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Changing direction to value-based foreign policy and its impact on international trade

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Within the last four years, Lithuania has faced different foreign policy challenges due to geopolitical situations such as the Ukraine-Russia war, the migration crisis on the border with Belarus, and the conflict with China. After opening a Taiwanese representative office in Vilnius, China downgraded diplomatic relations with Lithuania. The purpose of the article is to assess the impact of the changes on international economic relations between Lithuania and China. The paper employs descriptive statistics, correlation-regression, sensitivity analysis, and agglomerative hierarchical cluster analysis. The research is based on the impact of international economic relations on international trade by analyzing separately imports and exports. Our research fills a gap in international relations and globalization theory by focusing on international collaboration between small and large countries, while the large country implements economic sanctions. In the context of Lithuania, exports to China and imports from China comprise a small percentage in the structure of international trade. Lithuania’s GDP level reacts sensitively to changes in export and import data only if they change drastically (over 50%).
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Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
https://doi.org/10.24294/jipd.v8i12.6738
1
Article
Changing direction to value-based foreign policy and its impact on
international trade
Agnė Šimelytė1,*, Artūras Struca2, Andriejus Sadauskis3
1 Department of Insurance, Faculty of Economics, Higher Education Institution Vilnius College, Vilnius 08106, Lithuania
2 Department of Business Economics, Faculty of Economics, Higher Education Institution Vilnius College, Vilnius 08106, Lithuania
3 Faculty of Public Governance and Business, Mykolas Romeris University, Ateities st. 20, Vilnius LT-08303, Lithuania
* Corresponding author: Agnė Šimelytė, a.simelyte@ekf.viko.lt
Abstract: Within the last four years, Lithuania has faced different foreign policy challenges
due to geopolitical situations such as the Ukraine-Russia war, the migration crisis on the border
with Belarus, and the conflict with China. After opening a Taiwanese representative office in
Vilnius, China downgraded diplomatic relations with Lithuania. The purpose of the article is
to assess the impact of the changes on international economic relations between Lithuania and
China. The paper employs descriptive statistics, correlation-regression, sensitivity analysis,
and agglomerative hierarchical cluster analysis. The research is based on the impact of
international economic relations on international trade by analyzing separately imports and
exports. Our research fills a gap in international relations and globalization theory by focusing
on international collaboration between small and large countries, while the large country
implements economic sanctions. In the context of Lithuania, exports to China and imports from
China comprise a small percentage in the structure of international trade. Lithuanias GDP
level reacts sensitively to changes in export and import data only if they change drastically
(over 50%).
Keywords: Lithuania; China; foreign policy; international trade; value-based foreign policy
1. Introduction
Research on foreign policy and international trade has been pursued continuously
for years (Bulley, 2013; Buchowski, 2017; Chandler, 2003; Gilmore, 2014; Grygiel,
2024; Grygiel, 2024; Killian, 2021; Salvatore, 2023; Tuncer and Weller, 2022). For
example, Bulley (2013) advocates for a re-evaluation of values in foreign policy,
emphasising the importance of viewing foreign policy through an ethical lens. This
perspective suggests that ethical considerations should be the foundation of a nations
interactions with other countries, guiding decisions that are in line with moral
principles. However, there are a limited number of studies on changes in foreign policy.
Some studies find that sanctions as the instrument for the implementation of changed
foreign policy cause financial crises (Hatipoglu and Peksen, 2018) and sovereign debt
(Tuncer and Weller, 2023), reductions of trade (Salvatore, 2023) and incomes
(Gutmann et al., 2023; Neuenkirch and Neumeier, 2015), and declines in foreign direct
investment (Mirkina, 2018) While the greatest number of studies focus on
international trade and interlinkages with economic growth (Bajo-Rubio and Ramos-
Herrera, 2024; Celik et al., 2024; Rubavičius, 2021; Safi et al., 2021; Zheng et al.,
2023). Thus, there is a gap in the scientific literature relating to value-based foreign
policy and international economic relations. Further, the study provides the
methodology for evaluating the impact of international economic relations.
CITATION
Šimelytė A, Struca A, Sadauskis A.
(2024). Changing direction to value-
based foreign policy and its impact on
international trade. Journal of
Infrastructure, Policy and
Development. 8(12): 6738.
https://doi.org/10.24294/jipd.v8i12.6738
ARTICLE INFO
Received: 29 May 2024
Accepted: 30 August 2024
Available online: 1 November 2024
COPYRIGHT
Copyright © 2024 by author(s).
Journal of Infrastructure, Policy and
Development is published by EnPress
Publisher, LLC. This work is licensed
under the Creative Commons
Attribution (CC BY) license.
https://creativecommons.org/licenses/
by/4.0/
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
2
Additionally, it helps to identify the most important and promising business sectors.
Obviously, these sectors would be affected the most in the case of changing
international economic relations. The paper focusses on a specific situation when a
small country imposes values-based foreign policy on one of the most important
economies in the world. The research provides interdisciplinary benefits to the science
of politics and international economics. The paper is devoted to analyzing how
changes in foreign policy would influence economic growth. In the context of
Lithuanias foreign policy, the values-based approach has been a significant factor in
shaping its diplomatic stance. When considering the broader implications of values-
based foreign policy, the research by Pastore (2013) on small new member states in
the EU sheds light on strategies that countries like Lithuania may adopt. The concept
of asmall state smart strategy involves compromise-seeking behavior, persuasive
deliberation, and coalition-building, all of which can be influenced by the values and
principles that underpin a nations foreign policy. Furthermore, Buchowski (2017)
highlights how President Adamkus and subsequent heads of Lithuanian diplomacy
have consistently emphasized the moral foundations of the countrys new foreign
policy. This underscores the importance of ethical considerations and values in
guiding Lithuanias interactions with the international community.
