Bilateral trade and productivity: analysis for trading partners of China and the United States

The ongoing trade war between the US and China is at the center of various debates calling globalization into question and enhancing the importance of bilateral agreements (Vázquez-López, 2024). This issue has become even more salient in light of Donald Trump’s re-election and renewed emphasis on protectionism. In such a context, this article provides a holistic perspective on the bilateral trade between these two leading economic actors. The present study therefore investigates the effect of bilateral trade on the productivity of trading partners. Precisely, given the fact that bilateral agreements between the US and China and other countries can be used as political weapons in the current economic war between these two countries, it is worth conducting an empirical investigation on the potential benefit of bilateral trade for these trading partners. As a result, the study aims to examine this aspect by taking into account four different channels: imports, exports, trade balance and trade integration between the two countries over the period 2002–2019, as well as 99 trading partners of both China and the US.

While prior literature has extensively documented the positive link between trade and productivity, most studies focus on aggregate trade flows or single-country perspectives. Very few studies have comparatively assessed how bilateral trade with China versus the US affects productivity. Furthermore, existing research has often overlooked the conditional role of institutions, geopolitical risks and sectoral heterogeneity in shaping trade-productivity dynamics. The present study contributes to addressing these gaps by offering a comparative and channel-specific assessment of how trade with China and the US affects productivity, distinguishing between short-run and long-run effects and controlling for institutional quality, trade policy uncertainty and geopolitical tensions. The adopted approach explores a growing need in the literature to understand not only whether trade enhances productivity but also the conditions under which it does.

This study employs a two-step system generalized method of moments (GMM) estimator with panel data from 99 countries. The findings reveal that trade with both China and the US generally enhances productivity, but with key asymmetries. Trade with China supports total factor productivity (TFP) at constant prices through exports and imports, particularly in manufacturing, but may reduce welfare-related productivity due to structural trade imbalances and dependence on low-value sectors. In contrast, trade with the US exerts consistently positive effects across all channels and productivity dimensions due to stronger technological spillovers and institutional complementarities. These differences suggest that the identity of the trading partner, especially the trade structure, critically shapes productivity outcomes.

This study makes three key contributions. First, it deepens the empirical literature on the trade–productivity nexus by accounting for bilateral trade with two leading global economies using both country- and sector-level data. Second, it offers policy-relevant insights for emerging markets navigating the competing gravitational pulls of China and the US, especially in times of rising geopolitical tensions. Third, by incorporating a diverse global sample, notably from Latin America, it reinforces the study’s regional applicability and contribution to global policy debates on trade and development.

The present study is organized using the following structure: Section 2 reviews the literature on the influences of trade and institutions and the combination of institutions and trade on productivity. Section 3 outlines the methodology and data, followed by Section 4, which presents the empirical results. Section 5 discusses these findings, and Section 6 concludes the study by proposing key implications and recommendations.

This section examines the literature on the effects of trade on productivity, along with the underlying theoretical foundations. Trade activities are studied using the framework of import and export channels, trade openness and trade integration, by which they can influence aggregate productivity (Vázquez-López, 2024).

The theoretical foundations of the relationship between trade activities and productivity are supported by numerous theories. One of them is David Ricardo’s theory of the comparative advantage, which argues that countries should specialize in producing goods with a lower opportunity cost. Such specialization can lead to more efficient resource allocation by increasing the productivity of each nation (Findlay, 1991). Another study is the Heckscher–Ohlin Model, which employed factor endowments to claim that countries export goods that utilize their abundant factors of production. As countries engage in trade, they can achieve economies of scale and enhance productivity, contributing to higher levels of productivity (Leamer, 1995). A third important theory is the new trade theory, which highlights the role of increasing returns to scale and network effects. In industries with high fixed costs, trade allows firms to access larger markets, leading to greater efficiency and innovation and boosting productivity (Krugman, 2009). Finally, the endogenous growth theory can also be introduced, as it explains that technological change is an endogenous result of economic activities, including trade. As countries open their economies to trade, competition increases, driving firms to innovate and improve their production processes, which may increase their productivity (Tavani and Zamparelli, 2018). Holger et al. (2023) explained the mechanism by which exports and imports may improve productivity. First, the exporting channel enhances productivity through a “learning-by-exporting” process and innovation. Specifically, exporting firms that learn from international clients and competitors (particularly when engaging with advanced economies) improve their efficiency. Additionally, exporting increases the scale of operations, which supports innovation and strengthens productivity gains. More productive firms, in turn, can enter the global economy, as they are able to cover the costs associated with market entry. Second, importing also boosts productivity by providing access to inputs that may not be available domestically or are cheaper elsewhere. This enables firms to experiment with new combinations of inputs, optimize their production processes and develop new products. The combination of exporting and importing is crucial for productivity growth and policymakers should leverage Free Trade Agreements (FTAs) to lower tariffs and remove market entry barriers. By reducing these barriers, FTAs can stimulate both exporting and importing activities, leading to enhanced productivity for firms.

