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International Institutions and Domestic Compensation: The IMF and the Politics of Financial Liberalization Bumba Mukherjee Assistant Professor Dept. of Political Science and Dept. of Economics & Econometrics University of Notre Dame; and Visiting Associate Research Scholar, NCGG Princeton University [email protected] David Andrew Singer Assistant Professor Department of Political Science Massachusetts Institute of Technology E53-489, 77 Massachusetts Avenue Cambridge, MA 02139 [email protected] Abstract: Recent scholarship on economic liberalization argues that international institutions raise the reputational and financial costs to states of reneging on policy commitments, and make policymakers less likely to heed the demands of adversely affected domestic groups. We argue that institutions also serve a different purpose: they enable governments to commit to compensating the domestic losers from economic liberalization, thereby facilitating their acquiescence to policy reform. We illustrate our argument with an analysis of the impact of IMF stabilization programs on financial liberalization since the 1970s. IMF programs frequently include redistributive transfers to adversely affected sectors, thereby enabling borrowing governments to commit ex ante to compensating the opponents of liberalization. We provide a formal model of the interaction between a government, the IMF, and the domestic private financial sector. A key result of the model is that participation in IMF programs increases the probability of capital account liberalization, but only in the presence of a concentrated banking sector. We test this claim on a sample of 92 countries from 19752002 using an original spatial autoregressive Heckman selection model. The model accounts for the factors that lead countries to select into IMF programs as well as the possible influence of international diffusion on national economic policymaking. We find strong statistical support for the formal model, but no support for international diffusion. April 29, 2007 Preliminary Draft – Please do not cite without authors’ permission 1. Introduction At the heart of recent scholarship on international institutions is the problem of credible commitment: the challenge that states face in binding themselves to long-term policy decisions when they face short-term incentives to defect. In an anarchic international system, international institutions can serve as commitment devices for states that seek to tie their hands to specific policy decisions (Botcheva and Martin 2001). Membership in an international organization, for example, serves as a credible signal of a state’s commitment to follow the organization’s rules, whereas more specific legal commitments—including those delineated in international treaties—raise the reputational and financial costs to states for reneging on their promises (Goldstein, Rivers, and Tomz 2007; Simmons 2000; Stone 2002). The impact of international institutions is particularly salient for the study of economic liberalization, in which states must commit to painful international adjustments—such as allowing free trade and capital flows—whose aggregate benefits emerge only after a potentially lengthy transition period. Groups that are harmed by the economic reform are likely to organize in opposition; their success in derailing the government’s reform efforts depends on the government’s own cost-benefit calculus, which in turn reflects the government’s institutional commitments (e.g., Vreeland 2003). According to this view, international institutions cannot make opposition groups disappear, but they can raise the costs to governments that choose to heed their demands, thereby “tipping the balance” in favor of reform (Bird 2001).1 In this article, we suggest that international institutions may exert influence on state behavior through an alternative channel: they enable policymakers to credibly commit to compensating domestic groups that are adversely affected by economic liberalization. This argument acknowledges that governments face commitment problems in relation to their 1 Examples of this argument can be found in Goldstein et al (2007); and Simmons (2000). A related argument is that institutional commitments allow governments to shield themselves from blame for policy decisions; see Vreeland (2003). 1 domestic constituencies as well as their international policymaking. Governments seek to appease domestic groups that are negatively impacted by policy changes, but their promises to offer compensation ex post may lack credibility ex ante. International institutions, in short, can help governments to commit to redistributive policies in the wake of costly policy reforms, and thereby facilitate the political acquiescence of adversely affected domestic interest groups. We believe that scholars have understated the potential influence of international institutions on state behavior as a result of overlooking this domestic compensation channel. We illustrate our argument with an analysis of the International Monetary Fund’s influence on capital account liberalization since the early 1970s. During the period after World War II, most developed and developing countries insulated their economies from international capital flows. Indeed the IMF specifically allowed countries to impose capital controls because of the devastation caused by speculative currency attacks during the interwar period. However, two decades of relatively stable post-War growth combined with the international spread of telecommunications and computer technologies led many—but not all—countries to relax their restrictions on capital flows. After the collapse of the Bretton Woods monetary system in the early 1970s, economists at the IMF began to look more critically at capital controls, and by the end of the decade they initiated a behind-the-scenes effort to encourage capital account liberalization among its loan recipients. The policy reversal of the IMF leads to two questions. First, can pressure from the IMF account for the wide variation in capital account liberalization worldwide since the 1970s?2 Second, and more specifically, under what conditions do IMF stabilization programs have a positive effect on capital account liberalization? Capital account liberalization, like other economic reforms, creates winners and losers in the domestic economy. Internationally-oriented firms and financial institutions, for example, will generally benefit from the free movement of capital, whereas previously sheltered state-owned 2 See Quinn (2003) for an overview of the record of capital account liberalization globally since 1970. 2 enterprises will be harmed (Frieden 1991). Even those industries that stand to benefit from full capital mobility in the long run, such as financial institutions and export manufacturers, may face short-term costs if the domestic financial infrastructure is not sufficient to withstand volatile capital movements (Brooks 2004; Eichengreen 1999). States that seek to liberalize their capital accounts therefore require a strong constituency in favor of the reform, plus additional resources to compensate domestic groups that are harmed in the short term. We argue that the IMF’s influence on member states’ capital account policies works through two channels. First, the IMF imposes capital account liberalization as an implicit or explicit condition for governments that seek balance-of-payments financing. Governments that fail to follow through on their liberalization efforts are precluded from further borrowing. Moreover, reneging on a commitment to the IMF sends a negative signal to financial market actors, including creditors and investors (Simmons 2000; Vreeland 2003). Second, a portion of the loan obtained as part of an IMF stabilization program is offered as a redistributive transfer to societal groups that are adversely impacted by economic reform.3 The IMF not only provides the source of funds for the transfer4, but it also enforces the transfer as part of the conditions of the stabilization program. This domestic compensation channel allows governments to make credible commitments ex ante to provide financial transfers ex post to the losers from capital account liberalization. The two channels of influence are complementary: the first raises the reputational and financial costs of government noncompliance, whereas the second increases the benefits of reform to otherwise adversely affected domestic groups. For details of the compensatory financial assistance provided by IMF stabilization programs, see that the IMF often provides under its stabilization programs that foster capital account liberalization is given in chapters 2, 4 and 6 of IMF (2005): Annual Report of the Executive Board for the Financial Year Ended April 30, 2005, Washington D.C.: International Monetary Fund. 4 More concretely, a recent IMF report states that since financial sector reform measures (including capital account openness) supported by the IMF “often imply layoffs of public sector employees, programs have attempted within their macroeconomic constraints to spread retrenchment over time and to provide severance pay while promoting alternative job opportunities through a more flexible labor market, as well as retraining schemes.” See “Social Dimensions of the IMF’s Policy Dialogue,” IMF pamphlet series 1995, No.47, pp.3, Washington DC: IMF available at http://www.imf.org/external/pubs/ft/pam/pam47/pam4703.htm. 3 3 Despite the IMF’s dual-channel influence on government policymaking, states do not always follow through on their reform commitments. Just a quick glance at the empirical record (discussed in more detail below) reveals that IMF stabilization programs are not always associated with capital account liberalization. Instead, we argue that the strength of a key domestic constituency in favor of liberalization—namely, the private financial sector—must be sufficient to tip the balance in favor of reform. In the absence of a concentrated, politically powerful banking sector, the IMF’s influence will be insufficient to trigger capital account liberalization. We elucidate our arguments with a game-theoretic model that examines the interaction between the government of a financially-distressed country, the IMF, and the private banking sector. We then test the formal model’s key predictions by estimating an original statistical model, the Spatial Autoregressive Error Heckman selection (hereafter SAE Heckit) model, on a sample of 92 countries between 1975 and 2002. The SAE Heckit model accounts for the problem of selection: the factors that lead a government to participate in an IMF stabilization program might also determine the government’s subsequent policy behavior (Vreeland 2003). In order to isolate the independent effect of the IMF on state behavior, we must first explain the factors that lead countries to seek the IMF’s assistance. In addition, the SAE Heckit model developed here also accounts for spatial dependence in the data. Following the work of Simmons and Elkins (2004), Brune and Guisinger (2006), and Franzese and Hays (2006), we consider the possibility that economic policymaking is characterized by international diffusion channels. If one country’s policy choice influences another country’s choice—either through emulation, market pressure, or some other mechanism—then a failure to account statistically for this interdependence will lead to inconsistent and inefficient statistical results. Results from the SAE Heckit model provide strong statistical support for the interactive effect of IMF programs and financial sector concentration on capital account liberalization, but no support for international diffusion. 4 This paper proceeds as follows. In the next section, we discuss the literature on the politics of capital account liberalization and discuss the potential influence of the IMF. In section 3 we develop a formal model of capital account liberalization. In sections 4, 5, and 6, we present the statistical model, the data, and results. We conclude by discussing the implications of our findings for the study of the political economy of financial liberalization. 