Download Policy Diffusion in the International Political Economy

The Globalization of Liberalization:
Policy Diffusion in the International Political Economy
Beth Simmons
Department of Political Science
University of California, Berkeley
[email protected]
Zachary Elkins
Department of Political Science
University of California, Berkeley
[email protected]
Abstract:
One of the most important developments over the past four decades has been the spread of liberal
economic ideas and policies throughout the world. These policies have affected the lives of billions of
people, yet our most sophisticated political economy models do not adequately capture influences on
these policy choices. Evidence suggests that the adoption of these practices is highly clustered both
temporally and spatially. We hypothesize this might be due to processes of policy diffusion. We think of
diffusion as resulting from one of two broad sets of forces: one in which mounting adoptions of a policy
alter the benefits of adopting for others, and another in which adoptions provide policy relevant
information about the benefits of adopting. We develop arguments within these broad classes of
mechanisms, construct appropriate measures of the relevant concepts, and test their effects on
liberalization and restriction of the current account, the capital account, and the exchange rate regime.
Our findings suggest that domestic models of foreign economic policymaking are insufficient. The
evidence shows that policy transitions are influenced by international economic competition as well as the
policies of a country’s socio-cultural peers. We interpret this latter influence as a form of channeled
learning reflecting governments’ search for appropriate models for economic policy.
Prepared for delivery at the workshop, Internationalization of Regulatory Reforms: The
Interaction of Policy Learning and Policy Emulation in Diffusion Processes (Berkeley, CA, April 24-25,
2003).
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I. Introduction
One of the most important developments over the past four decades has been the widespread turn
to economic liberalization as a way to order both the national economy and economic relations with the
rest of the world. Increasingly, evidence indicates that transitions to economic liberalization cluster in
time and space. What can account for these tides of foreign economic policy liberalization and restriction?
An important but understudied part of the answer, we believe, lies in policy diffusion,1 in which the
decision to liberalize (or restrict) by some governments influences the choices made by others.2 We can
think of diffusion as resulting from one of two broad classes of mechanisms: one in which foreign policy
adoptions alter the benefits of adoption for others, and another in which these adoptions provide
information about the costs or benefits of a particular policy innovation. This article theorizes and tests
two particular diffusion arguments from each of these two classes of explanation. In developing these
arguments, we explicitly acknowledge the alternative. For example, the spread of economic liberalization
could be a response to commonly experienced phenomena (capital flight, economic recession) rather than
the result of interdependent state behavior. Similarly, liberal democracies may share preferences for open
foreign economic policies that give the greatest freedom to civil society to trade and invest across borders;
such attributes lead governments to respond similarly, but independently, to world conditions. Both of
these processes might lead to highly clustered policy making, but we would not classify either of them as
policy diffusion. For our purposes, they constitute null hypotheses against which accounts of
interdependent decision-making must compete.
1
There is a rich tradition of research on the geographic diffusion of a whole host of political, social, and economic
phenomena. In Political Science, the work of Walker (1969) and Gray (1973) on the diffusion of policy in the US
states prompted scholars to reconsider their assumptions about policy evolution. Since then, a number of studies
(e.g., Collier and Messick 1975 on social security and Tolbert and Zucker 1983 on civil service reform) have
confirmed these insights. In the last ten years, a convincing set of studies have shown that transitions to democracy
(Huntington 1991, Starr 1991, Markoff 1995, O’Laughlin et al. 1998, and Coppedge and Brinks 1999) and conflict
(e.g., Most and Starr 1980; Pollins 1989; Siverson and Starr 1991) are highly contagious. A parallel set of studies
exists in sociology with respect to institutional evolution (e.g., Meyer and Rowan 1977; Powell and Dimaggio 1991;
Strang 1991).
2
We use the terms “diffusion” very generally to refer to all processes in which “prior adoption of a trait or practice in
a population alters the probability of adoption for remaining non-adopters” (Strang 1991). There are a host of
related phenomena subsumed under this general definition (e.g., imitation, demonstration effects, mimicry,
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We focus on explaining changes within three foreign economic policy areas, each of which is
primarily monetary or financial in nature, but with profound impact on the real economy. The first is
liberalization of the current account. Increasingly, governments have committed themselves to refraining
from interfering in foreign exchange markets for purposes of blocking or otherwise influencing current
transactions (foreign debt repayment, payment for goods, services, or invisibles; see Simmons 2000a,
2000b). The second is liberalization of the capital account, or the removal of taxes, quotas, or other rules
that discourage the free movement of investment funds into and out of a country (Quinn and Inclan 1997;
Dooley 1996). The third policy is the unification of the exchange rate. Governments have over the course
of the past three decades largely eschewed multiple or tiered systems, which were quite popular in the
1950s and 1960s, but that can be used to discriminate against particular kinds of transactions or particular
trading partners. Together, these three policy areas constitute the major aspects of international monetary
and financial liberalization over the past three decades. Our argument is that these choices are influenced
by the choices of other governments, and not solely, as much of the political economy literature seems to
assume, by exogenously given domestic institutions or preferences that can be traced back to domestic
political or economic structures. Our task is to demonstrate how and why these policy choices diffuse
internationally.
The broad global trend toward liberalization in these three areas can readily be documented over
the past thirty years. In 1967, 25 members of the IMF (24%) had capital accounts that were practically
free from restrictions, 38 (37%) had fully liberalized current accounts, and 73 (75%) had unified
exchange rate systems. By 1996, 54 members (30%) had removed virtually all restrictions on the capital
account, while 79 (45%) had liberalized the current account and 158 (or 88% of the membership) had
unified their exchange rate systems.3 But what concerns us more is that transitions to and from these
policies tend to be highly concentrated in certain years. For example, the bulk of the transitions to unified
exchange rates occurred in the mid-1970s and mid 1990s. Similarly, the late 1960s and mid-1990s were
emulation, isomorphism, spatial autocorrelation, Galton’s Problem, contagion, dissemination, transfer, signaling,
reflection) which we will assume to be part of the more general phenomenon with which we are concerned.
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times of high activity in policy-making with respect to the current and capital account. Policy clusters can
be confirmed statistically: the distribution of transition counts (both liberal and restrictive transitions) fits
a negative binomial distribution (which expects clustered data) better that it does the distribution from a
random, non-clustered process such as the Poisson.4
Foreign economic policy transitions tend to cluster spatially as well. As the 1995 maps in Figure
1 demonstrate, the three economic policies appear to have a distinctly regional cast to them.
[FIGURE 1 ABOUT HERE]
One summary measure of such geographic clustering is the correlation between a government’s policy
and average policy score of border governments. For the three policies, the correlation is reasonably
strong: 0.52, 0.32, and 0.42 for the capital account, exchange rate, and current account policies
respectively. But why, exactly, should near neighbors choose similar policies? We suggest (1) foreign
economic policy choices elsewhere can alter the payoffs associated with choosing or maintaining a
particular policy, and (2) foreign economic policy choice elsewhere can change the information set on
which governments base their own policy decisions.5
3
IMF, Annual Exchange Arrangements and Restrictions, Analytical Appendix, various issues.
The Poisson is a rare events distribution which assumes the absence of precisely the two sources of convergence
which we purport to disentangle in this article. Specifically, the distribution assumes that events occur
independently and that the susceptibility of a particular event is homogenous across units, which in this case is years
(King, 1989; Siverson and Starr, 1991). The crucial difference between the Poisson and the negative binomial
distribution lies with their assumption about the variance in counts. The Poisson assumes that the mean equals the
variance. If there is over-dispersion (variance greater than the mean) -- an effect of highly clumped data -- then the
Poisson will not fit the data well. The negative-binomial, on the other hand, makes no assumption about the size of
the variance and in fact treats it as a parameter to be estimated. For each set of policy reversions, we performed a
chi-square test of the equivalence of distributions. In each case, a likelihood ratio test reveals that the data on policy
choice fit a negative binomial distribution significantly better than they do a Poisson.
5
Policy innovation elsewhere may affect both payoffs and information, but we view these mechanisms as
analytically if not always empirically distinct.
4
The Globalization of Liberalization
II.
