Introduction
The relationship between the two most powerful countries in the world – the United States and China – is deteriorating [1]. This increases the pressure on secondary or middle powers to choose sides in the growing great-power competition or conflict [2], which has already reached regions far beyond East Asia and the Americas [3]. On the one hand, the appeal of the US as a strategic partner is obvious: it remains the most powerful country in the world in both economic and military terms. On the other hand, China’s enormous economic expansion creates considerable opportunities, in particular for smaller states, even if these often come with attendant security worries.
An array of conflicts or flashpoints between these two great powers have appeared or intensified in recent years, such as Taiwan, the South China Sea [4], and the future of international institutions [5]. Moreover, in very recent years, scholars have increasingly turned their attention to the high-tech sector, which has become visibly securitized [6, 7]. Notably, China’s mammoth Belt and Road Initiative includes a major “Digital Silk Road” component [8]. The present article focuses on one specific foreign-policy conflict associated with China’s drive to expand into high-end infrastructure, namely, the Chinese telecom company Huawei’s drive to gain access to foreign markets. This endeavor is strongly opposed by the United States. There is widespread concern in Washington that Huawei is effectively a tool in the hands of the Chinese Communist Party (CCP). What is alleged is that Beijing might at will direct the company to spy on its clients, possibly also giving China a significant measure of control over other countries’ critical infrastructure. In the past few years, therefore, US foreign policymakers have toured the world spreading one simple message to other countries: Allowing Huawei to gain access to their markets carries significant security risks – with the implicit or explicit warning attached that choosing to deal with Huawei will come with major costs.
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A handful of recent studies have analyzed the policies of secondary states toward Huawei and its 5G investment undertakings [9‐15]. Valuable as they are, these studies only investigate small-N samples. Our project, in contrast, draw on a large-N sample of potential hosts of Huawei, aiming to gain new insight into the correlates of policies adopted vis-à-vis the Chinese company. For the dependent variable, we assemble a new dataset that provides a snapshot (as of October 15, 2021) of the attitudes of 70 states, selected by economic size, toward Huawei’s aspirations abroad. We place these states on a four-point scale ranging from fully acceptive to fully rejective. We then explore the driving forces behind these stances by way of a series of ordered logit regression models. Our dependent variable is correlated with various independent variables connected to three different strands of realist theory: balance-of-threat theory, patron-client theory, and Hirschman’s theory on trade relationships and foreign-policy convergence.
The article is structured in the following way: Next is a theoretical section where we review these three branches of realist theory, using them to derive the article’s three main hypotheses. Thereafter follow a description of the data and a batch of ordered logit regression models. The analysis identifies three main patterns in the data. First, those states that are most dwarfed by China’s power and capabilities seem more acceptive of Huawei. Second, those states that view the US as their patron and rely on its security guarantees tend to be far more rejective. Third, secondary states’ trade relations with the two great powers have relatively modest effects on how states position themselves vis-à-vis Huawei: Whereas results suggest that trade with China might be a (small) factor in the reasonings of other states, trade with the United States is not. The logistic regression models suggest that it is the second model – the patron-client theory – that offers the most cogent explanation of middle powers’ positioning on the Huawei issue.
The Case of Huawei
The United States has for some time led an intense pressure campaign against Chinese telecom giant Huawei, actively seeking to curb its market access and limit its role in building fifth-generation (5G) networks around the world. There is widespread concern in Washington that Huawei, to a substantial degree, is a tool that the Chinese Communist Party (CCP) could use at will to spy on the company’s clients, which could give China considerable influence over other countries’ critical infrastructure. This deeply worries US policymakers, who have long since started to prepare for an era of intense great-power rivalry [16]. It is within this context that Huawei is made to suffer what some refers to as the “liability of foreignness” in a setting concerning the commanding heights of the world economy [17]; Huawei, as it is, is being directly associated with its home country, an authoritarian state where the government enjoys considerable control over the national economy and its constituent actors.
It is well known that Huawei receives significant state financial support [18]. The company, for its part, has consistently denied more serious allegations that have been made, always maintaining that it is an eminently independent firm capable of withstanding any eventual pressure from the Chinese government, not least with respect to intelligence collection. Policymakers in the United States obviously think otherwise: As early as in 2012, the US House Intelligence Committee denounced Huawei as a threat to national security, advising private entities to avoid doing business with it [19].