2. The IMF’s Advocacy of Capital Account Liberalization How can we explain the international variation in financial liberalization since the 1970s? Students of international political economy have proposed and tested numerous theories of the ebb and flow of financial liberalization worldwide. One broad category of arguments privileges the role of domestic politics, including government partisanship (e.g., Alesina et al 1994; Kastner and Rector 2003; Quinn and Inclan 1997) societal interest groups and voter preferences (e.g., Frieden 1991; Quinn and Toyoda 2007; Sobel 1994), and rent-seeking politicians (Leblang 1997). A second, more recent line of scholarship suggests that international diffusion—in which countries are influenced by their “peers” due to geographical proximity, emulation, or financial market pressures—has a profound impact on capital account liberalization globally (Brune and Guisinger 2006; Garrett et al 2001; Simmons and Elkins 2004). Studies from these two distinct schools of thought have added immeasurably to our understanding of the causes of capital account liberalization. Yet these works tend to neglect altogether or underestimate the role of a key international institution—the International Monetary Fund (IMF)—in encouraging capital account liberalization.5 The relative neglect of the IMF in prior scholarship is not surprising considering that its original 1945 Articles of Agreement allowed countries to retain capital controls.. Restrictions on capital movements were in fact critical to the compromise embedded in the post-War Bretton Woods agreement (Frieden 2006). Countries maintained stable exchange rates (pegged to the A few studies include a simple control variable for country participation in an IMF program, but do not theorize about the conditional impact of the IMF or control for countries’ nonrandom participation in IMF programs. See Brune and Guisinger 2006, Quinn and Toyoda 2007, and Simmons and Elkins 2004. 5 5 U.S. dollar), but retained the ability to use monetary policy for domestic purposes—such as to stimulate employment in the event of an economic downturn. The combination of stable exchange rates and discretionary interest rates is only possible when governments prevent the cross-national flow of capital. The IMF was therefore an unambiguous advocate of capital controls during the first two decades of its operation. Even in the years immediately after the fall of the Bretton Woods system, when states throughout the world began to relax their capital account restrictions, the IMF maintained publicly that capital decontrol was not one of its explicit policy goals. But appearances can be deceiving. Indeed, we now know through a cursory examination of the empirical record and published reports that the IMF played an active role in promoting capital account openness since the 1970s via its short-run financial stabilization programs, including Stand-by Arrangements (SBAs), Extended Fund facilities (EFF) and Supplemental Reserve Facilities (SRF). A 1995 IMF occasional paper by an IMF staff team candidly admitted that “the Fund has in many cases encouraged developing countries to open their economies to foreign capital flows and to liberalize restrictions on capital account transactions” (IMF 1995, 6). More recently the IMF’s Independent Evaluation Office (IEO) examined the Fund’s position on capital account liberalization in a sample of developing countries during the post-Bretton Woods period and concluded that nearly 70 percent of stabilization programs contained either “explicit” or “implicit” clauses that encouraged borrowing countries to “open their capital account.” (IMF 2005, 49). Critics of the IMF have embraced the notion of an all-powerful institution that routinely foists austere liberalization policies onto unwilling (or unwitting) governments. Joseph Stiglitz, arguably the IMF’s most vocal critic, has charged that the institution’s steadfast advocacy of capital account liberalization for all member countries has contributed to speculative currency attacks and global financial instability (Stiglitz 2002). However, the political reality is evidently more complicated. A recent IMF staff paper indicates that since 6 1978 approximately “43% of IMF financial stabilization programs have influenced recipient countries to liberalize their capital account while the remaining 57% had no noticeable effect” (IMF 2002, 9). Thus, it seems clear that IMF programs are frequently an important catalyst for capital account liberalization, but that other factors may interact with the IMF’s influence in determining states’ ultimate liberalization trajectories. To unpack the influence of the IMF on financial liberalization, it is helpful to examine the process by which member countries seek and receive emergency financial support. Case studies of IMF programs reveal a standard sequence of events for recipient countries (Abdelal and Alfaro 2003; Alfaro and Hammel 2006; Baily et al 2000; Desai 2003; Stiglitz 2002). First, a government turns to the IMF for assistance after experiencing financial difficulties, such as rapidly depleting foreign exchange reserves, an increase in external debt, or some other balance of payments problem. The IMF in turn provides loans to the government provided that it participates in a financial stabilization program, such as a SBA, EFF, or SRF. These programs generally require governments to enact a variety of financial sector reforms, including the reduction of government-directed credit allocation from state-owned banks, the improvement of banks’ lending portfolios, and sometimes the privatization of state-owned financial institutions (Blustein 2001; Buira 2003; Hutchinson 2003; IMF 2005). Moreover, the IMF actively encourages countries that participate in stabilization programs to liberalize their capital accounts by adopting a unified exchange rate and removing regulatory barriers to capital and current account transactions (Desai 2003; IMF 2005; Joyce and Noy 2006; Stiglitz 2002). The extent to which the government actually complies with the IMF’s recommendations depends not just on participation in the IMF program per se but also on the domestic configuration of interest groups with a stake in economic reform. What are these domestic political constraints and how do they interact with the IMF program to affect liberalization? To elucidate the conditional nature of the IMF’s influence, we construct a formal model of the government’s decision to liberalize its capital account. 7 3. Formal Model of Capital Account Liberalization There are three players in the model: 1) the government of a financially troubled country that participates in the IMF’s stabilization program; 2) the IMF; and 3) private financial institutions. In the model, the IMF plays the role of an international creditor by providing financial assistance to the financial distressed country. For convenience, we denote the government of this financially distressed country as the “borrowing government” and label it b. When the government obtains financial assistance from the IMF, it agrees to participate in the institution’s short-run financial stabilization program. The IMF provides loans to the borrowing government under two conditions: the government must adopt financial sector reform (including capital account liberalization), and it must pay back the loan within a certain time frame as agreed by both parties. The IMF, like other financial intermediaries, is dependent on the revenue from repayment of its loans. The probability of timely repayment, in turn, is a function of the degree of effort that the borrowing government exerts in enacting financial reforms. Thus, the IMF is concerned ex ante about the observable reform effort that the borrowing government actually exerts ex post. The IMF’s utility therefore increases with (i) the degree of the reform effort, m, that the borrowing government exerts after receiving a loan; and (ii) the total net revenue, y, that the IMF obtains from providing loans to member countries. More formally, the IMF’s expected utility is defined by the following negative exponential constant absolute risk aversion (CARA) function, U IMF = E{−e−δy−γm } (1) where δ > 0 and γ > 0 . We describe below the functional form of the IMF’s utility in (1). The IMF provides a fixed amount of conditional loans, y0 , to the borrowing government. We define the returns that the IMF obtains from lending to the borrowing government as rb and the net revenue that it gets from the loans as y0 − t . The parameter t is the lump-sum transfer that the borrowing government provides from the IMF’s loan as compensation to groups in society for whom capital account liberalization is costly. This 8 assumption is plausible given that our detailed analysis of the IMF’s financial stabilization programs reveal that they often provide compensatory transfers to retrain workers and/or to supplement the benefits provided by the government for involuntary unemployment.6 The remainder of the loan is used for paying for financial restructuring, replenishing foreign reserves, reducing the debt burden and so on. Hence, ex ante the IMF knows that it will be paid back at most y0 − t of the loan amount. Note that at any one point of time the IMF’s net revenue is not obtained from providing loans to just one country. Rather the IMF’s total net revenue depends on the returns made on loans to governments of other countries that also approach the IMF for financial help. We denote the returns the IMF makes from loans to these other “foreign” governments by rf . If the IMF allocates (1 − α ) share of its net revenue to the borrowing government and α to foreign governments, then the total net revenue that the IMF makes is defined as, y = ( y 0 − t )[αr f + (1 − α )rb ] (2) Suppose that the returns from loans to the borrowing government and to governments of other foreign countries are risk free. Then in a world of completely mobile capital, the returns from loans will be equal to the world interest rate, i.e., rb = r f = i * . In the absence of full capital mobility, the IMF’s returns depend not only on the world interest rate but also on the degree of capital account openness of the borrowing and foreign governments. To keep things simple, we assume without loss of generality a specific functional form linking the extent of capital account liberalization, labeled as k, and the equilibrium rates of return, ( rb , r f ), for the IMF: 7 rb = i * − ρk rf = i * − ρk + ρ f k + kv 6 (3) See, for example, Abdelal and Alfaro (2003); Alfaro and Hammel (2006); Abdelal (2005); and IMF (2005). 7 Henry (2006) provides the derivation of the functional form in (3) that specifies the link between capital account openness and the equilibrium rates of return on loans offered by financial institutions. 9 where ρ ( ρf ) is the effectiveness with which the borrowing (foreign) government can implement capital account liberalization measures put forth by the IMF. The parameter v is a random variable with a normal distribution v ~ N(0,σ v2 ) ;8 v is the ex ante uncertainty that the IMF has with respect to obtaining its expected returns from providing conditional loans to foreign countries in the model.9 Substituting (3) into (2), we obtain the functional form of the IMF’s total net revenue, y = ( y 0 − t )[i* − ρk + αρ f k + αkv ] . Further, substituting the aforementioned expression in the IMF’s expected utility function in (1), we get, U IMF = −exp{−δ ( y 0 − t )[i* − ρk + αρ f k + αkv ](−γm )] (4) Having defined the IMF’s utility function, we now describe the utility of the privatesector banks in our model. We assume that private sector banks prefer greater levels of capital account liberalization since it allows them to diversify their portfolio into foreign assets, attract portfolio investment from abroad, and lower the costs they incur from rent seeking and financial repression that result from capital controls (Li and Smith 2002; Sobel 1994). Private sector banks, labeled n, exert a degree of political pressure, p, on the borrowing government to liberalize the capital account as part of the IMF’s stabilization program. Private banks earn a benefit, s, for capital account liberalization, where s>0. The amount of pressure that private sector banks exert on the government to open the capital account depends on three factors. First, we assume that market concentration, θ , nicely captures the economic power of private financial institutions and the degree of financial resources that they can potentially mobilize to lobby the government.. Second, observe that private financial institutions have to politically compete against state-owned banks and other groups that lose from liberalization when lobbying for greater capital account openness. Stateowned banks, public financial intermediaries and other potential “losers” are likely to demand 8 Ideally, we should assume a distribution for v that is bounded from above. For technical convenience and without loss of generality, we allow v to be an unbounded random variable. 