4
Explaining clustered transitions in foreign economic policy making
A. Altered Payoffs
In this class of mechanisms, the policy decisions of one government alter the costs and benefits of
the policy for others. One can think of these decisions as producing externalities which subsequent
adopters must factor into their decision calculus. One type of externality is highly material and works
through direct economic competition. The other is more ideational, and works through the more
subjective pressures of prevailing global norms.
1.
Altered material payoffs. One of the key insights of economists who pioneered
the early interdependence literature was that economic policies taken in one country can have economic
impacts elsewhere, with profound consequences for policy-making (Cooper 1968, Morse 1976). These
insights informed a generation of political economy work concentrated on issues of macroeconomic
policy coordination among the major economies (Hamada 1985, Iida 1999).
International markets for goods and especially capital are the conduit for policy interdependence
in these models. Here we focus on competition among policy makers to attract capital and international
business generally as a means to enhance aggregate economic growth (Stockman and Hernandez 1988).
Policy liberalization in country A may make it a relatively more attractive venue to invest or conduct
commercial relations. Indeed, economists have stressed that capital and trade respond positively to the
signal that policy liberalization sends (Bartolini and Drazen 1997). When foreign competitors liberalize,
traders and investors are drawn to those locations where they can do business more freely and securely.
Anticipating this, country B may experience competitive pressures to match its rival’s liberal policy. This
sets up the possibility of competition among jurisdictions, at least on the margins, for the location of
international economic activity. Such considerations could potentially explain a dynamic of competitive
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liberalizations, which the popular literature has sometimes referred to as a deregulatory race to the
bottom.6
Governments act strategically in this model in order to attract economic activity to their
jurisdiction with the ultimate aim of boosting aggregate growth. Pluralist renditions emphasize the
preferences of electorally significant firms or groups in clarifying to leaders the interests they have in
such policies (Goodman and Pauly 1993, Encarnation and Mason 1990). In more statist versions,
decision makers take such actions regardless of the immediate preferences of domestic political groups
(Krasner 1985); in the medium run, they are gambling on an aggregate growth payoff for which,
presumably, they will be rewarded by continued political support. In each case, however, the government
faces incentives to anticipate and counter decisions taken outside its jurisdiction, rather than waiting
passively for these decisions to work their way through the international economy, the domestic economy,
and the domestic electoral system. In an international environment that is assumed to be institutionally
thin and non-hierarchical, the result is competitive pressure to implement capital- and trade-friendly
policies when major competitors have done so.
Note that this model does not predict universal convergence on liberalization. It predicts
convergence towards either restrictive or liberal policies among competitors. We assume that a decisionmaker has good information about the identity of competitors, the policy choices they make, and the
expected material consequences attached to matching or failing to match their policies. The model leads
to the following prediction:
Governments’ liberalization policies will be positively influenced by the policies of their foreign
economic competitors.
The “race to the bottom” thesis is usually intended to convey the idea that in a competitive situation regulatory
standards tend to fall below an optimal level, and not that they literally crash to the level of the lowest existing
national standard. The thesis is propounded in a number of issue areas, including environmental standards (Porter
1999), corporate law (Daniels 1991), and capital adequacy standards (Bradley 1991 and Worth 1992). However for
critical analysis and contrary findings in the areas of trade and finance respectively, see D. Vogel 1995 and S. Vogel
1996.
6
The Globalization of Liberalization
2.
6
Altered reputational payoffs. The mainstream international political economy
literature has given scant attention to the effect that norms or ideas have on the values governments and
economic attach to reforms informed by predominant economic theories (for an exception, see McNamara
1998). One of the hallmarks of the current trend toward globalization is the ascendancy of market
mechanisms as engines of economic growth. The spread of liberalization both reflects and buttresses the
power of the neo-liberal ideational consensus.
Changes in prevailing global ideas and the practices they entail create externalities for
governments as well. As growing numbers of important actors articulate theories and implement
practices that reflect a normative consensus, the legitimacy of these ideas gather steam. In the absence of
ideational consensus, heterodox policies are difficult to distinguish and are readily tolerated. But
theoretical consensus on an appropriate economic model is associated with intangible costs of
nonconformity. Perceived policy failures associated with “heterodoxy” will suffer greater public
condemnation than similar failures of conforming policy. Governments that resist ideational trends face
reputational consequences that cast doubt on their approach to the economy and potentially the legitimacy
of their governance.
The logic that links normative consensus to legitimacy externalities maybe reflected in the
“tipping,” or “threshold,” models that Schelling (1978) and Granovetter (1978) have described. The basic
intuition in these models is that most governments are highly sensitive to the number, or proportion, of
other countries which have adopted a particular policy stance. The idea of “thresholds” or “critical mass
points,” is a useful (although not necessary) device with which to understand the process.
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7
Figure 2. Threshold Model of Policy Adoption
100
B
80
Percent
Adopting
Liberal
Policies
10
A
0
0
20
75
100
Percent Expected to Adopt Liberal Policies
Figure 1 illustrates the classic effect of “thresholds” on the probability of adopting liberal policies
that Schelling describes (1978). Imagine a group of actors, each of whom will adopt a given practice only
if a certain proportion of others adopt. We may say that this certain proportion is an actor’s “critical mass
point.” Assuming that the distribution of the various critical mass points is normal we can add them and
produce the s-curve in figure 1 (assuming here that the mean critical mass point was around 50%). Points
on the curve represent the proportion who would adopt a policy given the proportion of the population
which is expected to adopt. In this stylized scenario, one can see that a small set of actors (about 5%)
would adopt the policy even if no one else is expected to adopt, and a small percentage of actors (about
15%) at the top of the curve who will not adopt even if they expect everyone else to adopt. The y=x line
helps demonstrate the equilibria that result from this dynamic. Consider Point A, in which 10% of the
population would be willing to adopt if the proportion expected to adopt is 20%. Once those 10% adopt,
however, the expected adoption rate declines to 10% and one can see that the actual number adopting
given that expected rate will decrease. Essentially, points on the curve under the 45º line, will resolve to
bottom of the curve, since the critical proportion which actors require for their adoption is always higher
than the number who would adopt at that level. One can see the opposite dynamic for points above the
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8
45º line. For example, at point B, the percent willing to adopt (roughly 80%) exceeds the percent that
they expect to adopt (75%). As such, the percent expected to adopt will remain high since an even higher
proportion of actors are satisfied that there are enough actors adopting.
Our sense is that these sorts of tipping models capture the dynamics of global norms fairly well.
For reputational reasons, the proportion of others adopting matters a great deal. Given, that this is the
case, we may expect that the threshold effects that one sees in a Schelling-style tipping model should be
strong. Such reasoning implies a clear empirical expectation:
The proportion of liberalizations in the sample at large should exert a positive influence on a
government’s decision to liberalize.
B. Social Learning
A conceptually distinct motor for policy diffusion is informational. In contrast to the discussion
of payoffs above, this approach assumes that governments often lack the crucial information they need to
understand the consequences of economic policy innovation. Innovations elsewhere provide information
on policy consequences that may be more of less relevant in a particular case. Governments are assumed
here to use available information (including from foreign policy innovations) in a rational fashion to
maximize the chances of their own policy success. Such models have been explored in the federal
context (cites). We suggest a similar process may be at work at the international level (see for example
Westney 1987).
Clearly, however, policy makers are limited by the data available to them, their resources to
undertake analysis, and their own cognitive faculties. These limitations encourage a process of learning
characterized by a dependence on highly selective samples of policy models. Information, however, is
costly, limited and, most importantly, channeled. Governments do not learn about policy practices
randomly, but rather through common affiliations, negotiations, and institutional membership. They are
also rational to draw conclusions from cases that more closely resemble their own circumstances. Thus
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9
we suggest two processes, in particular, which have predictable diffusion effects: learning from those with
whom one communicates, and learning by reference group analogy.
1.
