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New Chinese Economic Policy to Latin America?
A QCA Approach to the Belt and Road Initiative
Juan Pablo Sims
Universidad del Desarrollo, Faculty of Government, Santiago, Chile
Fudan University, School of International Relations and Public Affairs, China
Yun-Tso Lee
Universidad del Desarrollo, Faculty of Government, Santiago, Chile.
Brice Tseen Fu Lee
Fudan University, School of International Relations and Public Affairs, China
Universidad del Desarrollo, Faculty of Government, Santiago, Chile
1 Introduction
Foreign relations between China and Latin America and the Caribbean (LAC) are relatively new
phenomena. Until 1979, apart from Cuba, Chile was the only country in the region that had diplomatic relations
with the Asian giant. Besides a handful of exceptions, Chinese engagement in the area was limited until the
early to mid-2000s, which was when China started prioritizing economic bonds with the region. At the turn of
the century, China-LAC trade only accounted for USD 5 to 8 billion a year. Today, China has become Latin
America’s second-largest trading partner, only behind the United States. Its engagement with the region has
evolved from a distant country in a faraway land to an ever-growing presence (Zhang & James, 2023).
Due to the growing Chinese economic presence in LAC, it has become crucial to understand the
implications of Chinese foreign policy in the region. In the region, politicians and academics alike claim that
China has become increasingly proactive across the Pacific, having moved past traditional economic exchanges
to new and novel ways of cooperation, including the expansion of cultural diplomacy, the development of new
Confucius Institutes, and the inclusion of LAC in the Belt and Road Initiative (BRI). Nonetheless, despite strong
statements by Chinese and LAC authorities regarding this new stage of bilateral and regional diplomatic ties,
the essence of the China-LAC relationship remains primarily economic.
In this context, this research will analyze current China-LAC economic relations by studying the
implementation of the BRI in the region. Announced in 2018, this inclusion into BRI has been described as the
most significant policy change toward LAC in decades and as a proxy for future Chinese economic strategy
toward the region (Pradhan & Mohanty, 2021). This paper will explore the central question: Can the BRI explain
Chinese FDI flows to LAC?
It will be argued that, despite the changes in rhetoric, the content of Chinese economic engagement
has remained stable, even during 2018-2020 following the region's inclusion in BRI. This finding presents
fundamental questions regarding the BRI rollout in LAC. The structure of this work will be as follows. Firstly,
it will review the literature focusing on three key topics, China-LAC relations, BRI, and FDI. Secondly, it will
develop a general theory of Chinese economic engagement in LAC. Thirdly, it will provide the methodological
framework. Finally, it will analyze the data and conclude that China-LAC relations have remained stable
throughout the last decades and that the BRI is not an element of change.
2 Literature Review
2.2 China-LAC Relations
2.2.1 Politics
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After the US-China rapprochement during the 1970s, most LAC countries switched diplomatic
relations from Taiwan to Mainland China. Nonetheless, it was not until the 1990s that China began increasing
its political engagement with the region. According to Jenkins (2019), China signed multiple “strategic
partnerships” with key regional players during this process. Firstly Brazil (1993), then Venezuela (2001),
Mexico (2003), Argentina (2004), Peru (2008), Chile (2012), Costa Rica (2015), Ecuador (2015), and Bolivia
(2018). Furthermore, some of these key relationships were later upgraded to “comprehensive strategic
partnerships”, starting with Brazil (2012), Mexico (2013), Peru (2013), Argentina (2014), Venezuela (2014),
Chile (2016), and Ecuador (2019).
On top of this wave of “strategic partnerships”, Chinese paramount leaders, including Hu Jintao and
Xi Jinping have made high-profile state visits to the region. Additionally, LAC leaders regularly travel to China,
a relatively uncommon practice decades ago. Moreover, great importance is placed on these state visits and are
compared only with high-ranking diplomatic missions to the U.S., the European Union, and Japan.
Despite the Chinese push to join multiple regional integration schemes, relations have remained mostly
bilateral (Lee and Hongying 2011; Rodríguez, 2015). In this context, China joined the Inter-American
Development Bank (IADB) in 2008. Also, it gained observer status at the Organization of American States
(OAS) in 2004. China has recently established dialogues with the MERCOSUR (Southern Common Market),
the Andean Community, and the Community of Latin American and Caribbean States (CELAC). Despite these
seemingly closer ties, the core of the China-LAC relationship has been economic.
2.2.2 Trade
During the 1990s, the exchange of goods and services became the cornerstone of China-LAC relations.
Total trade grew from 5 to 8 billion during the final years of the 20th century to USD 449 billion in 2021. Unlike
China’s other trade partnerships, including with Africa, China-LAC exchanges have been relatively balanced
from a macro regional perspective. However, looking at China’s trade country-by-country presents a different
story. According to Jenkins (2014), Brazil, Chile, Venezuela and Peru have trade surpluses with China, while
the rest maintains essential deficits. Even though trade has grown considerably in recent years, most China-
LAC trade is still dominated by specific industries. Dussel-Peters (2016) suggests the underlying reason behind
this is the lack of integration in global value chains.
This phenomenon is also analyzed by Rosales (2012) and Su (2017), which point out that most China-
LAC Trade is based on the dichotomy of raw materials and manufactured products, in which China imports
primary products and exports high-technology goods. The top Chinese imports from LAC are soybeans, iron
ore, petroleum, and copper. Although some Latin American manufactured products have gained significant
space within the Chinese market, like Chilean wine and engine components from Mexico and Brazil, they have
not significantly impacted trade patterns with China. Analyzing the LAC trade balance, it is possible to argue
that a similar trade relationship exists with most developed economies, including the U.S. and Europe.
Current China-LAC trade relations were made possible thanks to the Chinese accession into the World
Trade Organization (WTO) in 2001 and the subsequent signing of multiple Preferential Trade Agreements
(PTA) with a number of LAC economies: Chile (2005), Costa Rica (2007), and Peru (2009). These agreements
secured low tariffs and non-tariff barriers for fundamental raw materials needed by Chinese industry, such as
copper or iron ore, but also institutionalized agreed rules for Chinese investment in the region (Shaffer and Gao
2020).