In 2021, Lithuania has strengthen its international economic relationships with
Taiwan by opening a Taiwanese representative office in Vilnius. However, this
decision has elicited a negative reaction from China. Thus, Beijing stated that
Lithuania has violated the one-China principle. Lithuania refused to close Taiwanese
representative office, and was faced with Chinas full de facto embargo on Lithuanian
imports. This resulted, in Chinas downgraded political and economic relationships
(Jastramskis and Ramonaitė, 2022). Obviously, the bilateral economic relationships
have not been continuing and have been demolished. The main economic factors were
expected to decrease significantly. Further, in Germanys foreign policy, values were
played off against real-interest. Thus, Lau (2024) questions whether German foreign
policy should be value-based or interest-driven. Even more, the question arose about
what the costs of morality might be, not only in terms of monetary value, but also in
terms of international influence.
The aim of the paper is to assess the impact of the changes in international
economic relations between Lithuania and China. This study focusses on one of the
factors describing international economic relations, such as exports and imports. Thus,
it is the limitation of the study while the complicated international economic relations
have an impact on foreign direct investment, the movement of labor, and collaboration
in research and development. In order to reach its aim, the paper is divided into four
parts. In order, to implement this purpose, we have divided the paper into four parts.
The first part is devoted to a literature review; the second is dedicated to the
methodology, the third presents the results of the research and discussion, and the
fourth is dedicated to the conclusions and recommendations.
2. Literature review
2.1. The concept of foreign policy
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
3
International relations reflect world problems and foreign affairs, involving
intergovernmental organizations, non-governmental organizations, and multinational
corporations. Recently, international relations have been replaced or used as synonyms
for world politics, and global politics. Foreign policy and international relations tend
to be formed and changed in the context of significant events, especially those
associated with a large scale of violence. Thus, in the literature, from one point of view,
foreign policy is classified according to the periods as inter-war, post-war, Cold-War
period, post-Cold War, hegemony, and globalization. Each period of time is specific
and involves different political regimes, systems from autocracy, socialism, and
communism to liberal democracy (Tuncer and Weller, 2022). Furthermore, the post-
Cold War period stimulated the rise of hegemony and globalization.
Hegemony applies to leadership or dominance (Lauderdale and Amster, 2008).
In foreign policy, it refers to the dominance in power of one country over the others.
Even more, hegemony involves components of coercion and consent. The hegemony
of dominance might exist in political, social, and international economic relations
among states while the dominant state maintains its position over consent and coercion.
However, hegemony even refers to shared values, ideas, and ethics in society (Novelli,
2023). Further, over the past two decades, the U.S. hegemony has been challenged by
the growing strength of China and the aggression of Russia over in West (Grygiel,
2024).
Even though, the post-Cold-War period accelerated the process of globalization,
it has been developing for years. The later-known processes have been spreading over
the boarders, which reflect on the movement of materials, labor, goods, and capital.
Thus, obviously, the ties among the countries are getting tighter and stronger. Thus,
these strings among countries even negatively impact social, economic, and political
environments. It results in dependence on the dominating country. Due to the highly
attached supply chains around the world, the delivery of materials and goods has been
problematic since the start of the COVID-19 pandemic (Mačikėnaitė, 2022).
Especially, when China introduced lockdown. For example, at the beginning of the
Ukraine-Russia war, it became clear which countries were energetically dependent on
Russian gas. Despite, the globalization spread all over the world, the process has been
based on Western liberal values. Even more, China, being a communist country, has
partially accepted them, and it has become the worlds factory. Even though the
present international relations between the countries are shaped by the rules-based
international order that was developed by Western democracies after the Second
World War.
2.2. The significance of value-based foreign policy
Foreign policy values are intricately linked to moral and ethical considerations.
Scholars have highlighted the importance of values in shaping foreign policy attitudes
(Aggestam et al., 2018; Bulley, 2013; Chandler, 2003; Hanania and Trager, 2020;
Kertzer et al., 2014; Schalk, 2024). The integration of morality, values, and ethics into
foreign policy decision-making has become increasingly prevalent, with these ideals
becoming central to the American foreign policy community (Bulley, 2013). The idea
of good international citizenship has been explored in British foreign policy, where
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
4
ethical commitments to non-citizens have been justified by national interests and the
maintenance of a stable international order (Gilmore, 2014). In the realm of
international relations, the concept of moral courage has gained significance, as public
opinion, societal values, and morals can exert influence on foreign policy decisions
(Schalk, 2024). Additionally, the tension between sovereignty and cosmopolitanism
has been debated in the context of European foreign policy, highlighting the challenges
of balancing moral obligations and political legitimacy in shaping ethical foreign
policy frameworks. Overall, the incorporation of values, ethics, and morality into
foreign policy decision-making processes reflects a broader trend towards a more
conscientious and principled approach to international relations.
2.3. Foreign policy and trade
International economic relations are utilising economic tools within foreign
policy, such as economic aid and sanctions, to further national interests. Countries
leverage economic diplomacy to benefit from cross-border economic activities,
aiming to achieve economic objectives and enhance national interests (Ruffini, 2016).
This strategic approach is particularly crucial in international trade, where nations
actively participate in economic activities to foster economic development and
integration (Duginets and Omran, 2022). The integration of economic diplomacy into
foreign policy frameworks is evident in various countries strategies. For instance,
China is recognized for promoting economic prosperity through its foreign economic
policies, underscoring the significance of economic diplomacy in meeting national
goals (Busilli and Jaime, 2021). Countries like Australia, Japan, China, and Indonesia
have adjusted their foreign policy directions to include economic diplomacy as a key
component (Killian, 2021). International economic relations are a cornerstone of
foreign policy, enabling countries to navigate the complexities of international
economic relations, promote economic growth, and advance their national interests
globally.