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The US’s drive to implement restrictions on Huawei escalated sharply under the administration of Donald Trump. The company was dealt a particularly harsh blow in May 2019, when the President issued an executive order prohibiting any country or person deemed a “foreign adversary” from selling certain telecommunications equipment in the United States. This effectively banned Huawei from making bids to invest in the US’s 5G networks. On May 14, 2020, Trump extended the ban for another year; and the Biden administration has since upheld it [20], even if early indications were that the new team in the White House would pursue a somewhat more nuanced approach on this matter [21].
Much is surely at stake. There is broad agreement that whoever controls the world’s 5G technology will have an enormous strategic advantage in the future. Thus, even long before Huawei had grown to become the telecom behemoth it is today, lawmakers in Washington expressed worries that, “Any bug, beacon, or backdoor put into our critical systems could allow for a catastrophic and devastating domino effect of failures throughout our networks.” [22] Over the past few years, therefore, US foreign policymakers have toured the world in an attempt to stop other countries from giving Huawei market access. The message conveyed is that countries that use Huawei equipment are choosing a path that poses a significant risk to the United States and its allies. In the most egregious cases, former US Secretary of State Mike Pompeo warned, intelligence sharing, diplomatic and military ties, and partnerships with the United States would be off the table. [23] This constituted a thinly veiled threat against states that remained acceptive of the Chinese tech giant [24, 25]. The most sweeping act of the pressure campaign (as of yet) came in August 2020. Then, the US launched its “Clean Network” program, which aims to counter “long-term threats to data privacy, security and human rights posed to the free world from authoritarian malign actors, such as the Chinese Communist Party” [26].
The pressure campaign has, overall, arguably enjoyed some success. This was especially so in the last year of the Trump presidency, with additional countries imposing restrictions on Huawei practically every month. However, the world as a whole is still a considerable distance away from the unanimous Huawei ban that Washington seems to want. Granted, a handful of states have indeed implemented full bans on the company; others have signed 5G security agreements with the United States; and still others have already handed out contracts to Nokia or Ericsson, Huawei’s two main competitors (both of which are headquartered in Scandinavia). On the other hand, an even larger group of states are already actively using, or are planning to use, Huawei in their 5G networks. What, then, accounts for the variation in the way secondary or middle powers position themselves in the conflict between the United States and China on the issue of Huawei?
Theories of Alliance Formation and Dynamics
Many maintain that the United States is a benevolent hegemon that few fear and fewer still seek to balance [27‐29]. US engagement and US hegemony, they contend, are actively solicited by the majority of the world’s states, which essentially see the United States as a non-threatening sea-power whose global influence is to be embraced for the security and stability it brings [30]. For many of these scholars, the state to be feared, and thus to be balanced, is China, not the United States. John Mearsheimer, for example, maintains that China cannot rise peacefully [31, 32]. The United States and China, he predicted some years ago, would soon get caught in an intense rivalry, and most of China’s neighbors, as well as much of the rest of the world, would join forces with the US in order to contain Beijing [33]. Following the logic of his theory, states will balance against the most serious threat in terms of capabilities, which to states in the Asia–Pacific region – and likely even beyond – is China [34].
Threat Perceptions
Variants of the theory of balance of power offer plausible arguments for why counterbalancing will arise whenever too much power is concentrated in the hands of a single great power [35]. Stephen Walt’s balance-of-threat theory constitutes a refinement and modification of balance-of-power theory [36]. For Walt, a state’s alliance behavior is determined not just by the distribution of capabilities but rather by the broader threat it perceives from other states. Power or capabilities being only one component of a composite phenomenon, he identifies four interrelated criteria that states use to evaluate the severity of the threat posed by any other state.
The first is aggregate power. All else being equal, the greater a country’s total resources and power, the more potent is the potential threat it poses to others, and the more likely it is to provoke counterbalancing by, and alignment among, others [37]. The second is geographical proximity. It is a widespread assumption in geopolitics that the ability to project power is inversely related to distance [38]. By this logic, any given state faces a larger threat from nearby states than from distant ones, ceteris paribus. Alliance choices are thus shaped more as a response to the former [39].