Empirically, numerous studies investigated and explained the effects of trade activities on productivity at different levels (i.e. region, country and firm levels) (Bresnahan et al., 2016). In trading activities, the gains in productivity and output are derived from a better allocation of resources across sectors (Kawai, 1994). In general, this situation results from three factors: (1) trade liberalization implies lower tariffs or other trading constraints that stimulate greater competition from international producers against domestic producers. This situation leads to increased pressure on domestic producers to reduce the cost of their final products and exploit economies of scale (Helpman and Krugman, 1985), as they are able to sell their products at lower prices in larger, liberalized markets (Melitz, 2003; Pavcnik, 2002); (2) trade liberalization creates more opportunities for the transfer of modern technologies and advanced skills to domestic firms through spillover effects. This, of course, contributes to the productivity of domestic production (Aghion et al., 2005) and (3) the positive impacts of trade liberalization on productivity also contribute to improving the quality and variety of intermediate inputs for domestic production, which then increases productivity as well (Grossman and Helpman, 1991).

The positive impacts of trade liberalization and productivity are found in several empirical studies (e.g. see Ferreira and Rossi, 2003; Fernandes, 2007). For instance, Krammer (2010) conducted a study on a sample of 47 developed and developing countries from 1990 to 2006 and found that trade remains an important factor for a rise in TFP through the technological spillover effect. Ahn et al. (2019) used data from industrial sectors for 18 advanced economies and found a dominant role played by the indirect input market channel in fostering productivity gains. Precisely, the authors found that a one-percentage-point decline in input tariffs may increase TFP by approximately 2% in the sector under analysis. The implied potential productivity gains from fully eliminating remaining tariffs are estimated at around 1%, on average, which does not account for the potentially larger gains from removing existing non-tariff barriers. Along similar lines, Chand and Sen (2002) provided evidence suggesting that trade in intermediate-good sectors has a greater favorable impact on TFP growth than that of the final-good sectors in the case of Indian manufacturing. In the same vein, Amiti and Konings (2007) noticed that lower output tariffs may generate productivity gains by inducing tougher import competition, whereas cheaper imported inputs may raise productivity through learning, variety or quality effects. In this context, the authors found that the largest gains arise from reducing input tariffs rather than from lowering output tariffs, based on Indonesian manufacturing census data from 1991 to 2001.

There are many studies on trade liberalization examining the importance of trade costs, trade barriers, trade benefits, among others (Waugh and Ravikumar, 2016). Pioneers such as Barro (1995) and Mankiw et al. (1992) documented that larger trade leads to a greater level of trade openness and higher efficient techniques adopted by countries. A higher trade openness, in turn, leads to considerable TFP growth, especially in importing firms (Amiti and Davis, 2012). Abizadeh and Pandey (2009) used the ratio of total imported and exported value to gross domestic product (GDP) as a proxy for trade openness, concluding that this parameter has a positive effect on TFP growth in the aggregate economy, based on data obtained from Organization for Economic Co-operation and Development (OECD) countries for the 1980–2000 period. In addition, the gains in productivity from trade openness may differ across time periods and countries. Miller and Upadhyay (2000) used a sample of 83 countries covering the period from 1960 to 1989. They found that higher trade openness benefits TFP and outward-oriented countries experience higher TFP, over and above the positive effect of openness. More precisely, trade openness has to incorporate human capital development to achieve a positive effect in poor countries. Wong (2009) documented a positive and significant effect of trade openness on the productivity of manufacturing industries in Ecuador’s export-oriented sectors in the years following the implementation of trade reforms, although a decline in productivity was observed after 2000. Ferreira and Trejos (2011) pointed out that free trade could increase output by 89.8% compared to autarky; in other words, the raw productivity difference relative to the US is much larger than those of conventional measurements. In addition, Ferreira and Trejos (2011) found that gains from trade for other African nations (Congo, Mozambique and Rwanda, among others) range between 50 and 62% of productivity; for several Asian countries, the corresponding gains amount to around 15%. In conclusion, they noticed that gains from trade are reflected in total factor productivity, as the median size of that effect is about 6.5% of output, with a mean of 17% and a maximum of 89%.

However, in recent years, rising trade policy uncertainty and US–China geopolitical tensions, captured by the US–China Tension Index, have been shown to undermine the productivity-enhancing effects of trade by creating volatility, delaying investment and weakening firms’ adaptive capacity. This line of reasoning emphasizes that economic and policy uncertainty can significantly deter international economic engagement, including foreign direct investment and trade-linked productivity gains (Ölmez et al., 2024). Recently, the global financial crisis, in combination with the large trade balance deficit of advanced economies – the US being a particular example (Ferrero, 2010) – affects the trade relationships with emerging countries (Autor et al., 2013). This reduction in trade coincided with growing tariffs rising around the world as countries closed their economies to protect domestic producers and keep employment from falling (Constantinescu et al., 2016).