Learning through communication. The exchange of information among connected actors
is the presumed motor behind diffusion in most sociological studies (Rogers 1995, Coleman et al 1967,
see also Axelrod 1997).7 Learning depends on information, and information is largely channeled along
more or less intense actor networks. These networks are instantiated in specific forms of mediated or
unmediated government contacts. One can readily identify the kinds of network and communicative
linkages that could contribute to learning with respect to monetary and financial liberalization. Contacts at
the intergovernmental level may reflect well-established channels of communication: frequent
intergovernmental meetings at multiple official levels can transmit information to policymakers about
“what works” in other settings. It is well documented that the process of negotiating and maintaining
institutional affiliations may create opportunities to learn and persuade (Haas 1959, 1982). Conversely,
where official contacts are infrequent, information is less likely to be transmitted and less likely to
become salient to decision makers. Policy diffusion may follow communication channels carved out by
private actors as well. Business persons may transmit ideas about appropriate economic policy by looking
to the experiences of the countries with which they have especially intense trading contacts. Lessons
drawn from these contacts may inform the shape of the demands they make on their own governments,
thus feeding into the demand side of the policy equation.
In the case of nation-states, increased information about foreign practices translates into imitation
in a number of ways. For example, foreign models can encourage or expedite adoption by inserting a
policy on a legislature’s agenda, by offering a ready-made answer to domestic pressure for “change” and
Axelrod’s approach assumes actors are adaptive rather than fully rational: they follow simple rules
about giving and receiving influence, but they do not necessarily calculate costs and benefits in a
strategic, forward-looking way. The result of Axelrod’s model is pockets of ideational convergence,
based on the number of features and hence intensity of contacts that two neighbors share.
7
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10
“innovation,” by legitimating conclusions or predispositions already held, or by adding a decisive data
point in the evaluation of alternatives (Bennett 1991a, 1991b).
This argument suggests that policy diffusion should be strongest among governments that are in
especially close communication. We should expect a positive relationship between governments with
extensive opportunities to share information about the consequences of economic liberalization.
2.
Learning from Cultural Reference Groups. Actors in uncertain and information poor
environments rationally seek information relevant to their own policy context. Learning takes place at
least partially through analogy, and lessons are viewed as more relevant the extent to which a policy
innovator is perceived to resemble one’s own situation. The IPE literature readily distinguishes between
“advanced industrial,” “emerging,” and “developing” countries, but completely overlooks a far more
salient identity marker that may shape the emulation process: cultural similarity. We hypothesize that
governments are more likely to be influenced by the policy innovations in countries they consider to be
their cultural peers than countries with which shared cultural ties are low.
Unlike organizational sociologists who have concentrated on the apparently non-rational adoption
of policy models reflecting “world culture” (Meyer and Rowan 1977, Powell and DiMaggio 1991, Scott
and Meyer 1994), our argument is that cultural emulation reflects rational efforts to learn from the most
appropriate available examples of policy innovation. We are interesting in testing a constructivistinspired hypothesis: that ideas about appropriate models are likely to reflect deep identity concerns
(Ruggie 1975, Haas 1990, Checkel 1993, Risse-Kappen 1994). Sociologists have long assumed that
shared beliefs and values shape the channels along which ideas flow. Rogers (1995:274), for example
argues that “the transfer of ideas occurs most frequently between individuals...who are similar in certain
attributes such as beliefs, education, social status, and the like.” In fact, a reliable finding in the
voluminous literature on diffusion and social influence is that entities which share similar cultural
attributes tend to adopt the same practices. This is true not only of individual behavior like teen smoking
(Coleman 1960) and voting (Huckfeldt and Sprague 1991, Lupia and McCubbins 1998) but also of
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11
collective behavior with respect to corporations (Davis and Greve 1997), non-profit organizations
(Mizruchi 1989), states (Walker 1969), and indeed nations (Deutsch 1953). The most plausible
explanation of this finding, we believe, is that actors negotiating a complex set of political choices regard
the actions of actors with perceived common interests as a useful guide to their own behavior. A growing
number of political scientists describe mass political behavior this way (e.g., Brady and Sniderman 1985;
Lupia and McCubbins 1998) and we suggest that the same sort of process describes the reasoning of
leaders making difficult economic policy decisions.
But why should cultural groups be relevant reference groups? For one thing, cultural makers are
highly visible. If decision makers know one thing about another country, it is usually the language its
citizens speak or the religion they practice. Moreover, common culture embodies subjective notions of
identity contained in assumptions of commonly shared values and social purposes. The policies of
culturally similar countries are perceived to (and in fact may) contain highly relevant information on the
appropriateness of a particular policy in a specific context of shared values. In their search for relevant
economic models, decision-makers are hypothesized to look to the experiences of those societies with
whom they share an especially close set of cultural linkages. Such a tendency may be a cognitive short
cut for an individual, or a focal point to limit cycling over alternatives in a group decision-making
context. In either case, perceived cultural affinity assists in carving out the relevant models, or “reference
groups,” that inform policy development.8 Admittedly, economic policy making – a practice with very
material ends and theoretical underpinnings that make no explicit concessions to culture – may be
immune to culturally channeled learning. But given a high degree of uncertainty about the consequences
of a particular policy shift, governments may be influenced to follow the lead of a culturally or socially
similar group of states.
Our expectation is that cultural similarity will be a positive predictor of policy diffusion among
states.
Rosenau (1990: 213) terms these reference groups “cathectic,” suggesting that decision makers have a strong
cultural sense of whom their nation should look like.
8
The Globalization of Liberalization
III.
12
Data and Analytical Methods
The empirical problem now is to (1) identify meaningful measures of the pressures from altered
payoffs and channeled forms of learning, and to (2) estimate their effects on the liberalization and
restriction of foreign monetary and financial policy, while controlling for a reasonable battery of nondiffusion effects.
Dependent Variables
Our dependent variables are transitions in each of three policy areas – capital account openness,
current account openness, and exchange rate unification. (See the Data Appendix for all data definitions
and sources). Each of the variables is a binary measure of whether the government has or has not
imposed restrictions (or in the exchange rate case, a tiered or multiple system) in the given year (with
policy liberalization = 1). Admittedly, the dichotomous measure masks some complexity and cannot pick
up changes in the intensity of restrictions. But our purpose here is to model major policy shifts globally.9
For this purpose, and since we employ event history methods, dichotomous measures are most
appropriate.
Diffusion Variables
In order to assess the source and strength of policy diffusion paths, we must construct variables
that identify a country’s various competitive, communicative, global, and cultural influences, and
combine this information with the policy “cue” transmitted along that network. The growing field of
spatial econometrics offers a useful set of methods to incorporate these kinds of variables (Anselin 1988,
1999).10 Spatial regression models handle spatial dependence in one of two ways. One is to specify the
spatial dependence in the error term (spatial error models). This method is appropriate when spatial
9
For a finer polychotomous measure for fewer countries, see Quinn 1997.
A parallel set of methods has developed within network analysis. See, for example, Marsden and Friedkin (1993).
10
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13
dependence is nothing more than a nuisance that biases the interpretation of the parameters of interest.
When, as in our case, spatial dependence is itself the focus, researchers include spatial terms as regressors
in the model (spatial lag models).
Spatial lag models treat spatial dependence in the same way time-series models treat serial
correlation. Instead of lagging the value of the dependent variable one unit in time, one “lags” it one unit
in space. The spatial lag is the weighted average of the dependent variable in the actor’s “neighborhood.”
The neighborhood is mapped by an N by N spatial weights matrix conventionally labeled W. Thus, the
spatial lag for country i can be written as
Wy i 
W
j 1,... N
ij
 yj
where W is the spatial weights matrix and y is the dependent variable. In matrix form we write the
relationship as Wy, where y is an N by 1 vector of observations on the dependent variable. These
measures will vary by year as well.11
As with time series models, the spatial dependence can be modeled as an autoregressive or as a
moving average function depending on our assumptions about the effect’s rate of decay. Since we expect
spatial effects to reverberate throughout the network and not just from the closest actor, we adopt an
autoregressive function. We can express such a model as
Y = Wy+ X + 
where  is a spatial autoregressive coefficient, W is the n by n spatial weights matrix, X is a vector of nondiffusion regressors, and  is a vector of error terms.
