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The purpose of this study is to explore the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports.

Based on the theoretical analysis of the mechanism of the digital economy’s influence on the binary margin of agricultural exports, this study empirically examines the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports based on China’s customs export data from 2011 to 2016.

The relevant findings are threefold. (1) The digital economy significantly improves the binary margin of agricultural exports, and its effect on the intensive margin is stronger than that on the expansive margin. After the expansive margin is subdivided, the effects on the three sub-variables of the expansive margin are in the following order: old products exported to new markets > new products exported to old markets > new products exported to new markets. (2) The heterogeneity analysis reveals that the digital economy has a stronger role in promoting the binary margin of exports for enterprises in the eastern region, high-income countries as the destination of exports and state-owned enterprises. (3) Mechanism analysis shows that the digital economy promotes the binary margin of agricultural exports by reducing trade costs and intensifying market competition.

First, in terms of research perspective, although there are some studies on the impact of the digital economy on export trade in existing literature, the research objects mainly focus on manufacturing enterprises. In fact, agricultural trade is susceptible to natural conditions and seasonal factors, and countries may impose more SPS measures and TBT measures on agricultural trade due to risk considerations. The relationship between the digital economy and agricultural trade also has its own characteristics, but there are few research studies in this area. At present, only Liu and Gao (2022), based on the data of total imports and exports of different agricultural products from 2004 to 2018, have established a vector auto-regressive model to empirically analyse the heterogeneous dynamic impact of the digital economy on the trade volume of agricultural products. In addition, Ma and Guo (2023) conducted an empirical test on the total effect, regional heterogeneity and threshold effect of the digital economy on agricultural export trade based on China’s provincial panel data from 2011 to 2020. Therefore, under the new circumstances of continuous integration of digital technology and agriculture, this study interprets the impact effect and mechanism of the digital economy on the binary margin of agricultural exports from the perspective of the digital economy, providing new research perspectives and approaches for promoting the growth of agricultural exports. Second, in terms of theoretical analysis, the above studies have not been fully analysed in terms of the specific mechanism of the impact of the digital economy on agricultural exports. Based on the positive and negative characteristics of agricultural trade, this study introduces two kinds of roles into the theoretical analysis framework to comprehensively determine the trade impact effect of the digital economy. Third, in terms of research design, this study empirically examines the impact of the digital economy on the binary margin of agricultural products, passing a series of robustness tests and investigating the mediating roles of trade cost and market competition effects, producing an empirical basis for China to leverage the digital economy to promote the binary margin of agricultural exports.

The Russian export flow is highly concentrated on few trading partners that results in its high vulnerability to external shock. Furthermore, the Russian–Ukraine conflict and corresponding western sanctions has enhanced the need of export markets diversification for Russia. Therefore, this study is a baseline attempt to explore determinants of export flow along with identifying potential export markets. This objective is realized by employing an augmented version of gravity model on export flow of Russian Federation to 108 trading partners from 2000 to 2020.

The augmented gravity model of export flow is estimated by using employing contemporary panel econometrics such as panel generalized ordinary least square estimation technique with cross-sectional weight along with heteroskedasticity consistent white coefficients is employed to explore impact of selected macroeconomic and policy variables. Furthermore, the sensitivity analysis is performed by using panel random effect along with the Driscoll–Kraay standard errors with pooled ordinary least squares (OLS) regression and random effect generalized least square (GLS) estimator techniques. The estimated result of panel GLS technique is subjected to in-sampled forecasting technique to explore potential export markets.

The findings show that an increase in the income of trading partners and enhancement of domestic production capacity has significant positive impact on Russian export flow, whereas geographic distance has a significant negative impact. Income of trading partners emerged as major determinant of export flow with high explanatory power. Among augmented variables, the real exchange rate reveals a significant positive impact of lower intensity, whereas binary variables for the common border, common history and preferential/free trade agreement show a significant positive impact. The finding of export potential reveals a high concentration of export with existence of large potential for exports across the globe. For instance, many developing countries in Asia, Africa and America reveal high potential for Russian exports.

The findings urge Russian Federation to diversify its export markets by targeting potential export markets. Many emerging developing countries are witnessing a high potential for Russian exports, therefore attempts should be taken to diversify toward them. The expansion of existing transportation facilities along with development of cargo trade can be important policy instrument to realize objective of export diversification.

