2017) and the websites for each organization. Note that the UK exited from the European institutions in January 2020. painful economic adjustment. Additionally, many RFA members have struggled with IMF conditions in the past, which may drive an aversion to conditionality. Supporting this logic, the Fund notes in a policy paper: “Limited emphasis on conditionality attached to some RFA financing may be evidence that some RFA members consider Fund conditionality too onerous” (Miyoshi et al. 2013, 13). Last, while RFAs on average lend in smaller volumes than the IMF, their loans are still substantively large. Tables A3-A8 compare the volume of lending from a given RFA to the volume of IMF lending received by that RFA’s member states for each year from the RFA’s year of creation through 2014.27 In total, AMF lending is around 30 percent of IMF lending; FLAR lending is 40 percent of IMF lending; EFSD lending is over 400 percent of IMF lending; EU BoP lending is 65 percent of IMF lending; ESM lending is 125 percent of IMF lending; and EFSM lending is 60 percent of IMF lending. While IMF support carries unique benefits,28 these figures suggest that RFAs at least possess the financial capacity to substitute for a meaningful portion of IMF lending.

Data and Empirical Analysis

My empirical analysis consists of three sets of tests. The ﬁrst undertake a descriptive examination of which IOs tend to cooperate with one another in the emergency lending space, utilizing original data on co-ﬁnancing and information sharing. The second evaluate the impact of RFA membership on the number of conditions and policy areas covered by IMF programs. The third assess the stringency of the text of IMF conditions via structural topic modeling. I discuss the data used, research design, and results for each set of tests in turn. 27These Tables only cover RFAs that have lent; CMIM and the CRA are therefore excluded. 28For instance, IMF lending may send a signal to private creditors or allow leaders to scapegoat the institution (Vreeland 1999).

12 Descriptive Cooperation Analysis

To identify patterns of cooperation and contestation among emergency lending IOs, I hand-code cooperation among “plausible dyads” of organizations in the issue area. This means that I only consider the possibility of cooperation among IOs whose activities substantively overlap. The IMF is then a potential cooperation partner for each of the eight RFAs in the regime complex. In contrast, the RFAs are only rarely possible cooperation partners for one another, as they serve different geographic regions such that their memberships and activities do not overlap. The three European RFAs are the exception; they can plausibly cooperate with one another in addition to the Fund. As such, my data covers the entire universe of potential cooperation cases in the emergency lending area for each year that IOs have existed in the space. This means that an IO joins the data in its year of creation, with the exception of the IMF, because it has no potential cooperation partner until the AMF is created in 1976. The co-financing and information sharing variables are hand-coded based on my reading of thousands of policy papers, press releases, and legislative resolutions across all IOs in the emergency lending space. Given the breadth of documents examined and the high levels of transparency found at these institutions, I am confident that my coding is complete. Further details about coding procedures can be found in Appendix 3. There is important temporal variation in cooperation and competition among emergency lending IOs. Cooperation has become much more common with time, as Figure A1 shows. In fact, it is only after 2007 that these organizations started to engage in meaningful cooperation. In contrast, countries have long belonged to competitor organizations to the Fund, as Figure A2 indicates. The network diagrams in Figure1 map cumulative cooperation efforts among the IOs in the emergency lending regime. The size of the circles upon which the organizations’ names are written reflects the overall cumulative levels of cooperation undertaken by each IO, while the number of lines drawn from one IO to another reflects the number of country-

13 Co−financing Information Sharing

CRA CRA CMIM CMIM

AMF AMF

EFSD EFSD

IMF IMF

EUBOP EUBOP

FLAR FLAR

EFSM EFSM ESM ESM

Figure 1: Emergency Lending Network Diagram.

14 years during which the two IOs in question have cooperated. Last, the thickness of the lines reflects the number of incidences of cooperation undertaken by the two IOs in question during a given year. This means that IOs with more lines drawn between them have more frequent cooperation, while IOs with thicker lines tend to have deeper cooperation. Information sharing is much less common than co-financing, which might reflect the costs of sharing sensitive economic information about member states with other organizations. Carnegie and Carson(2019) show that member states are often hesitant to share sensitive information with IOs, and monetary and debt statistics might be considered sensitive. In an interview, a senior official from the EFSD suggested as much, noting that while “the IMF shares quite a lot on a public domain... get[ting] access to whatever goes beyond that would require an information sharing agreement,” and these agreements can be “very hard to implement” in practice.29 Co-financing, on the other hand, is potentially less risky, as “... each IO [often] finances a different stage of the program.”30 This means that costly information exchange can potentially be avoided under co-financing, as each organization monopolizes different portions of the program once the initial governance framework is agreed upon. Moreover, co-financing arrangements typically only last for the duration of the project, while information sharing arrangements tend to cover longer periods. The descriptive analysis also suggests that IOs whose most powerful stakeholders possess more similar geopolitical preferences tend to cooperate with one another. The only incidence of information sharing between the IMF and an RFA comes with the European Stability Mechanism (ESM), which is perhaps unsurprising given the pervasive influence of powerful European countries like Germany and France at both of these organizations.31 Similarly, co-financing is much more common between the European institutions and the IMF than between the Fund and the non-European RFAs. Interview data offers preliminary support for this intuition; a former IMF economist who worked on several co-financed programs in

29Interview A. 30Interview A. 31See Henning(2017) on the importance of these European states for IMF policymaking.

15 the 2010s said the following: “Geopolitics clearly matter for cooperation. IMF-Russia and IMF-China cooperation are therefore not very feasible.”32 However, the IMF and Russian-led EFSD have co-financed a few programs, including in Armenia in 2014, and the IMF and Chinese-led CMIM have conducted several joint crisis simulations together.33 CMIM’s rules also contain an explicit IMF link, as a country can only request 30 percent of its budgeted support before an IMF program is required.34 These outcomes could be the result of cooperation by autonomous IO staff – as was discussed in the previous section, two former IMF economists shared that member states largely delegate cooperation and financing decisions to staff.35 While powerful member state preferences are likely felt to some extent in cooperation agreements, as staff members themselves can be biased towards the positions of powerful principals (Nelson 2017; Clark and Dolan 2020), IO staff mostly possess the freedom to create or obstruct the exit environment for member states. This should minimize concerns about endogeneity in subsequent testing. In sum, while the IMF has expressed a desire to coordinate activities with its regional peers for fear of forum shopping and redundancies (Porter, Moriyama, Deb, Eugster, Huang, Menkulasi, Nor and Tovar 2017), results have been mixed. The IMF has more successfully negotiated co-financing than information sharing arrangements, and its most common partners are the European RFAs. The former trend may be explained by the perceived costliness of information sharing between IOs, while the latter is perhaps owed to political synergies, though IO staff retain high levels of autonomy to negotiate cooperation frameworks. How- ever, this analysis is purely descriptive, and subsequent research is needed to more completely disentangle these relationships.

32Interview C. 33See https://www.esm.europa.eu/sites/default/files/esmdiscussionpaper4.pdf. 34Ibid. 35Interviews B/C.

16 IO Competition and IMF Conditionality

How do cooperation and competition with outside institutions affect IMF policymaking? As a proxy for the intrusiveness of IMF conditionality, I utilize a count of the number of conditions applied in a project–year. This serves as my dependent variable. While the count measure is imperfect, extant work suggests that it adequately measures the stringency of conditionality.36 I also replicate my analysis with an alternate dependent variable from Stone(2008) – he measures the “scope” of conditionality as the number of policy categories covered by a conditionality program. Last, to further alleviate concerns about measurement, I probe the stringency of conditionality through original text analysis of the content of IMF conditions – the first such analysis of its kind. To construct my count variables, I use conditionality data from Kentikelenis, Stubbs and King(2016). They conduct extensive archival research, compiling an original database of conditions extracted from IMF source documents like letters of intent, covering all IMF program-years 1978-2014. They also categorize each condition by policy area. My sample thus includes all years in which a country is under an IMF program between 1978 and 2014. Subsequent analyses include a linear time trend. While conditionality became a cen- terpiece of IMF lending during the 1980s (Vaubel 1991, 210), programs have become more intrusive with time.37 The time trend accounts for this temporal variation. The intrusiveness of conditionality similarly varies geographically, as is illustrated in Figure2. The heav- iest average conditionality burdens fall on southeast Asian and eastern European countries, which is perhaps unsurprising given the legacy of state ownership in these regions. Coun- try fixed-effects account for this regional variation and other country-specific characteristics. Additionally, Figure A6 shows a histogram of the frequency of conditionality burdens across projects. The right tail tells us that the data is overdispersed. As such, I employ a negative binomial count model as my primary specification.

36See e.g. Dreher and Jensen(2007); Copelovitch(2010); Kentikelenis, Stubbs and King(2016). 37Both Dreher(2009 a) and Figure A5 conﬁrm this trend.

17 My main independent variables of interest, derived from my original data, are outside option member and cooperative member. A country is coded as a member of an outside option institution if the country is party to an RFA, participates in an IMF agreement, and the RFA fails to cooperate with the Fund; the variable takes the value corresponding to the number of non-cooperative RFAs that a country belongs to in a given year. This means that an RFA need not provide ﬁnancial support to the country to be characterized as a credible outside option – membership is suﬃcient, and it should lead to reduced condi-

tionality. Therefore, I anticipate a negative relationship between outside option member and the level of IMF conditionality. No such relationship should exist for more cooperative institutions, as cooperation undercuts the credibility of the exit option; an IO is coded as cooperative for a country if it either shares information with the IMF or has a co-ﬁnancing framework in place with the Fund.38 Figures A3-A4 illustrate which countries experience

changes in cooperative member and outside option member respectively. I also include outside option funding and cooperative funding as important control variables. Both are hand-coded from project documents, policy papers, and direct communication with the IOs.39 When countries receive funds from an outside option RFA, they should require smaller loans from the IMF – extant work shows that IMF loan size is positively correlated with conditionality (Vreeland 1999, 15). My mechanism, meanwhile, suggests that membership in competitive RFAs should matter regardless of whether members contemporaneously receive funding from the institutions.