The third leg of Walt’s theory is offensive power. All else being equal, states with large offensive capabilities are more likely to provoke a counter-alliance than are those less capable of threatening the sovereignty or territorial integrity of others [40]. Aggressive intentions constitute the fourth factor. States that are perceived by others as bellicose actors in international affairs are likely to provoke more counterbalancing. In other words, perceptions of malevolent intent (whether such perceptions are reasonable or not) are key to alliance choices [41].
Walt further identifies two distinct routes that states can take when faced with a significant external threat: They can either balance against the threat by allying with others, or they can choose to bandwagon, which implies aligning with the source of the threat itself [42]. Most realist scholars would contend that balancing is the most common of the two; but states that are very weak relative to the threatening state may be more inclined to bandwagon with it instead.
There are two main motives for states to opt for bandwagoning [43]. First, such a strategy functions as a form of appeasement: by joining with the ascendant state, one reduces the likelihood of being attacked by it. Second, in war and in geopolitical rivalry, others may align with the winning or ascendant power in order to share in the spoils of victory [44]. Balancing and bandwagoning entail drastically different realities. If balancing is the dominant feature of world politics, aggression is discouraged, as it causes counterbalancing and thereby raises the costs for the aggressor. Contrarily, if bandwagoning is the dominant trend, domineering behavior would in fact be the best way to gain allies.
In sum, Walt’s theory implies that the level of perceived threat from China should affect how states position themselves toward it. However, it is less clear what would be the expectations about the general tendency of responses; that is, whether states would tend to balance against or bandwagon with China. This means that the first hypothesis of this article is two-pronged:
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Hypothesis 1a: When a state perceives a threat from China, it becomes markedly more rejective of Huawei’s 5G, all else being equal.
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Hypothesis 1b: When a state perceives a threat from China, it becomes markedly more acceptive of Huawei’s 5G, all else being equal.
Patron-Client Relationships
In most of the literature on leadership in global governance, it is generally acknowledged that “successful leadership depends not only on resources and ambition but also crucially on the support of followers.” [45] For the transmission of influence, bilateral linkage – understood as dense, formal ties – is often seen as a precondition [46]. It is therefore plausible that having such ties to the United States influences the positions that those states adopt in the growing US-China rivalry.
The concept of patron-client systems originated in anthropological studies, which used it to describe intratribal and intraregional relationships between leaders and followers. In such settings, tribal chiefs – or patrons – dispense particular favors to their subjects – or clients – in return for their loyalty [47]. The field of International Relations has since adopted this concept, applying it to the relationship between states [48, 49]. A basic premise of the theory is that the larger the number of clients depending on a particular patron, the wider is the latter’s power base. In terms of behavior, a central characteristic of patron-client relationships is compliance, which the patron demands and expects from its clients on issues it deems important. Compliance can be measured in numerous ways, but an especially important conception is “the client’s policies toward the patron’s primary adversary.” [50]
In modern history, no state has mastered the art of acquiring friends and allies nearly as well as the United States. The wide network of partner countries that the United States constructed in the years following the Second World War has since been maintained; indeed, it has even expanded, especially after the end of the Cold War. David A. Lake argues that the subordinate states in what he calls “contractual relationships” cede certain aspects of foreign policymaking to the US in exchange for its security guarantees [51]. This is arguably one major advantage that Washington has over China [52].
In addition to entering into formal treaties and partnerships, the patron, in this case the United States, can demonstrate its commitment to this guarantee in many ways. Two of the main ones are the stationing of US military personnel in a client state [53] and arms sales to it [54]. We would expect such measures, then, to correlate positively with the degree of compliance by Washington’s clients. This leads to the article’s second hypothesis:
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Hypothesis 2: When a state has formal ties to the US and views it as its patron, thus relying on its security guarantee, it becomes more rejective of Huawei’s 5G, all else being equal.
Trade Relationships and Foreign Policy Convergence
In his seminal work on the foreign-policy consequences of trade, Albert Hirschman describes the political aspect of international trade relations and explores ways in which trade can be used as a resource by those seeking to augment national power and influence. He thereby confronts a main argument of economic liberalism; that is, that free trade creates bonds of interdependence between and among nations, which lowers the likelihood of interstate conflict [55]. According to Hirschman, however, to suggest that economic projects of a huge scale mainly aim at creating peace, friendship, and cooperation borders on the utopian, based as it is on a “belief that the dependence of A on B is roughly the same as the dependence of B on A.” [56] This, he says, is never truly the case; most, if not even all, such relationships are of an asymmetrical nature.