Regarding the trade between China, the US and other countries and its influence on the productivity of these countries, Autor et al. (2013) found that rising imports from China lead to higher unemployment, lower labor force participation and reduced wages in US labor markets competing with importing industries. In the same vein, Ahn and Duval (2017) studied the impact of fast-growing trade with China on productivity in 18 advanced economies during the 1995–2011 period by identifying the export and import channels separately. They found evidence of large productivity gains from trading with China, while adverse employment effects were also observed. However, they have excluded important drivers of productivity, such as accumulated physical or human capital (Arvanitis and Loukis, 2009).

Moreover, recent research highlights both the negative and positive effects of trading with China. For instance, Hou et al. (2022) used firm- and industry-level data from Ghana and China to examine how Ghana–China trade affects labor productivity in Ghanaian manufacturing firms. The authors concluded that trading with China offers greater potential in terms of productivity gains than trading with OECD countries. In contrast, Friesenbichler et al. (2024) investigated the impact of rising Chinese imports on intra-firm productivity growth in the European Union (EU) from 2005 to 2016. The authors found that increasing the Chinese import volume has a detrimental effect on productivity growth, especially after the 2008 financial crisis, with a stronger impact on firms with lower productivity. Particularly, after 2010, the overall negative effects on EU firms’ productivity growth have been more severe. Furthermore, the lack of regional productive integration in the Latin American context suggests that trade alone does not automatically translate into long-term productivity gains unless accompanied by structural reforms and regional coordination (Vázquez-López, 2024).

Although the existing literature has provided substantial insights into the trade–productivity relationship, a growing body of research suggests that other evolving factors may meaningfully intersect with or reshape this nexus. Notably, recent studies highlight the digital economy as a critical enabler of innovation and productivity. For instance, Pan et al. (2022) found a nonlinear positive relationship between digital economy development and total factor productivity across Chinese provinces, emphasizing the role of digital infrastructure, industrial scale and spillover capacity as drivers of sustained productivity gains. Furthermore, Ding et al. (2024) explored how corporate digital transformation, especially when aligned with environmental, social and governance (ESG) practices, enhances firm-level TFP. They argued that while digitalization directly improves efficiency, it also enables firms to better navigate sustainability challenges, particularly in the face of rising regulatory and stakeholder pressures. This view is supported by Otsuka (2024), who demonstrated that regional innovation networks and inter-regional infrastructure serve as vital conduits for productivity catch-up in lagging regions, even outside traditional agglomeration zones. These findings collectively suggest that digital connectivity, sustainability considerations and collaborative innovation networks now play a complementary role in unlocking the productivity potential of international trade.

In line with the existing literature, this study examines the effects of trading with China and the US on productivity, using a sample of 99 economies over the period 2002–2019. In this regard, the impacts of bilateral trade with China and the US on productivity were studied through four trading activities: import channel, export channel, trade integration and trade balance. Because bilateral agreements became a geo-economic weapon in the context of economic war between the US and China, it is important to investigate the extent to which such agreements can be profitable for partner countries in the short and the long run. To this end, the short-run and long-run effects of bilateral trade are estimated and discussed. Moreover, the present study incorporates institutional quality, the Trade Policy Uncertainty Index and the US–China Tension Index as key contextual variables to assess how geopolitical instability may offset or condition the productivity gains from bilateral trade. The following section presents the methodology and data of this study.

Based on the theoretical foundation discussed in the literature section on how exporting and importing impact productivity, and in accordance with a significant part of the literature addressing emerging countries (e.g. see Autor et al. (2013) and Ahn and Duval (2017)), the data related to a sample of 99 countries engaged in trading activities with China and the US are analyzed for the period 2002 to 2019. The primary variables in this research are TFP, which can be obtained from the latest version of the Penn World Table (PWT), specifically version 10.01 (Feenstra et al., 2015). The most recent data available on TFP dates back to the year 2019. Due to data availability, the timeframe of this study spans from 2002 to 2019.

Inspired by Ahn and Duval (2017), this study examines the impacts of trade activities on productivity and then expands the analysis to trade integration and trade balance separately. In addition, other major drivers for productivity are integrated, as documented in the existing literature: capital stock (Mukoyama, 2008) and human capital (Kumar and Kober, 2012). It is important to note that trade openness can have a strong correlation with bilateral trade, leading to the problem of coincidence effects in bilateral trade due to the influence of total trade openness. Therefore, the trade openness of each trading partner with China and the US, respectively, is calculated by excluding total bilateral trade with China or the US from total trade openness.

In summary, capital stock, human capital and adjusted trade openness (excluding total bilateral trade with China or the US) were included as control variables in the final analysis. Moreover, this allows for the analysis of consistent results across both short-run and long-run effects in the case of short-time panel data. The model takes the following form, based on the theories discussed above:

where i, t are the country i at year t, respectively; $β$ is the estimated coefficient; $ε$ is the error term.

