Abstract
Although there is a substantial amount of research that studies how environmental interest groups/non-governmental organizations (ENGOs) influence international environmental negotiations, both the theoretical work and the empirical evidence were not yet able to answer comprehensively if this makes it more likely that states, in turn, commit to stronger environmental agreements. This article seeks to contribute to clarifying this. First, the authors argue that a higher degree of ENGO access to official negotiations and a larger number of ENGOs actively participating during bargaining processes can facilitate outcomes of environmental negotiations. The authors then analyze quantitative data on international environmental regimes and their members’ commitment levels from 1946 to 1998 and obtain robust support for their claims. However, the rationale on the introduced explanatory factors also implies that the impact of ENGO access on states’ commitment levels should vary conditional on the number of ENGOs actively participating. The paper finds evidence for such an interaction, although the results go against our expectations.
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Notes
We focus on ENGOs defined as not-for-profit organizations that have not been established by state actors and whose aim is environmental protection. Thus, we exclude business associations and other groups that represent commercial interests (Betsill 2006: 175). We further limit our analysis to institutionalized global politics. More direct ways of shaping international environmental governance are not taken into account, but see Wapner (1995, 1996).
As we will elaborate below, we do not imply that participating in state delegations is the exclusive avenue for exerting influence. We do also not state that access to delegations always and necessarily induces access to higher levels of decision making. We claim, however, that it is one potential (and perhaps a sufficient) avenue for doing so.
For some notable quantitative exceptions see, for example, Bernauer et al. (2010), Fredriksson and Gaston (2000), Fredriksson et al. (2005), Fredriksson et al. (2007), Neumayer (2002), Roberts et al. (2004), and von Stein (2008). Those studies, however, exclusively look at states’ ratification behavior after the negotiations as such—which is related to our research, but essentially very different from the dependent variable we employ. Furthermore, those studies largely treat (E)NGOs—if dealing with them at all—as a control item, based on explanations from the domestic level. Our work focuses on the international level, though.
For a more comprehensive discussion, see Betsill (2006).
Against the background of Putnam’s (1988) two-level game, it is worth noting that we largely focus on the international level, that is, Putnam’s (1988: 436) first stage. Putnam’s (1988: 436) second stage primarily deals with domestic-level discussions about whether to ratify an agreement, for example, the literature we pointed to in footnote 3. With regard to the latter, research also indicates that domestic lobbying may be more effective in attaining (E)NGO influence (e.g., Skodvin and Andresen 2008; Kalt and Zupan 1984; Durden et al. 1991; Fowler and Shaiko 1987; Cropper et al. 1992; Smith 1995). However, neither does our approach implicitly assume that ENGOs can only influence negotiation outcomes at the international level nor does it fully neglect the exertion of influence at the domestic level. In fact, ENGOs are usually mass membership organizations. As representatives of voters, ENGOs then shape public opinion and signal electoral (i.e., domestic) preferences to policymakers. Further, ENGOs may signal voter preferences even in relatively closed negotiations through outsider strategies such as protests, demonstrations, or other types of direct action outside the negotiation forum.
Note, however, that ENGOs from rich, Western countries dominate and, consequently, there is a lack of ENGOs in the “South” (Beckfield 2003). However, a larger number of green organizations—regardless where these actually come from—still mean that there is a broader range of expert information and advice, as well as the ability to signal more credibly that their activities align with preferences of all segments in the population—including individuals, people, and groups that are not well represented in policymaking at any level.
The impartiality of ENGO information may arguably be questioned in some cases, but the “plurality of sources provides a check on exaggeration, obfuscation, and poor logic and data” (Raustiala 1997: 727).
We thank an anonymous reviewer for correctly emphasizing that the data do not sufficiently reflect quality of ENGO participation as much as they do quantity, that is, the data do not really explain the nuances of ENGO participation. Therefore, the data used for the analysis do have their flaws. However, we assess the quality of the data thoroughly in the Appendix, where we do not find much evidence for substantial inconsistencies in the data. Furthermore, more accurate data on ENGO access and participation do not yet exist to the best of our knowledge.
For a comprehensive description on how the experts obtained the data, see Breitmeier et al. (1996).
Please see the Appendix 1 for the discussion on the κ scores.
These commitment levels should reflect the policy positions of ENGOs, since we would not be able to claim an association or a relationship between ENGO access/number and stronger environmental commitments otherwise. An objective measurement may appear rather difficult in this context as there are generally numerous non-governmental organizations involved in international environmental negotiations, with individual—sometimes not necessarily overlapping—goals. However, since our analysis only examines the engagement of ENGOs, it is plausible to assume that states’ higher environmental commitments also mirror the interests of these organizations (Betsill and Corell 2001: 75).
