Key Trade Partners of Malaysia

Abstract

Malaysia, as one of the most open economies in Asia, requires the strategic selection of key trade partners to ensure sustainable trade-led economic growth. This study employs two alternative approaches—the global extraction method (GEM), which is Malaysia-centric and measures Malaysia’s vulnerability to the removal of specific partners, and the Intercountry Trade Impact Index (ITII), which is system-wide and captures partners’ global significance through their interdependencies and economic weight—to analyse and rank Malaysia’s trade partners, comparing these results with actual trade performance data. The analysis utilises the 2020 OECD Inter-country Input-Output tables, aggregated to reflect Malaysia’s trade relationships. Results from the GEM highlight the Association of Southeast Asian Nations (ASEAN), South Korea, New Zealand, Australia and Japan as key trade partners. Alternatively, the ITII approach identifies major partners, including China, New Zealand, the United States, Other Europe and the rest of the world. An integrated ranking across all methods, including trade volume considerations, consistently positions ASEAN, Japan, China, South Korea and New Zealand as Malaysia’s top-five trade partners, offering a comprehensive and balanced strategy for re-evaluating Malaysia’s trade policies.

JEL Codes: C67, F13, F14, F23, F63

Keywords

Trade policy global extraction method average propagation length input-output modelling Intercountry Trade Impact Index Malaysia

Introduction

Trade policymaking is a critical yet complex endeavour for nations worldwide. This complexity is particularly pronounced in Malaysia, a developing country where trade constitutes a substantial portion of the economy. According to VanGrasstek (2008), trade typically accounts for a significant share of Malaysia’s gross domestic product (GDP). In 2024, Malaysia achieved a remarkable trade record, with total trade reaching RM 2.879 trillion (Malaysia External Trade Statistics, 2024). According to the World Bank (2025), the degree of economic trade openness in Malaysia reached 131% of its GDP by the end of 2023. This underscores the pivotal role of trade in Malaysia’s economic growth. However, the challenges of trade policymaking are multifaceted. Policymakers must uphold trade-driven growth while ensuring that Malaysia’s high trade share remains stable in the years ahead. The Ministry of International Trade and Industry of Malaysia publishes an annual report on the country’s trade performance, known as the Trade Performance of Malaysia. This report aligns closely with the data provided in the WITS (2025), ensuring consistency and accuracy in trade statistics. In its latest report, the Association of Southeast Asian Nations (ASEAN), China, the United States, the European Union and Taiwan were identified as Malaysia’s major trading partners, collectively accounting for over 68.4% of the country’s total trade volume (Malaysia External Trade Statistics, 2024). ASEAN has been recognised as a strategic partner for Malaysia, with trade between Malaysia and ASEAN countries amounting to RM 720.66b, representing 27.3% of Malaysia’s total trade. Within ASEAN, Malaysia’s top-three trading partners are Singapore, Thailand and Indonesia. In addition, trade with China constitutes 17.1% of Malaysia’s total trade, making China the second most significant trading partner. Since 2015, the United States has held its position as Malaysia’s third-largest trading partner. The European Union ranks fourth, accounting for 6.5% of Malaysia’s total trade, with major contributors including the Netherlands, Germany, Belgium, Italy, Spain, France, Poland, Hungary, the Czech Republic and Sweden.¹ This study argues that future trade partner selection should not rely only on statistical reports of trade volumes with other countries or regions. In other words, trade volume does not necessarily reflect the true structural importance of a partner within global value chains (GVCs) or the extent of Malaysia’s vulnerability to potential disruptions. For example, two partners with similar trade volumes may differ substantially in how their extraction affects Malaysia’s output and in how their influence propagates on Malaysia through the global economy. Hence, two dimensions are critical: first, the degree of Malaysia’s interdependence with trading partners in goods and services, both through intermediate and final products, which reflects the country’s vulnerability in international trade, and second, the systemic significance of these partners in the global economy, which makes them strategically important for Malaysia’s long-term trade resilience. By integrating these dimensions, trade policy can better align with Malaysia’s objective of sustaining trade-driven growth while enhancing resilience in an interconnected global economy.

Malaysia’s trade landscape demonstrates both concentration in traditional partners, such as ASEAN, China, the United States and the European Union, and diversification through Malaysia’s FTAs and emerging markets. These developments illustrate Malaysia’s deepening integration into global trade networks and its efforts to broaden economic linkages. Yet, despite the centrality of trade to Malaysia’s development, a large body of existing academic research has not kept pace with these evolving dynamics.

First, studies on trade openness and economic growth confirm that openness has contributed positively to Malaysia and ASEAN’s development (Anoruo & Ahmad, 2022; Narayan, 2025; Sulaiman & Ramli, 2019). Nguyen Thi and Nguyen (2025) investigated the conditional impact of trade openness on economic growth in ASEAN countries. They found a positive relationship between trade openness and economic growth. This research suggests that the benefits of trade openness are not uniform across all ASEAN countries but are influenced by the specific characteristics and policies of individual nations. Chandran and Munusamy (2009) examined the relationship between trade openness and manufacturing growth in Malaysia. The findings indicate that trade openness has a significant positive impact on the growth of the manufacturing sector, highlighting the importance of open trade policies in fostering manufacturing development. Nguyen et al. (2023) explored the impact of trade openness on economic stability in ASEAN countries. They reveal that trade openness contributes to economic stability; however, such stability varies across different countries based on different macroeconomic indicators. Yet, these studies mostly rely on aggregate indicators and therefore provide limited insights into the differentiated roles of individual partners.

A second group of research investigates bilateral and multilateral trade determinants, such as GDP, exchange rates and foreign direct investment (FDI), particularly with China, Japan and ASEAN (Abidin et al., 2013; Hammed Agboola et al., 2018; Ky, 2025; Murad et al., 2025). Sasana and Fathoni (2019) identified several critical factors influencing FDI inflows into ASEAN countries. These include market size (as measured by GDP), exchange rate stability, infrastructure quality and institutional frameworks. It highlights the importance of these determinants in shaping the investment environment and fostering economic growth within the region. Abidin et al. (2013) examined Malaysia’s bilateral trade, finding that stable exchange rates and strategic FDI enhance integration with key partners. Murad et al. (2025) found that Malaysia’s exports to Japan significantly boost economic growth, while imports and development aid from Japan have positive but insignificant effects. The results suggest that long-term export promotion policies have strengthened Malaysia’s export-driven development. Yusuf et al. (2021) showed that while stronger institutions in African OIC countries negatively affect bilateral trade with Malaysia, good institutional quality supports Malaysia’s economic growth, highlighting the need for institutional reforms in these countries to boost trade and development. While these contributions explain important drivers of trade, they do not evaluate the systemic importance of Malaysia’s partners within its overall trade portfolio.