Furthermore, motivated by Ölmez et al. (2024) and in line with recent discussions on the role of geopolitical and institutional contexts, the empirical specification is extended by incorporating three additional control variables: institutional quality, the US–China Tension Index and the Trade Policy Uncertainty Index. These variables are introduced to capture the broader structural and political dynamics under which bilateral trade unfolds. Specifically, institutional quality reflects the strength of legal frameworks, enforcement of property rights and regulatory capacity (Simo-Kengne and Bitterhout, 2023). Those factors can facilitate a country’s ability to absorb external knowledge and convert trade exposure into productivity gains (Krammer, 2015). Trade policy uncertainty captures the unpredictability of trade-related regulations, which may discourage long-term investment and reduce firms’ incentives to pursue productivity-enhancing activities (Ren et al., 2022). Lastly, the US–China Tension Index quantifies the intensity of geopolitical frictions between the two major economies, which can distort trade behavior, disrupt supply chains and influence strategic investment decisions, particularly during episodes of trade retaliation or diplomatic tension (Rogers et al., 2024). By integrating these variables, the model is better equipped to isolate the net effects of bilateral trade on productivity by controlling for institutional frictions and geopolitical risks that may otherwise bias the estimated relationship.

Specifically, to enhance the robustness and applicability of the findings, a sectoral perspective is incorporated, as it allows for more granular insights into which industries particularly benefit or suffer from bilateral trade with China or the US. Such an approach offers a deeper understanding of the heterogeneity in trade–productivity dynamics across sectors. Accordingly, this study extends the analysis by examining bilateral trade flows in two key sectors: agriculture and manufacturing. These sectors are selected due to their distinct exposure to global value chains and varying sensitivity to international market conditions. Sector-specific trade data between China or the US and their 99 trading partners are sourced from the Observatory of Economic Complexity database developed by Simoes and Hidalgo (2011), ensuring consistency and comparability across countries and sectors.

Nevertheless, the regressions face challenges related to endogeneity, given the complex interconnections within trade activities and economic indicators such as human capital, trades and TFP (Hartmann et al., 2017). The dataset comprises a large N (99 countries) and a relatively small T (18 years, 2002–2019), which aligns with the conditions for using the panel GMM estimators. Due to the dynamic nature of this model and the existence of endogeneity, the two-step system GMM approach is adopted. Owing to the inclusion of a lagged-dependent variable in the dynamic models, all estimated coefficients reflect the short-run effects of the explanatory variables. This is a reasonable approach, given that total factor productivity typically requires time to produce its full effects, thus limiting the coefficients to represent short-term impacts. As the timespan of the study is relatively short (18 years), this study follows the approach proposed by Papke and Wooldridge (2008) to estimate the long-run coefficients for the key variables in equations (1a) and (1b). The GMM-estimated long-run coefficients exhibit strong performance, with their median values aligning closely with the true long-run effects, as proposed by Reed and Zhu (2017).

From equations (1a) and (1b), TFP is the proxy for productivity, which is calculated as the logarithm of four different productivity indicators, including TFP at constant national prices (2011 = 1) (TFP1) and welfare-relevant TFP at constant national prices (2011 = 1) (TFP2), respectively. It is worth indicating that logarithmic transformations for the variables TFP1 and TFP2 were used for the following two reasons:

• (1)

  Improving linear regression modeling. Applying logarithmic transformations can reduce skewness in the data, particularly when the original distribution is highly skewed or uneven. This ensures that statistical models adhere more closely to their assumptions of normality and homoscedasticity (Feng et al., 2014; Wooldridge, 2016).

• (2)

  Facilitating interpretation. Logarithmic transformations diminish the influence of large outliers and allow changes in variables to be interpreted in percentage terms rather than as absolute differences. This is particularly valuable in economic and productivity analyses, where proportional changes often provide more meaningful insights than raw differences (Osborne, 2002).

Cap is the proxy for capital stock per capita, calculated as the logarithm of real capital stock per capita; HC is the proxy for human capital, which is proxied by a log of an index of human capital per person (based on years of schooling and returns to education). Those variables are obtained from the Penn World Table, version 10.1 (Feenstra et al., 2015). Then, ToCN and ToUS are measures of adjusted trade openness, obtained by excluding total bilateral trade with China and the US, respectively. BitradeCN and BitradeCN are the proxies for bilateral trade with China and the US, respectively, based on the ratio of the value (in thousand US dollars) of imported goods from China and the US to GDP (ImCN/ImUS), the ratio of the value (in thousand US dollars) of exported goods to China and the US to GDP (ExCN/ExUS), the ratio of total trade with China and the US to GDP (TradeCN/TradeUS) and the ratio of trade balance with China and the US to GDP (TBCN/TBUS), respectively, to examine the aspects of the import channel, export channel, trade integration and trade balance with China and the US. The trade variables are employed from the Observatory of Economic Complexity.

Table 1 presents the data calculations, sources and descriptions for 99 countries (see  Table A1, Appendix, for the list of countries). Table 2 shows the correlation matrix between variables. The correlations between imports, exports and total bilateral trade with China and the US are significant and high (0.85, 0.79 and 0.14 for China; 0.94, 0.91 and 0.26 for the US); thus, a separate analysis of the four aspects of bilateral trade is justified.

