How Will Chinese Exports Respond to Trump’s Tariffs? A Sector-Level Analysis in The Light of The Current US-China Trade War

How Will Chinese Exports Respond to Trump’s Tariffs? 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A Sector-Level Analysis in The Light of The Current US-China Trade War Badr MOUGHAMIR, Jihad Ait soussane, Zahra MANSOURI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7943060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aims to analyze the elasticity of Chinese exports to the United States to tariffs applied by the U.S. on these exports across 23 product categories over the period 1993–2022. The study employs sector-level empirical analysis using bivariate cointegration techniques to estimate the elasticity of Chinese exports with respect to US-imposed tariffs. By applying logarithmic transformations to export values, the model calculates the percentage change in exports triggered by a 1% change in tariffs. Results reveal significant heterogeneity in Chinese export responses to imposed U.S. tariffs. While many product categories exhibited negative responses to tariff, some demonstrated resilience or even positive export elasticity. These outcomes were influenced by factors such as production cost structures, supply chain integration, and alternative market access. US-China trade war Export elasticity Tariffs Chinese exports Sectoral analysis Trade protectionism 1. Introduction Over the past decade, economic tensions between the United States and China have escalated into a full-blown trade conflict, most notably during the administration of President Donald Trump. Initiated in 2018, the US-China trade war marked one of the most significant disruptions in global trade since the formation of the World Trade Organization. Framed under the banner of correcting trade imbalances and protecting domestic industries, the Trump administration imposed a series of tariffs on a broad array of Chinese goods. The US-China trade war (2018–2019) presents a natural experiment in protectionist policy and its cascading economic effects. The United States, under President Trump, imposed tariffs on approximately 250 billion USD worth of annual imports from China, raising average tariffs from 3.7% to nearly 25%. In response, China retaliated with tariffs covering about 110 billion USD in US goods, increasing its average tariff rate from 8% to over 20% on the targeted products. This tit-for-tat tariff escalation represents the most significant tariff conflict between the two countries since the Great Depression era of the 1930s. A large body of empirical research has evaluated the outcomes of this trade war, and the consensus is broadly negative for both economies. On the American side, studies by Amiti et al. ( 2019 ) and Fajgelbaum et al. ( 2020 ) reveal that US importers and consumers bore the brunt of the tariffs. Since Chinese exporters only marginally lowered their prices, the tariffs were almost entirely passed on to American consumers. Cavallo et al. ( 2019 ) confirmed this trend through import and retail price data, showing a near-complete pass-through of tariffs to US retail prices, without notable reductions in foreign suppliers' margins. Consequently, American households experienced increased prices for goods such as electronics and household appliances, which eroded their purchasing power. From a macroeconomic perspective, Fajgelbaum et al. ( 2020 ) estimated a real income loss of 7.8 billion USD for the US economy in 2018 alone, equivalent to roughly 0.04% of GDP. This figure masks significant redistributive effects: while consumers and industries dependent on imports lost approximately 114 billion USD, domestic producers gained 24 billion USD, and the federal government collected around 65 billion USD in tariff revenues. Thus, while the government and certain protected industries benefited, the overall efficiency and welfare impact was negative, as wealth was transferred away from consumers. Contrary to expectations, the US manufacturing sector did not benefit as hoped. Study by Flaaen and Pierce ( 2019 ) indicates that manufacturing industries targeted by tariffs actually lost more jobs than other sectors. This counterintuitive outcome is explained by three main factors: higher production costs due to tariffs on imported inputs (e.g., steel, aluminum), retaliatory tariffs from China (especially targeting agriculture and manufacturing), and a resultant decline in export competitiveness. High-profile examples include the US automotive and machine tool industries, which saw falling profit margins, and soybean farmers, whose exports to China plummeted by 75% in 2018. As Bellora and Fontagné ( 2020 ) aptly described, this amounted to a form of “economic self-sabotage”. China, while also significantly impacted, managed to mitigate some of the negative effects through strategic responses. Chinese exports to the US dropped sharply in tariff-targeted sectors, often by as much as 50%, leading to a noticeable slowdown in manufacturing activity by 2019. However, Beijing employed two key mechanisms to cushion the blow: first, it diversified its export destinations, increasing trade with markets in Asia, Europe, and Africa; second, it allowed for a controlled depreciation of the renminbi, improving price competitiveness by roughly 10% between mid-2018 and the end of 2019. These steps partially absorbed the impact of US tariffs, but they did not fully offset the downturn in total Chinese exports. In addition to short-term trade disruptions, the trade war accelerated long-term shifts in global value chains. Firms in both countries sought to sidestep tariffs by relocating production to third countries (e.g., Vietnam, Malaysia), rerouting goods through intermediaries, or reclassifying product codes. These avoidance strategies were not without cost; they introduced inefficiencies and raised operational expenses. As noted by Dong and Kouvelis ( 2020 ), the reconfiguration of supply chains added considerable complexity and cost to international business operations. The uncertainty generated by the trade war also caused companies to delay or redirect investment, with some US and Chinese firms choosing to scale down bilateral operations or reinvest in less exposed regions such as Southeast Asia and Mexico. These developments underscore the classic economic understanding that protectionist measures often yield unintended consequences. While tariffs can reduce imports in the targeted sectors, they also tend to raise prices for domestic consumers, disrupt supply chains, and invite retaliatory measures that harm exporters. Moreover, in highly integrated global economies, isolating the impact of tariffs is difficult due to interdependencies in production networks and trade relationships. This study seeks to provide an empirical investigation of how Chinese exports to the United States responded to these increased tariffs across 23 product categories using time series over the period 1993–2022. Specifically, the study examines the elasticity of Chinese exports to US tariffs, distinguishing between sectors that were negatively elastic and inelastic. Understanding these differences is essential for crafting effective trade and industrial policies in a context of heightened geopolitical tension and economic nationalism. The remainder of this paper is organized as follows: Section 2 reviews the existing literature on the effect of trade tariffs on exports. Section 3 outlines the methodology and empirical design. Section 4 presents the results of the econometric analysis with discussion in light of theoretical and empirical insights. Finally, Section 5 concludes the study and offers policy recommendations. 2. Literature review 2.1. Theoretical review: 2.1.1. Tariffs, competitiveness, and export performance: One unexpected effect of tariff policies is that measures intended to protect the national productive apparatus may weaken domestic companies' competitiveness in international markets over the long term. Several theoretical systems explain this phenomenon. On the one hand, this policy of protecting the internal market from international competition through high customs tariffs can significantly reduce the incentive for national firms to innovate and maximize their productivity. In the absence of competitive import pressure, some industries risk becoming inefficient, which harms their export price and non-price competitiveness. For example, the creative destruction model applied to trade states that openness to trade forces the least productive firms out of the market while the most productive firms grow, leading to an overall gain in productivity (Melitz, 2003 ). Conversely, the protection of the domestic market through tariffs can delay this process of natural selection, artificially keeping underperforming local firms alive and thus impeding national productivity growth. On the other hand, several manufacturing industries depend on imported inputs (raw materials, components, and capital goods). High tariffs on these inputs increase local production costs. Theoretically, an increase in input costs is partly reflected in the export prices of finished goods, making them less competitive internationally. Conversely, lower tariffs on inputs can reduce production costs and improve the international competitiveness of domestic producers. Thus, generally, tariff liberalization on inputs stimulates exports, while increased protection on inputs discourages them. This phenomenon has been formalized in the concept of effective protection rate, which considers the structure of an industry's inputs and outputs: a tariff on a key input can negate the protective effect of a tariff on the final product and even generate negative adequate protection. In addition, tariffs on finished products can lead to retaliatory measures by trading partners. If foreign countries impose tariffs on the exports of the initiating country, domestic exporters lose market share abroad. This risk of retaliation accentuates the negative impact of tariffs on export performance. It is worth noting that some recent work has also explored the effects of trade policy uncertainties on competitiveness. The argument is that the unpredictability of future tariffs may deter companies from investing in export capacity. 2.1.2. Global Value Chains and tariff policy uncertainty: The structure of international trade has changed significantly with the advent of global value chains (GVCs). Now, goods move across multiple borders at different stages of the international division of the production process. On average, a significant portion of the value of an exported product comprises imported inputs (Johnson and Noguera, 2012 ). In this context, tariffs affect the final products consumed and all intermediate upstream trade. Theoretically, this means that the effect of a tariff can be amplified throughout the production chain: a tariff imposed in one country on an intermediate component can impact the export competitiveness of the downstream country that utilizes that component. For example, Johnson and Noguera ( 2012 ) show that traditional protectionist measures can have more complex effects on the distribution of trade value added because of the international sharing of production. Recent models increasingly consider these dimensions of vertical specialization and value-added trade. They predict that a bilateral tariff dispute between two countries can have cascading effects on the trade flows of other nations not directly targeted, as long as the latter are linked to the first two through supply chains. For example, a trade war can lead to a geographical reorganization of value chains: some companies will seek to divert trade via third countries to evade tariffs. Other companies facing rising costs of importing strategic inputs may choose to repatriate specific production steps or alter foreign suppliers. These adjustments carry costs and do not occur instantaneously, which implies temporary disruptions to trade flows. In addition, political uncertainty regarding the volatility of tariff policies can dampen international trade. Without clarity on future market access conditions (tariffs, quotas), firms may adopt a cautious approach, foregoing the opportunity to penetrate specific markets or invest in export capacities. A theoretical framework developed by Handley and Limão ( 2017 ) explained that reducing political uncertainty (e.g., via a trade agreement) can significantly boost exports by eliminating the risk premium. Conversely, as was the case during the changing announcements during the Sino-US trade conflict, an increase in uncertainty is equivalent to a major negative shock to international trade, potentially as significant as an actual increase in tariffs. 2.2. Empirical evidences: 2.2.1. Macroeconomic and aggregate trade flow effects: According to several econometric studies, tariff increases tend to reduce trade flows and negatively impact the macroeconomic performance of countries that adopt them. Analyzing data spanning over five decades and a sample of 150 countries, Furceri et al. ( 2020 ) explain that episodes of tariff increases are followed by significant declines in national GDP and productivity in the medium term. For example, a tariff increase of 1 percentage point leads to a decrease of approximately 0.5% of GDP on average after a few years. Similarly, higher tariffs are associated with rising unemployment and increasing income inequality. Significantly, despite the resulting import contraction, tariffs do not improve the country's trade balance. Adopting a tariff barrier, exports also tend to decline, largely offsetting the reduction in net imports. This empirical result, consistent with Lerner's theorem, explains why protectionist policies fail to correct trade imbalances while depressing economic activity. Work conducted by the IMF confirms these findings. Likewise, Furceri et al. ( 2019 ), in a separate study, estimate response functions (local projections) following exogenous tariff shocks: they explain that a tariff increase leads to an appreciation of the real exchange rate and a simultaneous decrease in the country's exports and imports, leaving the trade balance nearly unchanged. This appreciation of the national currency, resulting from the effect of the terms of trade or the rise in domestic goods prices, penalizes the exporting sectors. Conversely, tariff liberalization generally has positive effects on trade flows. Numerous studies of free trade agreements and WTO accessions conclude that the reduction in customs duties has substantially increased the countries' exports and imports. For example, tariff reductions as part of China's accession to the WTO in 2001 catalyzed a boom in Chinese exports and a reorganization of global industrial production. At an aggregate sectoral level, trade elasticities regarding tariff variations appear to be high. Recent estimates at the product level suggest that import volumes of a given good decline significantly in response to introducing a tariff on that good. During the 2018–2019 period, characterized by escalating trade tensions, there were substantial declines in bilateral trade flows between countries imposing tariffs on one another. For instance, China's exports to the United States of products subject to the new tariffs dropped sharply (approximately − 40% to -50% by volume for specific categories) immediately after the U.S. tariffs took effect. This trade-reducing mechanism has particularly impacted manufactured goods with higher technological value that the United States aimed to target through its measures. However, recent experience has also revealed trade diversion effects. When two major economies drastically reduce their trade with each other, other third countries can benefit by capturing market share. For example, studies show that following US tariffs on Chinese goods, many importers in the United States have purchased from suppliers in other emerging countries (Vietnam, Mexico, Taiwan, etc.), replacing Chinese products that have become more expensive (Mao and Görg, 2020 ). At the same time, Chinese exporters have redirected some of their sales to other markets in Asia, Europe, and Africa to compensate for the loss of access to the US market. According to Shen et al. ( 2021 ), Chinese exports destined initially for the United States have, in part, been redirected to third markets, a particularly pronounced phenomenon for differentiated (i.e., specific or branded) goods. However, this diversion has generally only partially offset the decline in bilateral trade: overall, global trade slowed during the rise in tariff barriers in 2018–2019, contributing to the first contraction in the volume of world trade since the 2009 crisis. 