Additionally, I include a number of economic covariates in the models, including gdppc, trade openness, debt service (% of exports), short-term debt (% of exports), and fdi / gdp. Countries with healthier economies should receive fewer conditions. I also include relevant political covariates, namely polity2 democracy scores, institutional checks, u.s. aid, un voting (ideal pt dist from u.s.) in the UN General Assembly,

38outside option member varies between 0 and 1, while cooperative member varies between 0 and 2. 39I especially thank the FLAR for providing lending data.

18 2.5 38.4

Figure 2: Average Number of Conditions Applied by the IMF Globally 1978–2014. Data comes from Kentikelenis, Stubbs and King(2016).

whether or not a country is at war, whether it is a legislative or executive election year, whether a country is a rotating unsc member, and the extent to which government oﬃcials hold liberal ideology. Countries with stronger democratic institutions might receive more stringent conventionality, as IMF conditions are less likely to be reversed (Vreeland 2003, 2005). Meanwhile, the Fund may give breaks to countries that are at war or in the midst of elections (Stone 2011). Next, countries that are closer to the U.S. as measured by aid receipts and UN voting positions, and rotating UNSC members, tend to receive less stringent conditionality (Stone 2008; Dreher 2009b; Dreher, Sturm and Vreeland 2015). Similarly, the

IMF demands less of countries whose oﬃcials possess more liberal ideology (Nelson 2014).

19 Last, I control for several IMF-speciﬁc characteristics. Poverty Reduction and Growth

Facility (PRGF) programs are more development-oriented and concessional, which may affect average conditionality levels (Dreher, Sturm and Vreeland 2009).40 The duration of a program should be positively associated with the stringency of conditions assigned by the Fund, as duration is increasing in the anticipated difficulty of economic reforms. Next, quota might be negatively correlated with conditionality insofar as it proxies for a country’s pull at the Fund. Last, time from last imf program, which is measured as the number of years since a country last lent from the Fund, accounts for potential serial correlation. Summary statistics for these data can be found in Table A9, and a complete description of variable names, definitions, and sources, can be found in Table A2. In subsequent tests, continuous variables are standardized for ease of interpretation and to assist the convergence of the negative binomial models.41 All independent variables are lagged by one year with the exception of program characteristics (duration and prgf). I opt for a one-year lag because there is a temporal gap between when a country applies for IMF assistance and when loan terms are agreed upon, which is when projects first appear in the data. To assess the impact of outside options on IMF conditionality, I perform two sets of regressions.42 The first restrict covariates to only the focal independent variables for which there is no missing data, namely outside option member, cooperative member, outside option amount, and cooperative amount. Because the economic covariates exhibit substantial missingness, missing data are imputed via multiple imputation in the second set of models, which include the full cohort of controls discussed above.43 A very similar

40The PRGF dummy also includes SAF and ESAF loans, which preceded PRGF, and PRGT programs, which succeeded PRGF as of 2009. See https://bit.ly/2z4zJM8. 41Standardization is done with the scale function in R, which subtracts the column mean from a given value and then divides by the column standard deviation. 42Because I utilize negative binomial models and standardized data, the coefficients on continuous variables should be interpreted as follows: for a one standard deviation change in the predictor variable, the difference in the logs of expected counts is expected to change by the respective regression coefficient, given the other predictor variables in the model are held constant. For binary or count measures, such as war or outside option member, the relevant change is simply one unit. 43See Lall(2016) on the benefits of multiple imputation. Imputation is done with the mice (multivariate

20 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.302∗∗∗ −0.196∗∗∗ (0.110) (0.061) Cooperative member 0.136 −0.072 (0.101) (0.070) Outside option amount −0.0003 −0.013 (0.016) (0.010) Cooperative amount −0.019 0.004 (0.014) (0.009) Year 0.022∗∗∗ 0.013∗∗∗ (0.002) (0.001) Country fixed effects Yes Yes Model type Negative binomial Negative binomial N 2121 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table 2: Preliminary Analysis (Select Controls and No Imputation). All independent variables are lagged by one year. Robust standard errors are clustered at the country-level. approach to missing data in the emergency lending space is taken by Schneider and Tobin (2020); as they note, data is not missing at random: “crisis countries with missing data tend to be poorer and have weaker democratic institutions... coefficient estimates would likely be inefficient and biased if listwise deletion [was utilized] for missing data.” The first set of tests can be found in Table2, while the second set can be found in Table 3. Results largely accord with expectations. States that become members of outside option institutions receive fewer conditions covering fewer issue areas. This is not the case for those that become members of more cooperative IOs; if anything, these countries receive harsher conditionality packages. These results therefore suggest that cooperation and competition among IOs affect the credibility of member states’ exit options. The effect of membership in an outside option RFA is substantively meaningful. Using the coefficients from the model with the full cohort of control variables included, becoming a member of an outside option institution is associated with a 22 percent decrease in the number of conditions imposed on a country by the IMF, and these conditions cover around imputation by chained equations) function and package in R. This package allows me to use logistic and OLS regressions to impute dichotomous and continuous variables respectively. The cart method is used, which relies on classification and regression trees. I replace missing data with five sets of simulated values, adjusting the estimates for uncertainty.

21 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.207∗∗ −0.140∗∗ (0.104) (0.061) Cooperative member 0.369∗∗∗ 0.070 (0.082) (0.071) Outside option amount −0.010 −0.021∗∗ (0.016) (0.010) Cooperative amount −0.023∗ 0.006 (0.013) (0.009) Duration −0.061∗∗ −0.027∗ (0.025) (0.015) Quota 0.153∗∗∗ 0.090∗∗∗ (0.055) (0.027) PRGF 0.635∗∗∗ 0.432∗∗∗ (0.067) (0.040) Time from last IMF program −0.003 −0.0001 (0.007) (0.004) Neoliberal ideology −0.022 −0.001 (0.016) (0.010) Polity2 0.018 0.032∗ (0.030) (0.017) UN voting (ideal pt dist from U.S.) 0.159∗∗∗ 0.101∗∗∗ (0.027) (0.017) UNSC member −0.104 −0.068 (0.065) (0.043) Checks 0.023 0.015∗ (0.015) (0.008) U.S. aid −0.121∗∗∗ −0.057∗∗∗ (0.021) (0.013) FDI / GDP −0.039∗∗ −0.012 (0.016) (0.010) GDPPC 0.126∗∗ 0.072∗∗ (0.060) (0.035) Openness 0.033 −0.002 (0.024) (0.017) Debt service / exports 0.002 0.003 (0.016) (0.010) Short-term debt / exports −0.010 −0.009 (0.018) (0.010) War −0.060 0.025 (0.052) (0.029) Election year 0.092∗ 0.041 (0.055) (0.029) Year 0.006∗ 0.002 (0.003) (0.002) Country ﬁxed eﬀects Yes Yes Model type Negative binomial Negative binomial N 2121 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table 3: Primary Analysis (with Controls and Imputation). All independent variables except for duration and prgf are lagged by one year. Robust standard errors are clustered at the country-level. Missing data is imputed with multiple imputation.

15 percent fewer policy areas. These results suggest that the Fund is cognizant that member states may exercise their outside options, and they award concessions to prevent them

22 from doing so. My interview research supports this claim, as an EFSD oﬃcial noted that the primary motive for cooperation is to mitigate forum shopping: “The purpose... [is] to communicate the same message to authorities to prevent facility shopping.” Covariate results are also broadly consistent with expectations. Countries that receive larger sums than usual from outside option IOs appear to receive less burdensome conditionality packages, which could be evidence of substitution away from IMF lending. Consistent with Stone(2008), countries that become closer to the U.S. receive breaks on conditionality. Last, countries that develop more institutional checks receive more conditions in accordance with Vreeland(2005). The only covariate that exhibits counterintuitive behavior in my

models is quota, as countries that gain formal power at the Fund receive more stringent conditionality packages. It could be that these countries possess greater capacity to implement reforms, which leads the IMF to ask more of them.

Robustness Checks

I perform a series of robustness checks to increase confidence in my results. First, I attempt to adjust for selection into IMF programs. In contrast to important work in the literature (Stone 2008; Copelovitch 2010), I opt not to control for selection in my primary model specifications. This is not for a lack of desire to account for selection bias, but instead a lack of feasibility, as existing selection approaches in the context of IMF conditionality are problematic. Stone(2011, 134-135) details the challenges, suggesting that selection approaches have poor convergence properties and often introduce additional bias into the second stage models. Nonetheless, I replicate my main analysis with the addition of a selection term. I first predict the probability that a country will participate in an IMF program in a given year with a binary probit that includes the instrument from Forster et al.(2019). 44 Specifically, the instrument is an interaction between the Fund’s budget constraint (as measured by the ratio

44This is similar to the approach taken by Lang(2016) and defended as excludable by Stubbs et al.(2020).