2.3 Chinese Policy, BRI and FDI
Even before the conception of the BRI, scholars have debated whether domestic policies or economic
objectives most impact Chinese FDI. Some authors such as Cheung and Qian (2009) and Chen et al. (2018)
have argued that Chinese companies were heavily influenced by the “Going Global” policy advocated by the
Chinese government, which led to a significant increase in Chinese OFDI. By contrast, other authors (Cheung
et al. 2012, 2014) suggest that Chinese firms primarily base their investment decisions on traditional economic
factors. Although this debate is still ongoing, scholars like Yu et al. (2019) have pointed out that it is likely that
the BRI might have had a similar impact on OFDI as the aforementioned “Going Global” strategy. This finding
implies that Chinese companies respond to political incentives, such as the BRI, to determine their investment
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patterns. Nonetheless, in spite of extensive research on this topic, the case of Latin America has not been
sufficiently studied.
2.4 Belt and Road Initiative in LAC
The origin of the BRI can be traced back to two speeches by President Xi Jinping in 2013, one given
in Kazakhstan and the other in Indonesia. In these speeches, he laid the ground for the BRI as a continental Silk
Road Economic Belt and a 21st Century Maritime Silk Road (Rolland 2019). A series of official declarations
solidified and institutionalized the project as the cornerstone of Chinese foreign policy. After the BRI was
launched, In May 2017, China hosted the Belt and Road Forum for International Cooperation, in which 60
nations and 28 heads of state attended (Bhattarai 2019). During this event, its internationalization process began,
as BRI evolved to include Oceania, South America, Africa, and cyberspace (Zou 2018). With the BRI, China
promised a new engine for global growth, sustained by cooperation and shared goals – not by competition
(Bhattarai 2019; Beeson & Crawford, 2023).
LAC relies heavily on China for sustained economic growth and funding for critical infrastructure
projects including roads, ports, and railways (Gransow 2015; Chiyemura et al., 2023). Kohli and Basil (2011)
have noted that LAC needs to invest at least 4% of its annual GDP in crucial infrastructure to avoid constraining
economic growth. This number is relevant when analyzed in light of multiple social movements across the
region that demand better wages and equal distribution of resources (Mayol 2019; Acelas and Perales 2021;
Huanca-Arohuanca 2021). With this in mind, China can be seen as not only a crucial economic partner for
LAC, but also as a source of stability. Therefore, Chinese foreign policy can be understood as a critical element
for the Latin American subsystem (Blanchard, 2021).
From this perspective, the Belt and Road Initiative is of particular interest. Multiple scholars have
pointed out that the BRI is the most ambitious Chinese international endeavor to date (Huang 2016; Swaine
2015; Tekdal 2018). This is of particular importance to LAC because the BRI’s scope can impact multilateral
and bilateral relations between China and BRI countries (Indeo 2018). Moreover, this initiative could also shape
environmental, trade, and development policies in BRI partners’ economies (Hong 2016; Iqbal et al. 2019; De
Soyres et al. 2019).
Despite the persuasive nature of this initiative that promises substantial investment and resources, the
BRI does not have a clear roadmap (Gyamerah et al., 2022). Moreover, it lacks official guidelines or even a
public policy structure. The Chinese government has not published any BRI comprehensive framework to date.
Consequently, there is no consensus regarding under what conditions a state officially participates in the BRI.
Although China has signed several MoUs with LAC countries, there is not any clarity on which Chinese
investments, policies, and initiatives can be considered part of this scheme. Countries such as Mexico,
Colombia, and Brazil have not signed any BRI-related MoUs, but are actively seeking out more Chinese
investment and trade. In that sense, Brazil has had a strong presence in BRI forums and is an AIIB prospective
member (Serrano et al. 2020).
This lack of clarity regarding what it means to participate in this initiative raises two fundamental
questions: What is the BRI? Moreover, how can a country be part of it? According to Zhou and Esteban (2018),
the BRI is strategically and tactically acting as a vehicle of soft balancing against the U.S. Contrary to this view,
authors like Wang (2016) have defined the BRI based on what it is not: Despite western concerns, the BRI is
not a Chinese geopolitical grand strategy to expand China’s influence and instead can be viewed as an
international economic cooperation project (Wu, 2023). Another perspective is the functional view adopted by
Wang (2019), which argues that the BRI is a network of bilateral and multilateral non-binding mechanisms
loosely connected with China at its core, which coordinates financial integration, trade liberalization, and
people-to-people connectivity.
Based on Wang’s pragmatic definition of the BRI, it is possible to analyze engagement with this policy
beyond the constraints of “official” membership determined by signing official documents, like MoUs or trade
agreements. In that sense, Serrano et al. (2020) suggest that, utilizing Wang’s definition (2019), a better way to
measure engagement with this policy is to take a broader view that includes mechanisms like formal
participation (MoU or other agreements), AIIB membership, and official involvement in BRI forum. Using this
framework, it can be argued that, so far, BRI presence in Latin America has been modest despite 19 countries
officially signing non-enforceable MoU by 2020 (Serrano et al. 2020).
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From a discursive point of view, it is relevant to note that after the 2013 launch of the BRI, Xi Jinping
engaged in vigorous “infrastructure diplomacy”, promoting future Chinese investments in multiple countries
especially within South-East Asia (Ye 2019). Like Li Keqiang and Zeng Peiyan, other leaders have followed a
similar template by promoting Chinese investments, both public and private, within the BRI framework. In this
context, Ye (2019) has compiled 125 official speeches and statements in which the main subject was BRI
promotion. Despite this effort to align the diversity of Chinese actors, state and private, the BRI implementation
has been suboptimal.
This brief review illuminates two defining factors of the BRI: On the one hand, the promise of mutually
beneficial economic activity is mainly linked to infrastructure investments, especially in regions with a chronic
investment deficit, like in Latin America (Pradhan, 2018). On the other hand, it is characterized by a lack of
planning and clarity regarding its structure, goals, and policies (Jones, 2020).
Regardless of its impact or implementation, it is clear that the BRI holds considerable importance for
the current Chinese government. The Chinese authorities have devoted significant resources to expanding the
project’s scope. By 2022, most LAC countries had already engaged at different levels of BRI participation, only
four years after the BRI in LAC was announced. To analyze the development of this project and overall China-
LAC economic relations, this research will observe Chinese FDI flows to LAC since the BRI was expanded to
the region to measure potential changes in Chinese economic engagement with LAC.