In geographic models, the spatial weights matrix, W, is often a matrix of geographic distances
among units. In our case, we are interested in measuring influence along other channels in addition to
geography: through competitors whose policies alter payoffs, through norms that gain global adherents,
through communication partners that foster learning, through cultural groups that provide appropriate
models for emulation. The elements of W differ according to the nature of the measure of “distance”
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14
between units. These measures come in two principal forms: (1) direct bilateral data which records a
level of interaction between states (e.g., amount of trade, number of telephone calls), or (2) affiliation
data which identifies shared membership in various groups (e.g., regional trade groups and language
communities). These kinds of “distances” are indeed different. One implies direct contact between states
and is measured continuously, the other implies contact through membership in a group and is measured
dichotomously. Despite these differences, the weighting procedure is similar.
One of our key hypotheses is that policy innovation alters payoffs in other countries, thus
encouraging them to change their policies as well. Economic competition for capital and trade is
suspected to be a prime explanation for the liberalization of economic policy over the past three decades.
In order to measure the effect of competitive mechanisms, we develop two indicators of “competitive
distance.” The first, export similarity, is a measure of the degree to which nations compete in the same
export markets.12 Since an importing country may find ways to reciprocate or reward policy liberalization
in country A, country B has an incentive to liberalize to the extent that A and B compete for market share
within that third market. Using the bilateral direction of trade data available from the IMF, we produce an
n by n by t matrix of correlations of each country’s total exports to each of the 182 partner countries. The
result is a matrix of yearly, dyadic measures of the degree to which nations possess the same trade
relationships. We use these distances to weight the mean of the dependent variables for a nation’s peers
for each year. If the behavior of competitors is more important than that of other peers, then this
weighted mean should predict a nation’s choice. We expect a positive association between innovations of
close trade competitors.
Our second competition indicator identifies competitors for investment capital. Since investors
want the freedom to repatriate their assets once invested, among otherwise similar investment venues they
will favor countries that allow for the liberal movement of capital and currency at non-discriminatory
exchange rates. Ideally, we seek an indicator that allows us to predict which countries compete for the
11
W, then, is an N x N x T matrix and y is an N X T matrix.
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15
same pool of international capital. We begin by assuming that international investors’ decisions depend
on their varying tastes for risk. Portfolio theory suggests that investors will want to create a portfolio with
a share of low risk, medium risk, and high risk investments (according to their tastes). Investors may
decide, for example, that 10 percent of their portfolio will be reserved for high risk, potentially high return
investments. Given this assumption, it is reasonable to posit that countries that pose similar risks are close
substitutes from an investor’s point of view. In this view, Brazil and Argentina which had similar
sovereign bond ratings in 1996, may have competed for the same capital, while Chile, whose bonds were
rated two points lower in 1996, did not compete as directly with the other two. Our measure groups
countries by their yearly Standard and Poor’s sovereign bond rating, and calculates the mean policy score
(for each policy area) for a country’s rating category for each year.13 If competition over the same “slice”
of international capital provides incentives to liberalize, then we expect a positive coefficient.
We have also argued that changes in less tangible payoffs such as legitimacy or prestige might be
influenced by the prevalence of increasingly global norms, theories, or beliefs embraced by governments
elsewhere. Heterodox policy failures are likely to earn a government more criticism than would policy
failures that are consistent with a global consensus about what constitutes wise, sustainable economic
policy. Our measure of global norms is quite simple. It is the yearly mean of the dependent variable
across all countries in the sample. One may think of this as a diffusion pressure which is unchanneled,
something diffusion scholars sometimes call homogeneous, as opposed the heterogeneous, mixing (Strang
1991).
Our second cluster of arguments concerns changes in the information set governments face. Our
first set of information indicators taps communication networks. Since we cannot directly observe
communication – at least about the policies we are concerned with here – the best we can do is to rely on
a set of reasonable proxies. At the official level, information about economic policy options can be
12
For a similar approach see Finger and Kreinen (1979). Network analysts often use this measure to identify
competitors (see Wasserman and Faust 1994).
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16
transmitted through negotiations and discussions among the members of economic agreements and
groupings. Members of the European Union or NAFTA, for example, share information on policy
arrangements in an effort to persuade members to converge on their favored economic policy model. We
therefore test communications linkages forged through preferential trade arrangements (PTAs). Another
plausible channel for communicating expectations and information about capital account openness is
through bilateral investment treaties (BITs). Again, for each nation-year, we calculate average policy
scores weighted by PTA and BIT partnerships, respectively. Our expectation is that these common
memberships should predict channeled policy diffusion, based on the diffusion of policy-relevant
information.
These affiliation-based information channels concentrate on unmediated official learning.
Information can also flow through private channels. Private actors that are exposed to liberal regimes for
the movement of goods and services may become convinced of the virtues of these arrangements and
attempt to persuade their governments to liberalize. Learning may even take place (though less plausibly)
at the mass level: extensive private communications may persuade a relatively broad-based segment of
the populace in the home country that liberalization is an appropriate policy. To allow for this
possibility, we have also gathered data on business contacts (proxied here as direct bilateral trade
linkages) and estimates of telephone traffic across pairs of countries. Once again, we weight the policy in
the foreign country by the intensity of these communications channels.
Good measures of cultural reference groups are difficult to pin down, but ideally what we seek
are measures that tap perceived similarity of values and shared identity across countries. Common
dominant language, common colonial heritage, and common dominant religion come close to capturing
these shared orientations. Dominant language may also reflect communication channels, and common
colonial heritage may pick up a number of structural similarities which on balance maybe more historical
than cultural. Dominant religion, on the other hand, we view as a fairly good measure of the identity and
13
Because it is possible that the policies themselves help determine the bond ratings, we experiment with models
which lag the ratings by two and three years. We find that the results do not change significantly with this
The Globalization of Liberalization
17
values held by a society, and a sense of cultural linkage with other countries groups with similar spiritual
commitments. We would argue at this point for inclusion of all three measures; combined they are likely
to capture the sense of identity we have argued is important in channeling a government’s search for
relevant “information-bearing” models. For each language, colonial, or religious grouping we compute
yearly means as described above. Note that these three variables, while quite similar, correlate only
between -0.03 and 0.22 when combined with the dependent variable as described above (i.e., as Wy).
Control Variables
We are open to the possibility that geography alone may continue to exercise an independent
influence on economic policy diffusion through mechanisms that have not explicitly been theorized
above. We experiment with two geographical variables – the logged distance between capitals and
common border. As we note above, diffusion along geographical lines is well established. Our inclusion
of spatial variables in a fully specified model allows us to isolate the effect which is due exclusively to
geographic identity and not to the competitive, communication, and cultural elements which would
otherwise be summarized in a geography term.14
It is certainly likely that policy transitions are influenced by conditions that have nothing to do
with policy diffusion. The most likely alternative explanation is that governments, especially those in
close regional proximity, face similar economic conditions, and find it independently rational to respond
in similar ways. For example, commonly experienced economic conditions that are largely exogenous to
countries’ decisions to liberalize could account for similar policy choices across time and space.
Variation in world interest rates, for example, could cause region-wide capital outflows, capital and
current account deterioration, and exchange rate pressure (Bartolini and Drazen 1998), with predictable
pressures on policy. Similarly, robust growth rates or an improving balance of payments could increase
modification.
14
Diffusion scholars may recognize this as the difference between homogenous and heterogenous mixing (Strang
1991).
The Globalization of Liberalization
18
policymakers’ confidence in liberalization (Goodman and Pauly 1993; but see Haggard and Maxfield,
1996).
In order to test these hypotheses, we enlist global and country-specific economic variables with a
close association with liberalization. We control for world interest rates (using United States interest rates
as a proxy), each country’s current account balance as a proportion of GDP (lagged two periods to
minimize problems of endogeneity), and GDP per capita (a rough indicator of developmental level).
Finally we control for the business cycle, using a measure of change in GDP growth. If convergence is
simply an uncoordinated response to economic conditions, we should see strong effects for this battery of
variables. Liberalization should be associated with reduced world interest rates, an improving balance of
payments, high GDP per capita, and an upswing in the business cycle.
A second sort of exogenous external shock for which we control is the use of International
Monetary Fund resources. As an alternative to the diffusion processes we have outlined here,
governments might simply be responding to pressures by the IMF to liberalize their economies. This
effect should be especially strong when members need to draw upon IMF resources. Hence we control
for the use of fund resources, and anticipate that liberalization should follow borrowing from the Fund.