Countries engage in foreign trade activities to drive economic development,
foster international economic integration, and liberalize trade relations (Duginets and
Omran, 2022). Trade policies and agreements significantly impact the development of
international trade, serving as essential components for the functioning of modern
economies. Although the concept of foreign policy might differ, the main functions
are the same (Figure 1). Hence, countries such as Lithuania are highly dependent on
their allies due to their membership in the EU, NATO, and the OECD (Pukšto et al.,
2014). For example, the European Union, of which Lithuania has been a part since
2004, is Lithuanias largest trading partner and has a direct impact on Lithuanias
international economic relations. Until 2019, the EUs strategy on China has been to
wait and see, and no drastic decisions have been taken. Since then, this relationship
began to change drastically when the EU adopted a much tougher foreign policy with
China. In addition to the official announcement of sanctions, China began to restrict
Lithuanian business activities in import and export matters (Rubavičius, 2021).
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
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Figure 1. The framework of the foreign policy.
Source: authors.
Further we have developed two hypotheses:
H1. Export to China has positive impact on the Lithuanian GDP.
H2. Import from China has negative impact on the Lithuanian GDP.
3. Methodology and data
3.1. Variable selection
A literature review clearly suggests that there is an impact of foreign policy on
international trade (Bajo-Rubio and Ramos-Herrera, 2024; Gutmann et al., 2023;
Salvatore, 2023). International trade, as it involves exports, and imports has an
influence on economic growth. Even more, every country promotes international trade
(Salvatore, 2023). International trade is a crucial driver of economic growth across
various regions. Numerous studies have consistently demonstrated a positive
correlation between international trade and economic growth (Cruz, 2011; Celik, 2024;
Frederic et al., 2017; Mitsek, 2015). For example, recent research in Africa has
highlighted the significant positive impact of international trade, particularly when
combined with the digital economy, on economic growth (Abendin and Duan, 2021).
Similarly, in Miami, Florida, international trade has been identified as a key factor
influencing both long-term economic growth and short-run business cycles (Cruz,
2011). Research supports the idea that international trade is a fundamental catalyst for
economic growth. From Europe, Africa, to Asia, and from small regional economies
to major countries like Japan, there is a consensus that trade openness, liberalization,
and the digital economy play crucial roles in fostering economic development and
prosperity (Table 1).
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
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Table 1. Previous research on economic growth and international trade.
Authors (year)
Analysed period
Territories
Indicators
Methods
Findings
Abendin, S., and Duan,
P. (2021)
20002018
52 African countries
Export, import, GDP,
trade openness
(difference of export and
import)
ordinary least
square, fixed
effects, and the
system generalized
methods of
moments
estimation
approaches.
Trade openness has a
mixed influence on
economic growth
Salvatore, D. (2023)
20042019
132 countries
Export, GDP, inflation,
capital inflows
FIML modelling,
dynamic policy
simulation
Trade has no impact on
growth for large
advanced countries.
For small advanced
countries trade is
relatively significant.
Gutmann, J. et al.
(2023)
19602016
158 countries
Trade, consumption,
investment, military
expenditures, government
expenditures, GDP
Descriptive
statistics, OLS
panel data
modelling, SUR
modelling
Significant negative
effect of sanctions on
the growth rate of GDP
and its components
(consumption and
investment), trade and
foreign direct
investment.
Bajo-Rubio, O., and
Ramos-Herrera, M.
(2024)
Different periods
(before WWII, after
WWII
20 European
countries
Export, import, GDP
panel data models
with large cross-
sectional and time
series dimensions.
Granger causality
test
Results supported the
existence of a bi-
directional relationship
between both trade
variables and GDP, for
the whole period and
across subperiods.
The previous studies considering the relationship between international trade and
GDP applied the following indicators:
1) Exports are expressed as a percentage of GDP (Abendin and Duan, 2021), in the
monetary value of goods (Bajo-Rubio and Ramos-Herrera, 2024)
2) Imports are expressed as a percentage of GDP (Abendin and Duan, 2021), in the
monetary value of goods (Bajo-Rubio and Ramos-Herrera, 2024)
3) International trade is the difference between exports and imports. The value
might be negative as well. International trade is expressed as a percentage of GDP,
or in the monetary value (Gutmann et al., 2023).
4) Economic growth is expressed as the real GDP per capita (Gutmann et al., 2023),
nominal GDP (Mitsek, 2015), real GDP (Bajo-Rubio and Ramos-Herrera, 2024),
and a difference in percentage in comparison to the previous year (Salvatore,
2023).
International trade is analyzed in percentage due to the fact that it is the part of
GDP estimated in the expenditure approach. Additionally, previous research includes
other indicators that might have an impact on economic growth, such as fixed capital
formation (Gutmann et al., 2023; Mitsek, 2015; Robinson and Thierfelder, 2024),
industrial output (Robinson and Thierfelder, 2024), capital flow, consumer price index
(Mitsek, 2015; Salvatore, 2023), consumption (Gutmann et al., 2023), government
expenditures (Gutmann et al., 2023), foreign direct investment (Gutmann et al., 2023;
Mitsek, 2015), employment (Celik et al., 2024; Mitsek, 2015), and urbanization (Celik
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
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et al., 2024). Some of the studies focus on the sectoral impact as well (Robinson and
Thierfelder, 2024).
Thus, for our study, we set limitations and measured only the impact of foreign
policy on economic growth while the volume of international trade might change. In
addition, the data set for the study is used in quarters and covers the 20132022 period.
3.2. Empirical research process
The empirical approach is divided in five steps (Figure 2). First of all, the data
has been retrieved from Statistics Lithuania and Eurostat. The descriptive statistics
provide the tendency and volume of exports of Lithuanian products to China and
imports from China. In addition, the data has been analyzed according to the sectors.
The sectorial analysis is based on the classification of Combined Nomenclature (CN)
2024 of European Commission Implementing Regulation (EU) No. 2023/2364.
Figure 2. The structure of methodology.