The following analytical procedures were applied to process the data:

• Step 1. Prior to the regression analyses, descriptive statistics were calculated and the correlation matrix for all observations was examined to assess the data’s underlying characteristics and relationships.

• Step 2. Given the dataset’s structure, which comprises a large cross-sectional dimension (N = 99) and a relatively short time dimension (T = 18, from 2002 to 2019), along with the presence of endogeneity and dynamic relationships, a two-step system GMM was used as the main estimation technique. This method allows for consistent estimation of short-run effects in the presence of lagged dependent variables and endogenous regressors. Long-run effects were derived by dividing the estimated short-run coefficient by one minus the coefficient on the lagged dependent variable. GMM statistics, including the Hansen and the Arellano–Bond tests, confirmed model validity and instrument relevance.

• Step 3. The results were subsequently analyzed within a theoretical framework and a practical context to derive meaningful insights and provide actionable policy implications for decision-makers. Significant coefficients were interpreted regarding theory and sectoral decomposition was added to identify industry-specific effects.

Table 3 reports the results from the GMM estimators for bilateral trade with China, based on 99 economies in Eq. (2a) and reveals notable findings.

The study’s key variable, bilateral trade with China, is examined through the lens of import channels, export channels, total trade and trade balance in relation to productivity. The results indicate that both exports and imports have significant positive impacts on total factor productivity at constant prices, while total trade and trade balance exert negative effects. Regarding the welfare-relevant TFP at constant national prices, which is used as a meaningful indicator for comparing living standards across countries in one year (Feenstra et al., 2015), the findings reveal that only imports from China have a positive effect on productivity, whereas total trade, exports and the trade balance all have negative impacts on productivity.

While trade openness is generally expected to promote productivity through specialization, economies of scale and knowledge spillovers, total trade with China appears to have the opposite effect in emerging economies. This outcome may reflect asymmetrical trade structures, where exports to China are predominantly composed of raw materials destined for large-scale Chinese factories, contributing minimally to domestic productivity (Fang et al., 2023). Subsequently, with regard to the import channel, Ahn and Duval (2017) revealed that China is the leading emerging economy with advantages in labor, raw materials and economies of scale. Thus, large volumes of low-cost final products imported from China can potentially enhance TFP1 by reducing production costs, improving input complementarities and exposing local firms to competitive pressures that stimulate innovation and efficiency gains (Amiti and Konings, 2007; Halpern et al., 2015). Although the impact of exports and imports on TFP1 is positive, the negative effect of total trade suggests that gains from technology spillovers may be limited due to the concentration of trade in resource-based sectors and trade patterns driven by cost arbitrage, which risk locking emerging economies into low-productivity roles in global value chains (Baldwin, 2016; Rodrik, 2018).

The trade balance channel exhibits negative effects on both TFP1 and TFP2, possibly because emerging economies are typically net importers in bilateral trade with China. This may lead to production leakage, whereby domestic demand is increasingly met through Chinese imports, weakening the domestic industry. Even when imports are productivity-enhancing, persistent trade deficits can constrain fiscal space for investment in key productivity-related sectors, such as infrastructure, education or research and development (R&D).

Overall, these results from TFP1 and TFP2 highlight the dual nature of bilateral trade with China: while exports and imports support TFP at constant prices, total trade and trade balance introduce structural challenges, especially in the context of welfare-relevant productivity (Diao et al., 2019). Moreover, as Freund and Bolaky (2008) explained, trade benefits may not translate into improved living standards unless domestic structural inefficiencies are addressed. Therefore, emerging economies must strategically manage their trade engagement with China, balancing short-term productivity gains with long-term structural and welfare considerations.

Table 4 reports the results for trade with the US and the productivity of trading partners. The results show that the effects of capital stock per capita are positive with TFP2, while the effects of human capital are positive with TFP1.

Table 4 reveals that bilateral trade with the US consistently exerts a significant and positive impact on both productivity at constant national prices (TFP1) and welfare-relevant TFP at constant national prices (TFP2). Specifically, all trade channels, including total trade, exports, imports and the trade balance, have significant positive effects on TFP1. For TFP2, while total trade, exports and imports remain productivity-enhancing, the trade balance exhibits a significant negative impact, suggesting a more nuanced welfare implication. These findings underscore the crucial role of access to the US market in fostering productivity gains, likely through exposure to advanced technologies (Taglioni and Winkler, 2016) and competitive pressures that improve firm-level efficiency (Halpern et al., 2015).

