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Policy Osmosis: Rethinking Diffusion Processes
when Policies have Substitutes
Federica Genovese
Stanford University
[email protected]
Florian Kern
University of Konstanz
[email protected]
Christian Martin
University of Kiel
[email protected]
March 20, 2014∗
Abstract
Existing analyses of policy diffusion operate under the assumption that diffusion processes can be observed by focusing on the diffusion of one policy. In this paper, we
take issue with the notion that these processes entail only the same policy diffusing
across units. Instead, we argue that interdependence can take the form of countries
substituting a policy with different instruments that are still interrelated to that policy
elsewhere. We call this ‘policy osmosis’ because the boundaries of political units can
exhibit a certain degree of flexibility to accomplish the goal of the policy being diffused
while staying open to the mechanisms that trigger the introduction of a different policy.
Methodologically, considering just one policy that is diffused although alternatives are
being implemented risks overestimating the one–policy diffusion and underestimating
the overall range of interdependence. Substantively, we argue that strategic political
considerations determine the degree to which interdependence results in policy diffusion
(same policy) or policy osmosis (policy alternatives). We use the case of environmental
policies to test our claim.
∗
We are thankful to participants at the EPSA 2012 conference in Berlin for useful and constructive
comments, and for Xun Cao and Georg Zachman for kindly sharing data. This is a working draft and
comments are very welcome, but please do not cite without the authors’ permission.
1
Introduction
The notion of diffusion is by now firmly established in mainstream political science research.
Hays et al. (2010) classify more than 200 articles that consider topics and applied work under
‘contagion’, ‘spatial interdependence’, or ‘network dependence’ (p. 406 and webappendix).
Yet, diffusion, most notably of policies, is generally cast as the diffusion (or non-diffusion)
of the same policy. Of the reviewed literature in Hays et al. (2010), no published research
concentrates mainly on indirect processes that diffusion takes through other policies.1 This
is somewhat surprising given the width and breadth to which the mechanisms of diffusion
have been discussed. We argue that these mechanisms are by no means limited to the same
policy diffusing. Rather, if policies have externalities that spill over to units that are somehow connected to the focal unit, these externalities may very well lead to a policy reaction
that is the result of a diffusion process but can be observed as the implementation of an
alternative policy.
We call such processes osmosis to capture the idea that the boundaries of jurisdictions
(e.g. national borders) may not exhibit uniform permeability to policies. Accordingly, political, institutional or cultural factors may compel governments – subject to an externality
from a policy implemented elsewhere – not to react with the same policy. Rather, a government may respond to policy spillovers by turning to a substitute or complementing, i.e.
alternative policy.
We claim that, if the processes of policy osmosis are present but are not modelled in
a policy diffusion context, the consequences are potentially severe. First, if the long-run
steady state only includes the effects of one policy diffusing but not the potential diffusion
of the alternative policy, the overall effect of interdependence risks to be underestimated.
Second, if one analyzes the independent diffusion of one specific policy without assessing
possible alternative policy choices, the magnitude of the diffusion of that one policy risks to
be overestimated.
In this paper, we use the environmental policy area to illustrate these dynamics. Empirically, we use two different cases. In a first step, we start with varying the analysis of an
established article in the literature to see whether our setup changes the substantive results.
We test our theory against Hugh Ward and Xun Cao’s argument on the diffusion of green
taxes in OECD countries (Ward and Cao, 2012). We re-analyze their data by adding a
spatially lagged variable for environmentally relevant subsidies, which constitute an interlinked yet separate policy in our spatiotemporal autoregressive regressions. We show that the
1
More specifically, neither of the titles in the papers mentioned by Hays et al. nor the abstracts of a
selected subsample address concepts such as alternative diffusion or substitute policies.
1
new results weaken the effect of other plausible determinants of green tax policy diffusion.
In other words, incorporating policy osmosis – defined as simultaneous alternative policy
choices – in the analysis captures a more adequate and recalibrated image of international
green policy interdependence than found in Ward and Cao (2012).
In a second step, we test the osmosis process in the more specific context of climate
change mitigation. We present an original dataset of alternative CO2 market instruments
between 2000 and 2010 in EU neighbouring countries. We show that including the choice
for carbon trading as a reserve policy alongside the diffusion of carbon taxes weakens the
significance of the international explanations for the latter. This empirical evidence indicates
that, while some countries have de facto adopted carbon taxes following international trends
(mainly EU conditionality or EU ‘mimicking’), some governments have embraced other green
agendas and diversified their environmental policy portfolio by focusing on permit trading,
which receive less domestic resistance than carbon levies (Stavins, 2003). Together with the
precedent findings, our results paint a more nuanced picture of the benefit–cost analyses of
domestic governments vis–à–vis environmental policy instruments.
We proceed as follows: In section 2 we review the core literature on interdependent
decision making and policy diffusion, concentrating in particular on the realm of pollution
abatement policies. We then present the main theoretical contribution, focusing on the microfoundations of interdependent policy choices that may result in the implementation of
alternative policies. The next section delineates the empirical strategy to investigate policy
osmosis and discusses the empirical examples. We first augment the analysis of Ward and
Cao (from now onwards, W&C), and then we turn to our own data on carbon taxes and
trading permits as alternative policies. The final section concludes.
2
Osmosis in perspective: the state of diffusion research
The study of spatiotemporal interdependence has received renewed interest in the past two
decades. Understanding how the outcomes, actions, or choices of some unit–times depend
on those of others is of fundamental importance for political inference. However, investigating these dynamics directly is rather challenging. Our goal of dissecting the phenomena
that occur in specific instances of policy competition hence takes over some of the discussions in the ongoing empirical and methodological discovery of complex domains of policy
interdependence (Boehmke and Witmer, 2004; Shipan and Volden, 2008; Franzese and Hays,
2008b).
2
The political science literature that focuses on diffusion refers to both static as well as
dynamic concepts of equilibrium: ‘interdependence’ (Case et al., 1993), ‘adoption’ (Berry
and Berry, 1990) as well as evolutionary phenomena under the terms of ‘emulation’ (Dolowitz
and Marsh, 2000), ‘adaptive innovation’ (Mintrom, 1997), or simply ‘learning’ (Gilardi, 2010;
Grossback et al., 2004; Rose, 1991). It is agreed that a conception of diffusion is made open
to interpretation in order to allow a flexible definition of time and space, or alternatively
to simplify the intricate nature of political decision-making. Nonetheless, it is increasingly
evident how this concept is left too broad to capture the whole spectrum of specific types of
phenomena that speak to the interconnectedness of policy instruments.
The concept of diffusion in the social world seems to have taken off from the precise
definition of the phenomenon in the natural sciences. We claim that it is worth lingering
on original inspiration because a refined conceptualization of the type of diffusion at hand
may facilitate the understanding of the strategic decisions undertaken under policy interconnectedness. Consequently, we argue that the concept of ‘osmosis’ may be more theoretically
generous – and perhaps more insightful – in some instances of policy adoption. We believe
this nuance is particularly important when the analysis of complex diffusion is not merely
reserved to mechanical processes or clustered events (Elkins and Simmons, 2005), but rather
focuses on a hybrid of strategic and evolutionary states of decision-making.2
In a standard ‘policy diffusion’ scenario, one may think of a unit of analysis, e.g. a
country, featuring a certain political topography that differentiates it from other countries
within a group of such units. Assuming that country i has adopted a new policy which to
some extent has externalities affecting country j, and the latter had to decide to the rest
of other states how to behave in relation to this new idea, classical diffusion theory would
program the actions of such state as a binary distribution: “adopt” or “not adopt” the policy,
or – in terms of policy levels – “more” or “less” policy. We claim that this mechanism may
2
In its original biological meaning, ‘diffusion’ describes a passive process of molecular motion between
units, e.g. cells, from a region of high molecular concentration to one of low molecular concentration.
Evidently, policy diffusion is anything but a passive process: it is not the adoption of country i’s policy
itself that automatically triggers country j’s adaptation. Rather, it is the choice of country j to react to
country i’s policy decision and its emanating consequences. As with essentially any attempt to model social
phenomena, policy diffusion therefore paints a simplified, highly abstracted picture of reality. Yet, even if the
biological phenomenon is not a fully adequate analogy, the metaphor of units (cells, states, municipalities,
etc.) interacting and adapting to interdependence-related pressures (molecular or political, economic, etc.)
is intuitive. Thus, we choose to remain consistent with the existing analogy and take a natural science
phenomenon to describe the more complex type of interdependence that we call ‘osmosis’. According to
the Encyclopedia Britannica Online (2013a,b), osmosis describes the diffusion of molecules between units,
e.g. cells, along semi-permeable membranes. With the risk of oversimplifying the natural matter, the latter
membranes separate the units and make sure that not every particle can passively enter – and potentially
alter – the ‘domestic’ milieu of a unit. As such, osmosis is a type of diffusion, occurring between adjacent,
but separated cells, each concerned with their inner molecular balance given outside pressures.
3
work to explain incumbent behaviors in some policy areas, namely where little is implied of
substitutive (and, to some extent, complementary) policies. However, when alternative policies are existent and play a relevant role in the debate, diffusion becomes a more complex,
‘osmosis-like’ type of process.
Drawing on the lesson from the natural sciences and moving towards a disentangled
meaning of policy learning, the distribution of actions may as well be more diverse, in
the sense that there may be more strategic options to choose from than a simple dichotomous adoption/non-adoption scenario. This observation does not necessarily require reconceptualizing the characteristics of the policy under analysis. As Franzese and Hays
(2008a,b) point out, scaling the options in the diffusion scenario rather implies integrating the ignored side-effects of political instruments that belong to the same ‘policy universe’.
However, going beyond the theoretical claim of Franzese and Hays, we will argue that allowing for a process of “policy osmosis” may inform the analysis of strategic decision-making
not only over multiple policy options, but also for policy instruments that differ across implementation arenas or even across policy issues.
2.1
Bridging policy diffusion debates and policy osmosis
Taking into account unit interdependence in social science research dates back to the 19th
century when Sir Francis Galton famously pointed out that observing the same outcome
across units is not necessarily linked to a common influence but can also be explained by
the units influencing each other (Galton, 1889; Ross and Homer, 1976; Jahn, 2006). Political scientists have taken up this notion and modeled interdependence in fields as different
as, for example, democratic transitions (Starr, 1991; Starr and Lindborg, 2003; Brinks and
Coppedge, 2006; Gleditsch and Ward, 2006), tax competition (Wilson, 1986; Wildasin, 1989;
Swank, 2006), foreign economic policies (Elkins and Simmons, 2004; Martin and Schneider,
2007), and public policies (Daley and Garand, 2005; Prakash and Potoski, 2006). In American politics, there exists a rich tradition of diffusion research among the American states
(starting with Walker, 1969), but also among cities (starting with Crain, 1966).
These and other works of the diffusion variant share a common understanding of interdependence: Units are linked by some measurement, and this link can vary in degree
according to the theory underlying its construction. The most common approach for linking
units is geographic proximity, for instance a shared border. The assumption behind such a
link is that units influence each other more the closer they are to one another. But of course,
‘closeness’ need not be expressed in terms of geographic proximity. Beck et al. (2006) make
a compelling case that “space is more than geography”. In their paper, they use both trade
4
flows and geographic distances to establish different connections between countries. In a
similar vein, cultural similarity has been used in accounts that use policy learning as a diffusion mechanism (e.g. Levi-Faur, 2005; Meseguer, 2005). Martin and Schneider (2007) employ
trade weighted averages of trade restrictions in other countries to model interdependence in
economic policy making. Here, as in Beck et al. (2006), states are “closer” to one another if
they have closer trade relations.
There is, however, an important caveat to modeling policy choices as interdependent.
Even though policies may be similar in different jurisdictions, these policy choices may have
an underlying common cause instead of being brought about by unit interdependence – Galton’s problem reversed. In the words of Levi-Faur (2005, 23): “[...] what may on the surface
look like a diffusion process (a forest of umbrellas in rainy day) is not necessarily driven by
mechanisms of diffusion.”
