Download comparing institutional influence

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COMPARING INSTITUTIONAL INFLUENCE:
THE RELATIVE EFFECTIVENESS OF THREE ENVIRONMENTAL AGREEMENTS
Ronald B. Mitchell1
Department of Political Science
University of Oregon
[email protected]
http:/www.uoregon.edu/~rmitchel/
Glenn Deane
Department of Sociology
University at Albany, State University of New York
[email protected]
Draft of Saturday, March 8, 2008 not for citation.
Criticism and suggestions -- whether constructive or otherwise -- welcome.
1
This paper extends the argument made in an article in Global Environmental Politics 2:4 (November 2002). The
material in this article is based upon work supported by the National Science Foundation under Grant No. 0318374
entitled “Analysis of the effects of environmental treaties” September 2003–August 2006. Any opinions, findings,
and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect
the views of the National Science Foundation. The paper has benefited from comments from Judith Kelley and
Layna Moseley. Early drafts of the paper benefited from generous support from a Sabbatical Fellowship in the
Humanities and Social Sciences from the American Philosophical Society and a 2002 Summer Research Award
from the University of Oregon.
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Abstract
International relations scholars have engaged the debate over whether and when international institutions influence
state behavior for over a quarter of a century. This paper argues for shifting the debate from the sterile evaluation of
whether individual institutions matter to the more policy-relevant question of "which institution worked better." We
develop and demonstrate an approach for selecting institutions and evaluating their influence in ways that allow
comparative assessment of whether, how much, and what type of influence those institutions had. In particular, we
demonstrate the value of including multiple institutional variables that correspond to different claims about
institutional influence. Our empirical analysis, using three protocols under the Convention on Long-Range
Transboundary Air Pollution, brings to bear new variables and new data for 53 countries and 23 years that
demonstrate that our approach can distinguish whether institutions influence state behavior, how much influence
they had, the pathways by which they had influence, and the types of country on which they had influence.
Mitchell/Deane Page 3 of 25
Introduction
Which treaty worked better? Although the international relations has engaged the question of institutional influence
for over a quarter century, efforts to compare the influence of different international institutions have been
remarkably rare. While scholars have debated the question of "do regimes matter" {Haas, 1989 #1258}, diplomats
have gotten on with the business of institutional design, adopting certain design elements while eschewing others
with little systematic comparative input from the scholarly community. This article identifies a next step for research
into the influence of international institutions, namely, comparing their influence. Such a comparison involves three
analytic tasks: a) convincingly demonstrating the causal influence, if any, of each institution being compared, b)
identifying the magnitude of each institution's influence in a comparable metric, and c) providing a means to
evaluate whether the conditions under which each institution operated were comparable. The first task requires
seriously and carefully addressing numerous theoretical and methodological challenges that obstruct efforts to
accurately attribute causal influence to an international institution, including spurious correlations and endogeneity.
The second task requires comparing institutions that face nominally comparable political problems and estimating
their influence in ways that use plausibly comparable metrics of their degree of influence. The third of these tasks
requires explicitly evaluating the difficulty of the problems being addressed.
The present analysis takes up these three tasks as follows. First, it seeks to convincingly demonstrate the causal
influence of multiple institutions through quantitative analyses that include a range of non-institutional variables,
explicitly address problems of endogeneity, and remove both the "inertial" and the "country-specific" components of
the available data before evaluating the influence of international institutions. Second, the analysis takes three
protocols regulating pollution emissions under a single environmental treaty and measures their influence in terms of
annual proportional changes in those emissions, thereby allowing direct comparison of the influence of institutional
variables. Third, the analysis uses the inertial trends in the different datasets as well as the influence of other
included variables as proxies by which to compare whether the problems the agreements faced were relatively
comparable.
Analysis of the influence of international environmental institutions has now reached a stage at which quantitative
methods can both complement and extend the valuable insights generated by qualitative research methods {Mitchell,
2002 #4725}. Quantitative analysis allows reexamination of questions of institutional influence already addressed
by qualitative methods as well as initial examination of questions that cannot be, or usually are not, answered by
such methods. Careful quantitative modeling offers a range of different, and arguably superior, ways of
distinguishing institutional influence from random variation, of identifying the magnitude of institutional influence
relative to other factors, and even of accounting systematically for the social processes of institutional influence.
When applied to multiple institutions, quantitative techniques provide opportunities to quantify the magnitude of a
given institution's influence relative to other institutions.
This paper argues that analysis of comparable institutions within a comparable statistical model -- in which the
dependent variable (DV) is measured in identical "annual percentage change" units and the same right-hand side
variables are included -- allows direct comparison of institutional influence. It also demonstrates the value of
including multiple indicators of institutional influence that reflect different arguments regarding how and who
institutions influence. In particular, we argue for examining the influence of institutions on members separately from
examining whether institutions may influence non-members as well as members and whether, among member states,
institutions influence laggards differently than leaders. Finally, it illustrates these arguments by evaluating three
protocols under the Convention on Long-Range Transboundary Air Pollution, demonstrating variance in both their
degree and type of influence.
Theoretical setup
Do international institutions have influence? That is, do they cause states to behave differently than they would
otherwise? Under what conditions do they wield influence? What states do they influence? By what institutional
mechanisms do institutions influence state behavior? An increasing number of scholars have engaged these and
related questions with increasing sophistication {Goldstein, 2007 #5351; Kelley, 2007 #5391; Morrow, 2007 #5390;
Simmons, 2005 #5173; von Stein, 2005 #5172; Neumayer, 2005 #5208; Hathaway, 2002 #5140; Simmons, 2000
#4434}. Although conceptually straightforward, the question poses several significant analytic challenges. This
article develops a general model for analyzing the effects of international institutions quantitatively in ways that
build on recent concerns and insights about mis-estimating their influence. It highlights several analytic tasks
necessary to engage different claims that have been made about institutional influence. In particular, it highlights the
often-overlooked need to avoid both Type I and Type II errors, i.e., simultaneously to avoid claims of institutional
influence where it does not exist and to avoid claims of the lack of institutional influence where it does exist.
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The initial debate between realists and institutionalists (or neo-realists and neo-institutionalists) over whether
institutions ever influence state behavior has expanded, over time, to include constructivists and a broader range of
questions, including "under what conditions do institutions matter?", "what features cause institutions to matter?",
"how do we know if (and show that) institutions matter?", "by what causal pathways do institutions matter?", and
"did this institution matter?" The debate has been engaged at a theoretical or "strategic" level, with some claiming
that the very nature of international institutions precludes them from wielding any independent influence on state
behavior {Waltz, 1979 #3185; Mearsheimer, 1995 #2020; Strange, 1983 #2979} while others have developed a
strong theoretical foundation for believing that states will, under certain circumstances, create international
institutions and will adjust their behavior to conform with the dictates of those institutions in ways they would not
otherwise {Keohane, 1984 #1624; Haggard, 1987 #1276; Martin, 2001 #4530}. The debate has also been engaged at
an empirical or "tactical" level, with some scholars presenting -- and others contesting -- evidence and arguments of
international institutions, at least certain institutions under certain circumstances, wielding influence.
There is no debate over whether states form international institutions -- states clearly do. And there is little debate
about whether states comply with international institutions -- states also clearly do. Indeed, realists that argue that
international institutions do not influence states generally expect and accept that states comply with institutional
dictates much of the time. The debate is over causation, i.e., it is over whether state behavior conforms to
institutional dictates because of institutions or for other reasons. Indeed, the debate is simply a new version of the
older question "why do states behave as they do," asked within a context that allows for the possibility that
international institutions are part of the answer. One line of counter-institutional argument involves a claim of
"spurious correlation," i.e., that whenever institutions appear to have influenced behavior, it is actually the influence
of exogenous non-institutional factors. The other, more recently engaged, line of counter-institutional argument
involves a claim of "institutional endogeneity," i.e., that, because international institutions are voluntary "contracts"
among states, their membership lists include only those states that expect their behavior will conform with
institutional dictates, but for non-institutional reasons. If institutional endogeneity is complete or "perfect," then
institutions wield no influence in a counterfactual sense, and institutions serve only to "screen" states rather than to
constrain their behavior {Downs, 1996 #897; Simmons, 2000 #4434; Keohane, 2003 #5171; Simmons, 2005 #5173;
von Stein, 2005 #5172; Mitchell, forthcoming #5194; Werner, 2005 #5373}.
Definitions
We focus here on the influence of those international institutions that derive from treaties, conventions, protocols,
and other formal agreements "negotiated among international actors, that prescribe, proscribe, and/or authorize
behavior" {Koremenos, 2001 #4555, 762}. Although international institutions can take many forms, the formal and
public nature of international legal agreements facilitates analysis by removing analytic ambiguity about the
commitments that states have made to each other.
To clarify the central concern of the debate over institutional influence, we distinguish between behavioral
conformance and behavioral adjustment. Much of the debate to date has been framed in terms of behavioral
"conformance," i.e., the degree to which a state's behavior conforms to institutional rules. Although the term
compliance is often used, we prefer conformance for several reasons. First, "compliance" dichotomizes behaviors in
ways that ignores institutionally-induced behavior that falls short of compliance, does not distinguish coincidental
and institutionally-induced compliance, and conflates institutional influences that produce "bare minimum"
compliance with those that induce behaviors that far exceed legal minimums. Second, there are many international
institutions whose rules are sufficiently vague that "compliance" cannot be determined but that should not, therefore,
be definitionally precluded from being influential or effective {for an elaboration of this discussion, see \Mitchell,
2007 #5307}. Thus, "conformance" involves the comparison of observed behaviors to institutional standards,
recognizing that that comparison should be continuous rather than dichotomous and that the institutional standards
may not always make such a comparison straight-forward.
But, as already noted, most important aspects of the debate are not about behavioral conformance but about
behavioral adjustment. We use the phrase "behavioral adjustment" to capture the causal aspect that is central to the
debate, namely, whether states adjust their behaviors in response to international institutions {Chayes, 1991 #650;
Simmons, 1994 #4117}. States can adjust either by changing their behavior in ways they would not have done
otherwise or by refraining from changing their behavior when, absent institutional influences, they would have. Our
goal in distinguishing between conformance and adjustment is for the latter term to refer to those behaviors that are
responses to international institutions regardless of the extent to which such behaviors conform with institutional
dictates.