23 of member states receiving support from the Fund in a given year) and a country’s likelihood of drawing on Fund support (as measured by the ratio of years in the data that a country has participated in an IMF program). As Stubbs et al.(2020, 46) suggest, this instrument meets the exclusion restriction because: “country-specific changes in conditionality that deviate from its long-run average are brought about only by decisions of the IMF that do not pertain to any given country, such as the introduction of social spending floors in the late-1990s or the streamlining initiative of the early 2000s.” They further suggest that the primary barriers to identification are the “potential direct effects of the general propensity of a country to obtain a specific amount of conditions in any given year on the outcome variable,” but such effects are absorbed by country fixed-effects. The first stage model also includes all covariates from the primary analysis aside from program characteristics (duration and prgf) and proportion neoliberal because the data only covers IMF program participants. I also add measures of demand for IMF assistance from Stone(2008)– reserves and current account / gdp. First stage results can be found in Table A10. Notably, the instrument passes a weak instrument Wald test (F = 30). I then incorporate these probabilities as inverse probability weights in the second stage.45 Results are even stronger with the selection adjustment than in the primary models, as outside option member is statistically significant at the 0.01 level for both DVs (Table A11).46 Second, while the results in Table2 should mitigate some of the concern that my results are driven by imputation alone, I also perform an analysis with an additional set of the most theoretically important covariates (i.e. those related to political leverage) that have relatively few missing observations, and I drop observations with missing data.47 Results remain robust

45This approach has been utilized in extant work. See e.g. Clark and Dolan(2020). 46I also attempt a Heckman selection adjustment utilizing the heckit function from the sampleSelection package in R, but the models will not converge with either DV. Convergence problems are common with these techniques, as Stone(2011) details. 47I run a set of models where all covariates are included and rows with missing data are dropped. Only around 300 observations remain, and results are not robust (Table A17). However, given the limited number of observations, I do not put much stock in these results.

24 (Table A13). Results similarly hold when independent variables are not lagged (see Table A14). This is important because the time between when a country initially applies for IMF assistance and when a country shows up in the data (i.e. when a loan is approved) may be much less than a year in some cases; approval takes several months on average, though it can be expedited during emergencies.48 Further, to alleviate concerns that the Greek Eurocrisis case, which involved protracted cooperation among the IMF and European RFAs, is an outlier driving my results, I drop all Greek project-years from my data and repeat my analysis. Results are similar (Table A15). Next, while the inclusion of a time trend is more common than year fixed-effects in the IMF conditionality literature (Stone 2008; Copelovitch 2010), I swap year dummies for the time trend in a robustness check (Table A16). Results are substantively similar. An additional question is whether co-financing and information sharing have distinct effects on conditionality when disaggregated. Table A18 replicates the primary analysis with membership in IOs that co-finance and share information included as separate covariates.

These measures also capture the depth of cooperation among IOs – co-financing and information sharing capture the number of active cooperative agreements between RFAs and the IMF relevant to a given country in a given year. outside option member remains highly statistically significant with the coefficient signed in the negative direction. The only cooperative term to obtain statistical significance, meanwhile, is information sharing, which is positively associated with the number of conditions included in an IMF program. This result suggests that when IOs pool otherwise private information, they might opt for more burdensome conditionality packages. More broadly, I conclude that membership in cooperative IOs is decidedly not associated with a reduction in the stringency of conditionality; if anything, as the consistently positive sign on cooperative member across models indicates, cooperation may drive harsher conditionality. Next, I drop all concessional and development-focused loans (financed with SAF, ESAF,

48See https://bit.ly/2JS69eZ.

25 PRGF, and PRGT) from the data, as these loans might face more competition from the World Bank and MDBs than the Fund (Table A19) – this eliminates nearly half of the sample. The coeﬃcient on outside option member remains negative and substantively large, though it does not achieve statistical signiﬁcance for the count of conditions DV. The results suggest that RFAs do compete with the IMF on concessional, conditional loans. This is possibly because the IMF and World Bank have carved out a division of labor; the Fund is careful to point out that PRGF and related instruments pertain only to policy areas that fall under the Fund’s mandate, such as macroeconomic policies and structural reforms,49 while World Bank conditional lending (DPF) covers softer areas like the environment (Clark and

Dolan 2020). Moreover, as the coeﬃcients on PRGF in Table3 indicate, these programs actually tend to include harsher conditionality packages than standard SBAs. It is then perhaps unsurprising that my core results are reliant on the inclusion of PRGF programs – it is when programs are most burdensome that countries should most aggressively attempt to bargain down conditions. A ﬁnal concern is that the count of conditions utilized as my primary dependent variable fails to capture enforcement. Indeed, conditions are often waived by the IMF such that countries do not have to implement the requested reforms (Stone 2011; Nelson 2017). For robustness, I therefore adjust the count of conditions DV by subtracting out all conditions that were waived by the IMF.50 The alternate DV then captures the total number of conditions that were actually enforced by the IMF in a given program-year. Results are again robust (Table A20); this suggests that countries are asked to implement fewer reforms when they have access to outside option institutions.

Text Analysis

While my regressions suggest that as countries join competitive RFAs, they receive fewer conditions covering fewer policy areas, I supplement my regression results with text analysis

49See https://www.imf.org/external/np/exr/facts/prgf.htm. 50Data on waivers comes from Kentikelenis, Stubbs and King(2016).

26 Public spending

Privatization

Foreign exchange policy

Bureaucratic tasks

−0.04 −0.02 0.00 0.02 0.04

Change in topical prevalence when joining outside option IO

Figure 3: Text Analysis (Outside Option IO). designed to capture whether the substance of IMF conditions also becomes less stringent. I utilize the text of conditions from Kentikelenis, Stubbs and King(2016) and estimate a structural topic model on these data.51 The topic model results showing the eﬀect of joining an outside option IO can be found in Figure3. The ten most common words found in each topical category appear in Figure A7. When a country becomes a member of a competitive RFA, they receive more IMF conditions covering bureaucratic tasks like the publication of reports or generation of statistical information. For example, “the government will produce a road-map with a strategy to build capacity and improve the institutional framework to respond promptly to economic crime.”

51To do so, I use the stm package in R to tune the number of topics and estimate parameters. The ﬁnal models ﬁt 9 topics with outside option member and cooperative member as covariates.

27 Public spending

Monetary policy

Publications

Trade policy

Privatization

Tax law

Bureaucratic tasks

External debt payments

−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15

Change in topical prevalence when joining cooperative IO

Figure 4: Text Analysis (Cooperative IO).

These countries are similarly asked to pursue liberalized foreign exchange policy, as in: “en- sur[ing] free availability of foreign exchange cash.” These tasks are much less burdensome than the privatization of large public corporations or cuts to public spending campaigns. For instance, countries that join competitive IOs are less likely to be asked to undertake “[the] privatization of the drilling company SONAFOR, the tapestry weaving company MSAD, and the reinsurance company SENRE” or “[the] adoption of a three-year public investment program... consistent with... medium-term macroeconomic objectives.” Privatization often involves shedding public sector employees and wage cuts for those that remain, while many of the public spending conditions speciﬁcally mention slashing public health-care and education spending.

28 In contrast to countries that join outside option institutions, those that become members of more cooperative IOs do not receive unambiguously softer conditions, as can be seen in Figure4. While members of cooperative IOs do receive more conditions covering softer areas like bureaucratic tasks and the publication of reports, the IMF is also much more likely to demand burdensome reforms to tax laws and cuts to public spending. Tax reforms typically involve ﬁnding ways to extract more revenue from consumers and corporations, as when the IMF asks “parliament to approve a tax reform package including... the elimination of several tax exemptions and preferential regimes under the corporate income tax.” While several topics change prevalence when a country joins a cooperative IO, these countries are not clearly better oﬀ, in contrast to those joining competitive IOs. These results then reinforce those from the interviews and regression analyses.

Conclusion

In this paper, I develop a theory of when overlapping IOs serve as credible outside options for member states. I argue that cooperation is an effective way for IOs to undercut exit options. As such, countries can only credibly threaten to forum shop when IOs compete with one another. Doing so allows these states to bargain down their obligations in IOs. Evidence from regression analysis performed on an original dataset of cooperation among emergency lending IOs offers support for this theory, as do supplemental interviews and text analysis. This paper makes several theoretical contributions. First, I refine existing accounts of when states can forum shop between institutions. While the literature views all IOs whose activities overlap as potential forum shopping destinations, I highlight the importance of the interactions between these organizations. Specifically, I show that states can only exercise their exit options when they belong to multiple IOs whose activities overlap, outside institutions can plausibly offer them more lenient terms, and the IOs in question do not cooperate

29 with one another. Second, in line with extant research on IO autonomy and bureaucratic culture,52 and in contrast to the forum shopping literature,53 I find that IOs have agency under regime complexity.54 My findings carry important policy implications, as it may be wise for IO staff to pursue cooperation with other organizations in their issue spaces. Recently, IMF President Kristalina Georgieva agreed, emphasizing the importance of IMF cooperation with RFAs to combat COVID-19.55 However, my interviews and network analysis suggest that staff may bear the interests of powerful member state principals in mind when they cooperate. A former senior economist at the IMF said the following on the matter: “In a perfect world, there would be perfect coordination... but it is most seamless when it is between the IMF and institutions like the World Bank due to locational and U.S. advantages. The shareholders should be weighing in on the program design before it is presented to the Board, and those shareholders are the same and weighted the same at both institutions.” In other words, while the IMF would like to co-opt all potential competitors to eliminate the threat of forum shopping, political misalignment and friction between staff from different IOs can be obstructive. This relates to existing work showing how member state preferences affect even decentralized and autonomous policymaking decisions in IOs.56 This research also points to several compelling areas for future work. Scholars could conduct similar empirical exercises in other issue areas to evaluate the generalizability of my theoretical framework; I believe that it ought to apply to any area where member states bargain with functionally similar, overlapping IOs over the stringency of policies. It must also be the case that organizations sometimes formally cooperate. While the emergency lending space is characterized by relatively low levels of institutional density, there is little reason to believe that the argument should not apply to areas where there are a larger number

52See Barnett and Finnemore(1999); Johnson(2014). 53See Alter and Meunier(2009); Davis(2009). 54Others have made similar claims, including Pratt(2017). 55See https://bit.ly/3bEbwuN. 56See e.g. Clark and Dolan(2020).

