African attitudes toward foreign countries: A hierarchical approach

Abstract

French

This study uses a hierarchical statistical model to explore what drives African attitudes toward foreign countries, an understudied and underappreciated topic in the fields of African studies, political psychology and international relations. Enabled by increasing access to information, technology and resources, African peoples across the continent are directing their national stages like never before. Yet when it comes to international engagements on the continent, there is little scholarly focus on African sentiment toward foreign countries. The present study finds that the drivers of attitudes toward foreign countries vary, depending on which foreign country is under consideration by the respondent. For China, positive sentiment is associated with individuals who report having assets, belong to the president’s party, view domestic governance positively, are more politically interested, are more trusting, are educated and are more frequent news consumers. While political interest, trust, education, news consumption and positive evaluation of domestic governance also correspond to positive attitudes toward the USA, so does being employed and perceiving the domestic government as corrupt. When it comes to attitudes toward former colonies, trust, education, news consumption, positive evaluations of domestic governance, perceiving the domestic government as corrupt, employment, identification with the president’s party and support for democracy are associated with positive sentiment. For China, the USA and former colonies, negative sentiment is associated with individuals who identify as Muslim.

Keywords

African public opinion international relations in Africa political psychology

Introduction

In many ways, Africa is rising. Economists predict that many African countries are poised to meet or exceed the economic success of the Asian Tigers,¹ overall conflict on the continent is declining (see Williams, 2011 for review of conflict trends) and many African countries are democratizing (Diamond & Plattner, 2010; Lynch & Crawford, 2011; Young, 2012). These positive trends, combined with diverse natural resources, make the continent attractive for foreign economic engagement. At the same time, remaining conflicts on the continent call for international peacekeeping, foreign military interventions and humanitarian assistance. In short, foreign engagement, of all flavors, in Africa is quite high. At the same time, Africa is a continent with a long and sober history – shaped primarily by its colonial experience and the Cold War – of international meddling. Given the contentious history the continent has had with the international community, how do Africans view foreign country engagement in Africa today? As foreign countries look to Africa as a place of opportunity, do African citizens perceive this as a threat – reminiscent of colonial times – or an opportunity?

While a few case studies examine attitudes toward China (e.g. Esteban, 2010; Monson & Rupp, 2013; Rebol, 2010; Rotberg, 2008), systematic and cross-national studies of African attitudes toward a variety of outsiders are scarce to non-existent. There is one cross-national academic study that examines African views toward China (Gadzala & Hanusch, 2010) and a handful of cross-national studies that examine African attitudes toward the USA (e.g. Blaydes & Linzer, 2012; Chiozza, 2009b, Moehler & van de Walle, 2010);² there are no studies that cross-nationally compare African attitudes toward a variety of foreign countries systematically.³ Because of the lack of systematic research in this area, the western media have too often become the de facto source for ‘evidence’ of African public opinion. This state of affairs is problematic because the media often perpetuate severe biases and stereotypes, especially with regard to our understanding of the Sino-African relationship: ‘[M]edia outlets … report a highly skewed image against the dictates of raw facts that provide a more positive view’ (Maru, 2013: 2). Chiozza identifies a similar problem regarding global opinions of the USA: ‘Systematic studies of how the U.S. is perceived by foreign publics are scarce, while impressionistic accounts abound. Academics, policy makers, and journalists have so far offered overviews rich in intuition at best or mired in prejudice at worst’ (Chiozza, 2009b: 3). There is clearly a need for more systematic research on this topic.

The dearth of public-opinion analysis leaves significant holes in a few different literatures: for Africanists, there is a lack of comparative, comprehensive and systematic research on how (and why) Africans feel about foreign countries operating in their homelands. This ought to be of significant interest to the academic community as a matter of post-colonial African narrative (see Sylvanus, 2013). For international relations scholars, comparing host-country reactions to different foreign countries can illuminate successful – and unsuccessful – foreign policy, identify unintended consequences, suggest triggers to international conflict, and so on. For political psychologists, identifying what kinds of respondents develop pro- or anti-opinions about complex objects such as foreign countries, and why, is an important part of understanding international relations (Herrmann, 2013; Jervis, 1976; Kahneman et al., 1982; Neto et al., 2013).

In this study, I seek to address some of these gaps in the literature by systematically examining determinates of African attitudes toward foreign countries. I investigate what kinds of respondents are more likely to form negative opinions of foreigners, to investigate whether there is a difference in opinion toward five foreign countries: China, the respondent’s former colony, Nigeria, South Africa and the USA, and why. I test a variety of hypotheses based on individual predispositions, as well as three sources of information that are thought to shape opinions at the individual level of analysis: material self-interest, social group affect and political values. I test these hypotheses by nesting⁴ Afrobarometer Round 4 (AB4) survey data within country-level factors using the Generalized Linear Latent and Mixed Model (GLLAMM) technique.⁵ Findings suggest that, consistent with the anti-Americanism literature, African attitudes toward foreign countries are complex and inconsistent. Individual predispositions are particularly strong and clear indicators of attitudes toward other countries, while the effect of information is diverse. Finally, there is no apparent trend in the way Africans view all foreign countries: instead, the drivers of individual-level attitudes vary for each of the five foreign countries tested in this study.

In the next section, I discuss drivers of attitudes. Then I review the relevant literature in order to portray the state of academic research on African attitudes toward foreign countries. I use this literature to derive hypotheses and theory, which I then test using a nested model of survey and country-level factors. I end with a discussion of the findings and ways forward.

Attitude formation and foreign countries

Generally speaking, attitudes are based on individuals’ evaluations of objects (Caprara & Vecchione, 2013; Taber & Young, 2013). In other words, an attitude ‘is a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor’ (Eagly & Chaiken, 1993: 1). In the world of politics, attitudes are formed through a combination of information and predispositions – such as individual traits and cognitive abilities – that lead an individual to evaluate an object favorably or unfavorably (Caprara & Vecchione, 2013; Chiozza, 2009b; Zaller, 1992).

Kinder posits three types of information that can affect individuals’ evaluations as they form opinions: a person’s material self-interest, affect toward social groups and political values (Kinder, 1998: 800). Values, in particular, have a strong impact on the evaluations people make about social issues because they are more central and enduring to individuals’ psyche (Zaller, 1992: 23). Political information, such as information about foreign countries and international relations, is supplied largely by political elites and disseminated through mass media (Blaydes & Linzer, 2012; Katzenstein & Keohane, 2007; Zaller, 1992).

The ways in which individuals process information varies according to their political awareness (McDermott, 2004; Zaller, 1992) and cognitive abilities (Taber & Young, 2013). The extent to which people seek to make themselves politically aware, as well as their ability to process information, is largely determined by their predispositions. Predispositions are based upon basic traits which can both ‘precede and predispose one to adopt specific self-beliefs, values, and social attitudes’ (Caprara & Vecchione, 2013: 27), as well as regulate the information communicated through mass media and an individual’s stated political preferences (Zaller, 1992: 23).

Attitudes are aggregated into what Converse (1964) calls belief systems. When belief systems are ‘constrained’, there is a logical connection between attitudes. That is, ‘one sort of attitude (logical orientation) constrains other attitudes (policy preferences), thereby linking a range of attitudes into a cohesive “belief system”’ (Zaller, 1992: 113). However, when it comes to individuals’ opinions about the USA, scholars have consistently uncovered contradictory and ambiguous belief systems about the USA because, as Chiozza explains, ‘America is an inherently multi-dimensional “object”’ (2009b: 47; see also Katzenstein & Keohane, 2007). Given the multi-dimensional nature of all foreign countries, we should expect the same kind of results for individual attitudes toward the countries under examination in this study: China, the USA, South Africa, Nigeria and respondents’ former colonies.

To summarize, many factors may enter a person’s evaluation when thinking about a foreign country; these factors are shaped by a person’s predisposition and by information. A person could think about the geography of the foreign country, its culture, its people, its political leadership, its policies, its similarity to the person’s country of origin, and so on (Chiozza, 2007; Katzenstein & Keohane, 2007; see Herrmann 2013 for detailed review). Scholars suggest that the images and factors which come to mind and the way those images and factors influence a person’s stated opinion about another country are affected by the individual’s judgment of their own group versus those they perceive as outside their group (e.g. McDermott, 2004; Mummendey et al., 2010). As Herrmann explains, ‘The pictures people have of other countries become central building blocks in their identification of the threats and opportunities their country faces. These images of others can become assumptions that are so taken for granted that they produce routinized habits that define basic parameters of what is seen as in a country’s interest or contrary to it’ (Herrmann, 2013: 337). Attitudes are also influenced by domestic elite framing of political issues (Blaydes & Linzer, 2012; Zaller, 1992).