Another possibility is that comparative political economy is sufficient to explain policy
transitions (Simmons 1994, Alesina, Grilli, and Milesi-Ferretti 1994, Epstein and Schor 1992, Giovannini
and de Melo, 1993, Quinn and Inclan 1997, Broz 2000, Leblang 1997). Domestic preferences, capacities,
and institutions themselves may be correlated over time or spatially, producing a pattern that resembles
policy contagion. If the underlying political-economic conditions common to many states in a particular
region “co-evolve” over time, we could mistakenly be focused on policy diffusion when we should be
looking at political, institutional, or developmental diffusion. Economic policy liberalization may simply
be the result of independent decision making in similar domestic political/institutional contexts.
We therefore control for a set of measures that captures both the nature of “demands” the polity
might make (Frieden 1991, Rogowski 1989, Milner 1988) as well as the institutional landscape that
translates those demands into policy (Garrett and Lange 1995). The extent to which the polity is likely to
The Globalization of Liberalization
19
demand more open policies is approximated by the penetration of international trade, measured here in
the traditional way (imports plus exports as a proportion of GDP). Garrett, Guisinger, and Sorens (2000)
find that democracy is associated with a smaller probability that a developing country will liberalize the
capital account. This may well be due to the difficulty of reconciling democratic politics with popular
demands in the developing country context. We therefore include a measure of democracy. Finally, by
including a measure of the ruling party’s level of nationalism, we consider the possibility that nationalistleaning governments will be reluctant to initiate liberalization.
Table 1 summarizes the mechanisms, concepts, and measures in the model.
[TABLE 1 ABOUT HERE]
Sampling and Estimation
Our sample includes the 182 IMF-member states with yearly observations from 1967 to 1996.15
We measure our dependent variables in binary form (see above), and our observations are collapsed in
yearly intervals.16 In order to model policy transitions, we employ a semi-Markov Model, which is
commonly used for estimating transitions among mutually exclusive states of being. This approach allows
us to consider transitions in both directions as well as vacillations between policies (see Allison 1984). In
our case, we run two hazard models for each dependent variable: one for transitions to liberal policies and
one for transitions from liberal policies. We do not have especially strong distributional assumptions
except that we expect that there will be some effect of time on the hazard rate.17
15
Our data are both left and right censored: roughly 100 governments are under observation since the beginning of
our analysis time (1967) and another 80 or so enter the analysis in the 1970’s. Except in the case of state
dissolution, all remain at risk after 1997.
16
It is possible for more than one transition to occur within a year, and for the conditions in January of that year to
be very different from those in December. However, for the most part these policy shifts are months in the making
and a yearly summary of thirty years of policy transitions is a good approximation of major trends. The modeling
technique chosen here assumes continuous time, but we experiment with models designed to improve estimates with
such data (see Jenkins 1995) and find no significant differences.
17
There are a number of possibilities for the effect of time on the hazard rate. For example, one may expect (1) an
initial decline during a wait-and-see period immediately after a policy shift, (2) a decrease over the long term as
policy makers become more invested in the policy; or (3) an increase over the long term as policy makers view the
policy as increasingly outmoded.
The Globalization of Liberalization
20
Incorporating spatial variables introduces a number of statistical complications.18 One is that
spatial lags, as weighted averages of the dependent variable in the “neighborhood,” often capture omitted
variables that are highly correlated with membership in the group.19 As such, specifying the model as
completely as possible is important. While we are reasonably confident that we have identified some
important predictors of liberalization, no model is fully specified and caution is in order before inferring
strong effects for diffusion. Multicollinearity is another potential concern with this type of analysis. As
noted above, networks of influence tend to overlap. For example, countries that are geographically
clustered are also likely to be important trade partners, competitors, or cultural peers. Indeed, this is the
case but not to an alarming degree. The correlations across the network variables range from 0.03 to 0.43.
IV.
Findings
What conditions lead to these policy transitions, whether toward liberalization or restrictions?
We turn here to the semi-Markov multiple regression results which test the effect on the duration of both
liberal and restrictive spells for each of our three policies (for a total of six models). Table 3 reports the
Weibull hazard ratios for each of the six equations. 20
[TABLE 3 ABOUT HERE]
The estimates are remarkably comparable across each of the three economic policies. Among the
diffusion variables, for example, the policies of capital competitors, partners in PTAs, policies of
countries with the same religion, and to a lesser degree policies of geographically close countries seem to
have effects of roughly similar magnitude and direction for all three policy areas. The same is true of
18
See Anselin 1999 for a discussion of these issues with respect to spatial terms and Blalock 1984 or Przeworski
1974 on contextual variables more generally.
19
For this reason, many spatial models of this kind use 2SLS or even 3SLS estimators, an approach which comes
with its own set of complications, especially in event history models.
20
Hazard ratios can be roughly understood as the change in the odds of transition associated with a one-unit change
in the explanatory variable. Therefore, hazard ratios over one represent an increased probability of transition, zero
to one a decreased probability of transition, and a hazard ratio of one represents zero effect. For the transitions to
restrictive states, we reverse the scoring of the diffusion variables (originally scored as the weighted proportion of
The Globalization of Liberalization
21
several of the controls as well. However, several of the diffusion mechanisms can explain both
liberalization and restriction. Most notably, the policies of common members in a PTA can explain both
exchange rate and current account liberalization and restriction, while the policies of competitors for
capital and societies with shared religious values explain both liberal and restrictive transitions across all
three policy areas. The economic and political controls on the other hand are far less able to account for
policy transitions symmetrically. Nationalist executives, for example, are highly unlikely to liberalize
capital controls, but are not significantly more likely to replace an already liberal capital regime with a
restrictive one. Nor can world interest rates explain transitions to restrictive policies. Many of the
diffusion variables, on the other hand, are able to explain policy shifts in both directions.
Conditions that affect payoffs: economic competition and global norms
The most pronounced effect on policy transition is that of economic competition. We
hypothesized that policy diffusion could be explained by the need for governments to attract capital and
trade in competition with a similarly situated set of countries. Policy choices of capital competitors are
significant at the 90% confidence level in each of the six models (Table 3). Trade competition, on the
other hand, is a less convincing causal mechanism. Liberalization among trade competitors is likely to be
important with respect to liberalization of the capital account, and multiple exchange rate regimes among
trade competitors may explain reversions to a multiple system. However, the direction of the effect is
counter-intuitive with respect to current account restrictions, and ambiguous in three cases. If policy
diffusion is linked to economic competition, it seems likely that the desire to attract capital is a more
powerful motive than trade competition.
What are the size of these effects? We plot survival curves for three different values of the
explanatory variable for each of four variables used to explain liberal transitions in the capital account
(Figure 3). The curves can be interpreted as estimates of the probability of maintaining restrictive capital
liberal policies) to be the weighted proportion of restrictive policies. Accordingly, the estimates of diffusion effects
in Table 3 should be in the same direction for both transitions. Not so for the control variables.
The Globalization of Liberalization
22
account policies. The distances between the middle and outside lines represent the independent effect of
moving one standard deviation above and below the mean on the explanatory variable. For the trade
competition variable, there is a full 8 to 12 point change (depending upon the length of the spell) in the
probability of transition associated with a move of one standard deviation and a 5 to 7 point change with a
similar move on the capital competition measure (see figure 3). These are significant effects in real
terms. In the case of Brazil, for example, a change in capital policy in Peru or Colombia – two countries
whose export markets correlate over 0.90 with that of Brazil -- will increase the predicted probability of a
similar policy move in Brazil twelve percentage points.
[FIGURE 3 ABOUT HERE]
We also hypothesized governments might experience a more subtle change in the payoffs they
face when a norm is increasingly adopted by the rest of the world. In particular, there seems to be some
evidence that the costs of implementing restrictive policies may fall the more widely distributed are such
policies. Signals from the world at large are positive and sometimes significant for restrictive transitions
in both the current and capital account. Exchange rate policies, however, tend to take place against the
global trend. With this result, we remain open to the possibility that the spread of global norms may
change the payoffs associated with policy choice, but the results are not especially convincing.