9 We also solved the model assuming that rb = i * − ρk + kv . Introducing this assumption in the model did not substantively change the model’s results that we present below. 10 more transfers t in order to get compensated for any new reform policies that move the economy toward greater financial openness. If the demand for t is substantial, it may act as a serious political constraint on the borrowing government toward moving forward on the IMF’s reform measures and discourage it from implementing reforms pertaining to capital account liberalization.10 Since private sector banks prefer capital account openness, they have incentives to resist demands for more transfers by state banks because they may directly hinder the prospects for further capital account liberalization. More formally, we assume in the model that private financial institutions use their resources determined by θ to not only lobby for greater financial liberalization but to also lobby against the demand for more transfers t by state-owned banks and other losers from capital account openness. We capture this behavior by private financial institutions in their utility function via (θ t ) −1 .11 Third, applying pressure on the government to further liberalize the capital account is not costless. Therefore, banks pay a cost c to lobby for more capital account openness that helps them to obtain the benefit s. The costs of lobbying are a function of the state of the macroeconomy, φ , and the degree of financial repression, k, stemming from capital controls.12 The state of the macroeconomy will, in all likelihood, affect the profitability of private banks therein influencing the costs they are willing to bear to lobby the government. Moreoever, it is well known that financial repression adversely affects the profitability of private sector banks.13 10 If public-sector banks and state financial institutions demand large amounts of lump sum transfers, it may also dissuade the IMF from lending to the government in the first place thus making it very difficult for the latter to liberalize the capital account. 11 Note that lim (θ t ) −1 → 0 ; this indicates that higher market concentration and hence financial θ →∞ leverage of private financial institutions allows them to resist transfers to “losers” more effectively. 12 We assume that φ ′ > 0 . Further c is assumed to be convex with respect to φ , which is intuitive. 13 For this, see Bacchetta and Ramon (1992) and Roubini and Sala-i-Martin (1995) 11 In short, from the preceding paragraphs, it is clear that the amount of political pressure p that banks can exert on the borrowing government with respect to capital account liberalization depends on θ , t and c. Since the utility of private financial institutions will be determined by the net benefit they obtain for exerting political pressure on the government to liberalize the capital account, we define their expected utility function as, U N ≡ p(t ,θ , c ) = s − tcφ ( s / t )(θ t ) −1 (5) Finally, we turn to describe the borrowing government’s utility function. The government conducts a “balancing act” between state-owned financial institutions and other domestic groups that lose from capital account openness, on the one hand, and the IMF and private financial actors that gain from liberalization, on the other. Four factors directly affect the government’s reform effort, m, and thus its utility function. First, the government faces political constraints from public sector banks that are averse to the IMF’s request for financial liberalization. The government will, therefore, attempt to reduce the resistance of state financial institutions to capital account openness by compensating them via the lump-sum transfer amount t drawn from y 0 . Second, the borrowing government will face political pressure for liberalization from private financial interests. The government accounts for this political pressure via the term p(t , θ , c) in its utility function. Third, the government requires fiscal resources to pay for the economic costs of adjustment that arise from implementing the IMF’s reform measures, including financial liberalization. We assume that these resources consist of an un-weighted average of two factors. For one, fiscal resources that are used for financial liberalization are derived from the net implicit revenue the government gets from the difference between lump-sum transfers t and the amount that private financial institutions invest when exerting pressure p (t , θ , c) for liberalization, i.e. from [t − p(t ,θ , c )] . Additionally, resources are derived from the seigniorage revenue that the government obtains from the fiscal effects of the IMF’s loan y 0 on the 12 economy after paying the lump-sum transfer t. Because the fiscal effect of the IMF loan will depend on the state of the country’s macroeconomic fundamentals ( φ ) and the effectiveness with which capital account liberalization measures are implemented ( ρ k ), the resources derived in this case is defined as [φρk ( y 0 − t )] . Thus the total fiscal resources the borrowing government can use to implement the IMF’s reform measures including capital account liberalization are [t − p (t ,θ , c )] + [φρk ( y 0 − t )] . Fourth, the IMF can always withdraw some proportion of the conditional loan and transfer it to provide loans to other countries if it believes that the borrowing government is not effectively implementing the stabilization program. Thus the borrowing government will account for the possibility that the IMF may withdraw some proportion of the conditional loan with probability q ∈ [0,1] and invest it in another foreign country that is perhaps implementing liberalization measures more effectively ( ρ f ). We incorporate this uncertainty by introducing the term qρ 2f / σ 2 additively in the government’s utility function. Gathering the above information together, we can define the full form of the government’s utility function. Specifically, the government’s decision problem is to optimally choose a level of capital account liberalization k and lump-sum transfer level t to maximize: U G = γ h[t − p (t , θ , c ) + φρk ( y 0 − t )] + δ ( y 0 − t )(i * − ρk ) + qρ 2f / σ 2 (6) The sequence of moves in the model—illustrated in figure 1—is as follows. First, the state of the financially distressed country’s macroeconomic fundamentals ( φ ) and capital account openness (k) are given exogenously by nature and are common knowledge to all the players. Second, the borrowing government obtains conditional loans y 0 from the IMF and participates in the IMF’s financial stabilization program by agreeing to implement the IMF’s reform measures. The government provides lump-sum transfers t as compensation from the IMF’s loan. Third, private banks exert political pressure p(t ,θ , c) on the borrowing government to further liberalize the country’s capital account. After observing y 0 13 and p(t , θ , c ) , the government exerts some reform effort, implements its optimal level of capital account openness (k*) and provides an optimal lump-sum transfer amount (t*) to compensate groups that are hurt by capital account liberalization. Finally, the IMF observes k* and t* and chooses whether or not to continue lending to the distressed country. The solution concept that we use to solve the model is subgame perfect Nash equilibrium. We state below the model’s equilibrium and comparative static results from which we derive our testable hypothesis. <<Insert Figure 1 about here>> 3.1 Equilibrium and Comparative Static Results Solving the model described in the following section leads to the following result. Lemma 1: The optimal degree of capital account liberalization that the government implements in a subgame perfect equilibrium after participating in the IMF’s financial stabilization program is, t − p (t , θ , c ) + φρk ( y 0 − t ) = ( h ′) −1 [δ /(γφ )] ≡ k * (7) and the optimal lump-sum transfer that the government provides to groups who lose from capital account openness is: t * = ρk (1 / h −1 ) − φi * (8) Proof: See appendix Lemma 1 formally characterizes the optimal degree of capital account liberalization set by the government in equilibrium. The lemma also characterizes the optimal lump-sum transfers that the government provides in equilibrium to groups hurt by liberalization after obtaining y 0 from the IMF. While useful, Lemma 1 does not provide substantive insights per se. Rather comparative statics conducted on the equilibrium solutions leads to the following substantive result Proposition 1: Suppose the government of a financially distressed country with declining macroeconomic fundamentals φ turns to the IMF for financial assistance and obtains y 0 as part of the IMF’s financial stabilization program. Then from the subgame perfect equilibrium solution in Lemma 1, ∂t * / ∂y0 > 0 , and, (ii) ∂k * / ∂y 0 ∂θ > 0 (i) Proof: See appendix 14 The comparative static result in Proposition 1 suggests that the level of capital account liberalization increases when the government (i) obtains loans y 0 from the IMF and participates in the institution’s financial stabilization program and (ii) the degree of market concentration of private-sector banks in the financially troubled country is relatively high.14 Therefore, unlike the literature that focuses on either the impact of domestic politics or international diffusion mechanisms on financial liberalization, our model predicts that international institutions (the IMF) and domestic political economy factors (the market concentration of private banks) matter for capital account liberalization. The causal mechanism that explains the comparative static result in Proposition 1 is simple. To begin with, the borrowing government that participates in the IMF’s financial stabilization program will use some proportion of the financial assistance package y 0 for compensating groups that lose from financial liberalization via a lump-sum transfer t. Indeed, the financial assistance from the IMF provides an opportunity for the government to not only offer but to also increase the lump sum transfer amount ( ∂t * / ∂y0 > 0 ) to losers from reform that would not have been possible in the absence of the IMF program. This is vital because providing compensation to the potential “losers” from financial liberalization may help to ease political resistance against capital account openness. Second, an IMF program also allows the government to credibly commit itself ex ante to provide compensation via lump sum transfers to those who lose from capital account openness ex post. Since the loan amount provided by the IMF is transparent and because the government’s decision to provide some share of these loans as transfers is transparent as well, it is difficult for the borrowing government to divert the amount meant for transfers for rent-seeking. Second, observe that the IMF will monitor whether its assistance package is being utilized properly, not just for implementing financial reforms but also for compensation purposes. Since domestic groups know that the IMF can monitor the government’s activity in this regard, they will be less 14 In the model, “relatively” high market concentration implies that the parameter θ increases. 