Finally, since it is a central component of the debate, it proves useful to reiterate Sprinz and Vaahtoranta'
classification of states in terms of their interest in, and support for, international environmental regulation. Sprinz
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and Vaahtoranta argue that state positions on international environmental policy can be predicted based on the
interplay between a state's ecological vulnerability to an environmental problem and the abatement costs it would
have to incur in any collective effort to address that problem {Sprinz, 1994 #2912}. The two variables of their
framework distinguish pushers (high vulnerability and low costs) and draggers (low vulnerability and high costs) but
also intermediates (high vulnerability and high costs) and bystanders (low vulnerability and low costs). We adopt the
more alliterative terminology in which pushers are "leaders" and all other states are "laggards."
Possible mechanisms and locations of institutional influence
Accurately assessing institutional influence requires knowing both where and how to look for it. That, in turn,
requires delineating extant claims about institutional influence. We believe there are essentially four major claims
about the influence of international institutions: they do not have such influence, they influence members but not
non-members, they influence leader members more than laggard members, and they influence both members and
non-members.
Institutions have no influence
Realist theory argues that international institutions have no independent influence on behavior. In the terms just
delineated, although there may be institutional conformance, there will be no adjustment. States join or reject
institutional membership based on calculations of which choice better aligns with their short-term, institutionindependent interests. After institutional formation, the behaviors of member states will tend to conform with
institutional dictates but will diverge from those dictates if and when they became "inconvenient." The behaviors of
non-member states will tend not to conform with institutional dictates but will do so if and when exogenous forces
make doing so in their interests. In this view, leader states promote, join, and comply with treaties while laggard
states oppose, do not join, and ignore the dictates of treaties. These dynamics produce a correlation between
conformance and membership but one that is spurious. States that have exogenous preferences for certain behaviors
(i.e., "leaders") create and join institutions that promote those behaviors and then follow their dictates; states that do
not share those preferences (i.e., "laggards") reject membership in those institutions and do not follow their dictates.
But for neither set of states, however, does post-agreement behavior differ from what it would have been otherwise:
members change their behavior only in ways that they would have had there been no institution and non-members
do not change their behavior.
Institutions influence members but not non-members
The dominant view of institutional influence is that they influence members but not non-members. States create
institutions to improve on the suboptimal outcomes created by independent decision-making. Institutions can create
conditions that make states more willing to take the risks involved in contingent, interdependent behaviors that can
produce Pareto-improving outcomes. States join or reject membership based on calculations of whether institutional
features reduce the risks of defection by other members sufficiently to warrant incurring the costs of their own
adjustment, in essence, supplementing the instrumental calculations predicted by realists with consideration of
additional longer-term and more interdependent considerations. Member states will not merely conform but may
adjust, i.e., they may adopt behaviors they would otherwise eschew to the extent they see others as doing likewise.
This line of reasoning has been adopted by most research on international institutions, with institutional influence
often estimated by comparing the behavior of members to otherwise-similar non-members {Goldstein, 2007 #5351}.
Although scholars disagree over exactly which institutional mechanisms lead states to adjust, they share an
underlying assumption that the mechanisms either only operate on -- or, at least, only have influence on -- member
states. Careful research has highlighted that the membership/non-membership distinction may not accurately
categorize which states are subject to institutional mechanisms and which are not: many institutions allow members
states to opt out of certain obligations {Rosendorff, 2001 #3844} and others "create rights and obligations for nonmembers" {Goldstein, 2007 #5351, 38}. But these claims largely argue simply for more accurate definitions and
categorizations of whether a state was legally subject to institutional provisions or not.
Institutions influence laggard members more than leader members
Other scholarship, however, suggests that using the membership/non-membership distinction risks much deeper
mis-estimation of institutional influence. Theories of institutional formation suggest that membership is composed
of both leaders and laggards {Sprinz, 1994 #2912}. Laggards may become members due to pressure from more
powerful states {Zürn, 1997 #4090; Martin, 1992 #1963} or because they have mixed motives that make their
interests in taking institutionally-required action contingent on the behavior of others. In either case, however, there
is a theoretical expectation that institutional influence on laggard states should be different, and most likely greater,
than on leader states. This perspective suggests that three types of states join international institutions: a)
unilateralist "leaders" that want other states to adopt the behaviors that they already plan to engage in or have
already undertaken for non-institutional, exogenous reasons; b) "contingent" states who view it as in their interest to
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adjust their behavior if other states do and who accept that adjustments by others are contingent on their own
adjustments; and c) "recalcitrant" or "free rider" states who join because they view themselves as benefiting from
behavioral adjustment by other states or because they receive explicit side-payments but who seek to avoid the costs
that their own adjustment would involve. Indeed, a dominant theme in the literature is that institutions are responses
to Prisoners' Dilemma or Tragedy of the Commons problems in which the countries that negotiate and join an
institution are all "contingent" states that would act independently -- and suboptimally -- were it not for the
institution. To the extent this is true, members will adjust but non-members will not and, so, the member/nonmember distinction captures institutional influence adequately. But a closer reading of theory suggests a different
interpretation, namely, that leader members should be distinguished from laggard (i.e., non-leader) members with an
expectation that we should expect institutional influence only among the latter. The non-contingent leaders will
conform with institutional dictates but due to the exogenous forces that make them leaders. But contingent and
recalcitrant states who become members and conform their behavior to institutional dictates provide evidence that
adjustment is occurring. It is these non-leaders that we would expect, absent the agreement, to have behaviors
different than the institution dictates.
Institutions influence members and non-members alike
Finally, there are reasons to believe that both that institutions may influence the behavior of both members and nonmembers. As constructivists argue, states that have already accepted a norm may create institutions to strengthen or
expand its influence among states that have not yet accepted it. Yet, if institutions influence behavior through
normative -- rather than instrumental -- processes, then their influence need not stop at the member/non-member
divide. To be sure, some norms are defined in ways that mean they only apply in contexts of reciprocity and such
reciprocity may be limited to members of an institution. Thus, to the extent that granting most-favored nation status
to other states is based in normative, rather than instrumental, calculations, it would be likely to operate almost
exclusively among institutional members who have shown their willingness to reciprocate. But many norms are
defined in more universal and non-contingent terms. Thus, human rights and prisoner of war agreements promulgate
relatively non-contingent norms, that states are expected to follow even in dealings with states that do not follow
them and, to a lesser extent, even if a significant fraction of states fail to follow it. To the extent an institution
strengthens a norm, states may adjust their behavior to conform to the norm, even if not the legal specificities, of an
institution and such adjustments may not be limited to member states. In short, the expectation of conformance may
"detach" from institutional membership, creating pressures that may lead even non-member states to adjust.
Institutions may also foster technological, economic, or even transnational professional changes that make
adjustment easier and more attractive or non-adjustment more difficult and more costly, regardless of a country's
membership status. Coordination problems that are truly global may need only a portion of relevant states to accept
a particular standard to "tip the balance" in favor of that standard vs. alternatives. Thus, [Air Traffic Control doesn't
need all parties but all parties will abide by rules.] The 1987 Montreal Protocol's efforts to protect the stratospheric
ozone layer induced considerable research into alternatives to chlorofluorocarbons (CFCs) which identified
substitute chemicals that were, in at least some cases, cheaper than CFCs with the effect that most states, regardless
of their membership status, began shifting away from CFC use relatively rapidly. Although such adjustment clearly
resulted from "DuPont's decision to phase out CFCs and develop substitutes [which] changed the market conditions
and forced other CFC manufacturers to follow suit," evidence strongly suggests that Dupont would not have made
those decisions in the absence the Montreal Protocol {Haas, 1992 #1263, 221}. In a process that contrasts with
Gruber's "go it alone" power theory {Gruber, 2000 #4482}, institutions may be able to entrain social processes such
that behaviors that previously were available and attractive become difficult or costly to engage in, even for states
that would, otherwise, have preferred not to adopt those behaviors.
One analytic goal of this paper is to model institutional influence in ways that allow us to identify institutional
influence by any of these mechanisms as well as to distinguish among them. As developed below, we include
institutional variables to reflect these different possible mechanisms and locations of institutional influence.
The influence of international environmental institutions
To assess the possibility of comparing the influence of international institutions within a quantitative framework, we
examine three environmental protocols under the Convention on Long-Range Transboundary Air Pollution
(LRTAP). States negotiated the LRTAP Convention in 1979 and have negotiated eight additional protocols. We
focus on two that address sulfur emissions and one that addresses nitrogen oxides, the 1985 Protocol On The
Reduction Of Sulfur Emissions Or Their Transboundary Fluxes By At Least 30 Per Cent, the 1988 Protocol
Concerning The Control Of Emissions of Nitrogen Oxides Or Their Transboundary Fluxes, and the 1994 Protocol
On Further Reduction Of Sulfur Emissions. We refer to these as the first Sulfur Protocol, the Nitrogen Protocol, and
the second Sulfur Protocol, respectively.
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These agreements have several analytic virtues. First, they foster comparison across protocols by controlling various
factors that are challenging to control statistically but can be controlled for through case selection. Analyzing these
protocols controls for many issue and institutional characteristics since the three protocols address different aspects
of the same environmental problem within the same overarching institutional framework. These cases also control
for potential member states, regional political background conditions and temporal changes, and level and trends in
economic development. In short, they pass at least an initial hurdle of addressing similar problems in a similar
setting.
Second, environmental treaties present interesting cases for evaluating institutional influence because the "tit for tat"
reciprocity considered central in most theoretical claims about -- and empirical evaluations of -- institutional
influence is unlikely to be the mechanism of institutional influence {Axelrod, 1984 #306; Downs, 1996 #897;
Morrow, 2007 #5390}. Morrow notes that international agreements "do change what states do by setting common
standards that strengthen reciprocal enforcement" {Morrow, 2007 #5390, 570}, but in environmental agreements (as
in human rights agreements) states are unlikely to adopt a reciprocity involving contingent pollution or contingent
wildlife protection because of both normative and bureaucratic constraints.