If the individual ‘pictures’ a foreign country as a threatening outsider or an enemy, then their evaluation of the country will likely be negative, even if evidence or arguments exist to contradict this attitude (e.g. Gentzkow & Shapiro, 2004; Jervis, 2010). If the individual views a foreign country as having more in common with their own country, they will likely evaluate the foreign country more positively (e.g. Nincic & Russett, 1979; Tims & Miller, 1986). If the individual sees opportunity in the foreign country, they are also more likely to view that country positively (this argument is implied in Nye, 2004). For example, Baker and Cupery (2013) find that domestic publics are especially likely to hold pro-American sentiment in countries with high levels of economic exchange with the USA or, rather, where economic opportunities are high.

These themes of the in-group/out-group and threat/opportunity effects on public opinion toward foreign countries have been developed and tested primarily in the literature on how Americans view foreign countries and how foreigners view America. Classic studies examined how Americans viewed the Soviet Union as the threatening outsider (e.g. Finlay et al., 1967; Holsti, 1967). The literature on how foreigners view Americans exploded after 11 September 2001 (e.g. Baker & Cupery, 2013; Chiozza, 2009a,2009b; Chiozza & Choi, 2012; Katzenstein & Keohane, 2007), and much of this literature focuses specifically on how Muslims in the Middle East and North Africa view the USA (e.g. Gentzkow & Shapiro, 2004; Nisbet & Myers, 2010, 2011; Nisbet et al., 2004). The focus on foreign attitudes toward the USA stems from the view that America is the unipole in the current unipolar international system; the implicit, sometimes explicit, undercurrent to this so-called ‘anti-Americanism’ literature, then, is to explore whether this unipole is considered threatening or non-threatening by those in the international community (see Chiozza, 2009b).

The much smaller body of literature on African opinions of foreigners and foreign countries does not typically employ in-group/out-group and threat/opportunity terminology, but these themes permeate the literature nonetheless. For example, studies of citizenship in Africa often uncover severe out-group divisions – often in the form of race, ethnicity and religion – that impede national-identity-building and enhance suspicion and intolerance of foreigners (e.g. Gray, 1998; Nyamnjoh, 2002, 2010). The effects of out-grouping and the creation of an ‘enemy image’ are especially evident in the xenophobia literature (e.g. Dodson, 2010). Murray (2003), for example, shows how socio-economic fragility and uncertainty in South Africa leads South Africans to view immigrants as a – real or imagined – menacing ‘other’, which leads to a real panic about ‘foreign invasion’ into South Africa. Contrary to Gentzkow’s and Shapiro’s (2004) findings that more economic exchange contributes to pro-American sentiment, Gadzala and Hanusch (2010) find that increasing Chinese imports have a negative impact on sentiment toward China, which suggests that economic exchanges do not have the same effect on attitudes toward all foreign countries. Instead, Gadzala and Hanusch’s findings suggest that Africans may view Chinese economic engagement as threatening.

As discussed above, few existing studies on the anti-Americanism literature examine sub-Saharan African opinions of the USA. One reason for this gap in research may be that, even if one takes the view that the USA is the unipole in the international community, it is not a major foreign actor in Africa.⁶ Current events suggest that other foreign countries, especially former colonies, China and even other Africa countries – especially South Africa – are much bigger foreign actors on the continent. This suggests that studies of African attitudes toward foreigners should encapsulate more than just attitudes toward the USA.

Expectations

Kinder’s three information types are essential in the calculus of in-group/out-group and threat/opportunity evaluations. For example, one’s economic status should impact how an individual views the economic activity of foreign countries in their homeland. If the individual views foreign economic activity as an opportunity to improve their own economic status, we would expect them to feel positively toward that country (or countries). Social-group identity, which delineates who shares the individual’s identity and who does not, is expected to have a strong impact on in-group and out-group evaluations. Shared political values are also expected to yield positive opinions of a foreign country because such shared values tend to make individuals feel like they are part of the same group and therefore should not view the foreign country as threatening. Individual predispositions also affect opinion formation because they help determine how information is processed (Chiozza, 2009b; McDermott, 2004; Zaller, 1992).

Individual-level opinions do not form in a vacuum. Rather, as Katzenstein and Keohane argue, ‘[T]o understand both the sources and consequences of anti-Americanism it is necessary to understand the political context that fosters or discourages negative attitudes toward the U.S. and that magnifies or minimizes the effects of these attitudes on policy’ (Katzenstein & Keohane 2007: 12). Democratic governments, for example, may be more likely than their non-democratic counterparts to be transparent about their engagement with foreign actors; citizens in democracies also have the opportunity to vote politicians out of office if they dislike their government’s foreign policies. To the extent that transparency and the opportunity to vote politicians out of office lessen the threat of foreign countries, we would expect citizens in democracies to feel more positively. Country-level economic circumstances may also impact sentiment toward foreign countries: individuals living in countries that are relatively wealthy are expected to hold more positive opinions of foreign countries, especially the USA (Blaydes & Linzer, 2012). The level of social heterogeneity within a country may also affect threat perceptions of ‘others’. Specifically, where there are many ethno-linguistic groups, in-group/out-group perceptions may be especially salient and translate into how individuals living within such contexts view all outsiders, including foreigners.

Measures and data

The individual-level data in this study come from Round 4 of the Afrobarometer survey (AB4).⁷ AB4 surveyed 20 sub-Saharan African countries⁸ between March 2008 and June 2009: Benin, Botswana, Burkina Faso, Cape Verde,⁹ Ghana, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Nigeria, Namibia, Senegal, South Africa, Tanzania, Uganda, Zambia and Zimbabwe.¹⁰ The individual-level factors taken from AB4 align to the hypothesized drivers of opinion discussed above: material self-motivation, political orientations, group orientations and predispositions.

The macro-level data – Human Development Index (HDI), economic growth, ethnic fractionalization and political rights – come from a variety of sources; the specific sources for each variable are detailed below. All measures are from 2008, to correspond with the AB4 data collection timeframe. Table 1 provides descriptive statistics on all variables across the 19 countries.

Table 1.

Country- and individual-level variation in independent variables.