Against this backdrop, Franzese and Hays (2006, 2007, 2008b) have argued that one
of the more important issues in modeling unit interdependence empirically is to distinguish
between common exogenous shocks that may entice a similar reaction and actual interdependence. To do so requires carefully modeling domestic influences and circumstances that
are common to all units or a subgroup of units. In addition, researchers might add period
and/or unit dummies to control for common trends and shocks in their data.
As the notion of policy interdependence became more firmly established in the literature, research becomes increasingly interested in the theoretical and empirical implications
of the described diffusion processes. In particular, the idea of unconditional diffusion processes has come under scrutiny (Martin, 2009; Gilardi, 2010; Neumayer and Plümper, 2012).
The argument of conditional diffusion boils down to the notion that policy interdependence
need not result in uniform policy outcomes across units. Rather, it depends on domestic
conditions in unit j how the effect of policy choices in unit i affect policy choices in unit j.
Our notion of policy osmosis is related to the concept of conditional diffusion but expands on it. Rather than focusing on just one policy we allow for interdependent policy
choices that involve alternative policies. The reasons for alternative policy choices are similar to the ones discussed in the conditional diffusion literature – different units may have
different sensitivities to external influences.3 However, these different sensitivities – or the
variation in the degree of permeability of unit boundaries, as we call it with reference to
the notion of diffusion in the sciences – need not be reflected solely in the level of a policy
diffusion effect. If policies have alternatives, it is equally conceivable and theoretically plau3
As (Gilardi, 2010, 651) puts it in the context of policy learning: “All policy makers need not be equally
sensitive to the experience of others.”
5
sible that these alternatives are implemented instead of (or alongside) the originally diffused
policy. In this way, there is not only unit interdependence, but also policy interdependence.
We now turn to the diffusion of different policy instruments in the policy area of pollution
abatement to highlight this point.
2.2
The case area of environmental policy
The environmental policy compound is a field of comparative research that is increasingly receiving attention among political economy researchers (Cao et al., 2014). While the diffusion
of environmental policy instruments has been discussed for about two decades (see review
in Urwin and Jordan, 2008), the literature has recently sped up in the process of identifying policies tackling issues such as waste disposal, water management and, most noticeably,
climate change. Territory-oriented policies such as local programs and small–scale projects
have increasingly caught the attention of researchers interested in the local mechanisms of
green policy diffusion (Fredriksson and Millimet, 2002). And yet, international pollution
abatement still constitutes a central level of environmental preservation. Thus, we will focus
only on national policies for the environment.
According to economic policy research (Stavins, 2003), the instruments for pollution
mitigation can be subset into four broad categories: standard controls, industry–tailored programs such as subdues and subsidies, industry–permissive programs (cap-and-trade schemes),
and environmental taxes (see also McKibbin and Wilcoxen, 2002; Aldy et al., 2003; Goulder
and Stavins, 2011). Classically, the evolution of these instruments has been studied separately. For example, Busch and Jörgens (2005) investigate energy taxes in the OECD and
Eastern Europe between 1948 and 2000, and show that diffusion has taken off following the
Nordic European front-runners. Zhang and Baranzini (2004) derive inferences for North
America based on the diffusion of energy taxes from the US to Canada. Focusing instead
on emission trading systems, Betsill and Hoffmann (2011) document the spread of cap-andtrade in different policy ‘venues’ due to emerging networks across industries and civil groups.
While all these works hint to the fact that each environmental policy has diffused in a
non-spurious way, they do not control for the spilling effects of other similar policies being
simultaneously implemented in foreign countries. Only few studies have in fact addressed the
strategic decision tree that links these policies together, and virtually none has modeled the
substitution incentives for countries to implement an environmental policy that is different
from what neighboring countries do.
Among the most nuanced arguments on complex policy interdependence, Aldy et al.
(2003) suggest that policy makers use hybrid policies because policy ‘shapers’ have heteroge-
6
nous interests that meet in a mixed policy equilibrium. Put differently, while most dirty firms
have historically resented environmental regulation, some – e.g. Enron in the US, or British
Petroleum in the UK – have embraced the green agenda in order to become leaders in new
technologies and environmental markets that at one point were still under–invested. This
argument is indirectly addressed also in theoretical explorations of green lobbies (Chaudoin
and Urpelainen, 2014). Accordingly, we may think that mixed policy attitudes are correlated
with the degree of linkage across different policies, and that these should trickle down to
‘osmosis–like’ governmental decisions.4
In the toolkit of green market instruments, the motivations for separate policies may
be intertwined. For example, environmental taxes induce dirty firms to reduce emissions up
to the level of the charge, so their justification is based on the industries’ price elasticity
(Pigou, 1924). Tradable permits, instead, induce dirty firms to exchange allowances capped
to a limited level, but their justification is often based on whether firms have the capacity
to exchange permits, so whether they are already ‘burdened’ with charges on not. Arguably,
both green taxes and permit trading can decrease pollution if efficiently implemented. However, if only one instrument is preferred and other countries have adopted one, a government
may decide either to pursue the policy that targets emission prices (tax) or emission quantities (cap–and–trade) based on the efficiency of neighboring policies.
This interconnectedness across green market policies is not subtle and in fact it has not
gone fully unobserved. Holzinger et al. (2011) indicate that several types of environmental
policies have emerged in modern Europe despite known cases of industrial malaise. However,
not all policies have survived with the same strength in each country. Taking a more refined
look, Holzinger (2001) shows that environmental programs in Germany have resulted in more
green investments (e. g. subsidies for waste treatments) rather than stringent environmental
taxes. By contrast, countries like the UK have been much less keen on engaging in subsidies,
and have been the leader of emission trading markets (Victor and House, 2006).
Similarly, Bernstein et al. (2010) highlight that governments around the world are increasingly debating the carbon tax and cap-and-trade options as alternative instruments.
For example, the United States Congress has so far only addressed (but failed to implement)
a nation–wide trade option, while countries like South Africa has exclusively debated the
4
The ‘mixed policy’ observation emerges also in the literature that studies the environmental decisions
adopted by international organizations. For example, Victor and House (2006, 2110) describe the European
Union’s emission trading scheme as a ‘safety valve’ for times in which green taxes cannot be tolerated.
Also, looking at the United Nations Convention on Climate Change, Genovese (2014) distinguishes countries
that prefer ‘liberalist’ policies from countries that prefer ‘protectionist’ policies, based on the evidence that
the former want to rip off the benefits of credits and permits while the latter are less comfortable with
environmental markets.
7
carbon tax.5 Some have switched their positions from one policy to the other, as if these
were policy substitutes. A point in case is Australia, where the national parliament agreed
on the introduction of a fixed-price carbon tax commencing July 2012. Then in November
2013 the executive scrapped the carbon tax in favour of a ‘gentle’ emission trading scheme.6
But was the Australian decision at all influenced by the carbon policy discussions in other
countries? And if so, were the foreign carbon taxes or the carbon trading implementations
that mattered for Australia’s switch? Whether environmental policy interdependence is explained by alternative policy interdependence has not yet been directly tested.
In short, the scholarly discussions suggest that the strategic link between different countries’ environmental instruments is an intuitive, yet understated international reality. Be it
partial complements or imperfect substitutes, separate green policies leave room for stakeholders to choose their most profitable instrument. What is left unexplained is whether this
logic is in place across space and time. Expanding on this last point, we now move to the
theoretical expectations on policy osmosis.
3
Osmosis in action: unpacking policy complexity
3.1
Theoretical expectations
In light of the literature reviewed, policy osmosis can be thought of as a special case of policy
interdependence which is in turn brought about by externalities emanating from policy decisions.7 Policy externalities link political units to each other, as they are the reason why a
policy implemented in one unit has an effect on another unit. In the absence of externalities,
there is no reason why a policy implemented in unit i should have an effect on unit j.
So, under policy osmosis, we should observe a situation in which the introduction of
a policy in country i increases the probability that a functionally equivalent policy will be
introduced in country j. We are thus within the framework of what Most and Starr (1990)
have dubbed “positive spatial diffusion”, while keeping with Franzese and Hays (2008b) no5
South Africa should have implemented a carbon tax in 2010 but this was recently delayed to 2016.
See South Africa Treasury. 2013. Tax Policy Paper. http://www.treasury.gov.za/public%20comments/
Carbon%20Tax%20Policy%20Paper%202013.pdf. Accessed 17 March 2014.
6
The tax would have transited to cap-and-trade on July 2015. See BBC. 2013. Australia carbon tax:
Abbott introduces repeal bill. http://www.bbc.com/news/world-asia-24923094. Accessed 17 March 2014.
7
Or non-decisions. It is worth mentioning that the whole discussion on policy interdependence centers on
actions of policy makers. In a situation of disequilibrium, however, policy non-decisions may bear externalities
just as well. Think of the US budget stand-off in 2011 where political deadlock almost resulted in a nondecision that surely would have had tremendous repercussions not only in the US. Another example is
inaction in severe economic crises where non-action of policy makers results in higher costs for the country
in crisis, but also for actors outside the country, e.g. the country’s trade partners or holders of its foreign
debt.
8
tion of policies being used as strategic complements rather than substitutes. However, we
contend that the same policy is not likely to spill over into other political units if those
units are not politically conducive to their reception. Rather, only the functional core of
the policy is preserved, while political-institutional conditions in j (historical contingencies,
special interest groups, upcoming elections, sensitive policy areas) may lead to the adoption
of a policy that is different yet functionally equivalent.
To illustrate, think of regulatory competition over a hypothetical tax that has certain
unintended consequences (e.g. environmental repercussions). Lowering taxes on a mobile
factor K in i (i.e. labour in a polluting industry) attracts this factor from spatially connected unit j. We have a negative externality of the policy that i introduced, and j is now
in disequilibrium because its tax base is lower and its tax policy is no longer optimal. Is it
politically feasible for j to now lower taxes on K? The answer is: it depends. Basinger and
Hallerberg (2004) have pointed to the domestic costs that may prevent races to the bottom
in a situation like this, i.e. unit i does not introduce tax reductions on K.
An alternative reaction to the functional pressure emanating from unit i’s decision on
j would be to leave the tax on K untouched because it is politically sensitive, but turn to
another source of taxation in order to adjust from the disequilibrium situation. For example,
i could tax non-elastic consumer goods more heavily. This would mitigate the functional
pressure yet avoid the political costs associated with lowering the taxes on K. If the effects
of i’s policy decision are not primarily fiscal, j may turn to active green investment policies
in order to address these political concerns.
What matters here, then, is first the fact that all the mechanisms of diffusion may be
given but the effect may not be observable if we only look at the same policy and whether or
not it “diffuses” across political units. If a policy is introduced in j as a reaction to a policy
that is introduced in i, and if there is interdependence between i and j with respect to that
policy, but these policies are not the same, we address this phenomenon as osmosis.
Second, if such policy osmosis exists, we are interested in when it is more likely to be
present. Obviously, this is the more important question than the conceptual preliminaries
outlined above. The likelihood of policy osmosis, i.e. introducing a substitute policy in j,
might depend on several factors: if such substitute exists; if j is conscious about the existence of such substitute; if j prefers the substitute over the policy adopted by i; if j has the
technological know-how to implement the substitute in case the latter is more demanding
on this level than the policy adopted by i; if others have adopted either the policy or its
substitute already; and if j can “learn” about both policies and their effectiveness.
From the perspective of positive political economy, policy osmosis will be more likely
to occur if the political costs of adopting a policy vary across units. To our case of environ9
mental policy, assuming functional or political incentives to achieve pollution abatements
that are triggered through an interdependent process, the actual choice of a specific policy
to achieve this goal will be predicated partly on its political cost. Following the example,
policy makers may well have learned through an interdependent process that the best way
of abating pollution is a stiff taxation of carbons emissions. However, if the political costs
of implementing such policies are too high, the policy maker may well turn to an alternative
policy, even though this policy may not be the first best solution.