30 of functionally similar institutions, such as the development space. If anything, we might expect competition to be a bigger problem in areas like development, as countries have access to more outside options, which could translate to additional bargaining leverage.57 Beyond development, another potential area of study is operational military cooperation, which scholars have previously examined through the lens of regime complexity.58 In both the development and security spaces, IOs share information and cooperate on the imple- mentation of programs; for instance, the World Bank and AIIB have signed a cooperation framework,59 and the UN, African Union, and EU cooperate on security and peacekeeping in Africa (Brosig 2011). Importantly, even in spaces where co-ﬁnancing or information sharing are less common, the concept of cooperation among IOs can be broadened – other forms include summits organized and attended by staﬀ from various IOs and the joint author- ship of technical reports. Widening the scope of cooperation studied might permit further opportunities for generalization.

57See Lipscy(2015); Pratt(2017). 58See Hofmann(2009), for instance. 59See https://bit.ly/2Y72baj.

31 References

Abbott, Kenneth W., Philipp Genschel, Duncan Snidal and Bernhard Zangl, eds. 2015. In- ternational Organizations as Orchestrators. Cambridge : New York: Cambridge University Press.

Alter, Karen J. and Kal Raustiala. 2018. “The Rise of International Regime Complexity.” Annual Review of Law and Social Science 14:329–349.

Alter, Karen J. and Sophie Meunier. 2009. “The Politics of International Regime Complex- ity.” Perspectives on Politics 7(1):13–24.

Bailey, Michael A., Anton Strezhnev and Erik Voeten. 2017. “Estimating Dynamic State Preferences from United Nations Voting Data.” Journal of Conﬂict Resolution 61(2):430– 456.

Barnett, Michael N. and Martha Finnemore. 1999. “The Politics, Power, and Pathologies of International Organizations.” International Organization 53(4):699–732.

Brosig, Malte. 2011. “The Emerging Peace and Security Regime in Africa: The Role of the EU.” European Foreign Aﬀairs Review 16(1):111–115.

Bulir, Ales and Soojin Moon. 2004. “Is Fiscal Adjustment More Durable When The IMF is Involved?” Comparative Economic Studies 46(3):373–399.

Busch, Marc L. 2007. “Overlapping Institutions, Forum Shopping, and Dispute Settlement in International Trade.” International Organization 61(4):735–761.

Carnegie, Allison and Austin Carson. 2019. “The Disclosure Dilemma: Nuclear Intelligence and International Organizations.” American Journal of Political Science 63(2).

Clark, Richard and Lindsay Dolan. 2020. “Pleasing the Principal: U.S. Inﬂuence in World Bank Policymaking.” American Journal of Political Science . Forthcoming. URL: https://bit.ly/2VGMKmG

Copelovitch, Mark S. 2010. “Master or Servant? Common Agency and the Political Economy of IMF Lending.” International Studies Quarterly 54(1):49–77.

Davis, Christina L. 2009. “Overlapping Institutions in Trade Policy.” Perspectives on Politics 7(1):25–31.

Dreher, Axel. 2009a. The Development of IMF and World Bank Conditionality. In The Law and Economics of Globalization: New Challenges for a World in Flux, ed. Linda Yueh. Cheltenham: Edward Elgar. Chapter 6.

Dreher, Axel. 2009b. “IMF Conditionality: Theory and Evidence.” Public Choice 141:233– 267.

32 Dreher, Axel, Jan-Egbert Sturm and James Raymond Vreeland. 2009. “Development Aid and International Politics: Does Membership on the UN Security Council Inﬂuence World Bank Decisions?” Journal of Development Economics 88(1):1–18.

Dreher, Axel, Jan-Egbert Sturm and James Raymond Vreeland. 2015. “Politics and IMF Conditionality.” Journal of Conﬂict Resolution 59(1):120–148.

Dreher, Axel and Nathan M. Jensen. 2007. “Independent Actor or Agent? An Empirical Analysis of the Impact of U.S. Interests on International Monetary Fund Conditions.” The Journal of Law & Economics 50(1):105–124.

Feldstein, Martin. 1998. “Refocusing the IMF.” Foreign Aﬀairs pp. 20–33.

Forster, Timon, Bernhard Reinsberg, Alexander Kentikelenis, Lawrence King and Thomas Stubbs. 2019. “How Structural Adjustment Programs Aﬀect Inequality: A Disaggregated Analysis of IMF Conditionality, 1980-2014.” Social Science Research 80:83–113.

Gehring, Thomas and Benjamin Faude. 2014. “A Theory of Emerging Order within Institu- tional Complexes: How Competition among Regulatory International Institutions Leads to Institutional Adaptation and Division of Labor.” The Review of International Organi- zations 9(4):471–498.

Goldstein, Morris. 2003. IMF Structural Programs. In Economic and Financial Crises in Emerging Market Economies. Chicago: University of Chicago Press pp. 363–437.

Helfer, Laurence R. 2009. “Regime Shifting in the International Intellectual Property Sys- tem.” Perspectives on Politics 7:39–44.

Henning, C. Randall. 2017. Tangled Governance: International Regime Complexity, the Troika, and the Euro Crisis. First edition ed. Oxford, United Kingdom: Oxford University Press.

Henning, Randall C. 2011. “Coordinating Regional and Multilateral Financial Institutions.”. Working Paper 11-9. URL: https://www.piie.com/publications/working-papers/coordinating-regional-and- multilateral-ﬁnancial-institutions

Hernandez, Diego. 2017. “Are “New” Donors Challenging World Bank Conditionality?” World Development pp. 529–549.

Hofmann, Stephanie C. 2009. “Overlapping Institutions in the Realm of International Secu- rity: The Case of NATO and ESDP.” Perspectives on Politics 7(1):45–52.

Humphrey, Chris and Katharina Michaelowa. 2013. “Shopping for Development: Multilat- eral Lending, Shareholder Competition and Borrower Preferences.” World Development 44(3):142–155.

Jaggers, Keith and Ted Robert Gurr. 1995. “Tracking Democracy’s Third Wave with the Polity III Data.” Journal or Peace Research 32(4):469–482.

33 Johnson, Tana. 2014. Organizational Progeny: Why Governments Are Losing Control over the Proliferating Structures of Global Governance. Transformations in governance Oxford: Oxford University Press.

Kentikelenis, Alexander E., Thomas H. Stubbs and Lawrence P. King. 2016. “IMF Con- ditionality and Development Policy Space, 1985-2014.” Review of International Political Economy 23(4):543–582.

Keohane, Robert O. and David G. Victor. 2011. “The Regime Complex for Climate Change.” Perspectives on Politics 9(1):7–23.

Lall, Ranjit. 2016. “How Multiple Imputation Makes a Diﬀerence.” Political Analysis 24:414– 443.

Lang, Valentin F. 2016. “The Economics of the Democratic Deﬁcit: The Eﬀect of IMF Programs on Inequality.” pp. 83–113. Unpublished manuscript. URL: https://bit.ly/2K5WhhR

Li, Larry, Malick Sy and Adela McMurray. 2015. “Insights Into the IMF Bailout Debate: A Review and Research Agenda.” Journal of Policy Modeling 37:891–914.

Lipscy, Philip Y. 2015. “Explaining Institutional Change: Policy Areas, Outside Options, and the Bretton Woods Institutions.” American Journal of Political Science 59(2):341–356.

Lipscy, Phillip Y. 2017. Renegotiating the World Order by Phillip Y. Lipscy. Cambridge : New York: Cambridge University Press.

Miyoshi, Toshiyuki, Stephanie Segal, Preya Sharma and Anish Tailor. 2013. “Stocktaking the Fund’s Engagement with Regional Financing Arrangements.”. URL: https://www.imf.org/external/np/pp/eng/2013/041113b.pdf

Montinola, Gabriella R. 2010. “When Does Aid Conditionality Work?” Studies in Compar- ative International Development 45(3):358–382.

Morse, Julia C. and Robert Keohane. 2014. “Contested Multilateralism.” Review of Interna- tional Organizations 9(4):385–412.

Nelson, Stephen C. 2014. “Playing Favorites: How Shared Beliefs Shape the IMF’s Lending Decisions.” International Organization 68(2):297–328.

Nelson, Stephen C. 2017. The Currency of Conﬁdence: How Economic Beliefs Shape the IMF’s Relationship with Its Borrowers. Ithaca: Cornell University Press.

Porter, Nathan, Kenji Moriyama, Pragyan Deb, Johannes Eugster, Chengyu Huang, Jeta Menkulasi, Tania Mohd Nor and Camilo E. Tovar. 2017. “Collaboration between Regional Financing Arrangements and the IMF–Background Paper.”. IMF Policy Paper. URL: https://bit.ly/31tBMDx

34 Porter, Nathan, Kenji Moriyama, Pragyan Deb, Johannes Eugster, Jeta Menkulasi, Tania Mohd Nor and Camilo E. Tovar. 2017. “Collaboration between Regional Financing Arrangements and the IMF–Background Paper.”. IMF Policy Paper. URL: https://www.imf.org/en/Publications/Policy-Papers/Issues/2017/07/31/pp073117- background-paper-collaboration-between-regional-ﬁnancing-arrangements-and-the-imf

Pratt, Tyler. 2017. “Angling for Inﬂuence: Institutional Proliferation in Development Bank- ing.”. Unpublished. URL: https://bit.ly/2LBRo2M

Pratt, Tyler. 2018. “Deference and Hierarchy in International Regime Complexes.” Interna- tional Organization 72(3):561–590.