One essential term in Hirschman’s book is trade dependence, which evidently produces foreign-policy convergence. The more states trade with one another, the more costly are interruptions to those trade relationships. Both or all sides, therefore, have incentives to converge on matters of foreign policy, fearing that major disputes could interfere with the trade flows, lowering the benefits accruing therefrom. In other words, trade dependence increases any given country’s “responsiveness to external demands, including unspoken ones, due to fears of losing market access and other economic benefits, and causes the economically weaker states to accommodate the foreign policy interests of the more powerful one.” [57] To paraphrase Hirschman: A can increase its hold on B, C, or D by creating a situation where the latter three would do anything to continue the trade they have with A. [58] Put differently, by increasing others’ dependence on itself, A bolsters its own influence.
Hart and Jones are among those who apply Hirschman’s theory directly to the case of China. They hold that, due to Beijing’s numerous trade relations with relatively weak states, China is in a particularly beneficial position to use its economic leverage to influence them [59]. In most cases, therefore, China can afford to walk away from trade partnerships, which places it in a superior bargaining position vis-à-vis those whose suffering will be much greater if trade is disrupted. These kinds of disparities in trade dependence can act as “a powerful weapon in the struggle for power,” causing the more dependent country to become more willing to make political concessions to the stronger one [58]. The question of interest, then, is whether or not trade suffices to bend states away from the United States and toward China.
This leads to a two-pronged hypothesis, which is our third and final one:
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Hypothesis 3a: When a state has close trade relations with China, it is put under Chinese influence, making it more acceptive of Huawei’s 5G, all else being equal.
These mechanisms should, of course, apply to the US in similar ways:
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Hypothesis 3b: When a state has close trade relations to the United States, it is put under US influence, making it more rejective of Huawei’s 5G, all else being equal.
Data and Variables
Our sample of 70 independent states is drawn from the International Monetary Fund’s (IMF) ranking of countries by estimated nominal gross domestic product (GDP). [60] We limit our sample to the upper one-third of the GDP list, so as only to include those states whose economies are likely large enough to carry some international weight. These are states that the US and China, one would presume, care the most about; their support, or their lack of support, can plausibly make a significant difference in the growing great-power rivalry. Our panel thus encompasses 70 sovereign states that are, albeit to varying degrees, caught in the middle of the intensifying tug-of-war between the United States and China. A majority of the states included are, again to varying degrees, friends and allies (i.e., clients) of the US, though there are also several states in the sample that take a more neutral position. The panel also includes some countries that lean toward Beijing, most notably Iran and Russia [61‐63].
The Dependent Variable
Using information from international news media, we coded the dependent variable – Y_Huawei – on a scale from 1 to 4, where 4 denotes a position of full rejection of Huawei (in accordance with US, but not Chinese, preferences), and 1 denotes a stance that is fully acceptive (in accordance with Chinese, but not US, preferences). Table 1 describes the content of each value on the scale.
Table 1
Categories of the dependent variable
Stance on Huawei’s 5G equipment and infrastructure | |
|---|---|
4 | Full ban: The state’s government has issued a full ban of Huawei’s equipment in their 5G network |
3 | Considerable government restrictions/operators are rejective: The state’s government has imposed laws that pose substantial barriers for Huawei; has signed 5G security agreements with the US; and/or some of the state’s major telecom operators have given 5G contracts to Nokia, Ericsson, and/or local actors, thus circumventing Huawei |
2 | Open approach/future use: To some degree, the state’s government is ignoring the US’s warnings, stating that it will not meddle in operators’ decisions on 5G; and/or the state uses Huawei in various technological areas, for example in 5G trials. The 5G network may still be in its early phases, but most evidence is pointing toward potential full use of Huawei in the future |
1 | Full use: The state’s government is using Huawei without any apparent restrictions, and their 5G networks have launched or are close to launching |
Table 2 shows the value given to each of the sample’s 70 countries on the dependent variable, while the Appendix presents a brief account and justification of the coding decision for every state in the sample. Two important notes are in order. First, there are, to be sure, fluid borders between some of these categories. A few states, such as Japan and Australia, placed full bans on Huawei early on [64, 65]. Others, such as Russia [66] and Indonesia [67], have already signed major deals signaling full acceptance of the company. Cases such as these ease the coding. A handful of countries fall in-between multiple categories, such as Mexico [68], Greece [69], and Ireland [70]. Such cases involve challenges that are also related to a second caveat, namely that the policies of some individual states on this issue are in flux. For example, responses to Huawei investments by European states have been mixed even if they seem, over time, to have become more rejective. There have also been some signs lately of a push-back against Huawei from some Asian countries. Others, like Brazil and the United Arab Emirates, are, at the time our coding concluded, in the midst of particularly intense pressure from the US to drop Huawei as a telecom partner, and the outcomes are unknown [71, 72].