2.5 General Theory of Chinese FDI
Investment literature is exceptionally complicated. There is no consensus among scholars concerning
the critical determinants of FDI. Many have argued that this phenomenon is country-specific, meaning that each
case might have specific determining factors (Blonigen 2005). Nonetheless, from a theoretical perspective,
authors such as Rugman (1980), Coase (1988), Buckley and Casson (1976), and Dunning (1980; 1977) have
developed a general theory of FDI, which states that MNEs seek internalization in response to imperfections in
the goods and factors markets. This point is further developed by Buckley et al. (2015), who argue that firms
internalize imperfect external markets until the costs outweigh the benefits; from this point onwards, MNEs
invest in locations that can minimize the overall operational costs. In short, MNEs have three main motivations
for going overseas: foreign-market seeking; efficiency-seeking; resource seeking (Buckley et al. 2015). In this
context, Buckley et al. (2015; 2018) tests this theory in the Chinese context, to identify if Chinese FDI has any
country-specific determinants. Despite their expectation that case particularities like political risk may have an
impact, they determine that Chinese FDI essentially behaves within the margins of the general theory of FDI.
These findings, however, are difficult to operationalize because, within the general theory of FDI, there
is no consensus regarding what factors explain FDI. For this reason, based on Walsh & Yu’s (2010) framework,
this research will focus on the most accepted and used determinants of FDI, which will be organized into two
subgroups: market and institutional factors. Market factors will be composed of: GDP growth (Dritsaki,
Dritsaki, & Adamopoulo 2004; Abbes et al. 2015), GDP per capita (Jaiblai and Shenai 2019; Moudatsu and
Kyrkilis 2011), inflation (Mustafa 2019), market size (Taylor 2002; Chakrabarti 2001), and trade openness
(Sánchez-Martín et al. 2014; Liargovas and Skandalis 2012). Institutional factors will be determined by four
sub-variables: property rights (Li 2006, 2009; Nieman 2012), rule of law (Staats and Biglaiser 2012; Hewko
2002), quality of bureaucracy (Muhammad and Siddiqi 2020), and corruption (Al-Sadig 2009; Helmy 2013).
Based on Buckley et al. (2015, 2018), Chinese FDI in LAC is expected to respond to market incentives rather
than political ones. Consequently, this paper hypothesizes that the BRI, characterized by its sui-generis
approach, and lack of material inducements, has not changed Chinese FDI flows to LAC.
3 Theory of China-LAC Relations
Based on the reviewed material, it is theorized that China-LAC economic relations have remained
stable. For decades, China has engaged in the region, establishing mutually beneficial economic agreements,
expanding its ever-growing exporting sector, and importing crucial raw materials (Cai, 2023). In this context,
this research will study the expansion of the BRI to the region as a case study to demonstrate that, despite the
rhetoric of multiple leaders and numerous political gestures, the expansion of the BRI to LAC has not
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substantively changed China-LAC economic relations. To that end, this research will look at China’s FDI flows
to LAC.
H1: BRI engagement cannot explain Chinese FDI flows to LAC for the period 2018-2020.
H2: Chinese FDI flows to LAC are better understood by the general theory of Chinese FDI instead of BRI
engagement.
4 Research Design
4.1 Set theory and QCA
As this study relies on a medium-sized number of cases (N = 26, based on Serrano’s model and data
availability), this research is not suited for traditional qualitative or quantitative methods. Consequently, it will
use set theory and Qualitative Comparative Analysis (QCA) to identify complex causal patterns: the necessary
and sufficient conditions between Chinese FDI in LAC between 2018 and 2020, as well as their relationships
with BRI implementation or mainstream FDI determinants. This period was selected based on that the BRI was
launched in LAC in 2018 and data availability.
QCA is a methodology that enables the analysis of multiple cases in complex situations by combining
quantitative and qualitative data. QCA is designed for use with an intermediate number of cases, typically
between 10 and 50. It can be used in situations where there are too few cases to apply conventional statistical
analysis. QCA is a methodology that looks for patterns across multiple cases to understand better why some
changes happen and others do not. In this context, QCA is particularly suited for this study because it focuses
on combining multiple conditions to analyze their relationships with the outcome. Consequently, this method
allows finding several causal pathways to the studied phenomenon, which is in line with the research interest
of this article that aims to improve the understanding regarding what conditions are necessary or sufficient to
explain Chinese FDI flows to Latin America, and the overall essence of recent Chinese engagement in the
region under the BRI framework.
QCA utilizes set theory to analyze causal relationships. This logic has two variants, dichotomous crisp-
sets, where each condition is dichotomously coded as a one or zero (set-membership or non-membership,
respectively), and fuzzy-sets, where values are a number between one and zero, representing the degree of
membership in a given set. Based on this general approach, the raw data is calibrated into sets according to
three points: full set membership, non-membership, and the crossover point. The specific calibration will be
explained in a later section, where it will follow different strategies according to the specificities of the observed
data. This method tries to represent the qualitative information of each condition.
Once the conditions have been coded into their respective fuzzy sets, QCA uses an algorithm to match
all the conditions and find solutions. Depending on the solution’s logical remainders (understood as
theoretically possible but not observed combinations), different results are provided. Specifically, this study
will utilize the results of the parsimonious and intermediate solutions, based on deep theoretical knowledge
about each case and condition. The results are then interpreted according to two critical parameters, consistency
and coverage. The former represents how closely the data is related to the results; the latter how scores offer
details on the percentage of the outcome explained by the results.
Despite the benefits of QCA, it is relevant to mention that the scope of this method is limited by the
feasibility of analyzing a large number of conditions. When QCA processes all possible combinations, each
new condition under scrutiny increases the number of potential matches exponentially. Consequently, as
previously mentioned, some of these cases might not correspond with empirically observed instances – a
phenomenon known as “limited diversity”. Methodologically, QCA does not have a proper answer for this other
than to keep the number of conditions small, typically no more than six.
QCA can identify multiple pathways, represented by a combination of conditions, to the outcome that
is being studied. However, even though QCA suggests a causal relationship between conditions and outcome,
it does not explain the mechanism by which these two elements are connected. For this reason, even though it
is beyond the scope of this study to employ another supplementary methodology, such as case study or process
tracing, the analysis of the results will be complemented by deep qualitative insight anchored in typical cases,
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in addition to an overview of contextual factors that help to explain specific China-LAC patters (Mross et al.
2022).
It is important to explicitly mention that this study is not interested in finding correlations but set
relationships. To that end, QCA is the right tool for the task because it can accurately point out the specific
relationship and significance between conditions and outcomes. It is a valuable input to producing a plausible,
causally complex explanation of a given phenomenon. Furthermore, equifinality, conjunctural, and asymmetric
relations, as mentioned by Schneider and Wagemann (2013), are believed to be rightly expressed in terms of
necessity, sufficiency, INUS, and SUIN conditions, which supports QCA as the chosen method.