15 inclined to believe that the loans will be misused for rent-seeking. Hence, transparency and monitoring by the IMF enhance the credibility of the government’s commitment to provide transfers to domestic groups for whom capital account liberalization is costly. This, in turn, helps to ease political resistance against liberalization. Note that the government’s ability to compensate domestic losers via the IMF loan is not sufficient to bring about capital account liberalization. Rather, the model suggests that the “demand side”—namely, pressure from private banks—also matters in providing political and economic incentives for the government to enact economic reforms. Highly concentrated banking sectors have additional financial resources that can be directed towards exerting more intense and sustained pressure on the government for financial liberalization. Also, note that higher market concentration indicates an oligopolistic market structure where the sum of the financial assets of a small number of large banks constitute a relatively large share of the total financial assets in the country’s banking sector. This is important insofar as the concentration of wealth among fewer banks minimizes collective action problems.15 And, as a result, it maximizes their ability to collectively apply political pressure on the government to liberalize the capital account after the latter opts to participate in the IMF’s program. The model shows that both the two aspects described above translate to higher political pressure for liberalization from private financial institutions ( ∂p (.) / ∂θ > 0 ).16 Furthermore, it predicts that it is the interaction of the IMF program which allows the government to make credible promises to compensate losers and increased political pressure for more liberalization by highly market-concentrated highly market-concentrated private financial institutions that leads to a positive effect on the level of capital account liberalization ( ∂k * / ∂y 0 ∂θ > 0 ). The preceding discussion leads to the following hypothesis tested below: 15 In the appendix, we prove as part of claim 1 that it is individually rational for highly concentrated private and MN banks to exert more pressure on the government to liberalize the capital account since the optimal benefits from lobbying, s*, is convex in θ and strictly increasing in t. 16 We prove this claim, labeled as claim 2, in the appendix. 16 Hypothesis 1: IMF financial stabilization programs will have a positive effect on the level of capital account liberalization if the degree of market concentration of private sector banks in countries participating in IMF programs is high. 4. Statistical Model The formal model in the previous section predicts that IMF financial stabilization programs will have a positive effect on capital account liberalization in countries that participate in the IMF’s program only when the degree of market concentration of private-sector and MNC banks in these countries is high. The model also posits a “selection” and an “outcome” process since it suggests that a country that voluntarily participates in an IMF stabilization program owing to poor macroeconomic fundamentals (the selection process) will adopt capital account liberalization measures (the outcome) only when the market concentration of private and MN banks in that borrowing country is high. Since the participation of countries in IMF stabilization programs is non-random and because the degree of capital account liberalization is conceptualized as a continuous variable in the formal model and for the empirical tests (see below), we use a Heckman selection model to test the prediction in hypothesis 1. But unlike a standard Heckman model, we estimate a sample selection model with spatial autoregressive errors (SAE) in the selection and the outcome equation. The sample selection model is estimated with spatial autoregressive errors (SAE) that specifies spatially autocorrelated disturbances in both the selection and the outcome equation. We estimate the SAE selection model, also known as the SAE Heckit model, on our data because of two reasons. First, as mentioned earlier, numerous scholars have suggested that geographical proximity plays a critical role in the international diffusion of the practice of capital account openness; that is, a country is more likely to increase the degree of liberalization of its capital account when it observes “neighboring” countries liberalize their capital accounts (see, for e.g., Simmons and Elkins 2004).17 As described below, a key advantage of the spatial Heckit model we employ is that it 17 Simmons and Elkins (2004) use spatial econometric techniques to test the effects of diffusion – including diffusion resulting from geographic proximity—on capital account liberalization. The spatial econometric techniques are similar to those used by Franzese and Hays (2005, 2006) and Beck, Gleditsch 17 allows us to control for the influence of international diffusion on capital account liberalization via geographic proximity by explicitly incorporating spatially autocorrelated disturbances in the outcome equation of the empirical model when testing hypothesis 1.18 Second, it is plausible that the participation of countries in IMF stabilization programs may also exhibit diffusion effects or, in other words, spatial dependence in the data. For example, a brief examination of our data reveals that several Latin American countries participated in IMF financial stabilization programs such as Stand-By Arrangements (SBAs) and Extended fund Facility (EFF) within two particular time periods, 1982-1987 and 19962001. Likewise, we find a spatial concentration of many East and South-East Asian countries participating in IMF stabilization programs between 1995 and 2000. If it is indeed the case that participation of countries in IMF programs exhibits some spatial dependence, then we should explicitly control for this phenomenon when empirically modeling the selection of countries into IMF stabilization programs to avoid bias. To econometrically account for both sample selection and spatial dependence in the data –with respect to participation in IMF stabilization programs and the extent of capital account liberalization – we need to estimate a two-stage empirical model that accounts for the selection of countries into IMF stabilization programs and spatially autocorrelated disturbances. Hence, we estimate a Heckman-type sample selection model with spatial autoregressive errors (SAE) in both the selection and outcome equations, which is called the SAE Heckit model. This model, developed by Kelejian and Prucha (1999) and expanded upon by Flores-Lagunes and Schneier (2006), specifies spatially autocorrelated disturbances (after dropping subscript t that denotes time for notational convenience): y1*i = α 0 + x1′iα 1 + u1i , u1i = δ ∑ cij u1 j + ε 1i (9) j ≠i and Kyle (2006). But none of these scholars estimate a spatial Heckman selection model, as done here. 18 If diffusion in terms of geographic proximity may be playing a critical role in determining increasing levels of capital account liberalization, then failing to account for such diffusion-mechanisms –i.e. spatial dependence in the data – will lead to inconsistent and inefficient parameter estimates. Since a SAE Heckit model statistically accounts for diffusion that occurs via geographic proximity in the data, it prevents bias in the parameter estimates and is thus a valid econometric tool. 18 y 2*i = β 0 + x 2′ i β1 + u 2i , u 2i = γ ∑ cij u 2 j + ε 2i (10) j ≠i where y1i* and y2i* are latent variables with the following relationship with respect to the observed variables y1*i = 1 if y1*i > 0 and y1i = 0 otherwise, and y 2i = y 2*i × y1i . Therefore, (9) is the selection equation that accounts for participation of countries in the IMF’s financial stabilization programs while (10) is the outcome equation that estimates the impact of covariates on the degree of capital account liberalization. The two equations (9 and 10) exhibit spatial dependence in their respective error term, as u1i and u 2i depend on the other u1 j and u2 j through their location in space, as given by the spatial weights cij and the spatial autoregressive parameters δ and γ . I briefly discuss the operationalization of the spatial weights in 1 and 2 below. At this stage, observe that we assume that the errors ε 1i and ε 2i , i =1,…N are iid N (0, ∑) .19 Hence, the statistical model in (9)-(10) can be presented in a reduced form y1*i = α 0 + x1′iα1 + ∑ wij1 ε1 j (11) j y2*i = β 0 + x2′ i β1 + ∑ wij2ε 2 j (12) j where the weights wij1 and wij2 are the (i,j) elements of the inverse matrices (1 − δC ) −1 and (1 − γC ) −1 , respectively, with C the matrix of spatial weights cij . Note that both sets of weights wij1 and wij2 depend upon the unknown parameters δ and γ . Many weighting schemes have been used to operationalize elements of the geographic diffusion parameter wij in spatial regression models by political scientists. For e.g., Simmons and Elkins (2004: 178) use directed trade-flow shares of country j in country i’s total for wij based on the hypothesis neighboring countries tend to trade more with each other. 19 ⎛σ 2 ∑ = ⎜⎜ 1 ⎝ σ 12 σ 12 ⎞ ⎟ σ 22 ⎟⎠ 19 Franzese and Hays (2006: 174), however, code wij = 1 for countries i and j that share a border and wij = 0 for countries that do not. Since scholars suggest that a key component of diffusion of capital account liberalization may operate via geographic proximity – i.e. “neighbor effects” where a country in, say, Latin America may liberalize its capital account further because its neighbors are doing the same – we use a geographic measure of spatial contiguity. This geographic measure of spatial contiguity is operationalized as the inverse distance between states i and j, where wij = 1 d ij . As the distance between states i and j increases (decreases), wij increases (decreases), thus giving less (more) spatial weight to the state pair when i ≠ j . While there is no consensus on how distance between cross-sectional units should be measured, we consider the distance between capital city of countries; data for this variable is drawn from EUgene in the COW dataset. The results we report below remain robust when we use other measures of spatial contiguity including directed trade-flow shares of country j in country i’s total and whether or not states share a border. We use GMM estimation to estimate the SAE Heckit model, as suggested by Kelejian and Prucha (1999), Pinske and Slade (2006) and Flores-Lagunes and Schneier (2006).20 The GMM estimation technique for the SAE Heckit model is described in the appendix. One key advantage of the GMM estimator is that it accounts for heteroskedasticity induced by the SAE process in the selection and outcome equation. Additionally, to correct for serial correlation, we use panel-robust standard errors suggested by Arellano (1987).21 Fixed effects are included in the outcome equation of the SAE Heckit model to minimize omitted variable bias.22 5. The Data To test the prediction in hypothesis 1, we put together a time-series-cross-sectional (TSCS) dataset of 92 countries between 1975 and 2002. We include countries that both did and 20 The SAE Heckit model is estimated by using the MATLAB Spatial Statistics Toolbox, version 2.0. 21 Arellano’s (1987) panel-robust standard errors accounts for the possibility that the errors may be serially correlated for a given country. 22 The outcome equation is also estimated with random effects but the results from the SAE Heckit models with random effects are not reported to save space. 