The role of comparative advantage in enhancing trade in value-added using a dynamic gmm model

Wuri, Josephine Article The role of comparative advantage in enhancing trade in value-added using a dynamic gmm model Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Wuri, Josephine (2024) : The role of comparative advantage in enhancing trade in value-added using a dynamic gmm model, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 7, pp. 1-21, https://doi.org/10.3390/economies12070187 This Version is available at: https://hdl.handle.net/10419/329113 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Wuri, Josephine. 2024. The Role of Comparative Advantage in Enhancing Trade in Value-Added Using a Dynamic GMM Model. Economies 12: 187. https://doi.org/ 10.3390/economies12070187 Academic Editor: Bruce Morley Received: 9 June 2024 Revised: 14 July 2024 Accepted: 16 July 2024 Published: 18 July 2024 Copyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Role of Comparative Advantage in Enhancing Trade in Value-Added Using a Dynamic GMM Model Josephine Wuri Department of Economics, Faculty of Economics, Sanata Dharma University, Yogyakarta 55281, Indonesia; [email protected] Abstract: Currently, international trade has evolved into international production fragmentation captured in GVCs. Countries must enhance intermediate exports in comparative advantage sectors to increase their trade in value-added (TVA) in global production chains. However, traditional measurements of revealed comparative advantage (RCA) based on gross exports need to be updated due to overvaluation, double counting, and implicit distortions in international trade. This study uses a new comparative advantage measure, “new revealed symmetric comparative advantage” (NRSCA). Using a dynamic General Method of Moment (GMM) approach, we investigate the role of comparative advantage in driving TVA regarding backward and forward linkages and examine the impact of the COVID-19 pandemic. We use data from the current Asian Development Bank multi-regional input–output database for 2010–2020. Our findings reveal that comparative advantage significantly impacted international TVA, along with the support of quality institutional services in each country. Implementing a new comparative advantage measure, NRSCA, provided accurate estimation results to overcome the overvaluation problem. Moreover, the COVID-19 pandemic disrupted value-added trade. Keywords: comparative advantage; trade in value added; dynamic GMM model; multi-regional input output; pandemic 1. Introduction In the new international trade theory, international trade has developed into trade in value-added (TVA) and is included in global value chains (GVCs). International fragmentation in the production of goods has enabled each country to specialize in producing high-value-added goods (Amendolagine et al. 2019;Fan et al. 2023;Hummels et al. 2001; Inomata 2013;Johnson and Noguera 2017). Furthermore, the opportunities for GVC participation have attracted foreign investment and allowed for profit-sharing and economic, technological, and industrial upgrading (Gereffi et al. 2005;González and Kowalski 2017;Prete et al. 2018;Zhang 2024), thus enabling developing countries to catch up with developed nations. The participation of countries in GVCs is measured by decomposing gross exports to trace domestic value added (DVA) and foreign contributions (Z. Wang et al. 2018). Backwardand forward-linked approaches are used to determine the level of countries’ participation in TVA, which traces the value added in each production phase (Asian Development Bank 2019;Koopman et al. 2014;Prete et al. 2018;Z. Wang et al. 2018;Wuri et al. 2023). Involvement in GVC facilitates the emergence of new global competitors and has challenged the trade dominance of developed countries. Consequently, concerns over competitiveness have been researched in recent years. Each country needs to enhance its comparative advantage to increase participation in GVCs. Understanding comparative advantages enables countries to optimize resource allocation and focus on industries with significant potential for enhancing value-added. Economies 2024,12, 187. https://doi.org/10.3390/economies12070187 https://www.mdpi.com/journal/economies Economies 2024,12, 187 2 of 21 This promotes sustainable economic growth, reinforces economic integration, and enhances global competitiveness in international trade (Elsalih et al. 2021;Laursen 2015). Balassa (1965) introduced revealed comparative advantage (RCA), which ranked product-specific specialization in cross-country trade. A country has a comparative advantage in a specific sector if the share of that sector in its total exports exceeds its share of total world exports (Ito et al. 2017). Most countries continue to rely on traditional measures of comparative advantage, which remains a critical issue for policymakers and academics. However, the traditional RCA (TRCA) measure based on gross exports does not accurately reflect countries’ comparative advantage, as the calculation includes foreign resource components (Johnson and Noguera 2017). Furthermore, trade measurements can become overvalued and develop implicit distortions (Asian Development Bank 2019; Athukorala and Yamashita 2006;Inomata 2013;Koopman et al. 2014). To address the gap in current research, this study developed a new comparative advantage measure, NRSCA. The symmetric calculation was based on DVA and excluded foreign value-added (FVA) and pure double-counted terms in gross exports to obtain an accurate measure of a country’s comparative advantage. The NRSCA encourages TVA using high-quality institutions as control variables such as government effectiveness and control of corruption. Therefore, countries with high-quality institutions can ensure efficiency and fair contracts, reduce corruption, reduce environmental degradation, promote policy coordination and trade, and ultimately stimulate economic growth (Fan et al. 2023; Gereffi et al. 2005;Mouanda and Gong 2019;Q. Wang et al. 2024;Zergawu et al. 2020). The pandemic disrupted trade intensity among countries due to border closures and quarantine-related restrictions (Baldwin and Mauro 2020;Vidya and Prabheesh 2020). Globally imposed restrictions continue to cause supply chain losses. We further estimated the role of the comparative advantage in driving value-added trade by considering institutional, time-invariant variables that can differ by country because of heterogeneity among them (Faheem Ur et al. 2024;Zergawu et al. 2020). Additionally, we use a new comparative advantage measure to overcome problems with traditional measures. The model was utilized to investigate the impact of the COVID-19 pandemic on TVA. We employed a dynamic GMM approach to address this issue and to overcome potential endogeneity problems. The dynamic GMM technique offers the advantage of evaluating dynamic adjustment observations, which are valuable for measuring the dynamics of adjustment between countries concerning TVA. To this end, this study used data from the current Asian Development Bank multi-regional input–output (ADB MRIO) to trace the interconnected value added among countries from 2010 to 2020, which was a challenge to obtain. This aspect enriches the growing body of research on the role of comparative advantage in international production fragmentation. The remainder of this article is structured as follows: a literature review is introduced in Section 2. Data and Methodology is explained in detail in Section 3, while the empirical results and analysis are presented in Section 4. The final section concludes with significant findings and policy recommendations. 