The impact of uncertainty on trade: The case for a small port

Noor Zahirah Mohd Sidek; Kiranantawat, Bhuk; Khaengkhan, Martusorn Article The impact of uncertainty on trade: The case for a small port Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Noor Zahirah Mohd Sidek; Kiranantawat, Bhuk; Khaengkhan, Martusorn (2022) : The impact of uncertainty on trade: The case for a small port, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 10, Iss. 8, pp. 1-18, https://doi.org/10.3390/economies10080193 This Version is available at: https://hdl.handle.net/10419/328493 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 10: 193. https://doi.org/10.3390/ economies10080193 Academic Editors: Ralf Fendel and Brantley Liddle Received: 28 May 2022 Accepted: 28 July 2022 Published: 8 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Impact of Uncertainty on Trade: The Case for a Small Port Noor Zahirah Mohd Sidek 1,* , Bhuk Kiranantawat 2and Martusorn Khaengkhan 2 1Department of Economics, Faculty of Business Management, Universiti Teknologi MARA, Sungai Petani 08400, Kedah, Malaysia 2 College of Logistics and Supply Chain, Suan Sunandha Rajabhat University, Dusit, Bangkok 10300, Thailand *Correspondence: [email protected] Abstract: In the present paper, we show how uncertainty emanating from fluctuations in economic uncertainty, news-based uncertainty, and geopolitical risks affect the number of containers exported from Thailand via Penang Port, Malaysia. Our sample extends from January 2009 to May 2020 from three main entry points in the Northern Peninsular Malaysia–Thailand Border: Padang Besar, Surat Thani, and Bukit Kayu Hitam. Two modes of transportation of containers are mainly used for export purposes, namely, road and rai. This study examines the nonlinear effect of uncertainty on trade by employing a two-regime Markov regime-switching approach. The empirical results show that, overall, uncertainty significantly affects the movement of containers in the high-uncertainty regime. Therefore, small ports must continue to diversify their client base to cushion the impact of fluctuations in global trade due to uncertainty. Keywords: Malaysia–Thailand cross-border trade; economic policy uncertainty; geopolitical news; economic news 1. Introduction In recent years, studies have shown that economic uncertainty tends to dampen and reduce the volume of trade as a result of the delayed investment, increased precautionary savings, and decreased consumption, which later leads to economic slowdown (Dogah 2021;Grier and Smallwood 2013). Although the impact of uncertainty on the stock market, financial market, and firm performance has been extensively examined (Gupta and Wohar 2017;Balcilar et al. 2016;Kollias et al. 2017), the effect of uncertainty on trade has yet to be subjected to serious empirical investigation. Therefore, there are still gray areas regarding how uncertainty affects trade. Previously, the main problem with assessing the impact of uncertainty on trade was the lack of a reliable dataset that allows for comparative analysis for countries. With the availability of new datasets to capture news, geopolitical events, and economic policy uncertainty, an in-depth analysis on trade uncertainty can be conducted to comprehend its impact. The assessment of the impact of uncertainty on trade is expected to have important implications for at least two (2) reasons. First, uncertainty may trigger a decline in both trade volume and growth. For example, uncertainty arising from trade tension between the United States and China may later lead to financial war or any other existing economicrelated cooperation involving both countries. If the trade war persists, other countries would be able to adjust given the knowledge (news) and extent of the impact of the trade war on other countries. For example, the trade war results in disturbances in oil prices where oil prices plummeted during the trade war since China is one of the world’s biggest oil consumers and the United States has large oil reserves, and the recent discovery of shale oil provides abundant oil supply. This information would be vital for the identification of risk-inducing shocks and how to assuage such shocks. Second, examining the different types of uncertainty serves as an effective learning mechanism for institutional investors, traders, and risk managers in their risk assessments. Discerning how different types of Economies 2022,10, 193. https://doi.org/10.3390/economies10080193 https://www.mdpi.com/journal/economies Economies 2022,10, 193 2 of 18 uncertainty would allow them to strategize and mitigate such risks on their trading position by diversifying trade with other countries. Malaysia and Thailand’s trade with the US and China accounts for more than 10 percent of their total trade, which mainly consists of semi-finished products; raw materials, such