	Benin	Botswana	Burkina Faso	Ghana	Kenya	Lesotho	Liberia	Madagascar	Malawi	Mali	Mozambique	Namibia	Nigeria	Senegal	South Africa	Tanzania	Uganda	Zambia	Zimbabwe	Total AB
Country-level variables
Human Development Index^[1]	0.425	0.626	0.322	0.520	0.429	0.441	0.346	0.486	0.392	0.332	0.306	0.597	0.453	0.461	0.613	0.414	0.437	0.420	0.345	0.449
Freedom House^[2] (1 = not free, 7 = free)	6.0	6.0	4.0	6.5	4.5	5.5	4.5	4.5	4.0	5.5	5.0	6.0	4.0	5.6	6.0	4.5	3.5	4.5	1.5	4.8
Ethnic fractionalization	0.30	0.00	0.00	0.44	0.57	0.00	0.62	0.00	0.55	0.13	0.36	0.55	0.66	0.14	0.49	0.59	0.63	0.71	0.41	0.38
Individual-level variables
Age (mean)	35.4	40.2	36.6	39.0	35.2	41.4	35.8	39.5	35.5	39.1	30.6	34.8	31.3	38.9	37.8	37.5	33.7	35.1	36.6	36.2
Urban dwellers	41 %	55 %	27 %	45 %	23 %	26 %	47 %	24 %	15 %	27 %	32 %	36 %	49 %	45 %	66 %	26 %	20 %	37 %	37 %	37 %
Education mean (0 = none, 9 = doctorate)	2.1	3.4	1.5	2.9	3.8	2.8	3.0	2.9	2.4	1.2	3.1	4.0	4.4	2.0	4.3	3.1	3.4	3.4	3.8	3.2
News (0 = never get news, 4 = get news daily)	1.7	2.3	1.4	2.1	2.2	1.3	1.2	1.6	1.3	1.6	1.6	2.4	2.4	2.3	3.1	1.9	1.7	1.6	1.4	1.9
Perception of international donors (0 = no help, 4 = help a lot)	2.6	2.6	2.9	2.6	2.7	3.3	2.8	3.0	2.3	2.8	2.5	2.7	2.2	2.6	2.1	2.6	2.7	2.7	3.3	2.6
Employed	19 %	29 %	15 %	58 %	41 %	15 %	18 %	56 %	16 %	16 %	25 %	41 %	51 %	25 %	50 %	45 %	40 %	23 %	23 %	33 %
Assets mean (0 = own nothing, 1 = own all three)	0.44	0.44	0.36	0.41	0.42	0.33	0.24	0.35	0.30	0.36	0.39	0.40	0.53	0.40	0.61	0.30	0.32	0.35	0.33	0.40
Remittance receivers	12 %	11 %	23 %	22 %	11 %	30 %	22 %	5 %	11 %	24 %	10 %	16 %	24 %	31 %	15 %	5 %	13 %	10 %	30 %	17 %
Muslim	27 %	0.5 %	58 %	16 %	14 %	0 %	9 %	3 %	11 %	88 %	21 %	0 %	38 %	95 %	3 %	33 %	10 %	0.1 %	0.4 %	22 %
Perceptions of elite corruption (0 = none are corrupt, 4 = all are corrupt)	2.0	1.3	1.7	1.7	2.1	1.4	2.0	1.6	1.7	2.1	1.5	1.6	2.2	1.9	1.7	1.5	2.1	1.8	1.9	1.8
Perceptions of governance (1 = very bad, 4 = very well)	2.8	3.3	2.6	3.2	2.6	2.5	2.7	3.1	3.2	2.7	3.2	3.1	2.6	2.4	2.8	3.0	2.8	2.3	3.0	2.8
Support for democracy (0 = democracy doesn’t matter, 2 = democracy is important)	1.7	1.8	1.5	1.7	1.7	1.2	1.6	1.3	1.6	1.6	1.4	1.5	1.5	1.6	1.5	1.6	1.7	1.7	1.6	1.6
Party identification (0 = opposition or no party ID, 1 = president’s party)	19 %	55 %	38 %	36 %	14 %	33 %	20 %	24 %	48 %	10 %	63 %	44 %	22 %	31 %	29 %	71 %	38 %	22 %	8 %	34 %
Political interest (0 = not interested; 3 = very interested)	1.9	2.0	2.0	1.9	2.0	2.0	1.5	1.6	1.8	2.0	1.9	1.7	1.7	1.9	1.6	2.2	1.7	1.7	1.7	1.8
Trust (0 = no trust; 3 = trust a lot)	1.6	2.0	2.0	1.8	1.4	1.7	1.4	1.6	2.0	1.8	2.1	1.9	1.1	1.6	1.5	2.0	1.5	1.6	1.4	1.6

The original HDI combines measures along three dimensions – education, health and living standards – into a composite value between 0 and 1 (see http://hdr.undp.org/en/statistics/hdi/ for further details on the construction of the HDI). Here, I transform the original HDI to a scale from 1 to 4, where 1 = HDI is less than 0.4, 2 = HDI is between 0.4 and 0.5, 3 = HDI is between 0.5 and 0.6, and 4 = HDI is greater than 0.6.

The original Freedom House score ranges from 1 = not free, to 7 = free. I invert the scale in order to make the regression analysis more intuitive. Freedom House data are available at www.freedomhouse.org. Values are from 2008.

Dependent variables

I construct the dependent variables – named ‘China’, ‘USA’, ‘former colony’,¹¹ ‘Nigeria’ and ‘South Africa’ – using the AB4 question series: ‘In your opinion, how much do each of the following do to help your country, or haven’t you heard enough to say?’ Responses are coded 0 to 4, where 0 = ‘do nothing/no help’ and 4 = ‘help a lot’. ‘Don’t know’ responses are recoded to the midpoint on this scale (see Appendix). While this question does not allow us to examine specific types of foreign activity, it offers a good measure, overall, for how Africans feel about the foreign countries.

The data in Table 2 indicate how African opinions toward China, the USA, former colonies, Nigeria and South Africa compare: Africans have the highest opinions, on average, of the USA (mean = 2.61), followed by the respondent country’s former colony (mean = 2.43). China is ranked third (mean = 2.41) and South Africa fourth (mean = 2.10). AB4 respondents have the lowest opinion of Nigeria (mean = 1.80).

Table 2.

African opinion toward foreign countries (Scale is 0–4).

Country	Mean African opinion	Standard deviation	Minimum	Maximum
USA	2.61	1.21	1.9 (South Africa)	3.3 (Liberia)
Former colony	2.43	1.21	1.8 (South Africa)	2.8 (Lesotho)
China	2.41	1.25	1.7 (Uganda)	3.0 (Lesotho)
South Africa	2.10	1.24	1.5 (Uganda)	3.3 (Lesotho)
Nigeria	1.80	1.20	1.3 (Kenya)	3.0 (Benin)

Figure 1 shows the comparison of mean responses to the five dependent variables across the 19 African countries sampled in AB4. As Figure 1 demonstrates, there is wide variation in how respondents feel toward foreign countries: the lowest mean opinion of China is 1.7 (Uganda) while the highest is 3.0 (Lesotho). Botswana and Lesotho hold the highest opinion of their former colonies¹² (2.8), and respondents in Liberia report very high opinions of the USA (3.3). South Africans hold the lowest opinions of their former colony (1.8) and the USA (1.9). Respondents in Lesotho feel the most positively about South Africa¹³ (3.3), while Kenyans and Ugandans feel the most negatively (1.5). Finally, respondents in Benin report the highest opinion of Nigeria (3.0), while Kenyans hold the lowest opinion (1.3).

Figure 1.

African opinions of foreign countries – AB4 data.

Independent variables

In this section, I describe how I operationalize the four main hypotheses – material self-interest, group orientation, political values and predispositions. Material self-interest is measured by three variables: an individual’s self-reported employment status, possessing goods and whether or not the individual receives remittances. Group orientation is measured by party affiliation and whether or not the individual identifies as Muslim. Political values are measured by perceived elite corruption, support for democracy and an individual’s evaluation of governance. Finally predispositions are measured by education, political interest and frequency of news consumption. These variables are not correlated,¹⁴ so we can be confident that each is measuring a unique aspect of each hypothesis. I now discuss the logic underlying the decision to use each variable to test its respective hypothesis.

Material self-interest

Employment – which provides income – measures how well an individual is doing materially and is measured here using AB4 question 94. Missing and ‘don’t know’ responses are dropped. I then create a binary variable where 0 = not working and 1 = working (this includes part-time and full-time employment).

Remittance flows to sub-Saharan Africa were approximately 20% of GDP in 2008 (Ratha et al., 2008), and recent research shows that remittances to Africa in 2010 outpaced western official development assistance.¹⁵ Remittances in Africa provide an alternative income and therefore increase the recipient’s material well-being. Remittance recipients may thus feel less of the direct economic impact of foreign country activity in their homelands, and may therefore have more neutral attitudes toward foreign countries than non-remittance recipients. Remittance recipients also presumably have a connection with family and friends abroad, which may improve their perceptions of foreign actors (Moehler & van de Walle, 2010 suggest such a relationship with regard to opinions of the USA). The original remittance variable, AB4 question 87, is coded 0 to 5 in AB4, where 0 = the respondent never receives remittances and 5 = the respondent receives remittances at least once a month. I recode this variable to make it binary, where 1 = the respondent is a remittance recipient and 0 = the respondent is not a remittance recipient. Missing observations are dropped and ‘don’t know’ responses are recoded to 0.

A person’s material self-interest is also measured by the goods they own. Owning goods may be impacted by the activity of foreign countries. For example, importing inexpensive Chinese goods may allow more Africans to buy things (Rupp, 2008). Thus, we might expect that the more Africans own, the more they may view countries like China favorably. The concept of access to goods is operationalized in this article using an additive index based on whether or not respondents own a radio, a television and a motor vehicle (missing and ‘don’t know’ responses are dropped before creating the index), resulting in the assets variable where 0 = the respondent owns nothing and 1 = the respondent owns all three.