20 did not participate in IMF financial stabilization programs during the 1975-2002 time period. Our data set also includes countries that exhibit substantial cross-sectional and temporal variation with respect to the extent of capital account liberalization. These two features of the data allow us to more carefully analyze the conditions under which IMF stabilization programs help to increase or decrease the level of capital account liberalization. Further, because our sample is comprehensive, it provides an opportunity for us to make much more generalizable claims about the conditions under which IMF financial stabilization programs lead to an increase in capital account liberalization if our hypothesis is indeed supported by the empirical evidence. The countries included in our data set are listed in Table 1.23 << Insert Table 1 about here>> Although our data set has some advantages, it has some drawbacks as well. For instance, the sample used here only starts from 1975 and not earlier primarily because lack of data on some critical economic and political control variables described below for many developing countries prevented us from extending the temporal range of the sample. Additionally, the lack of specific data that is required to operationalize one of our independent variables –market concentration of private sector and MN banks – also forced us to limit the cross-sectional size of our sample to 92 countries. 5.1 Dependent variable(s) In the paper’s formal model, the key dependent variable of interest, the level of capital account liberalization, is conceptualized as a continuous variable. To operationalize this dependent variable in the outcome equation, we use a well known and recently developed continuous measure of capital account liberalization by Chinn and Ito (2005) that covers the entire range of countries in our sample from 1975 to 2002. Chinn and Ito (2005) used the data 23 The data set in Table 1 includes developed and developing countries. Although IMF financial stabilization programs have been primarily implemented in developing (i.e. non-OECD) countries, we must point out here that IMF programs have not been applied only to developing countries. Indeed, there are example of developed (i.e. OECD) countries participating in IMF financial stabilization programs (for e.g., Spain and Italy). Hence, it is erroneous to completely exclude OECD countries from the sample. That said, we not only estimate our SAE Heckit model on the full sample of OECD and non-OECD countries, but also a separate subsample of non-OECD developing countries. 21 reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAR) to create their index of capital account liberalization. Specifically, they create an index based on four binary measures: existence of multiple exchange rates, restrictions on capital and current account transactions and requirement of surrender of export proceeds. Because Chinn and Ito (2005) (and we) focus on capital account liberalization –rather than controls – they reverse the values of these binary variables, such that the variables are equal to one when (i) capital and current account restrictions do not exist, (ii) surrender of export proceeds is not required and (iii) there does not exist of multiple exchange rates. Moreover, for controls on capital transactions, Chinn and Ito (and we) use the proportion of a five-year window (encompassing year t and the preceding four years) when capital controls were not in effect.24 The operationalization procedure described above leads to a 0-5 continuous index of capital account liberalization for all country-years in our sample.25 This index, which we label as Capital—takes on higher values the more open a country is to cross-border capital transactions. In addition to the continuous measure of the dependent variable in the outcome equation capital account liberalization, we need to develop a dichotomous measure for IMF Financial Stabilization Program, which is the dependent variable in the selection equation of the SAE Heckit model. The dummy IMF Financial Stabilization Program (labeled as IMF Program in the tables for convenience) is equal to 1 for countries that voluntarily participate in the IMF’s financial stabilization program when they encounter serious financial difficulties and thus obtain IMF funds that are specifically designed to solve their financial problems. Since the dummy IMF Program is also an independent variable in the outcome equation of the SAE Heckit model, we describe in more detail below how this variable is operationalized. 24 The Chinn and Ito (2005) dataset is widely used by political scientists including Brooks (2004), Satyanath and Berger (2006) and Brune and Guisinger (2006). 25 The original Chinn-Ito (2005) index ranges from -2.5 to +2.5. We rescaled this measure on a 0-5 scale to aid interpretation of coefficient estimates from the SAE Heckit model. The Phillips-Perron test failed to reject the null of stationarity for the Chinn-Ito and Brune-Guisinger Capital series. 22 5.2 Independent variables To test the prediction in hypothesis 1, we need two independent variables in the outcome equation of the SAE Heckit model. As mentioned above, the first independent variable is the dummy IMF Program that is coded as 1 when the IMF provides funds to countries – that voluntarily opt for IMF stabilization programs – in order to (i) assist them in dealing with the effects of externally generated and temporary export shortfalls, (ii) to provide financial assistance for exceptional balance-of-payments difficulties, (iii) to increases reserves and (iv) finally to increase confidence in financial markets. When the IMF provides conditional loans to countries under its financial stabilization program, it almost always “requests” the recipient country to further liberalize its capital account. Note that IMF short-run financial stabilization programs do not include programs for long-term economic reform and structural adjustment. Following the criterion described, three types of IMF funding are provided under its stabilization program: (i) Stand-by (and extended stand-by) Arrangements (SBA), (ii) Supplementary Reserve Facility (SRF) and (iii) Extended Fund Facility (EFF).26 Therefore, the dummy IMF program is coded as 1 when the IMF provides either one or some combination of these three types of funds mentioned above to financially distressed countries that opt to participate in its stabilization program. We note here that fund facilities such as the Structural Adjustment Fund (SAF) and the Poverty Reduction and Growth Facility (PRGF) are not included in the IMF program dummy as these funds are used only for long-run structural adjustment. Data for IMF program is drawn from several sources: the IMF’s (2004) Review of Fund Facilities, Hutchison (2001), Vreeland (2003) and Joyce and Noy (2005). 26 The other three types of IMF short-run financial stabilization programs are (i) Contingency funding facility (CFF), (ii) Buffer Stock funding facility (BSFF) and (iv) Currency Stabilization funds (CSF). Unfortunately, information on when and where these types of funding programs were initiated by the IMF as well as the contents of these programs are extremely weak. Given the poor information we have about these programs, we chose not to include them in our coding of IMF program. Fortunately, these programs are rarely offered by the IMF; indeed they make up only 7% of all IMF financial stabilization programs that have been initiated and implemented since 1975. 23 Descriptive statistics for IMF program shows that between 1975 and 2002, 572 programs –specifically SBA, SRF, EFF – were approved. A regional breakdown of program approvals reveals that financial stabilization programs are primarily directed toward countries in Latin America and Africa. For instance, 30% of all IMF short-run financial stabilization programs were directed toward Latin America while 35% of these programs were approved for African countries. Across time, we find that the number of IMF financial stabilization programs that were approved reached a peak in the early 1980s and in the late 1990s. We operationalize our second independent variable –the degree of market concentration of private sector banks –by using two measures of the market concentration of firms (within industries) that are commonly used in the industrial organization literature.27 These two measures are: the Hirschman-Herfindahl index and the standardized Theil index of market concentration. To conserve space, we only describe the operationalization and the results from our measure of the Hirschman-Herfindahl index of market concentration of private-sector and MN banks. Specifically, the Hirschman-Herfindahl index of market concentration of private-sector and MN banks is given for each country-year by n Bank Concentration = ∑ si2 i =1 si = Ai / nA (13) where si is the share of each private or MN bank’s financial assets in the total financial assets of the banking sector per year for each country. Therefore, put together, the HirschmanHerfindahl index is the sum of the squared market shares in terms of financial assets of private and MN banks in the banking sector.28 Data to operationalize this variable has been drawn from several sources including Barth, Caprio, and Levine (2003), the Bank for 27 See, for example, Silber 1989a and Silber 1995. The standardized Theil index of market concentration of private and MN banks that is also used n for the tests (but is not reported here) is Bank Concentration = − ⎛⎜ ∑ si ln si ⎞⎟ ln n . The results from ⎝ i =1 ⎠ using this measure of concentration are available on request. 28 24 International Settlements (2005), the Bankers Almanac (2005), GTAP (2005) and Beck et al (2005, 2006).29 A key advantage of the Hirschman-Herfindahl (HH) index of market concentration of banks we use is that it directly operationalizes the parameter θ from the model, which allows us to carefully test the prediction in hypothesis 1 from our model. Moreover, as recognized by many scholars of industrial organization, the HH index is a powerful and accurate proxy for the extent of market concentration of firms or in our case, banks within countries. A second advantage of the HH index is that data used to construct this variable is comprehensively available for almost all countries in our sample. Third, the Hirschman-Herfindahl measure contains substantial cross-sectional and temporal variation even though this continuous measure is bounded between 0 and 1. Hypothesis 1 predicts that the impact of IMF stabilization programs have a positive impact on the level of capital account liberalization conditional on the degree of market concentration of private sector and multinational banks in the banking sector of a country that borrows from the IMF under the latter’s financial stabilization program. To test this claim, we therefore interact IMF program with Bank Concentration and introduce this interaction term IMF Program x Bank Concentration in the outcome equation of the SAE Heckit model where the continuous dependent variable is Capital. From hypothesis 1, we expect that the coefficient of IMF Program x Bank Concentration will be positive in the outcome equation. We also control for the individual components of this interaction term in the outcome equation. 5.3 Control Variables in Outcome Equation Diffusion Variables The most popular alternative explanation that is put forth to account for capital account liberalization focuses on the effects of international diffusion. Scholars suggest that the influence of international diffusion on capital account liberalization primarily occurs 29 We are grateful to Ross Levine and Thorsten Beck for letting us use their extensive pooled data on financial assets of private sector banks from several countries. 