Ultimately, we expect countries to choose their ideal policy based on the ways in which
domestic actors conditions the influence of international factors, which should include not
only the internationally diffusing policy but also its alternative policy. So, while still investigating the impact that domestic factors have on transnational relations, our approach is to
endogenize alternative policy diffusion in our analysis. Empirically, we expect estimations
to change significantly as we include the ‘twin’ policy dynamics in the estimation equation.
More specifically, we predict that the influence of the singularly diffusing factors diminishes,
while the overall explanatory capacity of the model increases.
If we found policy osmosis to be an active phenomenon, our results would suggest that
models of international diffusion that do not account for the parallel trends in alternative
policy realms are biased compared to models that integrate them. This means that international variables like geographical distances and ideological alliances, which are said to spread
consistently across international networks, could have less significant impacts on, say, taxation choices than previously found if the alternative policy is strongly conditional. By the
same token, modeling policy osmosis should bring more support for the domestic variables
that are often found too rough or too static in standard large–N analysis (Jensen and Spoon,
2011).
3.2
Considerations for the application to environmental policies
Before moving to discuss the empirical estimation of policy osmosis, we need to pause on the
case–specific explanations of diffusion first, which in our study is the international diffusion
of environmental policies. The literature tells us that the international determinants of environmental policy adoption can be of different kind: Holzinger and Sommerer (2011) indicate
the importance of international integration and institutional harmonization as a source of
green policy diffusion. Tews et al. (2003) point to trade connections and ‘wealth sharing’
as a reason why countries may be willing to learn environmental practices from each other.
Finally, in the environmental context one cannot dismiss the importance of geographical
distance. As many environmental goods are non-excludable public goods that do not know
10
frontiers, one country acting on environmental preservation may have positive externalities
on a physically neighbouring country and therefore incentivize that other country to free
ride on the environment.
These international explanations of environmental policy interdependence were only
recently lent to empirical modelling, partly due to the missing methodology up to Franzese
and Hays. One unique case is the Ward and Cao (2012) article on green taxation. In their
analysis, W&C evaluate the statistical impact of multiple international factors on the diffusion of green taxes in the OECD countries. In their framework, the international variables
boil down to three separate phenomena: geographical distances, institutional environmental
integration, and trading networks.8 W&C then estimate the diffusion effects with both uniparametric and multiparametric spatiotemporal autoregressive models (STAR and m-STAR)
presented in Franzese and Hays (2008a) and Hays et al. (2010).
The findings in W&C are highly significant for virtually all explanations. Their concluding remark that international factors have mattered greatly in the cross-national evolution
of green taxes is in line with other environmental diffusion claims (e.g. Jänicke and Jacob,
2004). However, in light of the discussion of green policy substitution in the economic literature and our osmosis intuition, one might ask whether their story is robust to an analysis
that incorporates interactions on other interrelated green policies. Moreover, the strikingly
homogenous results on international factors that W&C obtain at the p-value < 0.05 raise
questions on how foreign influences are really captured if modelled in the STAR setup. As
the authors themselves point out in their conclusion,
“The way information is combined at the domestic level is probably complex, and building a coalition
for change may take time and involve forms of lobbying power not well captured by spatial models
of legislative politics such as the one we employed here” (W&C 2012, 1094)
We think the authors’ assertion begs for a re-evaluation of their models by testing political
decisions that may not be channelled through the international variables they choose but
that may still emerge from foreign countries. While this is not strictly an analysis estimating
legislative domestic politics as they intend, we believe osmosis can help at least indicating
whether strategic domestic decisions are in action from one country to another.
One should note that W&C are certainly aware of the potentially relevant mechanisms
from alternative policies. In the footnotes of their article (ft. 27), they write,
8
The actual variables are geographical distances, integration in international environmental organizations, trade volumes, plus networks among more developed economies, competition for export markets, and
ideological discrepancies across countries.
11
“Although cap-and-trade emissions quotas have more predictable effects than pollution taxes given
difficulties with estimating elasticities, green taxes raise revenue that can be used to subsidize environmental cleanup or to correct other distortions in the tax system, and they may be the only
practical option with regard to citizen or consumer behavior.” (W&C 2012, 1098)
The strong take on policy interdependence evidently plays a minor role in W&C’s argument. However, we think this assumption is of crucial importance for the analysis of policy
osmosis. If country j observes country i that has a high green tax but does not use that
revenue to clean up, the green tax in country i may have a marginally lower impact on its
pollution level than if country j also introduced it. So i’s high energy taxes conditional
on low energy subsidies incentivize j to increase taxes for the sake of abating pollution.
However, i’s high energy taxes conditional on high energy subsidies reassure j of positive
environmental externalities – and this could lead to significant changes in environmental
strategies, as j now can enjoy the environmental externalities of i and essentially free ride.
The same logic applies to the relation between green taxes and cap–and–trade. As
W&C affirm, trading permits have more predictable effects than pollution taxes because
it is difficult to estimate price elasticities. So, if country i exogenously adopts green taxes
this would signal a positive change in i’s consumer behaviour. At that point, neighbouring
country j may benefit from i’s tax decision by ripping off the benefit of i’s industries having
to relocate or buying new technology. It would be then j’s best response to adapt to a more
flexible system such as carbon trading.
The traceable qualitative evidence seems to support this expectation. Take the example
of Slovenia and its preparation to new environmental policies at the time it negotiated its
European Union membership with Bruxelles. In a communication of 2002, the Slovenian
government wrote:
Slovenia’s National Environmental Protection Programme aims to achieve a reduction of greenhouse
gas emissions chiefly through the use of economic instruments [...]. The strategy sets out green tax
reform as the fundamental policy. The effective application of economic instruments within the
framework of green tax reform will call for an increase in budgetary funds. [...] For this reason, it
has been envisaged that part of the CO2 tax will be used specifically for promoting these activities
and, first and foremost, for subsidising the interest rate of investments. The introduction of an
emission permits market is a measure that may contribute to reducing the total costs of emission
reductions. For Slovenia, an interesting alternative to the emission permits market is the introduction of trade in exemptions as part of CO2 tax. (Slovenia First National Report to the United
12
Nations Framework Convention for Climate Change, 2002, p. 40)
These statements grant the valid grounds for a more thorough estimation of diffusion processes in the environmental field. The question is then, how to proceed methodologically
with the estimation of policy osmosis.
3.3
Estimation strategy
As of today we are aware of only few solutions that directly capture the simultaneous benefit–
cost analysis over substitutive policies in countries distributed across space and time.9 So,
to empirically address the puzzle, we resort to a strategy that relies on the specification of
the multi parametric spatiotemporal lag (m-STAR) models elaborated by Hays et al. (2010).
Our general model follows the linear (OLS) equation
yt = ϕ yt−1 + Xt β + ρ Wyt
(1)
where Y is an N T × 1 vector of observations for N countries over T time periods, X is a
battery of domestic variables that are extant within country borders, and W is the connectivity matrix where international variables and foreign policies that we are interested in
testing (p) are measured.10 The connectivity matrix is calculated as N T × N T with T N × N
submatrices along the block diagonal. Here, Wyt is the policy–specific spatial lag that gives
a weighted average of other observations in the year concerned.11
In Equation (1), the spatial coefficient is estimated with the parameter ρ, which captures the strength of policy interdependence. We add the time-lagged dependent variable
yt−1 to capture dependency in time, and also because most political and economic decisions
in a country (i.e. a budget) are based on last year’s policies. Methodologically this is also
important to help eliminate serial error correlation and estimate bias, given that the dependent variable is on both sides of the equation and we estimate a linear model.
In addition, we include country fixed effects and (when noted) time fixed effects. The
former captures national path dependencies and cross-sectional heterogeneity. The latter controls for the evolution of the outcome variable across the years under observation.
9
We do recognize that some research is currently devoted to elaborate techniques for this type of questions,
e.g. Franzese et al. (2014). However, the methodological discussions are still open rather than resolved
(Plümper and Neumayer, 2010).
10
In this notation vectors are expressed as bold lowercase (y) and matrices are in bold uppercase (W).
11
For each W, the typical element wijt captures the influence from unit j to i at time t. The weights in
the spatial lag are also specified by wijt .
13
Measurement–wise, our W includes policies that are interlinked with the analyzed dependent variable. So, practically, we collect alternative policy measurements and scale them
in a spatiotemporal lag that we use just as any other connectivity matrix. We then test
the effects of adding and ignoring this alternative policy in the policy world under consideration. Following the intuition that substitute policies trigger late–mover advantages and
may incentivize one country to choose alternative policies, we expect the estimated spatial
coefficient, ρ, to be consistently negative.
Our m-STAR estimations are conducted in two scenarios. First, we confront our theory
against the findings in W&C (2012). We use their data on green taxation in the Organization
for Economic Cooperation and Development (OECD), and replicate their analysis with an
additional variable that - we argue - constitutes a substitute policy to environment–relation
taxation: green subsidies. This exercise allows us to understand what may be missing when
estimating simple diffusion instead of osmosis. Then we move our theory to a second test,
this time asking whether we can expect the “osmosis–like” results to emerge in different environmental policy contexts. We then focus on a new dataset that we collected on greenhouse
gas taxation and carbon permits in the EU neighbourhood region.
4
4.1
Empirical analyses
Problems of singular diffusion analysis: the case of green taxation in the OECD
As pointed out, W&C (2012) is the first article in the political science literature to focus on
the diffusion of environmental taxation using large-N methods. The authors take a broad
approach, considering a variety of possible explanations for why countries have made their
decisions on green levies in the decade between 1995 to 2005. These include domestic factors like legislators’ preferences and domestic lobbies’ positions, as well as the international
factors discussed in the previous section.
This ‘bird’s–eye view’ ultimately suggests that some domestic factors and international
networks explain green taxation, but there is no evidence that “the ideological affinity between states conditioned the reception of other states’ policies” (2012, 1094). We challenge
this finding by arguing that an additional – and properly chosen – measurement of alternative policy choices may activate the mechanism that W&C do not find. More specifically, we
claim that, by observing other countries’ domestic environmental debates, a focal country
may learn indirectly and therefore react as a consequence.
14
We believe that the most interesting mechanisms for osmosis occur when policies have
substitutes. So, we are left to decide which additional variable can capture a substitute
policy for W&C’s green taxes. We opt for energy subsidies for the following two reasons.
First, although subsidies can be linked to a tax, in developed countries this is not often the
case. The OECD (2014) reports an average of 1 out of 10 subsidies connected to a green tax
– a number that is largely driven by the European countries which have subsidy linkage with
EU Directives.12 Second, while both subsidies and taxes are market instruments, the former
are considered more ‘regulatory–friendly’ than taxes.13 This creates the strategic edge that
is factored in the benefit–cost analysis of decision makers when they observe other policies
being discussed internationally.14
We collected data on the 25 OECD countries from the “Database on instruments used
for environmental policy”, which reports the net financial costs (i.e. amount of grants,
soft loans and guarantees) of different subsidy schemes provided in a given year.15 Since the
dataset has some missing values and the STAR models do not perform under listwise deletion,
we perform linear interpolations and use the estimated mean of ten simulated missing values
for our analysis.
To first evaluate the general patterns in the data, in Figure 1 we plot the evolution of the
subsidy levels against per capita green taxation from 1994 to 2006. The top graph illustrates
the OECD averages. It noticeably shows that, despite the downward spikes in subsidies
during the late 1990s, since 2001 both economic instruments have increased substantially,
which is generally in line with the comprehensive policy transition observed in the literature
(Holzinger et al., 2008). However, the most interesting picture is delivered by the country–
specific graphs, which plot the trends – i.e. change – in green tax and subsidies from one
year to the other.
12
A major Directive that explicitly links incentives for clean practices and environmental charges is the
Rural Development Programs Regulation (1257/99).
13
http://www.oecd.org/env/tools-evaluation/48164926.pdf.