4.2 Market or Politics? FDI Determinants: A Fuzzy set approach
This section will determine whether the BRI can explain Chinese FDI flows to LAC by developing a
framework to analyze and compare this initiative and traditional FDI drivers. To that end, it will use five FDI
determinants widely accepted in the literature: market size, inflation, GDP per capita, trade openness, and
institutional factors (also known as “qualitative factors”). These are theorized to explain Chinese investments
in the region. This study will also add a fifth condition to measure if the level of political engagement with the
BRI, as a proxy of BRI participation, can provide an alternative explanation. As previously mentioned, fuzzy
set qualitative analysis introduced by Ragin (2000) will be used to measure and study factors that cannot be
accounted from a quantitative approach or small datasets, such as LAC’s BRI participation.
4.3 Cases and Outcome
The case selection was based on Serrano et al. (2020), in which the authors developed a general
framework to analyze BRI participation in LAC. However, due to the data availability, the number of cases was
changed from 24 to 26. This increase is explained by the inclusion of nations in LAC that do not have diplomatic
relations with China. The paper also removes countries without enough available data. It was impossible to find
institutional information for nations such as Cuba and Venezuela, as this indicator was built using corruption,
rule of law, level of bureaucracy and property rights data.
The outcome to be studied is Chinese FDI flows to LAC. This element is measured by firms and
projects data based on Dussel Peters dataset (Red ALC-China 2022) and is divided by country. Although this
dataset is far from perfect, as stated by Serrano (2021), not much is known about Chinese direct investments in
the region and the infrastructure projects contracted by LAC governments to Chinese companies. Therefore,
the Dussel Peters-LAC Network dataset is the best alternative currently available to study Chinese FDI in the
region.
4.4 Selection, Conditions Operationalization and Specific Calibration
Based on Serrano et al. (2020) and Serrano (2021), this study will analyze BRI engagement in LAC.
To that end, this study will use fuzzy logic (Schneider & Wagemann, 2013) and the direct calibration method
developed by Ragin (2000, 2009) to determine the level of engagement by assigning an equal value from one
to zero to each of the three elements of BRI participation (BIR-MoU signing, participation in BRI forums, and
AIIB membership) described by Serrano et al. (2020). This indicator was built based on Serrano et al. (2020)
and publicly available data on LAC-BRI MoUs, AIIB potential membership, and BRI forum participation.
According to a vast body of literature, this study will use four “market conditions” that help determine
FDI flows: market size, GDP per capita, inflation, and trade openness. These indicators were built based on raw
data and then transformed into percentiles using the highest value as percentile 100. The rest of the values were
calculated accordingly, and the average for each country between the years 2018 and 2020 was used. GDP
growth was left outside the model due to the COVID-19 distortion of the 2020 data. Each of the four
determinants will be coded using fuzzy set logic based on a scale from one to zero. Fuzzy sets were selected
above the more traditional crisp logic, given the ability to highlight partial truth, which better reflects the
complex international reality and is more applicable to social science research that is not simply a collection of
binary choices. Following Ragin’s (2000, 2009) suggestions and best practices for direct fuzzy calibration, this
study follows the direct calibration system proposed by Russo and Confente (2019), but with a slight
modification due to the data distortion caused by the relative size of the Brazilian economy comparison to the
rest of the region.
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Calibrating the QCA procedure is one of the process’s most challenging aspects (Misangyi et al. 2017;
Greckhamer et al. 2018). Proper calibration is crucial in this method because any errors can affect the reliability
and validity of the results. Russo and Confente’s (2019) system is preferable to this research, as it is based on
standardized calibration, rather than arbitrary measurement (one of the main criticisms of QCA). They argue
(Russo and Confente, 2019) that, although sample-based calibration is generally not recommended, this
decision can be justified because it provides a more accurate representation of the data than simply assigning
the scale’s midpoint. Using the median as a crossover point represents a reliable demonstration of “mostly in”
or “mostly out” of any given set. Furthermore, the recommendations for the other two anchor points (full and
full non-membership) are justified based on the endpoints of the scale used in this research (0-100).
Consequently, this research will utilize the following fuzzy scale anchor points:
Firstly, market size and the outcome will be coded based on the three qualitative anchors direct
calibration system proposed by Russo and Confente (2019). However, there will be slight modification due to
the data distortion caused by the relative size of the Brazilian economy in comparison to the rest of the region.
To that end, the calibration rule for these two conditions will be as follows: The lowest percentile will be
identified as the non-membership anchor, the median score of each condition as the crossover point, and the
95th percentile as full set membership.
Secondly, following a similar calibration method, GDP per capita, trade openness, inflation, and
institutional factors will be coded based on Russo and Confente (2019) without modification. The anchors for
non-membership, full set membership, and crossover points will be the 5th and 95th percentile and the median,.
The inflation indicator was organized using 2 percent as the benchmark for “good” or “healthy inflation” and
the data was registered based on its variance to that number. Also, as grounded in the reviewed FDI literature,
this study follows the consensus regarding the significance of institutional factors as a potential determinant of
FDI flows.
To better understand the influence of institutional factors on FDI, this research grouped four robust
indicators that measure different aspects of the institutional framework: property rights, rule of law, corruption,
and bureaucracy quality. These elements function as proxies to represent institutional information as FDI
determinants in the QCA process . The main reason for grouping these together is that QCA works at its best
when the number of conditions are no more than six. Consequently, given that the aforementioned factors refer
to the same type of variables (institutions), they were clustered into one. These four factors are built in
percentiles. Accordingly, the “institutional indicator” was built by averaging the data for each country for the
years 2018 to 2020.