2.2.2. Sectoral and firm-level impacts: Beyond macroeconomic aggregates, the empirical literature has examined how pricing policies affect firm and sector performance across various national contexts, especially in developing countries. These micro-econometric analyses allow a better understanding of the specific mechanisms through which tariffs impact competitiveness and exports. The distinction between input tariffs and tariffs on final goods is clear from empirical studies. In line with theoretical expectations, input tariffs are particularly harmful to firms' productivity and export performance. For example, according to Bigsten et al. ( 2016 ), in the case study of Ethiopia in the 1990s-2000s, the reduction of tariffs on imported intermediate inputs led to a significant productivity gain for local firms, especially those that were most dependent on foreign inputs. This gain results from both access to higher-quality raw materials and technologies and the learning effect of using a variety of inputs. Conversely, Bigsten and his co-authors do not observe any improvement in productivity following the reduction of tariffs on finished goods. In other words, the protection of local producers through high tariffs on finished products did not translate into downstream efficiency gains, but rather into productivity losses for the users of these protected products. This observation reinforces the idea that excessive tariff protection can have cascading perverse effects on the economy as a whole. China serves as a particularly illuminating case study of the effects of trade reforms, having undergone significant tariff changes. During the 1990s and 2000s, China drastically lowered its average customs duties (from more than 30% to less than 10% in two decades) as part of its economic opening and accession to the WTO. The analysis by Mao and Sheng ( 2017 ) reveals the impact of these tariff reductions on firm dynamics and productivity in China. They find that lower tariffs on final goods have tended to increase competitive pressure, leading to the exit of the least productive firms from the market, which has helped to raise the average productivity of the sector. Additionally, the reduction in input tariffs has stimulated both the entry of new firms (attracted by the availability of cheaper inputs) and the exit of the least efficient, accentuating the renewal of the productive fabric. Crucially, these positive effects of liberalization on productivity were most pronounced in China's provinces where the degree of domestic liberalization and market reform was high. This suggests a complementarity between domestic reforms (deregulation, business climate improvement) and trade reforms: the gains derived from lowering tariffs are maximized when the domestic competitive environment allows resources to be reallocated to the most efficient firms. In short, the Chinese experience illustrates that a gradual reduction in tariff barriers, coupled with structural reforms, can be a powerful lever for industrial modernization and competitiveness gains. Other sector studies confirm the potentially negative impact of tariffs on export performance due to value chains. For example, one study analyzed how tariffs on computer components in the United States affected exports of finished electronic products; it found that a tariff on a critical component can significantly reduce exports of final downstream products, as domestic producers pass on the cost or lose competitiveness vis-à-vis foreign competitors not subject to the additional cost. In addition, the impact of tariffs may vary depending on the characteristics of the firms. Analysis of firms has revealed that the most productive exporting firms are generally also those that import a significant proportion of their inputs (the phenomenon of efficient imports). Thus, a tariff on inputs severely affects these "champion" export firms, which increases costs. The results show that lower input tariffs due to trade liberalization in India have disproportionately favored the most successful firms, enabling them to increase exports with cheaper inputs. Similarly, a tariff increase penalizes large import-intensive exporters, while domestic companies can benefit in the short term by gaining local market share. Finally, the role of related trade policies, such as tariff quotas and export taxes, should be highlighted, as these can also impact export performance. For instance, an analysis of tariff quotas in the agri-food sector reveals that these mechanisms (which combine a reduced tariff import quota with a high tariff above that) are likely to restrict access to export markets significantly. According to the study conducted by Muchopa et al. ( 2019 ) regarding South African fruit exported to the European Union under quota, they conclude that these limiting quotas slow down the growth of South African fruit exports, creating a distortion in the distribution of market shares between supplying countries and disrupting the competitiveness of specific horticultural sectors. This conclusion shows that unconventional "tariff" barriers (such as quotas or variable tariffs) have effects similar to simple tariffs in reducing the export flows of the target country. 3. Empirical methodology 3.1. Sample of the study: This study is based on time series data covering Chinese exports to the United States and the tariffs applied by the U.S. on these exports over the period 1993 to 2022 across 23 product categories. The selection of this time span is driven by the availability of consistent and comprehensive data from the World Integrated Trade Solution (WITS) database provided by the World Trade Organization (WTO). The analysis is conducted across a wide range of product categories, reflecting the diversified structure of Chinese exports to the U.S. market. The product categories are classified according to the WTO trade nomenclature: Agricultural Raw Materials, Animal Products, Capital Goods, Chemicals, Consumer Goods, Food Products, Footwear, Fuels, Hides and Skins, Intermediate Goods, Machinery and Electronics, Machinery and Transport Equipment, Manufactures, Metals, Minerals, Ores and Metals, Plastic or Rubber, Raw Materials, Stone and Glass, Textiles and Clothing, Transportation Equipment, Vegetable Products, Wood Products. 3.2. Empirical model: To examine the direct effect of tariffs applied by the United States on Chinese exports, this study employs a bivariate cointegration model. This approach is consistent with common practice in empirical studies that adopt a bivariate cointegration framework (Ait Soussane and Manousri, 2020; Herzer and Nunnenkamp, 2012; Khan and Khan, 2011; Mehmoud and Seddiqui, 2013; Ajaga and Nunnenkamp, 2008; Chakraborty and Nunnenkamp, 2008). The model is specified as follows: Log(Exports) t ​=α + β(TARIFF) t​ +εt t Where: EXPORTS t refers to the volume of Chinese exports to the United States, measured in thousands of USD, disaggregated by product category. TARIFF t represents the average weighted tariff effectively applied by the U.S. on Chinese exports in each corresponding product category. Drawing on the theoretical and empirical literature reviewed in the second section, there are multiple transmission channels through which tariffs can affect exports, including price competitiveness, supply chains, and trade policy uncertainty. However, the goal of cointegration analysis is not to isolate individual mechanisms, but rather to capture the total long-run effect of tariffs on exports. This consideration provides strong justification for adopting a bivariate approach, rather than including additional control variables. Including such controls in time series cointegration models can introduce serious estimation problems due to potential multicollinearity, endogeneity, or non-stationarity. By focusing on the core relationship between tariffs and exports, the bivariate framework offers a parsimonious and robust specification to assess the long-run effects of trade policy on export performance. 3.3. Variable description and source of data: This study uses two main variables: Chinese exports to the United States (dependent variable) and U.S. tariffs applied on Chinese exports (explanatory variable), across a range of product categories. The two variables are described as follows: Exports The dependent variable is measured as the volume of Chinese goods exported to the U.S., expressed in thousands of USD. To interpret the results in terms of elasticities, we transform the export values using the natural logarithm. This transformation helps stabilize the variance and allows us to interpret the estimated coefficients as percentage changes. Export data by product category are collected from the WITS database . Tariffs The explanatory variable is the weighted average tariff effectively applied by the United States on Chinese exports. This measure reflects the actual tariff burden on Chinese products entering the U.S. market, as it accounts for the specific tariff rates applied to each product and the relative share of each product in total exports. It is therefore a more accurate and comprehensive indicator than simple average tariffs, as it captures both the tariff structure and the composition of trade. Tariffs data by product category are collected from the WITS database . 3.4. Estimation techniques: The Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) are two widely used estimation techniques in cointegration analysis to derive robust long-run relationships between non-stationary variables. Both methods were developed to address the limitations of traditional Ordinary Least Squares (OLS) when applied to cointegrated time series, particularly issues of serial correlation and endogeneity. FMOLS, proposed by Phillips and Hansen ( 1990 ), modifies the OLS estimator by correcting for serial correlation in the residuals and for the endogeneity caused by the long-run correlation between the cointegrating equation’s regressors and the stochastic error term. This correction results in asymptotically unbiased and normally distributed estimates, making FMOLS particularly useful in small samples where traditional estimators may perform poorly. On the other hand, DOLS, introduced by Stock and Watson ( 1993 ), enhances the standard cointegration regression by including leads and lags of the first differences of the explanatory variables. This dynamic specification helps to eliminate the feedback effects and corrects for endogeneity by capturing short-term dynamics that could bias the estimates. Both estimators are preferred over simple OLS in cointegrated systems, as they yield efficient and consistent long-run parameters even in the presence of endogenous regressors and serially correlated errors. FMOLS is non-parametric and based on semi-parametric corrections, while DOLS is parametric and allows for easier inference, especially when comparing nested models. According to Kao and Chiang ( 2001 ), DOLS often performs better than FMOLS in finite samples, while FMOLS remains more robust under certain forms of heteroskedasticity and serial correlation. In practice, the use of both methods together strengthens the empirical findings, as consistent and significant results across both estimators confirm the robustness of the estimated cointegrating relationship. 4. Results and discussion Table 1 Unit root test using the Augmented Dickey-Fuller: Category Exports Tariff At level At 1st difference At level At 1st difference Agricultural Raw Materials -1.274635 -4.973532*** -1.382993 -8.375569*** Animal -1.291020 -3.614928** -2.927781 -4.681773*** Capital goods 1.132421 -6.575318*** -7.178555*** - Chemicals 1.546961 -4.083676*** -3.066349 -5.921851*** Consumer goods 1.337295 -5.401312*** -2.757510 -3.235572** Food Products -0.751656 -5.140423*** -3.068747** - Footwear 0.378089 -3.326086** -1.607390 -4.125284** Fuels -2.882226 -7.713915*** -3.356449* - Hides and Skins -2.031237 -4.677099**** -1.814478 -3.983689** Intermediate goods -2.867653 -5.406446*** -4.120158** - Machinery and Electronics 0.884244 -5.943664*** -3.999471** - Machinery and Transport Equipment 1.164021 -7.057782*** -5.771119*** - Manufactures 1.319334 -5.996969*** -3.210108 -3.814382** Metals -2.843599 -5.579072*** -2.315932 -3.793659** Minerals -3.211625 -5.660244*** -2.652028 -5.275172*** Ores and Metals -2.321371 -4.477092*** -1.542203 -4.859266*** Plastic or Rubber -1.506621 -2.729811 -2.602961 -4.31762** Raw materials -1.887965 -3.745360** -2.081625 -9.807525*** Stone and Glass -3.124690 -4.071942** -3.507654* - Textiles and Clothing -2.410265 -5.458680*** -3.319915* - Transportation 0.976754 -8.746360*** -1.759646 -5.571525*** Vegetable 0.493646 -4.728712*** -2.671858* - Wood 0.361471 -5.233195*** -3.335905* - Note: *, **, *** indicate the significance levels 1%, 5% and 10%. Null hypothesis: the variable has a unit root. Trend and intercept are included in the test equation. Source: Authors’. Table 1 presents the results of unit root tests using the Augmented Dickey-Fuller (ADF) method for the variables exports and tariffs across the product categories. The results show that exports are non-stationary at level for all product categories, as the ADF statistics do not allow rejection of the null hypothesis. However, when first differenced, exports become stationary in all categories, with test statistics strongly rejecting the null at the 1% level in most cases. This indicates that exports are integrated in the first order I(1), meaning they become stationary after first differencing, and thus exhibit a unit root process. In contrast, tariffs show a more heterogeneous behavior across product categories. For certain products, such as Chemicals, Ores and Metals, and Machinery and Transport Equipment, the ADF statistics suggest that tariffs are non-stationary at level but become stationary at first difference, thus indicating that tariffs are I(1) in these cases. However, for other categories such as Agricultural Raw Materials, Animal Products, and Raw Materials, tariffs are already stationary at level, as the test statistics reject the null of a unit root at the 1% significance level. This implies that for these product categories, tariffs are integrated at level I(0). This divergence in the integration order of the two variables, exports and tariffs, has important econometric implications. Since exports are consistently I(1) and tariffs are a mix of I(0) and I(1) depending on the product category, it is essential to determine whether a long-run equilibrium, i.e. cointegration relationship, exists between the two variables in each category in the follow table. Table 2 Johansen cointegration tests: Tests of cointegration: Trace test Maximum Eigenvalue test Hypothesis: No cointegration At most 1 cointegration No cointegration At most 1 cointegration Agricultural Raw Materials 26.47144** 8.200679 18.27076* 8.200679 Animal 66.27206*** 13.02703** 53.24503*** 13.02703** Capital goods 23.71794* 4.584528 19.13341* 4.584528 Chemicals 20.19137*** 2.107337 18.08403** 2.107337 Consumer goods 32.00804*** 1.3942180 30.61382*** 1.394218 Food Products 18.08738** 0.940208 17.14717** 0.940208 Footwear 34.55383*** 9.272046 25.28179*** 9.272046 Fuels 24.69427* 8.705992 15.98828 8.705992 Hides and Skins 53.72021*** 8.200848 45.51936*** 8.200848 Intermediate goods 77.70952*** 0.008980 77.70054*** 0.008980 Machinery and Electronics 30.48816** 3.896201 26.59196*** 3.896201 Machinery and Transport Equipment 22.44306*** 1.700294 20.74277*** 1.700294 Manufactures 27.11487*** 1.357954 25.75691*** 1.357954 Metals 29.57124** 9.387034 20.18420** 9.387034 Minerals 68.64788*** 1.076037 67.57185*** 1.076037 Ores and Metals 35.58031*** 1.526654 34.05366*** 1.526654 Plastic or Rubber 18.88425** 1.528027 17.35622** 1.528027 Raw materials 26.80595** 8.496865 18.30909* 8.496865 Stone and Glass 68.17875*** 0.395935 67.78282 0.395935 Textiles and Clothing 17.87012*** 0.385416 17.48471*** 0.385416 Transportation 34.98963*** 1.138665 33.85097*** 1.138665 Vegetable 60.68148*** 8.930021 51.75146*** 8.930021 Wood 66.55646*** 0.978956 65.57750*** 0.978956 Note: *, **, *** indicate the significance levels 1%, 5% and 10%. linear deterministic trend is used for trend assumption. The optimal lag length is based on the VAR model’ lag length criteria. Source: Authors’. Table 2 presents the results of the Johansen cointegration tests, using both the Trace test and the Maximum Eigenvalue test, to test the existence of a long-run equilibrium relationship between exports and tariffs across the product categories. The results strongly indicate that exports and tariffs are cointegrated in nearly all product categories. For most sectors, both the Trace test and the Maximum Eigenvalue test reject the null hypothesis of no cointegration at significance levels of 1% and 5% mostly. At the same time, the null hypothesis of at most one cointegrating relationship is not rejected, suggesting the presence of a unique long-run equilibrium relationship between the two variables for each category. Overall, these results demonstrate that tariffs and exports are cointegrated across the majority of product categories, indicating that changes in tariffs are associated with long-term movements in export levels. This long-run relationship validates the theoretical expectation that tariff barriers have persistent and systematic effects on trade flows over time. Furthermore, the presence of cointegration justifies proceeding to cointegration regression methods as run in the following table: Table 3 The Chinese export elasticity with respect to tariffs applied by the United States across product categories: Category DOLS FMOLS Constant Tariff Constant Tariff Agricultural Raw Materials 10.88701*** 2.275157*** 10.43978*** 2.622024*** Animal 15.87563*** -10.51565*** 16.36317*** -12.18867*** Capital goods 19.23609*** -1.456101** 19.31897*** -1.590429*** Chemicals 20.18753*** -1.688708** 20.63546*** -1.835595** Consumer goods 21.51884*** -0.581288** 22.25116*** -0.695695** Food Products 17.83734*** -0.527196*** 18.73098*** -0.644788*** Footwear -0.258272*** -0.258272*** -0.258272*** -0.258272** Fuels -0.128620*** -0.128620* -0.128620*** -0.128620** Hides and Skins 21.03294*** -0.641035** N/A N/A Intermediate goods 20.15809*** -1.192186*** 22.78297*** -2.064054*** Machinery and Electronics 19.31834*** -1.218354*** 19.54570*** -1.342169*** Machinery and Transport Equipment 19.82527*** -1.316433*** 19.62776*** -1.260478*** Manufactures 22.29638*** -0.812868** 22.36947*** -0.849497*** Metals 19.10766*** -1.053298** 19.22835*** -1.120741*** Minerals 12.95923*** -0.911064*** 12.97422*** -0.923089*** Ores and Metals 11.45054*** 1.102042*** 11.58181*** 1.061716*** Plastic or Rubber 3.260004 3.257404** 9.326943 1.699709 Raw materials 14.66797*** -0.017387 15.31838*** -0.689905*** Stone and Glass 16.51285 -0.213702*** 82.72575*** -12.61069*** Textiles