Third, scholars have increasingly applied quantitative methods to assess the influence of non-environmental
international institutions, including those related to international political economy {Goldstein, 2007 #5351;
Simmons, 2000 #4434; von Stein, 2005 #5172} and to human rights agreements {Hathaway, 2002 #5140;
Neumayer, 2005 #5208}. Quantitative techniques have been used only infrequently, however, to assess individual
environmental institutions {Auer, 2001 #5376; Neumayer, 2002 #5145; Helm, 2000 #4775; Murdoch, 1997 #4444;
Murdoch, 1997 #4770; Murdoch, 1997 #4747; Murdoch, 2003 #5255; Schofer, 2005 #5377; Ringquist, 2005
#5243}. This article seeks to build on the small body of work that has used quantitative techniques to assess the
influence of individual international environmental agreements in such a way that the quantitative assessments of
different agreements can be meaningfully compared {e.g., see \Neumayer, 2002 #5348; Roberts, 2004 #5349}.
Fourth, the agreements chosen illustrate what appears to be a more general problem of analyses of international
institutions, namely, that qualitative analyses of various international institutions provide compelling process
tracing, counterfactuals, and narratives of institutional influence that quantitative analyses often fail to support. That
pattern is evident specifically with respect to these agreements. These agreements have received both quantitative
and qualitative attention, with most quantitative analyses finding the institutions non-influential and the qualitative
analyses finding them influential. We strive here to conduct a quantitative analysis that holds the possibility of
assessing whether these apparently contradictory findings are actually consistent. Indeed, by building a quantitative
model that reflects qualitative insights while carefully addressing endogeneity, we believe we can contribute to this
debate. Qualitative research has found the LRTAP regime (or one or more of these protocols) has led at least some
states to change their behavior in ways that have improved air quality {Levy, 1993 #1818; Wettestad, 2002 #4714;
Wettestad, 2002 #5238}. By contrast, quantitative studies have contended that LRTAP protocols have "made no
difference" {Ringquist, 2005 #5243, 99}, merely codifying emission reductions members would have made "even in
the absence of the treaty" {Murdoch, 1997 #4770, 157; see also \Murdoch, 1997 #4444; Murdoch, 2003 #5255;
Finus, 2003 #5388}. By contrast, other quantitative studies of the same agreements have concluded that the 1988
Sofia Protocol led to reductions that were 2% greater than they would have been otherwise {Bratberg, 2005 #5387;
for a contrasting conclusion, however, see \Helm, 2000 #4775}. The most visible disconnect between the analytic
approaches fits with the theoretical claims made above. The major finding of the qualitative literature is that many
members conform but only relatively few adjust, i.e., many members reduce but only some reduce because of
institutional influence. By contrast, the quantitative literature finds that the average institutional affect, across all
members and after accounting for other factors, does not distinguish members as a group from non-members as a
group. Although quantitative analyses have not explicitly accounted for the difference between leaders and nonleaders, the qualitative analyses, by necessity, have built their findings on careful analysis of relatively few countries
and without the advantages of more systematic evaluation of variables that is fostered in quantitative analysis.
Finally, the extensive involvement of the International Institute for Applied Systems Analysis (IIASA) in the
operations of the LRTAP regime have produced data on the dependent variable (DV) and several independent
variables (IVs) that has been created and/or carefully reviewed by experts, making it of far higher quality than
corresponding data for other agreements. Given that analyses of data of low quality can fail to identify real
correlations while also identifying spurious ones, analyses of LRTAP data are more likely than in many realms to be
credible.
The analytic tasks
To make a convincing comparison of the influence of two or more international institutions, i.e., to compare the
relative effectiveness of two or more regimes, requires attention to several analytic tasks. The first involves
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specifying the model so that it accurately addresses the questions of theoretical interest. The second involves
specifying the model in ways that, as much as possible, move toward supporting valid causal claims by addressing
alternative explanations and endogeneity. The third involves estimating the magnitude of the influence of the
institutions being compared in units and with models that allow those estimates to be meaningfully and convincingly
compared. This includes demonstrating that the institutions operated in reasonably-similar circumstances that posed
reasonably-similar obstacles to progress in resolving the problem.
Specifying the model to address institutional influence
The foregoing discussion of possible mechanisms and locations of institutional influence highlights the need to
include three different institutional variables. The first is a membership variable. This is the standard approach in
which, if the average behavior of members differs sufficiently from that of otherwise-similar non-members, it is
taken as evidence of institutional influence. This variable is coded as 0 for non-members and 1 for members.
Although an important variable to include, this approach fails to model an important feature of institutional theory,
properly. A regression that compares all members to all non-members identifies institutional influence as the
average across both leader and non-leader member states. Yet, membership ranks often include, and may be
dominated by, leader states that theory predicts will not be influenced by the institutions (i.e., will conform but not
adjust). In such cases, institutional influences on non-leader states that join an institution may be missed because that
institutional influence is "watered down" by leader states that are members but are not influenced by the institution.
Therefore, we include a second institutional variable in the form of a non-leader-membership interaction term. This
variable, which we do not believe has been used in prior quantitative analyses of international institutions, evaluates
whether the behavior of non-leader members differs from the behavior of otherwise-similar leader members. This
variable is coded as 0 for non-members, 0 for leader members, and 1 for non-leader members for the period after
they become members. That interaction term also (when combined with the membership term) evaluates whether the
behavior of non-leader members differs from the behavior of otherwise-similar non-members. Finally, to address
the possibility of institutional influence on non-members, we include an institutional existence variable. This
variable is coded as 0 for all countries before an agreement enters into force and 1 thereafter. Inclusion of this
variable allows for identification of whether an institution influences states through processes that are not dependent
on the usually instrumental impacts connected to institutional membership. Although the simplified model here
sheds little light on what those processes might be, the influence of institutions through pathways involving norms
and ideas or through economic or technological changes can be captured through such a variable.
Addressing alternative explanations and endogeneity
We develop our analytic model with the goal of avoiding the Type I error of finding institutional influence where it
does not exist. Besides including a range of generic and issue-specific independent variables identified by prior
researchers and placing analytic uncertainty on the side of institutional non-influence rather than institutional
influence, we address the problem of endogeneity directly.
Our analytic approach was influenced significantly by the desire to address the problem of endogeneity, i.e., the fact
that countries are not randomly assigned to receive the "treatment" of protocol membership but "self select" into the
"treatment" group. States may decide whether to become members of an agreement based on their assessment of
how they are likely to behave rather than deciding how to behave based on whether they are members or not. In
short, the endogeneity claim is that countries become members of international institutions at the point in time that,
for exogenous reasons, they are ready to change their behavior to conform with institutional dictates. Therefore,
before/after and member/non-member comparisons may exhibit significant differences even if the institution has no
independent influence on behavior. An increasing number of scholars have recognized that, because states selfselect into institutional membership, behavioral differences between members and non-members may arise because
international institutions "screen" rather than constrain {Simmons, 2000 #4434; Keohane, 2003 #5171; von Stein,
2005 #5172; Simmons, 2005 #5173; Kelley, 2007 #5391; Mitchell, forthcoming #5194}. Indeed, the problem has
been directly addressed in relation to the influence of LRTAP-related protocols {Murdoch, 2003 #5255; Ringquist,
2005 #5243}. Intuitively, endogeneity involves the notion that states that becomes members of an international
institution are, even before they become members, different than states that do not become members. In this view, to
the extent that members and non-members behave differently after an agreement takes effect, these post-protocol
differences are due to pre-existing differences between members and non-members rather than to any influences of
the protocol itself.
Endogeneity can be addressed in various ways. The first, and preferred, method is to analyze two-stage models that
estimate the likelihood that a state will join an institution and use that likelihood as inputs to models of subsequent
behavior. Second, the analyst can and should include variables considered to drive both behavior and membership so
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that the influence of institutional variables is evaluated after having accounted for other differences between
members and non-members. Third, the analyst can use matching techniques to compare members to non-members
that have been carefully selected to be "otherwise-similar" {Barabas, 2006 #5569}.
We address endogeneity in three ways. First, we include proxies for a broad range of independent variables, as
identified in prior analyses, to ensure that institutional variables are not biased by those factors being omitted
variables in the analysis. Second, we do not directly instrument our variables in a two-stage approach that includes
the modeling of negotiating positions on these protocols. But we do include issue-specific variables that correspond
to the "ecological vulnerability" and "abatement costs" that have been found to be important drivers of state
positions in international environmental negotiations {Sprinz, 1994 #2912}. Third, we include a variable to capture
whether states are leaders or non-leaders that is identified based on evaluations by other scholars of the negotiating
positions states took rather than on whether states became members or not. Including this variable allows us to
distinguish leader members from non-leader members in line with the theoretical implications delineated above.
Estimating the magnitude of institutional influence in comparable terms
If the foregoing steps are necessary to convincingly evaluating any international institutions influence, there are
additional constraints imposed by our desire to compare institutional effects. To rank agreements in terms of their
effectiveness, rather than to simply categorize them dichotomously as effective or not, requires models that meet at
least three additional criteria.
First, the models must be analytically similar. We adopt the view that, indeed, the models should include the exact
same explanatory variables and vary only in the dependent variable being regressed. Thus, as noted below, we
model both sulfur dioxide and nitrogen oxide emissions as functions of the same independent variables.
Second, if we want to answer the question with which this article began, we must create models that generate
coefficients for institutional variables which are meaningfully comparable across regressions. That, in turn, requires
that the dependent variable be measured in the same units. Annual percentage change (APC) scores are an important
means of allowing comparisons of institutional influence that do not depend, as various proposed alternative
methods do, on qualitative and often subjective assessments of institutional influence {Mitchell, 2002 #4725; Hovi,
2003 #5014; Hovi, 2003 #5017; Young, 2003 #5018; Underdal, 2004 #4774; Miles, 2002 #4230}.
Finally, meaningful and convincing comparison of the effects of different institutions requires selecting institutions
that appear to have faced relatively similar problems that were equally malign (or benign) in their
character{Underdal, 2002 #4221}, and then, in addition, demonstrating that the evidence confirms that the problems
were relatively equal in their susceptibility to resolution. In short, the institutions being compared must be shown to
have operated in circumstances that posed reasonably-similar obstacles to progress in resolving the problem.
Analytic and data improvements
Our approach builds on, but differs from, previous quantitative studies of LRTAP protocols in using significantly
enhanced models, data, and estimation techniques. First, we develop and analyze a panel (cross-sectional timeseries) dataset of relevant variables that is significantly larger in countries, years, and variables than previous
analyses. Our dataset includes annual data on emissions from 1980-2003 and corresponding annual data for a core
subset of independent variables for 53 countries and for all modeled independent variables for 36 countries.