4.5 Data and Results
Table 1: Calibrated Condition Values
Country
GDP Per
Capita
Inflation
Market
Size
Trade
Openn
ess
Institutional
BRI
Engage
ment
Outco
me
Argentina
0.61
0.04
0.68
0.03
0.63
0.33
0.91
Bahamas
0.96
0.95
0.18
0.02
0.52
0
0.32
Barbados
0.8
0.04
0.05
0.59
0.63
0.33
0.32
Belize
0.15
0.04
0.05
0.96
0.17
0
0.05
Bolivia
0.07
0.96
0.5
0.56
0.18
0.66
0.56
Brazil
0.55
0.5
0.96
0.02
0.62
0.33
0.96
Chile
0.74
0.91
0.61
0.63
0.96
1
0.8
Colombia
0.34
0.76
0.63
0.07
0.58
0
0.71
Costa Rica
0.68
0.96
0.52
0.72
0.84
0.33
0.53
Dominica
0.53
0.58
0.05
0.07
0.57
0.33
0.05
Dominican
Republic
0.53
0.79
0.52
0.5
0.33
0.33
0.05
8
Ecuador
0.34
0.44
0.53
0.33
0.33
0.66
0.56
El Salvador
0.11
0.47
0.18
0.81
0.3
0.33
0.05
Guatemala
0.15
0.5
0.52
0.3
0.29
0
0.05
Guyana
0.45
0.94
0.05
0.02
0.24
0.33
0.52
Haiti
0.02
0.04
0.18
0.3
0.04
0
0.05
Honduras
0.04
0.42
0.18
0.94
0.29
0
0.32
Jamaica
0.22
0.38
0.18
0.92
0.76
0.33
0.32
Mexico
0.58
0.43
0.89
0.86
0.5
0
0.92
Nicaragua
0.03
0.32
0.18
0.93
0.1
0
0.05
Panama
0.74
0.49
0.52
0.89
0.58
0.33
0.61
Paraguay
0.26
0.86
0.5
0.76
0.27
0
0.32
Peru
0.45
0.98
0.58
0.36
0.53
0.66
0.74
St. Vincent
0.51
0.73
0.05
0.02
0.48
0
0.05
Trinidad and
Tobago
0.78
0.73
0.18
0.02
0.72
0.33
0.52
Uruguay
0.8
0.04
0.51
0.46
0.92
0.66
0.52
1. Market size, GDP per capita, and inflation data correspond to the World Bank database for the period 2018-2020, except for
inflation in Argentina, in which case data from the Argentinian government was used.
2. Trade openness data was retrieved from The Global Economy database for the period 2018-2020.
3. The institutional indicator was built based on property rights index, rule of law, bureaucracy quality, and control of corruption
data from the Heritage Foundation, World Bank, The Global State of Democracy, and The Global Economy, respectively.
4. As previously mentioned, BRI engagement was built based on the work of Serrano et al. (2020).
5. The outcome was built based on the Dussel Peters database on Chinese ODI and infrastructure products.
Table 2: Truth Table for the Outcome = Chinese Investment in LAC
GDP
Per
Capita
Inflation
Market
Size
Trade
Openness
Institutional
B
R
I
Outcome
Raw
consist
Cases
1
0
1
0
1
1
1
0.9130
Uruguay
1
0
1
0
1
0
1
0.9116
Argentina
1
0
1
1
1
0
0
0.8937
Panama
1
1
1
1
1
1
0
0.8815
Chile
1
1
1
1
1
0
0
0.8801
Costa Rica
0
0
1
0
0
1
0
0.8750
Ecuador
0
1
1
0
1
1
0
0.8638
Perú
0
1
1
0
1
0
0
0.8416
Colombia, Brazil,
Mexico
1
0
0
1
1
0
0
0.7842
Barbados
0
0
0
1
1
0
0
0.6860
Jamaica
1
1
0
0
0
0
0
0.6801
St. Vincent and
The Grenadines
1
1
0
0
1
0
0
0.6743
Dominica,
Bahamas,
Trinidad and
Tobago
9
0
1
0
0
0
0
0
0.6510
Guyana,
Dominican
Republic,
Paraguay
0
0
0
0
0
0
0
0.5597
Haiti, Guatemala,
Bolivia
0
0
0
1
0
0
0
0.4516
Belize, Honduras,
Nicaragua, El
Salvador
The truth table is the fundamental tool used in QCA. It helps determine connections between the causal
conditions and the outcome (Ragin, 2009). To that end, this study used the latest version of fsQCA software to
create multiple configurations fusing the conditions to analyze and map rationally possible and either
empirically or hypothetically occurring configurations of fuzzy sets (Ragin 2009). On the one hand, consistency
represents how the data shows a coherent relationship between the conditions and the outcome. Consistency is
measured from 1 to 0. A “1” means complete relation between outcome and condition, which is uncommon in
fuzzy set logic. On the other hand, coverage assesses how strongly or relevant these causal conditions are.
According to Ragin (2009), coverage is comparable to an R2 statistic in quantitative methodologies.
Consistency (Xi ≤ Yi) = Σ [min (Xi, Yi)] / Σ (Xi).
Coverage (Xi ≥ Yi) = Σ [min (Xi, Yi)] / Σ (Yi).
As part of the truth table configuration, this study followed Ragin’s (2009) general recommendations
to set the frequency cut-off at one. Furthermore, going beyond QCA standard procedures (Ragin, 2006;
Schneider and Wagemann, 2012), this research established a consistency cut-off at 0.9, despite general
guidelines suggesting a threshold of only 0.8. This decision was made because a higher consistency reduces the
likelihood of logical contradictions (Schneider and Wagemann, 2012). Additionally, a higher consistency helps
to control for potentially skewed calibrations (such as the unaccounted effects of the COVID-19 pandemic in
Chinese FDI flows to LAC), given that a high consistency establishes an exceptionally high benchmark to
identify necessary conditions.
This work also performed a comprehensive robustness test, accounting for 61 alternative variations,
which included changing the crossover point for all conditions, including the outcome. The results were either
identical or very similar, indicating a high confidence level in the calibration, as suggested by Schneider and
Wagemann (2012, pp. 244-249) and reducing the likelihood of skewed data.
Table 3: Necessary Conditions
Conditions tested
Consistency
Coverage
GDP per Capita
0.720074 0.683566
Inflation
0.738490 0.560839
Market Size
0.779926 0.848697
Trade Openness
0.568140 0.510339
Institutional
0.779926 0.684168
BRI
0.540516 0.807428
Table 4: Truth Table Solutions
RAW
COVERAGE
UNIQUE
COVERAGE
CONSISTE
NCY
COMPLEX SOLUTION
~Inflation*Institutional*~Trade Openness*Market
Size*GDP per Capita
0.327
0.327
0.917
10
PARSIMONIOUS SOLUTION
~Inflation*Institutional*~Trade Openness
0.370
0.007
0.761
~Inflation*~Trade Openness*GDP per capita
0.363
0
0.794
~Trade Openness *Market Size*GDP per Capita
0.453
0.125
0.889
INTERMEDIATE SOLUTION
~Inflation*Institutional*~Trade Openness*Market
Size*GDP per Capita
0.327
0.327
0.917
4.6 Findings
This study analyzed the relations of 26 Latin American countries with China after the official launch
of the BRI to LAC (2018-2020). To determine how Chinese FDI is linked with standard macroeconomic
conditions, this research used QCA to search for patterns to better understand the potential impact of the BRI
in China-LAC economic affairs. Tables 1, 2, 3 and 4 summarize the key findings.