More recently, scholars have turned towards digitalisation and e-commerce as emerging sources of competitiveness, especially for SMEs (Ammeran et al., 2023; Ayob, 2021; Hongqiangh et al., 2025; Lei et al., 2025; Narayan, 2025). For instance, Lei et al. (2025) analysed the effects of digital and sustainable trade facilitation on trade flows among ASEAN countries. Employing a gravity model with data from the UN Global Survey, the findings indicate that measures such as greater transparency in trade procedures, trade facilitation and cross-border digital trade significantly boost bilateral trade. Also, Narayan (2025) found that growing e-commerce in Indonesia and Malaysia enhances their exports, while higher e-commerce development in partner countries can reduce exports.

Another line of research focuses on geopolitical shifts and regional trade agreements, particularly the Regional Comprehensive Economic Partnership (RCEP) (Chong, 2021; Husaini Mohammad Rohimi et al., 2024; Xu et al., 2025). For example, Xu et al. (2025) found that RCEP enhances agricultural value-added exports, strengthens regional participation in value chains and delivers growing short- and long-term benefits, especially when non-tariff barriers are reduced.

In addition, the literature emphasises GVC participation as another point of study, particularly in manufacturing, energy and agriculture sectors, where Malaysia is deeply integrated with China, Japan and ASEAN (Athukorala, 2016; Ilmi & Hastiadi, 2019; Ji et al., 2022; Jiang & Husin, 2024; Phillips & Henderson, 2009; Zhang et al., 2024). For instance, Ji et al. (2022) revealed that forward-linkage GVC participation lowers energy use per value-added chain, while backward linkage increases it, highlighting that moving up value-added chains can reduce resource and environmental costs, especially for lower-middle income countries. These studies capture value-added linkages but usually focus on specific sectors or bilateral ties, without translating GVC participation into a systematic evaluation of partner importance.

Finally, sectoral and commodity-based studies—for instance, on palm oil or agri-food exports—provide insights into Malaysia’s comparative advantages (Hassanpour & Ismail, 2010; Liew et al., 2021; Lugo-Arias et al., 2024; Mizik et al., 2020). Lugo-Arias et al. (2024) examined factors affecting palm oil export competitiveness in 30 countries from 2011 to 2019. Using the symmetric comparative advantage index, they found that emerging countries show greater specialisation than industrialised ones. Results indicate that lower palm and vegetable oil prices, higher exchange rates and rising soybean and biodiesel prices improve palm oil export competitiveness. While these results are valuable, they cannot fully capture the aggregate implications for the wider economic approach.

Taken together, these aspects of research confirm the significance of openness, bilateral flows, digitalisation, regional agreements, GVC participation and sectoral competitiveness for Malaysia’s trade performance. However, three limitations stand out: first, many studies rely on aggregate measures that overlook the relative importance of individual partners; second, much of the work is confined to bilateral or sectoral cases; and third, few studies offer a systemic framework for identifying Malaysia’s most strategic partners. This study addresses the research gap by applying the global extraction method (GEM) and the Intercountry Trade Impact Index (ITII) to the OECD Inter-country Input-Output (ICIO) tables to systematically identify and rank Malaysia’s key trade partners. Unlike existing bilateral, digital or sectoral analyses, this approach provides a partner-specific, economy-wide perspective that captures both trade interdependencies and systemic significance, thereby ensuring a more robust assessment of Malaysia’s trade-driven growth through the measurement of global multipliers.

Two criteria guide the analysis. First, the degree of trade interdependence within global production processes must be assessed. To this end, GEM is employed to measure Malaysia’s vulnerability by estimating the reduction in output under the hypothetical extraction of a trading partner, thereby identifying partners critical to production networks. Second, the broader economic influence of each partner must be evaluated. For this, the ITII is applied, which extends beyond direct interdependencies by incorporating the average propagation length (APL), global multipliers and country size.

While both methods use global multipliers, they differ fundamentally in scope. GEM is Malaysia-centric, assessing the country’s vulnerability to the removal of specific partners (Dietzenbacher et al., 2019) and identifying which partners contribute most to Malaysia’s trade-driven growth (Hertwich et al., 2024). In contrast, ITII is system-wide, capturing partners’ global significance through their interdependencies and economic weight, thereby highlighting Malaysia’s strategic partners in a forward-looking perspective to ensure resilient and stable trade in the long term. Accordingly, GEM highlights Malaysia’s most critical partners in terms of direct exposure, while ITII identifies those whose global standing makes them strategically important for Malaysia’s long-term trade resilience. Together, these complementary perspectives provide a robust framework for evaluating Malaysia’s trade partners in the international economy.

The remainder of this article is organised as follows: The second section outlines the mathematical framework underpinning this study, introducing two alternative approaches derived from the relevant literature. The third section focuses on the data and presents the results of the analysis. Finally, the fourth section offers concluding remarks along with policy recommendations pertinent to the findings.

Mathematical Frameworks

Global Extraction Method

The hypothetical extraction method (HEM), first introduced by Paelinck et al. (1965) (in French) and later developed by Lahr and Miller (2001), measures interindustry linkages and the economic significance of individual industries. HEM simulates the hypothetical cessation of a specific industry’s operations within an input–output framework. It calculates the total output required across the economy to meet original final demands; the resulting reduction in outputs (compared to the baseline) quantifies the linkages associated with the extracted industry.

Dietzenbacher et al. (1993) first extended HEM to intercountry linkages, applying it to selected EU countries. With the emergence of global databases such as the World Input–Output Database, the approach was adapted at the global level by Tukker and Dietzenbacher (2013) and Los et al. (2016). More recently, the GEM, introduced by Dietzenbacher et al. (2019), incorporates more realistic assumptions about international trade. GEM remains under development and has been applied in studies by Tormo García et al. (2024), Wang et al. (2023) and Yan et al. (2020).

Based on the work of Dietzenbacher et al. (2019), suppose there are N countries with n industries in the framework of ICIO. As discussed earlier, to focus on trade partners, we assume that the GEM model is simplified to include only countries with all industries aggregated in the original table. Accordingly, consider the N × N matrix Z of intermediate deliveries, the N × N matrix F of final demands, the N-element output vector x and the N-element value-added vector v are (in a partitioned form) given by:

\begin{matrix} Z = [\begin{matrix} \begin{matrix} Z^{11} \\ \begin{matrix} ⋮ \\ \begin{matrix} Z^{R 1} \\ \begin{matrix} ⋮ \\ Z^{N 1} \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \dots \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} \begin{matrix} \begin{matrix} Z^{1 R} \\ \begin{matrix} ⋮ \\ \begin{matrix} Z^{R R} \\ \begin{matrix} ⋮ \\ Z^{N R} \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \dots \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} Z^{1 N} \\ \begin{matrix} ⋮ \\ \begin{matrix} Z^{R N} \\ \begin{matrix} ⋮ \\ Z^{N N} \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix}], F = [\begin{matrix} \begin{matrix} f^{11} \\ \begin{matrix} ⋮ \\ \begin{matrix} f^{R 1} \\ \begin{matrix} ⋮ \\ f^{N 1} \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \dots \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} \begin{matrix} \begin{matrix} f^{1 R} \\ \begin{matrix} ⋮ \\ \begin{matrix} f^{R R} \\ \begin{matrix} ⋮ \\ f^{N R} \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \begin{matrix} \dots \\ \begin{matrix} ⋱ \\ \dots \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix} & \begin{matrix} f^{1 N} \\ \begin{matrix} ⋮ \\ \begin{matrix} f^{R N} \\ \begin{matrix} ⋮ \\ f^{N N} \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix}], \\ x = [\begin{matrix} x^{1} \\ \begin{matrix} ⋮ \\ \begin{matrix} x^{R} \\ \begin{matrix} ⋮ \\ x^{N} \end{matrix} \end{matrix} \end{matrix} \end{matrix}] v = [\begin{matrix} v^{1} \\ \begin{matrix} ⋮ \\ \begin{matrix} v^{R} \\ \begin{matrix} ⋮ \\ v^{N} \end{matrix} \end{matrix} \end{matrix} \end{matrix}], \end{matrix}