Diffusion due to new information: communication and cultural reference groups
The effects of the communication mechanisms are mixed, but they largely conform to our
intuitions. Individual measures of private communication generally had no effects. The evidence on
extent of private business contacts (proxied here as major import and export partners) as well as telephone
contacts gives no systematic purchase on policy diffusion. On the other hand, official contacts – in
particular common membership in a PTA – predict transitions well. Not only are the hazard ratios
statistically significant (at the 90% confidence level), the results are also consistent across policy areas
The Globalization of Liberalization
23
and explain policy changes in both directions. Governments were statistically more likely to switch to
current account restrictions, and to implement either multiple or unified exchange rate systems, according
to what members in a common PTA did.
The policies of countries with whom a country has signed a bilateral investment treaty appear, if
anything, to have a negative effect on policy (statistically significant in the case of capital account
liberalization). This is probably due to the fact that BITs are usually signed precisely to establish a
common legal framework between countries for whom this is lacking.21 It is very likely that the pool of
countries with which a government would feel the need to sign a BIT are those whose policies raise
questions about the protection of property rights; grossly speaking, these are likely to be countries that are
low on mutual trust and shared values. A null or even negative coefficient here is understandable in light
of the reasons for negotiating BITs in the first place.
Consider next the effects of cultural association. We have argued that governments may
reasonably search out information from cases they perceive to have cultural relevance to their own
situation. The most conspicuous finding here is that the policies of countries with similar dominant
religions are remarkably influential. The effects of the other two measures – common language and
common colonial heritage – wash out except in select policy areas (note that policies among similar
language and religious groupings are positively but mildly correlated at .22). The effects of the policies of
countries with shared religious values are significantly positive for each policy transition with the
exception of liberalization of the capital account. The strength and consistency of these results were a
surprise to us. We had expected cultural similarities generally to have a small effect across all policy
transitions, given the very material nature of the policies we are considering. As figure 3 shows, a move
of one standard deviation one way or another on the mean policy of those countries with similar dominant
21
BITs are almost always signed between developed and developing countries; only rarely do they exist within
these groups. See Guzman, 1998. The dominant north-south pattern can be observed by looking at the list of
existing treaties deposited with the International Center for the Settlement of Investment Disputes:
http://www.worldbank.org/icsid/treaties/treaties.htm
The Globalization of Liberalization
24
religions is associated with a change of roughly eight points in the probability of a country’s policy
transition.
Control variables:
Finally, we consider the effects of the control variables. Once we control for other factors, it
appears that geography per se is not a convincing explanation of policy diffusion. The only effects that
appear at all significant in table 3 are those for the impact of current account restrictions. Again, though,
the effect of geography is strangely asymmetrical: nearness may have a weak positive effect on
liberalizations across the board, but if anything a negative effect on restrictions.
The domestic political variables behave weakly, but as expected. As the political economy
literature has long held, there is evidence that publics residing in open economies demand and probably
get greater policy liberalization, and revert to restrictions with less frequency. Nationalist governments
are much less likely to liberalize (especially on the capital account) while democracies tend to favor
liberal transitions. The substantive effects, however,are not large: Figure 3 plots the substantive impact of
democracy on liberalizing capital, suggesting an increase in the probability of liberalization of roughly 4
percentage points with a move of one standard deviation (3.5) on the polity democracy scale.
Curiously, the economic determinants of policy have very little impact. The directions of effects
are generally as one would expect, however. High world interest rates may tend to decrease the
probability of liberalization and increase that of restriction. GDP Growth may encourage liberalization
and reduce the probability of restrictive transitions, though the effect is statistically significant only with
respect to current account liberalization. The substantive effects are quite moderate as well. As Figure 3
shows, raising interest rates two percentage points (one standard deviation) from the period’s average
(6.9%) lowers the probability of liberalization only two points. Surprisingly, we found absolutely no
evidence in these tests that making use of the resources of the IMF was associated with policy
liberalization.
The Globalization of Liberalization
25
Another way to look at the plausibility of the diffusion mechanisms we have proposed is to
examine their effects as a block. In Table 4, we summarize the strength of these blocks by comparing the
full model with nested models in which the blocks of diffusion effects are constrained to zero. The
likelihood-ratio test of such comparisons indicates the improvement in fit associated with the addition of
each block of variables.22
[TABLE 4 ABOUT HERE]
Viewed in this way, the aggregate effects of clusters of measures are quite robust. Economic
competition is an especially important and consistent part of the explanation for change in both directions
for each of the three policy areas. On the other hand, communication networks (both mass and official)
do not clearly add much explanatory power to policy choice. The cultural identity/shared values variables
taken as a block are consistently important in explaining policy choice, except for the case of capital
account liberalization. The blocks of control variables are much less convincingly associated with policy
choice in these areas. The block of geography variables has a significant impact in at best only two of the
six models. The economic variables jointly contribute in an improvement in model fit in only half of the
cases. And in only one model – liberal transitions in the capital account – are the political variables
jointly significant.
V.
Conclusions
There are good reasons to believe that governments are sensitive to external signals to liberalize
and to restrict their monetary and financial policies. Temporal and spatial clustering lend support to the
proposition that something systematic must be driving states’ policies in this way. It is hard to reject the
conclusion that there is some kind of policy diffusion involved in the formulation of foreign economic
policy internationally.
22
The null hypothesis of these tests is that the joint effect of the block of variables is zero. A rejection, therefore,
The Globalization of Liberalization
26
Indeed, this characterization is easy to accept intuitively. Scholars and laypersons alike find it
easy to grasp the competitive implications for Mexico of free trade between the United States and Canada
(Gruber 2000) as well as the socially emulative impulses of countries (Finnemore, 1996). Nonetheless,
the recent political economy literature has concentrated primarily on the domestic sources of foreign
economic policy (Epstein and Schor 1992, Simmons 1994, Broz 2000), or at most, economic policy
choice in response to price signals from the unmediated international economy (Rogowski 1988; Frieden
1991; Keohane and Milner 1996). Furthermore, some scholars and observers have attributed policy
liberalization to exogenous pressures from the International Monetary Fund as the primary avatar of the
“Washington Consensus.” The analysis in this paper suggests that these approaches do not sufficiently
explain why governments decide to open or restrict their economies.
We have explored two broad sets of mechanisms that might explain patterns that appear to
involve policy diffusion among countries: altered payoffs and new information. As an example of
changing payoffs, competition for international capital seems to be an especially compelling explanation
for the international diffusion of liberal economic policy. Across all policy areas, and for both liberal and
restrictive transitions, policy choice is highly correlated with the orientation of other governments who
compete for the same risk capital. Could this simply be due to the fact that similarly rated countries are
economically similar in a number of ways, and so have independently similar incentives to open and
restrict their capital and current accounts? This is possible, though it is rendered much less likely by the
inclusion of a battery of economic controls (wealth, growth, balance of payments, world interest rates)
that should to some extent control for this problem. The relationship between competition for capital and
policy diffusion is so empirically strong and theoretically plausible in these tests that it should be a high
priority for future research. A model in which “global norms” might account for changing payoffs and
hence policy diffusion was not similarly supported by the data.
Next we tested arguments that new information and learning contributes to economic policy
diffusion. Empirical work on learning must be grounded in plausible, observable proxies for this
suggests that the variables improve the fit of the model.
The Globalization of Liberalization
27
essentially psychological process. Our strategy has been to look for the observable implications of
learning, which we believe in the first instance might be linked to channels of communication and
persuasion. Our research suggests that the most convincing communication linkages with respect to
foreign economic policy making are likely to be governmental, rather than private or unofficial. Official
communication and association, proxied here through countries’ common membership in a regional
preferential trade arrangement, are plausible channels for policy diffusion. Such associational contacts –
which we assume intensify information flows among economic policymaking elites – seem to have a
sporadic positive effect on policy choice. This finding corroborates claims that “epistemic communities”
and other intellectual and institutional linkages among leaders are important sources of policy change.
Few political economists (though of course many more sociologists) would have nominated
religion as a central explanation for policy diffusion. The results here, however, are difficult to ignore.