This study is the first comprehensive analysis that employs augmented gravity model to explore potential export markets for Russian Federation by using panel data of 108 global trading partners from 2000 to 2020. This finding of this study provides a framework of export diversification toward potential markets across the globe.

This study examines the impact of China's trade and foreign direct investment (FDI) on Africa's global value chain (GVC) participation and economic upgrading.

The study covered 48 African countries, cutting across the western, eastern, central, southern and northern subregions to cover the heterogeneity of the continent. The study adopted feasible generalized least squares panel VAR-Granger causality Wald test and system generalized methods of moments techniques for estimation.

Overall, China's FDI to Africa and US-Africa trade have a linear relationship with Africa's GVC involvement and economic upgrading. The findings suggest that although China-Africa trade has a positive impact on GVC engagement and upgrading, the marginal effect decreases in the face of US-Africa and EU-Africa trade.

This study provides new evidence on the impact of China's FDI and trade on African economies' GVC participation and economic upgrading. To the best of the authors’ knowledge, this is the first study to empirically explore the effects of China's FDI and trade on Africa's GVC integration and economic upgrading as well as from the perspectives of backward and forward GVC participation. Furthermore, the study empirically examines whether the effects of Africa's economic cooperation with China relative to its GVC engagement differ from those of Europe (EU) and the US via a comparative regression.

This investigation aims to determine the effect of trade uncertainty on domestic investment (DI) and foreign direct investment (FDI) for the Turkish economy from the first quarter of 2005 to the first quarter of 2020.

The authors adopt the vector autoregression (VAR) model augmented with Fourier terms. Using this methodology, the authors obtain the empirical results of the impulse-response functions and the variance decomposition analysis.

The empirical results demonstrate that a shock to trade uncertainty has a slight negative impact on DI for up to approximately 1.5 years, whereas its impact on FDI is negative but long-lasting. Moreover, the contribution of trade uncertainty to FDI is relatively higher than to DI in the error variance decomposition for the investigated period. These empirical results can be beneficial for shaping the Turkish authorities' trade policies in the following periods.

These findings have implications within the macroeconomic setting. Government authorities can provide tax exemptions for specified sectors and debureaucratize investment processes for both domestic and foreign entrepreneurs. Additionally, institutional quality and property rights should be protected strictly and developed gradually.

This study is the first to examine the impact of world trade uncertainty on Türkiye’s DI and FDI. Because trade uncertainty might act as fixed costs, this creates the option value of waiting and seeing the market, and firms hesitate to incur investment.

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

This article summarizes the international scientific research output of global forest product models, infers future research trends and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

In 1999, Joseph Buongiorno, a scholar at the University of Wisconsin in the United States of America, proposed the global forest products model (GFPM), which was first applied to research in the global forestry sector. GFPM is a recursive dynamic model based on five assumptions: macroeconomics, local equilibrium, dynamic equilibrium, forest product conversion flow and trade inertia. Using a certain year from 1992 to present as the base period, it simulates and predicts changes in prices, production and import and export trade indicators of 14 forest products in 180 countries (regions) through computer programs. Its advantages lie in covering a wide range of countries and a wide variety of forest products. The data mainly include forest resource data, forest product trade data, and other economic data required by the model, sourced from the Food and Agriculture Organization (FAO) of the United Nations and the World Bank, respectively.

Compared to international quantitative and modeling research in the field of forest product production and trade, China's related research is not comprehensive and in-depth, and there is not much quantitative and mathematical modeling research, resulting in a significant gap. This article summarizes the international scientific research output of global forest product models, infers future research trends, and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

On the basis of summarizing and analyzing the international scientific research output of GFPM, sorting out the current research status and progress at home and abroad, this article discusses potential research expansion directions in 10 aspects, including the types, yield and quality of domestic forest product production, international trade of forest products, and external impacts on the forestry system, in order to provide new ideas for global forest product model research in China.

This study deals with changes in European Union's (EU's) trade potential in Machinery (HS 84–85) and Transportation (HS86-89) products.

The study uses a Structural Gravity model, Poisson Pseudo Maximum Likelihood (PPML) estimation together with panel data for the years 2002–2018 and a two-step procedure that employs predicted values of bilateral trade to compare potential to actual trade.

Results for Machinery products suggest a potential to expand trade with existing Regional Trade Agreements (RTAs) in the American continent, and countries of the IGAD region in Africa. In Transportation, a high trade potential with RTAs is found in the Americas, Africa and the Middle East. Policy suggestions concentrate on opportunities for enhancing trade relations through trade liberalization and agreement proliferation.