25 through four channels: geography, trade, finance and membership in similar international institutions or agreements (Simmons and Elkins 2004; Brune and Guisinger 2006). The SAE Heckit model that we use directly captures the possibility of geographic diffusion via the spatial weights matrix in the estimation procedure. Diffusion of capital account liberalization via the trade channel includes two aspects – the degree to which countries compete against each other for export markets and the extent of trade interdependence between countries. To operationalize the extent to which countries compete for similar export markets, we use the following measure: Export Competitionijt = ∑ c ∑ d ⎛ X cjd X idc ⎞ ⎜ c × c⎟ ⎜X ⎟ ⎝ ⋅d X i⋅ ⎠ (14) The measure in equation (14) captures the importance of country j as a trade competitor for the home economy i. More specifically, it captures the degree of competition of country j for the home economy i in the export market of commodity c ( X c ) in the third market d. The intuition for this measure is that country j is a stronger competitor for country i if (i) the larger the export market share of country j in region d ( X cjd / X ⋅cd ) and (ii) the higher the share for country i of total exports of that commodity c to region d ( X cjd / X ic. ).The sources for the trade data used to operationalize the export competition variable in (x) includes the World Trade Analyzer and Global Trade and Analysis Project (2005) version 6.0. Both these data sources measure commodities at the 2-digit SITC level.30 To operationalize the degree of real trade linkages that accounts interdependence in trade between countries, we use the following measure: Tradeijt = ∑ c ∑ c 30 ⎛ X ijc X cji ⎞ ⎜ c× c⎟ ⎜X ⎟ ⎝ ⋅i X i⋅ ⎠ We also operationalized the Simmons and Elkins (2004:179) measure of competition for export markets. Including their measure instead of our measure of competition for exports did not alter substantively or significantly in the statistical sense any of the results described below. 26 (15) Equation (15) measures the degree of bilateral trade between two countries, implying that country i will be affected more by a devaluation in country j the greater the amount of bilateral trade between them. For data on bilateral trade flows, we use the IMF’s Direction of Trade Statistics (2004), Simmons and Elkins (2004) and Global Trade and Analysis Project (2005), V 6.0. Simmons and Elkins (2004) and Brune and Guisinger (2006) suggest that with respect to the finance channel, the diffusion of capital account liberalization is driven by the degree to which countries compete against each other for capital. To empirically test this claim, Simmons and Elkins (2004) and Brune and Guisinger (2006) use Standard and Poor’s (S & P) sovereign bond ratings –which is also used by us – based on the assumption that financial/portfolio investors not only perceive states with similar sovereign bond ratings as close competitors for capital but also as countries with a similar investment risk profile. We refer readers to Simmons and Elkins (2004: 179) for a description of how they use Standard and Poor’s sovereign bond ratings to operationalize their idea that competition for capital drives countries to liberalize their capital accounts. We label this diffusion variable as Competition for capital. Simmons and Elkins (2004: 179-180) claim that another set of international diffusion variables, which they classify as indicators of informational influence, increases the likelihood of financial liberalization. In this regard, they claim that common membership among countries in international agreements or institutions encourage government officials to communicate information and expectations about capital account openness and that this, in turn, fosters capital account liberalization. Building on this idea, they argue more specifically that transmission of information about capital account openness between government officials when negotiating bilateral investment treaties and preferential trading agreements fosters capital account liberalization. Hence, the final set of diffusion variables in our specification, that is taken from Simmons and Elkins (2004) and Brune and Gusinger (2006), is based on calculating for each country-year average policy scores weighted by BIT (bilateral investment treaties) and PTA (preferential trading agreement) partnerships. According to Simmons and 27 Elkins (2004: 180), “these common memberships should predict channeled policy diffusion, based on the diffusion of policy-relevant information.”31 Economic and Political Controls in Outcome Equation Following the vast literature on the determinants of the level of capital account liberalization32, we include an array of economic and political controls in the outcome equation of the SAE Hekit model. We simply list these variables below. The operationalization of these variables are described in Brooks (2004) and Brune and Guisinger (2006) and the data sources used for each variable are listed in the Appendix: • Real GDP growth • GDP per capita • Real Interest Rates • Lag of Currency Crisis dummy • Current Account Balance (% GDP) • Central Govt. expenditure (%GDP) • Democracy • Veto Players • Government Partisanship We also include a time trend in the outcome equation. Doing so minimizes the possibility of a spurious relationship between the variables of interest and the dependent variable. 5.4 Controls in Selection Equation Similar to the outcome equation, we include a set of economic and political controls in the selection equation of the SAE Heckit model where the IMF program dummy is the dependent variable. The formal model in the previous section suggests that financiallydistressed countries—suffering from low foreign exchange reserves, high external debt and balance-of-payment difficulties— often turn to the IMF for financial assistance and are thus more likely to voluntarily participate in the IMF’s financial stabilization programs. In addition 31 We also controlled for two dummy variables in the outcome equation, a dummy for countries if they were had the same colonial heritage and a dummy if they shared a common legal tradition. These dummies are meant to capture the “cultural” factors that drive diffusion of liberalization policies such as those associated with capital account openness. We excluded these dummies from the outcome equation because their effects on the dependent variable were extremely weak and insignificant; dropping these additional diffusion variables did not alter any of the statistical results that we report below. 32 The economic and political controls that we include in the outcome equation are based on empirical models of capital account liberalization by Simmons and Elkins (2004), Quinn (2000, 2002), Quinn and Inclan (1997), Quinn and Toyoda (2007), Kastner and Rector (2005), Li and Smith (2002), Garret et al (2001), Brooks (2004) and Brune and Guisinger (2006). 28 to the model, a number of extant empirical studies that test when governments are more likely to participate in IMF stabilization programs provide a list of variables that affect these participation rates (Knight and Sanatella 1994; Conway 1994; Bird 1996; Vreeland 2004). Based on the model and these extant studies, we include the following variables in the selection equation that are simply listed below to conserve space: • Real GDP Growth • External Debt (% GDP) • Log (Forex Reserves) • Domestic Investment Ratio • Log Inflation • GDP per capita • Veto Players • Lag of IMF stabilization Program dummy 6. Findings and Analyses In this section, we discuss the results from estimating the SAE Heckit model for (i) the full sample of countries in Table 1 and (ii) a set of developing, i.e. non-OECD, countries drawn from the complete list of countries in Table 1. We primarily focus on analyzing the estimates from the outcome equation since this equation includes the key independent variables that directly test hypothesis 1. We then briefly report the results from the selection equation before discussing findings from various robustness and diagnostic tests. 6.1 Results from Outcome Equation Model 1 in Table 3 reports the estimates from the outcome equation of the SAE Heckit model for the full sample in which we included the independent variables and the five diffusion variables as controls but excluded the other political and economic controls. In model 2 of this table, we present the results from the outcome equation of the SAE Heckit model for the full sample in which we included the independent variables, the diffusion variables and all remaining controls. The estimates of the selection equation for these two SAE Heckit models are reported in columns A and B, Table 5 respectively. <<Insert Table 3 about here>> The coefficient of IMF Program x Bank Concentration is positive and highly significant at the 1% level in the outcome equation in model 1 that is estimated for the full sample. This 29 result statistically corroborates the prediction in hypothesis 1 from the formal model. With respect to the individual components of IMF Program x Bank Concentration, one finds that IMF Program is positive but statisitically insignificant in model 1, while Bank Concentration is also positive but insignificant. This suggests that neither IMF financial stabilization programs nor the degree of market concentration of private and MN banks in countries that receive loans from the IMF individually have a significant and substantive effect on the likelihood of currency crises in a fully specified empirical model. Put differently, neither IMF stabilization programs nor Bank Concentration are doing all the statistical work here. Rather, it is the interaction of IMF Program with Bank Concentration that substantively and significantly increases the level of capital account liberalization, as predicted in hypothesis 1. The estimate obtained for the interaction term IMF Program x Bank Concentration in the outcome equation of model 2 where the diffusion and all other controls are included remains positive and significant at the 1% level. Similar to the outcome equation in model 1, the individual components of the interaction term IMF Program and Bank Concentration are each statistically insignificant in model 2. This suggests that even after we control for not just the diffusion variables but also other variables that affect capital account openness, the interaction term IMF Program x Bank Concentration continues to exert a positively significant effect on the dependent variable in the outcome equation. Note that each of the five diffusion variables is statistically insignificant in model 1. More specifically, the estimates of each of the five diffusion variables in model 1 have the predicted positive sign but a closer look at these coefficients indicate the estimated effect of these variables are weak. Interestingly, the spatial autoregressive error (SAE) term in the outcome equation of model 1 is insignificant as well. This indicates that international diffusion pressures for financial openness stemming for reasons related to geography does not significantly, in the statistical sense, affect the degree of capital account liberalization. Hence, although for example, it is widely believed by scholars alike that each country in Latin America 30 hastened the pace of liberalization of their capital accounts in the 1990s because their neighbors were rapidly opening their capital accounts, the statistical results reveal that claims about such spatial effects leading to further financial liberalization may be exaggerated. At the same time, however, we do not want to suggest from the statistical insignificance of the diffusion variables in our empirical models that international diffusion mechanisms have no effect on financial liberalization. It is possible, for instance, that the influence of diffusion on financial liberalization is conditional on other political or economic variables. Alternatively it is plausible that the absence of temporal variation in some of the diffusion variables – for example, BIT and PTA – may explain why they do not have a significant effect on the level of capital account liberalization that has changed so dramatically in the last 15 years. At a minimum, the results in outcome equation in Table 3 suggest that scholars need to theorize more about the influence of international diffusion on capital account liberalization before testing the impact of such systemic variables. While the outcome equation results in models 1 and 2 statistically support hypothesis 1, we are naturally interested to gauge the substantive effect that the interaction term IMF Program x Bank Concentration has on the degree of capital account liberalization. With respect to marginal effects, simple calculation from the estimate of the aforementioned interaction term in model 2 reveals that when Bank Concentration’s companion variable IMF Program is set equal to 1 and other variables in the outcome and