2. Literature Review The analysis of TVA has been widely discussed since the development of the new international trade theory, which traces the distribution of value added in international trade (Athukorala and Yamashita 2006;Borin and Mancini 2019;Johnson and Noguera 2012;Koopman et al. 2014;Z. Wang et al. 2018). Currently, countries do not need to proceed with the accepted production stages. Instead, they can specialize in a specific production stage (Amendolagine et al. 2019;Inomata 2013;Prete et al. 2018). GVCs occur when the various stages of the production chain for goods and services, from the product design to the distribution of goods to final consumers, are produced and assembled through the networking of various countries, across international borders (Hummels et al. 2001; Inomata 2013;Prete et al. 2018). Economies 2024,12, 187 3 of 21 The participation of countries in value-added trade is measured through the decomposition of exports. This approach identifies domestic and foreign value-added shares embedded in intermediate exports (Asian Development Bank 2019;Ceglowski 2017;Johnson and Noguera 2012;Z. Wang et al. 2018;Wuri et al. 2022). However, few studies have simultaneously performed both analyses. Our study employs both approaches to provide a more comprehensive analysis. Furthermore, a country’s international trade performance depends on its dynamic comparative advantage (Burlina and Di Maria 2020). Therefore, nations worldwide can increase their participation in value-added trade by specializing in products with a comparative advantage (Ito et al. 2017). Traditional methods of measuring comparative advantage based on gross exports are outdated due to an inability to track value added and mitigate the overvaluation problem (Athukorala and Yamashita 2006;Koopman et al. 2014;Timmer et al. 2013). A more precise method for calculating the value-added contribution and distribution of intermediate export goods is critical, as goods may cross national borders several times, thus leading to several impositions of tariffs and transportation costs (Asian Development Bank 2019;Athukorala and Yamashita 2006;Koopman et al. 2014). The new revealed comparative advantage measure precisely reflects the comparative advantage of domestic resources by excluding foreign resources to address the overvaluation issue (Asian Development Bank 2019;Athukorala and Yamashita 2006;Koopman et al. 2014;Leromain and Orifice 2014;Marcato et al. 2019;Shuai et al. 2022;Song et al. 2021). Furthermore, given the ever-increasing trade in intermediates, this methodology supports the argument that comparative advantage measures should focus more on forward-linked value-added indicators to measure RCA. It is because the use of value-added RCA provides more information regarding the working of a real economy than the gross value of RCA (Brakman and Van Marrewijk 2017;Burlina and Di Maria 2020;Ceglowski 2017;Liu et al. 2020;Song et al. 2021;Z. Wang et al. 2018). We incorporated this measure of value-added RCA as a symmetrical measure, which will henceforth be referred to as the “new revealed symmetric comparative advantage” (NRSCA). It is essential to adjust the symmetrical measure to be compared on both sides of unity. COVID-19 has placed an enormous strain on the global public health system and economy and weakened the trade sectors of most countries (Hayakawa and Mukunoki 2021;Qin et al. 2020). ASEAN countries experienced a 0.83% decrease in forward GVC participation due to the social restriction policies implemented to mitigate the spread of the virus. China experienced a 13.54% decrease in forward participation. As China is a global manufacturing hub, this severely disrupted supply chains across the world (Baldwin and Mauro 2020;Chen and Chen 2022). Similarly, the average GVC participation in the European Union (EU) and North American countries also decreased (Wuri et al. 2022). Larger international trade volumes are associated with better institutions, commonly found in developed countries. According to Levchenko (2007), disparities in institutional quality could be a source of comparative advantage and a crucial determinant of trade patterns. A country with high institutional quality can promote environmental quality in trade while avoiding negative consequences such as pollution and carbon emissions (Pata et al. 2023;Q. Wang et al. 2024). Therefore, this study defines two control variables based on institutional quality government effectiveness and control of corruption. A better institution contributes to less corruption and an improved regulatory environment. 3. Methodology 3.1. Data Description This study estimated the importance of comparative advantage in driving TVA using ADB’s annual MRIO data, covering 35 industries in 62 countries and 132 countries classified as “rest of world” (ROW) for 2010–2020 (Asian Development Bank 2019). Furthermore, institutional variables were considered control variables (Kaufmann et al. 2010). This study focused on 41 countries that were members of several groups: ASEAN, East Asia, the EU, and North America (NA) (Appendix A, Table A1). This study will investigate Economies 2024,12, 187 4 of 21 ASEAN interand intraregional value-added trade and provide in-depth insights into the dynamics of comparative advantages and value-added trade on a global level. The data were obtained from the ADB MRIO and World Bank World Governance Indicators. Multiregional Input-Output (MRIO) data were collected by the Asian Development Bank (ADB) by integrating various national and international data sources. ADB collects data from the national input–output (I-O) tables provided by each member country. These data are usually collected by national statistical agencies or economic ministries in those countries. ADB involves international collaboration, the integration of various data sources, and technologies for managing and analyzing MRIO data. The Asian Development Bank Multi-Regional Input–Output Database (ADB MRIO) covers 2010–2020. The year 2010 was chosen as a starting point since worldwide trade circumstances had begun to recover following the financial crisis. The details are presented in Table 1. Table 1. Description of Variables and Sources of Data. Variables Description Measurement Expectation Source BPR Backward GVC participation ratio Share of foreign value added (FVA) to total world exports (ratio) - Multi-Regional Input–Output (MRIO), computed by authors, 2010–2020 FPR Forward GVC participation ratio Share of domestic value added (DVA) to total world exports (ratio) -MRIO, computed by authors, 2010–2020 TVA Trade in value added FPR + BPR - MRIO, computed by authors, 2010–2020 NRSCA New revealed symmetric comparative advantage Share of an economic sector’s forward-linked measure of DVA in exports Positive MRIO, computed by authors, 2010–2020 COVID Coronavirus disease 19 pandemic Dummy COVID-19 pandemic (1 = 2019–2020, 0 = otherwise) Negative - GOV Government effectiveness Index lies between −2.5 and 2.5 Positive World Governance Indicators (WGI), 2010–2020 CC Control of corruption Index lies between −2.5 and 2.5 Positive WGI, 2010–2020 We extended the basic input–output framework for a single economy by using the MRIO model to trace the relationships between countries and sectors (Asian Development Bank 2019;Leontief 1936). In addition, the input–output model is useful for understanding the impact of various government policies on specific industries and the economy as a whole (Kee and Tang 2016). 