14
Felder and Schleiniger (2002) argue that environmental subsidies are never efficient because they disperse
the citizens’ benefits from industrial competition. However, cases like the EU, which has increased its
subsidies issuance by 30% in the past 20 years and still remains a global environmental leader, suggest that
stakeholders that use either taxes or subsidies can attain environmental efficiency (Holzinger et al., 2011).
15
The online database is at http://www2.oecd.org/ecoinst/queries/Default.aspx. At the time we
collected this data (October 2012) the OECD stored it in a multi–tab excel book. The current format of the
database, updated in November 2013, is much more informative but much richer and thus slower to navigate.
15
40
50
200
gr green tax
500
60
70
green subsidy
80
800
90
Figure 1: Green policy substitution: energy taxes and subsidies, 1995–2005
1995
1998
2001
2004
year
CANADA
NORWAY
-60
-100
-40
0
-20
100
0
20
200
40
average OECD green
subsidy (M EUR)
300
average per capita
OECD green tax (W&C)
1995
1998
2001
1995
2004
1998
2001
2004
200
year
SWITZERLAND
1995
1998
2001
-200
-100
-50
-100
0
0
change
50
100
100
FRANCE
2004
1995
1998
2001
2004
100
year
UNITED KINGDOM
1995
0
-100
-100
-50
100
0
200
50
300
400
JAPAN
1998
year
2001
1995
2004
1998
2001
2004
Plot at the top shows yearly OECD averages of tax/subsidy values across time. Plots at the bottom show
the change of amounts of energy taxes (solid line) and energy subsidies (dotted line) for six selected OECD
countries.
16
The general point conveyed by the country plots is one of substitution at minimum.
Looking at France and Japan, for example, one notices that the 1997–98 biennium was a
time in which green taxes were exponentially increased, while subsidies decreased. This can
be in part traced back to pressing energy crises (for Japan), but also seems to be related
to genuine reconstructions of environmental programs (in France). A similarly anti-cyclical
image is shown in Norway’s mid–2000s, where an increasingly rising tax moved alongside a
decreasing subsidy level. The illustrations then show a shift of investments from one policy
to the other. However, the shifts do not seem to be perfectly harmonized, as some countries
(e.g. Switzerland) had their subsidy peak in 1999, while the United Kingdom had it in 2002.
indent Evidently, Figure 1 feeds into the puzzle that two apparently parallel policies may
in fact be related by interdependent politics that spread through time and space. However,
these descriptive plots do not allow for a full interpretation of the substitution dynamics
that, we contend, is in the data. In order to take a further step ahead, we estimate two
types of models that should at least identify whether the two policies are interconnected as
we believe they are.
First, we run an m-STAR of subsidies on all the domestic independent variables, the
dependent lag and the international lags reported in W&C. The domestic variables include
OECD national legislators’ left–right position and their environmental agenda based on the
Comparative Manifesto Project. Additionally, we use W&C’s energy production per GDP
to capture the power of the polluting lobbying sectors, and the usual controls of GDP per
capita, unemployment indicators, actual flows of economic resources, and income tax rates on
the average citizen. As per the international factors, we use the authors’ row standardized
connectivity matrices, W, which we then multiply to the subsidy dependent variable to
generate the spatial lag.16 For parsimony we focus on the three international factors that
W&C discuss as the most plausibly immune to collinearity. The geographical distance in
kilometers is based on Gleditsch’s Distance between Capital Cities dataset. Bilateral trade
is computed as dyadic trade flows from one country to another based on the Correlates
of War trade data (Barbieri et al., 2009). Finally, shared memberships in environmental
international organizations (IGO) is based on Ingram et al. (2005).
Second, we estimate a singular STAR model of green taxes per capita where we include
one spatial lag based on the connectivity matrix for subsidies. More specifically, we define
the connectivity matrix Wsubsidy , whose elements wijt are the relative disequilibrium (i.e.
difference) between country i and country j’s cost of subsidy at each time point. In other
16
The row standardization is important to allow that the estimated values of the spatial coefficient, ρ,
reflect the average influence of other countries’ policy choice.
17
subsidy
words, wijt
= Subsidyit – Subsidyjt .
Table 1 reports the results from the two sets of models, which we estimated both without
and with country fixed effects. The first two columns refer to the regressions of environmental
subsidies. Here one sees that the mechanisms that W&C found on the green taxation policy
are generally operating for environmental subsidies. Lower geographical distances and more
shared memberships on IGOs are orderly influencing the international diffusion of subsidies,
just as expected in analogy to W&C. The coefficient for bilateral trade is also statistically
significant but negative, which is the opposite of what W&C obtain with respect to taxes.
However, the finding is reasonable if one considers that subsidies are actions that by definition
distort the smooth flow of trade in goods, often defined as “unfair discrimination” by the
World Trade Organization.17 So, taken the subsidy subfield alone, the theory in W&C seems
to apply across environmental policies. We can affirm that countries learn from each others’
experience with subsidies programs.
Table 1: International influences on environmental policies
Domestic variables in W&C
Green subsidy lagged
Subsidy Outcome
Three lags Three lags
(time F.E.)
Yes
Yes
0.283∗∗∗
(0.0642)
0.267∗∗∗
(0.0587)
Green tax lagged
Geography distance ρ
Bilateral trade ρ
IGO env network ρ
-0.612∗
(0.266)
-0.333∗
(0.144)
1.310∗∗∗
(0.369)
N
χ2
54.38∗∗∗
(2.540)
258
280.7
0.443∗∗∗
(0.0404)
0.565∗∗∗
(0.0466)
0.110∗∗
(0.0380)
47.75∗∗∗
(2.102)
258
11184.7
-0.142∗∗
(0.0551)
42.61∗∗∗
(1.876)
258
14107.6
-1.251∗∗∗
(0.296)
-0.350∗
(0.151)
-0.770
(0.690)
Subsidy ρ
σ
Green Tax Outcome
Subsidy lag
Subsidy lag
(time F.E.)
Yes
Yes
47.42∗∗∗
(2.466)
258
410.5
Fixed effects not reported. Time fixed effects in columns 2 and 4. Standard errors in parentheses
∗
p < .05, ∗∗ p < .01, ∗∗∗ p < .001. Full list of coefficients in Table A.1 and A.2 in the Appendix.
17
http://www.wto.org/english/thewto_e/whatis_e/tif_e/agrm8_e.htm.
18
The more interesting question, however, is whether the way countries are interlinked on
the subsidy level systematically affects the outcome of interest in W&C: green taxes. To take
a first empirical grip on our hypothesis, we implement the STAR model with the subsidy
connectivity matrix in the second part of Table 1. The results in the last two columns are the
summary of the full results (reported in the Appendix). According to the first specification
without year dummies (the one–way fixed effects model), the spatial lag exerts a positive
influence on the adoption of high green taxes. This is theoretically against our expectation of
counteracting effects among competing policies, suggesting rather a reinforcing influence that
country i’s adoption of subsidiary green programs has on country j adoption of more taxes.
However, this result is heavily drifted by time digressions. Just as international integration
has evolved with time, policymakers have updated their knowledge about policy tools with
time. By controlling for trends with year dummies, we check the effect of our spatial lag result
more conservatively. In fact, we find that our expectations are proven correct: counting for
time, the ρ for subsidy is negative and significant, as predicted. In other words, the marginal
utility of i’s green tax policy action depends on j’s actions, including the action of choosing
the opposite policy.
Now, as W&C note themselves, spatial models that estimate only the effect of one
spatial lag per time can be liable to give biased estimates of spatial effects, as the single lag
partly acts as a proxy for others. Moreover, our main goal is to check the robustness of green
tax policy diffusion by including the simultaneous effect of the alternative subsidies policy,
under the proposition that the determinants of singular diffusion will lose significance once
substitution is integrated in the analysis. We then estimate a series of m-STAR models with
different combinations of the four spatial lags reported in Table 1. While the full tables are
reported in the Appendix, here we show the general findings via coefficient plots (Figure 2).
The mean estimates reflect three sets of models. The reference model, illustrated in
red dots, correspond to the W&C m–STAR we compare our subsidy conjecture against.
This is a one–way fixed effect panel regression of the green taxation on the aforementioned
domestic and international factors.18 One sees that the coefficient for geographical distance
is significant and substantively negative, while the two bilateral trade indicators and the
environmental IGO memberships are less substantive but still significant and positive. Our
first full model, in black dots, corresponds to the one–way fixed effects specification (same
as the previous model) but with the additional spatial matrix for subsidies. The results
seem to leave the general picture unaltered, but we should point out that the bilateral trade
18
In W&C’s article, this is Table 8 at page 1092. The coefficient values are slightly different due to W&C’s
data adjustments after publication, but the substantive inference is unvaried.
19
estimate is less significant than in the baseline model. In fact, the additional estimations
in the Appendix show that the significance for the three original spatial lag coefficients is
rather unstable across regressions, hence hinting to the possibility that what is captured
by the subsidy connectivity matrix is indeed capturing variance that the other ones were
missing beforehand.
-.8
rho coefficient estimates
-.4
0
.4
.8
Figure 2: Estimated effect of alternative policy (subsidies) on green taxation
Geography
Trade
IGO networks
estimated spatial lags
Subsidy
20
Coefficient plots of estimates spatial lag (ρ) parameters from Table A.3 and A.4 in the Appendix. Bars are
95% confidence interval. Full dots in red correspond to the baseline MSTAR from Ward and Cao (Table
A.3). Full dots in black correspond to the (one way fixed effects) MSTAR with the green subsidy lag (Table
A.3). Hollow black dots correspond to the two–way fixed effects MSTAR (Table A.4).
Finally, the third set of coefficients, in hollow black dots, are the estimates for the last
model but with additional time effects. As already evinced in Table 1, time has played an
important role in the evolution of environmental decision making in the past two decades,
which have moved from the more ‘command–and–control’ systems in the 1980s to the liberalist system of the 1990s until the hybrid ‘diversification’ of today (Stavins, 2008). We then
find the last set of regressions the most supportive.
Do we observe osmosis in the field of environmental policymaking in the OECD? Based
20
on our last battery of regressions, the answer is yes. As the coefficient plots in Figure 2 show,
accounting for the alternative interdependence on the environmental subsidies recalibrates
the findings in W&C by a substantial amount. The ρ coefficients for bilateral trade and IGO
membership lose statistical significance, which entails that the previous results on green taxation may have overstated the international influences affecting green charges. Moreover,
the spatial lag for subsidies is now statistically significant and negative. This indicates that,
as expected, countries do consider the externalities of other countries’ action on one policy.
Hence, we provide first evidence that governments rationally decide what policy to choose
based on what other countries may not be doing on that policy.
4.2
The merit of diffusion as osmosis: the case of climate change
policy in EU neighbouring countries
The altered replication of W&C (2012) gives us a reference point for the case of policy osmosis. In this final part of the paper, we provide an additional verification of our theory and
explore a second case of environmental policy tools where we expect the substitution dynamics to emerge. We focus on the world of carbon policies, which are a subset of environmental
policies that are only targeted to greenhouse gas contents. In this subgroup of policies we
choose carbon taxes and cap–and–trade allowances, which can be easily distinguished from
hybrid types of measures that work as ‘tax–and–reward’ systems, such as deposit refund
schemes.19
Empirically, we know that states may employ a mixture of carbon tax and cap–and–
trade policies. Some scholars have argued that these two policies are capable to coexist
provided they contribute to energy efficiency and support for renewable energy (Sorrell and
Sijm, 2003). Others however have raised reservations to their complementary (Stavins, 2008).
While the debate is far from concluded, it clearly points to the imperfect nature of these
climate policy responses overall. This allows us to use them to observe the strategic decision
making process across countries.