Hausman tests demonstrate that the regular ordered probit estimator is less efficient than our approach.
The original IRD item also includes non-governmental groups that do not pursue “pro-environment” goals. We identified those groups and dropped them from our data, however.
The results reported below are virtually identical when recoding the different values of ENGO access into binary variables and including these items instead of our ordinal scale.
More precisely, 2.58 = \( \frac{1}{{\left( {\frac{1.5}{3.5}} \right)^{2} + \left( {\frac{1.5}{3.5}} \right)^{2} + \left( {\frac{0.5}{3.5}} \right)^{2} }} \).
One might object to our approach for testing the third hypothesis that including an interaction term is unnecessary as we already use the level of ENGO access as a weight to obtain estimates for number of ENGOs. It can be easily shown, however, that we do have to incorporate the multiplicative term for our third hypothesis due to the calculation (see Laakso and Taagepera 1979; Ljiphart 1999: 65ff) of number of ENGOs. More specifically, a case with three ENGOs that all have an access level of 0.5 would display the exact same value on number of ENGOs as a case with three ENGOs as well that all receive a value of 1.5 on the access scale. Thus, merely employing number of ENGOs for the third hypothesis (and, hence, a simple count item for the second hypothesis) is insufficient.
See also Miles et al. (2002: 37) who cluster this concept under a regime’s problem structure.
For example, the first collective action problem of the first component of the whaling regime (1946–1982) cannot draw upon pre-existing regime structures and, thus, receives the value of 0. This does not apply to the same collective action problem of the second component (1982–1998), however, which then receives a value of 1. While we will discuss the selection issue in the conclusion again, note that the Appendices 3 and 4 also includes a robustness check using three-stage least-squares regression models that test for reversed causality (i.e., deep environmental commitments affect ENGO participation) as induced by the selection problem here.
Note, however, that we do not make any claims on causality, although we find an association for our core variables of interest. Put differently, to say that ENGO access, number of ENGOs, and their interaction are statistically associated with depth of cooperation is different from the claim that the former cause the latter.
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This article has been written in the context of the Swiss National Research Program on Democracy in the 21st Century. We are indebted to Thomas Bernauer, Vally Koubi, Gabriele Spilker, Ulrich Pilster, and Jürg Vollenweider for useful comments on a previous draft. We also thank the anonymous reviewers and the editor of International Environmental Agreements for advice. The replication materials for the data analysis can be obtained from the authors.
Appendices
Appendix 1: Cohen’s κ scores
For assessing the degree of consistency in the IRD dataset, its overall quality, and with regard to a possible violation of inter-coder reliability, the Table 5 shows Cohen (1960) κ scores. Cohen’s κ is defined as:
where Pr(a) is the relative observed agreement among coders and Pr(e) is the hypothetical probability of chance agreement. If the raters are in complete agreement, then κ = 1. If there is no agreement among the coders other than what would be expected by chance (as defined by Pr(e)), then κ = 0. However, all variables have inter-coder reliability scores significantly above the expected values. In terms of size and public good, we even have full agreement among the experts. The κ of most variables is at least at 0.2, which can be considered as fair agreements (Landis and Koch 1997).
Appendix 2: Coding instructions in IRD (Breitmeier et al. 1996)
Depth of cooperation: Is the regime shallow or deep as measured by the density and specificity of its rules?
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1 = Very shallow: Compared to the density of rules considered necessary for managing the problems in the issue area, the regime comprises only a very limited number of rules, and/or established rules are rather weak compared to the specificity of the rules considered necessary for managing the problems in the issue area.
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2 = Shallow: Between 1 and 3 on the scale.
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3 = Medium: Compared to the density of rules considered necessary for managing the problems in the issue area, the regime comprises a sizable number of rules to manage the problem and/or established rules have developed some strength compared to the specificity of the rules considered necessary for managing the problems in the issue area.
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4 = Deep: Between 3 and 5 on the scale.
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5 = Very deep: Compared to the density of rules considered necessary for managing the problems in the issue area, the regime comprises a very comprehensive set of rules and/or established rules are rather strong compared to the specificity of the rules considered necessary for managing the problems in the issue area [e.g., the adjustments and amendments to the Montreal Protocol (1987) adopted in London (1990) and Copenhagen (1992) led to a rather deep regime with comprehensive and strong rules].
ENGO access: What roles did non-state actors play in the negotiations? For each non-state actor, check as many as apply.
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0 = Not applicable.
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1 = Observer role.