Table 2
Countries’ values on the dependent variable (N = 70)
1 | 2 | 3 | 4 |
|---|---|---|---|
Argentina | Algeria | Belgium | Australia |
Bangladesh | Angola | Bulgaria | Japan |
Ethiopia | Austria | Canada | Poland |
Hungary | Brazil | Czech Republic | Romania |
Indonesia | Chile | Denmark | Sweden |
Kazakhstan | Colombia | Ecuador | United Kingdom |
Kenya | Cuba | Finland | |
Kuwait | Dominican Republic | France | |
Morocco | Egypt | Germany | |
Oman | Ghana | Greece | |
Philippines | Guatemala | India | |
Qatar | Iran | Israel | |
Russia | Iraq | Italy | |
Saudi Arabia | Ireland | Luxembourg | |
South Africa | Mexico | Malaysia | |
Switzerland | Myanmar | Netherlands | |
Thailand | Nigeria | New Zealand | |
Unit. Arab Emirates | Pakistan | Norway | |
Peru | Portugal | ||
South Korea | Singapore | ||
Sri Lanka | Slovakia | ||
Turkey | Spain | ||
Ukraine | Vietnam | ||
18 | 23 | 23 | 6 |
(25.71%) | (32.86%) | (32.86%) | (8.57%) |
The use of prominent and readily available news media for the coding of the dependent variable was a necessary strategy considering our objective to include a large sample of states. When selecting the sources on the basis of which coding decisions were made, we pursued a fairly simple method. Using Google News as our point of departure, we applied three search terms – Huawei, 5G, [state X] – for each of the 70 states in our sample for the relevant time frame (which was primarily the year up to October 15, 2021, although in a few cases where information was deemed insufficient, we also needed to rely on news articles – and sometimes also official reports – from before or after this time period). We then made a pre-selection of articles based on the perceived relevance of the headlines and the trustworthiness of the sources. Following an initial reading of this material, we selected a more limited final sample of sources (typically consisting of three to four per country, some of which are referenced in the Appendix).
Granted, such a strategy does come with some selection-bias and reliability concerns.To mitigate these, we made two additional steps in the coding process. First, we scrutinized recent scholarship in order to check for any potential mismatches that would require further inspection. In the literature, coverage of the policies of secondary states is generally good with respect to Europe [73‐76] and South Korea, Japan, Australia, and New Zealand [77‐79]. Moreover, Gregory Moore’s study, which was recently published in the Journal of Chinese Political Science, largely corroborates our coding also for (a few) states situated outside of these regions (such as Kenya and Thailand). [75] Overall, this comparison with other, recent works, many of which draw on official government statements, diplomatic white papers, and the like, worked to increase our confidence in our own coding.
Second, in the present analysis we only separate between four categories of responses to Huawei. Identifying countries belonging to the two “extreme” categories is a relatively uncomplicated task. The “middle” categories are a bit more challenging; but judgments proved easier, also when compared with assessments made by the aforementioned studies, when restricting these to just two (a point helpfully made by one of the anonymous reviewers). However, we also constructed a more nuanced six-category dependent variable, where we split each of the two middle categories into two separate ones, to check for the robustness of our models. Results were substantially the same, though coefficients of the six-category version were generally significant at a somewhat higher level of confidence (and naturally so since this increases the variation in the dependent variable).
