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Influence and support for foreign aid: Evidence from the United States and China

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Abstract

When do members of the public support outgoing foreign aid? Existing research often focuses on individual and sociotropic sources of support based on economic, ideological, and emotional considerations. This article examines a potentially under-appreciated source of attitudes toward outgoing aid: foreign policy influence for one’s country. I argue that observers can make intuitive associations between different types of aid and different influence outcomes, and that the prospect of influence will generally increase support for outgoing aid. To test these claims, I conduct parallel survey experiments in the two largest donor and lender countries, the United States and China. I find that an aid project’s mode of delivery and degree of visibility affect its perceived value for influence-seeking, and that respondents understand and generally support the use of aid for influence. While direct aid to governments does not appear to increase support, project visibility does, and support is particularly high for visible aid provided directly to host country governments. The findings contribute to existing international relations and political economy research on aid and public opinion, international influence, and Chinese development finance.

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Data Availability

Replication data and code are available on the website of the Review of International Organizations.

Notes

  1. See https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-rebranding-united-states-foreign-assistance-advance-american-influence/.

  2. Milner (2006) shows that concern about domestic public opinion is an important reason why governments opt to contribute substantial multilateral aid even though doing so relinquishes control relative to providing aid bilaterally. Others argue that public opinion toward aid only sometimes affects actual aid allocation (e.g. Otter , 2003.

  3. Though, governments with relatively strong domestic support bases for aid giving, such as the U.K., may be able to protect aid budgets during economic downturns (Paxton & Knack, 2012).

  4. As a recent example, in 2018 Japan’s Ministry of Foreign Affairs began a domestic promotional campaign featuring an “ODA-man” anime character to bolster popular support for its overseas development assistance amid rising domestic demographic and economic pressures (Lindgren, 2020).

  5. Public foreign policy preferences are shaped by political ideology, partisanship, as well as a host of individual-level attributes. Attitudes are also mediated by framing by the media and cue taking from elites (Zaller, 1992; Baum & Potter, 2008; Berinsky, 2009; Baum & Groeling, 2009).

  6. Relatedly, constituents may support aid to the extent it increases their own economic welfare or reflects humanitarian values they support (Milner & Tingley, 2011; Heinrich, 2013).

  7. Such perceptions may depend on respondents’ education levels (Hainmueller & Hiscox, 2006, 2010), in-group and out-group dynamics (Mansfield & Mutz, 2009), information on and comprehension of policies (Bearce & Tuxhorn, 2017), and other individual characteristics such as gender (Mansfield et al., 2015).

  8. A diverse set of economic, political, and security interests motivate donor governments’ provision of development finance (e.g. Maizels and Nissanke , 1984; Meernik et al. , 1998; Burnside and Dollar , 2000; Katada , 2001; Milner and Tingley , 2010).

  9. Soft power is usually defined as governments’ ability to advance their interests via attraction rather than coercion (Nye Jr, 2004). Favorable foreign public opinion is a common measure in international relations for soft power (e.g., Nye Jr , 2004; Goldsmith and Horiuchi , 2012; Rose , 2016).

  10. Not doing so can be costly. For instance, popular anti-American sentiment abroad serves as a symbolic resource for mobilizers in other countries to affect policy change locally and internationally (Schatz, 2021).

  11. Earlier studies consider how project-specific features, such as sector (e.g. economic or military) and financing type (e.g. grant or loan), affect the strategic utility of projects for both donors and recipients (e.g. Poe and Meernik , 1995; Dube and Naidu , 2015; Bermeo , 2016; Dreher et al. , 2018). Several other studies examine how variation in donor identity produces different popular reactions (Findley et al., 2017a, b; Cha, 2020; Blair & Roessler, 2021).

  12. Some exceptions exist. For instance, Dietrich et al. (2019) examine different levels of project branding, and Milner et al. (2016) investigate attitudinal effects caused by different project funder identities.

  13. A sizeable literature in political science conceptualizes influence. One classic definition is “a relation among actors in which one actor induces other actors to act in some way they would not otherwise act” (Dahl , 1973, 40). This literature suggests that states’ ability to translate raw power into political influence takes on various forms (Lukes, 1974; Nye Jr, 2004; Gaventa, 1982; Barnett & Duvall, 2005).

  14. Combinations of non-governmental actors such as NGOs, INGOs, and commercial contractors implement a growing share of development finance projects (Dietrich, 2013).