In essence, out of the 26 cases, the BRI engagement does not explain recent Chinese FDI flows to
LAC. None of these cases are contradictory, that is, instances where the exact configuration of causal conditions
grants a different result. Therefore, BRI engagement cannot be considered an element of change regarding
China-LAC economic relations for the period 2018-2020. In the following paragraphs, the key findings will be
systematically unpacked. Beforehand, as suggested by Schneider and Wagemann (2013), this study will
perform a necessary breakdown as what qualifies as good QCA practice.
The necessity threshold is typically set at 0.9. Hence, based on Table 3, not a single condition can be
considered necessary because the highest consistency is only 0.7799, which is the case for market size and
institutional factors. However, by analyzing Table 2 in detail, it is possible to observe that market size, GDP
per capita, and institutional conditions are present in the complex solution, in combination with the absence of
trade openness and the inflation condition (representing the lack of healthy inflation). Consequently, it is
possible to claim that these five elements are an insufficient, but necessary, part of an empirical solution. In
other words, when these components are combined, the outcome can be observed.
As shown in Table 3, none of the individual conditions are necessary. Furthermore, as Table 2 displays,
variables such as GDP per capita, market size, and institutional factors, which are present in all the
configurations with the outcome (according to consistency cut-off 0.9), are present in several situations without
the outcome. Typically, this fact would suggest necessity but insufficiency. However, if the parsimonious
solutions (representing theoretical counterfactual solutions) are also considered, as pictured in Table 4, there
are potential solutions without any of these three conditions. This demonstrates that no condition is necessary
or sufficient by itself. This finding can be partly explained due to the shortcoming of QCA as a method. In that
regard, methodologically, it is impractical to add a larger number of conditions that might control for all the
potential FDI determinants found in the literature because it would render the results almost impossible to
interpret.
The analysis shows that no single condition can explain Chinese FDI flows to LAC. These findings
are in line with H1 and H2. Firstly, it means that the BRI cannot explain Chinese investments in the region
during the studied period. Secondly, by analyzing the QCA solutions, it is suggested that the combination of
the other five conditions (a large economy, high GDP per capita, a solid institutional framework, low inflation,
and trade openness) is a better indicator to explain Chinese FDI than the BRI. However, this study’s QCA
methodology cannot determine the specific condition combination that explains this phenomenon. Thus, it is
necessary to develop a system that can account for country-specific variations, which was impossible for this
particular research due to data limitations. Nonetheless, the absence of the BRI as a necessary or sufficient
condition for Chine FDI is relevant to further exploring China-LAC economic relations.
Furthermore, based on the parsimonious solution (Table 4), a combination containing market size,
institutional elements, or GDP per capita is always the most efficient path to the outcome (Chinese FDI flows
11
to LAC). Using Fiss’s (2011) framework, it can be argued that these three variables are core conditions that
have more substantial causal effects on the outcome than the peripheral conditions. At the same time, the low
consistency of the BRI condition, as well as the absence of that element in the parsimonious solution, confirm
H1 and H2. Hence, it can be argued that BRI engagement is neither sufficient nor necessary to explain a causal
path to Chinese FDI flows to LAC for the period 2018-2020.
Counterintuitively, Table 4 points out the absence of the inflation (meaning a worser inflation score)
and trade openness variables as components of a potential parsimonious solution. On the one hand, this
phenomenon can be explained by the natural risk involved in investing in LAC, including the region’s high
inflation. Most foreign investors in the region have a high-risk tolerance. On the other hand, the absence of
trade openness as a causal condition can be understood by the high tariffs of large Latin American economies
such as Brazil and Argentina. In that sense, FDI to those countries can be explained by the desire to overcome
local trade regulations and access those markets. For example, many American and European manufacturing
corporations have established operations in Brazil to overcome tariffs and avoid MERCOSUR’s restrictions.
4.7 Robustness Test
These results hold, even following a significant number of robustness tests. Based on Schneider and
Wagemann’s (2013) good practice recommendations for QCA, this study undertook three types of robustness
checks. The first was conducted by changing consistency thresholds. The second was conducted by altering the
condition’s crossover calibration point. Finally, the third was conducted through the variation of case selection
by dropping individual cases. In conformity with these strategies, this research runs the QCA across sixty-one
configurations (Appendix: Table 5) and the data shows that the numbers are highly robust. This is because
almost all of the QCA solutions are part of a subset or superset of the leading solution formula, despite minor
variations in values including raw coverage, unique coverage, and consistency that do not lead to substantively
dissimilar interpretations (Mross et al. 2022).
Furthermore, in most tests, the results were either identical or highly similar, suggesting a high
confidence level in the solutions. The QCA is further supplemented by insights from two brief case studies to
further analyze the plausibility of the results and gain a deeper understanding. These examples were selected
because Argentina and Uruguay demonstrate the most consistent path to the outcome of Chinese FDI flows in
almost all QCA configurations.
4.7.1 The Uruguayan Case
Uruguay is the quintessential LAC example of a country with a conservative macroeconomic policy
and efficient institutions. According to Peluffo (2015), these are crucial elements to explain FDI flows and are
present in the country (Peluffo 2015). Uruguay established diplomatic relations with China in 1988, and both
countries have had cordial relations ever since. Chinese investments in the country have been modest, which
can be explained by the relatively small size of the Uruguayan economy. Nonetheless, these FDI flows are
disproportionate compared to the overall size of Uruguayan GDP. Respectively, Uruguay can be considered a
typical case of a small, but predictable, economy. This makes the country ideal for FDI flows in the region, due
to its relative security, compared to its neighbours.
In this context, the BRI rollout in Uruguay has been slow despite signing a BRI MoU in August 2018
and being an AIIB prospective member, which rendered Uruguay with a high score in the BRI engagement
indicator. For the 2018-2020 period, there were only two registered new Chinese investment projects in the
country (Red ALC-China 2022). If BRI engagement was a relevant element, it would be expected that Chinese
FDI flows to Uruguay to change. This has not been the case.