3.2. Measuring Trade in Value Added Each country’s participation in TVA was measured by decomposition gross exports (Johnson and Noguera 2012;Leontief 1936;Z. Wang et al. 2018). The relationship between production and final demand is as follows: X=(I−A)−1Y(1) where Xand Yare the vectors of gross output and final demand, respectively, provided by the economy sector and Iis an N × N identity matrix. Ais the N × N matrix of the input coefficient. We disaggregated each country’s gross output by rearranging the final demands of both countries into a matrix format based on sources and destinations. Economies 2024,12, 187 5 of 21 This decomposition is distinguished by the forward and backward linkages. The forward-linked perspective, or forward GVC participation ratio (FPR), measures the share of domestic value added (DVA) embedded in intermediate exports compared to total world exports. Conversely, the backward-linked perspective or backward GVC participation ratio (BPR) reveals the share of FVA used to produce a country’s export goods compared to total world exports (Asian Development Bank 2015;Koopman et al. 2014;Z. Wang et al. 2018; Wuri et al. 2022). Furthermore, TVA was the sum of the BPR and FPR (Amendolagine et al. 2019;Ayadi et al. 2021;Prete et al. 2018). 3.3. Measuring Comparative Advantage To enhance TVA, countries worldwide should increase their intermediate exports in sectors where they have a comparative advantage. Z. Wang et al. (2018) proposed a new approach for measuring comparative advantage (NRCA), which is based on forwardlinked DVA exports. DVA refers to the domestic value-added generated by the country’s sector and is ultimately embodied in exports, regardless of the place of consumption of these exports (Asian Development Bank 2019;Ceglowski 2017). This new measure was analogous to the Balassa measure, except that it was based on DVA. The NRCA index is the share of an economic sector’s forward-linked measure of DVA in its exports. The NRCAsk of country sin sector k(i, s = 1, 2, ... ,G;k= 1, 2, N) is as follows (Asian Development Bank 2019;Burlina and Di Maria 2020;Liu et al. 2020;Z. Wang et al. 2018): NRCAsk =VAX_Gsk ∑N k=1VAX_Gsn ,∑G i=1VAX_Gik ∑N k=1∑G iVAX_Gin (2) The subscript irefers to all countries except country s, and subscript nis all sectors except sector k.VAX_Gsk is the DVA exports of country sin sector k: VAX_Gsk =∑k∑rDVA_FINk sr +DVA_INTk sr +DVA_INTrexk sr (3) The VAX_G formula is presented in Table 2(Liu et al. 2020;Z. Wang et al. 2018). The first category was DVA in final exports (DVA_FIN). The second category was DVA in intermediate exports used by direct importer rto produce the final local products (DVA_INT). Summing up the third, fourth, and fifth categories yielded the DVA of economy sin its intermediate exports used by the direct importer rto produce exports and ultimately absorb other economies, except for the source economy s(DVA_INTrex). Table 2. The VAX_G Decomposition Equation. Category Term Description Formula DVA FIN 1 Domestic Value Added in final use commodity exports (VsBssT#Ysr DVA_INT 2 DVA in intermediate exports utilized by direct importers to manufacture final local products. (VsLssT#(AsrBrrYrr) DVA_INTrex 3 DVA in intermediate exports used by the direct importer to produce intermediate exports and consumed in other countries except for the source country s. (VsLssT# Asr G ∑ t=s,r BrtYtt! 4 DVA in intermediate exports utilized by the direct importer to produce final-use exports to other countries except for the source country s. (VsLssT# AsrBrr G ∑ t=s,r Yrt! 5 DVA in intermediate exports utilized by the direct importer to produce intermediate exports to other countries except for the source country s. (VsLssT# Asr G ∑ t=s,r G ∑ u=s,t BrtYtu! Source: (Asian Development Bank 2015;Z. Wang et al. 2018). Economies 2024,12, 187 6 of 21 In Table 2, Vs represents the DVA in country s. Bss shows the inverse Leontief N × N matrix as the total requirement matrix representing the number of gross outputs needed by country sto produce a unit of final demand increase in country s, Ysr is the N × 1 matrix of the final demand of country rfor the final products produced in country s, Lss illustrates the local Leontief inverse, and Asr is the N × N input–output matrix coefficient. Additionally, Xs is the N × 1 matrix of the gross output of country s. The symbol # means an element-wise matrix multiplication operation (Z. Wang et al. 2018). Following Laursen (2015), we modified the NRCA index into a symmetrical index as follows: NRSCAsk =(NRCA sk−1) ,(NRCA sk+1) (4) The NRSCAsk (hereafter, NRSCA) index ranges from − 1to1( − 1 ≤ NRSCA ≤ 1). An NRSCA greater than 0 indicates that country shas comparative advantages in sector k. Conversely, an NRSCA of less than 0 suggests that country shas comparative disadvantages in sector k. 3.4. Dynamic GMM Model Specification A dynamic panel technique was used to investigate the role of comparative advantage based on the DVA in driving the TVA. The application of the SYS-GMM model begins with identifying the study objectives. It then identifies the variables and panel data that will be utilized in the model spanning from 2000 to 2020. The next stage is to assess the stationary and analyze the SYS-GMM model to estimate the relationship between variables. The Hansen and Arellano–Bond tests assess instrument validity. Finally, these methods were employed as robustness validators to determine the consistency of the relationships between the variables of interest. Dynamic panel data estimation investigates dynamically adjusted observations, controls for unobserved individual heterogeneity, provides more information and data volatility, and reduces the possibility of multicollinearity (Baltagi 2005;Wuri et al. 2022). Arellano and Bond (1991) and Arellano and Bover (1995) developed a generalized method of moments (GMM) panel estimator for dynamic models. Dynamic panel characteristics are represented in the model by lag-dependent variables (Rahayu et al. 2024). If the lag of the dependent variable is correlated with the error term, the ordinary least square estimator is biased and inconsistent. Thus, the GMM approach was used to produce a consistent and unbiased estimator. Within the GMM framework, econometric analysis uses two estimation techniques: the system GMM (SYS-GMM) and first-difference GMM (FD-GMM). Because of the limitations of the FD-GMM estimator, specifically, its weak instrument, the SYS-GMM analysis was used in this study. Therefore, the SYS-GMM estimator was developed to reduce bias and overcome this limitation (Baltagi 2005;Blundell and Bond 1998;Rahayu et al. 2024). The empirical research indicates that dynamic panel data estimation based on the SYS-GMM can address unobserved individual heterogeneity, omitted variable bias, and potential endogeneity. Therefore, the