One last point to consider is that our coding was conducted – and concluded – as of October 15, 2021. This means that our data constitute a snapshot of the policies in existence at that date. The implied attendant caveat, of course, is that some of these policies, and the circumstances surrounding them, may have changed recently – or they may change in the near or far future. To cite but two such examples. Turkey has now arguably moved from “2” to “1” on our dependent variable (see Table 1), as Turkish telecom provider Türk Telecom in March 2022 signed a memorandum of understanding with Huawei to develop the country’s 5G network [80]. At the other end of the scale, In May 2022. Canada moved to ban the Chinese company altogether from its 5G network, thus placing the country in the category “4.” [81]
Independent Variables
The independent variables can be separated into three clusters, each of which corresponds to one of the theoretical models reviewed earlier. First, we operationalize and measure three of Stephen Walt’s four components of threat perceptions. (The fourth – “perceived aggressive intentions” – is virtually impossible to operationalize.) First, considering that the relational aspect of power is notoriously difficult to measure [82], we employ size of the economy as a proxy of a country’s aggregate power. We therefore include nominal GDP, drawing on estimated numbers for 2021 from the International Monetary Fund [83]. This variable is highly skewed, so we use the logarithmically transformed version of it in all models (Nominal GDP). Second, the military dimension of (offensive) power is also of import. We therefore use the measure of military expenditure for 2020 estimated by the Stockholm International Peace Research Institute (Military expend.). [84] Third, for the “geographical proximity” dimension of Walt’s balance-of-threat-theory, we use a measure of the distance from a state’s capital to Beijing (Distance). This we extracted from the CShapes dataset [85]. On the basis of this distance, we introduce a fourth variable as well, a dummy that categorizes a country as belonging to China’s “neighborhood” if its capital is separated from Beijing by no more than 6,000 km (Neighbor).
For the second cluster of independent variables, we include three essential indicators of states’ patron-client relationship with the United States. The first variable – Alliance – is a dichotomy that is coded 1 for any country that is either a member of NATO, a major non-NATO ally of the United States [86], or a strategic partner of Washington [87]. Second, we include a logged measure of the number of forward-deployed active-duty US military personnel (ustroops), as of June 2021, with data from the US Defense Manpower Data Center [88] (US troops). Third, we use an estimation of a country’s arms imports to calculate the percentage of a state’s total arms purchases accounted for by sales from the United States (Arms imports). [89] To account for any “outlier” years with unusually large/small arms imports for specific countries, we use the average for the period 2015–2020.
The third cluster of independent variables is associated with Hirschman’s theory on trade relations and foreign-policy convergence. We retrieved the numbers for imports from China (Import China), imports from the US (Import US), exports to China (Export China), exports to the US (Export US), and total trade for both (Trade China and Trade US) from the World Bank’s World Integrated Trade Solution (WITS). We use the latest data available, which for most countries is 2019 [90]. All numbers are calculated as percentages of the states’ total imports, exports, and trade, respectively.
Methods and Analysis
Methods
We present below three ordered logit regression models, which is the method of choice considering the dependent variable’s level of measurement (1–4). This is the correct way of modeling a dependent variable when the real distances between the categories are unknown but where the categories can still be ranked from low to high. We obtain one estimate for each independent variable, and our assumption is that the transition from one value to another on Y follows the same process. Our observed variable is viewed as providing incomplete information about a latent variable Y* ranging from -∞ to ∞. When the latent Y* crosses a cut-point, the observed category in Y changes [91], which provides the following regression equation:
$$L = \ln \{ P(Y \le m)\} = \tau_{m} + \beta_{1} X_{1} + \beta_{2} X_{2} + \beta_{3} X_{3} + \varepsilon$$
(1)
We ran sensitivity models using both binary logistic regression and multinomial logistic regression, which yielded substantially similar results to that of our main analysis.
One challenge is that our dataset, for a quantitative one, is rather small (N = 70). For the central limit theorem to hold, the sample size should be 30 or larger. However, the relatively low N still puts a limit on the number of explanatory variables that can justifiably be included in the same model [92]. Therefore, we choose to test no more than four or five in each model. Regarding significance testing, we are not investigating a sample, but rather the whole population of countries with large or medium-sized economies. According to sample theory, we would get perfect predictions when investigating the whole population. In our case, we follow the logic of stochastic model theory; hence, we are generalizing from the observation made, to the process or mechanism that brings about the data. Any lack of statistical significance in our models indicates that the association produced by nature is no more probable than that produced by chance [93, 94].
Results
Table 3 presents three models testing the effect of the different explanatory variables on Y_Huawei, which we sum up in an overview table presenting the significant and non-significant variables according to their respective groups (Table 4). The largest effect – with a z-score of 2.95 – is exhibited by US troops, followed by Arms import and Military expenditure. All three are significant at the 5%-level. Nominal GDP, Alliance, Import China, and Military expenditure are significant at the 10% level, while none of the other independents are significant. It is worth noting, though, that due to the low N we should not overlook substantial effects that are significant at the 10% level. As mentioned previously, we also ran a sensitivity analysis using a more fine-grained dependent consisting of six categories, achieving similar, though slightly more significant, results than in the presented models.