as wood; unrefined petroleum; and finished goods. From January to June 2020, Malaysia’s trade value with China and the US was approximately USD 23 billion. Moreover, Malaysia’s export increases to China and the US’s is 46.8% and 27.6%, respectively, compared to 2019. This indicates that, in a volatile situation, such as COVID-19, it is difficult to predict the impact of trade uncertainty (Malaysia External Trade Development Corporation 2020). Thus, it is crucial that the impact of trade uncertainty be studied to better predict the outcome and mitigate affected trade. According to the IMF, global growth is expected to contract by − 4.9 percent in 2020 (WEO 2020). The deadly COVID-19 pandemic coupled with the US–China trade war, which has persisted since 2017 until the outbreak of COVID-19 in December 2019, has brought the global economy to a possible V-shaped recession. The United States projected growth is − 8.0 percent, which is much lower than China’s projected growth of 1.0 percent in 2020 (WEO 2020). The increase in tariffs arising from the US–China trade war has not only reduced trade between the two countries, but is expected to disrupt global supply chains, consumers having to pay higher prices due to higher tariffs, and dent businesses in connection with China and the United States. The COVID-19 outbreak worsened the situation, but somewhat muted the trade war. In July 2020, the United States regulators began to push for more transparency from Chinese companies with share trading in the United States. For the past decade, several Chinese-owned companies used reverse mergers with dormant United States companies, which were later made public on the United States’ stock exchanges. Disputes arising from the trade war may persist in another form, which is a financial war between these two economic powerhouses. The objective of this study is to examine the impact of economic uncertainty on container throughputs in Penang Port and how it affects trade in Malaysia and Thailand. The Penang Port is used in this study due to several interesting reasons. It serves as a feeder port of raw-material exports from Southern Thailand and the Northern part of Malaysia to other parts of Asia, mainly to China, Hong Kong, and Japan. Another vital addition is the period of study that covers the US–China trade war period; the extended operating hours at the Malaysia–Thailand border from 18 h to 24 h daily at the Immigration, Customs, Quarantine, and Security (ICQS) Complex at Bukit Kayu Hitam, Malaysia; and Custom, Immigration and Quarantine (CIQ), Sadao, Thailand from 18 June 2019 to 17 June 2020, and covers the onset of the COVID-19 pandemic. In addition, Penang’s total exports and imports of goods in 2020 amounts to RM 500.4 billion, equivalent to 31.8% of total Malaysia’s trade with a trade surplus of RM 124.4 billion (Lee 2021). Penang also contributed 68.2% of the total RM 189.3 billion exports of electronic integrated circuits and 44% of RM 88.6 billion exports in electric and electronic products in Malaysia. Hence, despite being a small port, Penang Port is responsible for exports to China, the US, Singapore, Taiwan, Hong Kong, Japan, South Korea, Germany, and Vietnam. This study discusses the impact of economic uncertainty from the perspective of newsbased economic policy uncertainty, and uncertainty due to geopolitical risks in a nonlinear framework. A nonlinear relationship implies a non-direct relationship between variables where changes in one variable may not be proportional to changes in another variable. We argue that the nonlinear method is more appropriate to capture these uncertainties. The subsequent section overviews how the United States–China trade war impacted the trade in South-East Asian countries, focusing on Thailand and Malaysia. Section 3reviews the selected literature pertaining to uncertainty arising from events and disturbances and the impact on the economy. Section 4reviews the estimation methodology. The penultimate section discusses the results and policy recommendations, and the final section provides an overall conclusion and way forward for the supply chain ecosystem. Economies 2022,10, 193 3 of 18 2. News (US–China Trade War) and Its Impact on South-East Asian Trade The US–China Trade War was initiated when the United States initiated an initial investigation in late 2017 using the Trade Act of 1974 and Trade Expansion Act of 1962 for alleged unfair trade practices. The trade tension began on 22 January 2018 when the United States imposed global safeguard measures to imports of solar panels and washing machines from China. In addition, the United States invoked Section 232 of the Trade Expansion Act 1962 to levy additional tariffs on aluminum and steel imports on national security grounds. The situation was further aggravated when the European Union (EU) imposed retaliatory measures in