Since we are still investigating the area of environmental policymaking, we expect the
same setup in W&C to be helpful in assisting our analysis of carbon taxes. We construct our
outcome variable from the so-called ‘Country Chapters’ produced by the OECD in conjunction with the European Commission. These reports present a section on environmental taxes,
19
As we explain below, the carbon tax measure that we use here cannot be entirely disentangled from the
green taxes analyzed in W&C. This is because, as the authors say, many pollution taxes are appended under
the title of ‘environmental tax’. However, we feel more confident about measuring carbon–specific taxes,
since these have emerged around the same time of carbon trading options. Therefore, we leverage on the
fact that these tools are truly alternative in the political debates that precede them.
21
under which it is reported the existence of an energy tax on fossil fuel contents. As these are
generally recognized as the main type of CO2–based taxation (Zhang and Baranzini, 2004),
we use this information. We then standardize the variable by dividing the gross value by
each country’s population.20
We believe that domestic actors such as lobbying industries and executives should affect
whether a government adopts (and maintains) a carbon tax. Internationally, we expect geography to be the most influential determinant of spillover effects over carbon taxes. This is
in part due to the robust significance of the geographical distance matrix in W&C, but also
to the general understanding that strategic choices related to climate conditions are a most
salient issue for the units bordering the affected country (Perkins and Neumayer, 2009). Additionally, and in line with the osmosis conjecture, we argue that alternative policy decisions
– this time, carbon trading implementation – have an indirect yet substantial effect on the
politics of carbon taxation.
Our study analyzes the so–called European Union ‘neighbouring’ countries, which is a
sample comprised of 17 states. These are all countries that have special economic interactions with the EU (Schneider, 2009) or that at some point in the history of the Union (1958
onwards) expressed interest in becoming members.21 We realize that this particular set of
states is rarely analyzed altogether. However, we believe it presents itself as an instrumental
case for osmosis investigation. After all, the history of the carbon tax and cap–and–trade
policy instruments in the wider European zone is well–reported in both national and EU documents that testify the international adoptions of these tools. This extra–European exposure
to the dual carbon policy option could then show us under what conditions an alternative
policy may affect another policy’s behaviour.
Our panel encompasses data from 2000 to 2010, starting when emission policy debates
were established following the Kyoto Protocol agenda (Aldy et al., 2003). Of course, we
are aware that in those years the policies in the EU neighbouring zone were not completely
independent from the EU. To our advantage, this means that the region had a certain ‘exogenous’ pressure to adapt to diffusing policies and that we can use the strong regional role
20
The information is available at the European Commission’s Taxation webpage, http://ec.europa.eu/
taxation_customs/taxation/gen_info/economic_analysis/tax_structures/article_6047_en.htm.
We additionally cross-checked with the OECD’s ‘Inventory of Estimated Budgetary Support and Tax
Expenditures for Fossil Fuels.’
21
The countries under consideration are: Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Hungary,
Iceland, Latvia, Lithuania, Malta, Norway, Poland, Romania, Slovakia, Slovenia, Switzerland, and Turkey.
We exclude the countries that do not meet the minimum threshold of $5000 GDP per capita (Albania,
Morocco and Serbia), as we make this the threshold for true capacity of climate policy inaction (in line with
the 2.5% cost of policy implementations perceived by the Stern Review). We also exclude countries that the
European Commission categorizes as potential candidates as of the time of our data collection, i.e. summer
2012.
22
played by the Commission to see how countries delay or accelerate their preferred policies.
At the same time, the EU role has to be controlled and distinguished from a pure effect of
regulatory dependence and conditionality by the European Commission (Gupta and Grubb,
2000; Andonova, 2004). Consequently, in our regressions we include a binary measurement
for the enlargement effect of the EU in any country–year, where 1 stands for whether a
country at point t was integrated in the Union, and 0 otherwise. In addition, we control for
the incentives of policy adjustments that come with strong trade relations with the EU. In
a monadic fashion (Neumayer and Plümper, 2010), we introduce the sum of the balance of
trade of each of the neighbouring countries to the EU, where a negative value stands for a
deficit, and a positive one for surplus.22
Beyond the EU controls, the model in equation (1) includes our theoretical domestic
and international variables. The independent variables that we model as ‘lower level’ data
(i.e. without temporal or spatial lags) reflect domestic lobbying power. We proxy this with
the value added to GDP of two involved industries: transportation and manufacturing. Additionally we estimate the effect of real GDP per capita and CO2 emissions per capita.
Regarding the spatially lagged variables to estimate international spillover effects, we
proceed with three measurements. The first spatial weight matrix corresponds to the geographical distances across countries, which we construct from Gleditch’s “Distance between
capital cities” dataset. Secondly, we propose a connectivity matrix of ideological distances
based on the ‘orientation of the ruling party’ in the Database of Political Institutions (Beck
et al., 2001, database version 2010). The choice of this indicator is justified by the assumption that the likelihood of a country adapting to a neighbor’s policy is determined by the
similarities of the featured government and its policy positions. Since the DPI uses only
three values (1 to 3) to scale governments’ ideological orientations, we calculate the ideological distances by subtracting the values across countries and then squaring them.
Finally, we provide a matrix that captures the differences in allowances that countries
traded in each year since they established a carbon trading agency/registry. This carbon
trade connectivity matrix is modelled to reflect the standard total trade flows of country i
to country j plus the trade flows of country j to country i as per W&C. However, instead
of cash for goods, the value of the allowances is cash for prevented (or mitigated) carbon
22
Information on the dates in which countries submitted membership applications are at the European Commission Enlargement website, http://ec.europa.eu/enlargement/policy/glossary/terms/
application-eu-membership_en.htm. Data on the trade balances between the EU can be found at the
Eurostat webpage (http://epp.eurostat.ec.europa.eu/) for intra–trade with current member states and
at the European Commission Trade portal (http://ec.europa.eu/trade/) for all other countries.
23
emissions.23 We use the yearly deflated allowances in Abrell et al. (2011), which are the
national allowances minus the national verified emissions (i.e. the emissions for which most
allowances are used at the source) divided by GDP per capita.
Some objections can be raised to a panel analysis with this set up. First, on the substantive side, since carbon taxes and cap-and–trade have essentially been two top–down
mechanisms by the EU, we need to be sure that the countries under consideration did not
not directly implement policies the moment the EU (originally the EU15) introduced them.
This is something that can be graphically shown in Figures 3 and 4. Here the lightly shaded
maps correspond to countries that have not yet introduced these policies, while the darker
ones are the countries that have introduced them. One clearly sees that the carbon tax
measure diffuses more slowly than the cap–and–trade registrations, which instead were more
abruptly implemented around 2005–06 (i.e. the year of the enforcement of the Kyoto Protocol). However, this does not mean that there is no variation in policy levels after all. For
example, Iceland has never given in to the EU’s demand for more participation in environmental taxes,24 while Hungary in 2008 implemented the policy but was able to negotiate very
lose tax levels.25 Similarly, both allowance levels and timing of cap–and–trade registrations
varied according to Figure 4. Among other cases, Romania has been particularly slow at
adapting (and enacting) emission trading.26
Second, on a technical note, the fact that carbon taxes and carbon trading are policies
that really emerge only in the early–to–mid 2000s means that we have a significant amount
of zero values in the main variables (68 zeros out of 170 observations). Ideally we would
want to estimate a model with several zero counts in the dependent variable with a hurdle
model, which would take into account the ‘dual regime’ between observations without any
policy and observations with certain carbon tax values (Zorn, 1998). However, the model in
equation (1) is not suited to estimate such type of models, and no spatial-lag count model for
political science has yet been released for end–users.27 In order to continue working in the
framework of equation (1), we decide to simply transform the dependent variable (carbon
taxes) by taking its square root. As figures in the Appendix show, this normalizes the data
and decreases dispersion and inflation of zeros.
23
More precisely, one allowance unit is calculated as one tonne of Co2. Different types of allowances exist,
but the older one established by the EU Directive 2003/87/EC is the European Union allowance unit.
24
http://ec.europa.eu/enlargement/pdf/key_documents/2013/package/brochures/iceland_2013.pdf.
25
http://www.unicreditanduniversities.eu/uploads/assets/CEE_BTA/Dora_Fazekas.pdf.
http://www.reuters.com/article/2011/08/28/us-romania-co-idUSTRE77R0W920110828.
27
Work in progress on this end is (Franzese and Hays, 2009). Outside of political science, biologists
(Wu et al., 2013) have recently programmed a hierarchical bayesian spatio-temporal poisson model with
dynamic dispersion that may respond to this need. Further research may consider borrowing from these
other disciplines.
26
24
25
Co2 Tax
No Co2 Tax
2004
Co2 Tax
No Co2 Tax
2002
In colour countries under consideration (sample). Darker colours stand for carbon tax implementation.
Co2 Tax
No Co2 Tax
2008
Co2 Tax
No Co2 Tax
2006
Figure 3: Carbon tax adoption in EU neighbouring countries across time and space
26
Co2 Trade
No Co2 Trade
2004
Co2 Trade
No Co2 Trade
2002
In colour countries under consideration (sample). Darker colours stand for opening of verification agency/carbon trading registry.
Co2 Trade
No Co2 Trade
2008
Co2 Trade
No Co2 Trade
2006
Figure 4: Carbon trading set up in EU neighbouring countries across time and space
Our first test is to see whether the singular spatial lags are significant across spatiotemporal autoregressive models, granted the conditionality of all the domestic and Europe–
related variables already discussed. Table 2 reports the results from these analyses. The
odd columns, which refer to the models without time fixed effects, give the impression that
no international spillover effects may be in place after all, given that the only significant
variables are the lagged carbon tax, EU membership, and a strong manufacturing sector. As
per the previous exercise, however, time fixed effects models (even columns) provide a more
insightful picture of diffusion, as all the three lags attain a statistically significant coefficient.
Accordingly, countries that are physically closer and with more ideological similarities (i.e.
less different on the ideology scale) are prone to adopt the same (level of) carbon tax. More
importantly, the substitution with carbon trading is active, as ρ is significantly negative.
Table 2: Carbon taxation in EU neighbors: fixed effect STAR models
(1)
Geography
Carbon tax lagged
0.625∗∗∗
(0.0478)
(2)
Geography
(time F.E.)
0.578∗∗∗
(0.0518)
(3)
Ideology
(5)
CO2 Trade
0.629∗∗∗
(0.0476)
(4)
Ideology
(time F.E.)
0.588∗∗∗
(0.0514)
0.628∗∗∗
(0.0495)
(6)
CO2 Trade
(time F.E.)
0.591∗∗∗
(0.0520)
EU integration
14.25∗∗∗
(2.125)
10.76∗∗∗
(2.549)
14.93∗∗∗
(2.221)
11.32∗∗∗
(2.542)
14.87∗∗∗
(2.009)
11.05∗∗∗
(2.591)
Balance of trade
with EU
0.006
(0.020)
0.005
(0.019)
0.005
(0.020)
0.005
(0.020)
0.004
(0.020)
0.006
(0.019)
Value Added to GDP
of Transportation
-0.104
(0.115)
-0.113
(0.122)
-0.123
(0.112)
-0.113
(0.123)
-0.122
(0.113)
-0.114
(0.125)
Value Added to GDP
of Manufacture
0.350∗∗
(0.103)
0.348∗∗
(0.118)
0.345∗∗∗
(0.103)
0.312∗∗∗
(0.119)
0.345∗∗∗
(0.103)
0.327∗∗∗
(0.120)
Real GDP per capita
-0.0001
(0.0001)
-0.0001
(0.0001)
0.0001
(0.0001)
-0.0002+
(0.0001)
0.0000
(0.0001)
-0.0002+
(0.0001)
Co2 emissions
per capita
-0.776
(1.337)
-2.291
(1.446)
-0.439
(1.335)
-2.569+
(1.453)
-0.464
(1.298)
-2.814+
(1.478)
6.258
19.72∗
4.239
21.36∗
4.403
(10.94)
(11.91)
(10.99)
(12.02)
(10.87)
Spatial lag ρ
0.0833
-0.589∗
-0.003
-0.244∗
-0.0228
(0.136)
(0.274)
(0.0763)
(0.114)
(0.0420)
σ
7.35∗∗∗
6.92∗∗∗
7.36∗∗∗
6.99∗∗∗
7.36∗∗∗
(0.399)
(0.382)
(0.399)
(0.380)
(0.400)
N
170
170
170
170
170
χ2
1119.1
1270.0
1068.6
1202.8
1188.0
Dependent variable is carbon tax (squared root value). Standard errors in parentheses.
