Download International Institutions and Domestic Compensation: The IMF and the Politics of Financial Liberalization

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Preliminary Draft – Please do not cite without authors’ permission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.
Figure 2: Effect of Bank Concentration on Capital for IMF Program =1 with 95% CI.
IMF Program = 1
5
4
3
Capital
2
1
Bank Conc.
.5
1
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Figure 1: Sequence of Moves
IMF provides
y 0 and program
Private financial
institutions exert p (.)
……………
IMF chooses α and 1 − α
Players observe
φ and k
Gov’t requests
loans from IMF
Gov’t accepts
program and
transfers t
Gov’t exerts
reform effort;
optimally chooses
k* and t*