March 2018 on 23 iron and steel products, which was later followed by Turkey in May 2018. Being a small, open economy with a heavy reliance on exports, both Malaysia and Thailand were subjected to the pros and cons of the ongoing trade war. In the case of Malaysia, for example, the United States–China trade war created a trade diversion where outsourcing activities that were normally undertaken by China for the United States moved to Malaysia. For example, Poh Huat Resources Holding Berhad received a major outsourcing contract from the United States in place of China. The Malaysian Furniture Council estimated a 20 percent increase in furniture exports to the United States in the next three years (The Edge 2019). Thailand, on the other hand, faced a downturn in terms of exports for rubberwood to China due to the reduction in the production of furniture for United States imports for China. Another trade diversion was palm oil. Prior to the trade war, China’s cooking oil consumption was mainly fulfilled by soy oil imports from the United States. The trade war diverted cooking imports for palm oil from Malaysia and Indonesia. Between July 2017 and Jun 2018, palm oil exports from Malaysia to China increased by 6.7 percent. Investments in and the relocation of both multinational companies from the United States and China to Malaysia, Vietnam, and Thailand may occur if production costs and increases in tariff due to the trade war persist. More diverted investments are expected if the trade war persists. For example, Apple Inc. is expected to move 15–30 percent of production from China to a cheaper location in South-East Asia (Nikkei Asian Review, 19 Jun 2019). In Malaysia, for example, the first quarter of 2019 saw a surge in foreign investments of 74.2 percent in the manufacturing sector or approximately USD 7.75 billion, where USD 2.74 and USD 1.12 billion are from the United States and China, respectively. In July 2018, China was the second-largest trade partner for Malaysia after Singapore, whilst the United States came third. Malaysia’s main exports to the United States and China account for approximately 22.8 percent of the total exports during Jun 2017–Jun 2018. By July 2018–Jun 2019, Malaysia’s exports to the United States and China accounted for 38.7 and 53.5 percent, respectively, which mainly constituted electric and electronic products. Despite the 2017–2019 US–China trade wars, exports from Malaysia and Thailand still record growth (see Table 1). Malaysia’s exports to the US and China recorded growth of around 5.9 percent or approximately USD 32.33 billion. Robust exports were contributed to by LNG, metal, steel, seafood, electric and electronics, machinery, and optical equipment. In general, trade with the United States remains in the cases of export and import from Malaysia and Thailand to the United States. Table 1shows the export and import growth for pre-trade conflict (July 2017–June 2018) and during trade conflict (July 2018–June 2019). The export growth increased from 8.6 to 10.5 percent from Thailand. In the case of Malaysia, exports to the United States declined from 11.1 to 2.1 percent for the same period. In the case of imports, Thailand’s import growth from the United States increased from 2.5 to 19.0 percent, whilst Malaysia saw a smaller increase in import growth from 6.8 to 7.4 percent. Economies 2022,10, 193 4 of 18 Table 1. Trade growth of selected markets to the USA. Markets Export Growth Import Growth Pre-Trade Conflict Ongoing Trade Conflict * Pre-Trade Conflict Ongoing Trade Conflict July 2017–June 2018 % Growth July 2018–June 2019 % Growth July 2017–June 2018 % Growth July 2018–June 2019 % Growth Vietnam * 5.9 21.1 −13.8 17.3 Singapore 21.3 19.4 13.7 17.3 Taiwan * 7.7 14.7 1.0 20.3 Thailand 8.6 10.5 2.5 19.0 Malaysia 11.1 2.1 6.8 7.4 China 11.6 1.0 12.4 −18.7 Source: GTA IHS/DOSM; Notes: * sorted by export growth during the ongoing trade conflict period. Table 2presents the export and import growth from selected markets to China. Thailand’s export growth to China plummeted from 13.7 percent pre-trade conflict to − 6.6 percent during trade conflicts. Import growth saw a lower reduction from 11.4 to 3.8 percent. In the case of Malaysia, export growth contracted from 19.0 percent pre-crisis to 4.4 percent during the trade war. On a similar note, import growth dropped from 15.1 to 1.7 percent for Malaysia. Table 2. Trade growth of selected markets to China. Export Growth Import Growth Pre-Trade Conflict Ongoing Trade Conflict Pre-Trade Conflict Ongoing Trade Conflict July 2017–June 2018 % Growth July 2018–June 2019 % Growth July 2017–June 2018 % Growth July 2018–June 2019 % Growth Vietnam * 53.7 23.2 23.9 13.1 Malaysia 19.0 4.4 15.1 1.7 Taiwan * 17.9 −0.2 13.1 8.9 Thailand 13.7 −6.6 11.4 3.8 Singapore 7.6 −7.0 13.1 0.8 USA 9.7 −20.0 9.4 −3.2 Source: GTA IHS/DOSM, IPSOM 2020; Notes: * sorted