and Clothing 2.336493 1.353393*** -2.488578*** 1.802584 Transportation 18.26830*** -1.012231*** 18.52115*** -1.148693*** Vegetable 20.98046*** -3.226087** 24.82689*** -5.033896*** Wood 17.34031*** -1.279308*** 17.68112*** -1.474202*** Note: *, **, *** indicate the significance levels 1%, 5% and 10%. Depended variable: Log(Exports). The optimal lag length is based on the VAR model’ lag length criteria. N/A: non-applicable regression due to Near singular matrix in whitening regression. Source: Authors’. The results in Table 3 offer detailed empirical evidence on the effect of U.S. tariffs on Chinese exports across 23 product categories using DOLS and FMOLS methods. The results indicate that, across most product categories, the imposition of U.S. tariffs negatively affects Chinese exports, with varying degrees of elasticity. In the majority of categories, such as machinery and electronics, capital goods, intermediate goods, transportation equipment, metals, chemicals, and consumer goods, the elasticity of exports to tariffs is negative and statistically significant, suggesting that higher tariffs are associated with reduced Chinese exports to the United States. This aligns with theoretical expectations that tariffs act as trade barriers, increasing costs and reducing competitiveness. From a theoretical standpoint, as outlined in the literature, protectionist tariffs can reduce a country's export performance through several mechanisms. First, by shielding domestic industries from international competition, tariffs reduce the incentive for innovation and productivity improvements. As described by the creative destruction model (Melitz, 2003 ), trade openness encourages the exit of less productive firms and enhances overall efficiency, whereas protectionist policies delay this process and keep inefficient firms alive, diminishing export competitiveness. Second, many Chinese manufacturing sectors rely heavily on imported inputs such as raw materials and capital goods. Tariffs on these inputs increase domestic production costs, which are often passed on to export prices, rendering Chinese goods less competitive in international markets. This is captured by the concept of effective protection rate, which stresses that tariffs on intermediate goods can negate any intended protection on final products. Moreover, tariffs can trigger retaliatory measures from trade partners, further harming export performance by reducing access to foreign markets. Tariff uncertainty can also discourage firms from investing in export capacity due to unpredictability in future trade conditions. Within GVCs, where goods cross multiple borders during production, tariffs imposed on intermediate goods can ripple through the entire chain, affecting downstream exports, as emphasized by Johnson and Noguera ( 2012 ). This phenomenon magnifies the adverse effects of tariffs in today’s interconnected trade system. Empirical evidence from recent studies supports these theoretical insights. Furceri et al. ( 2020 ) demonstrate that tariff increases reduce GDP and productivity while increasing unemployment and income inequality. Similarly, the IMF highlights that tariff shocks lead to real exchange rate appreciation and simultaneous declines in both exports and imports, leaving the trade balance mostly unchanged and harming economic activity. However, the results also reveal a few exceptions where the tariff effect is either positive or statistically insignificant. For example, Chinese exports of agricultural raw materials, ores and metals, plastic or rubber, and textiles and clothing appear to be less sensitive, or even positively associated, with U.S. tariffs. These anomalies could be explained by factors such as product-specific demand inelasticity, trade diversion strategies, or the ability of Chinese firms to absorb costs or reroute exports to avoid tariffs. In some categories, such as ores and metals or agricultural raw materials, China might possess a comparative advantage or essential supply role, making demand less responsive to price increases caused by tariffs. Nonetheless, even among the categories negatively impacted, the elasticity of Chinese exports to tariffs varies significantly. In some sectors like animal products or vegetable goods, the elasticity is relatively high, indicating that exports are highly sensitive to tariff increases. In others, such as consumer goods or food products, the elasticity is lower, suggesting more inelastic responses. These variations may reflect differences in market structure, the availability of substitute suppliers, the strategic importance of products, and their integration within GVCs. Table 4 The categorization of Chinese export elasticities with respect to tariffs applied by the United States: Product category Negative elastic exports Product category Negative inelastic exports Animal -10,51565% Minerals -0,911064% Vegetable -3,226087% Manufactures -0,812868% Chemicals -1,688708% Hides and Skins -0,641035% Capital goods -1,456101% Consumer goods -0,581288% Machinery and Transport Equipment -1,316433% Food Products -0,527196% Wood -1,279308% Footwear -0,258272% Machinery and Electronics -1,218354% Stone and Glass -0,213702% Intermediate goods -1,192186% Fuels -0,12862% Metals -1,053298% Raw materials -0,017387% Transportation -1,012231% Product category Positive elasticities to exports Ores and Metals 1,102042% Textiles and Clothing 1,353393% Agricultural Raw Materials 2,275157% Plastic or Rubber 3,257404% Note: the results are based on the DOLS method considering its ability to encompasses the long-term effects. Source: Authors’. According to Table 4 , the imposition of tariffs by the United States on Chinese exports will lead to highly differentiated reactions across product categories, reflecting varying degrees of sensitivity or elasticity. Our classification of Chinese exports to the U.S. into three distinct groups, negative elastic, negative inelastic, and positive elastic responses, highlights the complex interaction between trade policy, supply chain structures, product characteristics, and global market dynamics. These categories not only reveal how different sectors absorb or react to external shocks like tariffs but also shed light on strategic vulnerabilities and strengths in Sino-U.S. trade relations. The first group, products exhibiting negative elastic responses to tariffs, includes product categories such as animal products (-10.52%), vegetable products (-3.23%), chemicals (-1.69%), capital goods (-1.46%), and machinery-related goods such as electronics and transport equipment. These sectors show that 1% increase in U.S. tariffs result in disproportionately large reductions in Chinese exports. One principal reason for this high sensitivity is price elasticity of demand: many of these goods, particularly in agriculture and heavy industry, have readily available substitutes in the global market. For instance, the U.S. has historically imported soybeans, meat, and other agricultural commodities from Latin America. When tariffs on Chinese animal or vegetable products increased during the Trump administration’s trade war (2018–2019), American importers quickly shifted to sourcing from countries like Brazil and Argentina. Similarly, in chemicals and capital goods, multinational firms with flexible supply chains could reroute orders to suppliers in Europe or Southeast Asia. The machinery and electronics sectors are also deeply embedded in GVCs, making them highly susceptible to tariff disruptions. When tariffs were imposed on Chinese tech products, companies like Apple began to accelerate production shifts to India and Vietnam, reducing their dependence on Chinese assembly lines. This strategic flexibility allows U.S. firms to minimize costs by avoiding tariffed imports, making Chinese exports in these sectors highly elastic. By contrast, Chinese exports in the second group demonstrate negative inelastic responses to U.S. tariffs. These include categories such as minerals (-0.91%), manufactured goods (-0.81%), hides and skins (-0.64%), consumer goods (-0.58%), and footwear (-0.26%). Inelasticity here indicates that export volumes declined in response to tariff increases, but not proportionally. Several structural factors help explain this. First, some of these goods may have limited global alternatives. For example, certain rare earth minerals and industrial metals are predominantly sourced from China, giving it near-monopoly status in specific niches. In such cases, U.S. buyers are forced to continue purchasing despite price hikes. Second, brand reputation and product standardization contribute to this inelastic behavior. Many Chinese consumer goods and footwear products are not easily substitutable in terms of quality or pricing, especially at scale. U.S. retailers like Walmart and Target depend heavily on Chinese suppliers for a wide range of affordable, consistent-quality products. Even with tariffs, the cost of switching to new suppliers (logistical, contractual, or quality-related) can be high, leading to continued demand for Chinese goods. Additionally, long-term supply contracts, particularly in sectors like hides and food processing, may reduce short-term responsiveness to tariff increases. These contracts often include clauses that buffer sudden price movements, allowing trade volumes to remain relatively stable over the contract duration. The third group consists of product categories with positive elasticities to tariffs, where Chinese exports to the U.S. actually increased despite higher tariffs. These include ores and metals (+ 1.10%), textiles and clothing (+ 1.35%), agricultural raw materials (+ 2.28%), and plastic or rubber products (+ 3.26%). At first glance, these results may seem counterintuitive, yet several plausible explanations emerge. One reason is preemptive stockpiling. During the 2018–2019 U.S.-China trade tensions, importers often rushed to buy goods ahead of new tariffs or in anticipation of further escalation, temporarily boosting export volumes. This was particularly evident in clothing and plastic goods, where U.S. wholesalers increased orders to maintain inventory levels. Another factor could be supply chain rigidity or gaps in domestic production. For example, the U.S. lacks significant domestic capacity to process certain ores and raw materials, and for these, import substitution is simply not viable in the short term. Chinese dominance in global production of rare earths and specific intermediate plastics means that, even with tariffs, U.S. industries remain reliant on Chinese inputs. Furthermore, in some sectors like textiles, Chinese producers may have absorbed part of the tariff cost by lowering export prices to remain competitive, especially when profit margins allowed it. Finally, some firms may have strategically reclassified products, routed trade through third countries, or used tariff exemptions to maintain access to the U.S. market. These complex adjustments can create short-term anomalies where exports rise despite increased trade barriers. In general, the elasticity of Chinese exports to U.S. tariffs varies widely by product category, driven by factors such as the availability of substitutes, integration in global value chains, contractual arrangements, and strategic firm behavior. Elastic responses typically occur in highly competitive and substitutable sectors, while inelastic responses are observed where China has market dominance, strong brand positioning, or long-term contracts. Positive elasticities, though rare, reflect strategic and anticipatory behavior by firms and buyers in response to trade policy uncertainty. Understanding these differentiated impacts is essential for policymakers assessing the effectiveness of tariffs, as well as for firms navigating shifting global trade conditions. It underscores that tariffs are a blunt instrument whose effects depend on the underlying economic structure of each sector and the strategic agility of the actors involved. 5. Conclusion and policy implications This study investigated the sectoral heterogeneity in the responsiveness of Chinese exports to U.S.-imposed tariffs in the light of the trade war initiated under the Trump administration. By focusing on the elasticity of export volumes to changes in tariff levels, the study aimed to uncover which product categories were most vulnerable to tariff shocks and which sectors showed resilience or counterintuitive growth. The methodological approach involved estimating export elasticities using a linear regression model, where the logarithm of export volumes served as the dependent variable and tariff rates as the key independent variable. The analysis was disaggregated across 23 product categories over the period 1993 to 2022 to highlight the varied sensitivities within the broader export portfolio of China. The elasticity coefficients were then classified into three main categories: negatively elastic, negatively inelastic, and positively elastic responses to tariff increases. The results of the analysis revealed a clear divergence in how different Chinese export sectors responded to U.S. tariffs. The first group, composed of sectors such as animal products, vegetable products, chemicals, capital goods, machinery, and intermediate goods, exhibited negative elasticities with coefficients more than 1%. These sectors saw a more-than-proportional drop in export volumes when tariffs were raised, indicating high price sensitivity and the presence of substitute suppliers elsewhere in the global market. The second group, which includes minerals, manufactured goods, hides and skins, consumer goods, and footwear, showed negative but inelastic responses with coefficients less than 1%. Here, exports declined less than proportionately, suggesting that while tariffs imposed a cost, the U.S. market continued to rely heavily on Chinese supply due to limited alternatives or established supplier relationships. The third group yielded surprising findings: in categories such as ores and metals, textiles and clothing, agricultural raw materials, and plastic or rubber, export volumes actually increased despite higher tariffs. These positive elasticities suggest strategic behaviors such as stockpiling by U.S. importers, price absorption by Chinese exporters, or rigidities in global supply chains that favored continued trade despite added costs. These findings have important policy implications for Chinese policymakers. Firstly, the identification of highly elastic sectors indicates where China is most vulnerable in the trade war with Trump administration. In these sectors, the U.S. has greater leverage to inflict economic damage by redirecting trade to other partners. Therefore, Chinese authorities should consider a dual strategy: short-term damage control and long-term diversification. In the short term, measures such as targeted subsidies, tax relief, or alternative market development (e.g., Belt and Road Initiative countries) could buffer export losses. In the long term, structural upgrades to improve quality, reduce costs, and enhance innovation in these sectors would make Chinese goods less substitutable. Secondly, for sectors showing inelastic responses, Chinese policymakers should recognize the strategic importance of these industries as trade anchors. These are areas where China holds considerable market power or production efficiency, and thus any disruptions would also significantly impact U.S. buyers. Leveraging these sectors diplomatically in trade negotiations could improve bargaining power. For instance, ensuring consistent supply and quality in rare earth minerals or advanced manufactured goods can reinforce China’s indispensability in global supply chains. The most counterintuitive findings lie in the sectors with positive elasticities. Policymakers should further investigate the causes behind these anomalies. If these increases are due to transitory factors such as pre-tariff stockpiling, Chinese exporters should not over-invest based on temporary demand surges. However, if they reflect deeper supply chain dependencies or consumer preferences, then these sectors could represent opportunities for expansion. In particular, the plastic and rubber industry, which posted the highest positive elasticity, could be nurtured through enhanced research and development, sustainability innovations, and quality improvements to gain further market share. In light of the ongoing trade war and broader geopolitical tensions, Chinese policymakers must adopt a more dynamic and adaptive trade strategy. This includes diversifying trade partners, investing in high-tech sectors to reduce dependency on tariff-sensitive categories, and forming stronger trade alliances. Efforts should be concentrated on sectors that demonstrate resilience or potential for future growth, such as textiles with advanced manufacturing techniques, biodegradable plastics, and high-grade agricultural raw materials. Simultaneously, reducing overreliance on the U.S. market by intensifying trade with ASEAN, Africa, and Latin America could mitigate external risks. However, this study is not without limitations. Firstly, the elasticity estimates are based on available trade and tariff data, which may not fully capture real-time business adjustments, informal trade routes, or strategic reclassification of goods. Secondly, the model does not account for firm-level heterogeneity; different firms within the same sector may respond differently based on size, export experience, and supply chain agility. Future research could improve upon these limitations by incorporating firm-level data, using dynamic panel models to assess lagged effects, and exploring interactions between tariffs and non-tariff barriers. Another promising avenue is the examination of re-routing behaviors, such as trade deflection through third countries, which has become increasingly common in response to tariff hikes. Declarations Funding: None Conflict of interest: None Data availability: upon request Author Contribution Badr Moughamir wrote the main manuscript, Jihad Ait soussane conducted the empirical analysis and Zahra Mansouri supervised the manuscript Acknowledgement None References Ait Soussane J, Mansouri Z (2020) L’impact Des Investissements Directs Marocains Sur La Reduction De Pauvrete Dans Les Pays De L’afrique Subsaharienne: Essai De Modelisation Econometrique. Int J Econ Manage Res 1(2):43–61 Amiti M, Redding SJ, Weinstein DE (2019) The impact of the 2018 tariffs on prices and welfare. J Economic Perspect 33(4):187–210 Bellora C, Fontagné L (2020) Shooting oneself in the foot? Trade war and global value chains. CEPII Working Paper 2019-18 Bergin PR, Corsetti G (2020) The macroeconomic stabilization of tariff shocks: What is the optimal monetary response? NBER Working Paper No. 27660 Bigsten A, Gebreeyesus M, Söderbom M (2016) Tariffs and firm performance in Ethiopia. J Dev Stud 52(7):986–1001 Caldara D, Iacoviello M, Molligoda P, Prestipino A, Raffo A (2020) The economic effects of trade policy uncertainty. J Monet Econ 109:38–59 Cavallo A, Gopinath G, Neiman B, Tang J (2019) Tariff pass-through at the border and the store: Evidence from US trade policy. NBER Working Paper No. 26396 Dong L, Kouvelis P (2020) Impact of tariffs on global supply chain network configuration: Models, predictions, and future research. Manuf Service Oper Manage 22(1):202–216 Fajgelbaum PD, Goldberg PK, Kennedy PJ, Khandelwal AK (2020) The return to protectionism. Quart J Econ 135(1):1–55 Flaaen A, Pierce JR (2019) Disentangling the effects of the 2018–2019 tariffs on a globally connected U.S. manufacturing sector. Finance Econ Discussion Ser No. 2019-086, Board of Governors of the Federal Reserve System. Furceri D, Hannan SA, Ostry JD, Rose AK (2019) Macroeconomic consequences of tariffs. IMF Working Paper 19/9 Furceri D, Hannan SA, Ostry JD, Rose AK (2020) Are tariffs bad for growth? Yes, say five decades of data from 150 countries. J Policy Model 42(4):850–866 Handley K, Limão N (2017) Policy uncertainty, trade, and welfare: Theory and evidence for China and the US. Am Econ Rev 107(9):2731–2783 Hayakawa K, Ishikawa J, Tarui N (2020) What goes around comes around: Export-enhancing effects of import-tariff reductions—Journal. Int Econ 126:103363 Johnson RC, Noguera G (2012) Accounting for intermediaries: Production sharing and trade in value added. J Int Econ 86(2):224–236 Kao C, Chiang MH (2001) On the estimation and inference of a cointegrated regression in panel data. Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, pp 179–222 Mao H, Görg H (2020) Friends like this: The impact of the US-China trade war on global value chains. World Econ 43(7):1776–1791 Mao Q, Sheng B (2017) The impact of tariff reductions on firm dynamics and productivity in China: Does market-oriented transition matter? China Econ Rev 45:125–142 Melitz MJ (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71(6):1695–1725 Muchopa CL, Bahta YT, Ogundeji AA (2019) Tariff rate quota impacts export market access to South African fruit products in the EU market. Agrekon 58(4):436–455 Phillips PC, Hansen BE (1990) Statistical inference in instrumental variables regression with I (1) processes. Rev Econ Stud 57(1):99–125 Shen G, Wang P, Xu Y (2021) Trade destruction and deflection effects of US-China trade frictions on China’s tariff-targeted products. World Econ 44(8):2216–2237 Stock JH, Watson MW (1993) A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: J Econometric Soc, 783–820 Footnotes https://wits.worldbank.org/CountryProfile/en/Country/CHN/StartYear/1993/EndYear/2022/TradeFlow/Export/Indicator/XPRT-TRD-VL/Partner/USA/Product/Total https://wits.worldbank.org/CountryProfile/en/Country/USA/StartYear/1993/EndYear/2022/TradeFlow/Import/Indicator/AHS-WGHTD-AVRG/Partner/CHN/Product/Total Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7943060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542584179,"identity":"5054bcac-a029-4b0d-8b9d-45e5d2ade4d7","order_by":0,"name":"Badr MOUGHAMIR","email":"","orcid":"","institution":"University Ibn Tofail","correspondingAuthor":false,"prefix":"","firstName":"Badr","middleName":"","lastName":"MOUGHAMIR","suffix":""},{"id":542584181,"identity":"dab38632-c837-4241-b788-00b596e64c9a","order_by":1,"name":"Jihad Ait soussane","email":"data:image/png;base64,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","orcid":"","institution":"University Ibn Tofail","correspondingAuthor":true,"prefix":"","firstName":"Jihad","middleName":"Ait","lastName":"soussane","suffix":""},{"id":542584183,"identity":"8108fbb7-9392-4719-8ee1-844e910cd5cc","order_by":2,"name":"Zahra MANSOURI","email":"","orcid":"","institution":"University Ibn Tofail","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"MANSOURI","suffix":""}],"badges":[],"createdAt":"2025-10-26 07:52:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7943060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7943060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95727359,"identity":"5cc459ef-266c-40ba-8f9b-6264e12ae374","added_by":"auto","created_at":"2025-11-12 10:43:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66741,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7943060/v1/d809aa30c321142f2dda363f.docx"},{"id":95727360,"identity":"e554bb4b-6312-4620-8883-b06aac0c2418","added_by":"auto","created_at":"2025-11-12 10:43:21","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4257,"visible":true,"origin":"","legend":"","description":"","filename":"703e72dbe66a42c18d5a01c19758a837.json","url":"https://assets-eu.researchsquare.com/files/rs-7943060/v1/7cd53d28efdd4fc5b56f5858.json"},{"id":95727362,"identity":"1117adcf-50ed-444a-a185-780bb8ec31ea","added_by":"auto","created_at":"2025-11-12 10:43:21","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120188,"visible":true,"origin":"","legend":"","description":"","filename":"703e72dbe66a42c18d5a01c19758a8371enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7943060/v1/58d512312cb29b9d67e3bd00.xml"},{"id":95799876,"identity":"80b9ce87-435d-47c1-b169-39122ddea133","added_by":"auto","created_at":"2025-11-13 08:21:01","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118851,"visible":true,"origin":"","legend":"","description":"","filename":"703e72dbe66a42c18d5a01c19758a8371structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7943060/v1/2f687c999196c4feb977a02a.xml"},{"id":95727363,"identity":"bbba0b5f-f38a-4317-8ec3-e65c0a110891","added_by":"auto","created_at":"2025-11-12 10:43:21","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121843,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7943060/v1/c3670bfef6c87273ec9b7eaa.html"},{"id":100737513,"identity":"4d543241-22fb-41cf-b155-8170a96f1d84","added_by":"auto","created_at":"2026-01-20 22:55:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":980418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7943060/v1/c5bb78f3-a4a5-4e55-930f-8d8f4b79abec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Will Chinese Exports Respond to Trump’s Tariffs? A Sector-Level Analysis in The Light of The Current US-China Trade War","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past decade, economic tensions between the United States and China have escalated into a full-blown trade conflict, most notably during the administration of President Donald Trump. Initiated in 2018, the US-China trade war marked one of the most significant disruptions in global trade since the formation of the World Trade Organization. Framed under the banner of correcting trade imbalances and protecting domestic industries, the Trump administration imposed a series of tariffs on a broad array of Chinese goods.\u003c/p\u003e\u003cp\u003eThe US-China trade war (2018\u0026ndash;2019) presents a natural experiment in protectionist policy and its cascading economic effects. The United States, under President Trump, imposed tariffs on approximately 250\u0026nbsp;billion USD worth of annual imports from China, raising average tariffs from 3.7% to nearly 25%. In response, China retaliated with tariffs covering about 110\u0026nbsp;billion USD in US goods, increasing its average tariff rate from 8% to over 20% on the targeted products. This tit-for-tat tariff escalation represents the most significant tariff conflict between the two countries since the Great Depression era of the 1930s.\u003c/p\u003e\u003cp\u003eA large body of empirical research has evaluated the outcomes of this trade war, and the consensus is broadly negative for both economies. On the American side, studies by Amiti et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Fajgelbaum et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reveal that US importers and consumers bore the brunt of the tariffs. Since Chinese exporters only marginally lowered their prices, the tariffs were almost entirely passed on to American consumers. Cavallo et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) confirmed this trend through import and retail price data, showing a near-complete pass-through of tariffs to US retail prices, without notable reductions in foreign suppliers' margins. Consequently, American households experienced increased prices for goods such as electronics and household appliances, which eroded their purchasing power.\u003c/p\u003e\u003cp\u003eFrom a macroeconomic perspective, Fajgelbaum et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) estimated a real income loss of 7.8\u0026nbsp;billion USD for the US economy in 2018 alone, equivalent to roughly 0.04% of GDP. This figure masks significant redistributive effects: while consumers and industries dependent on imports lost approximately 114\u0026nbsp;billion USD, domestic producers gained 24\u0026nbsp;billion USD, and the federal government collected around 65\u0026nbsp;billion USD in tariff revenues. Thus, while the government and certain protected industries benefited, the overall efficiency and welfare impact was negative, as wealth was transferred away from consumers.\u003c/p\u003e\u003cp\u003eContrary to expectations, the US manufacturing sector did not benefit as hoped. Study by Flaaen and Pierce (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicates that manufacturing industries targeted by tariffs actually lost more jobs than other sectors. This counterintuitive outcome is explained by three main factors: higher production costs due to tariffs on imported inputs (e.g., steel, aluminum), retaliatory tariffs from China (especially targeting agriculture and manufacturing), and a resultant decline in export competitiveness. High-profile examples include the US automotive and machine tool industries, which saw falling profit margins, and soybean farmers, whose exports to China plummeted by 75% in 2018. As Bellora and Fontagn\u0026eacute; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) aptly described, this amounted to a form of \u0026ldquo;economic self-sabotage\u0026rdquo;.\u003c/p\u003e\u003cp\u003eChina, while also significantly impacted, managed to mitigate some of the negative effects through strategic responses. Chinese exports to the US dropped sharply in tariff-targeted sectors, often by as much as 50%, leading to a noticeable slowdown in manufacturing activity by 2019. However, Beijing employed two key mechanisms to cushion the blow: first, it diversified its export destinations, increasing trade with markets in Asia, Europe, and Africa; second, it allowed for a controlled depreciation of the renminbi, improving price competitiveness by roughly 10% between mid-2018 and the end of 2019. These steps partially absorbed the impact of US tariffs, but they did not fully offset the downturn in total Chinese exports.\u003c/p\u003e\u003cp\u003eIn addition to short-term trade disruptions, the trade war accelerated long-term shifts in global value chains. Firms in both countries sought to sidestep tariffs by relocating production to third countries (e.g., Vietnam, Malaysia), rerouting goods through intermediaries, or reclassifying product codes. These avoidance strategies were not without cost; they introduced inefficiencies and raised operational expenses. As noted by Dong and Kouvelis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the reconfiguration of supply chains added considerable complexity and cost to international business operations. The uncertainty generated by the trade war also caused companies to delay or redirect investment, with some US and Chinese firms choosing to scale down bilateral operations or reinvest in less exposed regions such as Southeast Asia and Mexico.\u003c/p\u003e\u003cp\u003eThese developments underscore the classic economic understanding that protectionist measures often yield unintended consequences. While tariffs can reduce imports in the targeted sectors, they also tend to raise prices for domestic consumers, disrupt supply chains, and invite retaliatory measures that harm exporters. Moreover, in highly integrated global economies, isolating the impact of tariffs is difficult due to interdependencies in production networks and trade relationships.\u003c/p\u003e\u003cp\u003eThis study seeks to provide an empirical investigation of how Chinese exports to the United States responded to these increased tariffs across 23 product categories using time series over the period 1993\u0026ndash;2022. Specifically, the study examines the elasticity of Chinese exports to US tariffs, distinguishing between sectors that were negatively elastic and inelastic. Understanding these differences is essential for crafting effective trade and industrial policies in a context of heightened geopolitical tension and economic nationalism.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is organized as follows: Section 2 reviews the existing literature on the effect of trade tariffs on exports. Section 3 outlines the methodology and empirical design. Section 4 presents the results of the econometric analysis with discussion in light of theoretical and empirical insights. Finally, Section 5 concludes the study and offers policy recommendations.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Theoretical review:\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1. Tariffs, competitiveness, and export performance:\u003c/h2\u003e\u003cp\u003eOne unexpected effect of tariff policies is that measures intended to protect the national productive apparatus may weaken domestic companies' competitiveness in international markets over the long term. Several theoretical systems explain this phenomenon.\u003c/p\u003e\u003cp\u003eOn the one hand, this policy of protecting the internal market from international competition through high customs tariffs can significantly reduce the incentive for national firms to innovate and maximize their productivity. In the absence of competitive import pressure, some industries risk becoming inefficient, which harms their export price and non-price competitiveness. For example, the creative destruction model applied to trade states that openness to trade forces the least productive firms out of the market while the most productive firms grow, leading to an overall gain in productivity (Melitz, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Conversely, the protection of the domestic market through tariffs can delay this process of natural selection, artificially keeping underperforming local firms alive and thus impeding national productivity growth.\u003c/p\u003e\u003cp\u003eOn the other hand, several manufacturing industries depend on imported inputs (raw materials, components, and capital goods). High tariffs on these inputs increase local production costs. Theoretically, an increase in input costs is partly reflected in the export prices of finished goods, making them less competitive internationally. Conversely, lower tariffs on inputs can reduce production costs and improve the international competitiveness of domestic producers. Thus, generally, tariff liberalization on inputs stimulates exports, while increased protection on inputs discourages them. This phenomenon has been formalized in the concept of effective protection rate, which considers the structure of an industry's inputs and outputs: a tariff on a key input can negate the protective effect of a tariff on the final product and even generate negative adequate protection.\u003c/p\u003e\u003cp\u003eIn addition, tariffs on finished products can lead to retaliatory measures by trading partners. If foreign countries impose tariffs on the exports of the initiating country, domestic exporters lose market share abroad. This risk of retaliation accentuates the negative impact of tariffs on export performance.\u003c/p\u003e\u003cp\u003eIt is worth noting that some recent work has also explored the effects of trade policy uncertainties on competitiveness. The argument is that the unpredictability of future tariffs may deter companies from investing in export capacity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2. Global Value Chains and tariff policy uncertainty:\u003c/h2\u003e\u003cp\u003eThe structure of international trade has changed significantly with the advent of global value chains (GVCs). Now, goods move across multiple borders at different stages of the international division of the production process. On average, a significant portion of the value of an exported product comprises imported inputs (Johnson and Noguera, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this context, tariffs affect the final products consumed and all intermediate upstream trade. Theoretically, this means that the effect of a tariff can be amplified throughout the production chain: a tariff imposed in one country on an intermediate component can impact the export competitiveness of the downstream country that utilizes that component. For example, Johnson and Noguera (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) show that traditional protectionist measures can have more complex effects on the distribution of trade value added because of the international sharing of production.