Chinese FDI and trade to the country have been moving steadily, in line with flows from other regions,
which is likely linked with the general deterioration of macroeconomic conditions in LAC, which makes
Uruguay even more attractive thanks to its comparative stability (Oviedo 2020). In sum, despite the relatively
small size of the Uruguayan economy and high BRI engagement, Chinese FDI flows to Uruguay have remained
stable and cannot be explained by the BRI. Therefore, it can be argued that such a policy has not changed China-
Uruguay investments for the 2018-2020 period. This case can be said to showcase a successful alternative path
to explain Chinese FDI flows to LAC, which is based on traditional FDI theories.
12
4.7.2 The Argentinian Case
Contrary to the Uruguayan case, Argentina is an excellent example of Latin American expansionary
fiscal policy and lack of efficient institutions, making FDI flows to such an economy unattractive. Nonetheless,
Argentina has two specific elements that offset its institutional weakness. First, it has the third largest GDP in
LAC. Thanks to its MERCOSUR membership, it can access this significant trading block with preferential
tariffs, which is particularly noteworthy considering the size of Brazil. Second, Argentina has vast natural
resources, such as soybeans and other agribusiness products, which are particularly attractive for China’s focus
on food security.
Argentina only signed a BRI MoU in February 2022. Nonetheless, it had strong participation in both
the 2017 and 2019 BRI Forums. This participation, on top of being an AIIB prospective member, meant that
Argentina scored high in BRI engagement. However, if BRI engagement affected China-LAC relations, it
would be possible to observe a change in Chinese FDI flows to Argentina for the 2018-2020 period. Once again,
these numbers have remained stable, while experiencing steady increases. This fact suggests that Chinese FDI
flows to Argentina can be framed within the traditional forces that explain FDI rather than BRI engagement.
Furthermore, according to Oviedo (2015; 2018), given Argentina’s financial isolation during the 2001-
2016 period, the country became dependent on Chinese capital, becoming the third largest source of investment
measured by FDI stock (Oviedo 2018). In that regard, Chinese investment projects increased steadily before
the BRI expansion to LAC. Between 2018 and2020, 27 new projects began (Red ALC-China 2022).
Consequently, it is possible to argue that the Argentinian case also showcases a successful alternative path or
explanation of Chinese FDI flows to LAC, in line with general FDI theories. It is unlikely that during the studied
period, the BRI has caused an increase in FDI flows to Argentina.
4.8 Alternative Explanation
The QCA showed that the most reliable pathway to the outcome is a combination of factors: economic
size, high GDP per capita, a solid institutional framework, low inflation, and trade openness. The BRI
engagement did not show significant relevance in any configuration. Nonetheless, the fact that out of the 26
cases, the QCA only showed two reliable pathways to the outcome indicates there are significant factors that
were left out of the analysis. Even though this finding does not reject H1 or H2 (because the general theory of
FDI explains FDI flows better than the BRI), it strongly suggests this is only part of the explanation to
understand Chinese investment flows to LAC. With this in mind, this study’s analysis is complemented by the
inclusion of the Brazilian and Mexican cases. These countries are characterized by high levels of Chinese FDI
but without a pathway to the outcome, indicating the existence of an alternative explanation unaccounted for
by the model.
4.8.1 The Brazilian Case
One country-specific factor that might explain the lack of a pathway to the outcome for Chinese FDI
flows to Brazil could be natural resources, particularly in the areas of agriculture, mining, and energy. These
resources are vital for China’s industry, as well as augmenting its food and energy security (Shah, 2023). For
example, in agriculture, China is the largest importer of Brazilian soybeans, accounting for more than 60% of
Brazil’s soybean exports in 2020. The mining sector has also attracted substantial Chinese investment,
particularly in iron ore, which is critical for China’s steel industry. Brazil is the world’s second-largest iron ore
producer and China is its leading export market. In 2020, Brazil exported 64.5% of its iron ore to China, making
it the prime supplier of this commodity to China.
Additionally, China’s interest in Brazil’s energy sector is multi-layered, encompassing oil, natural gas,
and renewable energy resources. Brazil is one of the leading oil producers in Latin America and Chinese
companies have made noteworthy investments in Brazil’s offshore oil fields, including the Libra and Lula fields.
Summarizing, considering what multiple authors have suggested (Moran et al., 2012; Cheng et al., 2020; and
Zhang et al., 2022), it is plausible to argue that one extra element, unaccounted by the QCA model or the theory
of FDI, is the demand for natural resources, as it is a driving factor of Chinese FDI flows to Brazil.
4.8.1 The Mexican Case
13
Contrary to the Brazilian case, the Mexican one shows two potential factors unaccounted by the QCA
model that might explain the presence of high Chinese FDI, but the lack of a successful pathway to the outcome.
Like Brazil, Mexico has vast Chinese investments in the natural resource industry. For example, China has
invested in copper, cobalt, and zinc mines. China has also invested in deep-water oil and gas exploration in the
Gulf of Mexico. Furthermore, China has recently started investing in agribusiness in Mexico, one of the largest
agriproducts exporters in the region.
China has also been heavily investing in Mexican manufacturing, which differs from the Brazilian
case. This could be explained by Mexico’s strategic position in North America. It enjoys close proximity to the
U.S., facilitating shorter supply chains and lowering transport and logistical costs for products shipped across
the Western Hemisphere. Additionally, Chinese companies manufacturing in Mexico can circumvent some
tariff restrictions imposed on China since the beginning of the U.S.-China trade tensions that started in during
the Trump Administration.
Mexico is also a member of the United States, Mexico, and Canada Trade Agreement (USMCA).
Consequently, industrial products manufactured in Mexico, but originating from Chinese companies or capital,
can access the U.S. and Canadian markets with preferential tariffs. Subsequently, it can be argued that other
possible unaccounted elements by the QCA model or the theory of FDI Chinese are not only natural resources
(as is the case with Brazil), but also the geographic and institutional structure of Mexico.
5 Discussion
5.1 Analysis
This research’s scope is limited. Instead of theorizing about a causal mechanism that explains Chinese
FDI flows to LAC, it only addresses whether the BRI is a factor of change regarding China-LAC’s overall
economic relations through the examination of Chinese investments. To that end, the QCA data shows that the
combination of multiple conditions other than the BRI engagement presents a better explanation for Chinese
FDI flows to the region.