The positive coefficients for the export and import channels imply that both outward and inward trade integration with the US contributes to productivity improvements. Exports may incentivize upgrading and specialization, while imports – particularly of high-tech intermediate or capital goods – can complement domestic capabilities and enhance efficiency (Fernandes and Paunov, 2012). The negative association between trade balance with the US and welfare-relevant total factor productivity may reflect structural overdependence on external demand. Persistent trade surpluses, while boosting headline growth, can mask underlying inefficiencies, delay domestic reform and hinder internal demand rebalancing (Benigno et al., 2022). Moreover, if surplus-generating exports are not linked to high-value-added sectors, the long-term welfare impact remains limited (Hausmann et al., 2007). Overall, the results suggest that trade with the US is largely beneficial, but the structure and balance of trade are important factors. These findings are consistent with the argument by Freund and Bolaky (2008) that trade benefits are contingent on domestic conditions, without which, gains from openness may not fully translate into broad-based productivity improvements.

Following Roodman (2009), the validity of the instruments used in the GMM estimation was assessed using the Hansen and the Sargan tests, ensuring that the number of groups exceeded the number of instruments. As shown in Tables 3 and 4, the Hansen test results were non-significant, indicating that the methodology employed is valid. This suggests that the estimators used in the analysis are unbiased and consistent.

From the perspective of trade with China, trade enhances productivity through exports and imports. In particular, imports of final products from China contribute to improved TFP, potentially by reducing production costs, improving input complementarities and fostering competitive pressures that incentivize efficiency and innovation. However, the total trade and trade balance with China consistently exhibit significant negative long-run effects on both TFP1 and TFP2, suggesting that the benefits from trade are not uniform across channels and may be undermined by structural asymmetries, such as heavy reliance on raw material exports and persistent trade deficits (Hausmann et al., 2007).

In contrast, in trade with the US, all channels, including exports, imports, total trade and trade balance, display uniformly positive long-run effects on productivity. This pattern suggests that US-related trade offers broader and more consistent productivity gains, likely driven by technology diffusion, high-value trade flows and deeper knowledge spillovers (Eaton and Kortum, 2001). These differences underscore a fundamental asymmetry between the two trading partners: while trade with the US promotes productivity through technology-intensive and innovation-enhancing linkages, the productivity benefits from trading with China are more channel-dependent and contingent upon how emerging economies manage their trade composition and structural vulnerabilities (Chukwuma et al., 2024; Fang et al., 2023).

The results can be explained through several mechanisms. First, trade with the US enhances productivity across all channels, driven by technology transfer and managerial expertise, while trade with China shows mixed effects, with benefits concentrated in exports and imports but limited spillovers from overall trade. Second, exports to both partners boost TFP1, though the impact is stronger with the US due to higher demand for quality and innovation. Third, US imports complement domestic production, whereas Chinese imports – while beneficial for TFP at constant prices – may place pressure on low-value-added sectors (Autor et al., 2016; Halpern et al., 2015). Finally, a positive trade balance with the US supports long-term growth, while persistent deficits with China are associated with lower productivity, reflecting structural vulnerabilities.

Table 5 summarizes the effects of institutional quality (INS), trade policy uncertainty (TPU) and the US–China Tension Index (UCT) on total factor productivity (TFP1 and TFP2) (detailed results are reported in  Tables A2 to A7 in the Appendix). It highlights the impact of institutional and geopolitical dimensions on productivity. These variables are sequentially introduced into Eqs. (1a) and (1b) to isolate their individual contributions in the context of bilateral trade with China and the US.

The results suggest that institutional quality consistently exhibits significantly positive effects across all models for both TFP1 and TFP2, indicating that stronger institutional frameworks enhance the productivity gains from trading with China. In contrast, trade policy uncertainty and the US–China Tension Index show negative significant coefficients throughout, implying that rising uncertainty and geopolitical frictions systematically erode the potential productivity benefits of bilateral trade with China. These effects are robust across four trade channels. In the same vein, the pattern is largely consistent in the case of bilateral trade with the US. Institutional quality remains positively associated with both TFP1 and TFP2 across all cases. This result is consistent with the finding that institutional quality is a positive determinant of TFP (Krammer, 2015). Meanwhile, both TPU and UCT show significant negative effects, underscoring the sensitivity of productivity performance to policy stability and geopolitical tensions in trade relations with the US. Overall, these findings reinforce the view that while bilateral trade can be a driver of productivity, its effectiveness is conditional upon the broader institutional environment and geopolitical climate under which trade occurs (Caldara et al., 2020; Nguyen et al., 2021, 2022).

To further examine the trade–productivity relationship across sectors among the trading partners of China and the US, bilateral trade was broken down into agricultural and manufacturing components. Specifically, sector-level trade data between China or the US and their trading partners are obtained from the Observatory of Economic Complexity using HS2-level codes. These detailed trade flows are subsequently aggregated into two sectoral classifications: agriculture and manufacturing. Four sector-specific variables are then constructed for each bilateral relationship: exports and imports in agriculture and exports and imports in manufacturing. These variables are introduced into equations (1a) and (1b), respectively, to assess their differential effects on TFP1 and TFP2 in the context of trade with China and the US. Table 6 summarizes the effects of bilateral trade in the agricultural and manufacturing sectors on both TFP at constant national prices and welfare-relevant TFP (detailed results are reported in  Tables A8–A9; Appendix).