by export growth during the ongoing trade conflict period . Figure 1illustrates the entry of containers for export from South Thailand to Penang Port. Most of the exports from Thailand via Penang Port would be shipped to China, Japan, Taiwan, Hong Kong, and the Philippines. Exports from Thailand are mainly rubberwood and other rubber products. The preferred mode of entry is via rail through the Padang Besar entry point, which is the only means of rail access from Thailand to Malaysia. The mode of entry using the road is less popular due to relatively higher costs compared to rail. The use of Padang Besar rail as an entry point increased from 53.44 percent in 2016 to 59.96 percent in 2019. The road-entry point via Bukit Kayu Hitam gradually increased from the period of 2009 to 2017, but recorded a slight decrease in 2018–2019. In 2016, the total container entry via Bukit Kayu Hitam entry point was 35.82 percent, but by 2019 this was reduced to only 27.66 percent. Penang Port recorded a sharp decline in exports of rubberwood and other rubber products from Thailand to China. The number of containers (TEUs) from South Thailand to China via Penang Port substantially reduced during the trade war. This reduction was due to a contraction in the demand by the United States for wood molding products from China. During the trade war, the United States imposed an anti-dumping tax of 183.6 percent and an anti-subsidy tax of up to 194.9 percent on imported wood products from China. The Thailand government imposed the Agreed Export Tonnage Scheme (AETS) in 2018, which led to a reduction in export and rubber coupled with a decline in rubber prices to USD 60–70; the closure of furniture production in China due to renovation to comply with China’s new environmental standards substantially impacted Economies 2022,10, 193 5 of 18 the industries in South Thailand and Penang Port. With the COVID-19 pandemic and global recession, the number of trade activities is expected to contract further in 2020. Economies 2022, 10, x FOR PEER REVIEW 5 of 19 due to renovation to comply with China’s new environmental standards substantially impacted the industries in South Thailand and Penang Port. With the COVID-19 pandemic and global recession, the number of trade activities is expected to contract further in 2020. Figure 1. Mode of transportation for containers from Thailand to Penang Port for export. Source: PPSB (2020). 3. Review of the Selected Literature International trade has brought about unprecedented growth and development to a majority of countries involved in exports and imports. Over the years, the metamorphosis of international trade has taken the form of globalization, reduction in tariff and non-tariff barriers, and more bilateral and multilateral free-trade agreements, all of which aim to promote greater export and import rates. The rise of China as an export powerhouse prompted several countries to engage in FTAs with China or established their own trading blocs, such as the European Union (EU), North American Free Trade Area (NAFTA), ASEAN Free Trade Area (AFTA), amongst others. For example, FTA between China and the European Free Trade Association (EFTA) shows a high export volume from Iceland and Switzerland to China post FTA (Kristjánsdóttir et al. 2022). Uncertainty arising from domestic or international events causing changes or disturbances in the economy, economic policies, polity, political conflict, trade, or stock market would substantially affect economic and corporate behavior, especially in terms of asset allocation and investment decisions (Bouoiyour et al. 2019; Caldara and Iacoviello 2022; Wolfers and Zitzewitz 2009). Exchange-rate risks also give rise to uncertainty and have been subjected to rigorous empirical testing with varying conclusions (for example, Broll et al. 2020; Lin et al. 2020; Klaassen 2004; Perée and Steinherr 1989). Uncertainty tends to increase in the event of elections, which either leads to a change in the ruling government or otherwise. Ruling political party transitions would result in changes in policies since winning an election means an expectation from the public to observe noticeable changes in economic or social policies that would affect businesses (inter alia Lee and Lee 2018; Li et al. 2021) and trade (Hill et al. 2019). The main channel for uncertainty is via the news. Uncertainty arising from overall stock-price changes is often captured by the volatility index (VIX) and volatility index futures (NVIXs), uncertainty from economic policies is normally captured via the economic policy uncertainty index (EPU), and geopolitical risks are mainly captured by the geopolitical risks index (GPR). Policy uncertainty can be defined as ‘the economic risk associated with undefined future government policies and regulatory frameworks’ (Al-Thaqeb and Algharabali 2019). This can postpone corporate spending and investments and consumers’ spending. Baker et al. (2016) mentioned that uncertainty in government policies spiked just after the 2008 financial crisis, which was caused by uncertainty from corporate and consumers involving 0 20000 40000 60000 80000 100000 120000 140000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 No of containers (TEUs) Entry Points (Mode of Transportation) Padang Besar (Rail) Surat Thani (Rail) Padang Besar (Road) Bukit kayu Hitam (Road) Figure 1. Mode of transportation for containers from Thailand to Penang Port for export. Source: PPSB (2020). 