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The fight between two nations on each other’s exportable on the basis of tariff is known as tariff war. Although an economic policy, very ­often ­motivated by nationalism and politics, a nation imposes tariff on the ­exports of another nation which, in retaliation, again imposes tariff on the exports of its trading partner. The prime cause of such war is certainly to enhance employment opportunities in the home nation. But politics and nationalism provoke the opposite nation to follow the same policy. Effects of such tariff war, whether beneficial or harmful, are yet to be seen. In this context, we have adopted a general equilibrium model to illustrate the probable effect of the above-stated trade war in a structure consisting both H–O nugget and export sector dualism. The effect of imposition of tariff on multinational corporation (MNC) that has its own origin nation and production activities in other nation as well where it faces the war of tariff is considered. But it gets relief in the form of tax reduction in its origin nation. Under such a scenario, the study has shown the effect of tariff in the presence of full employment in the economy as well as in the presence of unemployment. It is seen that the MNC will continue its production procedure in both nations and enjoy profit, under some conditions. Further, in the presence of unemployment it is seen that if rate of tax on the MNC rises, unemployment may fall and welfare can increase under certain conditions.

The USA–China trade confrontation has become a major concern for all the nations involved in international trade. The retaliatory trade policies taken by both of these major trading nations have generated widespread impact among the trading nations especially on the developing and emerging nations. In this chapter, the macro-theoretic model is developed to show how a trade war can potentially arise in the wake of economic downturn led by some demand contracting force in one of the countries having trade ties and in turn can cause the recession to leap into global turmoil. This may prompt the countries to be more protective and averse to international exchange, thereby paving way to more intense trade frictions among the nations and stoking international macroeconomic propagation. Thus, the present introspection hints at tariff war among the nations engaged in restricted trade with each other being a plausible consequence of macroeconomic fault having cross-country repercussion implication and that in turn becomes more pronounced in the present tariff war leading to more fierce trade frictions among the countries.

The aim of this study is to examine the effect of trade wars between countries on social factors. For this purpose, six different criteria are selected based on the comprehensive literature review. Additionally, fuzzy DEMATEL approach is considered to weight these criteria. The findings indicate that working life is the most significant criterion. However, free time and leisure and family are at the bottom of the list. Additionally, it is also defined that working life is the most influencing criterion. These results demonstrate that trade wars have the highest impact on the working life of the people. In other sayings, people’s fear of losing their jobs goes up in case of the trade wars. To solve this problem, companies should make official statements to their employees about the situation caused by the trade wars. By making clear explanations, uncertainties in this process can be minimized.

Trade war among the nations dates back mainly to the nineteenth century. Some of the trade wars may be cited as (i) The First and Second Opium War Empire between 1839 and 1842; (ii) The Smoot-Hawley Tariff Act, 1930 signed by US President Herbert Hoover; (iii) Chicken wars in the early 1960s; (iv) The US–Japan automobile trade war in the 1980s; (v) 1985 Pasta War between America under the Regan Administration of United States and Europe; (vi) The Banana wars. However, trade becomes more intense in the present century with the increase of the economic trade instruments. Under the Obama Administration, currency war and tariff war both became strong between the United States and China with intense effect over the globe. After the Obama regime, came Donald John Trump with a number of controversial (aggressive) trade protectionism plans saying thereby “China’s accession to the World Trade Organization has enabled the greatest jobs theft in history” and “Trillions of our dollars and millions of our jobs flowed overseas as a result.” Even during the COVID-19 period in the 2020s, threats and counter-threats have been on the ascend. It is in this backdrop the present chapter mainly traces the history of trade wars in the twenty-first century, touching upon the nineteenth and twentieth century trade battles.