Radelet, Steven, Jeﬀrey D. Sachs, Richard N. Cooper and Barry P. Bosworth. 1998. “The East Asian Financial Crisis: Diagnosis, Remedies, Prospects.” Brookings Papers on Economic Activity 1998(1):1–90.

Scartascini, Carlos, Cesi Cruz and Philip Keefer. 2018. The Database of Political Institutions 2017. URL: https://publications.iadb.org/en/database-political-institutions-2017-dpi2017

Schneider, Christina J. and Jennifer L. Tobin. 2020. “The Political Economy of Bilateral Bailouts.” International Organization 74(1):1–29.

Stone, Randall W. 2008. “The Scope of IMF Conditionality.” International Organization 62:589–620.

Stone, Randall W. 2011. Controlling Institutions: International Organizations and the Global Economy. New York, N.Y.: Cambridge University Press.

Stubbs, Thomas, Bernhard Reinsberg, Alexander Kentikelenis and Lawrence King. 2020. “How to Evaluate the Eﬀects of IMF Conditionality.” The Review of International Orga- nizations 15:29–73.

Vaubel, Roland. 1991. The Political Economy of the IMF: A Public Choice Analysis. In The Political Economy of International Organizations, ed. Roland Vaubel and Thomas Willett. Boulder: Westview Press.

Vreeland, James R. 1999. “The IMF: Lender of Last Resort or Scapegoat.”. Unpublished. URL: https://www.ﬁles.ethz.ch/isn/30242/1999-03.pdf

Vreeland, James R. 2003. The IMF and Economic Development. Cambridge: Cambridge University Press.

Vreeland, James R. 2005. “The International and Domestic Politics of IMF Programs.”. Unpublished manuscript. URL: https://leitner.yale.edu/sites/default/ﬁles/ﬁles/resources/docs/vreeland.pdf

35 Supporting Information for “Bargain Down or Shop Around? Outside Options and IMF Conditionality”

June 23, 2020

1 Contents

1 Appendix 1: Interviews3

2 Appendix 2: Data and Sources4

3 Appendix 3: Cooperation Coding Procedures and Examples5

4 Appendix 4: Descriptive Supporting Statistical Information7

5 Appendix 5: Robustness Checks 18

2 1 Appendix 1: Interviews

I chose to interview officials who were involved in the negotiation of cooperation arrangements with other IOs during the time period under study. This includes both current and former officials from emergency lending IOs. Because only experienced, senior officials are involved in these high-level decisions and negotiations, I only spoke to a handful of decision- makers – a convenience sample. While three officials allowed me to quote them directly in this paper, officials from several other institutions pointed me to helpful online resources or allowed me to utilize their comments for deep background. I conducted all interviews myself.

ID Interviewee Date Mode of Contact Interview A Acting Director of Budget Support Loans Project Group of the EFSD September 16, 2019 Phone Interview B Former Senior Economist at IMF February 25, 2020 Phone Interview C Former Project Team Economist at IMF April 15, 2020 Phone

Table A1: Interviews.

Interviews were semi-structured and focused on the following questions:

1. What is the process for negotiating a loan at your IO?

2. Is there conditionality at your institution? If so, how does it diﬀer from conditionality at the IMF?

3. How are co-ﬁnancing and information sharing arrangements negotiated? Is one more diﬃcult than the other?

4. Do countries try to bargain down their conditionality? What does this look like, and is it relatively easy or diﬃcult?

5. Do you have any sense of why I observe reduced conditionality for some RFA members?

3 2 Appendix 2: Data and Sources

Variable Definition Source Number of prior actions Count of prior actions applied to a country in a project-year. Kentikelenis, Stubbs and King(2016). Number of categories Count of the number of policy categories that conditions span in a project year. Computed based on categorical codings and conditionality data from Kentikelenis, Stubbs and King(2016). Outside option member Binary indicating whether a country is a member of an IMF competitor IO in a given year. Hand-coded from IMF online resources. Cooperative member Binary indicating whether a country is a member of a cooperator of the IMF in a given year. Hand-coded from IMF online resources. Outside option amount Size of loan received from an IMF competitor IO in a given year in millions of constant USD. Hand-coded from RFA online resources and based on communications with the organizations. Cooperative amount Size of loan received from a cooperator of the IMF in a given year in millions of constant USD. Hand-coded from RFA online resources and based on communications with the organizations. Duration Length of IMF program in days. Kentikelenis, Stubbs and King(2016). Quota Size of a countryâĂŹs IMF quota in a given year in millions of constant USD. Downloaded from IMF. PRGF Binary indicating whether a project is of the Poverty Relief and Growth Facility type. Extracted from Kentikelenis, Stubbs and King(2016). Time from last IMF program Number of years since a country was last in an IMF program. Computed based on data from Kentikelenis, Stubbs and King(2016). Neoliberal ideology Neoliberal ideology scores from Nelson(2014), which code the proportion of the top policy officials in a given country-year that are neoliberals based on their place of education. Nelson(2014). Polity2 Polity2 democracy score received by a given country in a given year. Jaggers and Gurr(1995). UN voting (ideal pt dist from U.S.) Absolute distance between the ideal point of a given country and the U.S. in a given year based on UNGA votes. Bailey, Strezhnev and Voeten(2017). Checks Number of institutional checks that exist in a countryâĂŹs government. Scartascini, Cruz and Keefer(2018). U.S. aid Logged gross millions of current USD of ODA from the U.S. received by a country in a given year. World Development Indicators. UNSC member Binary indicating whether a country is a rotating UNSC member in a given year. Dreher, Sturm and Vreeland(2009) and hand-coding to update. FDI / GDP FDI as a fraction of GDP in constant USD. World Development Indicators. GDPPC Logged per-capita GDP in constant USD. World Development Indicators. Openness Trade as a fraction of GDP in constant USD. World Development Indicators. Debt service / exports Debt service as a fraction of total exports in constant USD. World Development Indicators. Short-term debt / exports Short-term debt as a fraction of total exports in constant USD. World Development Indicators. War Binary indicating whether a country is at war in a given year. I use the conflict incidence variable from the onset dataset from UCDP. Uppsala Conflict Data Program (UCDP). Election year Binary indicating whether a country held a legislative or executive election in a given year. National Elections Across Democracy and Autocracy (NELDA). Table A2: Variables, Definitions, and Sources. 4 3 Appendix 3: Cooperation Coding Procedures and Ex- amples

My coding effort covers two primary forms of cooperation among emergency lending IOs: co-financing and information sharing. I will discuss the coding procedures relevant to each form of cooperation in turn. To identify instances of co-financing, I utilized a combination of program documents, policy papers, and direct contact with relevant IOs. Fortunately, the IMF has documented its own co-financing operations in a series of policy papers on the topic of cooperation with RFAs. Relevant papers include Porter, Moriyama, Deb, Eugster, Huang, Menkulasi, Nor and Tovar(2017) and Porter, Moriyama, Deb, Eugster, Menkulasi, Nor and Tovar(2017). Beyond these papers, I utilized program documents from the IMF online archives and the eight RFAs, which can be found on their respective websites; these documents helped me to determine the funding breakdown between the IMF and the other organizations when it was not clear from the policy papers. Any cooperative funds supplied by RFAs were coded into the cooperative amount column. Program documents also allowed me to code cooperation among the European RFAs for the network analysis. Co-financing agreements are relevant to a given country if (a) two IOs sign a co-financing agreement specific to a program in that country or (b) two IOs sign a broader co-financing framework mandating that any loan be co-financed by two IOs. An example of the former is the AMF and IMF co-financing agreement discussed in the introduction of the paper, which applied only to Tunisia and terminated at the end of that country’s program. An example of the latter is the co-financing agreement active between the EFSM and IMF, which mandates that “[EFSM] activation will be in the context of a joint EU/IMF support.”1 Next, these policy papers and project documents were used to code the outside option amount variable, which covers outflows from RFAs that were not part of a co-financed program. All of the RFAs include program documentation and statistics on their financial po- sition relative to member states online aside from the FLAR. For instance, Arabstat is a ter- rific resource from the AMF (https://www.amf.org.ae/en/arabic_economic_database) and the ESM provides detailed program information on their website (https://www.esm. europa.eu/financial-assistance). Because FLAR lacks detailed online reporting, I directly contacted them to inquire about lending data, and they provided me with detailed statistics on the institution’s lending to member states over time. Coding information sharing was slightly more complicated. All information sharing agreements between two IOs are relevant to all member states, as they are typically much broader than co-financing agreements, and they persist indefinitely rather than applying specifically to one program. Once again, the IMF’s policy papers (cited above) were invaluable, as they discuss whether the Fund has formal information sharing provisions in place with each RFA. Information sharing among the European RFAs, meanwhile, occurs by definition, as all three institutions fall under the umbrella of the EU and share office space; I learned this through direct email contact with staff at the ESM. Otherwise, I primarily utilized press releases – each organization’s website contains press releases dating back to roughly the year 2000, and cooperation among emergency lending IOs did not originate until after 2000 (this

1See Porter, Moriyama, Deb, Eugster, Huang, Menkulasi, Nor and Tovar(2017).