Group orientation

The dominant research concerning how other countries view America post-11 September 2001 is the study of Muslim attitudes toward the USA. Conflict between Islam and the USA (or the West, more broadly) appear to be rooted somewhat in differences in philosophies about life (Lynch, 2007). While some believed that 11 September brought Samuel P. Huntington’s (1996) clash-of-civilizations hypothesis to life, his thesis has not received empirical support. Instead, Muslims display anti-Americanism over what America does, rather than what it is. For example, Furia and Lucas (2006) test a specific ‘clash-of-civilizations’ hypothesis, but find no support for the idea that majority Muslim countries systematically dislike non-Muslim countries (including the USA). This finding is corroborated by further research showing that spikes in anti-Americanism among Muslims – such as those that occurred around 2003 and the invasion of Iraq – are episodic and driven by what the USA does, rather than structural (Chiozza, 2009a). Other scholars find that Muslims are more likely to dislike the USA when their elites face competition between religious and secular ideologies. In fact, Blaydes and Linzer (2012) find that the most devout Muslim countries are less likely to hold anti-American opinions. To test whether identifying as Muslim has similar effects on attitudes toward foreign countries other than the USA, I include the variable Muslim (AB4 question 90). It is coded 0 to 1, where 1 = the respondent identified as being Muslim and 0 otherwise. To the extent that a shared Muslim identity makes individuals feel like non-Muslim countries are an out-group, and therefore possibly threatening, I expect that Muslim identifiers will view foreign countries more negatively.

Another group-orientation hypothesis derived from the broader literature concerns elite framing (e.g. Blaydes & Linzer, 2012; Zaller 1992). While it is outside the scope of this research to go through every AB4 country and study how elites frame foreign country engagement in their respective countries, it is possible to use a proxy measure – party identification – to evaluate how this may affect opinions. The expectation is that those who identify with the ruling party are more likely to view foreign engagement in their countries favorably because the ruling party is more likely to cast foreign engagement in their country positively, lest they risk breaking good diplomatic relations and being cut off from foreign assistance and political support. This effect should be especially strong for countries such as the USA, China and former colonies that are major humanitarian aid providers in Africa. The political opposition, on the other hand, can take more liberty in casting the engagement of foreigners in a negative light. For example, in research conducted on Equatoguinean attitudes toward China, Esteban argues:

Those with close links with the government are more likely to hold a positive view of the Chinese presence in their country. This view parallels the political opposition’s more critical view of the Chinese presence in Equatorial Guinea. This polarized view is underpinned by Beijing’s ‘non interference’ policy, which operates as a pro status quo policy, actively supporting incumbent authorities at the expense of the opposition. (Esteban, 2010: 235)

I measure party identification (PID) using AB4 question 86. For each country, PID is coded 1 if the respondent reports identifying with the president’s party,¹⁶ and 0 otherwise.

Political values

The more political values individuals share with foreign countries, the more likely individuals are expected to relate to the foreign country and view it as non-threatening.

I expect that the better individuals feel about their country’ governance, the more likely they are to approve of foreign countries because they like what their own government is doing. On the other hand, individuals who evaluate the governance of their countries negatively may attribute poor governance to other countries (Chiozza (2007, 2009b) calls this the ‘scapegoat hypothesis’). To capture the governance concept, I use an additive index of AB4 questions 57a through 57p. Diagnostics of correlation and Cronbach’s Alpha (0.90) justify placing these variables on one index. The index is scaled from 1 to 4, where 4 indicates that respondents think their officials are governing very well.

Corruption is often associated with many of the problems on the African continent (e.g. Bellamy, 2009: 19); therefore, perceived government corruption may be another way to test the scapegoat hypothesis. Some foreign countries have a reputation – real or imagined – among African citizens for contributing to Africa’s corruption problems. For example, China allegedly exacerbates the corruption problem because ‘easy access to soft loans has enabled many African governments to avoid the pressure of global governance institutions to meet norms of accountability’ (Maru, 2013, 6; see, also, Mbaye, 2010; Rupp, 2008). Assuming that citizens disapprove of elite corruption (many studies suggest this to be the case: see Anderson & Tverdova, 2003; Bowler & Karp, 2004; Bratton et al., 2005; Hope & Chikulo, 2000; Mbaku, 2007; Redlawsk & McCann, 2005; Seligson, 2002; Seligson & Carrion, 2002; Tavits, 2007), then to the extent that Africans associate foreign countries with corruption in their own countries, they should hold negative opinions of such countries. I operationalize the concept of perceived official corruption using AB4 questions 50a through 50g: The corruption index is created from a series of questions in AB4 that ask respondents ‘how many of the following people do you think are involved in corruption, or haven’t you heard enough to say?’ The components of this index are highly correlated, and the Cronbach’s Alpha of 0.87 is within the range to justify placing the variables on an index. The corruption index ranges from 0 to 4, where 4 corresponds to higher perceptions of corruption.

A third value that may drive individual opinions toward foreign countries concerns support for democracy. I expect that individuals who value democracy will hold more favorable opinions of other democracies (e.g. the USA) than non-democracies (e.g. China). Democratic countries such as the USA are more likely to promote democratic ideals (e.g. Barkan, 2009) in Africa, so Africans who value democracy may be more likely to view the USA favorably. On the other hand, countries that support the political status quo and that may even assist in augmenting autocratic regimes (e.g. China; see Rotberg, 2008: 9) may elicit negative attitudes from individuals who support democracy. Support for democracy is operationalized here using AB4 question 30. The variable is coded from 0 to 2, where 0 = democracy does not matter and 2 = democracy is important. ‘Don’t know’ responses are recoded to the midpoint; missing observations are dropped.

Predispositions

Media is a powerful driver of opinion because ‘it makes certain topics more salient and therefore more accessible when people make judgments’ (Nisbet & Myers, 2011: 686). This implies that the more news an individual consumes, the more likely they are to have well-formed and well-informed opinions of foreign countries. Here, news is an additive index of AB4 questions 12a, 12b and 12c, which ask how often respondents get news from the radio, television and newspapers. Missing and ‘don’t know’ responses are dropped. The resulting index is a scale of 0 to 4, where higher values signify respondents get news more frequently.

Immigration scholars find that education has a strong impact on how individuals evaluate outsiders. Specifically, studies in this vein suggest that more educated individuals are more tolerant of those outside their group (e.g. Citrin et al., 1997; Hainmueller & Hiscox, 2007). Following this research, I expect a similar impact of education on attitudes toward foreign countries: more educated individuals will have more positive sentiment toward foreign countries. Education is measured here using AB4 question 89.

Another predisposition that has been found in previous literature to affect how people evaluate others is trust. Miguet and Muller (2007) find, for example, that trust significantly affects European attitudes toward immigrants. Trust is also shown to affect attitudes toward favorability in outcomes (e.g. Brockner et al., 1997). Following these findings, I expect that individuals who are more trusting will evaluate foreign countries more positively than those who are less trusting because trusting people are assumed to be inherently less threatened by strangers. I measure trust using an additive index of AB4 questions 49a–49i. This question series asks ‘How often do you trust each of the following, or haven’t you heard enough about them to say: the president; the parliament; the national election commission; local government; the ruling party; opposition parties; police; courts of law; and traditional leaders?’ Diagnostics of correlation and Chronbach’s Alpha of 0.85 justifies creating an index. It is scaled from 0 to 3, with higher values indicating that the respondent is more trusting.

Finally a person’s predisposition for interest in politics should affect opinions of foreign countries because politically interested individuals are thought to be more attentive to news coverage and therefore more informed (e.g. Semetko et al., 1992). The expectation, then, is that less politically interested people will evaluate foreign countries more neutrally than people who are politically interested and therefore may have opinions of foreign countries. Here I measure political interest using AB4 question 13, which asks ‘How interested would you say you are in public affairs?’ The political interest variable is scaled from 0 to 3, with higher values corresponding to greater interest in politics.

Control variables

In addition to these expected hypotheses, there are a number of control variables that merit inclusion in the model. At the micro level, controls include the traditional battery of demographic controls – age, gender, residence – as well as attitudes toward international donors. These are common controls in studies of this nature (e.g. Gadzala & Hanusch, 2010; Moehler & van de Walle, 2010). At the macro level, controls include national economic growth, human development, ethno-linguistic heterogeneity and political freedoms.