GMM approach can produce consistent and unbiased estimators (Wuri et al. 2022;Xu 2016). The dynamic panel assumes that the disturbance is independent and identically distributed (IID). (2) The problem of unobserved individual heterogeneity is almost always time-invariant. (3) Comparative advantage is characterized as dynamic due to supply and demand fluctuations in domestic and international markets. (4) No perfect collinearity. Perfect multicollinearity is not allowed among independent variables in a model. (5) Assumption of endogeneity. Internal instruments are used to solve this problem. (6) Assumption of valid instruments. The instrument must be valid, meaning it must correlate with an endogenous independent variable but not with error terms—the assumption of no second-order serial correlation. (7) Error terms must not have second-order autocorrelation. It is tested using the Arellano-Bond test to ensure that the instrument used does not correlate with past error terms (Baltagi 2005). Economies 2024,12, 187 7 of 21 This estimator employed a lagged variable as an instrument, on the assumption that white noise errors would lose consistency if serially correlated. The following two specification tests determined the consistency of the SYS-GMM estimator: First, the Hansen test assessed the validity of an exogenous instrument by isolating over-identifying restrictions. In this test, the null hypothesis stated that the instrument was valid because there was no correlation with the error term. If the Hansen test rejected the null hypothesis, then the instrument and the error term were related, and the estimate was biased and inconsistent. Second, the AB test was a test for the presence of residual serial correlation. According to the null hypothesis, there is no second-order serial correlation or autocorrelation in idiosyncratic errors (Blundell and Bond 1998;Rahayu et al. 2024;Xu 2016). Therefore, based on the SYS-GMM model, the empirical model for analyzing the role of comparative advantage in TVA is as follows: Model 1 FPRit =α+∑p j=1βjFPRi,t−j+∑p j=1δjNRSCAi,t−j+ωCOVID19it +τ1Xit +µi+vit (5) Model 2 BPRit =σ+∑p j=1θjBPRi,t−j+∑p j=1ρjNRSCAi,t−j+ϵCOVID19it +τ2Xit +µi+vit (6) Model 3 TVAit =γ+∑p j=1φjTVAi,t−j+∑p j=1εjNRSCAi,t−j+ϑCOVID19it +τ3Xit +µi+vit (7) where subscripts iand tdenote the country and time index, respectively; µi is an unobserved time-invariant; and vit represents idiosyncratic error. Variables µi and vit are assumed~ IID0, σ2 v . Moreover, FPRit is the forward GVC participation ratio of country iduring period t, BPRit is the backward GVC participation ratio, TVAit is the TVA, and NRSCAit is the new revealed symmetric comparative advantage. In the model, we considered the economic impact of the COVID-19 pandemic by including a dummy variable. Xit is a set of control variables and τ denotes a column vector comprising the corresponding coefficients of these control variables. Our control variables were government effectiveness (GOV it) and control of corruption ( CCit) . The variables α , σ , and γ are constant, while β , δ , ω , τ , θ , ρ , ϵ , φ , ε , and ϑ are the estimated coefficients. The study period spanned from 2010 to 2020. In addition, we examined the influence of the COVID-19 pandemic shock on the TVA model. Finally, these approaches were used as robustness validators to assess the consistency of the relationships between the variables of interest. Before starting the empirical estimation, it was crucial to determine whether the variables of interest were stationary or not. We performed stationary analysis using the Augmented Dickey–Fuller (ADF) and Levin–Lin–Cu (LLC) tests proposed by Hao et al. (2015). This approach has been widely applied to avoid biased results for panel data with structural breaks and has attracted attention in international trade network analyses. 4. Empirical Results and Discussion 4.1. Summary Statistics Table 3presents the descriptive statistical results for all variables. It reports the mean, standard deviation (SD), variance, maximum, and minimum of the variables of interest. Table 3reveals the full sample and group countries. The full sample average for FPR was 0.420, with Malta (EU) having the lowest at 0.164 in 2013, and Brunei (ASEAN) having the highest in 2015 at 0.846. A standard deviation of 0.111 indicates minimal dispersion from the sample means. Similarly, the average sample value for BPR was 0.345 with a standard deviation of 0.128, thus indicating dispersion from the sample mean. The country with the lowest BPR of 0.077 was Brunei (ASEAN) in 2010, whereas Luxembourg (EU) showed the highest value of 0.726 in 2011. In addition, the country with the highest TVA was Bulgaria (EU) at 1.000 in 2011, whereas the lowest value of 0.431 was recorded for the Economies 2024,12, 187 8 of 21 Philippines (ASEAN) in 2011. A standard deviation of 0.093 revealed that the countries were significantly dispersed from a sample average of 0.765. Table 3. Descriptive statistics. Group Variable Obs Mean SD Variance Maximum Minimum Full Sample FPR 451 0.420 0.111 0.012 0.846 0.164 BPR 451 0.345 0.128 0.016 0.726 0.077 TVA 451 0.765 0.093 0.009 1.000 0.431 NRSCA 451 0.114 0.206 0.042 0.654 −0.824 GOV 451 0.952 0.724 0.524 2.335 −0.943 CC 451 0.738 0.949 0.900 2.405 −1.326 ASEAN FPR 99 0.464 0.162 0.026 0.846 0.210 BPR 99 0.286 0.128 0.016 0.563 0.077 TVA 99 0.750 0.111 0.012 0.960 0.431 NRSCA 99 0.132 0.155 0.024 0.654 −0.095 GOV 99 0.352 0.894 0.780 2.335 −0.943 CC 99 −0.117 0.977 0.955 2.180 −1.326 East Asia FPR 33 0.432 0.047 0.002 0.522 0.347 BPR 33 0.236 0.088 0.008 0.396 0.133 TVA 33 0.669 0.077 0.006 0.818 0.532 NRSCA 33 −0.095 0.073 0.005 0.022 −0.170 GOV 33 1.041 0.582 0.339 1.822 0.004 CC 33 0.592 0.791 0.626 1.695 −0.562 EU FPR 297 0.393 0.083 0.007 0.721 0.164 BPR 297 0.388 0.110 0.012 0.726 0.162 TVA 297 0.782 0.083 0.007 1.000 0.586 NRSCA 297 0.135 0.223 0.050 0.608 −0.824 GOV 297 1.092 0.564 0.318 2.241 −0.329 CC 297 0.975 0.787 0.620 2.405 −0.272 NA FPR 22 0.562 0.039 0.002 0.702 0.508 BPR 22 0.184 0.064 0.004 0.258 0.107 TVA 22 0.746 0.055 0.003 0.861 0.678 NRSCA 22 0.067 0.056 0.003 0.151 −0.012 GOV 22 1.629 0.147 0.022 1.854 1.319 CC 22 1.600 0.313 0.098 2.070 1.069 Note: FPR, forward GVC participation ratio; BPR, backward GVC participation ratio; TVA, trade in valueadded; NRSCA, new revealed symmetric comparative advantage; GOV, government effectiveness; CC, control of corruption; SD, standard deviation. The average sample value for comparative advantage measured using NRSCA was 0.114, with the lowest value of − 0.824 from Italy (EU) in 2018, whereas the highest value of 0.654 was recorded for Lao PDR (ASEAN) in 2019. Furthermore, the average NRSCA variance was 0.042. This value increased during the observation period, showing that countries competed worldwide to produce intermediate good exports by specializing in certain stages that provided high value added. The average values of government effectiveness and control of corruption are generally the highest in NA, while they are the lowest in ASEAN. Control of corruption has the lowest institutional score in ASEAN (−0.117), while NA has the highest (1.600). 