The third step is the estimation of correlation-regression. As mentioned above, a
vast of studies prove that international trade is the driver of economic growth. Thus,
the correlation has been estimated between GDP and export, GDP and import. Further,
two regression models were developed. Since the study is devoted to the impact of
international trade, the GDP is set as a dependent variable while exports and imports
are independent ones.
(1)
yi is a dependent variable, xi is an independent variable, ,
a slope, an error.
Further a sensitivity analysis has been performed. For the modelling, Phyton
software has been used. In the context of the study, sensitivity analysis can shed light
on how countries GDP levels will respond to changes in international trade. As the
analysis calculates the impact on the level of GDP of countries, it will be expressed in
terms of percentage changes. Based on the scientific literature, the following formula
is derived in the context of the study (You et al., 2021):
(2)
here:
SA is the percentage change in the dependent variable; X is the change in the
dependent variable due to changes in the independent variable; X is the baseline value
of the dependent variable.
The formula can also be used to assess how a change in several independent
variables affects the dependent variable. In this case, X represents the change in the
dependent variable due to changes in several independent variables.
When interpreting the analysis, it is worth taking-into-account the strengths and
weaknesses of the method. Some advantages of sensitivity analysis in an economic
context might be defined. The coefficients on which the independent variables will be
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
8
modelled must be determined for the analysis. The choice of coefficients must take-
into-account the questions to be answered. The context of the study is international
economic relations between Lithuania and China. Thus, the aim is to determine how
the level of GDP will react to sudden changes in the parameters. According to the
research carried out, it is logical to assume that export and import data will normally
change within a range of 5%10%. However, these changes are driven by natural
changes in the economy (Gruss and Kebhaj, 2019). Additionally, the authors believe
that in order to investigate how an economy may react to drastic changes (Table 2).
Sensitivity analysis was performed at three levels. Each level involved four
experiments. In the first level, the four experiments checked how GDP would change
if export and import as the independent variables were changed by +5% and 5%. In
the next level, the four experiments were run to check how GDP would react if exports
and imports varied by +10 and 10%, respectively. In the last level, it was modelled
how the GDP would react if there were extreme changes of at least +50% or 50%.
Table 2. Values of coefficients of change of sensitivity analysis parameters.
Coefficients:
Coefficient value:
Small increase in exports
+5%
Small increase in imports
+5%
Average increase in exports
+10%
Average increase in imports
+10%
Significant increase in exports
+50%
Significant increase in imports
+50%
Small decrease in exports
5%
Small decrease in imports
5%
Average decrease in exports
10%
Average decrease in imports
10%
Significant decrease in exports
50%
Significant decrease in imports
50%
The last step in our research is the application of clustering analysis. It is a
common method of statistical data analysis in many fields, including pattern
recognition, image analysis, information retrieval, and computer graphics, and is the
main goal of exploratory data analysis (Boulesteix et al., 2021). Cluster analysis is
very often used while classifying the objects by some features and prioritizing them.
This method is beneficial for comparing countries or business sectors based on the
chosen factors. Thus, cluster analysis in the context of foreign policy can provide
valuable insights into grouping countries based on similarities in their foreign policy
approaches, decision-making processes, or strategic orientations (Fenco and Šabič,
2017; Freire, 2020). In this case, the research focusses on the impact of the changes in
international relations between Lithuania and China. The study is based on two
variables: international trade and GDP.
Cluster analysis is a general problem to be solved rather than a specific algorithm.
In economics, agglomerative hierarchical cluster analysis is commonly used
(Ruitenbeek et al., 2023; Wang et al., 2024). The method is based on a similarity
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
9
matrix, which contains a similarity score for all pairs of elements. This shows that
several similarity or dissimilarity measures can be used for different types of variables
(quantitative, qualitative, and binary). In addition, different methods can be used to
assess the similarity of clusters: single relationship, full relationship, average
relationship, Wards method, etc. (Boulesteix et al., 2021; Hafeezallah et al., 2024).
The process starts by identifying each object as a separate cluster, then clustering
is performed, and then the clusters are merged into larger clusters until the objects fall
into a single cluster or a predefined termination condition is reached. There are several
ways to perform agglomerative hierarchical cluster analysis (Govender and Sivakumar,
2020). The distance between two clusters in one link is defined as the shortest distance
between any single data point in the first cluster and any single data point in the second
cluster. Based on this concept of distance between clusters, the two clusters with the
shortest single link distance are joined at each step of the process. The formula for this
method is given below.
(3)
󰇛󰇜a distance between observation vector x and observation vector y;
data of the first monitoring cluster;
data of the second monitoring cluster.
The distance between two clusters of complete linkage is defined as the
maximum distance between any individual data point in the first cluster and any
individual data point in the second cluster. Based on this concept of distance between
clusters, the two clusters with the shortest connection distance are merged at each step
of the process. The formula for this method is given below.
(4)
󰇛󰇜distance between observation vector x and observation vector y;
data of the first monitoring cluster;
data of the second monitoring cluster.
The formula measures the distance between two data points in the cluster with
the furthest distance.
In the case of average connectivity, the distance between two clusters is defined
as the average distance between the data points in the first and second clusters. Based
on this concept of inter-cluster distance, the two clusters with the shortest average
connection distance are merged at each step of the process. The formula for this
method is given below (Govender and Sivakumar, 2020):
(5)
󰇛󰇜distance between observation vector x and observation vector y;
data of the first monitoring cluster;
data of the second monitoring cluster.
The distance between two clusters is defined as the average distance between the
mean vectors of the clusters. Based on this concept of inter-cluster distance, the two
clusters with the shortest average connection distance are merged at each step of the
process.