\u003c/p\u003e\u003cp\u003eRecent models increasingly consider these dimensions of vertical specialization and value-added trade. They predict that a bilateral tariff dispute between two countries can have cascading effects on the trade flows of other nations not directly targeted, as long as the latter are linked to the first two through supply chains. For example, a trade war can lead to a geographical reorganization of value chains: some companies will seek to divert trade via third countries to evade tariffs. Other companies facing rising costs of importing strategic inputs may choose to repatriate specific production steps or alter foreign suppliers. These adjustments carry costs and do not occur instantaneously, which implies temporary disruptions to trade flows.\u003c/p\u003e\u003cp\u003eIn addition, political uncertainty regarding the volatility of tariff policies can dampen international trade. Without clarity on future market access conditions (tariffs, quotas), firms may adopt a cautious approach, foregoing the opportunity to penetrate specific markets or invest in export capacities. A theoretical framework developed by Handley and Lim\u0026atilde;o (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) explained that reducing political uncertainty (e.g., via a trade agreement) can significantly boost exports by eliminating the risk premium. Conversely, as was the case during the changing announcements during the Sino-US trade conflict, an increase in uncertainty is equivalent to a major negative shock to international trade, potentially as significant as an actual increase in tariffs.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Empirical evidences:\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Macroeconomic and aggregate trade flow effects:\u003c/h2\u003e\u003cp\u003eAccording to several econometric studies, tariff increases tend to reduce trade flows and negatively impact the macroeconomic performance of countries that adopt them. Analyzing data spanning over five decades and a sample of 150 countries, Furceri et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) explain that episodes of tariff increases are followed by significant declines in national GDP and productivity in the medium term. For example, a tariff increase of 1 percentage point leads to a decrease of approximately 0.5% of GDP on average after a few years. Similarly, higher tariffs are associated with rising unemployment and increasing income inequality. Significantly, despite the resulting import contraction, tariffs do not improve the country's trade balance. Adopting a tariff barrier, exports also tend to decline, largely offsetting the reduction in net imports. This empirical result, consistent with Lerner's theorem, explains why protectionist policies fail to correct trade imbalances while depressing economic activity.\u003c/p\u003e\u003cp\u003eWork conducted by the IMF confirms these findings. Likewise, Furceri et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in a separate study, estimate response functions (local projections) following exogenous tariff shocks: they explain that a tariff increase leads to an appreciation of the real exchange rate and a simultaneous decrease in the country's exports and imports, leaving the trade balance nearly unchanged. This appreciation of the national currency, resulting from the effect of the terms of trade or the rise in domestic goods prices, penalizes the exporting sectors.\u003c/p\u003e\u003cp\u003eConversely, tariff liberalization generally has positive effects on trade flows. Numerous studies of free trade agreements and WTO accessions conclude that the reduction in customs duties has substantially increased the countries' exports and imports. For example, tariff reductions as part of China's accession to the WTO in 2001 catalyzed a boom in Chinese exports and a reorganization of global industrial production.\u003c/p\u003e\u003cp\u003eAt an aggregate sectoral level, trade elasticities regarding tariff variations appear to be high. Recent estimates at the product level suggest that import volumes of a given good decline significantly in response to introducing a tariff on that good. During the 2018\u0026ndash;2019 period, characterized by escalating trade tensions, there were substantial declines in bilateral trade flows between countries imposing tariffs on one another. For instance, China's exports to the United States of products subject to the new tariffs dropped sharply (approximately \u0026minus;\u0026thinsp;40% to -50% by volume for specific categories) immediately after the U.S. tariffs took effect. This trade-reducing mechanism has particularly impacted manufactured goods with higher technological value that the United States aimed to target through its measures.\u003c/p\u003e\u003cp\u003eHowever, recent experience has also revealed trade diversion effects. When two major economies drastically reduce their trade with each other, other third countries can benefit by capturing market share. For example, studies show that following US tariffs on Chinese goods, many importers in the United States have purchased from suppliers in other emerging countries (Vietnam, Mexico, Taiwan, etc.), replacing Chinese products that have become more expensive (Mao and G\u0026ouml;rg, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the same time, Chinese exporters have redirected some of their sales to other markets in Asia, Europe, and Africa to compensate for the loss of access to the US market. According to Shen et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Chinese exports destined initially for the United States have, in part, been redirected to third markets, a particularly pronounced phenomenon for differentiated (i.e., specific or branded) goods. However, this diversion has generally only partially offset the decline in bilateral trade: overall, global trade slowed during the rise in tariff barriers in 2018\u0026ndash;2019, contributing to the first contraction in the volume of world trade since the 2009 crisis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Sectoral and firm-level impacts:\u003c/h2\u003e\u003cp\u003eBeyond macroeconomic aggregates, the empirical literature has examined how pricing policies affect firm and sector performance across various national contexts, especially in developing countries. These micro-econometric analyses allow a better understanding of the specific mechanisms through which tariffs impact competitiveness and exports.\u003c/p\u003e\u003cp\u003eThe distinction between input tariffs and tariffs on final goods is clear from empirical studies. In line with theoretical expectations, input tariffs are particularly harmful to firms' productivity and export performance. For example, according to Bigsten et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), in the case study of Ethiopia in the 1990s-2000s, the reduction of tariffs on imported intermediate inputs led to a significant productivity gain for local firms, especially those that were most dependent on foreign inputs. This gain results from both access to higher-quality raw materials and technologies and the learning effect of using a variety of inputs. Conversely, Bigsten and his co-authors do not observe any improvement in productivity following the reduction of tariffs on finished goods. In other words, the protection of local producers through high tariffs on finished products did not translate into downstream efficiency gains, but rather into productivity losses for the users of these protected products. This observation reinforces the idea that excessive tariff protection can have cascading perverse effects on the economy as a whole.\u003c/p\u003e\u003cp\u003eChina serves as a particularly illuminating case study of the effects of trade reforms, having undergone significant tariff changes. During the 1990s and 2000s, China drastically lowered its average customs duties (from more than 30% to less than 10% in two decades) as part of its economic opening and accession to the WTO. The analysis by Mao and Sheng (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reveals the impact of these tariff reductions on firm dynamics and productivity in China. They find that lower tariffs on final goods have tended to increase competitive pressure, leading to the exit of the least productive firms from the market, which has helped to raise the average productivity of the sector. Additionally, the reduction in input tariffs has stimulated both the entry of new firms (attracted by the availability of cheaper inputs) and the exit of the least efficient, accentuating the renewal of the productive fabric. Crucially, these positive effects of liberalization on productivity were most pronounced in China's provinces where the degree of domestic liberalization and market reform was high. This suggests a complementarity between domestic reforms (deregulation, business climate improvement) and trade reforms: the gains derived from lowering tariffs are maximized when the domestic competitive environment allows resources to be reallocated to the most efficient firms. In short, the Chinese experience illustrates that a gradual reduction in tariff barriers, coupled with structural reforms, can be a powerful lever for industrial modernization and competitiveness gains.\u003c/p\u003e\u003cp\u003eOther sector studies confirm the potentially negative impact of tariffs on export performance due to value chains. For example, one study analyzed how tariffs on computer components in the United States affected exports of finished electronic products; it found that a tariff on a critical component can significantly reduce exports of final downstream products, as domestic producers pass on the cost or lose competitiveness vis-\u0026agrave;-vis foreign competitors not subject to the additional cost.\u003c/p\u003e\u003cp\u003eIn addition, the impact of tariffs may vary depending on the characteristics of the firms. Analysis of firms has revealed that the most productive exporting firms are generally also those that import a significant proportion of their inputs (the phenomenon of efficient imports). Thus, a tariff on inputs severely affects these \"champion\" export firms, which increases costs. The results show that lower input tariffs due to trade liberalization in India have disproportionately favored the most successful firms, enabling them to increase exports with cheaper inputs. Similarly, a tariff increase penalizes large import-intensive exporters, while domestic companies can benefit in the short term by gaining local market share.\u003c/p\u003e\u003cp\u003eFinally, the role of related trade policies, such as tariff quotas and export taxes, should be highlighted, as these can also impact export performance. For instance, an analysis of tariff quotas in the agri-food sector reveals that these mechanisms (which combine a reduced tariff import quota with a high tariff above that) are likely to restrict access to export markets significantly. According to the study conducted by Muchopa et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) regarding South African fruit exported to the European Union under quota, they conclude that these limiting quotas slow down the growth of South African fruit exports, creating a distortion in the distribution of market shares between supplying countries and disrupting the competitiveness of specific horticultural sectors. This conclusion shows that unconventional \"tariff\" barriers (such as quotas or variable tariffs) have effects similar to simple tariffs in reducing the export flows of the target country.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Empirical methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Sample of the study:\u003c/h2\u003e\u003cp\u003eThis study is based on time series data covering Chinese exports to the United States and the tariffs applied by the U.S. on these exports over the period 1993 to 2022 across 23 product categories. The selection of this time span is driven by the availability of consistent and comprehensive data from the World Integrated Trade Solution (WITS) database provided by the World Trade Organization (WTO). The analysis is conducted across a wide range of product categories, reflecting the diversified structure of Chinese exports to the U.S. market.\u003c/p\u003e\u003cp\u003eThe product categories are classified according to the WTO trade nomenclature: Agricultural Raw Materials, Animal Products, Capital Goods, Chemicals, Consumer Goods, Food Products, Footwear, Fuels, Hides and Skins, Intermediate Goods, Machinery and Electronics, Machinery and Transport Equipment, Manufactures, Metals, Minerals, Ores and Metals, Plastic or Rubber, Raw Materials, Stone and Glass, Textiles and Clothing, Transportation Equipment, Vegetable Products, Wood Products.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Empirical model:\u003c/h2\u003e\u003cp\u003eTo examine the direct effect of tariffs applied by the United States on Chinese exports, this study employs a bivariate cointegration model. This approach is consistent with common practice in empirical studies that adopt a bivariate cointegration framework (Ait Soussane and Manousri, 2020; Herzer and Nunnenkamp, 2012; Khan and Khan, 2011; Mehmoud and Seddiqui, 2013; Ajaga and Nunnenkamp, 2008; Chakraborty and Nunnenkamp, 2008). The model is specified as follows:\u003c/p\u003e\u003cp\u003eLog(Exports)\u003csub\u003et\u003c/sub\u003e​=α\u0026thinsp;+\u0026thinsp;β(TARIFF)\u003csub\u003et​\u003c/sub\u003e+εt\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eWhere: EXPORTS\u003csub\u003et\u003c/sub\u003e refers to the volume of Chinese exports to the United States, measured in thousands of USD, disaggregated by product category. TARIFF\u003csub\u003et\u003c/sub\u003e represents the average weighted tariff effectively applied by the U.S. on Chinese exports in each corresponding product category.\u003c/p\u003e\u003cp\u003eDrawing on the theoretical and empirical literature reviewed in the second section, there are multiple transmission channels through which tariffs can affect exports, including price competitiveness, supply chains, and trade policy uncertainty. However, the goal of cointegration analysis is not to isolate individual mechanisms, but rather to capture the total long-run effect of tariffs on exports. This consideration provides strong justification for adopting a bivariate approach, rather than including additional control variables. Including such controls in time series cointegration models can introduce serious estimation problems due to potential multicollinearity, endogeneity, or non-stationarity. By focusing on the core relationship between tariffs and exports, the bivariate framework offers a parsimonious and robust specification to assess the long-run effects of trade policy on export performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Variable description and source of data:\u003c/h2\u003e\u003cp\u003eThis study uses two main variables: Chinese exports to the United States (dependent variable) and U.S. tariffs applied on Chinese exports (explanatory variable), across a range of product categories. The two variables are described as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExports\u003c/strong\u003e\u003cp\u003eThe dependent variable is measured as the volume of Chinese goods exported to the U.S., expressed in thousands of USD. To interpret the results in terms of elasticities, we transform the export values using the natural logarithm. This transformation helps stabilize the variance and allows us to interpret the estimated coefficients as percentage changes. Export data by product category are collected from the WITS database\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTariffs\u003c/strong\u003e\u003cp\u003eThe explanatory variable is the weighted average tariff effectively applied by the United States on Chinese exports. This measure reflects the actual tariff burden on Chinese products entering the U.S. market, as it accounts for the specific tariff rates applied to each product and the relative share of each product in total exports. It is therefore a more accurate and comprehensive indicator than simple average tariffs, as it captures both the tariff structure and the composition of trade. Tariffs data by product category are collected from the WITS database\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Estimation techniques:\u003c/h2\u003e\u003cp\u003eThe Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) are two widely used estimation techniques in cointegration analysis to derive robust long-run relationships between non-stationary variables. Both methods were developed to address the limitations of traditional Ordinary Least Squares (OLS) when applied to cointegrated time series, particularly issues of serial correlation and endogeneity.\u003c/p\u003e\u003cp\u003eFMOLS, proposed by Phillips and Hansen (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), modifies the OLS estimator by correcting for serial correlation in the residuals and for the endogeneity caused by the long-run correlation between the cointegrating equation\u0026rsquo;s regressors and the stochastic error term. This correction results in asymptotically unbiased and normally distributed estimates, making FMOLS particularly useful in small samples where traditional estimators may perform poorly. On the other hand, DOLS, introduced by Stock and Watson (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), enhances the standard cointegration regression by including leads and lags of the first differences of the explanatory variables. This dynamic specification helps to eliminate the feedback effects and corrects for endogeneity by capturing short-term dynamics that could bias the estimates.