Z_ij^RS are the elements of the N × N matrix of Z^RS that provide the money value of intermediate deliveries in country R to country S, f_i^RS are the elements of vector f^RS that provide the deliveries from country R for the final demand in country S. x_i^R of the N-element vector x^R provides the output of country R and v_j^R of the N-element vector v^R provides the value added generated by country R. Finally, based on input-output foundations discussed in Miller and Blair (2022), the N × N matrix with input or technical coefficients is given by A = Zx̂^–1, indicating A^RS = Z^RS(x̂^s)^–1, which gives the intermediate inputs per unit of the receiving country’s output. The N-element final demand vector is given by f = Fe_N. Consider that country K is hypothetically extracted from the global economic system. The input coefficients in the row and column of the Kth country is then nullified, and so is the final demand for products from this country. That is:

{\bar{A}}^{K S} = {\bar{A}}^{R K} = 0 \forall S, R

(1a)

{\bar{A}}^{R S} = A^{R S} \forall S, R \neq K

(1b)

{\bar{f}}_{K} = 0

(1c)

{\bar{f}}_{R} = f_{R} \forall R \neq K

(1d)

When extraction is applied to the ICIO table, it is assumed that the country S (≠ K) now must import its intermediate inputs of country K rather than supplying them domestically. The same principle holds for meeting the final demands associated with the products of the country K. The extraction of the country K will not change the circumstances in the country S. Therefore, imports from the country K to country S will not be substituted by inputs produced domestically. Hence, the differences between outputs observed in the new global economic system as the extraction of the country K and the initiative output level reveal the importance of the country K in this global economic system. That is, x^T can be measured by (I – A)^–1f = Lf, known as the global Leontief matrix or global production balance equation. Also, x̅^T is measured by (I – A̅)^{− 1} f̅ = L̅f̅ known as the extracted global Leontief matrix, and ΔOUTPUT = é_n(x̅^T − x^T), the difference in the total output of country T.

Introduction of Intercountry Trade Impact Index

The new index for the impact of trade in the global economic system is composed of three components. For composing this index, we introduce three determinants, which are discussed in the following sections. These components include APL for applying in an intercountry framework, economic impact measured by global multipliers and country size based on the ICIO tables. Hence, the vulnerability trade index for the country R is formulated as follows:

{ITII}_{R} = w_{1} I_{R 1} + w_{2} I_{R 2} + w_{3} I_{R 3},

(2a)

0 \leq w_{1} ​, w_{2} ​, w_{3} \leq 1,

(2b)

and

w_{1} + w_{2} + w_{3} = 1

(2c)

where ITII_R is the Intercountry Trade Impact Index of the country R, whereas w_i represents the weight assigned to the ith component of the index, reflecting its relative importance or preference within the overall calculation. Also, I_Ri denotes the performance value of the Rth country with respect to the ith component. Due to the varying ranges of values across components, we apply normalisation to provide a consistent measurement in the vulnerability trade index. For normalisation, we use the following index:

I_{R i} = \frac{e_{i}}{\sum_{n} e_{i}}

(2d)

where I_Ri represents the ith element of the normalised vector I_Ri e_i is the ith element of a representative vector e and Σ_ne_i is the sum of all elements of e. It is assumed that the elements of I_Ri consider only non-negative values. Therefore, it is clear that 0 ≤ I_Ri ≤ 1, so the value 1 for one country indicates the more relative importance of that country in the world economic system. The advantage of this index’s normalisation lies in its ability to preserve the relative ranking of countries in the final calculations.

Component I: Average Propagation Length

We employ the APL method in an interregional framework, based on Dietzenbacher and Romero (2007), Dietzenbacher et al. (2005) and Yu et al. (2014), as the first component of the ITII. The APL examines two key dimensions of international trade, drawing on Franco et al. (2011), De Benedictis and Taglioni (2011) and Xing (2017): the strength of interregional connections, assessed via backward linkages (buyers’ dependencies on supplying regions) and forward linkages (sellers’ dependencies on demanding regions). The distance (number of steps) in the trade process reveals the extent of direct connectivity and linkage size across countries in the global economy. APL quantifies this distance. In the backwards-looking approach, APL measures the average steps for an increase in the final demand in country A to propagate through the trade network and affect output in country B. In the forward-looking approach, APL measures the average steps for a cost push in country A’s production to impact output in country B. A key advantage of APL is its consistency: both backward- and forward-looking approaches produce identical results, as shown by Dietzenbacher et al. (2005).

For the derivation of the APL, the power series expansion of the global Leontief inverse ((I − A)⁻¹ = L) and global Ghosh inverse ((I − B)⁻¹ = G) is utilised. The elements of the APL matrix are mathematically specified as follows:

T_{R S} = {\begin{matrix} int (v_{R S}) if f_{R S} \geq a \\ 0 if f_{R S} < a \end{matrix}

(3a)

where v_RS represents the average number of iterations needed to propagate a demand pull from country S to country R or cost push from country R to country S. This measures the depth of the connection between the two countries in the trade network. f_RS reflects the average impact of a demand pull originating from country S to country R or cost push from country R to country S. It quantifies the strength of the trade relationship or economic influence between the two. a is an arbitrary threshold value introduced to ensure that only significant linkages are captured within the model, effectively filtering out irrelevant or negligible connections. Furthermore, v_RS is defined as:

v_{R S} = {\begin{matrix} \frac{{[L (L - I)]}_{R S}}{l_{R S} - δ_{R S}} if l_{R S} - δ_{R S} > 0 \\ 0 if l_{R S} - δ_{R S} = 0 \end{matrix}

(3b)

or alternatively,

v_{R S} = {\begin{matrix} \frac{{[G (G - I)]}_{R S}}{g_{R S} - δ_{R S}} if g_{R S} - δ_{R S} > 0 \\ 0 if g_{R S} - δ_{R S} = 0, \end{matrix}

(3c)

where L and G are the global Leontief and Ghosh inverse matrices, respectively. l_RS is the RSth element of the global Leontief inverse matrix and g_RS is the RSth element of the global Ghosh inverse matrix, both describing the degree of influence that country S (R) has on country R (S) in the trade network. δ_RS denotes the Kronecker delta, which takes on a value 1 when R = S (indicating an intra-regional relationship) and 0 otherwise.