4. Results and discussion
From 2013 to 2020, the volumes of export and import were very similar, 2014
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
10
2015 is the year of Lithuanias trade balance, but in 2020, Lithuanias imports from
China start to grow drastically several times compared to exports to China. In 2020,
China was the 22nd largest Lithuanian goods export market, accounting for 1.1% of
all Lithuanian goods exports. Almost 77% of Lithuanian goods exports to China in
2020 were the export of goods of Lithuanian origin, and the rest (23%) were the re-
export of goods. Compared to 2019, the export of Lithuanian goods to China increased
by 14%, mainly due to the increase in the export of cereals. In January 2021, the total
export of Lithuanian goods to China compared to the corresponding 2020 period
decreased by 9%. (Figure 3).
Figure 3. Lithuanian exports to China, and imports from China in Euros during the
period 20122022 (Eurostat).
In 2021, China was the 25th largest export market for Lithuanian goods,
accounting for 0.7% of the total export of Lithuanian goods. Almost 75% of
Lithuanian goods were exported to China. Compared to 2020, Lithuanias export of
goods to China decreased by 28%, mainly due to the decrease in grain exports. In 2021,
China was the 23rd largest export market, accounting for 0.8% of the total export of
goods of Lithuanian origin. In the structure of the export of goods of Lithuanian origin
to China, furniture accounted for the largest share (22%), optical, photographic,
measuring, medical, or surgical devices accounted for 15% (6th largest export market),
various chemical products accounted for 11% (7th largest export market), electrical
machines and devices accounted for 9.1% (16th largest export market), wood and
wood products; charcoal made 5.9% (21st largest export market). Compared to 2020,
the export value of goods of Lithuanian origin to China decreased by 30%. Cereals
accounted for the biggest decrease. During 2022 JanuaryFebruary exports of goods
of Lithuanian origin to China decreased by 99% compared to the corresponding period
of 2021. In 2021, China was the 7th largest import market for Lithuanian goods and
accounted for 4.2% of all Lithuanian goods imports. In 2021, China mainly imported
electrical machines and devices: 26% (3rd largest import market), machinery and
mechanical equipment: 13% (7th largest import market), ground vehicles made 6.9%
(9th largest import market), optical, photographic, measuring, medical, or surgical
devices accounted for 4.9% (2nd largest import market). Compared to 2020, the
imports of goods from China increased by 34%.
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
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The minimum indicator reveals that during the analyzed period, Lithuania
exported at least 2.6 million to China euros worth of goods, while China exported a
minimum of 1.5 million euros worth of goods (Table 3). Calculations for the Table 3
have been done by using data in quarters from 2013 to 2022.
Table 3. Descriptive statistics indicators.
Lithuanian Export to China in euros
Lithuanian Imports from China in euros
Min
2.6
1.5
Max
16.7
53.2
Mean
7.8
12.7
Moda
11.7
N/A
Standard deviation
3.6
13.6
Variance
13.4
188.4
The maximum indicator reveals that Lithuania exported the most 16.7 million to
China euros worth of goods and the most imported was 53.2 million euros worth of
goods. The mean reveals that during the analyzed period, Lithuania exported an
average of 7.8 million euros value of goods, and imported an average of 12.7 million
euros the value of goods. Such data can be interpreted indicating that historically,
Lithuanias international trade balance with China has been negative. The mode
indicator reveals that the only frequently recurring value during the analyzed period is
Lithuanias exports to China of 11.7 euros worth of goods. The standard deviation
indicator shows that the data on Lithuanias exports to China has a small deviation,
which means that the data is sufficiently concentrated. The variation indicator provides
data, according to which identical conclusions are drawn, that Lithuanias exports to
China are sufficiently centralized. In order to assess how the data on international
economic relations between Lithuania and China prevails in the general context of
exports and imports, the analysis of descriptive statistics is carried out as a percentage
of Lithuanian exports to China out of total Lithuanian exports, Lithuanian imports
from China as a percentage of all Lithuanian imports (Table 4).
Table 4. Indicators of descriptive statistics of percentage data.
Lithuanias Export to China as a percentage of
Lithuanias total export (%)
Lithuanias Imports from China as a percentage of
Lithuanias total imports (%)
Min
0.182
0.167
Max
1.002
2.155
Mean
0.344
0.723
Standard deviation
0.175
0.582
Variance
0.031
0.339
The minimum indicator reveals that Lithuanias exports to China during the entire
period, in the structure of the variable, accounted for at least 0.182%. Lithuanias
imports from China during the entire analyzed period, in the structure of the variable,
accounted for at least 0.167%. The average indicator reveals that Lithuanias exports
to China during the entire period, in the structure of the variable, averaged 0.344%.
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
12
Lithuanias import from China during the entire analyzed period, in the structure of
the variable, averaged 0.723%. The variance indicator reveals that Lithuanias exports
to China as a percentage of Lithuanias total exports are concentrated close to each
other. The data on Lithuanias imports from China as a percentage of Lithuanias total
imports is mostly scattered.
According to the results of the analysis, we can claim that the international
economic relations of Lithuania and China are not significant in the general structure
of international economic relations. However, during the entire analyzed period,
Lithuanias imports from China occupied the largest percentage share of the entire
structure at 2.155%, compared to other international economic relations between
Lithuania and China.
Further research examines the bivariate correlation between Lithuanian GDP and
exports to China and imports from China (Table 5). The results reveal that a moderate
relationship exists between GDP and exports (r = 0.727); a similar result has been
obtained for the relationship between GDP and imports (r = 0.716). The results are
statistically significant. Thus, regression modelling will include both variables.
Table 5. Correlation ratios.
GDP
Export
Import
GDP
1
0.727*
0.716*
Export
0.727*
1
0.756*
Import
0.716*
0.756*
1
Correlation is significant at the 0.05 level.
Both indicators are significant in terms of GDP. Two models have been
developed (Table 6). In the both models GDP is dependent. However, in model 1, the
export is an independent variable, while in model 2, the import is an independent
variable. The results reveal that both models are statistically significant. The first
model explains 52.8% of all variance. While, the second model explains 51.6% of the
total variance. Further, if exports would increase by 1 million, the GDP would increase
by 0.013 million. In addition, imports would increase by 1 million, and the GDP would
grow by 0.003 million.