3. Review of the Selected Literature International trade has brought about unprecedented growth and development to a majority of countries involved in exports and imports. Over the years, the metamorphosis of international trade has taken the form of globalization, reduction in tariff and non-tariff barriers, and more bilateral and multilateral free-trade agreements, all of which aim to promote greater export and import rates. The rise of China as an export powerhouse prompted several countries to engage in FTAs with China or established their own trading blocs, such as the European Union (EU), North American Free Trade Area (NAFTA), ASEAN Free Trade Area (AFTA), amongst others. For example, FTA between China and the European Free Trade Association (EFTA) shows a high export volume from Iceland and Switzerland to China post FTA (Kristjánsdóttir et al. 2022). Uncertainty arising from domestic or international events causing changes or disturbances in the economy, economic policies, polity, political conflict, trade, or stock market would substantially affect economic and corporate behavior, especially in terms of asset allocation and investment decisions (Bouoiyour et al. 2019;Caldara and Iacoviello 2022;Wolfers and Zitzewitz 2009). Exchangerate risks also give rise to uncertainty and have been subjected to rigorous empirical testing with varying conclusions (for example, Broll et al. 2020;Lin et al. 2020;Klaassen 2004;Perée and Steinherr 1989). Uncertainty tends to increase in the event of elections, which either leads to a change in the ruling government or otherwise. Ruling political party transitions would result in changes in policies since winning an election means an expectation from the public to observe noticeable changes in economic or social policies that would affect businesses (inter alia Lee and Lee 2018;Li et al. 2021) and trade (Hill et al. 2019). The main channel for uncertainty is via the news. Uncertainty arising from overall stock-price changes is often captured by the volatility index (VIX) and volatility index futures (NVIXs), uncertainty from economic policies is normally captured via the economic policy uncertainty index (EPU), and geopolitical risks are mainly captured by the geopolitical risks index (GPR). Policy uncertainty can be defined as ‘the economic risk associated with undefined future government policies and regulatory frameworks’ (Al-Thaqeb and Algharabali 2019). This can postpone corporate spending and investments and consumers’ spending. Baker et al. (2016) mentioned that uncertainty in government policies spiked just after the 2008 financial crisis, which was caused by uncertainty from corporate and consumers involving changing government fiscal, economic, and healthcare. Hence, it can be implied that when there is a Economies 2022,10, 193 6 of 18 considerable crisis leading to recession, policy uncertainty will impede recovery due to a delay or decrease in consumer and corporate investment and expenditure. As uncertainty has a considerable effect on the government, business, and individual spending and investment, this prompted many researchers to create concrete measures to determine uncertainty, especially in economic and policy uncertainty. As a result, the volatility index (VIX) was created to measure volatility in the stock market. It has been used by businesses and firms to measure uncertainty in the market. The major drawback is that it is only applicable to the established market as it measures market depth and liquidity (Al-Thaqeb and Algharabali 2019). A more recently developed measure is the news-based volatility index (NIVX). It was created by utilizing texts from the Wall Street Journal to measure uncertainty (Manela and Moreira 2017). However, it does not provide the overall picture of uncertainty as it only captures uncertainty based on news. The economic policy uncertainty index (EPU) was created to capture uncertainty encompassing policy, market, news, and economy (Baker et al. 2016). Thus, it is a more accurate index used to measure overall uncertainty in policy, which can help determine uncertainty and forecast the effect of uncertainty. From the study conducted by Huang and Luk (2020), EPU was used in China, in which media was heavily censored to measure economic