Micro-level control variables

Gender (AB4 question 101) and rural residence (AB4 variable ‘urbrur’) are unchanged from the AB4 original data. Gender is a binary variable where 1 = male, and 2 = female. Rural residence is also a binary variable where 1 = urban sampling unit, and 2 = rural sampling unit. Genders are represented evenly in all countries. However there is variation in the percentage of urban residences across the sampled countries: South Africa has the highest percentage of urban dwellers (66%), while Malawi has the lowest (15%). Missing data and ‘don’t know’ responses are dropped from the age (question 1) variable. Age is continuous, with higher values corresponding to greater age.

I follow Gadzala and Hanusch (2010) in controlling for views of other international donors to account for the effect that attitudes toward all international actors may have on opinions toward any foreign country. The attitude toward donors (question 98d) variable is adapted from original AB4 questions in the following ways: I drop missing observations for the attitudes toward international donors variable (hereafter, ‘donors’) and recode ‘don’t know’ responses to the midpoint on the scale. Here, 0 corresponds to a view that donors do nothing to help the country, while 4 signifies the opinion that donors do a lot to help.

Macro-level control variables

In this study I control for the nature of the national economy using the HDI.¹⁷ While other measures – e.g. Gross Domestic Product (GDP), Gross National Income (GNI) and GDP adjusted for purchasing power – are more common measurements of national economies, they are not necessarily valid or reliable in the African context (Jerven, 2013; see Gates, 2013 for a review of the issues associated with these traditional economic measures in developing countries). The HDI, while also imperfect, is nonetheless a more comprehensive measure of poverty and economic need because it includes health and education statistics in addition to GDP. The HDI is a composite index, scaled from 0 to 1, of a variety of variables. The higher the value on the index, the more ‘developed’ is the country. In the sample of countries under review here, Botswana has the highest HDI in 2008 (0.626) and Mozambique has the lowest (HDI = 0.306). The average HDI in this sample is 0.455. I collapse the original HDI scores onto a categorical scale between 1 and 4, as follows: 1 = HDI < 0.4; 2 = HDI > 0.4 and HDI < 0.5; 3 = HDI > 0.5 and HDI < 0.6; and 4 = HDI > 0.6. Ethnic fractionalization data come from Posner’s (2004) Politically Relevant Ethnic Groups (PREG). PREG is an index ranging from 0.0 to 1.0, where higher values correspond to greater ethnic heterogeneity. Finally, political freedom is operationalized using the Freedom House Freedom of the World indicators.¹⁸ The original variable is scaled from 1 to 7, where 1 = most free, and 7 = least free. I invert this scale in order to make interpretation of the results more intuitive: 1 = least free, and 7 = most free. Therefore, in this study, higher values on the Freedom House scale indicate more freedom.

Findings and discussion

The four main hypotheses are tested by nesting the individual-level and country-level data using GLLAMM with the ordered logic option to account for the ordered categorical nature of the dependent variables. The GLLAMM results for China, the USA, former colonies, Nigeria and South Africa are reported in Table 3.

Table 3.

GLLAMM regression results for attitudes toward foreign countries.

	Parameter	China	USA	Former colony	Nigeria	South Africa
Material self-interest	Employed	−.004 (.030)	.084** (.031)	.088** (.031)	−.065* (.033)	−.034 (.032)
	Assets	.194*** (.052)	.035 (.052)	−.052 (.053)	.060 (056)	.072 (.054)
	Remittances	.048 (.036)	.051 (.036)	.058 (.037)	.055 (.038)	.094** (.037)
Group orientation	Muslim	−072* (.035)	−.269*** (.035)	−.090* (.036)	−.013 (.041)	−.132*** (.036)
	Party identification	.058* (.029)	.045 (.030)	.085*** (.030)	−.035 (.031)	−.018 (.030)
Political values	Corruption	.026 (.016)	.060*** (.016)	.052** (.016)	.047** (.017)	−.005 .016
	Governance	.114*** (.019)	.107*** (.019)	.152*** (.019)	.139*** (.020)	.087*** (.020)
	Support democracy	−.005 (.020)	.038 (.020)	.055** (.020)	−.108*** (.021)	−.039 (.021)
Predispositions	Political interest	.029* (.013)	.047*** (.013)	.023 (.013)	−.006 (.013)	.021 (.013)
	Trust	.160*** (.021)	.174*** (.021)	.200*** (.022)	.249*** (.022)	.183*** (.022)
	Education	.023* (.009)	.055*** (.009)	.040*** (.009)	−.064*** (.009)	−.051*** (.009)
	News	.036* (.016)	.063*** (.016)	.087*** (.016)	−.112*** (.017)	−.031 (.016)
Individual-level control variables	Age	−.001 (.001)	.001 (.001)	.002 (.001)	.000 (.001)	.002 (.001)
	Gender	−.141*** (.028)	−.100*** (.028)	−.056* (.028)	.066* (.029)	−.003 (.029)
	Residence	−.045 (.031)	.043 (.032)	.087** (.032)	−.013 (.034)	−.013 (.032)
	Donors	.594*** (.013)	.775*** (.013)	.702*** (.013)	.415*** (.013)	.503*** (.013)
Country-level control variables	Ethnic fractionalization	−1.039*** (.066)	−.204** (.066)	−.631*** (.067)	.094 (.068)	−1.883*** (.067)
Country-level control variables	HDI	−.481*** (.021)	−.396*** (.021)	−.191*** (.022)	−.416*** (.021)	.107*** (.023)
	Freedom House	.338*** (.016)	.331*** (.017)	.180*** (.017)	.371*** (.016)	−.187*** (.016)
	Country-level	.136*** (.010)	.215*** (.016)	.186*** (.020)	.257*** (.013)	.166*** (.009)
	Individual-level Cut 1	−.330* (.148)	.962*** (.152)	.908*** (.151)	1.009*** (.157)	−1.852*** (.154)
	Individual-level Cut 2	.941*** (.148)	2.302*** (.152)	2.216*** (.151)	1.856*** (.156)	−.628 (.153)
	Individual-level Cut 3	2.230*** (.149)	3.680*** (.153)	3.580*** (.152)	3.652*** (.158)	.824*** (.153)
	Individual-level Cut 4	3.387*** (.150)	4.832*** (.155)	4.737*** (.154)	4.627*** (.160)	1.944*** (.154)
	Log likelihood	−26894.316	−25296.125	−25185.000	−23936.658	−25547.370
	AIC	880.219	888.490	874.176	891.391	877.572
	Level 1 N-size	18952	18952	17833†	16981††	17753†††

***

p ⩽ 0.001; **p > 0.001, p ⩽ 0.01; *p > 0.01, p ⩽ 0.05.

†

Not asked in Liberia.

††

Not asked in Nigeria.

†††

Not asked in South Africa.

AIC = Akaike Information Criterion.

Material self-interest

I hypothesized that the better off individuals are materially, the more favorably they will evaluate foreign country engagement in their country because they are more likely to see such engagement as an opportunity. The results partially support this hypothesis: employed respondents report more favorable sentiment toward the USA and former colonies. However, employed individuals report unfavorable sentiment toward Nigeria. There is no statistically significant relationship between being employed and opinions of China and South Africa, although the coefficient for Employed is negative in both models. Having assets has a positive, significant and large impact on opinion toward China, but no other foreign country. Receiving remittances has a positive impact on opinion toward South Africa but no significant impact on opinion toward and of the other foreign countries.

Why might employed people feel more favorably toward former colonies and the USA? One possibility is that both the USA and former colonies provide forms of humanitarian assistance aimed specifically at job creation in Africa. For example, both the European Investment Bank¹⁹ and the US Agency for International Development (USAID) have long supported microfinance projects aimed to assist small business owners. If Africans translate such assistance as contributing to their employment, the prevalence of international job creation efforts would explain these results. On the other hand, existing case studies suggest that Africans may see the Chinese as job-stealers. Specifically, unlike many other foreigners conducting business on the continent, the Chinese tend not to use African labor:

Unlike companies from other countries, Chinese companies in Equatorial Guinea, especially construction firms, bring the bulk of their labor force, including unskilled labor, from their country of origin. … this practice is deeply resented by the local population and blatantly breaks a local law dealing with foreign labor forces, which specifically stipulates that foreign nationals must not constitute over 10 per cent of their employees in Equatorial Guinea. … Even when they hire locals, Chinese companies, especially construction companies, are criticized for their poor working conditions; there are complaints about low salaries, long working hours, and lack of skills development. (Esteban, 2010: 239; see also: Obiorah et al., 2008; Rupp, 2008; Wells, 2011²⁰)

Thus, we might expect that unemployed people in Africa would hold unfavorable opinions of China. But the results do not support this expectation as a general pattern. Instead, it is Nigeria that unemployed people view unfavorably, not China.