4.2. Unit-Root-Test Result Table 4presents the stationary test results obtained using ADF and LLC tests. We conducted the tests using Stata 17. The results indicate that the p-value for the unit root test was less than 5%, thus rejecting the null hypothesis (Ho) that all panels contained a unit root. Furthermore, the test results showed that each variable was a stationary sequence and allowed us to proceed with further analyses. Economies 2024,12, 187 15 of 21 NRSCA value. The occurrence of the COVID-19 pandemic increased the primary sector’s comparative advantage by 38.55%. As a result, the NRSCA Canada primary sector value increased from 0.332 in 2019 to 0.460 in 2020. Meanwhile, other industries appeared to be contracting. The policy of the downstreamness of a country’s sectors utilizes FVA in conjunction with increasing the country’s involvement in GVCs. 4.5. The System GMM Dynamic Panel Estimation A dynamic GMM approach was employed to examine the influence of the comparative advantage in encouraging TVA and avoid any potential endogeneity issues. SYS-GMM-type instruments were employed in this model with the first and higher lags of the predetermined variable and the second and higher lags of the endogenous variable (Arellano and Bond 1991;Arellano and Bover 1995;Blundell and Bond 1998). The first lagged dependent variable, FPR, BPR, or TVA, was chosen as the predetermined variable. As TVA is a process, the models are the lagged form of the variable to allow for the partial adjustment of the TVA for its long-term equilibrium value. Thus, previous TVA levels influenced the current levels. We used Stata 17 for the analysis (STATA; StataCorp, College Station, TX, USA). Arellano and Bond’s (1991) residual serial correlation tests were AR (1) and AR (2). As previously stated, Hansen’s disease is a test for over-identification restrictions. Table 5summarizes the SYS-GMM estimation results for all models. The study estimated comparative advantage to drive TVA by considering institutional variables as a control variable for a sample of 41 countries using the two-step SYS-GMM Arellano–Bond estimator. Furthermore, we investigated the impact of the COVID-19 pandemic on the TVA. Table 5. The SYS-GMM results for trade in value-added (TVA), 2010–2020. Variables FPR BPR TVA (1) (2) (3) Lag of Dep Var 0.115 *** 0.831 *** 0.034 *** (0.009) (0.053) (0.004) NRSCA 0.265 *** 0.010 *** 0.171 *** (0.022) (0.014) (0.019) GOV 0.012 *** 0.029 *** 0.045 *** (0.004) (0.011) (0.004) CC 0.032 *** 0.021 ** 0.021 *** (0.008) (0.009) (0.004) COVID-19 −0.010 *** −0.010 *** −0.009 *** (0.001) (0.001) (0.0008) Constant 0.424 ** 0.002 0.726 *** (0.007) (0.010) (0.006) No. of observations 369 328 369 No. of countries 41 41 41 Hansen test, p-value 35.13; 0.972 34.21; 0.194 37.60; 0.488 AB–AR (1); p-value −1.76; 0.079 −3.55; 0.000 −1.70; 0.090 AB–AR (2); p-value −0.73; 0.464 −0.58; 0.560 0.44; 0.657 Note: *** and ** represent statistical significance at the 1% and 5% levels, respectively. SYS-GMM = system GMM estimator; in the forward linkage, the dependent variable is FPR, and in the backward linkage, the dependent variable is BPR; in trade in value-added, the dependent variable is TVA. Standard errors are shown in parentheses. Source: Calculated by the authors using Stata 17. The dependent lagged period includes the expected positive coefficient and is statistically significant in all models, thus indicating that the dependent variable of the previous period affects the current period. The positive sign of the coefficient suggests that the countries’ TVA in previous periods contributed positively to that of the current period because of raw material imports in the previous period. Approximately 11.5% of DVA exports reflect the last period’s exports (Model 1). The positive sign of the BPR coefficient indicates that approximately 83.1% of foreign value-added exports reflected the previous Economies 2024,12, 187 16 of 21 period’s exports (Model 2), and approximately 3.4% of the total value added reflected the previous period’s exports (Model 3). All findings support NRSCA’s goal of enhancing TVA by improving the institutional environment according to government effectiveness and the control of corruption. For all models, the estimation results revealed that comparative advantage (NRSCA) positively and significantly affected TVA. For the FPR, BPR, and TVA models, the NRSCA coefficients are 0.265, 0.010, and 0.171, respectively. The implementation of a new comparative advantage measure, NRSCA, provided accurate estimation results to overcome the overvaluation problem that arises when using TRCA. Given the increased intermediary trade, RCA should be more oriented toward forward-linked value-added exports (see also Z. Wang et al. 2018). In addition, with the emergence of dynamic comparative advantage, ASEAN’s pattern of comparative advantage may become similar to that of developed countries, following the flying geese (FG) paradigm (Widodo et al. 2018). In this framework, industry is transmitted from leader to follower countries. Thus, the comparative advantages of countries continue to evolve (Asian Development Bank 2019;Brakman and Van Marrewijk 2017;Ito et al. 2017). ASEAN is a key participant in GVC activities, playing a very important role in global trade and global policy. This is because ASEAN is the main production base and final assembler in production for the global economy and the dominance of ASEAN countries’ export share in the global market reaches 79.2% (Zhong and Su 2021). Furthermore, the COVID-19 pandemic reduced the average of value-added trade (Espitia et al. 2022;Kazunobu and Hiroshi 2020;Wuri et al. 2023). The coefficient of COVID-19 was negative and statistically significant for the FPR model ( − 0.010), BPR model ( − 0.010), and TVA model ( − 0.009). The pandemic has forced many countries to implement restrictive policies to contain the spread of this virus (Vidya and Prabheesh 2020). They restrict cross-border trade, which leads countries to use domestic resources, thereby increasing their comparative advantage based on DVA. The supply of intermediate inputs from other countries was limited, thus resulting in a reduced current aggregate demand and supply (Ayadi et al. 2021;Baldwin and Mauro 2020). Therefore, the output gap and final demand for countries’ products are low (Ghuzini et al. 2020;Wuri et al. 2024). This implies that the COVID-19 pandemic has adversely affected global trade (Espitia et al. 2022;Qin et al. 2020;Vidya and Prabheesh 2020;Zapata et al. 2023). However, the positive and significant coefficients on government effectiveness and corruption control suggested that high-quality institutions would promote TVA in international production sharing (Amendolagine et al. 2019;Faheem Ur et al. 2024;Gereffi et al. 2005;Mouanda and Gong 2019;Zergawu et al. 2020). The estimation results were consistent throughout the institutional coefficient analysis using SYS-GMM, with a positive sign and statistical significance for FPR, BPR, and TVA. The positive institutional coefficient suggests that a high-quality institution increases forward and backward linkage GVC participation (Mouanda and Gong 2019). Institutions, among others, play a role in contract enforcement, property rights, and shareholder protection. Countries could participate in TVA more when they had higher government effectiveness and better corruption control. In addition, a more effective government can provide a more conducive regulatory environment by eliminating quotas to increase the Eastern European clothing sector (Smith et al. 2014). 