uncertainty. The result revealed that despite heavy media censorship, EPU accurately determined the effect of high uncertainty, which coincided with a decrease in economic activities. Gupta and Sun (2020) also proved that EPU can be used to accurately predict economic policy uncertainty in BRIC and other developing countries. Furthermore, EPU is proven to be an ideal index that serves as a basis for further research on new-based uncertainty. As shown in the research of Ardia et al. (2019), new-based sentiment indices were developed, built on EPU to capture new-based sentiment values. These values were used to predict short-term US industrial production with great accuracy. Suitably, EPU was employed in this research to study the effect of uncertainty on trade, which can be used to forecast the impact of uncertainty on trade and export in Malaysia and Thailand. Trade policy uncertainty is another aspect that this research addressed. Trade policy has become a crucial factor that drives international trade due to rapid globalization. The ongoing US–China Trade War and the imminent change in trade policies due to the 2020 US election increased trade policy uncertainty, which could have a considerable impact on international trade. Caldara et al. (2020) found that unexpected changes in trade policies leading to high trade policy uncertainty can decrease business investment and economic activities in the US. In addition, it was also found that an increase in trade policy uncertainty can slow down Chinese companies’ investment in new foreign markets (Crowley et al. 2018). Therefore, in this study, it was important to investigate how trade policy uncertainty affected trade and export in Malaysia and Thailand. Geopolitical risk, as defined by Caldara and Iacoviello (2022), is the ‘risk associated with wars, terrorist acts, and tensions between states that affect the normal and peaceful course of international relations. Geopolitical risks can have an adverse effect on the economy as they contribute to a decline in economic activities, reduce stock returns, and cause outflows of capital (Caldara and Iacoviello 2022). Moreover, geopolitical risks can lead to considerable uncertainty causing an large and sudden impact on oil price, which affects trade (Brandt and Gao 2019). Gupta et al. (2019) showed that geopolitical risks in general negatively affect trade flows for 164 countries. Similar findings are echoed in Yang et al. (2022); Cheng and Chiu (2018); and Bouoiyour et al. (2019). In addition, geopolitical risks arising from trade wars would likely lead to higher tariffs and later reduced trade between the countries involved. In the worse cases, the tension might permeate to third-party countries involved indirectly with trade to both countries at war. Hence, it is important to measure the geopolitical risks as they can inherently affect trade and investment. The geopolitical risks index (GPR) is a widely accepted index largely used to capture geopolitical risks systematically. In this study, GPR was utilized to measure geopolitical risks and the impact on trade and export in Malaysia and Thailand. Economies 2022,10, 193 7 of 18 3.1. Empirical Estimation Method and Data Table 3offers the preliminary statistics for all variables used in the study. Table 4 presents no strong correlation between the variables, which reduces the possibility of multicollinearity in the estimation. Table 3. Descriptive statistics. Mean Maximum Minimum Std. Dev. Observations BKH_ROAD 4112.7740 10,556.0000 149.0000 2264.7630 137 CHINA_EPU 229.0956 852.0525 59.4412 157.2609 133 CHINA_NEWS 279.9389 970.8299 26.1441 230.9545 133 CHINA_TPU 155.4398 413.8014 83.5258 60.5949 138 GEPU_CURRENT 159.2704 425.6608 81.8783 65.5911 138 GPR 105.2298 370.4247 40.5062 56.1812 138 GPR_CHI 111.9746 251.2252 61.9459 32.3835 138 GPR_MAL 89.8874 271.0700 22.6284 35.6237 138 GPR_THA 94.4052 279.7898 35.7548 45.0354 138 GPR_THR 113.2368 408.9641 31.3702 64.7981 138 JAP_TPU 197.8658 699.9738 36.5727 143.9806 127 PB_RAIL 9282.0220 11,946.0000 649.0000 1338.5660 137 PB_ROAD 545.8467 1996.0000 0 531.4399 137 ST_RAIL 583.8832 2069.0000 0 516.7699 137 TP_EMV 0.0495 0.3540 0 0.0786 127 US_EPU 141.8978 284.1359 63.8773 44.7165 133 US_TPU 137.7891 1374.2800 10.5643 210.1710 127 3.2. Testing for Nonlinearity Subsequently, we performed a nonlinearity test based on Broock et al. (1996) or the BDS test as a preliminary test to understand the data-generating process of the underlying variables. This pre-testing serves as a basis for the nonlinear method used to understand the impact of uncertainty on trade. The BDS is estimated in the following manner: Vm,ε=√TCm,ε−Cm 1,ε Sm,ε (1) where Sm,ε is the standard deviation of √TCm,ε−Cm 1,ε . The BDS statistics converge towards N(0, 1) , so the null hypothesis of i.i.d. is rejected when |Vm,ε| > critical values. The BDS test is arguably more