The aim of the study is to evaluate role of trade war between the United States and China on oil price. For this purpose, global oil price and US trade balance with China are selected as variables. In addition to this issue, monthly data of these variables for the periods between 1990 and 2019 are taken into consideration. In the evaluation process, both Engle–Granger cointegration and Toda–Yamamoto causality analysis are considered. The results of Engle–Granger cointegration analysis indicates that there is a ­relationship between trade war and oil prices. Nevertheless, according to the results of Toda–Yamamoto causality analysis, it is identified that trade war does not cause oil prices. While looking at these results, it is determined that trade war between the United States and China has an influence on the oil price changes. However, it is also understood that it is not the main factor of this volatility. Thus, it is recommended that in order to identify the main indicator of the oil price volatility, some different factors should also be taken into consideration.

The purpose of this paper is to understand the impending relationship between the impact of the US–China trade war on Taiwanese firms' spending on R&D and their offshore investment in technologically advanced countries (TAC), the authors examined if changes in these firms' R&D ratios and the growing presence of skilled workers in Taiwan's labour market during the trade war have affected their offshore investments in TAC.

Using a model built on pooled cross-sectional time-series data from 2012–2019, the authors examined whether a change in R&D ratios of domestic firms in Taiwan and the growing presence of skilled workers in Taiwan's labour market have affected the offshore investment by these firms during the trade war. Using data from the Manpower Utilisation Survey, the authors applied differences–in–differences–in–differences and differences–in–differences–in–differences–in–differences estimation methods and found that the trade war indeed gave a boost to Taiwan's job market, particularly for skilled workers.

From the estimation results, the authors noticed a rise in employment opportunities alongside a decline in the earnings of skilled workers in industries where more firms have spent on R&D as well as invested in offshore operations. However, firms in Taiwan that had not heavily spent on R&D from industries where investment in foreign operations was otherwise high have also attracted skilled workers during the trade war.

An in-depth analysis of the impact of the trade war on domestic firms' spending on R&D and their investment in offshore operations in TAC should be helpful to policymakers interested in understanding the effects of the trade war and subsequent changes in firms' spending on R&D on labour market outcomes. If changes in the R&D ratios and a steady supply of skilled workers influenced the outflow of Foreign Direct Investment (FDI) to TAC, this insight could be helpful for those devising policies and measures to curb the impact of the trade war on domestic spending on R&D.

The study findings not only provide broad lessons to policymakers in Taiwan, but the country case study can guide growing economies that are equally careful while perceiving trade war as a significant deterrent to domestic R&D spending and the outflow of FDI.

This study aims to find the impact of the trade war between the USA and China on Asian economies. Apart from macroeconomic variables associated with trade, this study explicitly creates a trade war scenario and trade war participant dummies. Using the neural network multilayer perceptron, this study checks for the causal linkages between the predictors and target output for the panel of Asian economies and the USA.

A conceptual model of the after effects of trade war in a quadrant is developed. Variables related to trade and tariffs are included in the study for a panel of 19 Asian economies. The feedforward structure of neural network analysis is used to identify strong and weak predictors of trade war.

The hidden layers of the multilayer perceptron reveal the inconsistency in linkages for the predictors’ services exports, tariff measures, anti-dumping measures, trade war scenario dummy with gross domestic product. The findings suggest that to curtail the impact of the trade war on Asian economies, predictors with neural evidence must be paid due weightage in policy determination and trade agreements.

The study applies a novel and little explored AI/ML technique of Neural Network analysis with training of 70% observations. The paper will provide opportunity for other researchers to explore techniques of AI/ML in trade studies.

The purpose of this paper is to determine the effects of the 2018–2020 China–US trade war on US farm bankruptcies as filed under Chapter 12. The key task is to identify the economic factors affecting farm bankruptcies generally, and to then control for the trade war impacts including the Market Facilitation Program (MFP), floods, agricultural conditions and the health of agricultural finance leading into the trade war.

Results were obtained using ordinary least square regression and panel fixed effect model using bankruptcy rates and number as the dependent variable. Independent variables included market effects, credit conditions, yield variation, trade impacts, 2019 flooding, macroeconomic conditions and regional fixed effects. The authors use cubic splines to interpolate annual and quarterly data to a monthly base.