  15. High-visibility projects, in addition to their conspicuous physical presence relative to low-visibility projects, also tend to receive more publicity, particularly around key milestones such as a project’s announcement, commencement, or completion ceremonies (e.g. Menga , 2015).

  16. National leaders similarly often possess political incentives to pursue domestically-financed infrastructure and other visible development projects (Mani & Mukand, 2007; Harding, 2015; Lei & Zhou, 2022).

  17. Alternatively, opportunistic opposition politicians can exploit negative sentiment toward visible aid projects (or their proponents) for domestic political gain (O’Brien-Udry, 2020).

  18. See https://stats.oecd.org/.

  19. The surveys and embedded experiments were not pre-registered. All outcome questions included in the surveys are discussed in the manuscript and reported in Appendix 2. The online appendix is available on the Review of International Organizations’ webpage.

  20. Lucid was acquired by the Cint Group. See https://www.cint.com/blog/lucid-and-the-trade-desk-power-advertisers-brand-impact.

  21. It was placed in the fifth of six short question blocks prior to treatment. This follows suggestions to place attention checks shortly before treatment since attentiveness can vary while taking a survey (Kane et al., 2023). It asked respondents “To verify that you are paying attention, please select the ‘somewhat disagree’ response option.”

  22. In the power analysis, the minimum effect of interest was .5, the standard deviation was 1.5, and the significance level was .01, inputs that were informed by other recent experimental studies I have conducted in order to achieve 80% power. Power was calculated for direct tests of differences in means between treatment groups. This analysis suggested that each treatment group should include about 211 respondents.

  23. In Appendix 3, Figures 5, 6, 7, 8, and 9 plot treatment group balance for a set of pre-treatment covariates. Tables 2 and 3 further examine balance across treatment groups for the American and Chinese samples.

  24. Moreover, as reflected in Appendix 5 (for example, Tables 9, 10, 11, and 12), neither age nor other covariates for which the samples are unrepresentative appear strongly associated with perceptions of aid’s influence functions or outcomes. In the American sample, age is negatively associated with perceptions of influence functions while it is positively associated with them in the China sample. The results for other covariates–such as gender, education, and income–are generally not significant.

  25. See https://foreignassistance.gov/.

  26. https://www.foreignassistance.gov/cd/kenya/2017/disbursements/0. Disbursement data is not publicly available for Chinese development finance.

  27. The survey administered online to Chinese respondents initially used a seven-point scale to measure the same questions, and the results herein are transformed to five-point scale in order to make apples-to-apples comparisons about effect magnitudes. The substantive results are unchanged when using the original scale.

  28. Base covariates include respondent age, gender, level of education (whether one has a college degree), income (based on an 11- and 14-tiered scale of income ranges for the US and China, respectively), and political ideology (US only; coded as 1 if a respondent identifies as conservative along a seven-point numerical spectrum). In addition to the covariates included in these regressions, the surveys also captured additional pre-treatment factors such as measures of nationalism, support for democracy, and foreign policy ideology. Including these covariates does not change the results.

  29. Again, results here are presented in terms of raw five-point scales.

  30. As this study examines multiple outcomes to probe potential mechanisms, I also compute Bonferroni-corrected p-values for each hypothesis test. The significance of the main results holds and the differences in means remain statistically significant at more stringent thresholds (e.g. .01).

  31. Though it is also unclear the extent to which American respondents were familiar with bypass aid prior to the survey treatment.

  32. These questions also serve as implicit post-treatment manipulation checks. For example, respondents treated with a visible project should report higher level of agreement that the project will be highly visible.

  33. Non-governmental actors such as international NGOs also prioritize project visibility among donor and host country audiences to attract financing and improve their public reputation (Phillips, 2019).

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Acknowledgements

This work was supported by the Seed Fund for Basic Research for New Staff (“Understanding China’s High-Profile International Development Projects”) within the University of Hong Kong’s University Research Committee. The Harvard Digital Lab for the Social Sciences (DLABSS) supported the project’s early development. The author thanks the editor and three anonymous reviewers, Tyler Jost, Azusa Katagiri, Boram Lee, Dov Levin, Ryan Powers, and Kevin Troy for helpful comments on earlier versions.

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Strange, A. Influence and support for foreign aid: Evidence from the United States and China. Rev Int Organ (2023). https://doi.org/10.1007/s11558-023-09520-5

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