Country fixed effects (in all models) and time fixed effects (even columns) not reported.
+
p < 0.1 ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
Constant
27
22.41∗
(12.22)
-0.250∗
(0.118)
7.08∗∗∗
(0.385)
170
1328.6
Based on the results in Table 2, interacting with a country with a high level of tradable
allowances will lead a focal country to choose a carbon tax, ceteris paribus. We believe this
could be for two reasons. One the one hand, the country that specializes in the alternative
policy signals a comparative advantage (e.g. Switzerland signaling its capability to host
permit auctions and handle clearing houses for carbon markets), so interacting countries are
better off implementing alternative policies. On the other hand, countries specializing in
cap–and–trade have done it with the permission of firms willing to pass on their costs to the
consumers. This is potentially efficient for the profitable activity of the firms but not for the
environment. Countries observing this choice may then believe in the necessity to raise the
policy bar for themselves and enact a more stringent tax.
-1
-.5
rho coefficient estimates
0
.5
Figure 5: Estimated effect of alternative policy (carbon trading) on carbon taxation
Geography
Ideology
estimated spatial lags
Carbon trade
Coefficient plots of estimates spatial lag (ρ) parameters in Table A.6. Bars are 95% confidence interval.
Full dots in red correspond to a baseline MSTAR model. Full dots in black correspond to the (two way
fixed effects) MSTAR with volume of tradable carbon allowances.
Finally, we run the full m–STAR models with different combinations of the three spatial
lags. While the complete results are reported in the Appendix tables, here we briefly show
the general finding. As the coefficient plots in Figure 5 show, the model with only the two
‘classical’ lags (geographical distances and ideological differences) is only partly revealing,
because ideology is essentially non-significant. However, adding the alternative spatial lag
of carbon trading to the model modifies the initial results, as geographical distances become
28
less substantive and ideology gains more significance. Together with the results in Table 2,
we can confirm that policy osmosis is evinced in the EU neighbourhood dataset, and that
carbon mitigation instruments may indeed work as mutual alternative responses to climate
change policy making.
5
Conclusion
The argument we undertake in this paper follows a substantive concern with the current literature on international diffusion processes. We claim that considering just one policy that
is diffused although alternatives are being implemented risks overestimating the one–policy
diffusion and underestimating the overall range of interdependence. By under–investigating
what we call policy osmosis, i.e. patterns that do not depend on one focal policy but on
the diffusion of a substitute policy, we believe that comparative policy research may ignore
the indirect effects of complex interdependence and possibly bias empirical results. Consequently, we posit that different findings should emerge when integrating the logic of osmosis
into singular diffusion analysis.
Our results soundly support our theory. As such, they have several potential implications. First and rather broadly, we refocus the attention on the domestic decision-makers’
ability to learn from foreign affairs – instead of being subject to purely ‘international’ phenomena. Our results speak to Fearon’s argument that, “scholars of comparative politics
sometimes wonder what would explain foreign ‘policy’ if not domestic politics. One might
reasonably ask what kind of politics there is besides domestic politics. Foreign politics? But
isn’t that just the domestic politics of foreign countries, or the product of their domestic
politics?” (Fearon, 1998, 290).
Second and more specifically, we demonstrate that states that share international agreements and longstanding economic relations in fact may not be more likely to pass the same
environmental policy – granted the same exposure to the pressure of adopting one. That is
to say, taking policy osmosis into account ultimately paints a more realistic picture of policy
interdependence, and the toolset that states have at their disposal to react to externalities
(such as environmental pollution).
Looking back at the case of Australia’s swap of environmental approaches, our findings provide an explanation for why Canberra decided to go for a carbon tax in the early
2010s: foreign administrations pursued a series of cuts to solar panel and wind farm subsidies, which turned into a less optimistic future for international green manufacturing. For
example, the profits from clean technology investments in Australia would have been wasted
without a receptive demand in the North American market. So, as one reaction, the Aus29
tralian government decided to enforce an alternative environmental policy through a carbon
tax.28 Showing that this logic is persistent throughout countries and years, we have provided
a first evidence of the soundness of policy osmosis.
Ultimately our argument points to the merit of harder tests and better measurement of
political economy dynamics. As big data expands in the social sciences and methodological
questions are increasingly important to address, we believe theoretical tools such as the one
of policy osmosis will be useful to identify new empirical hurdles. It is then up to diffusion
researchers to seek for more consistent and robust instruments to analyze conditional policy
adoption and the strategic agency of decision makers.
28
The New York Times. 25 March 2008. On Carbon, Tax and Don’t Spend. http://www.nytimes.com/
2008/03/25/opinion/25prasad.html.
30
References
Abrell, J., Ndoye, A., and Zachmann, G. (2011). Assessing the impact of the EU ETS using firm
level data. Bruegel working paper, Bureau d’Economie Théorique et Appliqué, UDS.
Aldy, J. E., Barrett, S., and Stavins, R. N. (2003). Thirteen plus one: a comparison of global
climate policy architectures. Climate Policy, 3(4):373–397.
Andonova, L. B. (2004). Transnational politics of the environment: the european union and environmental policy in central and eastern europe. MIT Press.
Barbieri, K., Keshk, O. M. G., and Pollins, B. M. (2009). Trading data evaluating our assumptions
and coding rules. Conflict Management and Peace Science, 26(5):471–491.
Basinger, S. J. and Hallerberg, M. (2004). Remodeling the competition for capital: How domestic
politics erases the race to the bottom. American Political Science Review, 98(02):261–276.
Beck, N., Gleditsch, K. S., and Beardsley, K. (2006). Space is more than geography: Using spatial
econometrics in the study of political economy. International Studies Quarterly, 50(1):27–44.
Beck, T., Clarke, G., Groff, A., Keefer, P., and Walsh, P. (2001). New tools in comparative
political economy: The database of political institutions. The World Bank Economic Review,
15(1):165–176.
Bernstein, S., Betsill, M., Hoffmann, M., and Paterson, M. (2010). A tale of two copenhagens: Carbon markets and climate governance. Millennium - Journal of International Studies, 39(1):161–
173.
Berry, F. S. and Berry, W. D. (1990). State lottery adoptions as policy innovations: An event
history analysis. The American Political Science Review, page 395–415.
Betsill, M. and Hoffmann, M. J. (2011). The contours of ’cap and trade’ : the evolution of
emissions trading systems for greenhouse gases. Review of Policy Research, 28(1):83–106.
Boehmke, F. J. and Witmer, R. (2004). Disentangling diffusion: The effects of social learning and
economic competition on state policy innovation and expansion. Political Research Quarterly,
57(1):39–51.
Brinks, D. and Coppedge, M. (2006). Diffusion is no illusion. Comparative Political Studies,
39(4):463–489.
Busch, P.-O. and Jörgens, H. (2005). International patterns of environmental policy change and
convergence. European Environment, 15(2):80–101.
Cao, X., Milner, H. V., Prakash, A., and Ward, H. (2014). Research frontiers in comparative and
international environmental politics an introduction. Comparative Political Studies, 47(3):291–
308.
Case, A. C., Rosen, H. S., Hines, J. R., et al. (1993). Budget spillovers and fiscal policy interdependence:: Evidence from the states. Journal of public economics, 52(3):285–307.
Chaudoin, S. and Urpelainen, J. (2014). When is good news about pro-cooperation lobbies good
news about cooperation? British Journal of Political Science, Forthcoming.
31
Crain, R. L. (1966). Fluoridation: the diffusion of an innovation among cities. Social Forces,
44(4):467–476.
Daley, D. M. and Garand, J. C. (2005). Horizontal diffusion, vertical diffusion, and internal pressure
in state environmental policymaking, 1989-1998. American Politics Research, 33(5):615–644.
Dolowitz, D. P. and Marsh, D. (2000). Learning from abroad: The role of policy transfer in
contemporary policy-making. Governance, 13(1):5–23.
Elkins, Z. and Simmons, B. (2004). The globalization of liberalization: Policy diffusion in the
international political economy. American Political Science Review, 98:171–190.
Elkins, Z. and Simmons, B. (2005). On waves, clusters, and diffusion: A conceptual framework.
Annals of the American Academy of Political and Social Science, 598:33–51.
Encyclopedia Britannica Online (2013a). Diffusion (physics).
Encyclopedia Britannica Online (2013b). Osmosis (chemical process).
Fearon, J. D. (1998). Domestic politics, foreign policy, and theories of international relations.
Annual Review of Political Science, 1(1):289–313.
Felder, S. and Schleiniger, R. (2002). Environmental tax reform: efficiency and political feasibility.
Ecological Economics, 42(1):107–116.
Franzese, R. J. and Hays, J. C. (2006). Strategic interaction among EU governments in active labor
market policy-making. European Union Politics, 7(2):167–189.
Franzese, R. J. and Hays, J. C. (2007). Spatial econometric models of cross-sectional interdependence in political science panel and time-series-cross-section data. Political Analysis, 15(2):140–
164.
Franzese, R. J. and Hays, J. C. (2008a). Empirical models of spatial interdependence. In BoxSteffensmeier, J. M., Brady, H., and Collier, D., editors, Oxford Handbook of Political Methodology, page 570–604. Oxford UP, Oxford.
Franzese, R. J. and Hays, J. C. (2008b). Interdependence in comparative politics. Comparative
Political Studies, 41(4-5):742–780.
Franzese, R. J. and Hays, J. C. (2009). A comparison of the small-sample properties of several
estimators for spatial-lag count-models. Paper prepared for presentation at PolMeth 2009.
Franzese, R. J., Hays, J. C., and Cook, S. (2014). Spatial-, temporal-, and spatiotemporalautoregressive probit models of interdependent binary outcomes: Estimation, interpretation, and
presentation. Paper prepared for presentation at the Annual Meeting of the American Political
Science Association, 2010.
Fredriksson, P. G. and Millimet, D. L. (2002). Strategic interaction and the determination of
environmental policy across U.S. states. Journal of Urban Economics, 51(1):101–122.
Galton, F. (1889). Natural inheritance, volume 42. Macmillan.
Genovese, F. (2014). States’ interests and bargaining positions at the UN climate change negotiations: Exploring a new dataset. Environmental Politics, Forthcoming.
32
Gilardi, F. (2010). Who learns from what in policy diffusion processes?
Political Science, 54(3):650–666.
American Journal of
Gleditsch, K. S. and Ward, M. D. (2006). Diffusion and the international context of democratization.
International Organization, 60(04):911–933.
Goulder, L. H. and Stavins, R. N. (2011). Challenges from state-federal interactions in us climate
change policy. The American Economic Review, 101(3):253–257.
Grossback, L. J., Nicholson-Crotty, S., and Peterson, D. A. M. (2004). Ideology and learning in
policy diffusion. American Politics Research, 32(5):521–545.
Gupta, J. and Grubb, M. (2000). Climate Change and European Ledership. A Sustainabe Role for
Europe? Boston, Ma: Kluwer Academic Publishers.
Hays, J. C., Kachi, A., and Franzese, R. J. (2010). A spatial model incorporating dynamic, endogenous network interdependence: A political science application. Statistical Methodology,
7(3):406–428.
Holzinger, K. (2001). Negotiations in public-policy making: Exogenous barriers to successful dispute
resolution. Journal of Public Policy, 21(1):71–96.
Holzinger, K., Knill, C., and Arts, B. (2008). Environmental Policy Convergence in Europe? The
Impact of International Institutions and Trade. Cambridge University Press.
Holzinger, K., Knill, C., and Sommerer, T. (2011). Is there convergence of national environmental
policies? an analysis of policy outputs in 24 OECD countries. Environmental Politics, 20(1):20–
41.