Table 3
Determinants of Huawei reception, ordered logit regression
(1) | (2) | (3) | ||||
|---|---|---|---|---|---|---|
b | Std. E | b | Std. E | B | Std. E | |
Group 1 | ||||||
Nominal GDP | 0.503** | 0.219 | ||||
Distance | -0.013 | 0.056 | ||||
Neighbor | -1.100 | 0.703 | ||||
Military expend | 0.038** | 0.015 | ||||
Group 2 | ||||||
Alliance | 1.048* | 0.608 | ||||
US troops | 0.283*** | 0.107 | ||||
Arms imports | 0.018** | 0.008 | ||||
Group 3 | ||||||
Import China | -0.082** | 0.012 | ||||
Import US | -0.003 | 0.019 | ||||
Export China | 0.005 | 0.019 | ||||
Export US | 0.015 | 0.014 | ||||
Trade China | -0.038 | 0.039 | ||||
Trade US | -0.002 | 0.020 | ||||
Cut points | ||||||
\(\tau\) 1 | 1.172 | 1.282 | 0.322 | 0.794 | -0.934 | 0.629 |
\(\tau\) 2 | 2.576 | 1.304 | 1.412 | 0.808 | 0.031 | 0.618 |
\(\tau\) 3 | 2.979 | 1.313 | 1.734 | 0.812 | 0.531 | 0.622 |
\(\tau\) 4 | 3.833 | 1.338 | 2.441 | 0.831 | 1.429 | 0.646 |
\(\tau\) 5 | 5.458 | 1.432 | 3.963 | 0.941 | 3.045 | 0.764 |
N | 70 | 68 | 52 | |||
Log Likelihood | -106.661 | -111.372 | -82.228 | |||
Table 4
A summary of the results
Group 1 | Group 2 | Group 3 | |||
|---|---|---|---|---|---|
Significant | Not Signif | Significant | Not Signif | Significant | Not Signif |
Nominal GDP | Distance | Alliance | Import China | Import US | |
Military Exp | Neighbor | US Troops | Export China | ||
Arms Imports | Export US | ||||
Trade China | |||||
Trade US | |||||
If we consider Group 1, a high nominal GDP and large military expenditures increase the likelihood of a country belonging to the higher categories on the dependent. However, our two geographic measures – Distance and Neighbor – yield no effects. In other words, more powerful states are less likely to open their markets to Huawei than are smaller and weaker states, possibly because the former are better positioned to withstand any subtle or overt retaliatory measures by Beijing. Less powerful states, for their part, are significantly more inclined to welcome Huawei into their markets. Overall, however, Walt’s balance-of-threat theory gives at best only a partial explanation of secondary states’ positioning on the Huawei issue.
For Group 2, both high arms imports from the United States and hosting a large number of US troops significantly increase the likelihood of belonging to the high categories on the dependent, meaning that the likelihood of shunning Huawei increases. A military alliance or partnership with Washington, even if its effect is positive, is a somewhat weaker predictor. Taken together, though, these results lend significant credence to the patron-client understanding of alliance dynamics. It seems that those countries that are most dependent on the United States for their security are also the ones that are most likely to adhere to Washington’s warnings about Huawei.
Of the six measures tested for Group 3, however, only one is significant (Import China), while the others have no effect. This implies that if a country is dependent on imports from China, it is likely more acceptive of Huawei. Nonetheless, the overall results do not suggest that trade dependence has any substantial and unambiguous effect on the positioning of secondary states on the Huawei issue.
We predicted the likelihood of scoring the low (1) and high (4) value of our dependent variable for the minimum and maximum values of the explanatory variables included in Group 2, which exhibited the most consistent results, keeping the other variables in the models at their means. The results clearly indicate that dependency on the United States is a solid predictor of a country’s stance on Huawei.