Ringquist and Kostadinova's 2005 article uses data for only 19 countries that ends in 1994 and the most recent work
by Murdoch, Sandler, and colleagues (2003) uses data for only 25 countries that ends in 1990. Considerable
improvement has been made in agreement-specific variables, with annual national data included for ecological
vulnerability (measured as exceedances of critical loads), abatement costs (measured as costs of a 5% emission
reduction as a share of GDP), and self deposition and deposition import (measured as share of emissions deposited
domestically and share of deposition imported from other countries). Although data on all three variables have been
used previously, no previous studies have included annual data for these variables.
Second, past analyses either have used aggregate levels of emissions or percentage changes from a single base year
{see \Murdoch, 1997 #4444; Murdoch, 1997 #4770; Murdoch, 2003 #5255; Ringquist, 2005 #5243}. But the APC
scores that we adopt because of their value for comparing institutional effects also have the virtue of helping
transform panel data that, by its nature, contains considerable country-specific and time-specific trends, thereby
moving it closer to meeting the fundamental assumption of, and necessary condition that, observations in a statistical
model be independent and identically distributed (IID). Data on country emissions over time are, to be sure, not IID
in their "raw" form. To move country-year panel data significantly toward IID, one can remove trends over time and
trends due to country-specific factors. Modeling annual percentage changes "squeezes out" a significant amount of
the "embedded" correlation that arises in panel data due to both time-trends and country characteristics.
Mitchell/Deane Page 10 of 25
Third, and following previous work, we seek to identify and isolate institutional influence by modeling emissions as
a function of both institutional and non-institutional factors. We include proxies for all non-institutional (i.e., nonprotocol) independent variables used in previous analyses, but add proxies for economic performance, ecological
vulnerability, abatement costs, and strategic interaction that have either not been included in previous analyses or
have been included in only limited fashion. We initially model only non-institutional factors and then assess whether
institutional variables are both statistically significant (i.e., as measured by their t-statistics) and add to the
explanatory power of the model. To take advantage of the availability of emissions data for several non-European
countries not included in previous analyses, we run a baseline model that includes all 53 countries for the full time
period, using only those variables for which independent variable data is available for all countries.
Finally, a central aspect of the analysis here is comparing the influence of three different protocols, the first sulfur
protocol, the second sulfur protocol, and the nitrogen protocol. Conducting a quantitative analysis of multiple
agreements is intended to allow us to identify the relative influence of different agreements by generating a common
metric of effects within a model that explicitly accounts for other factors that may be driving the behaviors the
agreement tries to influence. This approach not only allows us to assess whether the influence of an institution is
larger or smaller than the influence of non-institutional factors in a given case as well as how that institutional
influence compares with the influence of other institutions. Looking across three institutions also allows us to
evaluate whether non-institutional factors play similar or different roles in different environmental contexts, e.g.,
evaluating whether economic forces make some problems more intractable than others {Underdal, 2002 #4221}.
This paper does not make full progress toward several more ambitious goals regarding institutional comparisons {as
delineated in \Mitchell, 2002 #4725} but represents an initial attempt to pursue them with some degree of
quantitative rigor.
Data and Variables
We model emissions as a function of broad-scale demographic, economic, and political forces; of more direct
factors including ecological vulnerability, abatement costs, emission reduction strategies; and of institutional forces.
Issues related to the treatment of missing data are discussed in Appendix II.
The dependent variable: emissions
Our dependent variables are annual national emissions of SOx and NOx (designated sox and nox). As noted, we
model emissions in terms of annual percentage changes, operationalized here by taking the natural log of emissions.
The dataset includes emissions data for 53 countries, combining commonly-used IIASA-RAINS emissions data for
23 years for 43 European states with at least 11 years of emissions data for 10 additional states (OECD emissions
data for two LRTAP members [Canada and the United States] and four non-members [Australia, Japan, Korea, and
New Zealand] and national data for four post-Soviet Asian republics [Kyrgyzstan, Turkmenistan, Tajikistan, and
Uzbekistan]). This more than triples the number of country-year observations for emissions used in recent analyses
{Ringquist, 2005 #5243}.
Generic independent-variables: metrics of demographic, economic, and political influences
Variation in emissions of most pollutants are likely to be influenced by both the population and level of economic
activity of a country. We include annual population data (designated pop) from the World Bank's World
Development Indicators dataset {World Bank, 2006 #5277}.
Growth in a country's economic activity also should produce growth in its pollution levels. We use Gross Domestic
Product (designated gdp) as our proxy for economic activity, using figures reported in 1990 US dollars and
purchasing power parity to allow meaningful comparison across countries and over time {Maddison, 2006 #5269;
Groningen Growth and Development Centre, 2006 #5268}.
Both the literatures on environmental Kuznets curves and on post-materialism engage, in different ways, the notion
that economic development has environmental benefits, i.e., that "richer" means "greener" {Grossman, 1995 #4562;
Harbaugh, 2000 #4563; Inglehart, 1990 #1493; Inglehart, 1995 #1492} To evaluate these arguments, we include per
capita income data, based on dividing GDP figures by population figures (designated gdp_capita).
We also evaluate the influence of trade openness (designated trade_gdp), including the standard metric of total trade
(exports plus imports) as a percentage of GDP, using annual UN Statistical Data for all three components as measure
in current US dollars {United Nations Department of Economic and Social Affairs, 2006 #5332}.2
2
To address the lack of UN Statistical Data for Soviet exports and imports, data on GDP, exports, and imports for
the Soviet Union was extracted from the annual CIA World Factbook {Central Intelligence Agency - United States,
1981-1992 #5363}.
Mitchell/Deane Page 11 of 25
To capture the influence of governmental structure, we follow standard practice and use annual Freedom House
sources of 0 for Not Free, 1 for Partially Free, and 2 for Free (designated free) {Freedom House, 2006 #4773}.3
Issue-specific independent variables
Leaders and laggards
For both NOx and SOx negotiations, we sought to distinguish leaders from non-leaders. For SOx, we combined
evidence of actual state negotiating positions from Sprinz and Vaahtoranta {, 1994 #2912, 100-101} with Levy's list
of countries that, as of 1989 (after the protocol was signed), had declared reduction targets greater than the 30%
required by the protocol {, 1993 #1818, Table 3.8, 118}.4 For NOx, we identified leaders as those countries that, in
addition to signing the NOx protocol (which required freezing emissions) signed an optional 30% reduction pledge
{Levy, 1993 #1818, Table 3.2, 97}. Since non-leaders are the countries most likely to show institutional influence,
we inverted the coding scheme and designate it as "laggard," coding leader countries as 0 and all other countries as
1. The codings are listed in Appendix I. Although a significant improvement over prior failures to address leaders
and laggards directly, these proxies for both the SOx and NOx cases are, nonetheless, likely to be rather "noisy", i.e.,
to lead to coding some leaders as laggards and vice versa.
Metrics of ecological vulnerability
As Sprinz and Vaahtoranta note, ecological vulnerability and abatement costs (essentially corresponding to the costs
and benefits of environmental protection) play an important role in determining a state’s positions in international
environmental negotiations and, presumably, their behavior in response to those agreements {Sprinz, 1994 #2912}.
Both Sprinz and Vaahtoranta as well as Murdoch and Sandler have undertaken analyses that include countryspecific cost estimates for SOx reductions as well as estimates of vulnerability to SOx emissions from other
countries {Murdoch, 1997 #4444}. The analysis here incorporates far more refined data that estimates the costs in
each year for each country for different pollutants.
We incorporate two different proxies to capture ecological vulnerability. The first proxy is forest cover as a
percentage of national land area (designated forest) from the World Bank's World Development Indicators dataset
for 1990, 2000, and 2003, with annual data being interpolated or extrapolated from available data points {World
Bank, 2006 #5277}. We incorporate forest cover in our model largely to provide comparability with previous
studies {Murdoch, 1997 #4444; Murdoch, 1997 #4770; Murdoch, 2003 #5255; Ringquist, 2005 #5243}. However, it
is a poor proxy, both because it has only weak construct validity (i.e., it does poorly at measuring that "variation in
the extent of the problem" in which we are interested) and because empirical data is so sparse.
To address these concerns, we developed a new dataset corresponding to ecological vulnerability as understood by
those negotiating these agreements. The RAINS research team at IIASA assesses environmental damage by
estimating the extent to which actual depositions exceed a "no damage" state for all ecosystems within a country
{Amann, 1998 #5364, 5} Since different ecosystems are differentially susceptible to acidification, these
"exceedances" must be "accumulated" across different ecosystems. Since ecosystems vary in size across countries,
these estimates are normalized by land area {Amann, 1998 #5364, 5}. The RAINS model estimates these average
accumulative exceedances (AAEs) in "averaged acid equivalents per hectare and year" or "aeq/ha/year") for all
countries in the LRTAP region, based on a given set of national emissions. For each year, national AAE figures
(designated aae_avg) were generated by entering actual emission levels for all countries in that year into the RAINS
model {International Institute for Applied Systems Analysis, 2006 #5334}.5
Metrics of abatement costs
Since emission levels are also, presumably, influenced by the estimated costs of reducing from those levels, for each
country-year observation we estimate the cost of a 5% reduction from then-current levels as a percentage of GDP.
The optimal dataset would consist of those costs that policy-makers estimated, at the time, that they would need to
3
That dataset does not provide scores for 1982, but the scores for all countries in the dataset are identical for 1981
and 1983 and so those scores are filled in with the 1983 scores.
4
An alternative rule for coding "leaders" would be to identify those countries that supported the "30% club" but, as
of the end of 1984, that group included all but 1 of the countries that -- six months later -- signed the protocol. The
list also a) codes Germany as a leader as a single country, based on West Germany being a leader and bringing East
Germany along after reunification; b) codes Russia, Belarus, and Ukraine as leaders since it was those three Soviet
republics that actually signed the protocol; and c) codes the Czech Republic and Slovakia as leaders since
Czechoslovakia was a leader at the time.
5
Chris Heyes of the International Institute for Applied Systems Analysis provided invaluable assistance in
developing and interpreting these estimates.