5 information was obtained through interviews and email contact with the organizations), so press releases cover all years of potential cooperation. Therefore, I searched each website by keyword for mentions of the other organizations by name and abbreviation and for the terms "information," "cooperation," and "information sharing." The only case of information sharing identified between the IMF and an RFA is with the ESM. The language of the information sharing agreement is as follows: “The ESM proposal provides for continued involvement of the IMF, for example, in surveillance, program design, and the triggering of collective action clauses in sovereign bond contracts. ESM General Terms for Financial Assistance Facility Agreements published in 2012 dictates that so long as any amount is outstanding, the beneficiary member state is obliged to supply all documents dispatched by the member state to the IMF under an IMF arrangement.”2 The membership variables utilized in the analyses – cooperative member and outside option member – were coded based on information from IMF policy papers as well as the organizations’ websites, which contain information about memberships and join dates for each member. Where information was incomplete (as for the FLAR), I relied on direct contact with staff at the organizations. Organizational membership data are then combined with the cooperation data described above to categorize countries as members of cooperative or competitive institutions. The maximum value taken by the cooperative and competitive membership variables are 2 and 1 respectively.

2See Porter, Moriyama, Deb, Eugster, Huang, Menkulasi, Nor and Tovar(2017) and Porter, Moriyama, Deb, Eugster, Menkulasi, Nor and Tovar(2017).

6 4 Appendix 4: Descriptive Supporting Statistical Infor- mation 15 10 Total Cooperation Total 5 0

1980 1990 2000 2010 2020

Year

Figure A1: Total Cooperation Among Emergency Lending IOs Over Time. Coop- eration is calculated as the number of information sharing and co-ﬁnancing arrangements that are active between all IOs in the space in a given year.

7 15 10 Total Competition Total 5 0

1980 1990 2000 2010

Year

Figure A2: Total Competition Among Emergency Lending IOs Over Time. Com- petition is calculated as the number of outside option (or non-cooperative) organizations with respect to the IMF that all countries participating in an IMF program belong to in a given year.

8 Armenia

Hungary

Jordan Country

Latvia

Portugal

Tunisia

2008 2011 2012 2013 2014 Year

Figure A3: Change in Membership in Cooperative IOs. All changes are from 0 to 1.

9 Armenia

Belarus

Cambodia

Comoros

Costa Rica

Indonesia

Jordan

Country Korea

Kyrgyz Republic

Lao PDR

Philippines

Tajikistan

Tunisia

Vietnam

1994 2000 2001 2009 2012 2013 2014 Year

Figure A4: Change in Membership in Outside Option IOs. All changes are from 0 to 1 except for Armenia 2014, Jordan 2012, and Tunisia 2013, which change from 1 to 0.

10 Year AMF IMF 1984 149.5 90.0 1985 123.6 234.1 1986 312.4 361.6 1987 270.4 293.2 1988 198.3 417.3 1989 800.3 266.6 1990 0.0 100.0 1991 382.0 481.2 1992 150.5 170.3 1993 5.6 400.0 1994 75.5 170.6 1995 106.5 1306.3 1996 97.6 472.2 1997 110.3 264.8 1998 22.4 0.0 1999 119.2 189.5 2000 0.0 0.0 2001 69.0 0.0 2002 12.5 85.3 2003 0.0 6.4 2004 0.0 297.1 2005 0.0 475.4 2006 88.4 16.1 2007 153.5 475.4 2008 97.0 40.3 2009 15.5 13.6 2010 25.5 2697.6 2011 1200.1 5481.4 2012 972 1146.0 2013 163.9 1146.0 2014 0.0 3600.4

Table A3: Comparison of AMF and IMF Lending. The ﬁgures reported in this table denote total annual lending to all AMF member states by both the AMF and IMF for a given year. Lending is reported in millions of current USD. Total AMF lending 1984-2014 is $5.7 billion, and total IMF lending to AMF members is $20.1 billion.

11 Year EFSD IMF 2009 0.0 446.3 2010 70.0 133.4 2011 1240.0 0.0 2012 440.0 0.0 2013 900.0 0.0 2014 210.0 82.2

Table A4: Comparison of EFSD and IMF Lending. The ﬁgures reported in this table denote total annual lending to all EFSD member states by both the EFSD and IMF for a given year. Lending is reported in millions current USD. Total EFSD lending 2009-2014 is $2.86 billion, and total IMF lending to EFSD members is $0.6 billion.

12 Year FLAR IMF 1984 686.0 250.0 1985 284.0 282.4 1986 440.0 125.4 1987 291.0 92.6 1988 250.7 211.4 1989 390.0 4197.9 1990 389.7 94.8 1991 451.3 75.0 1992 19.2 50.0 1993 0.0 1039.1 1994 0.0 231.0 1995 233.6 52.0 1996 33.6 1324.0 1997 0.0 125.0 1998 493.8 101.0 1999 500.0 2410.0 2000 0.0 376.7 2001 0.0 128.0 2002 180.7 2397.1 2003 156.0 286.8 2004 0.0 287.3 2005 400.0 1171.3 2006 0.0 65.0 2007 0.0 172.4 2008 0.0 0.0 2009 480.0 492.3 2010 0.0 0.0 2011 0.0 0.0 2012 514.6 0.0 2013 0.0 0.0 2014 617.6 0.0

Table A5: Comparison of FLAR and IMF Lending. The ﬁgures reported in this table denote total annual lending to all FLAR member states by both the FLAR and IMF for a given year. Lending is reported in millions of current USD. Total FLAR lending 1984-2014 is $6.8 billion, and total IMF lending to FLAR members is $16.7 billion.

13 Year BoP IMF 2002 0.0 240.0 2003 0.0 105.9 2004 0.0 447.0 2005 0.0 0.0 2006 0.0 0.0 2007 0.0 0.0 2008 2798.0 12059.0 2009 10314.0 11443.0 2010 3815.0 0.0 2011 1749.7 3090.6 2012 0.0 0.0 2013 0.0 1751.3 2014 0.0 0.0

Table A6: Comparison of EU BoP and IMF Lending. The ﬁgures reported in this table denote total annual lending to all EU BoP member states by both the BoP and IMF for a given year. Lending is reported in millions of current USD. Total BoP lending 2002-2014 is $18.7 billion, and total IMF lending to BoP members is $29.1 billion.

Year EFSM IMF 2010 0.0 45898.7 2011 36302.3 26832.6 2012 20848.1 23758.3 2013 0.0 2642.3 2014 3000.0 0.0

Table A7: Comparison of EFSM and IMF Lending. The ﬁgures reported in this table denote total annual lending to all EFSM member states by both the EFSM and IMF for a given year. Lending is reported in millions of current USD. Total EFSM lending 2010-2014 is $60.2 billion, and total IMF lending to EFSM members is $99.1 billion.

Year ESM IMF 2010 0.0 45898.7 2011 18793.5 23742.2 2012 72794.2 23758.3 2013 24712.7 891.0 2014 2722.3 0.0

Table A8: Comparison of ESM and IMF Lending. The ﬁgures reported in this table denote total annual lending to all ESM member states by both the ESM and IMF for a given year. Lending is reported in millions of current USD. Total ESM lending 2010-2014 is $119.0 billion, and total IMF lending to ESM members is $94.3 billion.

14 25 20 15 Average Number of Conditions Average 10 5

1980 1985 1990 1995 2000 2005 2010 2015

Year

Figure A5: Average Number of Conditions Applied per IMF Project–Year 1978– 2014. Data comes from IMF Monitor (Kentikelenis, Stubbs and King 2016).

15 800 600 Frequency 400 200 0

0 20 40 60 80 100 120

Number of Conditions

Figure A6: Dispersion of IMF Project Conditions 1978–2014. Data comes from IMF Monitor (Kentikelenis, Stubbs and King 2016). The positive skewness suggests that the data is overdispersed – the variance is greater than the mean. I correct for overdispersion in the data by employing a negative binomial model in subsequent testing.

16 Statistic N Mean St. Dev. Min Max Duration 2121 27.48 10.38 3 48 Number of conditions 2121 15.21 11.48 1 113 PRGF 2121 0.42 0.49 0 1 GDPPC 2121 2842.13 4574.60 153.93 48971.37 U.S. aid 2121 129.05 630.10 0.00 11227.79 Openness 2121 70.08 35.31 9.10 274.97 Debt service / exports 2121 15.63 13.76 0.13 155.43 Short-term debt / exports 2121 28.68 58.28 0.00 1111.66 FDI / GDP 2121 3.21 6.61 −28.62 103.34 Quota 2121 284.34 505.08 2.90 5945.40 UN voting (ideal pt dist from U.S.) 2121 3.00 0.61 1.21 4.78 Polity2 2121 1.99 6.22 −9 10 Checks 2121 2.55 1.51 1 11 Time from last IMF program 2121 1.50 2.06 1 27 Liberal ideology 2121 0.12 0.18 0 1 Cooperative member 2121 0.03 0.21 0 2 Outside option member 2121 0.13 0.34 0 1 Cooperative amount 2121 78.85 1283.65 0 30024 Outside option amount 2121 3.75 37.09 0 1200 Election year 2121 0.11 0.32 0 1 War 2121 0.19 0.39 0 1 UNSC member 2121 0.05 0.22 0 1 Number of categories 2121 5.31 2.49 1 13

Table A9: Conditionality Data Descriptive Statistics

17 5 Appendix 5: Robustness Checks

Participation in IMF program Budget constraint:Participation rate 0.156∗∗∗ (0.034) Outside option member −0.810∗∗∗ (0.135) Cooperative member 0.300 (0.223) Outside option amount 0.055∗∗∗ (0.020) Cooperative amount 0.121∗∗∗ (0.034) Quota 1.042∗∗∗ (0.398) Time to last IMF program 0.006 (0.010) Polity2 0.155∗∗∗ (0.043) Reserves −0.356∗∗∗ (0.063) GDPPC −0.416 (0.390) Current account / GDP −0.050 (0.040) UNSC member −0.214∗∗ (0.098) U.S. aid 0.159∗∗∗ (0.036) DAC aid 0.044 (0.050) UN voting −0.175∗∗ (0.070) FDI / GDP 0.094 (0.074) Inflation −0.018 (0.036) Openness −0.024 (0.054) Debt service / exports 0.238∗∗∗ (0.044) Short-term debt / exports −0.044 (0.062) War −0.166∗∗ (0.084) Election year 0.035 (0.075) Country fixed effects Yes Year fixed effects Yes Model type Probit N 6233 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A10: Selection Adjustment Robustness Check (First Stage). Binomial probit specification with robust standard errors clustered at the country-level. Data is standardized and independent variables are lagged by one year. Missing data imputed using multiple imputation to ensure that each country in the second stage sample has a fitted probability value.