Holzinger, K. and Sommerer, T. (2011). Race to the bottom or race to brussels? environmental
competition in europe. Journal of Common Market Studies, 49(2):315–339.
Ingram, P., Robinson, J., and Busch, M. L. (2005). The intergovernmental network of world
trade: IGO connectedness, governance, and embeddedness1. American Journal of Sociology,
111(3):824–858.
Jahn, D. (2006). Globalization as â ŸGaltonâ ès problemâ è: The missing link in the analysis
of diffusion patterns in welfare state development. International Organization, 60(2):401–431.
Jänicke, M. and Jacob, K. (2004). Lead markets for environmental innovations: A new role for the
nation state. Global Environmental Politics, 4(1):29–46.
Jensen, C. B. and Spoon, J. (2011). Testing the ‘Party matters’ thesis: Explaining progress towards
kyoto protocol targets. Political Studies, 59(1):99–115.
Levi-Faur, D. (2005). The global diffusion of regulatory capitalism. The Annals of the American
Academy of Political and Social Science, 598(1):12–32.
Martin, C. W. (2009). Interdependenz und ideologische position: Die konditionale diffusion der
zigarettenbesteuerung in den US-amerikanischen bundesstaaten 1971–2006. Politische Vierteljahresschrift, 50(2):253–277.
33
Martin, C. W. and Schneider, G. (2007). Pfadabhà ngigkeit, konvergenz oder regulativer wettbewerb: Determinanten der au\ssenwirtschaftsliberalisierung, 1978–2002. Transfer, Diffusion und
Konvergenz von Politiken, page 449–469.
McKibbin, W. J. and Wilcoxen, P. J. (2002). The role of economics in climate change policy. The
journal of economic perspectives, 16(2):107–129.
Meseguer, C. (2005). Policy learning, policy diffusion, and the making of a new order. The Annals
of the American Academy of Political and Social Science, 598(1):67–82.
Mintrom, M. (1997). Policy entrepreneurs and the diffusion of innovation. American Journal of
Political Science, page 738–770.
Most, B. A. and Starr, H. (1990). Theoretical and logical issues in the study of international
diffusion. Journal of Theoretical Politics, 2(4):391–412.
Neumayer, E. and Plümper, T. (2010). Making spatial analysis operational: Commands for generating spatial effect variables in monadic and dyadic data. Stata Journal, 10(4):585–605.
Neumayer, E. and Plümper, T. (2012). Conditional spatial policy dependence theory and model
specification. Comparative Political Studies, 45(7):819–849.
Perkins, R. and Neumayer, E. (2009). Transnational linkages and the spillover of environmentefficiency into developing countries. Global Environmental Change, 19(3):375–383.
Pigou, A. C. (1924). The Economics of Welfare. Transaction Publishers.
Plümper, T. and Neumayer, E. (2010). Model specification in the analysis of spatial dependence.
European Journal of Political Research, 49(3):418–442.
Prakash, A. and Potoski, M. (2006). Racing to the bottom? trade, environmental governance, and
ISO 14001. American Journal of Political Science, 50(2):350–364.
Rose, R. (1991). What is lesson-drawing. Journal of public policy, 11(1):1–22.
Ross, M. H. and Homer, E. (1976). Galton’s problem in cross-national research. World Politics,
29(1):1–28.
Schneider, C. J. (2009). Conflict, Negotiation and European Union Enlargement. Cambridge
University Press.
Shipan, C. R. and Volden, C. (2008). The mechanisms of policy diffusion. American Journal of
Political Science, 52(4):840–857.
Sorrell, S. and Sijm, J. (2003). Carbon trading in the policy mix. Oxford Review of Economic
Policy, 19(3):420–437.
Starr, H. (1991). Democratic dominoes. Journal of Conflict Resolution, 35(2):356–381.
Starr, H. and Lindborg, C. (2003). Democratic dominoes revisited. Journal of Conflict Resolution,
47(4):490–519.
34
Stavins, R. N. (2003). Chapter 9 experience with market-based environmental policy instruments.
In Karl-GÃ ran MÃ ler and Jeffrey R. Vincent, editor, Handbook of Environmental Economics,
volume Volume 1 of Environmental Degradation and Institutional Responses, pages 355–435.
Elsevier.
Stavins, R. N. (2008). Meaningful US cap-and-trade system to address climate change, a. Harvard
Environmental Law Review, 32:293.
Swank, D. (2006). Tax policy in an era of internationalization: explaining the spread of neoliberalism. International Organization, 60(4):847.
Tews, K., Busch, P. E. R. O., and JÃ rgens, H. (2003). The diffusion of new environmental policy
instruments. European journal of political research, 42(4):569–600.
Urwin, K. and Jordan, A. (2008). Does public policy support or undermine climate change adaptation? exploring policy interplay across different scales of governance. Global Environmental
Change, 18(1):180–191.
Victor, D. G. and House, J. C. (2006). BP’s emissions trading system. Energy Policy, 34(15):2100–
2112.
Walker, J. L. (1969). The diffusion of innovations among the american states. The American
Political Science Review, 63(3):880–899.
Ward, H. and Cao, X. (2012). Domestic and international influences on green taxation. Comparative
Political Studies, 45(9):1075–1103.
Wildasin, D. E. (1989). Interjurisdictional capital mobility: Fiscal externality and a corrective
subsidy. Journal of urban economics, 25(2):193–212.
Wilson, J. D. (1986). A theory of interregional tax competition. Journal of urban Economics,
19(3):296–315.
Wu, G., Holan, S. H., and Wikle, C. K. (2013). Hierarchical bayesian spatio-temporal Conway–
Maxwell poisson models with dynamic dispersion. Journal of Agricultural, Biological, and Environmental Statistics, 18(3):335–356.
Zhang, Z. X. and Baranzini, A. (2004). What do we know about carbon taxes? an inquiry into
their impacts on competitiveness and distribution of income. Energy Policy, 32(4):507–518.
Zorn, C. J. W. (1998). An analytic and empirical examination of zero-inflated and hurdle poisson
specifications. Sociological Methods & Research, 26(3):368–400.
35
Appendix
This appendix includes two sections:
Part A presents additional figures, summary statistics and the complete tables referenced in
the main text.
Part B shows a set of additional models that we ran to estimate our osmosis theory on the
EU neighbouring region. Here we focus on the simply binary indicator of whether a Co2 tax
(or carbon emission trading registry) is set up in country i at time t. To estimate the likelihood of this binary outcome, we employ conditional fixed effects logic regression analysis.
This allows us to compare a country’s policy adoption rates as a function of its own policy
behaviours as a control. The model follows the equation:
log Pij /Pij ’ = (zij - zij ’) α
where zij is a set of outcome-varying covariates, and α measures how these characteristics of
each outcome affect countries’ choices between them. A crucially indicator that we add in z
is the spatially lagged dependent variable described in the text, Wyt . Note that here the y
is binary, and W is calculated as the row standardized inverse of the (geographical or ideological) distance across countries for the Co2 tax (or Co2 trade) lag. We then test whether
these lagged variables exert any noticeable influence on the probability that a country introduces either CO2 taxes or, separately, CO2 trading.29 The results in Table B.1 show that
ideologically different countries are more likely to introduce different environmental policies.
Instead, the introduction of the same policy in a geographically distant country makes it
more likely that a focal country will introduce that policy. Although it is not easy to interpret the sign of the coefficients in Table B.1 substantively, these results overall support our
conjecture of international policy substitution.
29
In the regressions we include the set of domestic control variables already used in the main text. We
also add the effect of governmental effectiveness and regulatory quality, collected from the WBI.
36
Figure A.1: Adoption of climate change policies: parallel diffusion or policy osmosis?
Cumulative number of EU neighbouring countries adopting carbon taxes (dotted line) and carbon trading
registries (solid line) across time.
37
Figure A.2: Distribution and transformation of carbon taxes variable
0
Density
0.0005
.001
(a)
0
2000
4000
6000
CO2tax_value
8000
10000
80
100
0
.02
Density
.04
.06
(b)
0
20
40
60
squared root Co2tax_value
38
Table A.1: Domestic and international influences on green subsidies
Three lags
(baseline)
0.283∗∗∗
(0.0642)
Three lags
(time F.E.)
0.267∗∗∗
(0.0587)
Three lags
(with green taxes)
0.283∗∗∗
(0.0642)
Real GDP per capita
0.0163
(0.0153)
0.0151
(0.0152)
0.0163
(0.0153)
Real GDP per capita,
squared
-0.003
(0.002)
-0.003
(0.002)
-0.003
(0.002)
Unemployment rate
-1.890
(8.709)
-3.317
(8.224)
-2.090
(8.712)
Unemployment rate,
squared
0.0419
(0.308)
0.0728
(0.284)
0.0471
(0.308)
Income tax
-0.0179
(0.123)
-0.0614
(0.121)
-0.0373
(0.131)
Actual economic flows
-1.694∗
(0.861)
-1.475
(0.859)
-1.675∗
(0.862)
Legislative median,
left-right
-0.694
(0.597)
-0.585
(0.525)
-0.715
(0.598)
Legislative median,
environmental
-20.46∗
(8.697)
-19.16∗
(7.672)
-20.66∗
(8.705)
Legislative median,
environmental, squared
1.513
(0.864)
1.382
(0.762)
1.539
(0.865)
Green party
-10.27
(14.43)
-9.982
(12.86)
-10.43
(14.43)
18719.5
(106751.7)
31453.3
(96643.1)
25819.2
(107886.1)
Green subsidy lagged
Energy production
per GDP
Green tax lagged
0.0240
(0.0543)
Constant
66.01
(248.6)
-0.642∗
(0.273)
-0.337∗
(0.145)
1.339∗∗∗
(0.374)
258
281.2
-1252.3
parentheses.
69.82
336.4
(248.8)
(300.7)
Geography distance ρ
-0.612∗
-1.251∗∗∗
(0.266)
(0.296)
-0.350∗
Bilateral trade ρ
-0.333∗
(0.144)
(0.151)
IGO env network ρ
1.310∗∗∗
-0.770
(0.369)
(0.690)
N
258
258
χ2
280.7
410.5
Log likelihood
-1252.4
-1232.4
Dependent variable is green subsidies. Standard errors in
Country fixed effects (not reported) in all models.
Time fixed effects (not reported) in Column 2.
∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
39
Table A.2: Alternative influence on green taxes: green subsidy STAR models
Subsidy lag model
Green tax lagged
0.443∗∗∗
(0.0404)
Subsidy lag model
(time F.E.)