Mitchell/Deane Page 12 of 25
incur to make a small reduction from then-current emission levels. Since such estimates of emission reduction costs
were neither generated nor recorded for most countries nor in most years, we follow previous authors in relying on
estimates of costs generated by IIASA's RAINS team. At regular intervals, the RAINS team has estimated
abatement costs for several retrospective and prospective points in time {International Institute for Applied Systems
Analysis, 2007 #5372}. Thus, in 2000, they estimated cost curves for 1990, 1995, 2000, and 2010. Efforts to
combine data from different estimates demonstrated that variation in the data generated by such an approach
reflected changes in estimation techniques more than changes in underlying costs. Therefore, we have used the 1990
cost curves generated by IIASA in 2000 since, of cost curves available to the authors, they represented the estimates
closest in time (and were estimated closest in time) to the midpoint of the data series.
Using these 1990 cost curves, we estimated the costs for each country of making a 5% reduction from then-current
levels. For example, Belgium produced 400 units of SOx in 1985; a 5% reduction equates to a 20 unit reduction
leaving 380 units remaining; IIASA's 1990 cost curves estimate the marginal costs for Belgium to reduce to 380
units as 366.2 Euros per ton of SO2 removed. Costs for some countries in some years could not be estimated
because actual emission levels were above or below those included in the cost curves. Data for missing years was
filled in with data from the next subsequent year for which data was available (or, for missing years at the end of the
time period, from the last prior year for which data was available). Although RAINS estimates of marginal costs
increase monotonically, marginal abatement cost estimates derived by the foregoing method do not, with years with
higher emission levels than the previous year likely to have lower marginal costs. Yet, the logic of marginal costs
suggests that countries would choose the lowest marginal cost option to reduce emissions and that subsequent efforts
to reduce emissions would be more expensive. Therefore, annual estimates were revised to increase monotonically
by replacing cost estimates in a given year with the previous year's cost estimate wherever that prior year cost
estimate was higher. To estimate abatement costs (designated sox_expense_percent and nox_expense_percent) as a
share of GDP, we multiplied these estimates of marginal costs for a 5% reduction by the quantity of emissions
represented by a 5% reduction and divided the product by GDP.
Metrics of preferred emission reduction strategies
Although ecological vulnerability and abatement costs indicate whether and how strongly a country will want an
environmental problem addressed, other scholars have suggested the need to include other variables to identify what
policy they will prefer for addressing the problem {Murdoch, 1997 #4770; Murdoch, 2003 #5255}. For a country
that is vulnerable to an environmental problem, its preference ranking of unilateral or multilateral action will depend
on how transnational its own environmental problem is. In the acid precipitation case, a country's preference for
international cooperation depends on how much of its problem involves "self deposition," i.e., deposition of
emissions from its own emitters, and how much involves "deposition import," i.e., deposition of emissions from
emitters in other countries. We measure self deposition (designated sox_self_depo and nox_self_depo) as that
fraction of a country's emissions (deposited in the European region) 6 deposited on its own territory. This proxies a
country's incentive to restrict domestic production of emissions as a strategy to lessen depositions on its territory.
Thus, a country with a 100% self deposition rate receives all the benefits of any domestic emission reductions it can
induce in the form of reduced deposition in its own territory; a country with a 0% self-deposition rate receives no
direct benefits from any emission reductions it makes.
We measure deposition import (designated sox_depo_import and nox_depo_import) as that fraction of total
deposition on a country's territory that originates in other countries. This proxies a country's incentives to engage in
international cooperation as a strategy to reduce depositions on its territory. Thus, a country with a 100%
"deposition import" level receives all depositions from other countries and has strong incentives to find strategies
that induce other countries to reduce their emissions; a country with a 0% "deposition import" level receives no
depositions from other countries and has no incentives to engage in international efforts to get other countries to
reduce their emissions.7
6
EMEP and other sources {Murdoch, 1997 #4444} use total emissions to calculate the corresponding figures. We
use only emissions deposited in the EMEP-modeled region (some being deposited outside that region), because
those data can be assumed as strictly comparable to the national and aggregate deposition data in the model, since
total emissions are from separately reported sources.
7
To clarify that self deposition and deposition import are not complements, consider a country that emits 100 tons
of SOx of which 80 tons lands on its own territory (20 traveling to neighboring countries) and which receives a total
of 240 tons of SOx on its territory. Then, its self deposition rate would be 80% (80/100) while its deposition import
rate would be 66.7% (160/240).
Mitchell/Deane Page 13 of 25
In what constitutes a comprehensive new dataset, annual data have been derived from a series of source-receptor
matrices (also called "blame matrices," since they identify which countries are to blame for any given countries'
deposition levels) published periodically by the Norwegian Meteorological Institute (NMI) (data for 1985 and 19871992 {Sandnes, 1993 #5251}; 1996 {EMEP, 2001 #5352}, 1997 {EMEP, 1999 #5353}, 1998 {EMEP, 2000
#5354}, 2000 {EMEP, 2003 #5355}, and 2003 {EMEP, 2005 #5356}). For this variable, as with forests, available
data was used to interpolate or extrapolate missing data.
Institutional variables
As noted above, we incorporate three different variables to estimate the influence, if any, of the protocols being
analyzed.
Membership: The first, and most usual, metric involves institutional membership using the date an agreement enters
into force for individual countries (designated sox1_ceif, sox2_ceif, nox_ceif, where ceif stands for country entry
into force). Such an approach compares how states behave as members of an institution with how those same states
or comparable states behave when they are not members of the institution. Membership data are coded as 1 in those
years in which a country is in force for a country and 0 in all other years.8
Institutional existence: We also include a second metric for institutional existence as measured by an agreement's
general entry into force date (designated sox1_teif, sox2_teif, nox_teif, where peif stands for protocol entry into
force). Institutional existence variables compare the behavior of all countries (whether members or not) before and
after an agreement takes effect. This, less typical, approach assumes that institutions have the potential to influence
all states, regardless of membership status. By including both membership and institutional existence variables, we
can distinguish three possibilities: a) institutions influence all states equally regardless of their formal membership,
b) institutions influence members but have no influence on non-members, and c) institutions influence both
members and non-members but to different degrees. Data on institutional membership and existence comes from the
LRTAP Secretariat {United Nations Economic Commission for Europe, 2007 #5077}.
Laggard-membership interaction: As noted previously, theoretical considerations lead us to be interested in
assessing whether institutional influence is most evident among non-leader member states. To evaluate this
hypothesis, we include a laggard-membership interaction term that is simply the product of our laggard and
membership variables.
Modeling methodology
Our model's structural form also requires attention. First, we use APC for our DV and include a one-period lag of
the DV as a right-hand side (RHS) variable. The use of APC (implemented here by taking the natural log of
emissions) largely "de-nationalizes" the DV of country-specific influences while inclusion of a lagged DV "detrends" the DV of time-specific influences. Estimating APC rather than levels or first differences of emissions
already removes much of the time-related and country-related trends.9 Including a lagged DV is a way of modeling
the intuition that the best estimate of the APC in emissions in one period is the APC of emissions in the previous
period. In essence, including the lagged DV thereby removes "inertia" that is due to the range of included and
omitted variables that influenced the APC of emissions in the prior period. Of particular note, these strategies reduce
the variance that RHS variables can even potentially explain (since such variance is captured by the lagged DV).
While making finding correlations with RHS variables more difficult, the strategy has the advantage of reducing the
risk of falsely attributing variation in the DV to RHS variables.
Second, a major influence on our choice of how to model emissions is the fact that our institutional variables of
interest as well as several other of our RHS variables (e.g., average accumulated exceedances, self deposition, and
deposition imports) are either known or suspected of not being independent or exogenous of emission levels. To
address these challenges, we adopt the Arellano-Bond dynamic panel data estimator rather than the more traditional
method of using panel-corrected standard errors (PCSE) {Arellano, 1991 #5380; Beck, 1995 #4731}. RHS variables
fail to be exogenous to the extent that their values in one period correlate with the model's error term in current or
8
Since international agreements almost always enter into force only after a certain number of states have formally
accepted them (through ratification or other procedures), all countries that contribute to an agreement entering into
force will exhibit identical membership dates.
9
Thus, in our dataset, for any given countries, emission levels at time period t are very likely to correlate with
emission levels at time period t-1. By contrast, changes in emissions levels at time period t are far less likely to
correlate with changes in emission levels at time period t-1. Using APC further reduces the likelihood of correlation
between emissions in time period t and in time period t-1.
Mitchell/Deane Page 14 of 25
prior time periods, with the former referred to as endogenous, and the latter as predetermined, variables. Arellano
and Bond {, 1991 #5380} developed a method that identifies the number of periods needed to cleanse a RHS
variable of such correlations. Lagging RHS variables the appropriate number of periods creates valid instruments
that are independent of the dependent variable and therefore meet the requirements for inclusion in a model. The
Arellano-Bond method can identify variables as endogenous or predetermined and can identify the number of lags
needed to generate valid instrument. The Arellano-Bond approach also handles missing observations in the interior
of panels more elegantly than does a PCSE approach. Arellano and Bond also developed tests that ensure the
validity of the model's specification particularly with respect to serial correlation of the errors, using both a direct
test of serial correlation and Sargan tests (similar to Hausman tests) of over-identifying restrictions {Arellano, 1991
#5380, 281-283}.
Third, Arellano-Bond procedures, like any involving dynamic panel data estimators, require identification of the
appropriate lag structures for RHS variables. When attempting to identify causal influences, especially when
modeling a DV in which endogeneity is a significant possibility, it makes sense initially to identify the appropriate
lag structures for RHS variables based on theoretical considerations. Selecting the lag period (if any) of RHS
variables based on the highest correlations with the DV would bias the model toward finding those variables to be
influential, even if that "influence" runs counter to theoretical expectations about when and where such influence
should be evident. On the other hand, the selection of lag structure must take empirical considerations into account,
since that is the method by which endogeneity is accounted for. We have adopted a "middle ground" in which all
RHS variables are included with both contemporaneous and one-period lags with the exception of RHS variables
related to GDP (i.e., gdp, gdp_capita, and trade_gdp) that are included with contemporaneous and two-period lags.
This model passes all appropriate specification tests for both the NOx and SOx emissions cases.
The empirical model
We seek to take advantage of available emissions data, within the constraints imposed by data availability of our
RHS variables. We generate a baseline model of SOx and NOx emissions (as APC) using only the lag of the DV.