Based on a fixed effect model, the authors find that all other things being equal the China–USA trade war would have had a significant impact on Chapter 12 farm bankruptcies, increasing the bankruptcy rate by 25.7%. The flooding in 2009 had minor effects of increasing the rate by only 0.05%. The overall impact will, however be substantially lower than the 25.7% because of the MFP. The MFP variables (binary) had mixed effects and its true impact is unknowable at this time; however, the authors also find that a 1% increase in the producer price index decreases bankruptcy rates by 2.62% and farm bankruptcy numbers by 3.70%. Likewise a 1% increase in GDP reduces bankruptcies by 3.25%. These suggest that the MFP program will have likely reduced farm bankruptcies considerably than what would have occurred in their absence. The authors also find that states heavily dependent on trade faced lower market uncertainty. Broader economic factors (net charge-offs of farm loans held by insured commercial banks, US real GDP, the average effective interest rate on nonreal estate farm loans) affect farm bankruptcy.

The authors use monthly bankruptcy statistics, however not all data were available in monthly measures requiring interpolation using cubic spline functions to approximate monthly changes in some variables. Although the MFP had mixed effects in the model, the mid- to longer-term effects may be more impactful. These longer-term effects (and even shorter-term effects through 2020) are complicated by the coronavirus disease 2019 (COVID-19) pandemic, which will require a different identification strategy than that employed in this paper.

The analysis and results of this paper are, to the authors' knowledge, the first to investigate the impact of the China–US trade war on Chapter 12 farm bankruptcy filings. The use of cubic splines in the interpolation of agricultural data is also a technical innovation.

Applying a computable general equilibrium model to assess the impact of tariffs between the US and China, Taiwan stands to gain from trade diversion of the trade war between the two largest world economies in the short term.

Initially, Taiwan suffered a minor loss from the sector-specific tariff on steel and aluminum imposed by the US. However, its loss is mitigated after counting counter measures from foreign countries. The cumulated US tariffs and China's retaliations led to trade diversion effect. Taiwan's initial loss from the steel and aluminum tariffs was over compensated by a series of trade war between the US and China.

Under the scenario of the cumulated tariffs of $250 billion of US imports and China's retaliations of $110 billion on US goods, the social welfare, exports, import and trade balance in Taiwan increased. Its terms of trade improved as well. Real wage increases slightly more for unskilled labor than for skilled labor. The short-term effect of the trade war has positive effect on all macro indicators of Taiwan's economy.

On sectoral shift, Taiwan's export will gain the most in precision engineering products ($2,941.6 million), followed by electronics ($310.7 million) and agricultural products ($31.3 million). The negative effects are in sectors such as business services ($58.323 million), other services ($46.9 million), transportation service ($36.6 million), trade service ($25.3 million), and finance service ($24.5 million). Taiwan's total imports will increase by 0.59%, whereas its total export will increase by 0.33%. However, total trade balance still increases by $451.1 million.

The study also finds that Taiwan has a high degree of overlapping export commodities with China in the US market, much higher than most major trading partners for the US, yet its market share for those products in the US is ranged from 1% to 5% only. Moreover, more than 60% of Taiwan's export to the US is in intermediate goods which have less product differentiation than those in final consumption goods. These two factors will provide an opportunity for Taiwan to exploit the US market.

Though the short-term effect of trade war is positive, Taiwan needs to have a long-range planning amid the external shocks. Policy implications for Taiwan are to map out a cosmopolitan view of its geo-strategy by diversifying outward foreign direct investment and trade destinations. It needs to reduce the “systemic risk” of relying on single market in China which is vulnerable to the uncertainty in the US–China relations. If the trade war lasts too long, Taiwan would need to reevaluate its triangular trade-investment nexus with China and the US as well as its role in the global supply chain.