\u003c/p\u003e\u003cp\u003eBoth estimators are preferred over simple OLS in cointegrated systems, as they yield efficient and consistent long-run parameters even in the presence of endogenous regressors and serially correlated errors. FMOLS is non-parametric and based on semi-parametric corrections, while DOLS is parametric and allows for easier inference, especially when comparing nested models. According to Kao and Chiang (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), DOLS often performs better than FMOLS in finite samples, while FMOLS remains more robust under certain forms of heteroskedasticity and serial correlation. In practice, the use of both methods together strengthens the empirical findings, as consistent and significant results across both estimators confirm the robustness of the estimated cointegrating relationship.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnit root test using the Augmented Dickey-Fuller:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eExports\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eTariff\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAt level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt 1st difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAt 1st difference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural Raw Materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.274635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.973532***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.382993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.375569***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.291020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.614928**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.927781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.681773***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCapital goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.132421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.575318***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.178555***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.546961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.083676***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.066349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.921851***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.337295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.401312***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.757510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.235572**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.751656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.140423***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.068747**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFootwear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.378089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.326086**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.607390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.125284**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.882226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.713915***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.356449*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHides and Skins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.031237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.677099****\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.814478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.983689**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.867653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.406446***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.120158**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Electronics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.884244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.943664***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.999471**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Transport Equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.164021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.057782***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.771119***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufactures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.319334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.996969***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.210108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.814382**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.843599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.579072***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.315932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.793659**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinerals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.211625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.660244***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.652028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.275172***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOres and Metals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.321371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.477092***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.542203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.859266***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlastic or Rubber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.506621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.729811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.602961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.31762**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaw materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.887965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.745360**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.081625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.807525***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone and Glass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.124690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.071942**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.507654*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles and Clothing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.410265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.458680***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.319915*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransportation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.976754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-8.746360***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.759646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.571525***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.493646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.728712***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.671858*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.361471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.233195***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.335905*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, **, *** indicate the significance levels 1%, 5% and 10%. Null hypothesis: the variable has a unit root. Trend and intercept are included in the test equation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo;.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of unit root tests using the Augmented Dickey-Fuller (ADF) method for the variables exports and tariffs across the product categories. The results show that exports are non-stationary at level for all product categories, as the ADF statistics do not allow rejection of the null hypothesis. However, when first differenced, exports become stationary in all categories, with test statistics strongly rejecting the null at the 1% level in most cases. This indicates that exports are integrated in the first order I(1), meaning they become stationary after first differencing, and thus exhibit a unit root process.\u003c/p\u003e\u003cp\u003eIn contrast, tariffs show a more heterogeneous behavior across product categories. For certain products, such as Chemicals, Ores and Metals, and Machinery and Transport Equipment, the ADF statistics suggest that tariffs are non-stationary at level but become stationary at first difference, thus indicating that tariffs are I(1) in these cases. However, for other categories such as Agricultural Raw Materials, Animal Products, and Raw Materials, tariffs are already stationary at level, as the test statistics reject the null of a unit root at the 1% significance level. This implies that for these product categories, tariffs are integrated at level I(0).\u003c/p\u003e\u003cp\u003eThis divergence in the integration order of the two variables, exports and tariffs, has important econometric implications. Since exports are consistently I(1) and tariffs are a mix of I(0) and I(1) depending on the product category, it is essential to determine whether a long-run equilibrium, i.e. cointegration relationship, exists between the two variables in each category in the follow table.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eJohansen cointegration tests:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTests of cointegration:\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTrace test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMaximum Eigenvalue test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis:\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo cointegration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt most 1 cointegration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo cointegration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAt most 1 cointegration\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural Raw Materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.47144**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.200679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.27076*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.200679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.27206***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.02703**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.24503***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.02703**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCapital goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.71794*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.584528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.13341*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.584528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.19137***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.107337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.08403**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.107337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.00804***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3942180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.61382***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.394218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.08738**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.940208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.14717**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.940208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFootwear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.55383***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.272046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.28179***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.272046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.69427*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.705992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.98828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.705992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHides and Skins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53.72021***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.200848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.51936***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.200848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.70952***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.70054***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Electronics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.48816**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.896201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.59196***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.896201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Transport Equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.44306***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.700294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.74277***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.700294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufactures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.11487***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.357954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.75691***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.357954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.57124**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.387034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.18420**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.387034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinerals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.64788***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.076037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.57185***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.076037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOres and Metals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.58031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.526654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.05366***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.526654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlastic or Rubber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.88425**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.528027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.35622**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.528027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaw materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.80595**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.496865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.30909*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.496865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone and Glass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.17875***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.395935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.78282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.395935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles and Clothing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.87012***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.385416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.48471***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.385416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransportation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.98963***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.138665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.85097***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.138665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.68148***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.930021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.75146***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.930021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.55646***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.978956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.57750***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.978956\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: *, **, *** indicate the significance levels 1%, 5% and 10%. linear deterministic trend is used for trend assumption. The optimal lag length is based on the VAR model\u0026rsquo; lag length criteria.\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo;.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the Johansen cointegration tests, using both the Trace test and the Maximum Eigenvalue test, to test the existence of a long-run equilibrium relationship between exports and tariffs across the product categories. The results strongly indicate that exports and tariffs are cointegrated in nearly all product categories. For most sectors, both the Trace test and the Maximum Eigenvalue test reject the null hypothesis of no cointegration at significance levels of 1% and 5% mostly. At the same time, the null hypothesis of at most one cointegrating relationship is not rejected, suggesting the presence of a unique long-run equilibrium relationship between the two variables for each category.