0.565∗∗∗
(0.0466)
Real GDP per capita
-0.007
(0.009)
-0.0283∗
(0.0113)
Real GDP per capita squared
0.0002
(0.0001)
0.0004∗∗
(0.0001)
Unemployment rate
29.63∗∗∗
(6.439)
13.42∗
(6.500)
Unemployment rate squared
-1.088∗∗∗
(0.228)
-0.591∗∗
(0.221)
Income tax
1.228∗∗∗
(0.0915)
1.024∗∗∗
(0.0929)
Actual economic flows
-3.261∗∗∗
(0.668)
-1.838∗∗
(0.680)
Legislative median, left-right
-1.349∗∗
(0.467)
-0.828
(0.426)
Legislative median, environmental
10.56
(7.159)
12.66∗
(6.421)
Legislative median, environmental, squared
-1.523∗
(0.689)
-1.694∗∗
(0.618)
Green Party
17.60
(11.40)
24.73∗
(10.39)
-392687.8∗∗∗
(83106.1)
-220783.9∗∗
(77933.4)
Energy production per GDP
Constant
-171.4
326.8
(161.5)
(223.0)
Subsidy ρ
0.110∗∗
-0.142∗∗
(0.0380)
(0.0551)
N
258
258
χ2
11184.7
14107.6
Log likelihood
-1363.6
-1334.2
Dependent variable is green taxes. Standard errors in parentheses
Country fixed effects (all models) and time fixed effects (col 2) not reported
∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
40
Table A.3: Green taxation in the OECD revisited: one–way fixed effect MSTAR models
(1)
Baseline
(original W&C)
0.405∗∗∗
(0.0364)
(2)
Geography, Trade
& Subsidy lags
0.400∗∗∗
(0.0380)
(3)
Trade, IGOs
& Subsidy lags
0.392∗∗∗
(0.0380)
(4)
Geography, IGOs
& Subsidy lags
0.412∗∗∗
(0.0374)
(5)
All
lags
0.404∗∗∗
(0.0371)
Real GDP per capita
-0.018
(0.009)
-0.004
(0.009)
-0.012
(0.009)
-0.023∗
(0.009)
-0.0186
(0.009)
Real GDP per capita,
squared
0.0004∗∗
(0.0001)
0.0003
(0.0001)
0.0003∗
(0.0001)
0.0004∗∗
(0.0001)
0.0001∗∗
(0.0001)
Unemployment rate
19.85∗∗
(6.122)
24.52∗∗∗
(6.132)
21.33∗∗∗
(6.275)
19.20∗∗
(6.202)
19.82∗∗
(6.130)
Unemployment rate,
squared
-0.779∗∗∗
(0.214)
-0.879∗∗∗
(0.217)
-0.800∗∗∗
(0.220)
-0.785∗∗∗
(0.217)
-0.778∗∗∗
(0.214)
Income tax
0.967∗∗∗
(0.0933)
0.936∗∗∗
(0.0957)
0.934∗∗∗
(0.0958)
1.029∗∗∗
(0.0906)
0.967∗∗∗
(0.0942)
Actual economic flows
-2.192∗∗∗
(0.657)
-1.804∗∗
(0.684)
-1.621∗
(0.675)
-2.580∗∗∗
(0.660)
-2.181∗∗
(0.679)
Legislative median,
left-right
-0.672
(0.428)
-0.802+
(0.437)
-0.916∗
(0.435)
-0.723+
(0.433)
-0.673
(0.428)
Legislative median,
environmental
12.01+
(6.363)
13.56∗
(6.513)
14.39∗
(6.516)
11.99+
(6.458)
12.04+
(6.376)
Legislative median,
environmental, squared
-1.478∗
(0.625)
-1.819∗∗
(0.633)
-1.839∗∗
(0.634)
-1.396∗
(0.632)
-1.480∗
(0.625)
Green Party
22.45∗
(10.43)
18.86+
(10.66)
21.87∗
(10.74)
23.84∗
(10.57)
22.50∗
(10.46)
Energy production
per GDP
-257788∗∗
(78634)
-306739∗∗∗
(80652)
-283215∗∗∗
(81723)
-241744∗∗
(80499)
-258722∗∗
(79898)
Constant
-131.7
(176.5)
-0.506∗∗∗
(0.128)
0.247∗
(0.116)
0.507∗∗∗
(0.138)
-486.8∗∗
(150.6)
-0.149
(0.100)
0.536∗∗∗
(0.0923)
-374.6∗
(167.2)
32.34
(159.9)
-0.561∗∗∗
(0.138)
-132.0
(176.5)
-0.503∗∗∗
(0.136)
0.210+
(0.140)
0.506∗∗∗
(0.138)
-0.003
(0.047)
258
14964.7
-1341.7
Green tax lagged
Geographical distance ρ
0.323∗∗
(0.122)
IGO env network ρ
0.159
(0.102)
Subsidy network ρ
-0.0146
-0.060
(0.048)
(0.045)
N
258
258
258
χ2
16083.2
14582.6
14510.1
Log likelihood
-1341.7
-1348.3
-1348.2
Dependent variable is green tax per capita. Standard errors in parentheses.
Country fixed effects not reported. + p < 0.1 ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
Bilateral trade ρ
41
0.695∗∗∗
(0.105)
0.022
(0.045)
258
15873.9
-1343.8
Table A.4: Green taxation in the OECD revisited: two–way fixed effect MSTAR models
(1)
Baseline
(own figures)
0.526∗∗∗
(0.0461)
(2)
Geography, Trade
& Subsidy lags
0.522∗∗∗
(0.0456)
(3)
Trade, IGOs
& Subsidy lags
0.541∗∗∗
(0.0464)
(4)
Geography, IGOs
& Subsidy lags
0.528∗∗∗
(0.0458)
(5)
All
lags
0.525∗∗∗
(0.0458)
Real GDP per capita
-0.0340∗∗
(0.0110)
-0.0364∗∗∗
(0.0110)
-0.0365∗∗
(0.0113)
-0.0379∗∗∗
(0.0111)
-0.0376∗∗∗
(0.0111)
Real GDP per capita,
squared
0.00005∗∗∗
(0.00001)
0.00005∗∗∗
(0.00001)
0.00001∗∗∗
(0.00001)
0.00001∗∗∗
(0.00001)
0.00001∗∗∗
(0.00001)
Unemployment rate
12.31
(6.351)
10.95
(6.348)
11.16
(6.473)
11.37
(6.343)
10.97
(6.343)
Unemployment rate,
squared
-0.550∗
(0.217)
-0.485∗
(0.216)
-0.500∗
(0.221)
-0.519∗
(0.216)
-0.500∗
(0.217)
Income tax
0.974∗∗∗
(0.0921)
0.931∗∗∗
(0.0925)
0.941∗∗∗
(0.0949)
0.956∗∗∗
(0.0921)
0.941∗∗∗
(0.0930)
Actual economic flows
-1.934∗∗
(0.668)
-1.473∗
(0.688)
-1.274
(0.699)
-1.633∗
(0.688)
-1.549∗
(0.692)
Legislative median,
left-right
-0.297
(0.421)
-0.240
(0.419)
-0.480
(0.420)
-0.267
(0.420)
-0.261
(0.419)
Legislative median,
environmental
14.05∗
(6.117)
15.35∗
(6.083)
16.55∗∗
(6.189)
15.11∗
(6.102)
14.95∗
(6.092)
Legislative median,
environmental, squared
-1.602∗∗
(0.602)
-1.708∗∗
(0.597)
-1.973∗∗
(0.601)
-1.623∗∗
(0.599)
-1.659∗∗
(0.598)
Green Party
26.28∗∗
(10.12)
28.72∗∗
(10.15)
30.16∗∗
(10.35)
29.11∗∗
(10.17)
29.07∗∗
(10.15)
-205525.5∗∗
(76863.6)
-232131.8∗∗
(76449.9)
-248006.3∗∗
(77978.2)
-211518.3∗∗
(76069.9)
-223555.3∗∗
(76878.1)
-131.7
(176.5)
-0.600∗∗∗
(0.178)
0.122
(0.116)
0.241
(0.220)
-486.8∗∗
(150.6)
-0.486∗∗∗
(0.180)
0.203
(0.136)
-374.6∗
(167.2)
32.34
(159.9)
-0.601∗∗∗
(0.169)
-132.0
(176.5)
-0.527∗∗∗
(0.181)
0.146
(0.147)
0.217
(0.220)
-0.107+
(0.054)
258
16651.7
-1327.3
Green tax lagged
Energy production
per GDP
Constant
Geographical distance ρ
0.294∗
(0.138)
IGO env network ρ
0.159
(0.102)
Subsidy network ρ
-0.110∗
-0.145∗∗
(0.048)
(0.0547)
N
258
258
258
χ2
17639.1
16992.8
16363.5
Log likelihood
-1329.2
-1327.8
-1331.6
Dependent variable is green tax per capita. Standard errors in parentheses.
Time and country FE not reported. + p < 0.1 ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
Bilateral trade ρ
42
0.304
(0.205)
-0.102+
(0.054)
258
18056.0
-1327.8
Table A.5: Summary statistics of carbon policy instruments dataset
Variable
Co2 tax value
lag Co2 tax value
EU integration
Balance of Trade with EU (net)
VA (%) manufacture
VA (%) transportation
Co2 per capita
GDP per capita
Government effectiveness
Regulatory quality
43
Mean
Std. Dev.
18.502
22.365
15.614
21.458
0.417
0.494
-76.954
102.309
3.439
6.827
3.904
6.06
9.138
3.091
18076.334 18502.725
0.883
0.628
0.975
0.419
N
187
187
187
186
187
187
170
187
170
170
Table A.6: Carbon taxation in EU neighbors: two–way fixed effect MSTAR models
Geography
and Ideology
0.570∗∗∗
(0.0516)
Geography
and Co2 Trade
0.570∗∗∗
(0.0516)
Ideology
and Co2 Trade
0.570∗∗∗
(0.0516)
All
lags
0.505∗∗∗
(0.0610)
EU integration
10.64∗∗∗
(2.526)
10.64∗∗∗
(2.526)
10.64∗∗∗
(2.526)
9.685∗∗∗
(2.543)
Balance of trade
with EU
0.000124
(0.0198)
0.000124
(0.0198)
0.000124
(0.0198)
-0.00138
(0.0195)
Value Added to GDP
of Transportation
-0.116
(0.121)
-0.116
(0.121)
-0.116
(0.121)
-0.126
(0.120)
Value Added to GDP
of Manufacture
0.331∗∗
(0.117)
0.330∗∗
(0.117)
0.330∗∗
(0.117)
0.317∗∗
(0.116)
Real GDP per capita
-0.0001
(0.0001)
-0.0001
(0.0001)
-0.0001
(0.0001)
-0.0001
(0.0000)
Co2 emissions
per capita
-2.333
(1.432)
-2.333
(1.432)
-2.333
(1.432)
-2.602+
(1.421)
20.65+
(11.80)
-0.492+
(0.287)
-0.182
(0.118)
20.65+
(11.80)
-0.492+
(0.287)
20.65+
(11.80)
Carbon tax lagged
22.46+
(11.69)
Geography ρ
-0.405
(0.295)
Ideology ρ
-0.492+
-0.211+
(0.287)
(0.116)
CO2 trade ρ
-0.192
-0.192
-0.227+
(0.117)
(0.117)
(0.118)
N
170
170
170
170
χ2
1248.9
1248.9
1248.9
1256.6
Log likelihood
-570.9
-570.9
-570.9
-569.1
Dependent variable is carbon tax (squared root value). Standard errors in parentheses
Country fixed effects and time fixed effects not reported.
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
Constant
44
Table B.1: Conditional logit models with binary policy adoptions
Introduced
CO2 tax
Introduced
Carbon Trade
Introduced
CO2 tax
Introduced
Carbon Trade
Spatially lagged CO2 tax
weighted by geographical distance
0.308
(0.215)
0.855∗∗
(0.366)
Spatially lagged CO2 trade
weighted by geographical distance
-0.235∗
(0.132)
-0.384∗∗
(0.179)
Spatially lagged CO2 tax
weighted by ideological distance
0.453∗∗
(0.181)
0.132
(0.150)
Spatially lagged CO2 trade
weighted by ideological distance
-0.303∗∗∗
(0.112)
-0.016
(0.097)
Co2 emissions
per capita
0.034
(0.039)
0.047
(0.036)
0.040
(0.045)
0.038
(0.032)
Value Added to GDP
of Manufacture
-0.018
(0.058)
0.061
(0.055)
-0.013
(0.062)
0.045
(0.053)
Value Added to GDP
of Transportation
0.031
(0.057)
0.015
(0.061)
0.034
(0.065)
0.029
(0.058)
Government effectiveness
-2.102
(3.060)
-3.020
(2.798)
-2.079
(3.345)
-1.406
(2.558)
Regulatory quality
5.210
(4.027)
5.961
(3.946)
3.400
(4.159)
4.863
(3.642)
N
170
170
170
Pseudo R2
0.1333
0.2304
0.2227
The dependent variable is a dummy variable that takes on the value 1
if a country has introduced a CO2 tax in a given year, 0 otherwise.
Conditional (fixed-effects) logistic regression with standard errors in parentheses
∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
45
170
0.1515