According to Equations 3(a)-3(c), the normalised index for APL as defined by I_R₁ provides the sum of the propagation indices of all countries (regions) in the global economic system, as shown in Equation 3(d):

I_{R 1} = \frac{(\sum_{l = 1}^{n} T_{l R} + \sum_{l = 1}^{n} T_{R l} - 2 S_{R R})}{\sum_{R = 1}^{n} (\sum_{l = 1}^{n} T_{l R} + \sum_{l = 1}^{n} T_{R l} - 2 S_{R R})}

(3d)

where Σ ⁿ _l _{= 1} T_lR represents the backward APL of the country R. It quantifies the number of inter-country interactions triggered by a change in the final demand within country R, indicating the depth of backward linkages. Σ ⁿ _l _{= 1} T_Rl represents the forward APL of the country R. It measures the number of inter-country interactions resulting from a change in primary cost within the country R, capturing the extent of forward linkages. 2S_RR corresponds to twice the value of the diagonal element of the APL matrix. It reflects inter-country propagation effects, which are significant for understanding domestic interdependence. n is the total number of countries in the global economic system, representing the scope of the trade network being analysed.

By subtracting the diagonal element twice (i.e., 2S_RR), the initial self-effect of an exogenous change in country R—whether forward or backward—is removed. This adjustment eliminates direct self-linkages, ensuring the model focuses exclusively on inter-country trade interactions and enabling a cleaner analysis of external trade dynamics. This component measures the number of countries a nation is connected to, either as a consumer (backward) or as a producer (forward) in international trade. Its main purpose is to distribute the impact of allocated funds across the global economy through a given country. The more extensive a country’s trade linkages, the higher its priority in influencing global economic dynamics, underscoring the role of trade interconnectedness in determining economic importance and resource allocation.

Component II: Economic Impact

To assess the economic impact, we use the global output multiplier ratio as defined by Equation 4, derived from ICIO. The global output multiplier for each country or region is calculated using the global Leontief inverse matrix. Consequently, the normalised global output multiplier for each country is determined by Equation 4:

I_{R 2} = \frac{L_{R}}{\sum_{n} L_{R}}

(4)

When the global output multiplier approaches unity, it indicates that the country has a highly complex interconnectedness and significant dependencies on other countries or regions within the framework of international trade as defined by ICIO. Therefore, this component serves as an indicator of the extent to which a country influences the global economic system.

Component III: Country Size

This study utilises the output (x vector) to quantify a country’s size in relation to the global economy, defined as follows:

I_{R 2} = \frac{X_{R}}{\sum_{n} X_{R}}

(5)

As this metric nears unity, the economic significance of the country increases. Consequently, this index identifies countries that warrant a higher priority for receiving additional goods and services to stimulate international trade.

Data and Results

Data

This study utilises the OECD’s ICIO tables for 2020, the most recent version available, which provides a consistent framework for mapping international flows of goods and services across countries (OECD, 2025). The data set covers 76 countries plus the rest of the world and 45 economic activities. For this analysis, the table is aggregated to focus on Malaysia’s 25 major trade partners, as reported in Malaysia External Trade Statistics (2024) and Trade Performance of Malaysia (2020), accounting for specific economic activities.²

Aggregating economic activities in the ICIO tables simplifies complex data, eases analysis of trade flows and inter-country interactions, and emphasises major trade partners and their global economic significance. This country-centric approach aids policymakers in trade agreements, economic strategies and international collaboration, while ensuring data consistency and cross-country comparability, aligning with the study’s objectives outlined in the introduction section.

Results

As outlined in the introduction section, this study addresses the policy question: Which nations, regions or international organisations could serve as key trade partners to sustain Malaysia’s trade-led economic growth? To answer this, two methods are employed. The first method evaluates trade interdependency by simulating the complete hypothetical removal of one country or region from the global economy—the GEM. This approach quantifies the resulting reduction in Malaysia’s output, revealing which countries or regions exert the greatest impact on Malaysia’s economy. It also enables ranking countries or regions by the size of Malaysia’s output reduction after each extraction, assessing the effect of each extraction on global output and determining Malaysia’s average position in the international trade network. Table 1 reports the reduction in Malaysia’s output (in million USD) following the global extraction of various countries. Countries are ranked by the magnitude of this output reduction: a higher rank indicates greater output loss for Malaysia and thus stronger trade and economic interdependence.

Table 1.

Reduction in the Output of Malaysia After Global Extraction (Results by Country/Region, in Million USD).

Country/Region	Rank	Output Reduction	Country/Region	Rank	Output Reduction
South America	7	2,803	Hungary	10	2,457
Other Europe	23	630	India	8	2,731
Africa	16	975	Italy	24	498
Australia	4	3,194	Japan	5	3,084
Belgium	19	873	South Korea	2	3,523
ASEAN	1	8,153	Netherlands	11	2,214
Canada	14	1,005	New Zealand	3	3,462
Czech Republic	9	2,495	Poland	17	936
Germany	20	845	Slovenia	15	999
Spain	6	2,912	Turkey	13	1,513
France	22	643	The United States	21	742
China	12	2,089	Rest of the World	18	926

Source: Research findings based on global aggregated ICIO 2020.

Note: *Bold values indicate the five countries whose extraction leads to the largest reduction in Malaysia’s total output.

According to the GEM results (Table 1), ASEAN ranks first and registers the largest output reduction for Malaysia at $8,153m. This figure shows deep regional economic interdependence; any economic shock in Malaysia would create substantial reverberations across the entire ASEAN bloc. South Korea ranks second with $3,523m and demonstrates strong bilateral trade reliance. New Zealand ranks third at $3,462m. Despite the geographical distance, this result reveals substantial trade flows, likely in specific commodities or specialised industries, and points to a stronger economic bond than commonly perceived. Australia ranks fourth with $3,194m and confirms its vital role as a major trade partner. Geographical proximity and shared economic interests drive this significant relationship across various sectors. Japan ranks fifth at $3,084m and confirms its status as a major economic partner. Thus, the top-five key trade partners that exert the greatest influence on Malaysia’s output are ASEAN, South Korea, New Zealand, Australia and Japan.

According to Table 2, among the top-five trade partners (ASEAN, South Korea, New Zealand, Australia and Japan), China registers the highest output reduction after extraction from the global economy, followed by the United States (second) and Europe (third).

Table 2.

Reduction in Top-five Trade Partners’ Output of Malaysia after Global Extraction (Results by Country/Region, in Million USD).