According to these results, the more assets individuals have, the more likely they are to view Chinese activity favorably. This is not surprising, given some case-study research, which reveals that citizens in Africa like having access to cheap goods from China. On the one hand, as an elderly Cameroonian man explained in an interview, Chinese imports have been good for his personal circumstances: ‘I have worked [for] the government of Cameroon for over forty-three years, but my meager salary could not allow me to buy a television set. Today, with my pension allowance, I have bought a cheap Chinese television’ (Rupp, 2008: 69). On the other hand, the same study found that ‘Chinese goods represent an economic malignancy spreading through African markets, displacing African-made goods and, in the process, destabilizing African industries. It is in the marketplace that African anxieties and anger about Chinese “colonialism” are most often and most vigorously expressed’ (Rupp, 2008: 69). Thus, while this statistical model reveals general patterns, the reality of these relationships may be quite complex.

While the coefficient for Remittance is in the expected direction in each model, it is only statistically significant for South Africa. This could be driven by the countries in the model that are close to South Africa – Lesotho, Namibia and Zimbabwe, for example – who likely have large populations receiving remittances from South Africa. Perhaps this proximity causes individuals to associate receiving remittances with positive affect toward South Africa.

Group orientation

The more individuals identify foreign countries as being part of an out-group, the less favorably they are expected to evaluate those countries. Here I test the effects of two types of group orientation – religion and PID – on attitudes toward foreign countries. In many ways, the results are consistent with expectations: Muslims view the USA less favorably than non-Muslims, which corresponds to the contention that anti-Americanism is driven largely by America’s actions abroad: ‘The ceaselessness of Baghdad’s bloodshed has greatly undermined the US’ credibility [and] fanned anti-American feelings in Muslim regions like the Horn of Africa’ (Gettleman, 2006). Muslims also evaluate China, South Africa and former colonies more negatively than non-Muslims. Europe’s engagement in America’s global war on terror, and the subsequent terrorist attacks in Europe (e.g. the Madrid train bombings in 2004 and the 7/7 train bombings in London in 2005), might lead us to expect that being Muslim would have the same effect on attitudes toward former colonies as it does on attitudes toward the USA. Similarly China’s treatment of Muslims in the Uighur region of China supports the findings. A related argument is posited by Shinn and Eisenman:

To avenge the suppression of Uighur Muslims after the July 2009 Urumqi riots, al-Qaeda’s North African wing, al-Qaeda in the Islamic Maghreb (AQIM), killed twenty-four Algerians when they ambushed a convoy of Chinese engineers guarded by Algerian security forces. This was the first time an African terrorist network targeted Chinese in Africa in direct response to Beijing’s domestic repression of Muslims. (Shinn & Eisenman, 2012: 93)

The Muslim coefficient is also negative for Nigeria, although it is not significant. The lack of findings for Nigeria is unsurprising, given that half of Nigeria is Muslim, so it is the most likely foreign country in the study to be viewed as an ‘in-group’ for Muslim respondents.

Identifying with the party in office has a positive and significant effect on attitudes toward China and the former colonies but has no effect on opinions of the other countries. This partially supports the logic I posited above.

Political values

Similar to social group affiliation, shared political values should lead individuals to feel that outsiders are in their group and therefore are not threatening. Some of the results for political values are mixed, but there are strong findings for the governance variable: the better individuals evaluate their own governments, the more likely they are to hold positive sentiment toward all five foreign countries and this effect is large; conversely, negative evaluations of the government correspond to negative sentiment toward foreign counties. This supports the scapegoat hypothesis (Chiozza, 2009b).

Similar to the scapegoat theory, I expected perceived governmental corruption to lead to negative opinions of foreign countries if individuals associate foreign countries with their own country’s corruption problem. However, this expectation is only borne out in attitudes toward the USA, former colonies and Nigeria. It appears that corruption is a poor proxy for the scapegoat hypothesis in this study.

Support for democracy has mixed results. Support for democracy does not have a significant effect on attitudes toward the USA, although the effect is positive and significant for former colonies. Democracy supporters hold negative and significant attitudes toward Nigeria, which also makes sense, as Nigeria is one of the least democratic countries in this study (Freedom House rates Nigeria as 4, or ‘partly free’ in 2008). Valuing democracy has a negative, but insignificant effect on attitudes toward China or South Africa (which respectively rate 6.5, or ‘not free’, and 2, or ‘free’, by Freedom House in 2008).

Predispositions

Predispositions reveal the most consistent drivers of attitudes toward foreign countries. Among the four predispositions tested, Trust is especially strong. In all five models, the effect of the trust variable is large, positive and significant. The findings are clear: individuals who are more trusting evaluate all foreign countries more positively.

Education also has a strong effect but does not correspond to the expected hypothesis. Educated individuals feel more positively toward non-African countries (the USA, China and European countries) and more negatively toward African countries (South Africa and Nigeria). The underlying logic of the hypothesis is that education fosters tolerance. If this were the case then the coefficient should be positive for all countries. The negative Education coefficients in the Nigerian and South African models suggest that a different mechanism may underpin this relationship. This finding seems to better support research by scholars such as O’Rourke and Sinnott (2006) and Mayda (2006), who suggest that education is better understood as a proxy for human capital: an individual’s education impacts their labor prospects, and it is this factor that shapes their attitudes toward foreigners.

News consumption also reveals relatively consistent findings: news consumers report positive sentiment toward the non-African countries and negative sentiment toward the African countries (although the coefficient in the South African model is insignificant). Without knowing the content of the news that individuals consume, it is difficult to say more about this finding. However, it is interesting that the coefficients for news are in the same direction as the education variable.

Finally, politically interested individuals have more positive attitudes toward China and the US, but political interest has no impact on attitudes toward the other countries. Relatively speaking, Africans most likely have the least contact with China and the US so perhaps it is only the politically interested – those inclined to seek out political knowledge – who hold ‘meaningful belief systems’.

Controls

As we can see, age has no significant effect on attitudes toward foreign countries. Attitudes toward donors have the expected effect: the more positively individuals view international donors, the more likely they are to view all foreign countries positively, and this effect is large. Residence has no effect on attitudes toward China, the USA, Nigeria or South Africa. However, rural dwellers are more likely to hold positive opinions of their former colony. This might be due to humanitarian projects devoted to rural residents. Men are more likely to view non-African countries positively, whereas women are more likely to hold pro-Nigerian sentiment. There is no significant effect of gender on attitudes toward South Africa.

Country-level controls

The country-level controls are strong but somewhat inconsistent across the five models. People living in more politically free countries are more likely to hold positive attitudes on China, the USA, former colonies and Nigeria, but negative attitudes toward South Africa. This finding partially supports the hypothesis that people living in freer countries are more likely to view foreigners positively. As expected, individuals living in more heterogeneous countries hold more negative sentiment toward China, the USA, former colonies and South Africa. This supports the idea that for people who live in heterogeneous societies, in-group/out-group orientations may be especially high. However it is curious that the opposite holds for attitudes toward Nigeria, where people in more heterogeneous countries are more likely to view Nigeria positively. Perhaps this is because Nigeria itself is one of the most heterogeneous countries in Africa. It could be that the real mechanism here is that more heterogeneous countries view other heterogeneous countries positively because they can relate to those countries. However, if this were the case, we might also expect the same findings for all of the other models, since China, the USA and South Africa are all extremely heterogeneous. Finally those individuals living in less-developed countries hold more positive views of the USA, China, their former colony and Nigeria, whereas those living in more developed countries view South Africa more positively.

