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The Review of International Organizations

Erschienen in:

26.11.2016

Domestic politics and the WHO’s International Health Regulations: Explaining the use of trade and travel barriers during disease outbreaks

verfasst von: Catherine Z. Worsnop

Erschienen in: The Review of International Organizations | Ausgabe 3/2017

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Abstract

During the 2009 H1N1 pandemic, the World Health Organization (WHO), acting under the authority of the International Health Regulations (IHR), recommended against the imposition of trade or travel restrictions because, according to WHO, these barriers would not prevent disease spread. Why did 47 states impose barriers anyway? This article argues that states use barriers as political cover to prevent a loss of domestic political support. This logic suggests that governments anticipating high domestic political benefits for imposing barriers during an outbreak will be likely to do so. Logistic regression and duration analysis of an original dataset coding state behavior during H1N1 provide support for this argument: democracies with weak health infrastructure—those that stand to gain the most from imposing barriers during an outbreak because they are particularly vulnerable to a negative public reaction—are more likely than others to impose barriers and to do so quickly.

Vorheriger Artikel Good countries or good projects? Comparing macro and micro correlates of World Bank and Asian Development Bank project performance

Nächster Artikel The reputations of NGOs: Peer evaluations of effectiveness

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Evidence supports WHO’s position that barriers are rarely effective (e.g., Bell and World Health Organization Working Group on Prevention of International and Community Transmission of SARS 2004; Colizza et al. 2007; Cooper et al. 2006; Cowling et al. 2010; Ferguson et al. 2006; Poletto et al. 2014; Selvey et al. 2015; Vincent et al. 2009).

For an overview of commitment problems in international politics and how IOs can help to mitigate them see Fearon (1998) and Abbott and Snidal (2000).

Some states lack surveillance capacity and do not know that an outbreak is occurring. Not surprisingly, adequate surveillance capacity is a necessary condition for rapid outbreak reporting. The point here is that the more insidious barrier to reporting is that even when states are fully aware of an outbreak they purposefully conceal it to avoid economic costs. For evidence that this commitment problem is operating, see Worsnop (2016).

For more on the IHR revision, see von Tigerstrom (2005), Fidler and Gostin (2006), and Katz and Fischer (2010).

In her study of state commitments to the International Criminal Court (ICC), Kelley (2007) makes a similar point. One of the ICC’s central aims was to “assure delivery of indicted suspects to the Court,” and so examining variation in which states signed a non-surrender agreement with the US offered a good test of states’ commitments to the institution because such agreements clearly undermined the ICC’s purpose (Kelley 2007, 574).

During H1N1, the Food and Agriculture Organization, World Organization for Animal Health, and World Trade Organization (WTO) agreed with WHO that barriers would not prevent H1N1 spread (World Trade Organization 2009). Even when WHO has issued travel advisories warning against travel to certain areas because of disease, SARS being the most notable example, states often impose measures that are more restrictive than WHO guidelines.

Furthermore, domestic politics has been shown to be a key determinant of state behavior during domestic crises. Studies of derogations from human rights treaties and human rights violations during states of emergency are instructive on this count, showing that domestic political factors and regime type affect whether and how states uphold commitments to these treaties when they face the hard test of a crisis such as an armed conflict, economic shock, or natural disaster (e.g., Hafner-Burton et al. 2011; Neumayer 2013). With the potential to threaten the health of the population, economic productivity, and social stability, a major outbreak may be viewed as a potential crisis; and as such, governments may be particularly likely to base their response on domestic political incentives.

Note that Holy See was also a party to the IHR at the time of H1N1, but is not included in the analysis; its exclusion does not affect the findings.

Complete descriptions of variable measurement and tables of summary statistics can be found in the Online Appendix, which is available at this journal's webpage.

“WHO H1N1 Situation Reports” available at http://gis.emro.who.int/, WTO “Trade Monitoring Database” available at http://tmdb.wto.org/, USTR report available at http://www.ustr.gov/sites/default/files/SPS%20Report%20Final(2).pdf, and US government cables available at http://www.wikileaks.org.

Imputation is not compatible with clustering standard errors. Because clustering is particularly likely in time-series cross-sectional data, I use listwise deletion for Model 7 and cluster standard errors by country. To account for potential clustering in the other models, the Online Appendix presents results for the following: Models 1 through 3 using a generalized estimation equation (GEE) that allows for dependence within regional clusters (Zorn 2001) and Models 4, 5, and 6 using listwise deletion and clustering standard errors by country (see Tables A6 and A10).

Akaike information criterion for Table 2 models: Model 1: 193.78, Model 2: 187.38, Model 3: 169. The likelihood ratio (LR) test statistic also shows that adding the interaction term in Model 2, and then additional controls in Model 3, improves fit. To calculate the LR test statistic here and for Models 4–6 below, I re-run all models using listwise deletion (see Tables A6 and A10 in the Online Appendix). Comparing Models 1 and 2, the LR test statistic is 8.67, p < .01. For Models 2 and 3, the LR test statistic is, 31.85, p < .001.

Figures 1 and 2 based on Model 2. All simulations produced using the Zelig package in R (Imai et al. 2007, 2008).

N = 190 for Models 4, 5, and 6. Three observations are dropped because timing information was not available.

Akaike information criterion for Table 3 models: Model 4: 433.94, Model 5: 427.64, Model 6: 413.02, and Model 7: 311.76 (note that because Model 7 uses different data it cannot be directly compared to the others). And again, the LR test statistic also shows that adding the interaction term in Model 4, and then additional controls in Model 5, improves fit. Comparing Models 4 and 5, the LR test statistic is 9.68, p < .01. For Models 5 and 6, the LR test statistic is 23.54, p < .01.

Results are insensitive to different length observation periods.

Figure 3 is based on Model 5.

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Titel: Domestic politics and the WHO’s International Health Regulations: Explaining the use of trade and travel barriers during disease outbreaks
verfasst von: Catherine Z. Worsnop
Publikationsdatum: 26.11.2016
Verlag: Springer US
Erschienen in: The Review of International Organizations / Ausgabe 3/2017
Print ISSN: 1559-7431
Elektronische ISSN: 1559-744X
DOI: https://doi.org/10.1007/s11558-016-9260-1

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Weitere Artikel der Ausgabe 3/2017

Good countries or good projects? Comparing macro and micro correlates of World Bank and Asian Development Bank project performance

Ruth Ben-Artzi. 2016. Regional development banks in comparison: Banking strategies versus development goals (New York, NY: Cambridge University Press)

Balancing friends and foes: Explaining advocacy styles at global diplomatic conferences

The reputations of NGOs: Peer evaluations of effectiveness

Financing the United Nations: Explaining variation in how donors provide funding to the UN

Xu, Jiajun. 2017. Beyond US Hegemony in International Development: The Contest for Influence at the World Bank. (Cambridge: Cambridge University Press)

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