powerful compared to other nonlinear techniques, such as the threshold model and Markov-switching model (Ashley and Patterson 2006), to indicate nonlinear behavior in the data. If nonlinearity is established, the next step is to choose an appropriate estimation method to unpack the behavior of the focal variables. Table 5presents the results of the BDS test that indicate the presence of nonlinearity in the data; hence, this corroborates the use of the Markov regime-switching model for the analysis. Economies 2022,10, 193 8 of 18 Table 4. Correlation. BKH_R CHINA_E CHINA_NWS CHINA_TPU GEPU_CURRENT GPR GPR_CHI GPR_MAL GPR_THA GPR_THR PB_RAIL PB_ROAD ST_RAIL TP_EMV US_EPU US_TPU BKH_R 1 CHINA_EPU 0.398 1 CHINA_NEWS 0.411 0.963 1 CHINA_TPU 0.386 0.883 0.898 1 GEPU_CURRENT 0.383 0.925 0.929 0.992 1 GPR 0.575 0.568 0.537 0.411 0.443 1 CHINA GPR 0.386 0.710 0.676 0.579 0.614 0.731 1 MALAYSIA GPR −0.003 −0.236 −0.196 −0.252 −0.246 0.131 −0.114 1 THAILAND GPR −0.238 −0.389 −0.346 −0.426 −0.432 −0.221 −0.169 0.234 1 PADANG BESAR RAIL 0.568 0.590 0.555 0.434 0.467 0.996 0.755 0.095 −0.233 1 PADANG BESAR ROAD 0.037 0.191 0.184 0.205 0.210 0.014 0.098 −0.087 −0.220 0.037 1 SURAT THANI_RAIL 0.726 0.270 0.303 0.249 0.251 0.357 0.236 0.041 0.027 0.345 −0.054 1 TP_EMV 0.536 0.805 0.766 0.650 0.684 0.597 0.672 −0.303 −0.337 0.618 0.201 0.346 1 US_EPU 0.368 0.764 0.708 0.618 0.647 0.660 0.815 −0.275 −0.307 0.691 0.150 0.148 0.736 1 US_TPU 0.059 0.403 0.421 0.710 0.645 0.040 0.231 −0.295 −0.333 0.063 0.123 −0.080 0.248 0.291 1 CHINA GPR 0.316 0.831 0.770 0.735 0.759 0.630 0.751 −0.211 −0.323 0.663 0.149 0.126 0.699 0.878 0.416 1 Economies 2022,10, 193 15 of 18 Table 8. Regime-switching dynamic regression for number of containers arriving at Penang Port via different modes of transportation. Padang Besar—Rail Surat Thani—Rail Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Regime 1—Low Uncertainty U14.8811 107.4374 *** 11.4715 −0.0673 0.4847 −3.9667 ** 4.3748 *** 3.6683 *** (15.3455) (21.7428) (27.6069) (2.4521) (0.5604) (1.6268) (1.1088) (0.9429) c3253.858 −1664.528 6318.247 9453.659 *** 319.8347 *** 1514.241 *** 589.3852 *** 766.4135 *** (1906.433) (1735.280) (3459.406) (261.8097) (64.8436) (123.2746) (164.4627) (123.3398) Regime 2—High Uncertainty U−4.4634 −4.2927 4.7010 304.6323 *** −3.2913 *** −0.1907 −2.6127 −1.2005 (2.3880) (2.4724) (2.8374) (79.5825) (1.2256) (0.7061) (1.6422) (1.1225) c9852.230 *** 9887.763 *** 8920.490 *** −17741.13 *** 1461.771 *** 288.4942 *** 516.0702 *** 365.8604 *** (237.4110) (243.1765) (329.0029) (6308.632) (95.229) (74.8273) (159.5266) (96.1773) σ16.9331 *** 6.8941 *** 6.9319 *** 6.8615 *** 5.4967 *** 5.5045 *** 5.4637 *** 5.4688 *** (0.0720) (0.0634) (0.0649) (0.0653) (0.0626) (74.8273) (0.0623) (0.0623) Transition Probabilities p11 0.6351 1.8138 0.5256 3.5896 *** 4.9771 *** 4.6157 *** 4.6758 *** 4.6621 *** (1.9444) (1.0360) (1.3485) (0.6282) (1.2180) (1.3777) (1.3526) (1.3581) P21 −3.8820 *** −3.6955 *** −3.7873 *** −0.3852 −4.6029 *** −4.9908 *** −5.0289 *** −5.0205 *** (0.7240) (0.6209) (0.6220) (1.6568) (1.3842) (1.2122) (1.200) *** (1.2028) LL −1157.316 −1152.032 −1158.895 −1150.082 −953.0087 −953.8968 −848.4135 −949.1548 AIC 16.9973 16.9201 17.0203 16.8917 14.0147 14.0276 13.9476 13.9584 SC 17.1465 17.0693 17.1695 17.0409 14.1639 14.1768 14.0968 14.1076 HQC 17.0579 16.9808 17.0809 16.9523 14.0753 14.0883 14.0082 14.0190 Regime 1—Low Uncertainty U0.1191 −9.4019 ** 3.5221 *** 1.3719 −6.9956 −1.0767 *** 18.830 *** 11.4329 (1.2239) (3.9918) (0.8601) (2.7651) (7.0164) (4.9493) (7.0112) (7.5136) c1323.012 *** 2274.775 *** −82.4301 1208.596 *** 7327.821 *** 6143.562 *** 4589.201 *** 5509.893 *** (151.7525) (410.3890) (102.5792) (269.0571) (695.3123) (456.8329) (821.4908) (830.4401) Regime 2—High Uncertainty U−1.8106 ** −0.1968 1.6797 3.3478 *** −9.5735 *** 3.8857 21.8218 *** 16.7469 *** (0.7640) (0.8448) (2.5606) (0.7711) (2.8751) (4.8107) (3.9429) (3.6984) c480.2407 *** 338.2866 *** 1152.135 *** −6.3977 3822.469 *** 2078.717 *** 487.7106 1353.502 *** (75.8017) (78.6498) (284.1437) (80.8842) (322.7585) (469.4478) (450.7068) (366.2725) σ17.7307 *** 5.7371 *** 5.6812 *** 5.6799 *** 7.1899 *** 7.2115 *** 7.1055 *** 7.1533 *** (0.0687) (0.0662) (0.0669) (0.0686) (0.0632) (0.0627) (0.0635) (0.0635) Transition Probabilities p11 2.1497 *** 2.1192 *** 3.4748 *** 2.1087 *** 3.3964 *** 4.8563 *** 3.3611 *** 3.3567 *** (0.6583) (0.6065) (0.6255) *** (0.6249) (0.8405) (1.2821) (0.8431) (0.8445) P21 −3.5895 *** −3.5510 *** −2.0528 *** −3.5488 *** −3.9954 *** −4.9311 *** −3.9700 *** −3.9625 *** (0.6583) *** (0.6289) (0.6066) (0.6492) (0.7680) (1.2502) (0.7704) (0.7729) LL −999.3483 −999.8158 −993.9712 −992.9206 −1190.386 −1187.752 −1179.965 −1185.416 AIC 14.6912 14.6980 14.6127 14.5973 −17.4800 17.4416 17.3279 17.4075 SC 14.8404 14.8472 14.7619 14.7465 −17.6292 17.5908 17.4771 17.5567 HQC 14.7518 14.7586 14.6733 