On the one hand, for trade with China, the results indicate that exports in both sectors are positively and significantly associated with TFP1, suggesting productivity gains through learning-by-exporting mechanisms. Then, both imports and exports in the manufacturing sector consistently exert positive effects on TFP1 and TFP2, highlighting the sector’s critical role in value-added and innovation-driven growth when engaging with China. However, the effect of agricultural and manufacturing exports on TFP2 is negative, implying that while exports may raise aggregate efficiency, they do not necessarily translate into welfare-relevant productivity. In the case of trade with the US, the results are similarly nuanced. Exports in both agriculture and manufacturing positively impact TFP1 and TFP2, while agricultural imports exhibit a negative effect, possibly due to the displacement of local producers or lower domestic value capture (Bakari and Tiba, 2022).

In contrast, manufacturing imports are strongly associated with higher productivity, reinforcing the idea that imported intermediate and capital goods from advanced economies contribute to domestic technological upgrading (Halpern et al., 2015). These findings reinforce the need for sector-sensitive trade strategies, as the productivity implications of bilateral trade vary not only by partner country but also by industry.

These contrasting effects between trading with China or the US – particularly the divergence in how different trade channels affect TFP1 versus TFP2 – can be further contextualized by examining institutional, geopolitical and sectoral dimensions. First, institutional quality consistently exerts a positive and significant impact on both TFP, regardless of the trading partner, reinforcing the idea that strong institutions enhance a country’s ability to achieve productivity gains from trade. In contrast, trade policy uncertainty and the US–China Tension Index display robust negative effects across all models, indicating that political instability and geopolitical frictions systematically erode the benefits of bilateral trade. This highlights that the productivity returns from trade are not automatic, but instead depend on the stability and predictability of the broader policy environment.

Second, results at the sectoral level reveal that the composition of trade flows is a key determinant of productivity outcomes. For China, manufacturing exports and imports are positively associated with both TFP1 and TFP2, suggesting technology-driven gains in complex industries. However, agricultural trade with China, while beneficial for TFP1, has a negative effect on TFP2, likely due to limited domestic value capture and weak spillovers into broader welfare. In contrast, trade with the US shows more balanced benefits across sectors: both agricultural and manufacturing exports improve TFP1 and TFP2, while agricultural imports reduce TFP2, possibly due to displacement effects on local producers. These findings suggest that manufacturing sectors benefit the most from trade with both partners, while agriculture remains vulnerable, especially in welfare-related productivity terms. This sector-specific divergence provides important implications for industry-level planning and policy targeting. Manufacturing-oriented industries are more likely to gain from openness, whereas agricultural sectors may require support policies to mitigate adjustment costs and welfare losses.

Together, these results help explain the asymmetry observed between China and the US: trade with the US is more aligned with high-value, innovation-intensive sectors and institutional spillovers, while trade with China, especially in low-tech sectors or under weak institutions, may amplify structural dependencies and limit welfare-relevant gains. This contextual understanding reinforces the argument that trade effects are conditional and heterogeneous, not only across countries but also across policy environments and sectors.

This study contributes to the growing literature on bilateral trade and productivity by examining the nuanced impacts of trading with China and the US through different trade channels: imports, exports, total trade and trade balance. Furthermore, it offers an advanced understanding of trade and productivity dynamics by revealing how the impacts of trade on TFP vary across trading partners and by providing evidence for theories of technological spillovers and learning by exporting.

Contrary to conventional concerns, trading with China shows that both exports and imports positively affect TFP at constant national price productivity, supporting the notion of learning by exporting and technological diffusion through imports. However, these gains are not uniformly reflected in welfare-relevant productivity, where exports and overall trade show negative effects, suggesting limited value chain upgrading and structural dependence on low-tech sectors. This aligns with Engelbrecht’s (2002) findings on the importance of human capital and technological spillovers for productivity gains and supports the argument that exporting raw materials while importing final goods can constrain domestic value creation.

In contrast, trade with the US consistently improves both TFP1 and TFP2, with almost all trade channels underscoring the role of high-value trade, technology-rich imports and strong knowledge spillovers. These findings reflect the capacity of US trade to support both technical efficiency and welfare-enhancing productivity through exposure to competitive global standards, high-productivity sectors and advanced production networks. This suggests that while trading with the US enhances living standards and productivity, its effectiveness depends on the trading partner’s ability to absorb advanced technologies and develop complementary domestic capabilities. Moreover, the results highlight the asymmetry between trade partners: the US fosters productivity through technology-driven trade across channels, whereas the benefits from China are channel-specific and depend heavily on managing trade composition. This emphasizes the importance of fostering domestic value-added production and enhancing the capacity to assimilate advanced technologies through targeted investments in human capital and infrastructure.

Moreover, this study also provides a comprehensive view of the impact of trade, not only on total factor productivity but also on two specific aspects of it: TFP at constant national prices and welfare-relevant TFP at constant national prices.