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2 = Member of national delegation.
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3 = Member of negotiation body.
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4 = Exerted pressure inside the negotiations.
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5 = Exerted pressure outside the negotiations.
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6 = Missing.
Number of ENGOs: List of important non-state actors identified in the agreement.
Uncertainty: Was the nature of the problem well understood?
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1 = Very strongly established understanding: There was general consensus regarding nature, causes, and consequences of the problem, as well as regarding solutions and what should be maximized in the issue area.
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2 = Strongly established understanding: Between 1 and 3 on the scale.
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3 = Partially established understanding: Consensus was partially achieved, either by consensus on some but not all of the different variables (nature, causes, and consequences of the problem as well as solutions and what should be maximized in the issue area) or by generally growing, but still not fully developed, consensus on all of the different variables.
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4 = Low established understanding: Between 3 and 5 on the scale.
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5 = Not at all established: Understanding was not established with regard to nature, causes, and consequences of the problem, or to solutions or what should be maximized in the issue area.
Hegemon: Were the nations involved in regime formation roughly symmetrical in terms of issue-specific power or did the process involve sharp differences in power resources?
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1 = Completely even distribution: Issue-specific power resources are evenly distributed among nations.
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2 = Slightly uneven distribution: Besides slightly uneven distribution, no single nation has a greater ability to get other nations to do something they otherwise would not do.
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3 = Considerable unevenness: Uneven distribution of power resources can lead to more powerful actors being able to get other nations to do something they otherwise would not do with regard to a limited number of issues in the issue area.
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4 = Highly uneven distribution: Very uneven distribution of power resources can lead to more powerful actors being able to get other nations to do something they otherwise would not do with regard to a significant number of issues in the issue area.
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5 = Issue-specific hegemon present: One single actor can get all other actors to do things that they otherwise would not do with regard to nearly all issues at stake in the issue area.
Size: How many nations were regarded as being important because of their role in causing the problem?
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1 = 1–5.
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2 = 6–15.
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3 = 16–30.
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4 = 31–60.
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5 = 60–120.
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6 = More than 120.
Good type: Does the problem involve supplying a collective good, regulating the use of a common pool resource, managing a shared natural resource, or controlling transboundary externalities?
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0 = None of the types.
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1 = Collective (or public) good.
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2 = Common pool resource.
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3 = Shared natural resource.
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4 = Common pool resource and shared.
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5 = Transboundary externalities.
Appendix 3: General robustness checks
In order to ensure the robustness of our findings, we changed a variety of model specifications and reran the estimations again. First, Clarke (2005) shows that the inclusion of control variables may actually increase the bias instead of decreasing it. Hence, Models 1–2 in the manuscript do not include the control covariates, emphasizing that the results do not depend on whether the control covariates are included or not.
Second, instead of the random-effects ordered probit models, an alternative estimation strategy may be generalized linear latent and mixed models (Rabe-Hesketh, Skrondal, and Pickles 2004). Although employing these kinds of models seems more difficult in our case as the IRD is partly characterized by lack of coding of regimes as distinct from components, we calculated a multilevel generalized linear model (Model 5). However, these estimations do not alter the substance of our findings.
Third, although we argued for a strongest-link operationalization of ENGO access, it may be that this definition is the reason that the originally expected interaction effect cannot be observed. Thus, we also employed the average instead of the highest level of access. Model 6 reports our findings when using a mean/average operationalization of ENGO access, but the results do not significantly change.
Finally, we pointed to the difficulties and problems when estimating a fixed-effects nonlinear model (Greene 2004). Nevertheless, Baetschmann et al. (2011) recently developed a consistent fixed-effects ordered logit model, which we employ in Model 7. This model, however, reveals only minor differences to our main estimations in Table 4 of the original paper, which are likely to be caused by the following two problems of the fixed-effects ordered logit. First, this model is highly inefficient, since the method drops all observations that do not “flip” or change their values on the dependent variable over regime components. Second, the estimator artificially increases the number of observations (Baetschmann et al. 2011: 8) (Table 6).