Country/Region	Extraction of ASEAN		Extraction of South Korea		Extraction of New Zealand		Extraction of Australia		Extraction of Japan
Country/Region	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value
South America	10	28,701	10	36,692	14	11,570	11	16,680	9	34,653
Other Europe	4	412,462	5	418,613	3	350,276	4	232,347	4	429,487
Africa	17	5,870	15	4,858	18	3,284	18	2,720	15	5,114
Australia	11	20,780	9	38,180	7	71,975	2	2,172,472	8	41,080
Belgium	18	1,547	18	1,335	19	1,596	20	761	18	1,348
ASEAN	2	3,934,591	7	140,171	6	112,946	6	106,886	6	151,258
Canada	16	5,876	14	10,161	15	8,068	14	4,356	13	9,969
Czech Republic	22	241	22	283	22	228	23	162	22	167
Germany	9	57,876	8	75,927	9	55,557	8	37,703	7	58,011
Spain	25	3	25	4	25	6	22	334	25	5
France	12	19,754	12	19,887	12	13,982	12	10,713	12	14,144
China	1	4,418,863	1	3,708,723	1	1,899,005	1	2,181,561	2	2,223,818
Hungary	23	100	23	126	23	95	24	67	23	111
India	8	63,076	11	31,485	11	19,732	9	22,638	11	18,482
Italy	13	9,444	13	11,571	13	13,124	13	9,630	14	8,522
Japan	6	217,454	6	178,976	8	55,805	7	54,430	1	8,405,791
South Korea	7	87,070	2	2,505,169	10	35,417	10	17,967	10	33,978
Malaysia	14	8,153	17	3,523	17	3,462	16	3,194	17	3,084
Netherlands	15	6,179	16	4,640	16	3,522	15	3,490	16	3,671
New Zealand	21	769	21	648	5	162,214	17	3,152	21	624
Poland	20	1,067	20	1,075	20	1,152	19	897	19	757
Slovenia	24	10	24	11	24	10	25	6	24	7
Turkey	19	1,204	19	1,281	21	1,092	21	756	20	649
The United States	3	878,294	3	1,121,974	2	815,745	3	677,516	3	1,037,587
Rest of the World	5	375,175	4	537,532	4	306,932	5	116,672	5	379,225

Source: Research findings based on global aggregated ICIO 2020.

Note: *Bold values indicate the reduction in Malaysia’s total output after each regional/ country extraction.

Malaysia’s average ranking in output reduction within the international trade system after the extraction of these five entities stands at 16. Across the full set of 24 countries (or regions), Malaysia’s overall ranking is 19 (see Appendix B for details).

The second method, the ITII, evaluates the economic influence of Malaysia’s trade partners on the global economy and complements the first method (GEM). ITII applies the APL—introduced earlier in the ‘Mathematical Frameworks’ section—to measure both the distance and the size of trade linkages between partners in the global system. By also incorporating global multipliers and country size, ITII highlights additional criteria relevant to identifying key trade partners for Malaysia.

Based on ITII, Table 3 presents four columns: the three main components of ITII and the final composite ITII, which identifies Malaysia’s key trade partners. Component I (APL) measures the scope and distance of global linkages. New Zealand ranks first, followed by China (second) and the rest of the world (third). These rankings reflect the significant reach and potential global trade impacts of their multipliers. Component II (economic impact) uses normalised global multipliers to show direct and indirect effects on the global output. New Zealand again ranks the highest, followed by China (second) and Other Europe (third). Component III (country/region size) reflects the normalised share of output in the global economy. China, the United States and Other Europe hold the top-three positions among the 25 countries or regions analysed.

Table 3.

Intercountry Trade Impact Index (ITII): Components and Index Values.

Country/Region	Component I: APL		Component II: Economic Impact		Component III: Country Size		Intercountry Trade Impact Index
Country/Region	Rank	Index Value	Rank	Index Value	Rank	Index Value	Rank	Index Value
South America	20	0.0274	22	0.0264	7	0.0378	12	0.0317
Other Europe	13	0.0310	3	0.0583	3	0.0995	4	0.0644
Africa	16	0.0265	20	0.0289	15	0.0147	16	0.0258
Australia	4	0.0429	8	0.0370	14	0.0158	14	0.0306
Belgium	22	0.0538	19	0.0293	20	0.0064	21	0.0221
ASEAN	7	0.0398	7	0.0397	8	0.0342	8	0.0369
Canada	19	0.0305	21	0.0286	13	0.0175	15	0.0260
Czech Republic	18	0.0493	18	0.0297	22	0.0032	22	0.0218
Germany	14	0.0357	6	0.0431	6	0.0433	7	0.0402
Spain	25	0.1474	25	0.0171	16	0.0139	25	0.0168
France	15	0.0293	11	0.0335	10	0.0280	11	0.0319
China	2	0.0101	2	0.0725	1	0.2330	1	0.1184
Hungary	24	0.0734	24	0.0250	23	0.0018	24	0.0183
India	12	0.0229	15	0.0319	9	0.0290	10	0.0322
Italy	8	0.0267	10	0.0351	12	0.0215	13	0.0310
Japan	11	0.0242	12	0.0335	5	0.0527	6	0.0406
South Korea	5	0.0388	9	0.0366	11	0.0216	9	0.0323
Malaysia	6	0.0493	13	0.0333	21	0.0050	17	0.0254
Netherlands	21	0.0511	16	0.0309	17	0.0105	20	0.0241
New Zealand	1	0.0218	1	0.1473	24	0.0012	2	0.1070
Poland	17	0.0420	14	0.0323	19	0.0072	19	0.0244
Slovenia	23	0.0626	23	0.0259	25	0.0006	23	0.0185
Turkey	9	0.0249	17	0.0307	18	0.0083	18	0.0250
The United States	10	0.0126	5	0.0445	2	0.2155	3	0.0986
Rest of the world	3	0.0259	4	0.0490	4	0.0777	5	0.0559

Source: Research findings based on global aggregated ICIO 2020.

Note: *Bold values indicate the top five key trade partners of Malaysia based on ITII.

The fourth column of Table 3 shows the composite ITII, which combines the three components under equal weights. This index determines the overall ranking of Malaysia’s trade partners. China ranks first, reflecting the highest overall trade impact on the global economy. New Zealand and the United States hold second and third positions, respectively. Other Europe and the rest of the world rank fourth and fifth, respectively. Equal weighting of the three factors is not fully realistic. A sensitivity analysis via Monte Carlo simulation tests the ranking stability under varying weights generated with the R programming language (see Appendix C). Figure 1 displays the range of simulated average rankings. The results show minimal change: China remains firmly first; the United States and New Zealand stay second and third, respectively (in either order); and Other Europe and the rest of the world exhibit stable, closely matched positions across weight scenarios.

Figure 1.

Source: Research findings.

To further examine the ITII, a sensitivity analysis was performed using a Tornado diagram generated with the R programming language (see Appendix D). This approach evaluates how variations in ITII values affect country rankings. The analysis identifies the nations that consistently hold high or low ranks regardless of weight changes and countries whose rankings prove highly sensitive to shifts in weight preferences.

Figure 2 illustrates the range of possible ranks for each country under varying weights in the ITII. China exhibits the highest sensitivity, with its maximum ITII value surpassing all others, while the United States shows the second-highest sensitivity and experiences notable rank shifts depending on weight adjustments. New Zealand ranks third in sensitivity and maintains its position consistently, even under equal-weight scenarios. Figure 2 also orders countries by overall ranking volatility, from most to least volatile. These results closely match the original ITII findings, reinforce the identification of Malaysia’s key trade partners and highlight both the role of intercountry trade dynamics and the influence of methodological choices on rankings, thereby supporting the robustness of the framework.