18 Number of prior actions Number of categories Model 1 Model 2 Outside option member −0.306∗∗∗ −0.171∗∗∗ (0.094) (0.061) Cooperative member 0.260 0.121 (0.162) (0.080) Outside option amount −0.009 −0.017∗ (0.019) (0.010) Cooperative amount −0.005 −0.003 (0.022) (0.011) Duration 0.069∗∗ 0.017 (0.031) (0.015) Quota 1.000∗∗∗ 0.478∗∗∗ (0.149) (0.084) PRGF 0.206∗∗∗ 0.241∗∗∗ (0.073) (0.040) Time from last IMF program 0.001 0.001 (0.007) (0.004) Neoliberal ideology −0.016 0.002 (0.016) (0.010) Polity2 0.007 0.031∗ (0.033) (0.018) UN voting (ideal pt dist from U.S.) 0.135∗∗∗ 0.109∗∗∗ (0.031) (0.019) UNSC member −0.250∗∗∗ −0.138∗∗∗ (0.074) (0.044) Checks 0.040∗∗ 0.016∗ (0.016) (0.009) U.S. aid −0.046∗ −0.024 (0.026) (0.015) FDI / GDP −0.042∗∗ −0.019∗ (0.018) (0.010) GDPPC −0.120 −0.004 (0.073) (0.046) Openness −0.018 −0.023 (0.031) (0.021) Debt service / exports 0.002 0.007 (0.017) (0.010) Short-term debt / exports 0.010 −0.006 (0.025) (0.011) War −0.029 0.034 (0.048) (0.028) Election year −0.046 −0.020 (0.059) (0.030) Year −0.008∗ −0.003 (0.004) (0.003) Country ﬁxed eﬀects Yes Yes Inverse probabiliy weights Yes Yes Model type Poisson Poisson N 1961 1961 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A11: Selection Adjustment Robustness Check (Second Stage with Inverse Probability Weights). Robust standard errors are clustered at the country-level. All independent variables except for duration and prgf are lagged by one year. Model type is Poisson. Predicted probabilities of participation are incorporated as inverse probability weights.

19 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.188∗ −0.140∗∗ (0.096) (0.061) Cooperative member 0.322∗∗∗ 0.070 (0.085) (0.071) Outside option amount −0.020 −0.021∗∗ (0.018) (0.010) Cooperative amount −0.019 0.006 (0.014) (0.009) Duration −0.0005 −0.027∗ (0.028) (0.015) Quota 0.122∗∗ 0.090∗∗∗ (0.061) (0.027) PRGF 0.479∗∗∗ 0.432∗∗∗ (0.070) (0.040) Time from last IMF program −0.001 −0.0001 (0.007) (0.004) Neoliberal ideology −0.026 −0.001 (0.017) (0.010) Polity2 0.012 0.032∗ (0.030) (0.017) UN voting (ideal pt dist from U.S.) 0.130∗∗∗ 0.101∗∗∗ (0.028) (0.017) UNSC member −0.135∗∗ −0.068 (0.066) (0.043) Checks 0.039∗∗∗ 0.015∗ (0.015) (0.008) U.S. aid −0.108∗∗∗ −0.057∗∗∗ (0.023) (0.013) FDI / GDP −0.040∗∗ −0.012 (0.019) (0.010) GDPPC 0.045 0.072∗∗ (0.072) (0.035) Openness 0.019 −0.002 (0.026) (0.017) Debt service / exports −0.004 0.003 (0.017) (0.010) Short-term debt / exports −0.011 −0.009 (0.021) (0.010) War −0.058 0.025 (0.053) (0.029) Election year 0.051 0.041 (0.055) (0.029) Year 0.006∗ 0.002 (0.003) (0.002) Constant −8.472 −1.947 (6.012) (3.547) Country ﬁxed eﬀects Yes Yes Model type Poisson Poisson N 2121 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1

Table A12: Poisson Robustness Check. All independent variables except for duration and prgf are lagged by one year. Robust standard errors are clustered at the country-level. Missing data imputed by multiple imputation.

20 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.395∗∗∗ −0.194∗∗ (0.134) (0.081) Cooperative member −0.204 0.005 (0.178) (0.111) Outside option amount −0.007 −0.018∗ (0.016) (0.010) Cooperative amount 0.023 0.004 (0.030) (0.010) UN voting (ideal pt dist from U.S.) 0.340∗∗∗ 0.223∗∗∗ (0.032) (0.021) UNSC member −0.114 −0.068 (0.071) (0.048) U.S. aid −0.089∗∗∗ −0.036∗ (0.032) (0.020) Year 0.015∗∗∗ 0.009∗∗∗ (0.002) (0.001) Country ﬁxed eﬀects Yes Yes Model type Negative binomial Negative binomial N 1798 1798 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A13: No Imputation Robustness Check (Limited Covariates). Robust standard errors are clustered at country-level.

21 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.201∗∗ −0.163∗∗∗ (0.097) (0.057) Cooperative member 0.139 0.179∗ (0.121) (0.091) Outside option amount −0.038∗∗∗ −0.026∗∗∗ (0.015) (0.009) Cooperative amount 0.021 −0.001 (0.021) (0.015) Duration −0.044∗ −0.018 (0.025) (0.015) Quota 0.210∗ 0.111∗∗ (0.116) (0.055) PRGF 0.613∗∗∗ 0.418∗∗∗ (0.062) (0.038) Time from last IMF program −0.009 −0.005 (0.008) (0.005) Neoliberal ideology 0.003 −0.0004 (0.017) (0.010) Polity2 −0.016 0.019 (0.029) (0.017) UN voting (ideal pt dist from U.S.) 0.115∗∗∗ 0.061∗∗∗ (0.028) (0.017) UNSC member −0.047 −0.038 (0.056) (0.037) Checks 0.075∗∗∗ 0.040∗∗∗ (0.014) (0.008) U.S. aid −0.096∗∗∗ −0.054∗∗∗ (0.023) (0.015) FDI / GDP −0.046∗∗∗ −0.025∗∗ (0.016) (0.010) GDPPC −0.028 −0.034 (0.056) (0.041) Openness 0.073∗∗∗ 0.023 (0.023) (0.015) Debt service / exports 0.025∗ 0.018∗ (0.015) (0.010) Short-term debt / exports −0.039∗∗∗ −0.027∗∗∗ (0.015) (0.010) War −0.145∗∗∗ −0.050 (0.053) (0.031) Election year 0.019 −0.008 (0.049) (0.028) Year 0.005 0.002 (0.004) (0.002) Constant −7.479 −3.175 (7.712) (4.064) Country ﬁxed eﬀects Yes Yes Model type Negative binomial Negative binomial N 2121 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A14: No Lag Robustness Check. Robust standard errors are clustered at the country-level. Missing data imputed by multiple imputation.

22 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.206∗ −0.127∗∗ (0.105) (0.061) Cooperative member 0.380∗∗∗ 0.167∗∗∗ (0.103) (0.055) Outside option amount −0.010 −0.021∗∗ (0.016) (0.010) Cooperative amount −0.024∗ −0.001 (0.013) (0.007) Duration −0.062∗∗ −0.028∗ (0.025) (0.015) Quota 0.153∗∗∗ 0.091∗∗∗ (0.055) (0.027) PRGF 0.635∗∗∗ 0.430∗∗∗ (0.067) (0.040) Time from last IMF program −0.004 −0.0005 (0.007) (0.004) Neoliberal ideology −0.022 −0.001 (0.016) (0.010) Polity2 0.020 0.034∗ (0.031) (0.018) UN voting (ideal pt dist from U.S.) 0.163∗∗∗ 0.102∗∗∗ (0.027) (0.017) UNSC member −0.104 −0.068 (0.065) (0.043) Checks 0.021 0.014 (0.015) (0.008) U.S. aid −0.121∗∗∗ −0.056∗∗∗ (0.022) (0.013) FDI / GDP −0.039∗∗ −0.013 (0.016) (0.010) GDPPC 0.134∗∗ 0.067∗ (0.062) (0.036) Openness 0.033 −0.002 (0.024) (0.017) Debt service / exports 0.001 0.002 (0.016) (0.010) Short-term debt / exports −0.010 −0.009 (0.019) (0.010) War −0.061 0.025 (0.052) (0.029) Election year 0.088 0.038 (0.055) (0.029) Year 0.006∗ 0.001 (0.003) (0.002) Country ﬁxed eﬀects Yes Yes Model type Negative binomial Negative binomial N 2114 2114 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A15: Greece Excluded Robustness Check. Robust standard errors are clustered at the country-level. All independent variables except for duration and prgf are lagged by one year. Missing data is imputd by multiple imputation.