4.6. Robustness Tests We examined the robustness of our baseline results on the role of comparative advantage in driving countries’ participation in TVA using different indicators for the COVID-19 variable (Zergawu et al. 2020). We substituted the COVID-19 dummy measure with COVID-19 shocks to represent economic fluctuations (Wuri et al. 2023). The COVID-19 shocks were calculated based on the gross export gap, which is the difference between real and potential exports (Ghuzini et al. 2020;Hubbard et al. 2014;Wuri et al. 2024). The export variable was chosen because it was directly affected by trade fluctuations. To assess the impact of the COVID-19 pandemic on global country participation in TVA, it is critical to Economies 2024,12, 187 17 of 21 determine whether the performance of countries is above or below their potential (González and Kowalski 2017). When the potential value is greater than the real value, a recession would occur, which would then cause an economic recession, and vice versa. In reality, potential exports were not observed; therefore, they were often proxied by the expected value. We calculated the expected export value based on the Hodrick–Prescott filter (Hubbard et al. 2014). Therefore, the COVID-19 shocks indicate fluctuations in exports during the estimation period. This approach was used to examine the influence of other aspects of COVID-19 on TVA activity. Table 6displays the corresponding results. The results from the table illustrated the same conclusion in all the models. The dependent lagged period includes the expected positive coefficient and is statistically significant in all models. The positive sign of the coefficient suggests that the countries’ TVA in previous periods contributed positively to that of the current period. Approximately 11.6% of DVA exports reflect the last period’s exports. The positive sign of the BPR coefficient indicates that approximately 7.7% of foreign value-added exports reflected the previous period’s exports, and approximately 1.2% of the total value added reflected the previous period’s exports. For all models, the estimation results revealed that comparative advantage (NRSCA) positively and significantly affected TVA. For the FPR, BPR, and TVA models, the NRSCA coefficients are 0.248, 0.024, and 0.032, respectively. Thus, comparative advantage plays a crucial role in the promotion of TVA (Ito et al. 2017). Further, the impact of the COVID-19 shock was negative and significant with the institutional variable as a control variable, thus indicating that the pandemic reduced value-added trading activity (Qin et al. 2020;Vidya and Prabheesh 2020;Wuri et al. 2022). Table 6. The SYS-GMM results for robustness tests, trade in value-added (TVA) with COVID-19 shocks, 2010–2020. Variables FPR BPR TVA (1) (2) (3) Lag of Dep Var 0.116 *** 0.077 *** 0.012 ** (0.008) (0.011) (0.005) NRSCA 0.248 *** 0.024 * 0.032 *** (0.015) (0.013) (0.012) GOV 0.009 ** 0.069 *** 0.069 *** (0.004) (0.007) (0.009) CC 0.029 *** 0.036 *** 0.033 *** (0.009) (0.008) (0.008) COVID shocks −0.031 *** −0.063 *** −0.083 *** (0.005) (0.007) (0.003) Constant 0.406 *** 0.207 *** 0.733 *** (0.007) (0.019) (0.009) No. of observations 369 410 410 No. of countries 41 41 41 Hansen test, p-value 34.99; 0.973 35.51; 0.969 38.86; 0.652 AB–AR (1); p-value −1.69; 0.092 −2.50; 0.012 −1.33; 0.185 AB–AR (2); p-value −0.83; 0.408 −1.37; 0.172 −1.02; 0.307 Note: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. SYS-GMM = system GMM estimator; in the forward linkage, the dependent variable is FPR, and in the backward linkage, the dependent variable is BPR; in trade in value-added, the dependent variable is TVA. Standard errors are shown in parentheses. Source: Calculated by the authors using Stata 17. 5. Conclusions In this article, we employed the system GMM estimators for a dynamic panel model to investigate the role of the comparative advantage in driving TVA by considering institutional quality as a control variable, using the ADB MRIO for the period 2010–2020. Moreover, the COVID-19 pandemic variable was included in this study’s model to examine the pandemic’s impact on value-added trade. In particular, we focused on a new revealed symmetric comparative advantage measure, NRSCA. This symmetric measure was used Economies 2024,12, 187 18 of 21 for accurate calculations, using DVA through forward linkage, instead of gross exports. NRSCA addressed the issues of overvaluation, double counting, and implicit distortions in international trade across borders. The results suggested an increase in value-added trade with the significant role of the comparative advantage, along with the support of quality institutional services in each country. Worldwide countries’ comparative advantages changed dynamically. Indonesia had a comparative advantage in the primary and low technology manufacturing sectors. Malaysia remained very competitive in the primary sectors. In addition, Malaysia had a comparative advantage based on DVA in several sectors in low, medium, and high technology manufacturing. The Philippines remained highly competitive in low technology manufacturing. In addition, the Philippines had a comparative advantage based on DVA in several of the medium and high technology manufacturing and public services sectors. Singapore remained highly competitive in several of the medium technology manufacturing and business services sectors. Thailand remained highly competitive in primary industries such as agriculture, hunting, forestry, and fishing. Thailand also had a comparative advantage based on DVA in several low, medium, and high technology manufacturing and business services sectors. China had a comparative advantage in the East Asia Region in low, medium, and high technology manufacturing sectors. However, Japan and Korea had a comparative advantage in medium and high technology manufacturing. The positive NRSCA average value indicated that Germany had a comparative advantage in the medium and high technology manufacturing sectors. Furthermore, Poland and the Slovak Republic had a comparative advantage from the perspective of forward linkage in the low technology manufacturing sector. In the meantime, Greece had a comparative advantage in several primary industries and low technology manufacturing. In the US Area, the US had a comparative advantage from the perspective of forward linkage in the primary sector, business services, and personal and public services. Meanwhile, Canada had a comparative advantage in the primary sector. The COVID-19 pandemic slowed down TVA and potentially disrupted many sectors. Furthermore, governments should develop value-added exports based on NRSCA to establish a competitive advantage and enhance investment in research and development. Moreover, the government is expected to build transportation and logistics infrastructure to improve supply chain efficiency and facilitate interand intra-regional trade. Further research is required to incorporate control variables, such as gravity control, infrastructure, GDP per capita, and trade regulation, and to calculate the position of the leading sector from final use. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: The author is thankful to the ADB team for providing the required data. I would also like to thank anonymous reviewers for their valuable comments and suggestions. Conflicts of Interest: The author declares no conflict of interest. Economies 2024,12, 187 19 of 21 Appendix A Table A1. List of ADB multi-regional input–output countries. No. Group Countries 1 ASEAN Malaysia, Indonesia, Thailand, Philippines, Singapore, Viet Nam, Brunei Darussalam, Lao PDR, Cambodia 2 East Asia Japan, People’s Republic of China, Republic of Korea 3 EU Austria, Bulgaria, Belgium, Czech Republic, Cyprus, Germany, Denmark, Spain, Estonia, France, Finland, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Luxembourg, Malta, Latvia, Netherlands, Poland, Portugal, Romania, Slovenia, Slovak Republic, Sweden 4 NA Canada, United States Source: Authors’ compilations. References Amendolagine, Vito, Andrea F. Presbitero, Roberta Rabellotti, and Marco Sanfilippo. 2019. Local Sourcing in Developing Countries: The Role of Foreign Direct Investments and Global Value Chains. World Development 113: 73–88. [CrossRef] Arellano, Manuel, and Olympia Bover. 1995. Another Look at the Instrumental Variables Estimation of Error Component Models. Journal of Econometrics 68: 29–51. [CrossRef] Arellano, Manuel, and Stephen Bond. 1991. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies 58: 277–97. [CrossRef] Asian Development Bank. 2015. Global Value Chains Indicators for International Production Sharing. Key Indicators for Asia and the Pacific 2015. Manila: Asian Development Bank. Available online: https://www.adb.org/sites/default/files/publication/175162/gvc. pdf (accessed on 9 March 2022). Asian Development Bank. 2019. The Evolution of Indonesia’s Participation in Global Value Chains. Manila: Asian Development Bank. Manila: Asian Development Bank. [CrossRef] Athukorala, Prema-Chandra, and Nobuaki Yamashita. 2006. Production Fragmentation and Trade Integration: East Asia in Global Context. North American Journal of Economics and Finance 17: 233–56. [CrossRef] Ayadi, Rym, Giorgia Giovannetti, Enrico Marvasi, Giulio Vannelli, and Chahir Zaki. 2021. Demand and Supply Exposure through Global Value Chains: Euro-Mediterranean Countries during COVID. The World Economy 45: 637–56. Available online: https: //onlinelibrary.wiley.com/doi/epdf/10.1111/twec.13156 (accessed on 1 July 2023). [CrossRef] [PubMed] Balassa, Bela. 1965. Trade Liberalisation and ‘Revealed’ Comparative Advantage. The Manchester School 33: 99–123. [CrossRef] Baldwin, Richard E., and Beatrice W. di Mauro, eds. 2020. Economics in the Time of COVID-19. A VoxEU.org eBook. London: CEPR Press. Available online: https://voxeu.org/article/economics-time-covid-19-new-ebook (accessed on 2 April 2023). Baltagi, Badi H. 2005. Econometric Analysis of Panel Data. Hoboken: John Wiley & Sons Ltd. Blundell, Richard, and Stephen Bond. 1998. Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics 87: 115–43. Available online: https://www.ucl.ac.uk/~uctp39a/Blundell-Bond-1998.pdf (accessed on 15 June 2022). Borin, Alessandro, and Michele Mancini. 2019. Measuring What Matters in Global Value Chains and Value-Added Trade. WPS 8804. Available online: http://hdl.handle.net/10986/31533 (accessed on 30 November 2022). Brakman, Steven, and Charles Van Marrewijk. 2017. A Closer Look at Revealed Comparative Advantage: Gross-versus Value-Added Trade Flows. Papers in Regional Science 96: 61–92. [CrossRef] Burlina, Chiara, and Eleonora Di Maria. 2020. Manufacturing and Value-Added Dynamics in Global Value Chains: The Case of Italy. Competitiveness Review 30: 457–70. [CrossRef] Ceglowski, Janet. 2017. Assessing Export Competitiveness through the Lens of Value Added. World Economy 40: 275–96. [CrossRef] Chen, Ji-Ming, and Yi-Qing Chen. 2022. China Can Prepare to End Its Zero-COVID Policy. Nature Medicine 28: 1104–1105. [CrossRef] Elsalih, Osama, Kamil Sertoglu, and Mustafa Besim. 2021. Determinants of Comparative Advantage of Crude Oil Production: Evidence from OPEC and Non-OPEC Countries. International Journal of Finance and Economics 26: 3972–83. [CrossRef] Espitia, Alvaro, Aaditya Mattoo, Nadia Rocha, Michele Ruta, and Deborah Winkler. 2022. Pandemic Trade: COVID-19, Remote Work and Global Value Chains. World Economy 45: 561–89. [CrossRef] Faheem Ur, Rehman, Islam Md. Monirul, and Kazi Sohag. 2024. Does Infrastructural Development Allure Foreign Direct Investment? The Role of Belt and Road Initiatives. International Journal of Emerging Markets 19: 1026–50. [CrossRef] Fan, Zhaobin, Sajid Anwar, and Ying Zhou. 2023. Centralization of Trade Agreements Network and Global Value Chain Participation. Quarterly Review of Economics and Finance 94: 11–24. [CrossRef] Gereffi, Gary, John Humphrey, and Timothy Sturgeon. 2005. The Governance of Global Value Chains. Review of International Political Economy 12: 78–104. [CrossRef] Ghuzini, Diny, Josephine Wuri, and Kuntari Dasih. 2020. Structural Shocks and Macroeconomic Conditions in Indonesia. Journal of Applied Economic Sciences XV: 488–506. Available online: https://www.ceeol.com/search/article-detail?id=894712 (accessed on 25 June 2024). Economies 2024,12, 187 20 of 21 González, Javier Lopez, and Przemyslaw Kowalski. 2017. Global Value Chain Participation in Southeast Asia: Trade and Related Policy Implications. In Production Networks in Southeast Asia. Edited by Lili Yan Ing and Fukunari Kimura. New York: Routledge. Hao, Yu, Hua Liao, and Yi-Ming Wei. 2015. Is China’s Carbon Reduction Target Allocation Reasonable? An Analysis Based on Carbon Intensity Convergence. Applied Energy 142: 229–39. [CrossRef] Hayakawa, Kazunobu, and Hiroshi Mukunoki. 2021. Impacts of COVID-19 on Global Value Chains. Developing Economies 59: 154–77. [CrossRef] Hubbard, R. Glenn, Anthony P. O’Brien, Matthew Rafferty, and Robert J. Gordon. 2014. Macroeconomics. London: Pearson Education Limited. Hummels, David, Jun Ishii, and Kei-Mu Yi. 2001. The Nature and Growth of Vertical Specialization in World Trade. Journal of International Economics 54: 75–96. [CrossRef] Ing, Lili Yan, and Fukunari Kimura, eds. 2017a. Production Networks in Southeast Asia. Economic Research Institute for ASEAN and East Asia. New York: Routledge, Taylor and Francis Group. Available online: https://www.eria.org/publications/productionnetworks-in-southeast-asia/ (accessed on 24 September 2022). Ing, Lili Yan, and Fukunari Kimura, eds. 2017b. Global Value Chain Participation in Southeast Asia: Trade and Related Policy Implications. In Production Networks in Southeast Asia. New York: Routledge. Inomata, Sitoshi. 2013. Trade in Value Added: An East Asian Perspective. ADBI Working Paper Series; Tokyo: Asian Development Bank Institute. [CrossRef] Ito, Tadashi, Lorenzo Rotunno, and Pierre Louis Vézina. 2017. Heckscher–Ohlin: Evidence from Virtual Trade in Value Added. Review of International Economics 25: 427–46. [CrossRef] Johnson, Robert C., and Guillermo Noguera. 2012. Accounting for Intermediates: Production Sharing and Trade in Value Added. Journal of International Economics 86: 224–36. [CrossRef] Johnson, Robert C., and Guillermo Noguera. 2017. A Portrait of Trade in Value-Added over Four Decades. Review of Economics and Statistics 99: 896–911. [CrossRef] Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. The Worldwide Governance Indicators: Methodology and Analytical Issues. Hague Journal on the Rule of Law 3: 220–46. [CrossRef] Kazunobu, Hayakawa, and Mukunoki Hiroshi. 