Figure 2.

Source: Research findings.

Concluding Remarks and Policy Recommendations

This study builds on national reports on Malaysian trade policies (Trade Performance of Malaysia, 2020; Malaysia Trade Summary: WITS, 2025; World Bank, 2025), which traditionally highlight 25 countries as key trade partners under bilateral or multilateral agreements. Yet, a critical question arises: Does trade volume alone truly identify Malaysia’s most strategic partners? To address this, the study applies two advanced methods using the ICIO database. The first, the GEM, assesses the impact of removing individual partners from Malaysia’s trade network, identifying those whose absence would most reduce national output. The second, the ITII, incorporates factors such as APL, multiplier effects and country size to rank partners by systemic importance. Together, these approaches provide a more nuanced evaluation of Malaysia’s trade dependencies. Table 4 compares traditional trade-volume rankings with those generated by the two methodological approaches.

Table 4 highlights notable disparities between trade partner rankings derived from national reports and those obtained through this study’s methodologies. For instance, New Zealand ranks 3rd and 2nd under the GEM and ITII, respectively, yet is no higher than 16th in national reports. At the same time, several partners—such as ASEAN, the United States, Japan and South Korea—consistently remain highly ranked across both sources, reaffirming their pivotal roles in Malaysia’s trade landscape.

Table 4.

Ranking of Countries.

Country/Region	Trade Performance Reports Rank	Global Extraction Method Rank	Intercountry Trade Impact Index Rank	Average Ranking
South America	12	7	12	9
Other Europe	11	23	4	12
Africa	21	16	16	20
Australia	7	4	14	7
Belgium	22	19	21	24
ASEAN	1	1	8	1
Canada	14	14	15	14
Czech Republic	23	9	22	21
Germany	10	20	7	11
Spain	15	6	25	15
France	17	22	11	18
China	2	12	1	3
Hungary	19	10	24	19
India	6	8	10	6
Italy	13	24	13	17
Japan	4	5	6	2
South Korea	5	2	9	4
Netherlands	8	11	20	13
New Zealand	16	3	2	5
Poland	24	17	19	23
Slovenia	20	15	23	22
Turkey	18	13	18	16
The United States	3	21	3	8
Rest of the world	9	18	5	10

Note: *Bold values indicate the top eight key trade partners of Malaysia based on different indicators.

From a policy perspective, three approaches to identifying Malaysia’s key partners emerge. The first is a traditional reliance on trade performance reports and trade volume, which offers continuity but risks overlooking smaller yet strategically important partners and fails to capture the dynamic structure of global trade. The second involves the GEM, which quantifies the impact of losing access to specific markets and highlights countries with strong interdependencies, including ASEAN, South Korea, New Zealand, Australia and Japan. This approach underscores the importance of interconnectedness in sustaining Malaysia’s output. The third approach employs the ITII, which emphasises partners such as China, New Zealand, the United States, Europe and the rest of the world, based on their systemic influence, economic size and network connectivity. While valuable, this method also highlights the risks of heightened exposure to external shocks from globally dominant economies.

Hence, this study recommends that Malaysian policymakers trade with their key partners who can help with Malaysia’s domestic output and have similar economic structures and close geographical distance; use the GEM; and choose ASEAN, South Korea, New Zealand, Australia and Japan as key trade partners. If they pursue finding some countries to trade with, economic and political powers, China, New Zealand, the United States, Other Europe and the rest of the world might be selected. However, as mentioned, most of these countries experience more instability, like the countries in the rest of the world, and their economic structures are different from Malaysia’s. As a final point, the average ranking for all approaches shows that countries such as the ASEAN organisation, Japan, China, South Korea and New Zealand can be the key trade partners of Malaysia based on all these approaches.

Finally, this study recommends that Malaysian policymakers consider distinct strategies for selecting key trade partners based on their objectives. For fostering partnerships that align with Malaysia’s domestic output, economic structure and geographical proximity, the GEM suggests ASEAN, South Korea, New Zealand, Australia and Japan as major trade partners. Conversely, if the goal is to engage with countries wielding significant economic and political influence, partners such as China, New Zealand, the United States, Other Europe and the rest of the world may be prioritised. However, it is important to note that many of these nations, particularly in the rest of the world, face higher levels of instability and possess economic structures that differ significantly from that of Malaysia. Finally, the average rankings across all approaches highlight ASEAN, Japan, China, South Korea and New Zealand as consistent key trade partners, offering a balanced perspective for Malaysia’s trade strategy.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Appendix A

Table A1.

Twenty-Five Major Trading Partners of Malaysia by Aggregation of ICIO Tables.

No.	Countries in Aggregated ICIO Tables	List of Countries Included in Each Aggregation
1	South America	Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru
2	Other Europe	Austria, Bulgaria, Belarus, Switzerland, Cyprus, Denmark, Estonia, Finland, United Kingdom, Greece, Croatia, Ireland, Iceland, Israel, Lithuania, Luxembourg, Latvia, Malta, Norway, Portugal, Romania, Russian Federation, Slovakia, Sweden, Ukraine
3	Africa	Côte d’Ivoire, Cameroon, Egypt, Morocco, Nigeria, Senegal, Tunisia, South Africa
4	Australia	Australia
5	Belgium	Belgium
6	ASEAN	Brunei Darussalam, Indonesia, Cambodia, Lao (People’s Democratic Republic), Myanmar, Philippines, Singapore, Thailand, Viet Nam
7	Canada	Canada
8	Czech Republic	Czech Republic
9	Germany	Germany
10	Spain	Spain
11	France	France
12	China	China, Hong Kong, Taiwan
13	Hungary	Hungary
14	India	India
15	Italy	Italy
16	Japan	Japan
17	South Korea	South Korea
18	Malaysia	Malaysia
19	Netherlands	Netherlands
20	New Zealand	New Zealand
21	Poland	Poland
22	Slovenia	Slovenia
23	Turkey	Turkey
24	The United States	The United States
25	Rest of the world	Bangladesh, Jordan, Kazakhstan, Pakistan, Saudi Arabia, rest of the world

Source: Research findings based on global aggregated ICIO (2020).

Appendix B

Table B1.

Reduction in Trade Partners’ Output of Malaysia After Global Extraction (Results by Country/Region, in Million USD).