Our results show that governments tend to liberalize and to restrict the capital account, current account
and exchange rate regime along the lines of countries with whom they share a religious identity,
controlling a wide range of other factors. A key finding is that this relationship holds in all three policy
areas, and symmetrically for both liberalization and restriction.
We have argued that governments tend to orient their policy attention toward countries whom
they consider to be analogous to the situation they face. We agree with neither the “world culture”
sociologists, who emphasize the irrationality of absorbing global culture willy-nilly, nor the political
economists who remove culture from the calculus of policy choice altogether. We suggest that
governments systematically consider the lessons their cultural peers have to offer when fashioning their
own economic policy choices. Of course, religion should be thought of as just one indicator that taps the
broader value orientation or cultural identity of a society. Note also that we are not arguing that religion
speaks directly to the question of capital controls or exchange rate arrangements. These results do
suggest, though, that values common to a particular religious tradition shape attitudes toward risk,
individualism, equality, and materialism generally. Governments take these shared attitudes into account
when searching for appropriate models in the absence of perfect (or perfectly understood) information.
The Globalization of Liberalization
28
Nor do we assert that religion is the only indicator of commonly held values, but this research indicates it
is a plausible place to begin. Religion may serve as a fundamental source of identity for governments as
much as it does for individuals, with consequences for highly material arenas of policy choice.
Changing payoffs and new information revealed through policy change abroad both have an
important role to play in understanding how economic policies diffuse from one country to another.
Indeed, there are cases in which one of these mechanisms overwhelms the other. But it is only by
examining these alternative explanations side by side that it is possible to see that competition for capital
investment is a far more convincing explanation for liberalization of the capital account than any cultural
explanation examined here. When it comes to lifting capital controls, cultural similarities have far less
purchase than direct economic competition for capital and trade. Yet, the information gleaned from
cultural analogies are apparent in every other area of policy choice. One of the important research
agendas this work suggests is that of uncovering how changing payoffs and new (channeled) information
influence particular aspects of policy choice globally.
One thing is clear: economics and comparative political economy can take us only so far in
understanding the ebb and flow of foreign economic policy liberalization over the past three decades.
The apparent diffusion of policy choice over this time demands an explicitly international or indeed
transnational theory and testing. As we think in these directions, we may uncover under-emphasized
sources of authority that structure competition and channel the search for appropriate models of foreign
economic policy. Research into the dynamics of globalization and its underlying governance structures
should push us to understand how and why this takes place.
The Globalization of Liberalization
29
The Globalization of Liberalization
30
Table 1. Mechanisms, Concepts, and Measures
Mechanism or Concept
Explanatory Variable
Diffusion Mechanisms
Altered Payoffs
Competition
Policies of Capital Competitors
Policies of Trade Competitors
Global Norms
Mean Global Policy
New Information
Mass Communication
Policies of Import Partners
Policies of Export Partners
Policies of Telephone Partners
Official Communication
Cultural Similarity
Policies of BIT Partners
Policies of PTA Partners
Policies of Religion Partners
Policies of Colonial Partners
Policies of Language Partners
Control Mechanisms
Geography
Policies of Border Countries
Policies weighted by distance
Political Conditions
Degree of Openness
Democracy
Nationalist Executive
Economic Conditions
Current Accounts/GDP (t-2)
GDP Growth
GDP per Capita
Interest Rates
Contingent Policies
Multiple Exchange Rates
Current Account Restrictions
Capital Controls
IMF Imposition
Use of IMF Credits
31
Table 2. Transitions to and From Liberal Economic Policies
Universe: IMF Member Countries, 1966-1996
Transitions to Liberal Policies
Capital Account
Exchange Rate
Current Account
Transitions to Restrictive Policies
Capital Account
Exchange Rate
Current Account
Full Sample
Number of Countries Ever at Risk
Time at Risk
Number of Transitions
Markov Transition Rate (%)
169
4116
66
1.6
116
1279
150
11.7
146
2404
125
5.2
80
983
41
4.2
179
3147
123
3.9
136
2033
112
5.5
Pre-1973 Cohorts
Number of Countries Ever at Risk
Time at Risk
Number of Transitions
Markov Transition Rate (%)
105
2713
49
1.8
82
1039
120
11.5
97
2007
107
5.3
60
781
38
4.9
110
2500
108
4.3
95
1627
96
5.9
Post-1972 Cohorts
Number of Countries Ever at Risk
Time at Risk
Number of Transitions
Markov Transition Rate (%)
64
1403
17
1.2
34
240
30
12.5
49
397
18
4.5
20
202
3
1.5
69
647
15
2.3
41
406
16
3.9
* Countries “at risk” are those without the policy, and so eligible for transition to that policy. “Time at risk” is the number of years that all “at risk” countries are
without the policy.
32
Table 3. Effects on Transitions to and from Liberal Economic Policies
Universe: IMF Member Countries, 1966-1996
Transitions to Liberal Policies
Capital Account
Exchange Rate
Current Account
Transitions to Restrictive Policies
Capital Account
Exchange Rate
Current Account
Mechanism or Concept
Explanatory Variable
Diffusion Mechanisms
Geography Controls
Policies of Border Countries
1.052
0.995
1.016
0.699
1.049
1.007
Policies weighted by distance
1.249
1.033
1.054
0.353
0.741
0.639**
Policies of Capital Competitors
1.434**
1.896***
1.485***
1.197***
1.849**
1.295**
Policies of Trade Competitors
6.013*
0.850
1.198
0.007
1.359*
0.629*
Mean Global Policy
1.465
0.845
1.226
1.402*
0.652
2.092**
Policies of Import Partners
0.685
0.829
1.057
0.140***
1.316
0.905
Policies of Export Partners
0.434
1.022
1.210
0.694
1.141
0.855
Policies of Telephone Partners
1.084
1.010
1.056
0.587***
0.992
0.916**
Altered Payoffs
Competition
Global Diffusion
New Information
Mass Communication
Official Communication
Cultural Similarity
Control Mechanisms
Political Conditions
Policies of BIT Partners
0.828**
0.893
0.958
1.104
0.975
0.993
Policies of PTA Partners
1.033
1.047*
0.990
0.717
1.064*
1.086*
Policies of Religion Partners
1.249
1.538***
1.172**
21.092***
1.554***
1.251***
Policies of Colonial Partners
0.500
0.791
1.031
0.031
0.834
1.365***
Policies of Language Partners
0.836
1.154**
0.979
1.272
1.153**
1.149
Degree of Openness
3.169*
0.919
1.384
0.007***
0.578
0.449**
Democracy
1.125*
1.013
1.001
0.916
0.978
1.059
0.000***
0.975
0.942*
1.003
1.021
1.073
1.023
1.016
0.973*
1.007
1.005
1.024
1.021
0.890*
0.974***
1.010
1.011
0.954
GDP per Capita
1.000***
1.000
1.000
1.000**
1.000
1.000
Interest Rates
0.920**
0.960
0.928
1.180
1.051
1.211
2.082**
11.602
Nationalist Executive
Economic Conditions
Current Accounts/GDP (t-2)
GDP Growth
Related Policies
Multiple Exchange Rates
Current Account Restrictions
0.161***
14.277***
Use of IMF Credits
Time at Risk
Number of Subjects
Number of Transitions
Log Likelihood
Estimates are hazard-ratios from a Weibull Survival Model
* significant at 10%; ** significant at 5%; *** significant at 1%
0.050**
1.026
4.702***
1.004
1.002
1.001
2357
143
36
114.520
939
96
118
77.754
1801
126
85
485.959
Capital Controls
IMF Imposition
1.827*
0.485
0.322***
1.178
0.207***
1.003
1.002
1.001
700
62
22
261.056
2439
159
81
317.963
1549
120
87
283.501
33
Table 4. The Improvement in Model Fit Associated with each Explanatory Mechanism
Likelihood ratio tests of the joint significance of blocks of variables
Universe: IMF Member Countries, 1966-1996