The findings offer actionable insights for policymakers in countries with significant trade engagements with China and the US. First, countries trading with China should prioritize diversifying their trade partners and transitioning towards value-added industries. While exports and imports with China improve TFP at constant national prices, the negative effects of total trade and trade balance, especially on welfare-relevant TFP at constant national prices, highlight the risks of structural dependence on resource exports and persistent trade deficits. Policymakers should foster industries that enable domestic value creation and reduce reliance on raw material exports.

Second, for trade with the US, policymakers should leverage technological spillovers by enhancing human capital development and increasing capital investment while fostering high-tech, value-added exports to capitalize on spillover effects. The positive impacts across all channels indicate strong opportunities to gain from learning by exporting and high-tech imports. Governments should actively foster high-tech, value-added exports to the US and strengthen institutions that facilitate technology absorption and industrial upgrading. As such, strategic import policies should ensure that imports from China do not displace local industries, especially in low-value-added sectors. While imports from China improve technical efficiency, they may pose challenges to welfare-related productivity if not aligned with domestic production needs. Thus, targeted support for domestic firms, alongside selective import substitution policies, may be necessary to sustain inclusive productivity growth.

Furthermore, sector-specific insights from the analysis highlight the need for tailored trade strategies. For manufacturing industries, especially those engaging in trade with the US or China, policies should focus on expanding access to high-tech imports and export promotion schemes linked to quality upgrading. In contrast, agricultural sectors, particularly those affected by import competition, require supportive measures such as productivity-enhancing subsidies, rural infrastructure investment and technical training to mitigate welfare losses and boost competitiveness. Policymakers and industry leaders should collaborate to identify vulnerable sectors and design adaptive trade responses that align with both national development goals and evolving global trade dynamics.

Finally, amid US–China trade tensions, these results advocate for maintaining trade openness over the rise of protectionism. Liberalized trade facilitates global competitiveness and long-term productivity improvements by enabling technological transfer and economies of scale. Policymakers should focus on trade agreements that emphasize technology transfer and value-added production, ensuring that both technical efficiency and welfare productivity are optimized.

While this study provides valuable insights into the impacts of bilateral trade with China and the US, it has limitations. First, although institutional quality, trade policy uncertainty and geopolitical tensions have been incorporated into the analysis, their interaction effects with trade channels remain underexplored. Future research could investigate how institutional strength moderates the transmission of trade-induced productivity gains, especially in low-capacity states. Second, although this study integrates sectoral-level trade data for agriculture and manufacturing, more granular industry classifications (e.g. high-tech vs low-tech manufacturing and food vs cash crops) would allow for deeper insights into sector-specific dynamics. Third, given the intensifying US–China trade tensions and evolving global value chains, future research should focus on the dynamic responses of productivity to shocks in trade policy, supply chain realignment and decoupling trends across industries and regions. In addition, exploring the heterogeneity of trade impacts across income levels, institutional contexts and regional blocs could yield more nuanced policy insights.

In the context of ongoing trade tensions between the US and China, this study provides a comprehensive analysis of the impact of bilateral trade on productivity for 99 trading partners over the period 2002–2019. By employing the two-step system GMM estimator, the effects of bilateral trade are explored through four channels, namely imports, exports, total trade and trade balance, contributing to a deeper understanding of trade-productivity dynamics.

The study’s findings reveal nuanced and asymmetric effects. Bilateral trade with China enhances TFP at constant national prices through both exports and imports, but total trade and trade balance with China have negative impacts on welfare-related productivity. Sectoral analysis further indicates that manufacturing trade with China supports both TFPs, while agricultural trade, especially exports, is detrimental to welfare productivity. Moreover, the positive effects of trade are highly conditional on institutional quality, while policy uncertainty and geopolitical tensions systematically weaken the productivity benefits of bilateral trade.

In contrast, bilateral trade with the US exhibits consistently positive effects across all channels and productivity dimensions. Both agricultural and manufacturing exports to the US promote TFP1 and TFP2, while agricultural imports from the US reduce welfare productivity, likely due to displacement of domestic producers. These findings emphasize the importance of partner composition, sectoral structure and the institutional context in shaping trade-productivity outcomes.

The results offer actionable insights for policymakers. For countries trading with China, diversifying trade partners, upgrading industrial structures and managing sectoral exposure, particularly in agriculture, are essential to avoid structural dependency. For countries engaging with the US, policies should prioritize human capital development and technological absorption, leveraging high-value trade for long-term productivity gains. Across both cases, strengthening institutional frameworks and mitigating geopolitical risk are critical enablers of productivity-enhancing trade.

In conclusion, this study highlights the importance of a strategically balanced approach to trade liberalization. While bilateral trade with both China and the US can boost productivity, its long-term success depends on targeted industrial policies, institutional quality and geopolitical stability. These findings caution against protectionist responses and advocate instead for trade openness combined with strategic investments in value-added production, human capital and technological innovation to sustain inclusive and resilient economic growth.

This study is funded by the University of Economics Ho Chi Minh City, Ho Chi Minh City (700000), Vietnam.

The supplementary material for this article can be found online.