There are no studies to date, that examine “collective” measure of EU trade potential, that treats the EU as a single country. Changes in existing opportunities to expand trade, common for EU members, are of special interest for policy formulation, especially after the recent turmoil presented by the Global Financial Crisis (GFC) and the Greek Economic Crisis (GEC). Treating the EU as a single entity, is necessary for the formulation of an effective, common, EU trade policy. This study concentrates on the manufacturing sector to examine existing opportunities for the EU to expand trade, after the GFC and the GEC. This article deals with Machinery (HS 84 and 85) and Transportation (HS 86 through 89) products as they comprise a significant part of total EU exports, reaching 41% of total exports in 2016. Finally, this study offers a unique illustration of results through trade potential heat maps.

In international business (IB), the discussion of COVID-19-related global value chain (GVC) models driving resilience has taken momentum since May 2020. The purpose of this study is to uncover insights that the pandemic provided as a unique research opportunity, holistically, revealing the significant role of non-lead firms in GVC outcomes and resilience. This allows to extend theory as the authors critically identify impact criteria and assess interdependence and valence, thus progressing the traditional (pre-pandemic) IB view of GVC governance and orchestration.

This study opts for an integrative review to help create a much-needed extension of IB theory by means of a critical perspective on GVC theory. The authors examine the extant body of IB literature as the relevant stock of collective IB knowledge prompted by the COVID-19 pandemic, uncovering contributions – with a focus on the role of non-lead firms in orchestration and resilience – that allows to clarify what was not evident pre-pandemic. With this, the authors move the theory from its efficiency focus to a better recognition of the interdependencies of power and profit outcomes stemming from asymmetries of interrelationships. By design, the authors focus on the unique research period of the pandemic and orchestration complexities along the development of configurational arguments beyond simple correlations (Fiss, 2011), revealing key dependencies as key themes. The authors highlight further research avenues following Snyder (2019) that are called upon to strengthen that understanding and that helps extend theory.

This research provides a critical perspective on the application of the traditional IB views for GVC governance (designed for efficiency, cost and proximity to markets with pre-dominance for just in time), which has shifted during the pandemic to accommodate for adaptation and adjustment to resilience and just in case considerations. The holistic review reveals not only the key country- and multinational enterprise (MNE)-dependencies with residual impact determining the balance between just-in-time and just-in-case. Also, the authors advance the understanding of the (un)balance of the traditional GVC – focused on just-in-case rather than just-in-time through a lead and non-lead GVC participation and power lens yet rarely observed. The authors find that governance should not be construed as “management” such that it resolves into decisions undertaken in lead firms for execution in subordinate GVC participants. Autonomy allows to subsidiary units by MNE lead firms and/or exercised by (mainly, innovative) non-subsidiary GVC participant firms, is uncovered as a key driver in this. Greater delegation capacity appears to help provide resilience to loss in profit, with a recognition that there may be a dynamic trade-off between power and profit. In addition, the authors are able to identify correlations with innovation, demand elasticity, digital uptake, investment and other, that the authors trust will set the scene for additional research deepening and extending the findings.

Integrative literature reviews include a problem formulation (i.e. that is limited to published topics around an emerging theme) and are hence very focused in nature and approach. This applies to this paper. Data analysis in this method is not typically using statistical methods in contrast to meta-analyses. Also, the authors limit the sample to a relatively short time period with 33 publications analysed, purposefully focusing on the most prompt and “acute” insights into GVCs during the pandemic.

The traditional GVC governance model is designed for efficiency, cost and proximity to markets with pre-dominance for just in time. The authors reveal dependencies that are instrumental to better understand lead and non-lead interaction and relative autonomy, with a focus on residual impact determining the balance between just-in-time and just-in-case that, if in the sought equilibrium and agile, can allow alignment with context and this resilience. This paper specifically provides practical insights and visualization that highlights stages/“ripple” effects and their impact and the questions to ask as stakeholders look for GVC resilience. This includes, int.al., firms and their role as strategic agents, prompting participants through the learnings from exogenous shock to realign their strategies, redistributed manufacturing of production across subsidiary and non-subsidiary non-lead firms, greater competition and hence power for suppliers leveraging resilience and innovation, greater understanding of localization and regionalization of production of essential supplies, interaction with governments, and of investment impacts abroad especially to secure GVC participation.