We then model emissions for all 53 countries on lagged APC of emissions, population, GDP, GDP per capita, trade
openness, political status, forest cover, and laggard (i.e., lagged DV, gdp, gdp_capita, trade_gdp, free, forest,
laggard). We then run the same model with institutional variables -- membership, institutional existence, and
laggard-membership interaction terms -- included (sox1_ceif, sox1_teif, sox1_ceif*laggard, sox2_ceif, sox2_teif,
sox2_ceif*laggard or nox_ceif, nox_teif, nox_ceif*laggard). We follow this with a model that includes the issuespecific variables of average accumulative exceedances, abatement costs, self deposition, nor deposition import (i.e.,
aae_avg, sox_expense_percent, sox_self_depo, sox_depo_import, nox_expense_percent, nox_self_depo,
nox_depo_import). Adding these RHS variables reduces the number of countries to 36 because of the lack of
corresponding data on some RHS variables. We then re-run the model with institutional variables included.
The delineation of the models is summarized here.
Model 1: Lagged DV only
sox = f(sox_t-1); 53 countries
nox = f(nox_t-1); 53 countries
Model 2: Lagged DV plus control variables available for 54 countries
sox = f(sox_t-1, gdp, pop, gdp_capita, trade_gdp, free, forest, laggard); 53 countries
nox = f(nox_t-1, gdp, pop, gdp_capita, trade_gdp, free, forest, laggard); 53 countries
Model 3: Lagged DV plus control variables available for 54 countries plus institutional variables
sox = Model 2 + (sox1_ceif, sox1_teif, sox2_ceif, sox2_teif, sox1_ceif*laggard, sox2_ceif*laggard); 53 countries
nox = Model 2 + (nox_ceif, nox_teif, nox_ceif*laggard); 53 countries
Model 4: Lagged DV plus control variables available for 36 countries
sox = Model 2 + (aae_avg, sox_expense_percent, sox_self_depo, sox_depo_import); 36 countries
nox = Model 2 + (aae_avg, nox_expense_percent, nox_self_depo, nox_depo_import); 36 countries
Model 5: Lagged DV plus control variables available for 36 countries plus institutional variables
sox = Model 4 + (sox1_ceif, sox1_teif, sox2_ceif, sox2_teif, sox1_ceif*laggard, sox2_ceif*laggard); 36 countries
nox = Model 4 + (nox_ceif, nox_teif, nox_ceif*laggard); 36 countries
We predict the sign and strength of coefficients as follows:
 changes in the dependent variable will correlate positively and strongly with prior-period changes in
emissions (sox_t-1). Despite efforts to make observations as independent as possible, we expect the lagged
dependent variable to retain considerable explanatory power.
Mitchell/Deane Page 15 of 25

changes in economic activity (gdp) will correlate strongly and positively with changes in emissions levels.
Increases in economic activity are expected to produce relatively direct and immediate increases in
emissions.
 changes in population (pop) will correlate positively but weakly, if at all, with changes in emissions.
Population seems likely to be an alternate, and less compelling, proxy for levels of economic activity that
should be captured by gdp.
 changes in income/level of development (gdp_capita) will correlate positively and relatively strongly with
changes in emissions. Countries with higher average incomes will expend those resources in ways that lead
to increased pollution and that these influences will be mitigated only slightly by an increasing demand for
environmental protection (a la the environmental Kuznets curve literature).
 changes in trade openness (trade_gdp) will correlate weakly, if at all, with changes in emissions. The
extensive "free trade and the environment" literature has generated considerable debate but few robust
empirical findings about whether trade openness increases or decreases environmental degradation. The
relationship may depend on the types of trade and forms of environmental degradation involved. Trade may
increase emissions if imports are dominated by items (e.g., cars) whose operation increases emissions;
similarly, lower prices may increase total consumption. Trade may decrease emissions if imports replace
highly polluting domestic production; by lowering prices; it may also generate competitive pressures in
domestic sectors that improve the economic and energy efficiency of production. The various vectors of
influence between trade and environmental protection make it likely that trade's net effect on emissions is
weak and also make it difficult to estimate the sign of that net effect.
 political status (free) will correlate positively but weakly with changes in emissions. Political status is
largely invariant over time and therefore is not modeled in change terms. Since countries considered more
free receive higher Freedom House scores, and since freer countries are assumed to have both more vocal
citizens and more responsive governments, emissions should increase at a slower rate in freer countries
than otherwise similar countries that are less free.
 changes in ecological vulnerability as proxied by forest cover (forest) will correlate positively but only
weakly with changes in emissions. Declining forest cover should lead publics to mobilize and push their
governments (even in non-free countries) to take action to reduce this damage, including through reducing
emissions of the responsible acid precipitants. Both data quality and the inherent nature of forest cover,
however, leads us to expect that changes in forest cover will correlate weakly if at all with emissions.
 changes in ecological vulnerability as proxied by average accumulated exceedance (aae_avg) will correlate
positively but strongly with changes in emissions. High levels of exceedances should produce significant
pressure to address the problem. If exceedances get yet higher, that pressure should increase while, if
exceedances decline, we should expect that pressure to abate.
 changes in abatement costs (sox_expense_percent and nox_expense_percent) will correlate positively but
weakly with changes in emissions. Although marginal costs of abatement are monotonically increasing, our
analysis uses total abatement costs and thus can increase or decrease. We should expect emission levels to
decline as abatement (for a 5% reduction as a share of GDP) become cheaper, and to increase as abatement
becomes more expensive. Although we expect abatement costs to have a relatively strong effect, data
quality leads us to expect the observed correlation to be weak, at best.
 changes in self deposition (sox_self_depo and nox_self_depo) will correlate negatively but weakly with
changes in emissions. Increases in self deposition should increase pressure for unilateral action to reduce
emissions. Here we expect only a weak correlation because of the endogeneity of the variable -- increases
in emissions generate increased self deposition which counteracts the political vector just delineated.
 changes in deposition import (sox_depo_import and nox_depo_import) will correlate positively but weakly
with changes in emissions. Increases in deposition import should increase pressure for multilateral action to
reduce emissions and reduce unilateral efforts.
 laggard (laggard) will correlate positively and strongly with changes in emissions. The laggard variable is
invariant over time and therefore is not modeled in change terms. We expect emissions in leader countries
to decline more than, or increase at a slower rate than, those in otherwise similar laggard countries.
As denoted in the literature review above, the results from the three membership variables will address the debate
among realists, institutionalists, and constructivists with respect to institutional influence.
 membership (sox1_ceif, sox2_ceif, and nox_ceif) will correlate negatively and strongly with changes in
emissions. Membership is largely invariant over time and therefore is not modeled in change terms. We
Mitchell/Deane Page 16 of 25
expect emissions in member countries to decline more than, or increase at a slower rate than, those in
otherwise similar non-member countries.
 institutional existence (sox1_teif, sox2_teif, and nox_teif,) will correlate negatively and weakly with
changes in emissions. Institutional existence is largely invariant over time and therefore is not modeled in
change terms. We expect emissions in all countries to decline more than, or increase at a slower rate than,
emissions during otherwise similar periods of time.
 membership-laggard (laggard) will correlate negatively and strongly with changes in emissions. We believe
that laggard states that are members will show some differences from otherwise similar leader members.
We believe that they will show even greater differences from otherwise similar laggard non-members.
Interpreting results
Interpretation of results from a dynamic panel data estimation regression deserves comment. Because many of the
included variables (gdp, pop, gdp_capita, trade_gdp, forest, aae_avg, sox/nox_expense_percent, sox/nox_self_depo,
sox/nox_depo_import) are represented by two terms in the equation (first differences and one- or two-period lagged
differences), the overall effect of the variable and its statistical significance can only be identified through postestimation techniques that combine the effects of both terms.
In addition, coefficients are usually interpreted differently in light of inclusion of the lagged DV. The traditional
approach, which we adopt here, to interpreting the coefficients of RHS variables in such models is to identify "longrun" effects of the variables by correcting for the inertia in the time series. This entails using the lagged difference of
the DV as the "coefficient of adjustment," dividing the coefficients for all variants of each RHS variable in question
by the coefficient of the lagged DV and evaluating statistical significance by using the likelihood that that ratio is
equal to 0 by means of a chi-square statistic.10
Findings
Results from the regressions provide considerable evidence of institutional effects across all three protocols, make
sense of the divergence in conclusions of prior quantitative and qualitative analyses of the protocols studied here,
and represent a "proof of concept" that institutional influence across institutions can be compared convincingly.
Institutional variables: the independent variables of interest
The most interesting and consistent finding with respect to institutional influences is that membership does not
predict emissions in any of our model specifications in which laggard-membership terms are included. Institutional
membership is not statistically significant for any of the three protocols in the more refined country set (36
countries) or variable sets (limited or extended set of control variables) included in the model. We only present
Model 5 results here for clarity of presentation but, across all model specifications, other variants of institutional
variables are statistically significant. In light of the strength of the correlations in most cases despite the noisiness of
data on the laggard variable (see above), this strongly supports the argument that understanding institutional
influence requires modeling different types of potential institutional influence and the value of distinguishing
between leader and laggard members. In several instances, analyses that did not include a membership-laggard
interaction term would have either failed to find, or would have mis-estimated institutional effects, even though
those effects emerge when those terms are included.
Both our analytic priors and the empirical results suggest that Model 5 presents the most accurate results
(corresponding results from Model 3 are presented in Appendix III). In the Model 5 runs, institutional variables for
two of the protocols are statistically significant: the first SOx Protocol's entry into force (sox1_teif) and the NOx
Protocol's membership-laggard interaction term relative to non-members ((nox_ceif*laggard)+nox_ceif). Table 1
presents these results and suggest that institutions may be influential by different pathways, with different states, and
to different degrees.
Table 1: (Model 5 results for all variables are reported in Appendix IV)
Model 5
NOx
36 countries, all variables
Protocol
-1.5%
Effect of protocol entry into force
10
1st SOx
Protocol
-5.4%
2nd SOx
Protocol
5.4%
Thus, in the SOx Model 5, the "long run" coefficient for trade of -167.49 (-16749%) is generated by summing the
coefficients for the difference of trade and the two-period lagged difference of trade included in the equation (0.1434 and -64.3463, respectively) and dividing them by one minus the coefficient of the lagged DV (10.7699=0.2301), i.e., the coefficient of adjustment.