Country/Region	Extraction of South America		Extraction of Other Europe		Extraction of Africa		Extraction of Australia		Extraction of Belgium		Extraction of ASEAN		Extraction of Canada
Country/Region	Rank	Value	Rank	Rank	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value
South America	1	4,488,745	2	13,314	11	25,130	11	16,680	12	28,485	10	28,701	9	37,632
Other Europe	16	394,968	12	13,587,625	5	488,229	4	232,347	1	2,115,508	4	412,462	4	434,822
Africa	12	4,793	13	5,245	1	1,921,365	18	2,720	16	10,358	17	5,870	14	5,294
Australia	7	4,788	11	2,975	17	4,488	2	2,172,472	18	4,852	11	20,780	15	4,803
Belgium	14	1,545	7	6,376	18	3,168	20	761	4	592,622	18	1,547	18	1,763
ASEAN	21	56,013	20	38,172	9	48,661	6	106,886	11	50,443	2	3,934,591	8	49,553
Canada	24	15,530	9	7,622	16	4,736	14	4,356	15	12,129	16	5,876	3	2,027,426
Czech Republic	20	425	23	1,660	21	504	23	162	20	2,730	22	241	22	276
Germany	8	92,680	19	207,542	6	82,208	8	37,703	6	335,732	9	57,876	6	73,804
Spain	25	420	5	1	25	1	22	334	25	1	25	3	25	2
France	11	21,972	8	51,456	7	50,291	12	10,713	7	241,353	12	19,754	12	20,044
China	4	3,112,363	1	1,083,136	2	1,666,527	1	2,181,561	3	863,666	1	4,418,863	2	2,239,093
Hungary	3	198	24	616	23	178	24	67	22	757	23	100	23	138
India	22	30,362	25	20,134	8	49,647	9	22,638	13	28,442	8	63,076	10	26,992
Italy	2	17,830	4	34,331	12	23,620	13	9,630	10	53,989	13	9,444	13	16,905
Japan	13	71,863	15	47,782	10	34,654	7	54,430	9	60,654	6	217,454	7	69,233
South Korea	6	32,075	22	11,004	13	12,892	10	17,967	17	9,490	7	87,070	11	23,521
Malaysia	9	2,803	14	630	20	975	16	3,194	21	873	14	8,153	20	1,005
Netherlands	10	9,079	6	18,313	15	6,257	15	3,490	8	88,248	15	6,179	16	4,624
New Zealand	18	406	18	226	22	396	17	3,152	23	348	21	769	21	408
Poland	23	1,441	10	8,397	19	2,657	19	897	14	12,233	20	1,067	17	1,807
Slovenia	5	11	3	76	24	19	25	6	24	67	24	10	24	13
Turkey	17	1,692	21	4,466	14	7,983	21	756	19	4,784	19	1,204	19	1,736
The United States	15	3,034,280	17	926,596	3	559,904	3	677,516	2	1,299,721	3	878,294	1	5,008,121
Rest of the world	19	211,112	16	181,303	4	544,986	5	116,672	5	358,951	5	375,175	5	147,855
Country/Region	Extraction of Czech Republic		Extraction of Germany		Extraction of Spain		Extraction of France		Extraction of China		Extraction of Hungary		Extraction of India
Country/Region	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value
South America	15	11,313	12	16,158	8	150,940	14	12,742	9	31,926	16	12,467	10	24,468
Other Europe	2	1,540,541	2	1,205,631	2	3,326,540	3	1,041,463	4	173,590	2	2,601,546	5	336,098
Africa	17	7,508	15	7,152	11	86,486	13	13,500	15	2,716	19	6,447	11	18,514
Australia	20	3,625	20	3,112	19	11,243	19	2,515	10	18,497	21	3,964	13	12,191
Belgium	16	7,621	16	7,113	17	18,022	12	16,259	18	549	17	11,206	19	1,964
ASEAN	10	52,634	9	36,992	9	111,005	9	30,464	6	64,211	10	69,345	6	113,437
Canada	19	4,337	17	5,413	18	17,015	17	8,202	14	3,138	20	4,999	15	5,168
Czech Republic	5	291,138	19	3,278	20	5,212	20	1,511	22	139	15	12,677	22	162
Germany	3	605,600	1	5,522,084	5	736,452	5	267,985	7	35,037	3	871,244	8	48,183
Spain	25	1	25	1	21	4,349	25	1	25	3	25	1	25	2
France	8	67,750	6	60,900	6	404,832	1	3,478,322	12	8,909	7	114,010	12	14,792
China	1	3,663,074	3	1,139,187	1	4,904,453	2	1,058,070	1	34,940,669	1	2,992,726	2	1,800,838
Hungary	22	2,463	21	1,029	23	1,981	22	459	23	62	6	117,844	23	66
India	14	17,739	11	18,582	12	72,777	11	19,102	11	9,039	14	21,458	1	3,697,229
Italy	9	62,367	8	38,842	7	201,690	7	64,504	13	4,871	8	98,876	14	9,107
Japan	7	68,574	7	42,577	10	100,802	8	31,475	5	78,238	9	76,172	7	55,656
South Korea	12	35,563	14	10,215	14	37,156	16	8,311	8	33,982	11	65,070	9	30,433
Malaysia	21	2,495	22	845	<22	2,912	21	643	16	2,089	22	2,457	17	2,731
Netherlands	13	20,407	10	21,594	13	49,414	10	23,971	17	1,527	13	35,612	16	3,659
New Zealand	24	147	23	154	24	690	23	130	21	405	24	132	21	383
Poland	11	46,567	13	13,905	15	22,929	15	8,345	19	547	12	41,225	20	1,104
Slovenia	23	181	24	93	25	149	24	54	24	4	23	637	24	15
Turkey	18	6,380	18	3,714	16	21,469	18	3,596	20	497	18	10,127	18	2,130
The United States	4	603,115	4	651,237	3	2,197,740	4	699,727	2	298,430	4	845,480	4	643,503
Rest of the world	6	189,737	5	204,070	4	1,715,659	6	202,198	3	239,418	5	283,475	3	938,519
Country/Region	Extraction of Italy		Extraction of Japan		Extraction of South Korea		Extraction of Malaysia		Extraction of Netherlands		Extraction of New Zealand		Extraction of Poland
Country/Region	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value
South America	10	19,033	9	34,653	10	36,692	12	32,186	10	48,120	14	11,570	12	22,509
Other Europe	3	1,027,283	4	429,487	5	418,613	6	346,964	1	1,878,387	3	350,276	2	1,717,660
Africa	14	9,129	15	5,114	15	4,858	15	7,275	14	15,434	18	3,284	15	8,548
Australia	19	1,605	8	41,080	9	38,180	11	32,382	18	4,996	7	71,975	19	4,283
Belgium	16	7,026	18	1,348	18	1,335	19	1,353	13	25,015	19	1,596	16	7,859
ASEAN	8	25,013	6	151,258	7	140,171	5	388,090	9	74,541	6	112,946	10	40,264
Canada	18	3,590	13	9,969	14	10,161	16	5,615	17	9,424	15	8,068	20	3,298
Czech Republic	20	1,467	22	167	22	283	22	251	21	1,743	22	228	18	5,350
Germany	6	216,167	7	58,011	8	75,927	8	74,413	5	329,408	9	55,557	5	462,445
Spain	25	1	25	5	25	4	25	4	25	2	25	6	25	1
France	7	81,607	12	14,144	12	19,887	13	17,978	8	78,530	12	13,982	7	67,306
China	2	1,056,544	2	2,223,818	1	3,708,723	1	4,800,562	3	1,427,987	1	1,899,005	1	1,917,951
Hungary	21	622	23	111	23	126	23	108	22	645	23	95	21	1,237
India	11	17,833	11	18,482	11	31,485	9	58,654	12	31,362	11	19,732	14	18,077
Italy	1	2,766,062	14	8,522	13	11,571	14	11,403	11	32,818	13	13,124	8	66,296
Japan	9	22,950	1	8,405,791	6	178,976	7	235,244	7	138,092	8	55,805	9	49,366
South Korea	13	9,715	10	33,978	2	2,505,169	10	51,156	15	12,188	10	35,417	11	27,619
Malaysia	22	498	17	3,084	17	3,523	3	512,608	20	2,214	17	3,462	22	936
Netherlands	12	14,152	16	3,671	16	4,640	17	5,519	4	1,052,369	16	3,522	13	20,795
New Zealand	24	129	21	624	21	648	20	1,173	23	467	5	162,214	23	149
Poland	15	7,112	19	757	20	1,075	21	953	16	11,658	20	1,152	3	765,847
Slovenia	23	155	24	7	24	11	24	9	24	55	24	10	24	107
Turkey	17	6,169	20	649	19	1,281	18	1,431	19	3,706	21	1,092	17	7,209
The United States	4	431,395	3	1,037,587	3	1,121,974	2	1,182,778	2	1,571,046	2	815,745	4	549,141
Rest of the world	5	316,972	5	379,225	4	537,532	4	425,540	6	312,560	4	306,932	6	226,197
Country/Region		Extraction of Slovenia			Extraction of Turkey			Extraction of the United States			Extraction of Rest of the World
Country/Region		Rank		Value		Rank		Value		Rank		Value		Rank		Value
South America	12		22,814		12		34,529		9		18,218		9		27,925
Other Europe	1		2,571,049		2		1,169,470		3		237,633		4		468,691
Africa	18		8,448		14		21,064		16		1,939		13		10,405
Australia	21		3,836		19		3,672		14		2,390		15		4,715
Belgium	19		7,299		18		4,684		17		1,181		19		1,910
ASEAN	9		47,204		11		38,513		8		31,122		6		54,076
Canada	15		15,796		17		4,773		6		33,617		14		6,495
Czech Republic	20		5,949		21		1,506		22		134		21		467
Germany	5		442,635		6		188,391		7		32,590		5		61,124
Spain	25		1		25		1		25		1		25		1
France	7		89,751		8		50,291		12		8,548		10		24,864
China	2		1,949,753		1		1,711,127		2		858,126		2		1,435,141
Hungary	22		3,224		22		444		23		64		23		192
India	11		26,452		7		55,250		10		17,812		8		37,990
Italy	6		192,298		9		44,269		13		5,889		12		16,653
Japan	10		39,079		10		43,280		5		34,902		7		40,168
South Korea	13		22,188		13		31,391		11		11,238		11		17,847
Malaysia	23		999		20		1,513		19		742		20		926
Netherlands	17		14,019		15		7,615		15		2,045		16		4,680
New Zealand	24		162		23		114		21		182		22		381
Poland	14		20,575		16		5,278		20		683		18		2,381
Slovenia	8		50,396		24		50		24		5		24		33
Turkey	16		15,176		3		953,876		18		797		17		4,656
The United States	3		557,339		5		655,491		1		32,614,628		3		564,091
Rest of the world	4		491,166		4		706,641		4		95,897		1		10,931,770