2020. Impacts of COVID-19 on Global Value Chains. Institute of Developing Economies (IDE) Discussion Papers 791. Available online: https://ir.ide.go.jp/?action=pages_view_main&active_action=repository_view_ main_item_detail&item_id=51871&item_no=1&page_id=39&block_id=158 (accessed on 15 January 2023). Kee, Hiau Looi, and Heiwai Tang. 2016. Domestic Value Added in Exports: Theory and Firm Evidence from China. American Economic Review 106: 1402–36. [CrossRef] Koopman, Robert, Zhi Wang, and Shang-Jin Wei. 2014. Tracing Value-Added and Double Counting in Gross Exports. Amrican Economic Review 104: 459–94. [CrossRef] Laursen, Keld. 2015. Revealed Comparative Advantage and the Alternatives as Measures of International Specialization. Eurasian Business Review 5: 99–115. [CrossRef] Leontief, Wassily W. 1936. Quantitative Input and Output Relations in the Economic System of the United States. Review of Economics and Statistics 18: 105–25. Available online: https://orion.math.iastate.edu/driessel/15Models/1936_Input_Output.pdf (accessed on 15 August 2022). [CrossRef] Leromain, Elsa, and Gianluca Orifice. 2014. New Revealed Comparative Advantage Index: Dataset and Empirical Distribution. International Economics 139: 48–70. [CrossRef] Levchenko, Andrei A. 2007. Institutional Quality and International Trade. Review of Economic Studies 74: 791–819. [CrossRef] Liu, Xuepeng, Aaditya Mattoo, Zhi Wang, and Shang-Jin Wei. 2020. Services Development and Comparative Advantage in Manufacturing. Journal of Development Economics 144: 102438. [CrossRef] Lofrano, Giusy, Sureyya Meriç, Gülsüm Emel Zengin, and Derin Orhon. 2013. Chemical and Biological Treatment Technologies for Leather Tannery Chemicals and Wastewaters: A Review. Science of the Total Environment 461–62: 265–81. [CrossRef] Marcato, Marilia, Carolina Baltar, and Fernando Sarti. 2019. International Competitiveness in a Vertically Fragmented Production Structure: Empirical Challenges and Evidence. Economics Bulletin 39: 876–93. Available online: https://ideas.repec.org/a/ebl/ ecbull/eb-18-00788.html (accessed on 5 September 2023). Mouanda, GilhaiméM., and Jiong Gong. 2019. Determinants of Global Value Chains Participation for Landlocked Countries. International Journal of Social Science and Economic Research 4: 3265–94. Available online: https://ijsser.org/files_2019/ijsser_04__24 6.pdf (accessed on 25 June 2023). Natsuda, Kaoru, Noriyuki Segawa, and John Thoburn. 2013. Liberalization, Industrial Nationalism, and the Malaysian Automotive Industry. Global Economic Review 42: 113–34. Available online: https://www.tandfonline.com/doi/abs/10.1080/1226508X.2013.7 91475 (accessed on 8 September 2023). [CrossRef] Parobek, Jan, Hubert Paluš, Martina Kalamarova, Erika Louˇcanova, Anna Križanova, and Katarina Repkova Štofkova. 2016. Comparative Analysis of Wood and Semi-Finished Wood Product Trade of Slovakia and Its Central European Trading Partners. Drewno 59: 183–94. [CrossRef] Pata, Ugur Korkut, Mehmet Metin Dam, and Funda Kaya. 2023. How Effective Are Renewable Energy, Tourism, Trade Openness, and Foreign Direct Investment on CO 2 Emissions? An EKC Analysis for ASEAN Countries. Environmental Science and Pollution Research 30: 14821–37. [CrossRef] Economies 2024,12, 187 21 of 21 Prete, Davide Del, Giorgia Giovannetti, and Enrico Marvasi. 2018. Global Value Chains: New Evidence for North Africa. International Economics 153: 42–54. [CrossRef] Qin, Meng, Xiuyan Liu, and Xiaoxue Zhou. 2020. COVID-19 Shock and Global Value Chains: Is There a Substitute for China? Emerging Markets Finance and Trade 56: 3588–98. [CrossRef] Rahayu, Caecilia Wahyu Estining, Christina Heti Tri Rahmawati, Josephine Wuri, Yuliana Rini Hardanti, and Laurentius Bambang Harnoto. 2024. Role of Indonesia’s G20 Presidency and Exchange Rate on LQ45 Stock Price: Dynamic Panel Model Approach. Migration Letters 21: 926–39. [CrossRef] Shuai, Jing, Xinjie Peng, Yujia Zhao, Yilan Wang, Wei Zu, Jinhua Cheng, Yang Lu, and Jingjin Wang. 2022. A Dynamic Evaluation on the International Competitiveness of China’s Rare Earth Products: An Industrial Chain and Tech-Innovation Perspective. Resources Policy 75: 102444. [CrossRef] Smith, Adrian, John Pickles, Milan Buˇcek, Rudolf Pástor, and Bob Begg. 2014. The Political Economy of Global Production Networks: Regional Industrial Change and Differential Upgrading in the East European Clothing Industry. Journal of Economic Geography 14: 1023–51. [CrossRef] Song, Yiru, Chunjiao Yu, Lulu Hao, and Xi Chen. 2021. Path for China’s High-Tech Industry to Participate in the Reconstruction of Global Value Chains. Technology in Society 65: 101486. [CrossRef] Timmer, Marcel P., Bart Los, Robert Stehrer, and Gaaitzen J. de Vries. 2013. Fragmentation, Incomes, and Jobs: An Analysis of European Competitiveness. Economic Policy 28: 613–61. [CrossRef] Vidya, C. T., and K.P. Prabheesh. 2020. Implications of COVID-19 Pandemic on the Global Trade Networks. Emerging Markets Finance and Trade 56: 2408–21. [CrossRef] Wang, Qiang, Jiayi Sun, Rongrong Li, and Ugur Korkut Pata. 2024. Linking Trade Openness to Load Capacity Factor: The Threshold Effects of Natural Resource Rent and Corruption Control. Gondwana Research 129: 371–80. [CrossRef] Wang, Zhi, Shang-jin Wei, and Kunfu Zhu. 2018. Quantifying International Production Sharing at the Bilateral and Sector Levels. NBER Working Paper 19677. Cambridge: National Bureau of Economic Research, Revised February 2018. Available online: https: //www.nber.org/papers/w19677 (accessed on 5 March 2021). Widodo, Tri, Rini Setyastuti, and Sri Adiningsih. 2018. ‘Flying Geese’ Paradigm: Review, Analytical Tool, and Application. Munich Personal RePEc Archive. Available online: https://mpra.ub.uni-muenchen.de/87171/ (accessed on 10 February 2024). Wuri, Josephine, Tri Widodo, and Amirullah Setya Hardi. 2022. Global Value Chains Participation during the COVID-19 Pandemic: A Dynamic Panel Approach. Economies 10: 121. [CrossRef] Wuri, Josephine, Tri Widodo, and Amirullah Setya Hardi. 2023. Speed of Convergence in Global Value Chains: Forward or Backward Linkage. Heliyon 9: e18070. [CrossRef] [PubMed] Wuri, Josephine, Yuliana Rini Hardanti, Laurentius Bambang Harnoto, Caecilia Wahyu Estining Rahayu, and Christina Heti Tri Rahmawati. 2024. The Impact of Interest Rate Spillover on Output Gap: A Dynamic Spatial Durbin Model. Economies 12: 22. [CrossRef] Xu, Haifeng. 2016. Financial Intermediation and Economic Growth in China: New Evidence from Panel Data. Emerging Markets Finance and Trade 52: 724–32. [CrossRef] Zapata, Amadeo Navarro, María Arrazola, and Joséde Hevia. 2023. Determinants of High-Tech Exports: New Evidence from OECD Countries. Journal of the Knowledge Economy 15: 1103–17. [CrossRef] Zergawu, Y. Zewdu, Yabibal M. Walle, and JoséM. G. Gómez. 2020. The Joint Impact of Infrastructure and Institutions on Economic Growth. Journal of Institutional Economics 16: 481–502. [CrossRef] Zhang, Qian. 2024. The Impact of Digitalization on the Upgrading of China’s Manufacturing Sector’s Global Value Chains. Journal of the Knowledge Economy, 1–24. [CrossRef] Zhong, Sheng, and Bin Su. 2021. Investigating Asean’s Participation in Global Value Chains: Production Fragmentation and Regional Integration. Asian Development Review 38: 159–88. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.