Appendix 4: Estimates for determining reverse causality using three-stage least-squares regression (3SLS)
To estimate new regressions using 3SLS for determining whether our models might suffer from simultaneity, that is, reverse causality, we needed to specify an equation for either of the two variables that pertain to ENGOs (see Ward 2006). We explored possible specifications by running multiple models similar to that shown in Table 4 of the paper, based on the same theoretical rationale. In 3SLS, instruments for endogenous variables are generated by regressing each such variable on all exogenous variables in the system. Here, the endogenous variables are depth of cooperation and ENGO access or number of ENGOs (depending on the model specifications), respectively (we decided to consider one ENGO variable at each time, since introducing both ENGO variables as endogenous does not induce model convergence). The following regression models (as taken directly from the statistical software) are then re-estimates of Model 4 in the paper (while leaving out the interaction term and, as said, introducing the ENGO variables separately) using 3SLS. While the results are very similar to Table 4 in our manuscript, it is in particular striking that either ENGO access or number of ENGOs has a positive coefficient that is significant at the 5 % level. In the estimate of the associated equation for ENGO access or number of ENGOs, respectively, depth of cooperation is not significant, though. This supports the view that causality flows from ENGO access and number of ENGOs, respectively, to depth of cooperation—and not the other way round.
reg3 (depth_coop effective_ngo problem_understand_final power_setting_symmetry number_causers public publicXnumber) (effective_ngo depth_coop problem_understand_final power_setting_symmetry duration)
Three-stage least-squares regression
Coef. | SE | z | P > |z| | [95 % CI] | ||
|---|---|---|---|---|---|---|
Depth_coop | ||||||
Effective_ngo | 1.265444 | .6307391 | 2.01 | 0.045 | .0292179 | 2.50167 |
Problem_understand_final | −.1874489 | .215541 | −0.87 | 0.384 | −.6099015 | .2350037 |
Power_setting_symmetry | .0622026 | .2374787 | 0.26 | 0.793 | −.403247 | .5276523 |
Number_causers | −.0049348 | .0633936 | −0.08 | 0.938 | −.1291839 | .1193143 |
Public | −.132282 | .8083245 | −0.16 | 0.870 | −1.716569 | 1.452005 |
PublicXnumber | .0369074 | .2295219 | 0.16 | 0.872 | −.4129472 | .4867621 |
_Cons | .5531266 | 1.598661 | 0.35 | 0.729 | −2.580191 | 3.686444 |
Effective_ngo | ||||||
Depth_coop | 1.01936 | 1.420515 | 0.72 | 0.473 | −1.764798 | 3.803518 |
Problem_understand_final | .1718737 | .2538429 | 0.68 | 0.498 | −.3256493 | .6693967 |
Power_setting_symmetry | −.0539744 | .1930648 | −0.28 | 0.780 | −.4323744 | .3244256 |
Duration | −.1284077 | .8376114 | −0.15 | 0.878 | −1.770096 | 1.51328 |
_Cons | −1.017102 | 4.016572 | −0.25 | 0.800 | −8.889439 | 6.855234 |
reg3 (depth_coop ngo_influence problem_understand_final power_setting_symmetry number_causers public publicXnumber) (ngo_influence depth_coop problem_understand_final power_setting_symmetry duration)
Three-stage least-squares regression
Coef. | SE | z | P > |z| | [95 % CI] | ||
|---|---|---|---|---|---|---|
Depth_coop | ||||||
Ngo_influence | .9667695 | .4287744 | 2.25 | 0.024 | .1263871 | 1.807152 |
Problem_understand_final | −.3519225 | .1270197 | −2.77 | 0.006 | −.6008765 | −.1029684 |
Power_setting_symmetry | −.1865691 | .1849333 | −1.01 | 0.313 | −.5490317 | .1758934 |
Number_causers | .0727102 | .0996316 | 0.73 | 0.466 | −.1225642 | .2679845 |
Public | .388875 | .5077592 | 0.77 | 0.444 | −.6063149 | 1.384065 |
PublicXnumber | −.1175762 | .2023243 | −0.58 | 0.561 | −.5141246 | .2789722 |
_Cons | 3.122761 | .5639902 | 5.54 | 0.000 | 2.017361 | 4.228162 |
Ngo_influence | ||||||
Depth_coop | −.0385542 | .5073754 | −0.08 | 0.939 | −1.032992 | .9558834 |
Problem_understand_final | .0564301 | .1541004 | 0.37 | 0.714 | −.2456011 | .3584614 |
Power_setting_symmetry | .2284085 | .062192 | 3.67 | 0.000 | .1065144 | .3503026 |
Duration | .3215822 | .164923 | 1.95 | 0.051 | −.001661 | .6448254 |
_Cons | −.1167138 | 1.602743 | −0.07 | 0.942 | −3.258033 | 3.024605 |
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Böhmelt, T., Betzold, C. The impact of environmental interest groups in international negotiations: Do ENGOs induce stronger environmental commitments?. Int Environ Agreements 13, 127–151 (2013). https://doi.org/10.1007/s10784-012-9180-3
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DOI: https://doi.org/10.1007/s10784-012-9180-3