Transitions to Liberal Policies
Transitions to Restrictive Policies
Exchange
Exchange
Capital Account
Current Account Capital Account
Current Account
Rate
Rate
Log Likelihood of Full Model
169.834
579.497
361.669
109.802
313.464
404.392
Other Liberalization Policies = 0
13.44 (2)
p = 0.01
13.94 (2)
p = 0.00
1.72 (2)
p = 0.42
13.35 (2)
p = 0.01
21.00 (2)
p = 0.00
22.72 (2)
p = 0.00
Economic Determinants = 0
5.43 (3)
p = 0.14
0.05 (3)
p = 0.97
9.96 (3)
p = 0.01
14.16 (3)
p = 0.00
12.33 (3)
p = 0.00
0.71 (3)
p = 0.70
Political Determinants = 0
35.37 (4)
p = 0.00
2.65 (4)
p = 0.44
4.08 (4)
p = 0.25
3.79 (4)
p = 0.28
4.12 (4)
p = 0.24
2.30 (4)
p = 0.51
Geography = 0
3.23 (2)
p = 0.19
0.91 (2)
p = 0.63
0.66 (2)
p = 0.72
4.43 (2)
p = 0.10
0.93 (2)
p = 0.62
4.97 (2)
p = 0.08
Mass Communication = 0
5.65 (3)
p = 0.13
4.06 (3)
p = 0.25
5.35 (3)
p = 0.14
7.15 (3)
p = 0.06
0.36 (3)
p = 0.94
7.06 (3)
p = 0.06
Official Communication = 0
4.50 (2)
p = 0.11
3.87 (2)
p = 0.14
1.96 (2)
p = 0.37
0.56 (2)
p = 0.75
1.00 (2)
p = 0.60
3.13 (2)
p = 0.20
Cultural Similarity = 0
4.23 (3)
p = 0.23
10.16 (3)
p = 0.01
35.82 (3)
p = 0.00
25.34 (3)
p = 0.00
13.84 (3)
p = 0.00
56.27 (3)
p = 0.00
Competition = 0
12.55 (2)
p = 0.00
6.54 (2)
p = 0.03
24.83 (2)
p = 0.00
18.31 (2)
p = 0.00
33.21 (2)
p = 0.00
8.72 (2)
p = 0.01
Constrained Models
H0: Measures within blocks are jointly zero
Cells are the ChiSq (d.f.) and corresponding p values
34
Figure 3. Effect on Transitions to Liberalized Capital Accounts
Survival Curves from Weibull Regression (Model 1, Table 2)
Policies of Capital Competitors
Policies of Trade Competitors
.9
.9
.85
.85
.85
.8
.8
.75
.7
.8
Survival
Survival
Survival
Interest Rates
.9
.75
.7
.75
.7
.65
.65
.65
.6
.6
1970
1975
1980
1985
1990
1995
.6
1970
1975
1980
.
.
1990
1995
1970
1975
1980
1985
1990
.
.
Democracy
.
Policies of Countries with Same Relig ion
.9
.9
.85
.85
.8
Survival
Survival
1985
.
.75
.8
.75
.7
.7
.65
.65
.6
.6
1970
1975
1980
1985
1990
1995
1970
1975
1980
.
.
1985
1990
1995
.
.
For each chart, the middle line represents the effects of the variable at its mean, the outside lines the effect of the variable at its mean plus and
minus one standard deviation. All other variables are held at their means.
1995
The Globalization of Liberalization
35
Data Appendix
Dependent Variables:
Capital Account Liberalization. A dichotomous variable coded 1 if a country’s current account is free
from restrictions and 0 if it is not. Source: International Monetary Fund, Annual Exchange
Arrangements and Restrictions, analytical appendix, various issues.
Current Account Restrictions. Restrictions on current account; restrictions exist on payments in respect
of current transactions. 0 if restrictions exist; 1 if they do not. Note: when this dependent variable is used
only to analyze policies of Article VIII countries, it is interpreted as non-compliance. Source:
International Monetary Fund, Annual Exchange Arrangements and Restrictions, analytical appendix,
various issues.
Multiple Exchange Rates. Whether (0) or not (1) the country has some form of multiple exchange rate
system. The case was coded as a multiple exchange rate system if there were special rates for some or all
capital transactions and/or some or all invisibles; import rates that differed from export rates; more than
one rate for imports; or more than one rate for exports. Source: International Monetary Fund, Annual
Exchange Arrangements and Restrictions, analytical appendix, various issues.
Diffusion Variables:
1. Economic competition variables:
Policy of Export competitors. For country A, the yearly mean policy score weighted by export
similarity to country A. Export similarity is calculated as the correlation of country A’s share of exports
to 150 countries with country B’s. Source: Based on Fingers and Kreinin(1979) and calculated from the
IMF Direction of Trade Statistics dataset.
Capital Competitors. The yearly mean policy score of governments with similar sovereign bond ratings.
Source: Standard and Poor’s Historical Sovereign Bond Ratings.
2. Communication/contact variables:
Policy of Major Export Partners. The yearly mean policy score of governments weighted by their
share of country A’s total exports. Source: IMF Direction of Trade.
Policy of Major Import Partners. The yearly mean policy score of governments weighted by their
share of country A’s total imports. Source: IMF Direction of Trade.
Policy of PTA Partners. The yearly mean policy score of governments with a preferential trade
agreement with country A. Source: Helen Milner and Edward Mansfield. Original source: World
Trade Organization.
Policy of BIT Partners. The yearly mean policy score of governments with a bilateral investment treaty
with country A. Source: World Bank, http://www.worldbank.org/icsid/treaties/i-1.htm
The Globalization of Liberalization
36
Policy of Telephone Partners. The yearly mean policy score of governments weighted by their share of
country A’s total telephone minutes. Telegeography 1999.
3. Cultural reference groups:
Policy of Language Partners. The yearly mean policy score of governments with the same dominant
Language as country A. Source: World Bank.
Policy of Colonial Partners. The yearly mean policy score of governments with the same former
protectorate as country A. Source: World Bank.
Policy of Religious Partners. The yearly mean policy score of governments who share the same major
religion. The main religions coded were: Protestant, Catholic,
East Orthodox, Hindu, Buddhist, Muslim, Jewish, and Local/traditional. Sources: Countries of the World
and Their Leaders Yearbook 2000; The Europa World Year Book 1999, The CIA World Factbook.
http://www.odci.gov/cia/publications/factbook/
Control Variables:
1. Economic Variables:
Real GDP per capita. Source: Penn World Tables.
Current account balance/GDP. The current account balance (the sum of net exports of goods and nonfactor services, net factor income, and net private transfers as a percentage of GPD, before official
transfers) as a proportion of GDP for each country for the period under observation. Source: STARs
database, indicators 223/38, World Bank.
World interest rates. Annual average United States Treasury bill yield. Source: International Monetary
Fund, Time Series key 11160C..ZF... International Financial Statistics, May 1999.
GDP Growth. GDP average annual growth rate, for sum of GDP at factor cost and indirect taxes, less
subsidies. Source: STARs database, indicator 181, World Bank.
2. Domestic Political Variables:
Openness. Imports (total value of goods and services: sum of merchandise f.o.b., imports of non-factor
services and factor payments at market prices in current US dollars) plus exports (total value of goods and
services; sum of merchandise f.o.b, exports of non-factor services and factor receipts at market prices in
current US dollars), as a proportion of GDP. Source: STARs database, indicators (210 + 119)/38, World
Bank; also Penn Wold Tables.
Democracy. Democracy score (ranging from a low of 0 to a high of 10) denoting the degree of
democratic institutions within each country. Source: POLITY III Data set. For a complete discussion of
the conceptualization and coverage of this data set and comparisons with other measures of democracy,
see Jaggers and Gurr, (1995).
National Executive. Whether (1) or not (0), the chief executive of the government is from a Nationalist
Party, or coalition of parties. World Bank.
The Globalization of Liberalization
37
3. Geographic variables:
Policies of Border Countries. The yearly mean policy score of governments with the same dominant
Language as country A. Source: Arc-Info base data set.
Policies weighted by Distance. The yearly mean policy score of governments weighted by (1 / the log of
their distance) from their capital to that of country A. Source: Arc-Info base data set.
4. IMF coercion:
Use of Fund Credit. Whether (1) or not (0) a country has made use of IMF credits during a given year.
Source: World Development Indicators, World Bank.
The Globalization of Liberalization
38
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