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The Review of International Organizations
Published in: The Review of International Organizations 2/2019

01-04-2019

Popular non-support for international organizations: How extensive and what does this represent?

Authors: David H. Bearce, Brandy J. Jolliff Scott

Published in: The Review of International Organizations | Issue 2/2019

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Abstract

This paper investigates popular non-support for international organizations (IO), asking two questions. First, are attitudes within the mass public becoming less supportive of IOs? Second, how can we explain these IO attitudes, especially when the mass public appears to know relatively little about specific international institutions? Using survey data from the International Social Survey Programme’s National Identity module, fielded across multiple countries in 1995, 2003, and 2013, it reports that on average and within most countries, citizen attitudes about IOs have become less positive over time. To explain these attitudes, this paper argues that citizens tend to group things that appear as “international” such as cross-border economic flows and IOs. While citizens might feel positively or negatively about these international factors, this grouping implies that they view them similarly, based on what they can feel from the international level related to their job and income. Thus, less (more) skilled citizens who are hurt by (who benefit from) economic globalization should express more negative (positive) views about IOs. Controlling for cultural attitudes socialized through education, we find that skill is a statistically significant and substantively strong predictor of IO attitudes. We also show how this individual-level skill difference gets larger in countries that are more and/or less-favorably exposed to economic globalization.
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Appendix
Available only for authorised users
Footnotes
2
Michael R. Gordon and Niraj Chokshi. “Trump Criticizes NATO and Hopes for ‘Good Deals’ With Russia.” New York Times January 15, 2017.
 
5
Oliver Wright. “Euroscepticism on the rise across Europe as analysis finds increasing opposition to the EU in France, Germany and Spain.” Independent June 7, 2016.
 
6
Using a 2016 population-based American sample, Bearce and Cook (forthcoming) report that only 36% of respondents are willing to report themselves as familiar with the World Trade Organization and only 55% as familiar with the United Nations.
 
8
The introductory essay by Copelovitch and Pevehouse usefully separates populism, with its demands for economic redistribution, from nationalism, efforts to reclaim sovereignty from international arrangements. In their typology of the backlash against international organizations (Figure 1), Copelovitch and Pevehouse consider how these two dimensions (populism and nationalism) may sometimes overlap (i.e., in the top left cell). Our argument about non-support for IOs, which may include preferences for withdrawal, coming from those hurt by economic globalization, namely the less skilled, best fits into this cell.
 
9
These calculations thus include those who did not offer a response, so the favorable IO percentages in Table 1 would be slightly larger if we excluded non-respondents. But the very same trends are present either including or excluding non-respondents.
 
10
The ISSP makes their country samples more nationally representative by including a country-specific WEIGHT variable (GESIS 2015, 746), which we use for calculating the percentages for each country individually. But following the ISSP’s advice, this weighting variable is not used for the country-national mean because “there is no total weight usable for international comparison.” “While all ISSP data sets contain a weighting variable, the weights in this variable do not incorporate a common weighting scheme that can be applied to all countries of the same ISSP module” (ibid xv).
 
11
For 95% confidence, the margin of error is equal to 1.96 multiplied by the square root of (((p)*(100-p))/N) where p is the cell percentage in Table 1 and N is the sample size for that cell.
 
12
The same statistically significant trends are apparent if we consider only the 15 countries that were included in all three waves.
 
% agreeing with
IO Enforcement
% agreeing with
IO Compliance
% disagreeing with IO Threat
Wave:
1995
2003
2013
2003
2013
2003
2013
N=
17,392
18,535
17,526
18,535
17,526
18,535
17,526
Cross-national mean
(margin of error)
69.0
(±0.7)
66.0
(±0.7)
59.2
(±0.7)
32.9
(±0.7)
29.4
(±0.7)
21.0
(±0.6)
18.7
(±0.6)
 
13
Consistency with the margin of error is why we present the country percentages as whole numbers, unlike for the cross-national means (which are presented to the first decimal place).
 