Table 6. Regression modelling.
Model 1
Model 2
Constant
196006.245 (70191)
110727.671 (99479.234)
Export
0.013 (0.004)
Import
0.003 (0.001)
R
0.727
0.716
R2
0.528
0.513
F
8.960
8.149
t
2.993
2.902
Dependent is GDP, predictors export and import.
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
13
Further, a sensitivity analysis has been performed. Table 7 presents the data from
the analysis, where exports and imports are the independent variables that were varied
by +5% and 5%.
Table 7. Sensitivity analysis results, parameters change coefficient +5% and 5%,
Lithuanian GDP sensitivity.
Experiment
No. 1
No. 2
No. 3
No. 4
Parameter
Export data
Import data
Export data
Import data
Coefficient of change of parameters:
+5%
+5%
5%
5%
Min (%)
0.0015
0.0178
0.0071
0.0009
Max (%)
0.0071
0.0009
0.0015
0.0178
Mean (%)
0.0034
0.0049
0.0034
0.0049
After changing the data of Lithuanias exports to China by +5%, Lithuanias GDP
reacts in the interval [0.0015; 0.0071], with an average GDP sensitivity of 0.0034 and
GDP sensitivity of less than 1%. After changing the data of Lithuanias imports from
China by +5%, Lithuanias GDP reacts in the interval [0.0178; 0.0009], with an
average GDP of sensitivity 0.0049 and a GDP sensitivity greater than 1%. After
changing the data of Lithuanias export to China by 5%, Lithuanias GDP reacts in
the interval [0.0071; 0.0015] with an average GDP sensitivity of 0.0034 and a
GDP sensitivity greater than 1%. After changing the data of Lithuanias imports from
China by 5%, Lithuanias GDP reacts in the interval [0.0009; 0.0178] with a mean
GDP sensitivity of 0.0049 and a GDP sensitivity is less than 1% and greater than
1%. Further, we examine the changed in GDP if exports and imports vary by +10%
and 10% (Table 8).
Table 8. Sensitivity analysis results, parameter change coefficient +10% and 10%,
Lithuanian GDP sensitivity.
Experiment
No. 1
No. 2
No. 3
No. 4
Parameter
Export data
Import data
Export data
Import data
Coefficient of change of parameters:
+10%
+10%
10%
10%
Min (%)
0.0029
0.0357
0.0142
0.0018
Max (%)
0.0142
0.0018
0.0029
0.0357
Mean (%)
0.0068
0.0097
0.0068
0.0097
After changing the data of Lithuanias exports to China by +10%, Lithuanias
GDP reacts in the interval [0.0029; 0.0142], with an average GDP sensitivity of 0.0068.
In the analysis, the maximum GDP sensitivity was +1.42%. After changing the data
of Lithuanias imports from China by +10%, Lithuanias GDP reacts in the interval
[0.0357; 0.0018], with an average GDP sensitivity of 0.0097. In the analysis, the
maximum GDP sensitivity was 3.57%.
Table 9 presents the data from the analysis where exports and imports are the
independent variables that have been varied by +50% and 50% respectively.
Sensitivity analysis was performed on each independent variable separately.
According to these criteria, four experiments were performed.
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
14
Table 9. Sensitivity analysis results, parameter change coefficient +50% and 50%,
Lithuanian GDP sensitivity.
Experiment
No. 1
No. 2
No. 3
No. 4
Parameter
Export data
Import data
Export data
Import data
Coefficient of change of parameters:
+50%
+50%
50%
50%
Min (%)
0.0147
0.1783
0.0710
0.0088
Max (%)
0.0710
0.0088
0.0147
0.1783
Mean (%)
0.0338
0.0487
0.0338
0.0487
After changing the data of Lithuanias exports to China by +50%, Lithuanias
GDP reacts in the interval [0.0147; 0.0710], with an average GDP sensitivity of 0.0338.
In the analysis, the maximum GDP sensitivity was +7.10%. After changing the data
of Lithuanian imports from China by +50%, Lithuanian GDP reacts in the interval
[0.1783; 0.0088], with an average GDP of sensitivity 0.0487. In the analysis, the
maximum GDP sensitivity was 1.78. After changing the data of Lithuanias exports
to China by 50%, Lithuanias GDP reacts in the interval [0.0710; 0.0147], with an
average GDP sensitivity of 0.0338. In the analysis, the maximum GDP sensitivity
was 7.10%. After changing the data of Lithuanias imports from China by 50%,
Lithuanias GDP reacts in the interval [0.0088; 0.1783], with an average GDP
sensitivity of 0.0487. In the analysis, the maximum GDP sensitivity was +1.78%.
The first observation when evaluating the results of the analysis is the difference
between the data when the independent variables were analyzed separately and at
different times. Analyzing the variables separately, the maximum positive sensitivity
of Lithuanias GDP is 7.10%, when the volume of exports to China is increased by
50%. The maximum negative sensitivity of Lithuanias GDP is 7.10%, when
Lithuanian exports to China are reduced by 50%. On the other hand, when analyzing
the variables together, which were changed by the same coefficient, the maximum
positive sensitivity of Lithuanias GDP is 12.25%, when the data of Lithuanian exports
to China and imports from China are reduced by 50%. The maximum negative
sensitivity of Lithuanias GDP is 12.25%, when the data of Lithuanian exports to
China and imports from China are increased by 50%. Such sensitivity analysis data
reveals that Lithuania historically had a negative trade balance with China. This
explains why positive coefficients, when independent variables are analyzed together,
have a negative effect on Lithuanias GDP and vice versa.