selection equation are held at their mean in the sample, increasing Bank Concentration by one standard deviation above its mean increases the level of capital account liberalization by 27%. This marginal effect is illustrated in Figure 2. <<Insert Figure 2 about here>> As an initial test of robustness, we estimated the SAE Heckit model on a sample of only developing countries that are drawn from the complete list of countries in Table 1. By “developing countries”, we mean all non-OECD countries in our sample in Table 1. The rationale for focusing on just developing countries for conducting additional empirical tests is 31 two-fold. First, during the 1980s and 1990s many developing countries in Latin America and Asia made rapid strides toward substantially opening their capital accounts (Brune and Guisinger 2006; Garrett et al 2001). Second, since the 1970s, a vast majority of IMF financial stabilization programs have been approved by the IMF for developing countries. Given the rapid pace of capital account liberalization in the developing world in the last twenty years and the increasing use of IMF financial stabilization programs by these countries, it is plausible that our theory may hold empirically in the sample of developing countries. We checked whether or not this was indeed the case by first estimating two SAE Heckit models on the sample of developing countries; in the outcome equation of the first SAE Heckit model for developing countries we included the independent and the diffusion variables but excluded the remaining control variables. The results from this specification are presented in model 3, Table 4. In the outcome equation of the second SAE Heckit model estimated for developing countries, we include the independent and diffusion variables as well as all the other control variables. The results from this latter specification are reported in model 4. <<Insert Table 4 about here>> In models 3 and 4 respectively, the estimate of the interaction term IMF program x Bank Concentration is positive and highly significant at the 1% level. Similar to the earlier empirical specifications estimated for the full sample, we find that the estimates of the individual components of the interaction term used to test hypothesis 1, i.e. IMF program and Bank Concentration, are each insignificant. An examination of the substantive effects of the interaction term in model 4 indicates that when Bank Concentration’s companion variable IMF Program is set equal to 1 and other variables in the outcome and selection equation are held at their mean in the sample, increasing Bank Concentration by one standard deviation above its mean increases the extent of financial liberalization under a short-run IMF financial stabilization program from by approximately 31%, which is quite substantial. 32 Having discussed the results obtained for our key independent variables, we now turn to briefly report the results from the economic and control variables in the outcome equations in models 1, 2, 3 and 4. Unlike the strong statistical support we obtain for the prediction in hypothesis 1, the estimated results of the economic and political control variables in the outcome equations of the different empirical models provide mixed evidence. For example, the estimate of the political controls Democracy, Veto Players, Government Partisanship and Divided Government are largely insignificant in the outcome equations of the SAE Heckit models estimated for the full sample and the sub-sample of developing countries. However, the coefficient of Government Partisanship is positive and weakly significant in the outcome equation of the SAE Heckit model 5 that was estimated for developing countries. This suggests that there is some, albeit weak evidence, that partisanship may matter for capital account openness in developing countries. The economic control variables relatively fare much better in the outcome equation. For instance, the estimates of GDP per capita, Current Account/GDP and the lag of the Currency Crisis dummy are consistently significant and in the predicted direction in the outcome equations. But other economic control variables such as Real GDP Growth and Real Interest Rates are consistently insignificant in the specifications.33 6.2 Selection Equation Results and Robustness tests Turning to the estimates of the selection equation of models 1, 2, 3 and 4 –that are reported in Columns A, B, C and D in Table 5 respectively – we find that the coefficient of the only political control in the specification, Veto Players, is consistently positive but statistically insignificant in the selection equation. The results for the economic controls in the selection equation are, much like the outcome equation, “mixed.” For example, GDP per capita and Log Forex Reserves is in the predicted negative direction and statistically insignificant in the selection equation of each SAE Heckit model. Also, External Debt/GDP is positive and highly significant in the selection equations. The estimate of the SAE parameter δ in the selection equation of all 33 The estimate of the “adjusted” Inverse Mill’s ratio is positive and weakly significant in the outcome equations of all the estimated SAE Heckit models. 33 the SAE Heckit models is positive but insignificant. The insignificance of δ indicates that there is no statistically significant spatial dependence in the data with respect to countries participating in IMF financial stabilization programs. <<Insert Table 5 about here>> Finally, to check the econometric validity and consistency of the results reported earlier, we conducted some robustness tests and a series of diagnostic checks. First, we added some control variables to the outcome equation of each SAE Heckit model in tables 3 and 4 and then re-estimated the model, including the selection and the augmented outcome equation. These additional controls in the outcome equation are: Central Bank Independence (the Cukieman et al 2002 measure), capital/labor ratio (to proxy for factor endowments), log of inflation, education (to proxy for skill), log of foreign exchange reserves and an additional diffusion variable that captures similar economic structure of countries in the dataset.34 We do not report the results from the SAE Heckit models with the additional controls owing to space constraints. Including these additional controls did not substantively or significantly, in the statistical sense, alter any of the empirical results discussed earlier. Finally, we conducted standard post-estimation diagnostic checks. These diagnostic tests revealed that none of the empirical models estimated for this paper suffer from severe multicollinearity, serial correlation and omitted variable bias and that the residuals are normally distributed.35 7. Conclusion We suggest here that short-run IMF financial stabilization programs play a crucial role in fostering capital account liberalization. But these programs do not have an independent effect. Rather, our formal model predicts that IMF stabilization programs such as SBAs, EFF and SRF have a positive effect on capital account liberalization conditional on the degree of 34 See Brooks (2004), Quinn & Toyoda (2007) and Quinn & Inclan (1997) for details of these controls The largest VIF value in each empirical model is substantially lesser than 10, thus suggesting that multicollinearity is not a problem. The Breusch-Godfrey LM test failed to reject the null of no serial correlation in all the outcome equations, while Gourieroux et al’s (1982) score test failed to reject the null of no serial correlation in the selection equations. The RESET test indicates that the models do not suffer from omitted variable bias and the Jarque-Bera indicates that the residuals approximate a normal distribution. 35 34 market concentration of private sector and MN banks in countries that borrow from the IMF. Results from SAE Heckit models estimated on a large sample provide robust statistical support for the main prediction from our model. Our paper is the first to theorize about and test how IMF programs interact with certain domestic political economy variables to foster financial liberalization. This is in sharp contrast to the literature that focuses on the impact of either domestic political factors or international diffusion mechanisms on capital account openness. Moreover, the findings presented here have at least two key policy implications. First, it is fashionable among journalists and some academics to claim that the IMF is obsolete and should be disbanded. Such claims are premature. Instead, the analysis in this paper suggests that the IMF will in all likelihood continue to influence policy reform in particularly developing countries in the foreseeable future as long as domestic political and economic conditions in these countries are, on balance, receptive to IMF reform measures. Second, an important policy lesson for the IMF from this study is that the institution should pay much more attention toward developing short-run financial stabilization programs that incorporate compensation schemes for groups in society that lose from the IMF’s reform measures. Critics have charged the IMF in the past of being insensitive to the concerns of sections in society that find economic reforms costly and to some extent such criticisms are not completely invalid. In fact, quite ironically, our study suggests that the IMF can actually increase its leverage and influence policy reform in borrowing countries provided it encourages the governments of these countries to adequately and transparently compensate societal groups who find the IMF’s financial reform measures costly in the short-run. While useful, this paper suffers from two key drawbacks that may be addressed by further research. First, a crucial component of our causal story is that IMF programs help to foster financial liberalization in part because governments that participate in these programs can use IMF loans to credibly commit themselves ex ante to compensate those who lose from financial reforms ex post. We have not tested this causal claim here simply because a limitation 35 of statistical analyses –which includes the tests in this paper – is that it prevents one from carefully testing unobservable causal mechanisms from game-theory models. Second, a limitation of our formal model is that we do not explicitly model the strategic behavior of the “losers” from financial liberalization. Doing so adds substantially to the technical complexity of the model and makes it intractable. That said, we might gain new insights from introducing groups that lose from capital account liberalization as a fourth player in the model. Whatever future direction this project takes, we hope that we have provided some new theoretical and empirical insights that deserve future research. Appendix A. Proofs Proof of Lemma 1: For all t, UG is a concave function of k. The first order condition of UG with respect to k is γh ′(t − p(t ,θ , c ) + φρk ( y 0 − t )]φρ ( y 0 − t ) − δρ ( y0 − t ) = 0 (A.1) The above expression can be simplified to γh ′(t − p(t ,θ , c ) + φρk ( y 0 − t )]φ − ρ = 0 (A.2) (A.2) implies that (t − p(t , θ , c ) + φρk ( y 0 − t )] = ( h ′) −1 [δ /(γφ )] ≡ k * (A.3) To solve for the optimal t we need to use the first-order condition in (A.2) as a constraint in the domestic government’s optimization problem. Doing so leads to the following optimization problem for the government, arg max U G (k , t ) = γh[t − p (t ,θ , c ) + φρk ( y 0 − t )] + δ ( y 0 − t )(i * − ρk ) + qρ 2f / σ 2 t s.to. t − p (t ,θ , c ) + φρk ( y 0 − t ) = k (A.4) * From the constraint in (A.4), we get [k * − t + p (t ,θ , c )] φ = ρk ( y 0 − t ) . Substituting this expression into UG and simplifying, we obtain γh ( k * ) + δi * ( y0 − t )] − δρk ( y0 − t ) + qρ 2f / σ 2 (A.5) Since the reduced form of UG is concave in t, the first order condition of UG with respect to t is U G′ (t ) = −δi * + δ φ − δp (t , θ , c ) ρk * ] / φ = 0 (A.6) Substituting p (t ,θ , c ) = s − tcφ ( s / t )(θ t ) −1 and ( h ′) −1 [δ /(γφ )] = k * into (A.6) and then solving for t* from (A.6) leads after some algebra to t * = ρk (1 / h −1 ) − φi * as claimed. QED 36 (A.7) Proof of Proposition 1: (i) Since (1 / h −1 ) = [t − p(t , θ , c ) + φρk ( y 0 − t )] in equation (8), ∂t * ∂y 0 = φ ( ρk ) 2 > 0 . (ii) From equation (7) in the text, the derivative of k* with respect to y 0 is ⎛ ⎞ 1 ⎜⎜ δ γφ 2 ρ ⎟⎟ h ′(t − p(t ,θ , c ) + φρk ( y0 − t ) ∂k ⎠ =−⎝ 2 ∂y0 (h′(t − p(t,θ , c) + φρk ( y0 − t )(δ / γθ ) ) * (A.8) where p (t ,θ , c ) = s − tcφ ( s / t )(θ t ) −1 . From the proof of claim 2 (see below), ∂p (t ,θ , c ) / ∂θ > 0 . Therefore, ∂h (.) / ∂θ = h ′( −∂p(t ,θ , c ) / ∂θ ) . Substituting this into (A.8) leads to ⎛ ⎞ 1 ⎜⎜ δ γφ 2 ρ ⎟⎟ h ′[ −∂p (t ,θ , c ) / ∂θ ] + φρk ( y 0 − t ) ⎠ −⎝ (h′[−∂p(t,θ , c) / ∂θ ] + φρk ( y0 − t )(δ / γθ ) )2 (A.9) Using (A.8) and (A.9), the relevant cross-partial is: ⎛ ⎞ δγφ 2 ρ ⎜ ⎟ ⎜ * h ′[( 2θt )( cφst )] + φρk ( y 0 − t ) ⎟⎠ ∂k ⎝ =− ∂y 0 ∂θ (h′[(2θt )(cφst )] + φρk ( y0 − t )(δ / γθ ) )2 ( ) Because h ′[(2θt )( cφst )] > 0 , δγφ 2 ρ / h ′[( 2θt )( cφst )] + φρk ( y 0 − t ) < 0 . Hence (A.10) ∂k * > 0 QED. ∂y 0 ∂θ Proof of Claim 1: The first order condition of equation (5) in the text with respect to s is 1 − cφ ′( s / t )(θt ) −1 = 0 . From the aforementioned expression one can easily check that s* = tμ(θtc −1 ) = 0 where μ is the inverse of φ ′ > 0 and is thus decreasing. The derivative of s with respect to θ is tφ ′(θtc −1 )tc −1 > 0 since φ ′ is 0. Differentiating the aforementioned expression with respect to θ once again, we get φ ′(tc −1 )c −1 > 0 . This implies that s is convex in θ . The derivative of s with respect to t is λ + tφ ′θc −1 > 0 . Differentiating this expression with respect to t once again yields θc −1φ ′( x )[ xt ′′( x ) / φ ′( x ) + 2] (A.11) where x = tθc −1 . Note that [ xt ′′( x ) / φ ′( x ) + 2] > 0 which holds for φ ( x ) = ψ ( x + g )η + c1 , φ ( x ) = ψe βx + c2 and hence ∀ψ,η,g,c1,c 2 > 0 . Hence s strictly increases in t. Using a similar argument and the fact that sθ > 0 , it follows that stθ > 0 QED. Proof of Claim 2: Applying the quotient rule, we obtain ∂p (.) / ∂θ = cφst /(θt ) 2 > 0 ∀c, φ , s, t ∈ ℜ + QED. B. Estimation of Spatial Heckit Model: In the presence of spatial error dependence, MLE of the spatial Heckit model will result in inconsistent estimates of the parameters. Therefore, we follow Pinkse and Slade (1998, 2006), Kelejian 37 and Prucha (1999) and Flores-Lagunes and Schnier (2006) by using a feasible estimator for relatively large samples that achieves consistency by accounting for heteroskedasticity induced by spatial errors. Specifically, we use a two-step procedure a la’ Heckman (1976, 1979) that is estimated jointly in a Generalized Methods of Moment (hereafter GMM) framework. The selection equation is estimated using Pinkse et al’s (2005) GMM estimator, while the outcome equation is estimated with spatial methods proposed by Flores-Lagunes and Schnier (2006). An estimate of the Inverse Mills Ratio (hereafter IMR) is included in the outcome equation to correct for selectivity bias. To estimate these two parts simultaneously, the corresponding moment conditions are stacked, and a GMM criterion function is minimized with respect to all parameters in the model. To estimate the SAE Heckit model in (9)-(10) via GMM, we use these formulas from McMillen (1995): var(u1i ) = σ 12 ∑ (ωij1 )2 , var(u 2i ) = σ 22 ∑ (ω ij2 )2 and j j ( { ) } ' E (u1i , u 2i ) = σ 122 ∑ ωij1 ωij2 . Let θ1 = α 0,α 1 , δ be the parameters to be estimated in the spatial 2 j probit model, and ψ i (θ 1 ) = α 0 + x1' iα 1 var(u1i ) in the index function of the probit model weighted by the standard deviation of the residual. The corresponding “generalized residuals” of this model are: u~1i (θ1 ) = {y1i − Φ[ψ i (θ1 )]}⋅ φ [ψ i (θ1 )] . Φ[ψ i (θ1 )]{1 − Φ[ψ i (θ1 )]} (B.1) The GMM estimates for θ1 can be obtained as follows: θˆ1,GMM = arg min S N (θ1 )' M N S N (θ1 ) (B.2) θ1∈Θ1 ( ) where S N (θ1 ) = 1 ' z N u1N θˆ1 , z N is a data matrix of regressors plus at least one instrument (to identify N the extra parameter δ , u~ (θ ) is the vector of generalized residuals with elements as shown in (B.2), 1N 1 p and M N is a positive definite matrix such that M N → M . The estimates of θ1 can be used to construct the IMR to correct for sample selection bias. The conditional regression function for the outcome equation has the following form (McMillen 1995): [ ( E y 2i y1i > 0 = β 0 + x 2' i β1 + E [u 2i u1i ] > − α 0 + x1' iα 1 = β 0 + x 2' i β1 + = β 0 + x 2' i β1 + E (u1i , u 2i ) var(u1i ) ⋅ σ 12 ∑ ω ij1 ω ij2 j σ 12 ∑ (ω ij1 ) 2 j 38 φ [− ψ i (θ1 )] {1 − Φ[− ψ i (θ1 )]} ⋅ φ [− ψ i (θ1 )] {1 − Φ[− ψ i (θ1 )]} )] ∑ω ω 1 ij σ = β 0 + x 2' i β 1 + 12 ⋅ σ1 2 ij j ∑ (ω ) 1 2 ij ⋅ φ [− ψ i (θ1 )] {1 − Φ[− ψ i (θ1 )]} j Therefore, the selectivity correction implies the following adjusted IMR: ∑ω ω 1 ij λi ≡ 2 ij j ∑ (ω ) 1 2 ij ⋅ φ [− ψ i (θ1 )] . {1 − Φ[− ψ i (θ1 )]} (B.3) j ( ) Once estimated λ̂i , the adjusted IMR may be included as an additional variable in the outcome equation. Note that the adjusted IMR in (B.3) depends on a parameter that is not estimated in the first step: γ , which is included in the weights ω ij2 . To increase the efficiency of the estimator and directly obtain its variance-covariance matrix, we use GMM to estimate simultaneously all parameters of the sample selection model by rewriting it as a sequential estimator. Stacking the corresponding moment [ ] { } ' ' conditions leads to g ( z N , θ ) = s ( z1N , θ ) , m( z 2 N , θ ) , θ = α 0,α 1 , δ , β 0 , β1 , μ , γ with ' ' ' ' s (z1 N , θ ) = z1' N u~1 N (θ ) , u~1N (θ ) and m( z 2 N , θ ) = [ y1N ⋅ z 2 N ] u~ 2 N (θ ) , u~ 2 N (θ ) = y 2 N − β 0 − x 2' N β 1 − μλˆN (δ , γ ) where N denotes the corresponding matrix of data, we let ( ) z N' = z1' N , [ y1N ⋅ z 2 N ] , z1N and z2 N includes the regressors from the selection and outcome equation ' ' respectively with the estimated “adjusted” IMR in the outcome equation represented as λˆN (δ , γ ) to ( ) ' ' ' make explicit its dependence on both SAE parameters. Let u~N (θ ) = u~1N (θ ), u~2 N (θ ) ; then the parameters of the SAE sample selection model can be estimated as: θˆGMM = arg min g N (θ )' M N g N (θ ) θ ∈Θ where g N (θ ) = (B.4) p 1 ' ~ z N u N (θ ) , for a conformable positive definite M N such that M N → M . θˆGMM is the N estimator for the Heckit model with spatial autoregressive errors in the selection and outcome equation. Pinkse & Slade (2006) prove that θˆGMM is consistent (i.e. θˆGMM → θ 0 ) and asymptotically normal. 39 Table 1: List of countries, 1975-2002 Country Algeria Argentina Australia Austria Bangladesh Barbados Belgium Belize Bolivia Botswana Brazil Burundi Cameroon Chile China Colombia Costa Rica Cyprus Czech republic Denmark Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Ethiopia Fiji France Gabon Germany Ghana Grenada Guatemala Guinea-Bissau Guyana Haiti Honduras Hong Kong Hungary Italy India Indonesia Iran Ireland Italy Jamaica Country Japan Jordan Kenya South Korea Lao P.D. Republic Madagascar Malawi Malaysia Mali Malta Mauritius Mexico Morocco Mozambique Myanmar Nepal Netherlands New Zealand Nicaragua Nigeria Norway Pakistan Panama Paraguay Peru Philippines Poland Sierra Leone Singapore South Africa Spain Sri Lanka Swaziland Syria Sweden Thailand Trinidad & Tobago Tunisia Turkey Uganda United Kingdom United States Uruguay Venezuela Zambia Zimbabwe 40 Table 2: Data Sources for Control Variables GDP per capita: World Bank (WB) World Development Indicators (WDI) 2005 CD-ROM Currency Crisis Dummy =1: Leblang and Satyanath (2006); Mukherjee (2007) Real Interest Rates: WBWDI (2005), IMF (2004) IFS CD-Rom and PWT (2004). Current Account Balance (% of GDP): WB WDI and IMF (2004) International Financial Statistics CD-Rom Foreign Exchange reserves: WB WDI (2005) and IMF (2004) IFS CD-Rom Inflation: WB WDI (2005) Real GDP growth: WB WDI (2005) Trade Openness: WB WDI (2005) and IMF (2004) IFS CD-Rom Democracy (Polity IV Index) Central Bank Independence: Cukierman et al (2002), updated in Stasavage et al (2004) Government Consumption (% of GDP): WB WDI (2005) and IMF (2004) IFS CD-Rom Veto Players: World Bank’s Database of Political Institutions, Beck et al (2001) updated in 2004. Government Partisanship: World Bank’s DPI, Beck et al (2001) updated in 2004. Domestic Investment Ratio: WB WDI (2005), Penn World Tables (2004) and IMF (2004) IFS CDRom External Debt (% of GDP): WB WDI (2005) and IMF (2004) IFS CD-Rom 41 Table 3: Outcome Equation Results for Full Sample, 1975-2002 Independent variables IMF program IMF program x Bank Concentration Bank Concentration Diffusion variables Export Competition Trade Competition for capital BIT PTA Chinn-Ito (2005) Measure Model 1 Chinn-Ito (2005) Measure Model 2 .050 (.046) .137*** (.055) .023 (.084) .033 (.037) .125*** (.040) .067 (.119) .059 (.077) .037 (.041) .042 (.036) .014 (.059) .027 (.048) .022 (.055) .012 (.008) .020 (.051) .010 (.086) .021 (.020) Economic Controls GDP per capita .037*** (.010) .030 (.036) .059** (.030) -.051*** (.018) -.051*** (.020) .033*** (.012) Real GDP Growth CGE (% of GDP) Real Interest rates Lagged Currency crisis Current Account Balance (% of GDP) Political Controls Democracy(polity IV) .021 (.040) -.035 (.037) .048 (.037) .031* (.019) .424** (.061) .033 (.037) .249* (.136) .77 2124 Veto Players Partisanship Trend Constant SAE parameter ( γ ) ( ) “Adjusted” IMR λ̂i ρ N .044* (.026) .447*** (.062) .050 (.046) .326* (.185) .82 2289 Notes: ***, **, *: 1%, 5% and 10% levels of significance. Numbers in parentheses are panel-robust standard errors based on the procedure from Arellano (1987). The standard errors are also corrected for heteroskedasticity via the GMM estimation procedure. The outcome equation of the SAE Heckit models are estimated with fixed effects that are not reported above. 42 Table 4: Outcome Equation Results for Developing countries only, 1975-2002 Independent variables IMF program IMF program x Bank concentration Bank Concentration Diffusion Variables Export Competition Trade Competition for capital BIT PTA Chinn-Ito (2005) Measure Model 3 Chinn-Ito (2005) Measure Model 4 .064 (.075) .122*** (.043) .053 (.084) .031 (.077) .153*** (.031) .029 (.018) .042 (.039) .044 (.031) .009 (.012) .023 (.030) .073 (.082) .036 (.022) .039 (.054) .021 (.015) .014 (.029) .031 (.040) Economic Controls GDP per capita .080*** (.029) .049 (.038) .026*** (.011) -.040 (.039) -.092*** (.038) .065 (.092) Real GDP Growth CGE (% of GDP) Real Interest rates Lagged Currency crisis Current Account Balance (% of GDP) Political Controls Democracy(polity IV) .095 (.086) .021 (.045) .018 (.020) .063 (.088) 209*** (.065) .063 (.088) .167* (.091) .83 1925 Veto Players Partisanship Trend Constant SAE parameter ( γ ) ( ) “Adjusted” IMR λ̂i ρ N .046 (.035) .316*** (.078) .046 (.035) .245* (.133) .89 1987 Notes: ***, **, *: 1%, 5% and 10% levels of significance. Numbers in parentheses are panel-robust standard errors based on the procedure from Areallano (1987). The standard errors are also corrected for heteroskedasticity via the GMM estimation procedure. The outcome equation of the Heckit models are estimated with fixed effects that are not reported owing to lack of space. 43 Table 5: Selection Equation Results for Full Sample, 1975-2002 Real GDP growth Log Forex Reserves External debt (% GDP) Domestic Investment Ratio Log inflation GDP per capita Veto Players Lagged IMF program Constant SAE parameter ( δ ) selection equation for Model 1 -.068 (.074) -.039*** (.018) .024*** (.008) -.030 (.071) .054 (.041) selection equation for Model 2 -.065 (.092) -.032*** (.010) .031*** (.010) -.039 (.050) .042 (.039) selection equation for Model 3 -.045** (.020) -.047*** (.021) .075*** (.019) -.033*** (.011) .032 (.034) selection equation for Model 4 -.040** (.019) -.052** (.023) .080*** (.026) -.022 (.021) .041 (.040) -.028*** (.011) .041 (.040) .060*** (.011) .290*** (.089) .032* (.018) -.036*** (.014) .065 (.052) .073*** (.028) .316*** (.078) .030* (.017) -.026 (.023) .051 (.043) .021*** (.006) .290*** (.089) .065* (.038) -.031*** (.012) .057 (.044) .040** (.019) .316*** (.078) .053* (.032) Notes: ***, **, *: 1%, 5% and 10% levels of significance. 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