\u003c/p\u003e\u003cp\u003eOverall, these results demonstrate that tariffs and exports are cointegrated across the majority of product categories, indicating that changes in tariffs are associated with long-term movements in export levels. This long-run relationship validates the theoretical expectation that tariff barriers have persistent and systematic effects on trade flows over time. Furthermore, the presence of cointegration justifies proceeding to cointegration regression methods as run in the following table:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Chinese export elasticity with respect to tariffs applied by the United States across product categories:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDOLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eFMOLS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTariff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTariff\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural Raw Materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.88701***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.275157***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.43978***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.622024***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.87563***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.51565***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.36317***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-12.18867***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCapital goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.23609***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.456101**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.31897***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.590429***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.18753***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.688708**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.63546***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.835595**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.51884***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.581288**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.25116***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.695695**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.83734***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.527196***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.73098***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.644788***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFootwear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.258272***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.258272***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.258272***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.258272**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.128620***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.128620*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.128620***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.128620**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHides and Skins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.03294***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.641035**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.15809***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.192186***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.78297***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.064054***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Electronics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.31834***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.218354***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.54570***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.342169***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Transport Equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.82527***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.316433***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.62776***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.260478***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufactures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.29638***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.812868**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.36947***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.849497***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.10766***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.053298**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.22835***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.120741***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinerals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.95923***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.911064***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.97422***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.923089***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOres and Metals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.45054***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.102042***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.58181***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.061716***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlastic or Rubber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.260004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.257404**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.326943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.699709\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaw materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.66797***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.017387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.31838***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.689905***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone and Glass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.51285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.213702***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.72575***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-12.61069***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles and Clothing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.336493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.353393***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.488578***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.802584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransportation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.26830***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.012231***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.52115***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.148693***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.98046***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.226087**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.82689***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.033896***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.34031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.279308***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.68112***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.474202***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, **, *** indicate the significance levels 1%, 5% and 10%. Depended variable: Log(Exports). The optimal lag length is based on the VAR model\u0026rsquo; lag length criteria. N/A: non-applicable regression due to Near singular matrix in whitening regression.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo;.\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e offer detailed empirical evidence on the effect of U.S. tariffs on Chinese exports across 23 product categories using DOLS and FMOLS methods. The results indicate that, across most product categories, the imposition of U.S. tariffs negatively affects Chinese exports, with varying degrees of elasticity. In the majority of categories, such as machinery and electronics, capital goods, intermediate goods, transportation equipment, metals, chemicals, and consumer goods, the elasticity of exports to tariffs is negative and statistically significant, suggesting that higher tariffs are associated with reduced Chinese exports to the United States. This aligns with theoretical expectations that tariffs act as trade barriers, increasing costs and reducing competitiveness.\u003c/p\u003e\u003cp\u003eFrom a theoretical standpoint, as outlined in the literature, protectionist tariffs can reduce a country's export performance through several mechanisms. First, by shielding domestic industries from international competition, tariffs reduce the incentive for innovation and productivity improvements. As described by the creative destruction model (Melitz, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), trade openness encourages the exit of less productive firms and enhances overall efficiency, whereas protectionist policies delay this process and keep inefficient firms alive, diminishing export competitiveness. Second, many Chinese manufacturing sectors rely heavily on imported inputs such as raw materials and capital goods. Tariffs on these inputs increase domestic production costs, which are often passed on to export prices, rendering Chinese goods less competitive in international markets. This is captured by the concept of effective protection rate, which stresses that tariffs on intermediate goods can negate any intended protection on final products.\u003c/p\u003e\u003cp\u003eMoreover, tariffs can trigger retaliatory measures from trade partners, further harming export performance by reducing access to foreign markets. Tariff uncertainty can also discourage firms from investing in export capacity due to unpredictability in future trade conditions. Within GVCs, where goods cross multiple borders during production, tariffs imposed on intermediate goods can ripple through the entire chain, affecting downstream exports, as emphasized by Johnson and Noguera (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This phenomenon magnifies the adverse effects of tariffs in today\u0026rsquo;s interconnected trade system.\u003c/p\u003e\u003cp\u003eEmpirical evidence from recent studies supports these theoretical insights. Furceri et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrate that tariff increases reduce GDP and productivity while increasing unemployment and income inequality. Similarly, the IMF highlights that tariff shocks lead to real exchange rate appreciation and simultaneous declines in both exports and imports, leaving the trade balance mostly unchanged and harming economic activity.\u003c/p\u003e\u003cp\u003eHowever, the results also reveal a few exceptions where the tariff effect is either positive or statistically insignificant. For example, Chinese exports of agricultural raw materials, ores and metals, plastic or rubber, and textiles and clothing appear to be less sensitive, or even positively associated, with U.S. tariffs. These anomalies could be explained by factors such as product-specific demand inelasticity, trade diversion strategies, or the ability of Chinese firms to absorb costs or reroute exports to avoid tariffs. In some categories, such as ores and metals or agricultural raw materials, China might possess a comparative advantage or essential supply role, making demand less responsive to price increases caused by tariffs.\u003c/p\u003e\u003cp\u003eNonetheless, even among the categories negatively impacted, the elasticity of Chinese exports to tariffs varies significantly. In some sectors like animal products or vegetable goods, the elasticity is relatively high, indicating that exports are highly sensitive to tariff increases. In others, such as consumer goods or food products, the elasticity is lower, suggesting more inelastic responses. These variations may reflect differences in market structure, the availability of substitute suppliers, the strategic importance of products, and their integration within GVCs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe categorization of Chinese export elasticities with respect to tariffs applied by the United States:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative elastic exports\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProduct category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative inelastic exports\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10,51565%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinerals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,911064%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3,226087%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eManufactures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,812868%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,688708%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHides and Skins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,641035%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCapital goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,456101%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsumer goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,581288%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Transport Equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,316433%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFood Products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,527196%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,279308%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFootwear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,258272%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachinery and Electronics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,218354%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStone and Glass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,213702%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,192186%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,12862%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,053298%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRaw materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-0,017387%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransportation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,012231%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProduct category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive elasticities to exports\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c4\" namest=\"c3\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOres and Metals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,102042%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles and Clothing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,353393%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural Raw Materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,275157%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlastic or Rubber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,257404%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: the results are based on the DOLS method considering its ability to encompasses the long-term effects.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo;.\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the imposition of tariffs by the United States on Chinese exports will lead to highly differentiated reactions across product categories, reflecting varying degrees of sensitivity or elasticity. Our classification of Chinese exports to the U.S. into three distinct groups, negative elastic, negative inelastic, and positive elastic responses, highlights the complex interaction between trade policy, supply chain structures, product characteristics, and global market dynamics. These categories not only reveal how different sectors absorb or react to external shocks like tariffs but also shed light on strategic vulnerabilities and strengths in Sino-U.S. trade relations.\u003c/p\u003e\u003cp\u003eThe first group, products exhibiting negative elastic responses to tariffs, includes product categories such as animal products (-10.52%), vegetable products (-3.23%), chemicals (-1.69%), capital goods (-1.46%), and machinery-related goods such as electronics and transport equipment. These sectors show that 1% increase in U.S. tariffs result in disproportionately large reductions in Chinese exports. One principal reason for this high sensitivity is price elasticity of demand: many of these goods, particularly in agriculture and heavy industry, have readily available substitutes in the global market. For instance, the U.S. has historically imported soybeans, meat, and other agricultural commodities from Latin America. When tariffs on Chinese animal or vegetable products increased during the Trump administration\u0026rsquo;s trade war (2018\u0026ndash;2019), American importers quickly shifted to sourcing from countries like Brazil and Argentina. Similarly, in chemicals and capital goods, multinational firms with flexible supply chains could reroute orders to suppliers in Europe or Southeast Asia. The machinery and electronics sectors are also deeply embedded in GVCs, making them highly susceptible to tariff disruptions. When tariffs were imposed on Chinese tech products, companies like Apple began to accelerate production shifts to India and Vietnam, reducing their dependence on Chinese assembly lines. This strategic flexibility allows U.S. firms to minimize costs by avoiding tariffed imports, making Chinese exports in these sectors highly elastic.