The QCA suggests that factors such as market size, institutional stability, and GDP per capita are more
reliable for analyzing Chinese FDI flows to LAC. This finding fits in with the theories and literature reviewed
in this study. These conditions are typically considered good FDI predictors due to their reliability to observe
attractive foreign markets where MNEs can further develop their internationalization process and benefit from
the host nation’s market, efficiency, or natural resource. From that perspective, even though the QCA did not
satisfactorily show a relationship of necessity, it did ascertain that engagement with the BRI during the 2018-
2020 period is unlikely to have played a significant role in determining FDI flows. At the same time, the analysis
undertaken in this research also did not adequately establish sufficiency relations. This fact is probably
explained by the lack of country-specific conditions that can account for the specificities of each territory.
It has been well established in the FDI literature that country-specific elements, such as natural
resources or geographical proximity to value chains, can explain FDI flows to a large degree. In that regard,
LAC is a large and diverse region, encompassing almost 22 million square kilometers and approximately 670
million people. Accordingly, further research is appropriate to analyze Chinese FDI flows and overall economic
relations with smaller units within the region that share specific patterns like geographic location, exports, or
institutional development. For example, the Southern Cone or Central America are likely to have very different
trends in their FDI flows from China.
Furthermore, even though the results suggest the BRI has not changed Chinese FDI patterns to LAC
for the 2018-2020 period, it is still possible that this policy might become a relevant analysis factor in the near
future. This study’s results might be impacted by problems in the BRI rollout to the region instead of a
fundamental flaw in the policy design. For instance, slow implementation may have been caused by the COVID-
19 pandemic. Consequently, it is necessary to keep track of this phenomenon and measure potential future shifts
in the FDI flows, primarily because the end of the COVID-19 restrictions will likely unlock significant
investments out of China in 2023.
14
5.2 Limitations
The results presented in this research must be analyzed considering three crucial elements. Firstly, this
study did not control for country-specific variables due to the intrinsic restrictions of QCA as a methodology.
Nonetheless, due to the limited scope of the investigation (which only showed the unlikeliness of the BRI as an
element of change in the China-LAC economic relations, instead of trying to theorize about causal
mechanisms), it is argued that the findings are relevant to understanding the expansion of the BRI as a Chinese
policy to LAC.
Secondly, recent studies (Bai et al. 2021; Stone et al. 2022) have suggested that Chinese private and
state-owned companies follow different investment patterns based on their levels of exposure to government
influence. Accordingly, this element would require performing a more sophisticated QCA enquiry,
differentiating by the degree of state ownership. Unfortunately, such research escapes the scope of this study
due to data and sample limitations.
Thirdly, in line with Jenkins’ examination (2021), despite the preliminary conclusion regarding the
rollout of the BRI to LAC, the potential effects of the COVID-19 pandemic and the fact that the BRI was
launched in the region relatively recently means that further research is necessary once new data becomes
available. This new data will likely shed some light into the possible long-term consequences of the BRI in
LAC for the overall China-LAC diplomatic relations. In that sense, the short time period since the BRI’s 2018
arrival in the region is also an element that makes the study of this policy difficult. Jenkins (2021) argued that
investors likely need more time to redirect or make new investments, which is why recency might drive the
results. Accordingly, it would have been preferable to analyze a more extended period.
Despite the limitations associated with the years of this data, the study is still relevant, given that it
suggests that, at the very least, during the first three years of BRI political engagement, that policy cannot
explain Chinese FDI flows to LAC. Furthermore, Jenkins (2021) also found an average increase in FDI flows
for 2019 after most LAC countries signed BRI MoUs. Nonetheless, most of this increase was due to projects in
Chile and Peru, suggesting a country-specific explanation, rather than an overall attribution to the BRI.
Furthermore, despite the end of the COVID-19 restrictions, there is still no consensus regarding the
overall effects of this event on FDI in general and Chinese FDI flows to LAC specifically. According to some
(Badmus et al., 2022), the pandemic might have had dissimilar effects depending on the region – in some cases
harming FDI inflows and causing FDI outflows in others. On the other hand, some have suggested that there
might have been different impacts depending on the investment entry mode, such as greenfield FDI or cross-
border mergers and acquisitions (Hayakawa et al., 2022; Du, 2021). Similarly, in the case of China, there is no
consensus about the potential consequences of COVID-19 for FDI. For example, Jiang et al. (2021) estimate
that eastern Chinese SOEs probably reduced their investments more than other companies. Consequently, to
untangle the effects of the pandemic on Chinese FDI flows to LAC, better data and analysis will be required.
For this reason, this study uses the best available data on Chinese FDI flows to LAC, despite this evident
limitation.
Accordingly, given all these limitations, this research must be understood as a stepping stone in a
broader body of literature that will need to consider differences in Chinese private and state-owned companies’
investment patterns, COVID-19, the recency of the BRI rollout, and country-specific elements to disentangle
the consequences of all these factors once the data becomes available. Future studies might focus on a more in-
depth examination of how different countries in LAC have reacted to the BRI and why some have decided not
to join it formally.
6 Conclusion
After carefully examining the data, it can be argued that Chinese FDI flows to LAC are not determined
by BRI engagement. On the contrary, the QCA inquiry strongly suggests that such flows are explained by
multiple economic and institutional factors, as hypothesized by the general theory of Chinese FDI proposed by
Buckley et al. (2015, 2018). These findings indicate that the BRI has not been an element of change in China-
LAC economic relations. Despite all the recent political engagement and strong rhetoric that both China and
LAC leaders have employed, this initiative so far has not altered more than three decades of primarily economic
diplomacy. These findings are supported by the analysis of the Uruguayan and Argentinian cases, where it is
15
possible to observe specific trends that might ascertain alternative explanations for FDI in those countries,
instead of linking such flows with the BRI for the period 2018-2020. In that sense, even though LAC authorities
have repeatedly expressed their enthusiasm for the BRI and how they expect it will increase investment in the
region, this study suggests that for the period 2018-2020, such potential flows have been determined by market
and business opportunities, not by China-LAC political engagement. Nonetheless, it is crucial to mention that
these results could be driven by problems in the BRI rollout, given by recency of the data, and external shocks
such as the COVID-19 pandemic. Consequently, it is necessary to expand further on this research and analyze
further FDI information as it becomes available.
7 Declarations
All authors state that there is no conflict of interest and no relevant financial or non-financial interests to disclose
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