14
Tsai (2017, 342–3) examines Asian attitudes towards the UN, the World Bank, and the International Monetary Fund, finding them to be generally favorable at least in 2005–7, speculating that citizens in these “lower-income countries” may be more favorable towards these IOs (compared to Europeans) because they “have been recipients of overseas assistance and development aid”.
 
15
As Triandafyllidou (2017, 215) described the International Organization of Migration: “There is no formal institutionalised framework for the governance of international migration in the way for instance that it exists for trade…. Institutions like the International Organization for Migration (IOM) while transnational in nature are mainly service providers depending on individual states for their services.” As Thomas (2016, 899) similarly discussed, “the International Organization of Migration (IOM)… [is] not a treaty-based organization (like the UN or World Trade Organization (WTO)), despite the name, but an agency established by governments for the purpose of managing migrant resettlement and return.”
 
16
The obvious and important exception here is the EU’s free movement of workers, which regulates immigration policy among EU member-states. But each EU member-state sets its own national immigration policy vis-à-vis non-member-states.
 
17
It is important to note that this expectation differs from the one advanced by Ecker-Ehrhardt (2012, 486), arguing that “most societies tend to support internationalized governance” and proposing that economic interdependence should generally increase citizen support for IOs. Similarly, Tsai (2017, 327) posits that IO support should increase “when an individual becomes more exposed to globalization.”
 
18
In a factoral framework with four factors (low skill labor, high skill labor, capital and land), education is typically used as a proxy for human capital, helping to separate high skill from low skill labor. Income may be correlated with education, but it better proxies capital with wealthier individuals having greater endowments of this factor.
 
19
The factoral framework assumes that factors can move across sectors. This assumption is more immediately true for high skill labor, but may be true only in the long run for low skill labor.
 
20
GESIS 2015, 202.
 
21
GESIS 2015, 43.
 
22
GESIS 2015, 97.
 
23
GESIS 2015, 350.
 
24
If one is more concerned that Income and Employed capture post-treatment effects, rather than Education selection factors, we can report that our results are robust to their exclusion.
 
25
If one is concerned that our individual-level controls, intended to capture factors that select individuals into different levels of Education, are instead only measuring post-treatment effects, then we present another version of model 2.1 in the first column of Table 8 in the Appendix. Without these individual-level control variables, the Education coefficient increases by more than 20%.
 
26
World Bank 2016.
 
27
World Bank 2016.
 
28
World Bank 2016.
 
29
International Monetary Fund 2016.
 
30
Weakly consistent with H2a, the Immigrant Share constitutive term, capturing the effect of more immigration for those with the least skill (Education = 0), is negatively signed in model 3.1. This result is statistically significant using a one-tailed test, which would be appropriate given our directional hypothesis.
 
31
In accord with H2b, the Tariff Rate constitutive term takes on a statistically significant positive sign (0.111) in model 3.2, showing that even the least skilled individuals become more supportive of IOs with more trade protection.
 
32
Also consistent with H2c, the Investment Inflows constitutive term takes on a statistically significant positive sign (0.019) in model 3.3, indicating that even the least skilled individuals (Education = 0) become more Pro IO with more inward investment.
 
33
GESIS 2015, 121.
 
34
GESIS 2015, 79.
 
35
GESIS 2015, 85.
 
36
These significant positive correlations are present not only for individuals without a college degree (Education ≤ 4) but also for those with at least a college degree (Education ≥ 5).
 
37
World Bank 2016.
 
38
Correlates of War 2017. To arrive at a percent, we multiply the CINC scores by 100.
 
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Metadata
Title
Popular non-support for international organizations: How extensive and what does this represent?
Authors
David H. Bearce
Brandy J. Jolliff Scott
Publication date
01-04-2019
Publisher
Springer US
Published in
The Review of International Organizations / Issue 2/2019
Print ISSN: 1559-7431
Electronic ISSN: 1559-744X
DOI
https://doi.org/10.1007/s11558-019-09351-3

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