Mitchell/Deane Page 17 of 25
("xxx_teif" from model w/ interaction term)
Effect of leader/laggard variable on emissions
("laggard" from model w/ no interaction term)
Effect of membership across members and non-members
("xxx_ceif" from model w/ no interaction term)
Effect of membership on leaders
("xxx_ceif" from model w/ interaction term)
Effect of membership on laggards
("xxx_ceif+(xxx_ceif*laggard)" from model w/ interaction term)
* - Yellow highlighting identifies variables significant at the .05 level.
0.7%
-1.3%
-1.3%
-1.8%
-1.2%
2.6%
-0.4%
-0.3%
4.7%
-2.5%
-4.2%
-5.7%
Let us consider the interpretation of these results for each protocol in turn.
 Entry into force of the NOx Protocol generally (nox_teif) does not appear to have influenced state behavior.
To assess whether institutional membership influenced behavior, we ran our model with and without the
membership/laggard interaction term. The model without the interaction term identifies the overall effects
of "laggardness" and of "membership," assuming there is no interaction between the variables. For NOx,
the coefficient on laggard is not statistically significant: the average emissions of leaders cannot be
distinguished from those of laggards.11 The coefficient on membership also is not statistically significant:
the average emissions of members cannot be distinguished from those of non-members. Adding the
membership/laggard interaction term to that model allows us to assess the theoretical question, noted
above, of whether the protocol had different influences on leaders and laggards. Model results nicely fit
theoretical expectations: the coefficient of membership for leaders is not statistically significant but for
laggards it is negative and statistically significant, i.e., among leaders, members cannot be distinguished
from non-members, but among laggards, members can be distinguished by their significantly lower
emissions than non-members. Intuitively, this suggests the NOx protocol's effect was to lead laggard states
to "catch up" with leader states in ways that they would not have done otherwise, reducing their emissions
relative to corresponding non-members by an average of 2.5% per year.
 The first SOx Protocol appears to have led all 36 states included in the model -- both the 20 members and
the 16 non-members -- to reduce their emissions an average of 5% per year. Countries that joined did not
behave differently than corresponding non-members, either on average or based on whether they were
leaders or laggards but they did behave differently after the protocol entered into force than before.
 After taking the influence of the first SOx Protocol into account, the second SOx Protocol appears not to
have influenced behavior at all. No statistically significant differences are evident with respect to either the
second SOx Protocol's entry into force or country membership in that Protocol (whether evaluated
generally or by leader/laggard subgroups).
Since the motivating question for this article was "which worked better," a comparison of these results is also
warranted. Using Model 5 results, the most direct and convincing comparison is that between the first and second
SOx Protocols. The exact same DV is used (SOx emissions as APC) and the coefficients are estimated within the
same regression equation. The effect of the first SOx Protocol appears to have been to lead all European states,
regardless of membership, to reduce emissions by about 5% per year even after taking general time trends and the
influence of other RHS variables into account. By contrast, the second SOx Protocol appears not to have had any
influence. The NOx Protocol seems to have had about half the influence of the first SOx Protocol but only on
laggard members, causing them to reduce emissions by only 2.5% per year. In this case, the first SOx Protocol
appears to have had a larger influence on more states than the NOx Protocol but the analysis points to new questions
of institutional influence such as whether an institution that had a large influence on only laggard member states
should be considered more or less influential than one which had a smaller influence on a large group of states,
perhaps including non-member states.
Our model also provides a proxy by which to verify our claim that the problems were relatively similar in their
"level of difficulty" or malignity by examining the coefficient of the lagged DV. In Model 5 (i.e., among the 36
European states most directly affected by European acid precipitants), the coefficient of the lagged DV is almost
11
When relevant coefficients are examined in the model with an interaction term, the average emissions of leaders
cannot be distinguished from those of laggards either among members or among non-members.
Mitchell/Deane Page 18 of 25
identical, picking up 77% of the variance in the DV in the NOx case and 79% of the variance in the SOx case. These
figures tell us about the "speed" with which the time series for each country reaches equilibrium state, and that, once
all variables are included as in Model 5, that the trajectories of emissions of both pollutants is both similar and quite
slow to reach equilibrium levels. Another, more intuitive, interpretation of this result is simply that the "inertia" in
both SOx and NOx emissions that we have not captured by the variables we include in the model is both large and
relatively similar, i.e., for both pollutants the included RHS variables had the opportunity to explain only about onethird of emission variance with the rest already accounted for by other factors. The value of our analysis lies not in
any belief that these numbers reflect the "true" influence of these agreements but rather in demonstrating that the
effects of international institutions can be convincingly and meaningfully compared if the institutions are selected to
ensure they address relatively-similar problems, if the DVs employed are measured in similar units, and if the same
RHS variables are included in the model.
Other RHS variables
Although our interest here was in the institutional variables, the other variables included in the model as controls
deserve some attention. Two findings were particularly contrary to our original expectations. The first was that
economic activity (measured as GDP) was not statistically significant for either pollutant in any of the models
evaluated. We had strong theoretical priors that economic activity would drive emissions and strong confidence in
the quality of our data on both emissions and GDP. The absence of such a correlation has several possible
explanations: a) no correlation exists between GDP and emissions after accounting for other factors (which seems
unlikely), b) that including the lagged DV absorbs the correlation which would otherwise be captured by GDP, or c)
that the correlation is between the levels of GDP and emissions and that correlation was eliminated by transforming
emissions into APC form. We are inclined to believe that the third of these is the actual reason, and investigation of
that question will be the focus of future work.
The second unexpected finding is the frequency with which income (GDP per capita), trade (as a percent of GDP),
and population are statistically significant correlates of emissions, particularly for NOx. Contrary to the
environmental Kuznets curve literature, whenever income has a statistically significant correlation with emissions,
its sign is positive. Given that all the countries in the sample are relatively developed, they should be on the
downward sloping side of the famous $8,000 per person level of income at which income is alleged to correlate with
greater environmental protection {Grossman, 1995 #4562; Harbaugh, 2000 #4563}.
With respect to trade, as noted above, the debate is unresolved in terms of whether trade would tend to increase or
decrease emissions. Our results lend support to the notion that the net effect of trade is downward pressure on
emissions with the sign in all cases being negative, and with statistically significant correlations for most models and
pollutants. An economic interpretation would see this as evidence that trade leads to more efficient production of
goods, with corresponding benefits to the environment. A political interpretation would see this as evidence that
economic interdependence has environmental benefits in reducing transboundary pollution.
Population also has statistically significant correlations with emissions, at least in the 36-country runs. Here too,
however, the sign of the correlation does not fit comfortably with existing theory -- in the cases in which
population's correlation with emissions is statistically significant, it is strongly so (all below the .02 level of
significance) and has a negative sign. Although we can generate theories for why increases in population produce
decreases in emissions, such theories would appear to have a "post hoc" character that calls their plausibility into
question.
Our dual proxies for ecological vulnerability prove consistently significant. Our variable for forest cover (forest)
proves significant in all the SOx models and some of the NOx models. Given that the forest data was interpolated
from only three years of data, this correlation may be an artifact of common trending in the forest data.
The alternative proxy we introduced to improve on ecological vulnerability was average accumulative exceedances
(aae_avg) for all countries. That variable is consistent in both Model 4 (without institutional variables) and 5 (with
institutional variables) for both SOx and NOx but, contrary to expectation, the correlation with emissions is
consistently positive. This variable was introduced, following Sprinz and Vaahtoranta {, 1994 #2912}, as a proxy
for political pressure to make reductions. The most likely explanation of the strongly positive correlation found here
is that this correlation may be capturing the opposing direction of causality, i.e., that higher emissions in a country
lead to greater environmental damage in that country.
Evidence regarding abatement costs is mixed. In the NOx case, abatement costs are found to be significant (at least
at the .10 level) with positive signs. This fits with expectation -- as abatement costs increase, so would average
emission levels or, put differently, lower abatement costs lead to lower emission rates. The same findings are not
found with respect to SOx, however, either bringing the correlation into question or raising concerns about the
degree to which the data is sufficiently sound to find such a correlation.
Mitchell/Deane Page 19 of 25
Finally, strategic interaction variables (self_depo and depo_import) are consistently negative and usually statistically
significant, but with the correlation being stronger for SOx then NOx. Both self-deposition and deposition import
have negative correlations with lower emission levels. It is tempting to interpret this as saying that increases in
environmental damage, whether generated at home or abroad, lead to lower emission levels. But the findings just
presented regarding ecological vulnerability (aae_avg) suggest otherwise.
Alternative model specification
Appendix IV shows the results of initial efforts to evaluate the stability of our model specification. There, we
compare results from Model 5, just presented, with results from a model with the same countries but a more limited
set of variables as well as a model with that more limited set of variables run with all 53 countries for which such
data is available. A comparison of the results in a step-wise way clarifies some of the foregoing analysis.
Comparing Model 5 to a model that includes the same countries but a more limited set of variables (i.e., without the
pollutant-specific variables of aae_avg, xxx _expense_percent, xxx_self_depo, and xxx_depo_import) in which the
signs of institutional variables are generally the same but in which institutional influence appears quite different. In
that model, as already noted, membership alone again does not appear to have any influence for any of the protocols,
once other institutional variables are included. The NOx Protocol appears not to have any influence, the first SOx
protocol appears to have had both an "institutional existence" impact on all states regardless of membership as well
as a "laggard-membership" impact in which laggard members reduce emissions at a rate significantly different than
leader members. And, in this model, the second SOx protocol appears to have an influence through a laggardmembership mechanism as well, although it is significant at the 10% rather than 5% level. The differences between
these models is due simply to inclusion of additional variables in Model 5 and almost all those variables are
statistically significant when included. This suggests that the Model 5 results are more accurate, with the results of
Model 3 illustrating a spurious correlation due to the variables that are left out of Model 3 but included in Model 5.
Comparing this 36-country Model 3 with the same model that includes all 53-countries for which RHS variables is
available highlights additional sensitivities. In the 53-country Model 3, the NOx Protocol and both the SOx
Protocols appear to have laggard-membership influences on emissions. For all variables that are statistically
significant in both cases, the signs and the order of magnitude of the coefficients are the same, although the level of
statistical significance changes for many variables. The additional 17 countries included in this model introduce
significantly more variation across most variables than that among the original 36 countries, since most of those
countries are non-European countries. Efforts are underway to better understand the variation in results across
models and to decipher the sources of variation among the models and thereby improve model specification.
