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The author attempts to examine the existence and pattern of coalitions in international relations across countries, and investigates whether international relations of coalition partners influence a country's enaction of agricultural non-tariff measures (NTMs).

The author adopts a machine learning technique to identify international relation coalition partnerships and use network analysis to characterize the clustering pattern of coalitions with high-frequent records of global event data. The author then constructs a monthly dataset of agricultural NTMs against China and international relations with China of each importer and its coalition partners, and designs a panel structural vector autoregressive (PSVAR) model to estimate impulse response functions of agricultural NTMs with regard to international relation shocks.

The author finds countries to establish coalition partnerships. Two major clusters of coalitions are noted, with one composed of coalitions primarily among “North” countries and the other of coalitions among “South” countries. The United States is found to play a pivotal role by connecting the two clusters. The PSVAR estimation reveals reductions of NTMs against China following improved international relations with China of both the importer and its coalition partners. NTM responses are more substantial for measures that are trade restrictive. These results confirm that coalitions in international relations lead to coordination of agricultural NTMs.

The author provides international political insights into agricultural trade policymaking by showing interactions of NTM enaction across countries in the same coalition of international relations. These insights offer useful policy implications to predict and cope with hidden barriers to agricultural trade.

As technical barriers gradually become the important tools of trade protection, it is important to understand whether intensified enforcement of border controls is adopted as a hidden tool of trade protectionism and differs across periods and industries.

This article applies a panel structural vector autoregression (PSVAR) model to investigate the potential role of trade protectionism motives in Food and Drug Administration (FDA) import refusals on China's agricultural exports, utilizing newly constructed monthly data at the industry level.

The results show that import refusal is mainly driven by the inspection history, highlighting the importance of the intrinsic product quality and maintaining an excellent inspection history in border inspection. The novel finding is that US employment contractions would also lead to a small increase in FDA import refusals, especially those taking place within ten months and made without sampling tests. Such an association is driven by industry-specific employment shocks and becomes stronger after the financial crisis. It is also more evident in industries where the US lacks competitiveness against China, being manufactured without mandatory safety regulations, and with negative skewness of employment growth.

This research is one of the preliminary attempts to understand whether the de facto border controls are worked as a hidden tool of protectionism to agricultural products, and what the specific trajectory and duration of the impacts at the monthly level. This study provides empirical evidence showing the role of protectionism motives in FDA import refusals and is heterogeneous across industries, which generate new insights and policy implications to predict and cope with additional barriers on agricultural trade.

This paper aims to examine the asymmetric impact of the oil price increase and decrease on stock returns at the firm level.

To ascertain the impact oil price can exert on the stock price at the firm level, this study uses panel structural vector auto regression with various linear and nonlinear measures of oil price shock on a data set, containing 1,168 firms listed in Indian stock markets. This study also considers stock index returns, Fama-French factors and inflation as control variables.

This paper finds evidence that at firm level, net oil price increase and decrease have an asymmetric impact on stock returns. Other oil price shock measures, namely, shock because of oil price increase and decrease, do not show any sign of asymmetric impact on stock returns.

The comparison of firm-level return on its response towards oil price fluctuation can give valuable insights into a firm’s features.

The main objective of this paper is to examine the impact of COVID-19 on the tourism flows of eight Asia-Pacific Countries: Australia, Hong Kong, Malaysia, New Zealand, the Philippines, Singapore, Taiwan and Thailand.

Using monthly data from 2019M1 to 2021M10 and 48 origin and eight destination countries in a panel Poisson pseudo-maximum likelihood (PPML) estimation technique and gravity equation framework, this paper finds that after controlling for gravity determinants, COVID-19 periods have a 0.689% lower tourism inflow than in non-COVID-19 periods. The total observations in this paper are 12,138.

A 1% increase in COVID-19 transmission in the origin country leads to a 0.037% decline in tourism flow in the destination country, while the reduction is just 0.011% from the destination. On the mortality side, the corresponding decline in tourism flows from origin countries is 0.030%, whereas it is 0.038% from destination countries. A 1% increase in vaccine intensity in the destination country leads to a 0.10% improvement in tourism flows, whereas vaccinations at the source have no statistically significant effect. The results are also robust at a 1% level in a pooled OLS and random-effects specification for the same model.

The findings provide insights into managing tourism flows concerning transmission, death and vaccination coverage in destination and origin countries.

The COVID-19-induced tourism decline may also be considered another channel through which the global recession has been aggravated. If we convert this decline in terms of loss of GDP, the global figure will be huge, and airline industries will have to cut down many service products for a long time to recover from the COVID-19-induced tourism decline.

It is to be realized by the policymaker and politicians that infectious diseases have no national boundary, and the problem is not local or national. That’s why it is to be faced globally with cooperation from all the countries.

This is the first paper to address tourism disruption due to COVID-19 in eight Asia-Pacific countries using a gravity model framework.

1. Asia-Pacific countries are traditionally globalized through tourism channels

2. This pattern was severely affected by COVID-19 transmission and mortality and improved through vaccination

3. The gravity model can be used to quantify the loss in the tourism sector due to COVID-19 shocks

4. Transmission and mortality should be controlled both at the origin and the destination countries

5. Vaccinations in destination countries significantly raise tourism flows

Asia-Pacific countries are traditionally globalized through tourism channels

This pattern was severely affected by COVID-19 transmission and mortality and improved through vaccination

The gravity model can be used to quantify the loss in the tourism sector due to COVID-19 shocks

Transmission and mortality should be controlled both at the origin and the destination countries

Vaccinations in destination countries significantly raise tourism flows

This paper aims to investigate how differently the COVID-19 blockade regulations influence the prices of perishable and storable foods. The authors focus on the cases of the 2020 blockade at Hubei province and the 2021 blockade at Shijiazhuang city in China, and the authors examine how the blockade influenced the prices of Chinese cabbages (perishable) and potatoes (storable) within and around the blockade area.

The paper employs the fixed effects model, the panel VAR (PVAR) model, and the spatial dynamic panel (SPD) model to estimate the impacts of the blockade on the food prices. It constructs the unique data set of 3-day average prices of Chinese cabbages and potatoes at main wholesale markets in China during the two urban blockade periods from January 1 to April 8 in 2020 and from January 1 to March 1 in 2021.

The results from the SPD models indicate that the price of Chinese cabbages was more vulnerable and increased by 7.1–9.8% due to the two blockades while the price of potatoes increased by 1.2–6.1%. The blockades also significantly influenced the prices in the areas adjacent to the blockade area. The SPD results demonstrate that the impacts of the blockades would be overestimated if the spatial dependence is not controlled for in the fixed effects model and the PVAR model.

Because the research focuses on the cases in China, the results may lack generalizability. Further research for other countries is encouraged.

This paper demonstrates the importance of considering food types and spatial dependence in examining the impact of the COVID-19 blockades on food prices.