While research has addressed the effects of international nongovernmental organizations (INGOs) advocacy on human rights outcomes, less is known about how INGOs choose advocacy targets and tactics. We combine insights from political economy and constructivism to understand how INGOs come to choose targets and tactics through the concepts of information and leverage politics, first articulated by Keck and Sikkink (1998), and salience politics, or the need to select cases that energize organization members and donors. INGOs select potential targets for advocacy and choose their tactics based on considerations of leverage potential and political salience, both of which are a function of potential target states’ aid, trade, and security linkages with major Western powers. Using data on Amnesty International’s written advocacy efforts - background reports, press releases, and new data on Urgent Actions - we find robust evidence that Amnesty International accounts for these linkages with Western powers in choosing targets for its advocacy campaigns.
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Uppsala Conflict Data Program (2012/06/03) UCDP Conflict Encyclopedia: www.ucdp.uu.se/database, Uppsala University.
Cingranell and Richards (2010) physical integrity score = 0, Gibney and Wood (2010) Political Terror Scale = 5, both of which are the poorest possible grades. Both scales are at least in part based on AI’s reporting.
See Keck and Sikkink (1998), chapter 1.
Under the leadership of International Executive Committee Chair Peter Duffy (1989–1991), the Secretariat began developing its own means to fundraise, but this is a very minor part of its finances.
Beyond emphasizing the importance of leverage and salience politics, we recognize how INGOs can act through information and symbolic politics. When INGOs write extensive reports on a human rights situation in a particular country(ies), they are serving at least two purposes. First, they provide information on violations that might otherwise go unreported. Second, such reports often contain original research and policy recommendations, all of which can eventually help states, IGOs, and other actors formulate responses to human rights violations. By and large, background reports are done for specialist or policymaking audiences, rather than ordinary citizens.
The economic literature on home bias – the tendency to prefer domestic goods to international goods – indicates that consumers are highly aware of goods’ country of origin (see Lewis 1999).
The Leahy-Feingold Amendment restricted arms sales to Indonesia due to concerns about mass human rights abuses in East Timor, the island that at the time had been occupied by Indonesia since 1975.
However, the act can be circumvented in cases of demonstrated national interest; in October, 2010, US President Barack Obama exempted Chad, the Democratic Republic of the Congo, Sudan, and Yemen from the CSPA; the act was only applied to Somalia and Myanmar. See Brian Knowlton, “4 Nations With Child Soldiers Keep U.S. Aid,” New York Times October 28, 2010.
In 2001, AI’s International Council voted on a set of criteria by which to evaluate economic sanctions. Since then, AI has issued judgments on sanctions, including a 2009 document on the use of sanctions in Cuba (see “The US Embargo against Cuba: Its Impact on Economic and Social Rights” (2009)). There may be compelling reasons why the citizenry might demand sanctions against another country, and states may feel the need to concede to different domestic interests in extreme cases, such as the anti-apartheid movement (Kaemper and Lowenberg 1998). Domestic interests are also a key factor in the creation (or not) of preferential trade agreements (Ayres 1998, Hafner-Burton 2005).
UAs first emerged as part of the Campaign for the Abolition of Torture (CAT), a massive, worldwide campaign that increased substantially the demands on the International Secretariat at a time when AI’s letter writing campaigns were still run under the auspices of the THREES groups, standing groups which received coordination via the Secretariat. As demands on the Secretariat grew, they transitioned to the UA model, which replaced central coordination with member self-selection of campaigns in which to participate; see Wong (2012, ch. 3).
UAs are now also posted online, but the primary way to receive UAs during the period covered by our analysis was to sign up as part of a national-level network.
Based on sample-period panel means for the PTS score.
The use of AI-based evaluations limits sample size somewhat. Relative to the US State Department-derived variable, AI-based evaluations are missing in roughly 20 % of cases, due to an absence of AI reporting on particular cases.
For background reports, press releases, and UAs, zero observations (i.e., no AI targeting in a given country-year) account for 30.5 %, 66.2 %, and 41.5 % of country-year observations. Zero inflation can be thought of as an econometric nuisance, leading to biased coefficients and standard errors, or as a property of the actual data generating process. We do not propose different theoretical mechanisms governing the decision to target at all versus no targeting (i.e., the logistic regression step of the zero-inflated negative binomial estimator, ZINB) than for decisions about the volume of targeting (i.e., the count step of ZINB). Thus, we have no a priori theoretical reason to suspect the ZINB estimator would be better for modeling AI’s decision making process. However, AIC and Vuong (1989) specification tests confirm the ZINB estimator is a better fit for the data, though the results produced by the two techniques are quite consistent. Results of the standard negative binomial regressions are reported in Appendix 1, available online on this journal’s website.
In particular, the US and UK trade linkage variables (r = 0.8) and US and UK foreign policy similarity (r = 0.93 outside of Latin America & the Caribbean region) are highly correlated. When included in the same models, the trade variables and US foreign policy similarity variables return variance inflation factors (VIF) > 5.
A standard deviation increase in log ODA from the mean is associated with a 12.0 % increase in the frequency of background reporting the following year.
Save for its indirect effect, through suppressing AI’s assessments of in-country human rights conditions.
Table 1, model 5.
However, both coefficient estimates are positive and statistically significant when standard negative binomial regression is used. Aid from the USA (p < 0.10, p < 0.05) is positively associated with the frequency of targeting for UAs, with a one standard deviation increase from the mean value associated with an 8.4 % increase.
Table 1, model 5.
The standard negative binomial models, reported in the appendix, provide stronger evidence of a Latin bias in UAs, with the indicator variable significant in both specifications.
Likely due, in part, to AI’s past success in highlighting these cases; see Hafner-Burton and Ron (2012).
Based on Table 1, models 3 and 5, respectively.
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We thank Amnesty International, and Scott Harrison in particular, for graciously sharing their data. We thank Michael Widmeier and Marie Chalkley for research assistance. We would like to thank Courtenay Conrad, Julia Gray, Miles Kahler, Will Moore, Dan Nielsen, Emily Ritter, and the participants at the 5th Annual Conference on The Political Economy of International Organizations and the 5th Annual Meeting of the International Political Economy Society for their feedback and suggestions. Special thanks to Jacqueline DeMeritt, Matthew Krain, Idean Salehyan, Sarah Stroup, Michael Tierney, three anonymous reviewers, and Axel Dreher for detailed comments and recommendations.
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Hendrix, C.S., Wong, W.H. Knowing your audience: How the structure of international relations and organizational choices affect amnesty international’s advocacy. Rev Int Organ 9, 29–58 (2014). https://doi.org/10.1007/s11558-013-9175-z
- Human rights
- International non-government organizations
- Amnesty International
- Arms transfers