The insights provided through this extension of theory with its literature review reveal the importance of aligning IB research into GVCs to factors that became visible through alternative or unusual settings, as they have the power to reveal the limitations of traditional views. In this case, a mainly efficiency-led, just-in-time focused GVC governance model is reviewed through the literature that emanated during the pandemic, with a critical perspective, which helped uncover and underline the complexities and evolution of GVC governance, providing fundamental support to solutioning the continuing global supply chain challenges that started as a result of the pandemic and are yet again accelerated by the Ukraine and Middle Eastern wars and its impact with, int.al., concerns over possible severe global food, labour/migration and resources crises. IB holds a social responsibility to help identify critical challenges from the disciplinary perspective and help advance resilience for social benefit.

This paper supports the original IB theory development by extending GVC theory into the lead – non-lead dynamics that may, under certain conditions, provide a “Resilience wall” for GVCs. The value created through insights stemming from a unique period of time for GVC is significant. It allows us thus also to pave the way to an emerging and critical research adaption looking into equilibrium, nuancing demand elasticity, better understanding trade and investment impacts along GVCs and more. By examining views on the sources of pandemic risks in a possibly unique setting, the authors offer added value from extant IB research insights by combining them, revealing the importance for GVCs to investigate not only key dependencies between the exogenous shock, i.e. context, and the impacts assessed through this literature but to further use their inherent value to create a framework for further conceptualization and extension of the traditional IB view on GVC governance. This work illustrates the urgency and importance for IB to take a timely and possibly more critical approach to the investigation of governance models that have, to date, shown some significant limitations.

The present study aims to investigate the non-linear relationship between trade and income inequality to address goal 10 of sustainable development goals (SDGs) using the Kuznets Curve (KC) framework for major emerging countries during 1991–2020.

For this purpose, recent econometric techniques, such as Common Correlated Effect (CCE) and Dynamic Common Correlated Effect (DCCE) estimators have been employed to deal with the cross-section dependence (CD) that arises in panel data, while the robustness of the study is checked through Driscoll–Kraay standard errors method.

The empirical results of the study confirm the existence of inverted “U-shaped” relationship between trade and income inequality suggesting evidence for the trade-led KC in the panel of emerging countries. Along with the non-linear model, the threshold value is estimated to be between 3.5 and 4% of gross domestic product (GDP).

The authors' findings support that trade contributes significantly toward reducing income inequality and helps in achieving goal 10 of SDGs. Hence, trade policies appear to be more egalitarian. The results widen the scope for further research and provide insights for regulators and policymakers in modeling trade policies and changing the status quo trade policy framework accordingly.

The present study is a pioneering attempt to examine the non-linear relationship between trade and income inequality under the KC framework in light of the Agenda 2030 for sustainable development. The study also considers other explanatory factors that have an impact on income inequality. Furthermore, the study considers other explanatory factors that have an impact on income inequality, and the attempt to estimate the threshold value for the trade-led KC is novel and interesting.

The purpose of this research is to systematically scrutinize the influence of macroeconomic determinants on trade openness, through the lens of various trade theories, with a particular focus on the economies of the GIPSI countries – Greece, Ireland, Portugal, Spain and Italy.

This study investigates the macroeconomic factors influencing trade openness in the GIPSI economies from 1995 to 2020. Methods include stepwise regression (SR) for model selection, Pedroni panel cointegration test and panel regression results. The analysis uses advanced panel regressions, including FMOLS, Panel OLS and FEM. The long-term dynamics were tested using Pedroni cointegration, while Granger causality testing was used to examine the causal direction between the trade openness ratio and trade determinant.

The results show both long-term and short-term relationships between trade openness and (1) foreign direct investment, (2) labor force participation rate, (3) trade reserves and (4) trade balance. The researchers also detected unidirectional and bidirectional causality relationships between trade openness and these four factors. The study also revealed that trade reserves (TR) emerge as the most influential determinant of trade openness, and per capita income does not exhibit economic significance concerning the trade openness of GIPSI economies.

This research is conducted within the context of the GIPSI nations (Greece, Ireland, Portugal, Spain and Italy). As such, the outcomes may not be universally applicable to other economic systems due to the distinct institutional settings and governance structures across different economic groups. Future investigations may explore the relationship between trade openness and its determinants by incorporating different variables.

To the best of the authors' knowledge, this is the first study investigating the theory that suggested trade drivers drive the trade openness of GIPSI countries context. By focusing on GIPSI countries, the study offers a unique perspective on the dynamics of trade openness in economies that have experienced financial crises and stringent austerity measures.