Conclusion

The findings for material self-interest, social-group affiliation and political values vary significantly across the models. Taking material self-interest first: the results vary widely for employment. The most consistent results appear to be that employed individuals hold more positive sentiment toward western countries than the unemployed. Having assets appears to affect only attitudes toward China, and receiving remittances appears to affect only attitudes toward South Africa, in this study. Regarding social-group orientation, being Muslim increases the likelihood of holding negative attitudes toward all foreign countries except Nigeria, while PID appears to have an effect only on attitudes toward China and former colonies. The hypothesis related to political values also received partial support in this study. While the findings suggest strong support for the scapegoat theory, support for democracy and perceived corruption yielded mixed results across the five models.

The most consistent findings come from the predisposition hypothesis. Trust is an especially strong indicator of attitudes toward foreign countries: the coefficients are all positive and significant, and the size of the effect is magnitudes larger than the size of the effect for other predisposition variables. Education and news also yield relatively consistent results, with more educated individuals and news consumers being more likely to view the non-African countries positively and the African countries negatively.

Taking each model separately, it is clear that the drivers of domestic attitudes toward foreign countries vary, depending on which foreign country is under consideration by the respondent. For China, positive sentiment is associated with individuals who report having assets, belonging to the president’s party, view the domestic governance positively, are more politically interested, are more trusting, are educated and are more frequent news consumers. While political interest, trust, education, news consumption and positive evaluation of domestic governance also correspond to positive attitudes toward the USA, so does being employed and perceiving the domestic government as corrupt. When it comes to attitudes toward former colonies, trust, education, news consumption, positive evaluations of domestic governance, perceiving the domestic government as corrupt, employment, identification with the president’s party and support for democracy are associated with positive sentiment. For China, the USA and former colonies, negative sentiment is associated with individuals who identify as Muslim.

When it comes to attitudes toward the two African country-dependent variables in this study, trust and a positive evaluation of the domestic government correlate to positive sentiment, as with the non-African countries. However, contrary to the non-African countries, news consumption and education correspond to negative attitudes toward Nigeria and South Africa. Identifying as Muslim corresponds to negative attitudes toward South Africa, although remittance recipients are more likely to view South Africa favorably. Perceived domestic corruption correlates with positive attitudes toward Nigeria, but being employed correlates with negative attitudes toward Nigeria.

The findings in this study about African attitudes toward foreign countries corroborate findings from the broader literatures on foreign attitudes toward the USA: attitudes are inconsistent and complex. As the existing studies argue, this is likely due to the fact that each of the five foreign countries tested here is a very different ‘object’ and is highly complex in its own ways. The five countries provide a diverse array of ‘objects’, running the spectrum of political freedom, economic prosperity, heterogeneity, culture, history, policies and populations. Future research in international relations and political psychology should move beyond studying foreign attitudes toward the USA to explore the vast differences that exist toward object countries and the implications for research, policy and practice.

Footnotes

Appendix: ‘Don’t know’ responses

About 30% of the pooled sample of respondents chose ‘don’t know’ in response to the dependent variable questions about opinions toward foreign countries. The rate of ‘don’t know’ responses varied greatly across countries, from lows of 6% in Liberia to highs of 68% in Malawi (see Table A1). Rather than dropping these respondents from the analysis, I follow the convention of rescaling responses to a theoretically defensible point on the Likert scale (see Bratton et al. 2005: Appendix C). In this case, the ‘don’t know’ responses are recoded to the center point on the scale, based on the assumption that respondents were likely unable to distinguish between the available options to them in the survey instrument, thus prompting their ‘don’t know’ responses.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

Author biography

Kris L. Inman received her PhD from University of California – Davis, in Political Science. She is currently the Director of the Africa Research Initiative at the Center for Strategic Intelligence Research at the National Intelligence University, Washington DC, USA.

References

Anderson

Tverdova

(2003) Corruption, political allegiances, and attitudes toward government in contemporary democracies. American Journal of Political Science 47(1): 91–109.

Bakera

Cupery

(2013) Anti-Americanism in Latin America: Economic exchange, foreign policy legacies, and mass attitudes toward the Colossus of the North. Latin American Research Review 48(2): 106–130.

Barkan

(2009) Advancing democratization in Africa. In: Cooke

Morrison

(eds) US Africa Policy Beyond the Bush Years. Washington, DC: Center for Strategic International Studies Press, 99–110.

Bellamy

(2009) Making better sense of US security engagement in Africa. In: Cooke

Morrison

(eds) US Africa Policy Beyond the Bush Years. Washington, DC: Center for Strategic International Studies Press.

Blaydes

Linzer

(2012) Elite competition, religiosity, and anti-Americanism in the Islamic world. American Political Science Review 106(2): 225–243.

Bowler

Karp

(2004) Politicians, scandals, and trust in government. Political Behavior 26(3): 271–287.

Bratton

Mattes

Gyimah-Boadi

(2005) Public opinion, democracy, and market reform in Africa. Cambridge: Cambridge University Press.

Brockner

Siegel

Daly

. (1997) When trust matters: The moderating effect of outcome favorability. Administrative Science Quarterly 42(3): 558–583.

Caprara

Vecchione

(2013) Personality approaches to political behavior. In: Huddy

Sears

Levy

(eds) The Oxford Handbook of Political Psychology (2nd ed.). Oxford: Oxford University Press.

10.

Chiozza

(2007) Disaggregating anti-Americanism: An analysis of individual attitudes toward the US. In: Katzenstein

Keohane

(eds) Anti-Americanism in World Politics. Ithaca, NY: Cornell University Press.

11.

Chiozza

(2009a) A crisis like no other? Anti-Americanism at the time of the Iraq war. European Journal of International Relations 15: 257–289.

12.

Chiozza

(2009b) Anti-Americanism and the American World Order. Baltimore, MD: Johns Hopkins University Press.

13.

Chiozza

Choi

(2012) Going the American way: The surprising case of Korean pro-Americanism. The Pacific Review 25(2): 269–292.

14.

Citrin

Green

Muste

Wong

(1997) Public opinion toward immigration reform: The role of economic motivation. Journal of Politics 59: 858–881.

15.

Converse

(1964) The nature of belief systems in mass publics. Critical Review: A journal of politics and society 18(1–3): 1–74.

16.

Curini

Jou

Memoli

(2011) Satisfaction with democracy and the winner/loser debate: The role of policy preferences and past experience. British Journal of Political Science 42: 241–261.

17.

Diamond

Plattner

(eds) (2010) Democratization in Africa: Progress and retreat. Baltimore MD: Johns Hopkins University Press.

18.

Dodson

(2010) Locating xenophobia: Debate, discourse, and everyday experience in Cape Town, South Africa. Africa Today 56(3): 2–22.

19.

Doyle

(2013) Africans’ remittances outweigh Western aid. BBC News, 17 April. Available at: http://www.bbc.co.uk/news/world-africa-22169474 (accessed 25 January 2016).

20.

Eagly

Chaiken

(1993) The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich.

21.

Eagly

Chaiken

(1998) Attitude structure and function. In: Gilbert

Fisk

Lindsey

(eds) Handbook of social psychology. New York, NY: McGraw-Hill, 269–322.

22.

Economist . (2011) The hopeful continent: Africa rising. The Economist (December). Available at: http://www.economist.com/node/21541015 (accessed 25 January 2016).

23.

Esteban

(2010) A silent invasion? African views on the growing Chinese presence in Africa: The case of Equatorial Guinea. African and Asian Studies 9: 232–251.

24.

Finlay

Holsti

Fagen

(eds) (1967) Enemies in politics. Chicago, IL: Rand McNally.

25.

Furia

Lucas

(2006). Determinants of Arab public opinion on foreign relations. International Studies Quarterly 50(3): 585–605.

26.

Gadzala

Hanusch

(2010) African perspectives on China–Africa: Gauging popular perceptions and their economic and political determinants. Afrobarometer Working Paper no. 117.

27.

Gates

(2013) The problem with poor countries’ GDP. Project Syndicate, 6 May. Available at: http://www.project-syndicate.org/commentary/poor-countries-need-more-accurate-gdp-data-by-bill-gates (accessed 29 January 2016).

28.

Gentzkow

Shapiro

(2004) Media, education and anti-Americanism in the Muslim world. Journal of Economic Perspectives 18(3): 117–133.

29.

Gettleman

(2006) Anti-American sentiment is sweeping across Africa. New York Times, 24 December.

30.

Gray

(1998) Cultivating citizenship through xenophobia in Gabon, 1960–1995. Africa Today 45(3–4): 389–410.

31.