14.6580 17.5407 17.5022 17.3885 17.4681 Notes: *** and ** denote 1% and 5% significance levels, respectively. The switching variables: Model 1—GPR Thailand; Model 2—GPR Malaysia; Model 3—GPR China; Model 4—GPR. 5. Conclusions Uncertainty may originate from trade tensions, economic policies due to changes in government, or geopolitical problems or geo-economic fragmentation of the global economy. These uncertainties are expected to have several negative effects on trade. Uncertainty can lead to a decline in both trade volume and growth. The trade war, for example, could lead to an increase in oil and commodity prices, especially when the countries involved are major exporters or importers. If the trade war leads to economic sanctions, the oil and commodity prices will continue to soar, which in turn escalates uncertainty. The US–China trade war brought about several changes in existing trade constructs, such as trade diversion in the form of outsourcing contracts. Production in Economies 2022,10, 193 16 of 18 China was diverted to Malaysia and Thailand, which benefits all three parties. Another trade diversion is the increase in exports for palm oil from Malaysia to China as the US imposed trade sanctions on China’s importing of vegetable oil from the US. Another form of diversion is investment diversion where the US is expected to move investments from China if the trade war persists. This paper examines the effect of uncertainty on trade with a specific focus on crossborder trade between Malaysia and Thailand via Penang Port, which represent a case study for a small port. Uncertainty is represented by economic uncertainty, news-based uncertainty, and geopolitical risks, which affect the number of containers for export movements from Thailand to Malaysia. We relied on the two-regime Markov regime-switching model (MSM) technique to capture the impact of uncertainty on the movement of containers during highand low-uncertainty periods. The movement of containers in our sample covers three entry points that include Surat Thani (rail), Padang Besar (rail and road), and Bukit Kayu Hitam (road). Monthly data spanning from January 2009 to May 2020 were used for the purpose of this study. The results infer that, in general, uncertainty affects the movement of containers, especially during periods of high uncertainty, such as during the US–China trade war or when countries began to lockdown due to COVID-19. Several policy implications can be deduced based on the findings. First, since the movement of goods and services between Malaysia and Thailand is expected to continue in the near future, the authorities in Malaysia should expedite the construction of a transportation hub in Bukit Kayu Hitam. This transportation hub will act as a catalyst to further expand trade between Malaysia and Thailand via land transportation. Once trade volume increases, then it makes sense to have a 24 h operation at the ICQS, Bukit Kayu Hitam. Second, risks arising from geopolitical uncertainty or economic uncertainty that are exogenous in nature are difficult to control. However, countries can mitigate the effects by expanding the number of trading partners, create new exports destinations, and probably engage in more FTAs or trading blocs to cushion the impact of disturbances arising from such uncertainties. The 2022 Russian–Ukraine conflict caused a dramatic increase in commodity and oil prices, resulting in increased inflation and potential recession in less-developed countries. Elevated debt due to the COVID-19 pandemic compounded the effect of rising inflation, leading to greater uncertainty. As countries continue to brace these impacts, small ports would be affected somewhat directly or indirectly. An important strategy that must be adopted by small ports is to continue to diversify their client base and type of exports to help cushion the impact of fluctuations in global trade. Future work could focus on a few ports in Southeast Asia to better comprehend the nature and flow of trade to China and other major trading partners in Southeast Asia. Author Contributions: N.Z.M.S.—Original draft, data curation, methodology, run regression, Review and Editing; B.K.—Literature Review, data collection, introduction, conclusion; M.K.—Literature Review, data collection, conclusion. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the SSRU-UiTM Matching Grant 2019–2020. Institutional Review Board Statement: The views expressed in this paper are entirely by the authors and do not necessarily reflect those of UiTM and SSRU. Informed Consent Statement: Not applicable. Data Availability Statement: All data and underlying source codes are available upon request. Conflicts of Interest: The authors declare no conflict of interest. References Al-Thaqeb, Saud Asaad, and Barrak Ghanim Algharabali. 2019. 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