23 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.195∗ −0.125∗ (0.104) (0.066) Cooperative member 0.613∗∗∗ 0.178 (0.153) (0.125) Outside option amount 0.003 −0.013 (0.014) (0.010) Cooperative amount −0.033∗∗ −0.002 (0.017) (0.013) Duration 0.014 0.011 (0.022) (0.014) Quota −0.048 −0.018 (0.035) (0.020) PRGF 0.284∗∗∗ 0.223∗∗∗ (0.063) (0.039) Time from last IMF program −0.003 −0.0002 (0.007) (0.004) Neoliberal ideology −0.015 0.001 (0.014) (0.010) Polity2 −0.039 −0.006 (0.026) (0.015) UN voting (ideal pt dist from U.S.) −0.034 −0.011 (0.024) (0.016) UNSC member −0.121∗∗ −0.086∗∗ (0.059) (0.040) Checks 0.021 0.008 (0.013) (0.008) U.S. aid −0.041∗∗ −0.005 (0.020) (0.013) FDI / GDP −0.037∗∗∗ −0.014∗ (0.012) (0.008) GDPPC 0.065 0.050∗ (0.044) (0.029) Openness 0.018 −0.009 (0.021) (0.016) Debt service / exports −0.012 −0.003 (0.013) (0.009) Short-term debt / exports −0.003 −0.006 (0.016) (0.009) War −0.079∗ −0.006 (0.042) (0.026) Election year 0.028 0.009 (0.047) (0.026) Country fixed effects Yes Yes Year fixed effects Yes Yes Model type Negative binomial Negative binomial N 2121 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A16: Year Fixed-Effects Robustness Check. Robust standard errors are clustered at the country-level. All independent variables except for duration and prgf are lagged by one year. The year time trend included in the primary models are swapped out for year fixed-effects in this analysis.

24 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.017 0.031 (0.528) (0.287) Outside option amount −0.007 −0.011 (0.023) (0.015) Duration 0.047 0.085∗∗∗ (0.056) (0.030) Quota −0.073∗∗∗ 0.044 (0.317) (0.210) PRGF 0.073∗∗ 0.070 (0.146) (0.084) Time from last IMF program 0.024 0.022∗ (0.018) (0.013) Neoliberal ideology 0.037 −0.017 (0.033) (0.022) Polity2 −0.018 0.041 (0.061) (0.044) UN voting (ideal pt dist from U.S.) 0.149∗∗∗ 0.115∗∗ (0.076) (0.050) UNSC member −0.201∗∗∗ −0.154∗ (0.126) (0.093) Checks −0.011 −0.015 (0.033) (0.021) U.S. aid 0.017 −0.024 (0.064) (0.045) FDI / GDP 0.177∗∗ 0.072 (0.102) (0.069) GDPPC −0.857∗∗∗ −0.896∗∗∗ (0.444) (0.337) Openness −0.125∗∗ −0.038 (0.074) (0.043) Debt service / exports 0.001 0.004 (0.041) (0.026) Short-term debt / exports −0.053 −0.060∗ (0.053) (0.032) War 0.003 −0.048 (0.096) (0.060) Election year 0.060 −0.012 (0.090) (0.047) Year 0.064 0.032∗∗∗ (0.015) (0.010) Constant −125.008∗∗∗ −61.772∗∗∗ (30.804) (20.127) Country ﬁxed eﬀects Yes Yes Model type Negative binomial Negative binomial N 287 287 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A17: No Imputation Robustness Check (All Covariates). Robust standard errors are clustered at the country-level. All independent variables except for duration and prgf are lagged by one year. cooperative member and cooperative amount drop out of the models as there are no instances of cooperation in the remaining observations.

25 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.250∗∗ −0.142∗∗ (0.108) (0.063) Co-financing 0.059 0.051 (0.111) (0.075) Information Sharing 0.739∗∗∗ −0.006 (0.177) (0.162) Outside option amount −0.012 −0.021∗∗ (0.016) (0.010) Cooperative amount −0.010 0.008 (0.014) (0.009) Duration −0.060∗∗ −0.027∗ (0.025) (0.015) Quota 0.154∗∗∗ 0.091∗∗∗ (0.055) (0.027) PRGF 0.632∗∗∗ 0.431∗∗∗ (0.067) (0.040) Time from last IMF program −0.003 0.00005 (0.007) (0.004) Neoliberal ideology −0.021 −0.001 (0.016) (0.010) Polity2 0.017 0.032∗ (0.030) (0.017) UN voting (ideal pt dist from U.S.) 0.158∗∗∗ 0.102∗∗∗ (0.027) (0.017) UNSC member −0.107∗ −0.068 (0.065) (0.043) Checks 0.023 0.015∗ (0.015) (0.008) U.S. aid −0.118∗∗∗ −0.057∗∗∗ (0.021) (0.013) FDI / GDP −0.036∗∗ −0.012 (0.017) (0.011) GDPPC 0.137∗∗ 0.074∗∗ (0.059) (0.035) Openness 0.033 −0.002 (0.024) (0.017) Debt service / exports 0.002 0.003 (0.016) (0.010) Short-term debt / exports −0.009 −0.008 (0.018) (0.010) War −0.062 0.024 (0.052) (0.029) Election year 0.091∗ 0.040 (0.055) (0.029) Year 0.006∗ 0.002 (0.003) (0.002) Country fixed effects Yes Yes Model type Negative binomial Negative binomial N 2121 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A18: Disaggregated Cooperation Robustness Check. Robust standard errors are clustered at the country-level. All independent variables except for duration and prgf are lagged by one year. co-financing and information sharing are separated out from the cooperative member variable in this model.

26 Number of conditions Number of categories Model 1 Model 2 Outside option member −0.138 −0.153∗ (0.137) (0.089) Cooperative member 0.334∗∗∗ 0.018 (0.083) (0.068) Outside option amount 0.006 −0.006 (0.017) (0.010) Cooperative amount −0.028∗∗ 0.001 (0.014) (0.008) Duration −0.069∗∗ −0.026 (0.028) (0.016) Quota 0.085 0.065∗∗ (0.052) (0.028) Time from last IMF program 0.004 0.002 (0.011) (0.006) Neoliberal ideology −0.086∗∗∗ −0.043∗∗ (0.029) (0.017) Polity2 0.081 0.065∗∗ (0.054) (0.028) UN voting (ideal pt dist from U.S.) 0.163∗∗∗ 0.097∗∗∗ (0.039) (0.023) UNSC member −0.043 0.002 (0.087) (0.060) Checks 0.014 0.009 (0.022) (0.012) U.S. aid −0.103∗∗∗ −0.032∗∗ (0.025) (0.016) FDI / GDP −0.026 −0.004 (0.016) (0.011) GDPPC 0.111 0.055 (0.069) (0.040) Openness 0.058 0.005 (0.038) (0.028) Debt service / exports 0.007 −0.0004 (0.024) (0.016) Short-term debt / exports −0.015 −0.012 (0.019) (0.013) War −0.197∗∗ −0.037 (0.095) (0.051) Election year 0.143∗ 0.051 (0.076) (0.040) Year 0.015∗∗∗ 0.008∗∗∗ (0.005) (0.003) Country fixed effects Yes Yes Model type Negative binomial Negative binomial N 1221 1221 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A19: PRGF Dropped Robustness Check. Robust standard errors are clustered at the country-level. All independent variables except for duration are lagged by one year. All concessional loans are dropped from the dataset for this analysis; these include programs financed through SAF, ESAF, PRGF, and PRGT.

27 Number of enforced conditions Outside option member −0.219∗∗ (0.105) Cooperative member 0.399∗∗∗ (0.081) Outside option amount −0.006 (0.016) Cooperative amount −0.022∗ (0.013) Duration −0.058∗∗ (0.025) Quota 0.142∗∗∗ (0.054) PRGF 0.614∗∗∗ (0.067) Time from last IMF program −0.002 (0.008) Neoliberal ideology −0.025 (0.016) Polity2 0.019 (0.031) UN voting (ideal pt dist from U.S.) 0.149∗∗∗ (0.027) UNSC member −0.086 (0.066) Checks 0.021 (0.015) U.S. aid −0.114∗∗∗ (0.022) FDI / GDP −0.033∗∗ (0.016) GDPPC 0.147∗∗ (0.060) Openness 0.044∗ (0.025) Debt service / exports 0.001 (0.016) Short-term debt / exports −0.008 (0.018) War −0.070 (0.052) Election year 0.069 (0.057) Year 0.006∗∗ (0.003) Country ﬁxed eﬀects Yes Model type Negative binomial N 2121 ∗∗∗p < .01; ∗∗p < .05; ∗p < .1 Table A20: Waiver Adjustment Robustness Check. Robust standard errors are clustered at the country-level. All independent variables except for duration and prgf are lagged by one year. The count of conditions in this model is adjusted so that waived conditions are eliminated. The DV is then a count of enforced conditions.

28 Public spending public, budget, sector, adopt, expenditur, revenu, program, balanc, consist, line

Monetary policy net, domest, asset, intern, reserv, wage, bill, money, total, floor

Publications complet, account, audit, report, financ, financi, establish, system, manag, prepar

Trade policy percent, price, increas, elimin, import, rate, tariff, reduc, sale, product

Privatization paragraph, compani, custom, payment, measur, least, collect, polici, describ, author

Tax law tax, law, approv, parliament, submit, reform, servic, legisl, amend, mefp

Foreign exchange policy bank, foreign, exchang, commerci, central, market, loan, cash, rate, system

Bureaucratic tasks will, issu, nation, regul, includ, requir, review, strengthen, framework, procedur External debt payments debt, govern, arrear, mediumlongterm, credit, new, extern, shortterm, fiscal, deficit

Figure A7: Most Common Words from Topic Model Categories.