Exports, as a component of GDP, is a positive indicator of GDP, in other words,
the growth of the variable determines the growth of GDP. Imports, as a component of
GDP, is a negative indicator of GDP, in other words, the growth of the variable
determines the decrease in GDP. This explains why, when the independent variables
are analyzed separately, negative coefficients lead to a negative influence when
analyzing exports and a positive influence when analyzing imports, and vice versa.
The analysis reveals that Lithuanias GDP level can react sensitively only to large
changes in export and import data. But it is based on-the assumption that only imports
and exports change significantly, and all other indicators remain identical. In a
situation where the indicators change naturally, it is logical to expect that other
components of the economy will also change significantly, which in the final version
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
15
will determine a much lower actual GDP sensitivity. If exports and imports change
unnaturally (meaning the change happened suddenly, and other sectors of the economy
were not ready or had not yet reoriented), if we set a limit of 50% of the change
coefficient, where the impact on the economy will be sudden and obvious, which can
be negative or positive, depending on the situation. Additionally, it is worth noting
that exports and imports are indicators that are constantly changing. These standard
changes are not determined by the economic relations between Lithuania and China,
but based on circumstances, such as the global economic recession or changes in other
sectors. Thus, before assessing the international economic relations between Lithuania
and China, based on the sensitivity analysis, it has been noted that the existing
economic relations are not significant enough. Their impact is not great in the current
situation, and in order for them to have a big impact, there must be sudden changes in
exports and imports.
An identical analysis was carried out in the context of Chinas GDP, but the
results of the analysis are too insignificant to carry out their separate analysis, most of
the results are in the range [0.0000; 0.0004]. According to these data, we can draw the
conclusion that the international economic relations between Lithuania and China are
absolutely insignificant in the context of China (Figures 4 and 5).
Figure 4. Values of the Calinski and Harabasz index for Lithuanian export cluster
analysis (source: created by the authors).
Figure 5. Values of the Calinski and Harabasz index for Lithuanian imports cluster
analysis (source: created by the authors).
The data used in the cluster analysis includes Lithuanian and Chinese export and
import data by type of goods. The first step in the analysis is to determine which
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
16
number of clusters is most suitable for agglomerative hierarchical cluster analysis. The
values of the Silhouette index of the first evaluation criterion are presented in Table
10.
Table 10. Values of Calinski & Harabasz index, and silhouette index for Lithuanian
export cluster analysis.
Number of clusters
2
3
4
5
Calinski & Harabasz index
137.061
153.784
210.997
227.143
Silhouette index
0.864
0.862
0.862
0.853
According to the index data, two clusters are the most appropriate choice, as the
index value is the highest at 0.864.
Since the Calinski and Harabasz index values of the two and three clusters are
quite similar, but the value of the two clusters of the Silhouette index is higher, two
clusters will be used for the analysis. In the analysis of clusters, we can assume that
the second cluster includes the types of goods that Lithuania mostly exports to China,
and the first cluster includes all other exported types of goods, but their quantities are
not exclusive. The results of the agglomeration hierarchical cluster analysis are
presented in the Table 11, where the general classifications of goods are assigned a
number covering a specific quantity of goods.
Table 11. Results of agglomeration hierarchical cluster export analysis, according to general classification.
The first cluster
The second cluster
Commodities
Number of kinds of the commodities
Commodities
Number of kinds of the
commodities
Food products
23
Food products
1
Animal non-food products
4
Chemical goods
2
Raw materials and minerals
7
Timber products
1
Chemical goods
10
Raw materials and materials
1
Medical goods
1
Mechanical goods
2
Films and publishing
2
Films and publishing
1
Timber products
4
Furniture
1
Textile and clothing products
18
Ceramic goods
2
Steel products
2
Mechanical goods
6
Guns
1
Other goods
4
According to the results of the analysis, it is logical to say that Lithuania mainly
exports chemical and mechanical goods to China, where both classifications have two
types of goods each. Certain classifications of food, wood, raw materials, cinema,
publications, and furniture goods also stand out from the others, in the second cluster,
they belong to one type of goods each. The types of goods that belong to this
significant cluster of Lithuanian exports are the following:
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
17
X10Cereals
X27Mineral fuels, mineral, oils and their distillation products; bituminous
materials; mineral waxes
X38Various chemical products
X44Wood and wood products; charcoal
X74Copper and copper articles
X84Nuclear reactors, boilers, machines and mechanical devices; their parts
X85Electrical machinery and equipment and parts thereof; sound recording
and reproducing apparatus, television video and sound recording and reproducing
apparatus, parts and accessories for these products
X90Optical, photographic, cinematographic, measuring, checking, precision,
medical or surgical instruments and apparatus, parts and accessories thereof
X94Furniture; bedding, mattresses, mattress frames, decorative cushions and
similar stuffed furniture; lamps and lighting equipment, not elsewhere specified or
included; illuminated signs, illuminated signs and similar articles; prefabricated
buildings. The second part of the subsection describes the results of the cluster analysis
of Lithuanian imports by types of goods.
The first step in the analysis is to determine which number of clusters is most
suitable for agglomerative hierarchical cluster analysis. The values of the Silhouette
index of the first evaluation criterion are presented in Table 12.
Table 12. Cluster analysis silhouette index for cluster analysis of Lithuanian
imports.
Number of clusters
2
3
4
5
Calinski & Harabasz index
322.610
423.999
675.789
722.669
Silhouette index
0.926
0.802
0.796
0.628
According to the index data, two clusters are the most appropriate choice, as the
index value is the highest at 0.926.
Hence, the Calinski and Harabasz index values of the second and third clusters
are quite similar, but the value of the Silhouette index of the second cluster is much
higher. Two clusters will be used for the analysis. Thus, the second cluster includes
the types of goods that Lithuania mostly imports from China. The first cluster includes
all other imported types of goods, but their quantities are not exclusive. The results of
the performed agglomeration hierarchical cluster analysis are presented in Table 13,
where the general classifications of goods are assigned a number covering a specific
quantity of goods.