\u003c/p\u003e\u003cp\u003eBy contrast, Chinese exports in the second group demonstrate negative inelastic responses to U.S. tariffs. These include categories such as minerals (-0.91%), manufactured goods (-0.81%), hides and skins (-0.64%), consumer goods (-0.58%), and footwear (-0.26%). Inelasticity here indicates that export volumes declined in response to tariff increases, but not proportionally. Several structural factors help explain this. First, some of these goods may have limited global alternatives. For example, certain rare earth minerals and industrial metals are predominantly sourced from China, giving it near-monopoly status in specific niches. In such cases, U.S. buyers are forced to continue purchasing despite price hikes. Second, brand reputation and product standardization contribute to this inelastic behavior. Many Chinese consumer goods and footwear products are not easily substitutable in terms of quality or pricing, especially at scale. U.S. retailers like Walmart and Target depend heavily on Chinese suppliers for a wide range of affordable, consistent-quality products. Even with tariffs, the cost of switching to new suppliers (logistical, contractual, or quality-related) can be high, leading to continued demand for Chinese goods. Additionally, long-term supply contracts, particularly in sectors like hides and food processing, may reduce short-term responsiveness to tariff increases. These contracts often include clauses that buffer sudden price movements, allowing trade volumes to remain relatively stable over the contract duration.\u003c/p\u003e\u003cp\u003eThe third group consists of product categories with positive elasticities to tariffs, where Chinese exports to the U.S. actually increased despite higher tariffs. These include ores and metals (+\u0026thinsp;1.10%), textiles and clothing (+\u0026thinsp;1.35%), agricultural raw materials (+\u0026thinsp;2.28%), and plastic or rubber products (+\u0026thinsp;3.26%). At first glance, these results may seem counterintuitive, yet several plausible explanations emerge. One reason is preemptive stockpiling. During the 2018\u0026ndash;2019 U.S.-China trade tensions, importers often rushed to buy goods ahead of new tariffs or in anticipation of further escalation, temporarily boosting export volumes. This was particularly evident in clothing and plastic goods, where U.S. wholesalers increased orders to maintain inventory levels. Another factor could be supply chain rigidity or gaps in domestic production. For example, the U.S. lacks significant domestic capacity to process certain ores and raw materials, and for these, import substitution is simply not viable in the short term. Chinese dominance in global production of rare earths and specific intermediate plastics means that, even with tariffs, U.S. industries remain reliant on Chinese inputs. Furthermore, in some sectors like textiles, Chinese producers may have absorbed part of the tariff cost by lowering export prices to remain competitive, especially when profit margins allowed it. Finally, some firms may have strategically reclassified products, routed trade through third countries, or used tariff exemptions to maintain access to the U.S. market. These complex adjustments can create short-term anomalies where exports rise despite increased trade barriers.\u003c/p\u003e\u003cp\u003eIn general, the elasticity of Chinese exports to U.S. tariffs varies widely by product category, driven by factors such as the availability of substitutes, integration in global value chains, contractual arrangements, and strategic firm behavior. Elastic responses typically occur in highly competitive and substitutable sectors, while inelastic responses are observed where China has market dominance, strong brand positioning, or long-term contracts. Positive elasticities, though rare, reflect strategic and anticipatory behavior by firms and buyers in response to trade policy uncertainty. Understanding these differentiated impacts is essential for policymakers assessing the effectiveness of tariffs, as well as for firms navigating shifting global trade conditions. It underscores that tariffs are a blunt instrument whose effects depend on the underlying economic structure of each sector and the strategic agility of the actors involved.\u003c/p\u003e"},{"header":"5. Conclusion and policy implications","content":"\u003cp\u003eThis study investigated the sectoral heterogeneity in the responsiveness of Chinese exports to U.S.-imposed tariffs in the light of the trade war initiated under the Trump administration. By focusing on the elasticity of export volumes to changes in tariff levels, the study aimed to uncover which product categories were most vulnerable to tariff shocks and which sectors showed resilience or counterintuitive growth. The methodological approach involved estimating export elasticities using a linear regression model, where the logarithm of export volumes served as the dependent variable and tariff rates as the key independent variable. The analysis was disaggregated across 23 product categories over the period 1993 to 2022 to highlight the varied sensitivities within the broader export portfolio of China. The elasticity coefficients were then classified into three main categories: negatively elastic, negatively inelastic, and positively elastic responses to tariff increases.\u003c/p\u003e\u003cp\u003eThe results of the analysis revealed a clear divergence in how different Chinese export sectors responded to U.S. tariffs. The first group, composed of sectors such as animal products, vegetable products, chemicals, capital goods, machinery, and intermediate goods, exhibited negative elasticities with coefficients more than 1%. These sectors saw a more-than-proportional drop in export volumes when tariffs were raised, indicating high price sensitivity and the presence of substitute suppliers elsewhere in the global market. The second group, which includes minerals, manufactured goods, hides and skins, consumer goods, and footwear, showed negative but inelastic responses with coefficients less than 1%. Here, exports declined less than proportionately, suggesting that while tariffs imposed a cost, the U.S. market continued to rely heavily on Chinese supply due to limited alternatives or established supplier relationships. The third group yielded surprising findings: in categories such as ores and metals, textiles and clothing, agricultural raw materials, and plastic or rubber, export volumes actually increased despite higher tariffs. These positive elasticities suggest strategic behaviors such as stockpiling by U.S. importers, price absorption by Chinese exporters, or rigidities in global supply chains that favored continued trade despite added costs.\u003c/p\u003e\u003cp\u003eThese findings have important policy implications for Chinese policymakers. Firstly, the identification of highly elastic sectors indicates where China is most vulnerable in the trade war with Trump administration. In these sectors, the U.S. has greater leverage to inflict economic damage by redirecting trade to other partners. Therefore, Chinese authorities should consider a dual strategy: short-term damage control and long-term diversification. In the short term, measures such as targeted subsidies, tax relief, or alternative market development (e.g., Belt and Road Initiative countries) could buffer export losses. In the long term, structural upgrades to improve quality, reduce costs, and enhance innovation in these sectors would make Chinese goods less substitutable.\u003c/p\u003e\u003cp\u003eSecondly, for sectors showing inelastic responses, Chinese policymakers should recognize the strategic importance of these industries as trade anchors. These are areas where China holds considerable market power or production efficiency, and thus any disruptions would also significantly impact U.S. buyers. Leveraging these sectors diplomatically in trade negotiations could improve bargaining power. For instance, ensuring consistent supply and quality in rare earth minerals or advanced manufactured goods can reinforce China\u0026rsquo;s indispensability in global supply chains.\u003c/p\u003e\u003cp\u003eThe most counterintuitive findings lie in the sectors with positive elasticities. Policymakers should further investigate the causes behind these anomalies. If these increases are due to transitory factors such as pre-tariff stockpiling, Chinese exporters should not over-invest based on temporary demand surges. However, if they reflect deeper supply chain dependencies or consumer preferences, then these sectors could represent opportunities for expansion. In particular, the plastic and rubber industry, which posted the highest positive elasticity, could be nurtured through enhanced research and development, sustainability innovations, and quality improvements to gain further market share.\u003c/p\u003e\u003cp\u003eIn light of the ongoing trade war and broader geopolitical tensions, Chinese policymakers must adopt a more dynamic and adaptive trade strategy. This includes diversifying trade partners, investing in high-tech sectors to reduce dependency on tariff-sensitive categories, and forming stronger trade alliances. Efforts should be concentrated on sectors that demonstrate resilience or potential for future growth, such as textiles with advanced manufacturing techniques, biodegradable plastics, and high-grade agricultural raw materials. Simultaneously, reducing overreliance on the U.S. market by intensifying trade with ASEAN, Africa, and Latin America could mitigate external risks.\u003c/p\u003e\u003cp\u003eHowever, this study is not without limitations. Firstly, the elasticity estimates are based on available trade and tariff data, which may not fully capture real-time business adjustments, informal trade routes, or strategic reclassification of goods. Secondly, the model does not account for firm-level heterogeneity; different firms within the same sector may respond differently based on size, export experience, and supply chain agility.\u003c/p\u003e\u003cp\u003eFuture research could improve upon these limitations by incorporating firm-level data, using dynamic panel models to assess lagged effects, and exploring interactions between tariffs and non-tariff barriers. Another promising avenue is the examination of re-routing behaviors, such as trade deflection through third countries, which has become increasingly common in response to tariff hikes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003cp\u003eConflict of interest: None\u003c/p\u003e\u003cp\u003eData availability: upon request\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBadr Moughamir wrote the main manuscript, Jihad Ait soussane conducted the empirical analysis and Zahra Mansouri supervised the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAit Soussane J, Mansouri Z (2020) L\u0026rsquo;impact Des Investissements Directs Marocains Sur La Reduction De Pauvrete Dans Les Pays De L\u0026rsquo;afrique Subsaharienne: Essai De Modelisation Econometrique. Int J Econ Manage Res 1(2):43\u0026ndash;61\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmiti M, Redding SJ, Weinstein DE (2019) The impact of the 2018 tariffs on prices and welfare. J Economic Perspect 33(4):187\u0026ndash;210\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellora C, Fontagn\u0026eacute; L (2020) Shooting oneself in the foot? Trade war and global value chains. CEPII Working Paper 2019-18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergin PR, Corsetti G (2020) The macroeconomic stabilization of tariff shocks: What is the optimal monetary response? NBER Working Paper No. 27660\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBigsten A, Gebreeyesus M, S\u0026ouml;derbom M (2016) Tariffs and firm performance in Ethiopia. J Dev Stud 52(7):986\u0026ndash;1001\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaldara D, Iacoviello M, Molligoda P, Prestipino A, Raffo A (2020) The economic effects of trade policy uncertainty. J Monet Econ 109:38\u0026ndash;59\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCavallo A, Gopinath G, Neiman B, Tang J (2019) Tariff pass-through at the border and the store: Evidence from US trade policy. NBER Working Paper No. 26396\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong L, Kouvelis P (2020) Impact of tariffs on global supply chain network configuration: Models, predictions, and future research. Manuf Service Oper Manage 22(1):202\u0026ndash;216\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFajgelbaum PD, Goldberg PK, Kennedy PJ, Khandelwal AK (2020) The return to protectionism. Quart J Econ 135(1):1\u0026ndash;55\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlaaen A, Pierce JR (2019) Disentangling the effects of the 2018\u0026ndash;2019 tariffs on a globally connected U.S. manufacturing sector. Finance Econ Discussion Ser No. 2019-086, Board of Governors of the Federal Reserve System.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurceri D, Hannan SA, Ostry JD, Rose AK (2019) Macroeconomic consequences of tariffs. IMF Working Paper 19/9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurceri D, Hannan SA, Ostry JD, Rose AK (2020) Are tariffs bad for growth? Yes, say five decades of data from 150 countries. J Policy Model 42(4):850\u0026ndash;866\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHandley K, Lim\u0026atilde;o N (2017) Policy uncertainty, trade, and welfare: Theory and evidence for China and the US. Am Econ Rev 107(9):2731\u0026ndash;2783\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayakawa K, Ishikawa J, Tarui N (2020) What goes around comes around: Export-enhancing effects of import-tariff reductions\u0026mdash;Journal. Int Econ 126:103363\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson RC, Noguera G (2012) Accounting for intermediaries: Production sharing and trade in value added. J Int Econ 86(2):224\u0026ndash;236\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKao C, Chiang MH (2001) On the estimation and inference of a cointegrated regression in panel data. Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, pp 179\u0026ndash;222\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMao H, G\u0026ouml;rg H (2020) Friends like this: The impact of the US-China trade war on global value chains. World Econ 43(7):1776\u0026ndash;1791\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMao Q, Sheng B (2017) The impact of tariff reductions on firm dynamics and productivity in China: Does market-oriented transition matter? China Econ Rev 45:125\u0026ndash;142\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelitz MJ (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71(6):1695\u0026ndash;1725\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuchopa CL, Bahta YT, Ogundeji AA (2019) Tariff rate quota impacts export market access to South African fruit products in the EU market. Agrekon 58(4):436\u0026ndash;455\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhillips PC, Hansen BE (1990) Statistical inference in instrumental variables regression with I (1) processes. Rev Econ Stud 57(1):99\u0026ndash;125\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen G, Wang P, Xu Y (2021) Trade destruction and deflection effects of US-China trade frictions on China\u0026rsquo;s tariff-targeted products. World Econ 44(8):2216\u0026ndash;2237\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStock JH, Watson MW (1993) A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: J Econometric Soc, 783\u0026ndash;820\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wits.worldbank.org/CountryProfile/en/Country/CHN/StartYear/1993/EndYear/2022/TradeFlow/Export/Indicator/XPRT-TRD-VL/Partner/USA/Product/Total\u003c/span\u003e\u003cspan address=\"https://wits.worldbank.org/CountryProfile/en/Country/CHN/StartYear/1993/EndYear/2022/TradeFlow/Export/Indicator/XPRT-TRD-VL/Partner/USA/Product/Total\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wits.worldbank.org/CountryProfile/en/Country/USA/StartYear/1993/EndYear/2022/TradeFlow/Import/Indicator/AHS-WGHTD-AVRG/Partner/CHN/Product/Total\u003c/span\u003e\u003cspan address=\"https://wits.worldbank.org/CountryProfile/en/Country/USA/StartYear/1993/EndYear/2022/TradeFlow/Import/Indicator/AHS-WGHTD-AVRG/Partner/CHN/Product/Total\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"US-China trade war, Export elasticity, Tariffs, Chinese exports, Sectoral analysis, Trade protectionism","lastPublishedDoi":"10.21203/rs.3.rs-7943060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7943060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to analyze the elasticity of Chinese exports to the United States to tariffs applied by the U.S. on these exports across 23 product categories over the period 1993\u0026ndash;2022. The study employs sector-level empirical analysis using bivariate cointegration techniques to estimate the elasticity of Chinese exports with respect to US-imposed tariffs. By applying logarithmic transformations to export values, the model calculates the percentage change in exports triggered by a 1% change in tariffs. Results reveal significant heterogeneity in Chinese export responses to imposed U.S. tariffs. While many product categories exhibited negative responses to tariff, some demonstrated resilience or even positive export elasticity. These outcomes were influenced by factors such as production cost structures, supply chain integration, and alternative market access.\u003c/p\u003e","manuscriptTitle":"How Will Chinese Exports Respond to Trump’s Tariffs? A Sector-Level Analysis in The Light of The Current US-China Trade War","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 10:43:16","doi":"10.21203/rs.3.rs-7943060/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fb46c29-b80f-4a8d-ac08-c40e7b08b6c3","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T22:44:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 10:43:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7943060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7943060","identity":"rs-7943060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}