Hainmueller

Hiscox

(2007) Educated preferences: Explaining attitudes toward immigration in Europe. International Organization 61(2): 399–442.

32.

Herrmann

(2013) Perceptions and image theory in international relations. In: Huddy

Sears

Levy

(eds) The Oxford handbook of political psychology (2nd ed.). Oxford: Oxford University Press, 334–63.

33.

Holsti

(1967) American leadership in world affairs. In: Finlay

Holsti

Fagen

(eds) Enemies in Politics. Chicago, IL: Rand McNally.

34.

Hope

Chikulo

(2000) Corruption and development in Africa: Lessons from country case-studies. New York, NY: Palgrave.

35.

Huntington

(1996) The clash of civilization and the remaking of world order. New York, NY: Simon & Schuster.

36.

Jerven

(2013) Poor Numbers: How we are misled by African development statistics and what to do about it. Ithaca, NY: Cornell University Press.

37.

Jervis

(1976) Perception and misperception in international politics. Princeton, NJ: Princeton University Press.

38.

Jervis

(2010) Why intelligence fails: Lessons from the Iranian revolution and the Iraq war. Ithaca, NY: Cornell University Press.

39.

Kahneman

Slovic

Tversky

(eds) (1982) Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.

40.

Katzenstein

Keohane

(eds) (2007) Anti-Americanism in world politics. Ithaca, NY: Cornell University Press.

41.

Kinder

(1998) Opinion and action in the realm of politics. In: Gilbert

Fiske

Lindzey

(eds) The Handbook of social psychology, Vol. 2. Oxford: McGraw-Hill, 778–867.

42.

Lagarde

(2014) Africa rising – Building to the future. International Monetary Fund, 29 May. Available at: http://www.imf.org/external/np/speeches/2014/052914.htm (accessed 25 January 2016).

43.

Lynch

Crawford

(2011) Democratization in Africa 1990–2010: An assessment. Democratization 18(2): 275–310.

44.

Lynch

(2007) Anti-Americanisms in the Arab world. In: Katzenstein

Keohane

(eds) Anti-Americanism in world politics. Ithaca, NY: Cornell University Press, 196–226.

45.

Maru

(2013) China–Africa relations democracy and delivery. Mecca: Al Jazeera Center for Studies.

46.

Mayda

(2006) Who is against immigration? A cross-country investigation of individual attitudes toward immigrants. Review of Economics and Statistics 88(3): 510–530.

47.

Mbaku

(2007) Corruption in Africa: Causes, consequences, and cleanups. Boulder, CO: Lexington Books.

48.

Mbaye

(2010) Matching China’s activities with Africa’s needs. In: Harneit-Sievers

Marks

Naidu

(eds) Chinese and African perspectives on China in Africa. Kampala: Pambazuka Press, 39–54.

49.

McDermott

(2004) Political psychology in international relations. Ann Arbor, MI: University of Michigan Press.

50.

Miguet

Muller

(2007) Determinants of individual attitudes towards immigration: The role of trust. Presented to the Department of Economics, University of Geneva.

51.

Moehler

van de Walle

(2010) Pro and anti-Americanism in Sub Saharan Africa. Paper presented to the International Communication Association conference, Suntec City, Singapore.

52.

Monson

Rupp

(2013) Africa and China: New engagements, new research. African Studies Review 56(1): 21–44.

53.

Mummendey

Klink

Brown

(2010) Nationalism and patriotism: National identification and out-group rejection. British Journal of Social Psychology 40(2): 159–172.

54.

Murray

(2003) Alien strangers in our midst: The dreaded foreign invasion and ‘Fortress South Africa’. Canadian Journal of African Studies 37(2–3): 440–466.

55.

Neto

da Conceição Pinto

Mullet

(2013) The legitimacy of humanitarian military interventions: A preliminary study of Angolan and Mozambican university students’ views. International Perspectives in Psychology: Research, practice, consultation 2(2): 73–84.

56.

Nincic

Russett

(1979) The effect of similarity and interest on attitudes toward foreign countries. Public Opinion Quarterly 43(1): 68–78.

57.

Nisbet

Myers

(2010) Challenging the state: Transnational TV and political identity in the Middle East. Political Communication 27(4): 347–366.

58.

Nisbet

Myers

(2011) Anti-American sentiment as a media effect? Arab media, political identity, and public opinion in the Middle East. Communication Research 38: 684–709.

59.

Nisbet

Sheufele

Shanahan

(2004) Public diplomacy, television news, and Muslim opinion. The International Journal of Press/Politics 9(2): 11–37.

60.

Nyamnjoh

(2002) Local attitudes towards citizenship and foreigners in Botswana: An appraisal of recent press stories. Journal of Southern African Studies 28(4): 755–775.

61.

Nyamnjoh

(2010) Racism, ethnicity and the media in Africa: Reflections inspired by studies of xenophobia in Cameroon and South Africa. Africa Spectrum 45(1): 57–93.

62.

Nye

(2004) Soft Power: The means to success in world politics. New York, NY: PublicAffairs.

63.

Obiorah

Kew

Tanko

(2008) ‘Peaceful rise’ and human rights: China’s expanding relations with Nigeria. In: Rotberg

(ed.) China into Africa: Trade, aid, and influence. Washington, DC: Brookings Institution Press, 272–95.

64.

O’Rourke

Sinnott

(2006) The determinants of individual attitudes towards immigration. European Journal of Political Economy 22(4): 838–861.

65.

Posner

(2004) Measuring ethnic fractionalization in Africa. American Journal of Political Science 48(4): 849–863.

66.

Ratha

Mohapatra

(2008) Outlook for remittance flows 2008–2010: Growth expected to moderate significantly, but flows to remain resilient. In: Migration and Development Brief. Washington, DC: World Bank Group, 1–14.

67.

Rebol

(2010) Public perceptions and reactions: Gauging African views of China in Africa. African Journal of Agricultural Research 5(25): 3524–3535.

68.

Redlawsk

McCann

(2005) Popular interpretations of ‘corruption’ and their partisan consequences. Political Behavior 27(3): 261–283.

69.

Rotberg

(2008) China’s quest for resources, opportunities, and influence in Africa. In: Rotberg

(ed.) China into Africa: Trade, aid, and influence. Washington, DC: Brookings Institution Press, 1–20.

70.

Rupp

(2008) Africa and China: Engaging postcolonial interdependencies. In: Rotberg

(ed.) China into Africa: Trade, aid, and influence. Washington, DC: Brookings Institution Press, 65–86.

71.

Seligson

(2002) The impact of corruption on regime legitimacy: A comparative study of four Latin American countries. Journal of Politics 64(2): 408–433.

72.

Seligson

Carrion

(2002) Political support, political skepticism, and political stability in new democracies: An empirical examination of mass support for coups d’etat in Peru. Comparative Political Studies 35(1): 58–82.

73.

Semetko

Brzinski

Weaver

Willnat

(1992) TV news and US public opinion about foreign countries: The impact of exposure and attention. International Journal of Public Opinion Research 4(1): 18–36.

74.

Shinn

Eisenman

(2012) China and Africa: A century of engagement. Philadelphia, PA: University of Pennsylvania Press.

75.

Steenbergen

Jones

(2002) Modeling multilevel data structures. Journal of Politics 46(1): 218–237.

76.

Sylvanus

(2013) Chinese devils, the global market, and the declining power of Togo’s Nana-Benzes. African Studies Review 56(1): 65–80.

77.

Taber

Young

(2013) Political information processing. In: Huddy

Sears

Levy

(eds) The Oxford Handbook of Political Psychology (2nd ed.). Oxford: Oxford University Press, 525–58.

78.

Tavits

(2007) Representation, corruption, and subjective well-being. Comparative Political Studies 41(12):1607–30.

79.

Tims

Miller

(1986) Determinants of attitudes toward foreign countries. International Journal of Intercultural Relations 10(4): 471–484.

80.

Williams

(2011) War and conflict in Africa. Cambridge: Polity.

81.

Wells

(2011) You’ll be fired if you Refuse: Labor abuses in Zambia’s Chinese state-owned copper mines. New York: Human Rights Watch.

82.

Young

(2012) The postcolonial state in Africa: Fifty years of independence. Madison, WI: University of Wisconsin Press.

83.

Zaller

(1992) The nature and origins of mass opinion. Cambridge: Cambridge University Press.