Source: Research findings based on global aggregated ICIO (2020).

Appendix C

R Programming for Monte Carlo Simulation of Average Ranking of Countries with Different Weight Preferences

library(readxl)

data ← read_excel(“path_to_your_file.xlsx”)

colnames (data) ← c(“Country”, “APL”, “Economic_Impact”, “Country_Size”, “ITII”)

data$OriginalOrder ← seq_len(nrow(data))

num_simulations ← 1000

rank_matrix ← matrix(NA, nrow = nrow(data), ncol = num_simulations)

set.seed(123)

for (sim in 1:num_simulations) {

weights ← runif(3)

weights ← weights / sum(weights)

ITII ← data$APL * weights[1] +

data$Economic_Impact * weights[2] +

data$ Country_Size* weights[3]

rank_matrix[, sim] ← rank(-ITII)

}

average_ranks ← rowMeans(rank_matrix)

results ← data.frame(Country = data$Country, AverageRank = average_ranks, OriginalOrder = data$OriginalOrder)

results$FinalRank ← rank(results$AverageRank, ties.method = “min”)

results ← results[results$FinalRank <= 25, ]

results ← results[order(results$OriginalOrder), ]

library(extrafont)

extrafont::font_import(prompt = FALSE)

loadfonts(device = “win”)

library(ggplot2)

ggplot(results, aes(x = reorder(Country, OriginalOrder), y = AverageRank)) +

geom_bar(stat = “identity”, fill = “lightcoral”, color = “black”, width = 0.6) +

geom_text(aes(label = FinalRank),

vjust = -0.5, color = “black”, size = 5) +

theme_minimal(base_family = “Times New Roman”) +

labs(

x = “Countries”,

y = “Average Rank” ) +

theme(

axis.text.x = element_text(angle = 90, hjust = 1, face = “bold”, size = 12),

axis.title.x = element_text(face = “bold”, size = 14),

axis.title.y = element_text(face = “bold”, size = 14) )

Appendix D

R Programming for Tornado Diagram as Sensitivity Analysis of Intercountry Trade Impact Index (ITII) Variations

library(readxl)

data ← read_excel(“path_to_your_file.xlsx”)

colnames (data) ← c(“Country”, “APL”, “Economic_Impact”, “Country_Size”, “ITII”)

data$OriginalOrder ← seq_len(nrow(data))

num_simulations ← 1000

rank_matrix ← matrix(NA, nrow = nrow(data), ncol = num_simulations)

set.seed(123)

for (sim in 1:num_simulations) {

weights ← runif(3)

weights ← weights / sum(weights)

ITII ← data$APL * weights[1] +

data$Economic_Impact * weights[2] +

data$ Country_Size* weights[3]

sensitivity_data$MinComposite[i] ← min(composite_values)

sensitivity_data$MaxComposite[i] ← max(composite_values)

}

sensitivity_data$Range ←

sensitivity_data$MaxComposite - sensitivity_data$MinComposite

sensitivity_data ← sensitivity_data[order(sensitivity_data$Range, decreasing = TRUE), ]

ggplot(sensitivity_data, aes(x = reorder(Country, Range), ymin = MinComposite, ymax = MaxComposite)) +

geom_linerange(color = “blue”, size = 2.5) +

coord_flip() +

theme_minimal(base_family = “Times New Roman”) +

labs(

x = “Countries”,

y = “Intercountry Trade Impact Index (ITII)”) +

theme(

axis.text.x = element_text(face = “bold”, size = 12),

axis.text.y = element_text(face = “bold”, size = 12),

axis.title.x = element_text(face = “bold”, size = 14),

axis.title.y = element_text(face = “bold”, size = 14),

plot.margin = margin(10, 10, 10, 50) ) +

scale_y_continuous(expand = expansion(mult = c(0.1, 0.1)))

Notes

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