Conclusion
This paper has investigated the comparative influence of three international institutions as a means of illustrating the
importance of specifying quantitative models of institutional influence in ways that reflect the extant debate about
the different mechanisms and locations of institutional influence. Three major conclusions can be drawn from this
study.
First, we have developed and demonstrated an approach that allows modeling multiple institutions in sufficiently
comparable ways that comparison of the coefficients for institutional variables becomes plausible. A strategy that
combines selection of institutions to hold various factors constant, using annual percentage changes as the dependent
variable in all cases, including common right-hand side variables, and using statistical indicators of the "inertia" in
the DV provides a means to address the usual critique that institutions are "simply not comparable."
Second, we have shown the importance of including three different institutional variables to arbitrate between
arguments that institutions have no influence, that their influence operates on members and non-members alike, that
they operate on members more than non-members, and that they operate on laggard members more than leader
members. The differences in outcomes between models that do or do not include these variables and the statistical
significance of both the institutional existence and laggard-membership interaction terms confirm that their inclusion
is important to accurately identifying whether, and what type of, institutional influence is involved. Traditional
modeling that includes membership as the sole indicator of institutional influence is likely to miss or mis-estimate
institutional influence.
Third, the analysis has shown that the specific institutions investigated here vary not only in how much influence
they have but in the causal pathways by which they influence state behavior. Our results suggest that the first SOx
Protocol had a large influence on states through some mechanism (whether normative, technological, or economic)
that was not contingent on a state's membership. The NOx Protocol appears to have had less influence, only
influencing laggard member states and influencing those states to a smaller degree (in percentage terms) than the
first SOx Protocol. By contrast, the second SOx Protocol appears not to have had any significant influence on states,
regardless of either their membership or leader/laggard status. These conclusions are tentative and subject to
Mitchell/Deane Page 20 of 25
revision based on efforts to evaluate the sensitivity of the findings to different model specifications and country
inclusions and their proper interpretation requires making sense of some that run counter to existing theory. Yet,
overall, the results suggest that our approach allows more nuanced and accurate insights into institutional influence
than have been available through other approaches. Once confirmed these findings may help clarify the
inconsistencies that exist between previous quantitative and qualitative studies of these institutions.
Mitchell/Deane Page 21 of 25
Appendix I
Coding of Laggard variables
SOx "Leaders"
We identified leader states by combining evidence from Sprinz and Vaahtoranta regarding state negotiating
positions {, 1994 #2912, 100-101} with the list of SOx Protocol signatories that, as of 1989, had declared reduction
targets greater than the 30% required by the protocol from Levy {, 1993 #1818, Table 3.8, 118}. The following
countries were identified as "leaders" and coded as 0, with all other countries coded as 1, to create the laggard
variable for SOx.
Austria (Levy 1993; Sprinz and Vaahtoranta 1994)
Belarus (Sprinz and Vaahtoranta 1994)
Belgium (Levy 1993; Sprinz and Vaahtoranta 1994)
Canada (Sprinz and Vaahtoranta 1994)
Czech Republic (Sprinz and Vaahtoranta 1994)
Denmark (Levy 1993; Sprinz and Vaahtoranta 1994)
Finland (Levy 1993; Sprinz and Vaahtoranta 1994)
France (Levy 1993; Sprinz and Vaahtoranta 1994)
Germany (Levy 1993; Sprinz and Vaahtoranta 1994)
Hungary (Sprinz and Vaahtoranta 1994)
Italy (Sprinz and Vaahtoranta 1994)
Luxembourg (Levy 1993)
Netherlands (Levy 1993; Sprinz and Vaahtoranta 1994)
Norway (Levy 1993; Sprinz and Vaahtoranta 1994)
Russia (Sprinz and Vaahtoranta 1994)
Slovakia (Sprinz and Vaahtoranta 1994)
Sweden (Levy 1993; Sprinz and Vaahtoranta 1994)
Switzerland (Levy 1993; Sprinz and Vaahtoranta 1994)
Ukraine (Sprinz and Vaahtoranta 1994)
NOx "Leaders"
We identified NOx leaders as countries listed by Levy as having signed the NOx protocol and also made a further
30% reduction pledge {Levy, 1993 #1818, Table 3.2, 97}. The following countries were identified as "leaders" and
coded as 0, with all other countries coded as 1, to create the laggard variable for NOx.
Austria
Belgium
Denmark
Finland
France
Germany
Italy
Liechtenstein
Netherlands
Norway
Sweden
Switzerland
Mitchell/Deane Page 22 of 25
Appendix II
Treatment of missing data
For emissions, population, GDP (and GDP per capita), trade openness, political status, average accumulated
exceedances, membership, and institutional existence, data coverage for all countries was sufficiently extensive that
missing data was not interpolated or otherwise generated, and was left to be addressed through statistical techniques.
To generate annual data for forest cover based on data availability only for 1990, 2000, and 2005, we interpolated
missing values via quadratic polynomial regression for each country. In addition, we used Belgian and Czech
Republic forest cover to estimate Luxembourg and Slovakian forest cover, respectively, since data on the latter
countries were missing. We generated self deposition and deposition import data for missing years (data availability
only for 1985, 1987-1992, 1996, 1997, 1998, 2000, and 2003) using the same method as for forest cover, but we
retained observed values for the years with LRTAP reported data (with only three observed values for forest cover,
the quadratic fit always passed through the observed data points). We extrapolated forecasted values for years 2004
and 2005. For various countries, abatement costs (sox_expense_percent and nox_expense_percent) were not
available because emission levels in those years did not fall within the levels available on the cost curves. Missing
data was filled in from the nearest subsequent year for which corresponding country data was available, with
missing data at the end of the period filled in from the last year for which corresponding country data was available.
To develop a complete dataset that took advantage of all available emissions data required considerable
manipulation of data for states related to Czechoslovakia, Germany, the Soviet Union, and Yugoslavia. For the full
time period under study, emissions data was available on all the successor states to Czechoslovakia, the Soviet
Union, and Yugoslavia (although in the Soviet case, from separate sources) and on Germany but not on the former
West and East Germany. For ease of exposition, we refer to Czechoslovakia, Germany, the Soviet Union, and
Yugoslavia as "aggregate" states, and their successor or predecessor states as "constituent" states. Given the
different treatment of aggregate and constituent states by various data sources, generating independent variable data
that corresponds to the dependent variable proved challenging. For political status, self deposition, and other
deposition, the constituent states to Czechoslovakia, Yugoslavia, and the Soviet Union were filled in with the
corresponding data for those states in the pre-dissolution period; aggregate German data was filled in with the
average of West and East German data for the pre-reunification period. For GDP, GDP per capita, and trade
openness, aggregate German data is available, but data for successor states to Czechoslovakia, Yugoslavia, and the
Soviet Union were generated by determining each constituent states' percentage of the aggregate state's GDP over
the 1990-1992 period and then applying that percentage to the (known) aggregate state's GDP during the period
prior to 1990. For abatement costs, constituent state data is available for Czechoslovakia, Yugoslavia, and the Soviet
Union but aggregate German data was filled in with the average of West and East German data for the prereunification period.
Mitchell/Deane Page 23 of 25
Appendix III
Model 3 results
Model 3:
53 countries, "core" variables
Effect of protocol entry into force
("xxx_teif" from model w/ interaction term)
Effect of leader/laggard variable on emissions
("laggard" from model w/ no interaction term)
Effect of membership across members and non-members
("xxx_ceif" from model w/ no interaction term)
Effect of membership on leaders
("xxx_ceif" from model w/ interaction term)
Effect of membership on laggards
("xxx_ceif+(xxx_ceif*laggard)" from model w/ interaction term)
* - Yellow highlighting identifies variables significant at the .05 level.
NOx
Protocol
-1.3%
1st SOx
Protocol
3.2%
2nd SOx
Protocol
4.1%
2.1%
-0.6%
-0.6%
-2.4%
-7.1%
-5.4%
0.7%
-4.3%
-1.7%
-2.7%
-9.8%
-19.1%
Mitchell/Deane Page 24 of 25
Appendix IV: Model results
Long Run Effects
Model 5
(36 ctry)
NOx
Model 5
(36 ctry)
1st SOx
Model 5
(36 ctry)
2nd SOx
Model 3
(53 ctry)
NOx
Model 3
(53 ctry)
1st SOx
Model 3
(53 ctry)
2nd SOx
Model 3
(36 ctry)
NOx
Model 3
(36 ctry)
1st SOx
Model 3
(36 ctry)
2nd SOx
forest
2.9%
7.7%
3.5%
12.3%
10.4%
11.3%
pop
-0.8%
-1.2%
0.0%
0.4%
-1.4%
-2.1%
gdp
0.0%
0.1%
-0.0%
-0.1%
-0.0%
0.1%
trade_gdp
-28027.0% -16750.0%
-42976.4% -92152.6%
-2709.2% -55435.0%
gdp_capita
3906.0% -816.60%
4442.2% 11108.9%
3876.7% 1376.6%
free
0.1%
-1.4%
0.4%
-4.9%
-1.0%
0.3%
laggard
1.8%
1.3%
4.1%
3.5%
1.0%
5.4%
aae_avg
0.0%
0.1%
xxx_expense_percent
16.8%
-47.0%
xxx_self_depo
-91.9%
-154.0%
xxx_depo_import
-108.0%
-235.4%
xxx_teif
-1.5%
-5.4%
5.4%
-1.3%
3.2%
4.1%
-1.8%
-7.7%
5.2%
xxx_ceif
-0.4%
-0.3%
4.7%
0.7%
-4.3%
-1.7%
1.0%
-0.5%
6.7%
xxx_ceif*laggard
-2.0%
-3.9%
-10.5%
-3.4%
-5.5%
-17.4%
-2.7%
-11.8%
-19.2%
(xxx_ceif*laggard)+xxx_ceif -2.45%
-4.2%
-5.7%
-2.7%
-9.8%
-19.1%
-1.7%
-12.3%
-12.5%
Coefficients with statistical significance greater than 5% are designated with yellow highlighting, those with statistical significance between 5 and 10% are
designated with orange highlighting. xxx_ represents nox_ for the NOx columns and sox_ for the SOx columns.
Mitchell/Deane Page 25 of 25
References