A state with no US alliance has a 40.69% likelihood of full use of Huawei (value 1 on the dependent), compared to 20.37% for an allied country. The former has a 2.13% likelihood of a full ban (value 4), with the corresponding number for a US ally being 5.97%. A country with a minimum presence of US troops, which is the strongest explanatory variable in our models, has a 44.38% likelihood of full use (value 1), whereas a country with the largest presence of troops has a mere 5.12% likelihood of using Huawei without any restrictions. The former has a 2.23% likelihood of a full ban (value 4), while the latter has a likelihood of 25.26%. Lastly, a country with no arms imports from the US has a 31.47% likelihood of using Huawei without restrictions, whereas a country with the maximum score on arms imports has a 7.50% likelihood of full use. The former has a 3.68% likelihood of full ban, while the corresponding number for the latter is 18.08%.
Conclusions
The broader mission of this article was to explore some of the factors that shape how middle or secondary powers respond to China’s ambitions and its drive for influence abroad. Analyzing the case of Huawei is conducive to this task for four main reasons. First, it is of great importance for China in that it represents and symbolizes its ambition to control the commanding heights of the world economy; and it is of equal importance to the United States, which does not want to see China spearheading high-tech industries, let alone control the critical infrastructure of Washington’s friends and allies. Second, the controversy around this case has been long-running even as its contentiousness has risen lately, making the choices of secondary states a hugely topical issue in international relations. Third, the Huawei case is amenable to measurement across an array of countries in a way that most other US-China conflict issues are not. And fourth, this case can tell us something very useful about the overall rivalry between Beijing and Washington – which will likely be the most defining matter in international politics in the years and decades to come.
Under the administration of Donald Trump, the United States arguably abandoned any pretense of unambiguous, across-the-board engagement with China, instead pursuing a policy of intense strategic competition and even, to an extent, Kennan-like containment [95, 96]. Yet, such a stance is widespread within the corridors of Washington: there is now broad bipartisan agreement in Congress that China poses a great challenge to the United States and indeed to the world [97, 98]. All this puts enormous pressure on secondary states; the choices available to them – in the case of Huawei and on several other issues – are in a major way shaped by this overarching conflictual environment. Their room for maneuver is circumscribed by the preferences of both Washington and Beijing.
This is not in any way to argue, however, that the case of Huawei’s 5G investments is representative of the whole gamut of trade relationships between China and others, including the United States. The level of contentiousness, for example, is far meeker with respect to such products as chemicals, solar panels, and car production. Indeed, as regards the latter, Huawei now provides the operating system for the Aito M5 electric vehicle of Seres, a Silicon Valley-based affiliate of China’s Sokon [99]. China’s trade with the United States is still huge. Nonetheless, some key aspects of the overall relationship between these two behemoths are now patently securitized – 5G technology constitutes one such prominent example – and hence indicative of a growing superpower rivalry.
Neither should our study be taken to argue that middle powers are completely bereft of autonomy on these issues. To an even larger extent than during the Cold War, which witnessed secondary states often either ignoring the wishes of the superpowers or playing them up against each other, now “[m]any countries and regions have become big enough to walk away from both America and China. Most countries have also become shrewder at weighing and acting on their own geopolitical interests.” [100] Similarly, G. John Ikenberry points out how the “middle countries” of the world often have strong reasons to want such great-power duality to persist, as this could well enable them “to receive the security benefits of allying with the United States and the economic benefits of allying with China.”. [101]
These are surely among the main reasons why an analysis like ours cannot be expected to unveil patterns, and arrive at conclusions, that are fully uncontestable. Many secondary states simply fail to fall neatly in line with what the theories expect. But others do so. To repeat the main patterns revealed by our study: First, states that are highly dwarfed by China’s power and capabilities tend to be somewhat more acceptive of Huawei, while more powerful states are far more rejective of the Chinese telecom behemoth. Second, the most univocal result is that those that can be viewed as client states of the United States, depending on Washington for their security, tend to be considerably more rejective of the Chinese company. Third, secondary states’ trade relations with the two great powers seem to have only a very limited effect on how states position themselves vis-à-vis Huawei: Trade with China might matter a bit, but trade with the US does not.
In sum, therefore, it is the patron-client theory which offers the most cogent explanation of the patterns in our data. So, even though secondary states in general unquestionably do have a great deal of autonomy when making decisions such as those explored herein, it is an equally unquestionable feature of such choices that they have to be taken within a reality that is to a great – and growing – degree shaped by an intensifying great-power rivalry.
Declarations
Conflict of Interest
None of the authors have any conflict of interest.
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