Table 13. Results of agglomerative hierarchical cluster import analysis, according to general classification.
The first cluster
The second cluster
Commodities
Number of kinds of the commodities
Commodities
Number of kinds of the commodities
Food products
24
Mechanical goods
2
Animal non-food products
4
Raw materials and minerals
8
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
18
Table 13. (Continued).
The first cluster
The second cluster
Commodities
Number of kinds of the commodities
Commodities
Number of kinds of the commodities
Chemical goods
12
Medical goods
1
Films and publishing
3
Timber products
5
Textile and clothing products
18
Ceramic goods
2
Steel goods
2
Mechanical goods
6
Guns
1
Furniture
4
Other goods
1
According to the results of the analysis, we can claim that Lithuania mainly
imports mechanical goods from China, where the classification has two types of goods.
The sectors that are dependent on the significant cluster of Lithuanian exports are
the following:
X84Nuclear reactors, boilers, machines, and mechanical devices, their parts.
X85Electrical machinery and equipment and parts thereof; sound recording
and reproducing apparatus, television video and sound recording and reproducing
apparatus, parts and accessories for these products.
Generalizing, we may state that Lithuanias export interests in China mainly
include food products, chemical products, wood and wood products, machines and
mechanical devices or their parts, different types of electrical goods, furniture, etc. In
the context of China, these goods include its import interests from Lithuania.
Lithuanias import interests in China mainly include machines and mechanical
devices or their parts and different types of electronic goods. In the context of China,
these goods include its export interests to Lithuania.
5. Conclusions
Although international economic relations between Lithuania and China are
intense. The study revealed that exports to China and imports from China have a
statistically significant impact on GDP growth. Our analysis showed that Lithuanian
export interests in China mainly include food products, chemical products, wood and
wood products, machines and mechanical devices or their parts, electrical goods of
different types, furniture, etc. In the context of China, these goods include its import
interests from Lithuania. Lithuanian import interests in China mainly include
machines and mechanical devices, their parts, and different types of electronic goods.
However, it was before the conflict with China. Meanwhile, the exports are dropping.
According to our estimations, the cooling of international relations will negatively
affect Lithuanian GDP if exports decline by 50% or more. While the impact on China
would be insignificant at any level. In the case of Lithuania unfortunately, there is a
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
19
great possibility that exports to China will decline more than fifty percent. As China
rises as a global power, its engagements with different regions play a crucial role in
shaping the contemporary global order. The evolving dynamics between China and
Lithuania, as well as other regions, underscore the complexities of contemporary
international relations and the interplay of economic, political, and strategic interests.
Theoretical implications. The study fills in the gap in political and economic
science by focusing how value-based foreign policy influences on international
economic relations which leads to the consequences to economic growth. Furthermore,
the study delves into the impact of international trade on economic development and
macroeconomic indicators, providing insights into how trade policies can influence a
countrys economic growth trajectory and foreign relations. The study set the
macroeconomic indicators that would have been influenced by value-based foreign
policy. In addition, a methodology for evaluating the changes in international
economic relations has been created. The methodology might be used to evaluate
foreign policy changes of the any other country, specifically focusing the sectors that
might have been influenced most. The methodology might be used to measure the
impact of other factors that might be dependent on international economic relations,
such as foreign direct investment, foreign aid, the international labor movement,
unemployment, and others.
Managerial and political implications. By considering moral and ethical
dimensions, policymakers aim to navigate the complexities of global politics while
upholding principles of good international relations with others in the international
community. The multidimensional nature of the relationship between international
trade and foreign policy highlights the need for coherent and strategic trade policies
that align with broader foreign policy objectives to promote economic development,
enhance global competitiveness, and foster international cooperation. The research
identified the potential lower point of decreased exports that might have an impact on
the shrunk of GDP. Furthermore, it has been identified two clusters that are the most
significant in Lithuania-China trade. Thus, this result might be useful for politicians
to consider what business sectors would suffer the most due to the changed foreign
policy. In this respect, new alternative trade markets are supposed to be opened.
Further, this study contributes to the redirection of international economic relations by
improving the strategy for international trade, in order to strengthen existing
international relations or develop the new ones. Limitations and future research.
However, we face some limitations in our study because a very short period of time
has passed since Lithuania opened a Taiwanese representative office. In the future, it
will highly be important and necessary to replicate research. Thus, it would assist to
improve and adopt an international relations strategy with China and other countries,
not just the EU. Further research is limited to the analysis of trade impacts on GDP.
For future research, applying the same methodology, would be interesting to explore
other macroeconomic factors such as inward and outward FDI, R&D, inflation, or
unemployment.
Author contributions: Conceptualization, AS (Agnė Šimelytė) and AS (Andriejus
Sadauskis); methodology, AS (Artūras Struca); software, AS (Artūras Struca);
validation, AS (Agnė Šimelytė), AS (Artūras Struca) and AS (Andriejus Sadauskis);
Journal of Infrastructure, Policy and Development 2024, 8(12), 6738.
20
formal analysis, AS (Agnė Šimelytė); investigation, AS (Agnė Šimelytė) and AS
(Artūras Struca); resources, AS (Agnė Šimelytė) and AS (Andriejus Sadauskis); data
curation, AS (Artūras Struca); writingoriginal draft preparation, AS (Agnė
Šimelytė), AS (Artūras Struca) and AS (Andriejus Sadauskis); writingreview and
editing, AS (Agnė Šimelytė); visualization, AS (Agnė Šimelytė) and AS (Artūras
Struca); supervision, AS (Agnė Šimelytė). All authors have read and agreed to the
published version of the manuscript.
Acknowledgments: We are grateful to the reviewers whose encouragement has led to
better quality and completion of our paper.
Conflict of interest: The authors declare no conflict of interest.
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