The impacts of logistics performance index and other factors on exports of vietnam

Nội dung text: The impacts of logistics performance index and other factors on exports of vietnam

1. THE IMPACTS OF LOGISTICS PERFORMANCE INDEX AND OTHER FACTORS ON EXPORTS OF VIETNAM Tran Van Ngoc, Nguyen Van Son, Mai Khac Thanh, Hoang Chi Cuong1 Abstract: In this article, the authors will apply a Gravity Model with a panel dataset of 19 main trade partners of Vietnam during 2005-2018 and Hausman-Taylor estimator to examine the possible impact of overall Logistics Performance Index (LPI) score offered by the World Bank and other factors on exports of Vietnam. To have a robust check, the authors also employ the Fixed Effects (FE) and Random Effects (RE) estimation techniques. The estimated results show that the improvement of logistics performance in Vietnam has promoted the country’s exports recently. Other factors that induce the exports of Vietnam are the trade openness within the WTO, ACFTA and AIFTA. The factors that reduce Vietnam’s exports are 2008 global financial crisis, joining the AEC, AANZFTA and JVCEP. Some policy implications are also proposed in this study. Keywords: Export, LPI, Gravity model, Vietnam 1. INTRODUCTION Exports are goods and services that are produced in one country and sold to buyers in another. Exports, along with imports, make up international trade. Located in the Eastern Indochina Peninsula in Southeast Asia, Vietnam is a land of challenging myths and appealing scenic beauty. The elongated roughly S shaped country has a north-to-south distance of 1,650 km and a coastline of 3,260 km. The country started to launch the “innovation” since 1986 and officially “opened door” to the world since 1990. The country has deeply integrated with the world after the end of the US-embargo in 1995. Due to liberalization and globalization within the World Trade Organization (WTO) and Free Trade Agreements (FTAs), business organizations are forced to supply products beyond the national boundaries. The total exports of Vietnam in 2020 reached to 281.5 billion USD, according to General Statistics Office (GSO) of Vietnam, 2021. In such situations, the role of logistics is to provide time and place utility of the products to customers around the world. The concept of logistics means operations related to the production and delivery of goods and services. Logistics refers to the whole flow management, which includes freight transportation, sourcing, stock maintenance, warehousing, handling, border clearance, information system and some other functions. Today, logistics and supply chain management make a very attractive field for professional career in Vietnam. The logistics industry is one of the fastest growing industries in Vietnam and is estimated to grow at a greater pace than the GDP growth rate. Logistics service revenues currently account for 15-20% of GDP of the country. The research question is that is there the relationship between Logistics Performance Index and Exports of Vietnam? This article will examine this possible relationship by employing the gravity model and Hausman-Taylor 1 Vietnam Maritime University; Email: ngocdesign75@gmail.com 972
2. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 973 estimation with the robust check by using the Random Effects Model and Fixed Effects Model with the use of Hausman Test for choosing the suitable model between the two. 2. A BRIEF LITERATURE REVIEW The interaction between the logistics performance of a country and exportation has been investigated in recent years. Çelebi (2019) confirms that the efficiency of logistics systems is a significant determinant of bilateral trade, the magnitude of the effect may vary according to economic and geographic characteristics. Jouili and Allouche (2016a) investigate the relationship between countries’ merchandise exports and quality of logistics performance, seaport infrastructure quality, and liner shipping connectivity among the major maritime nations in the world. The findings confirm that there exists a significant relationship among the merchandise exports and the aforementioned variables. Kabak et al. (2018) investigate the relationship between logistics performance (the six sub-dimensions of LPI) and exports at country level of Turkey, Burundi, Zimbabwe, Brazil and Portugal. They conclude that improvement in some of the logistics performance indicators has an important positive impact on the export level of those countries. Wang and Choi (2018) show for 43 countries that logistics performance has a significant and positive impact on export volume. Munim and Schramm (2018) analyse the impacts of logistics performance on seaborne trade. Findings reveal that it is vital to continuously improve logistics performance to increase seaborne trade. Wang et al. (2018) examine the relationship between international trade and green logistics. They conclude that the logistics performance index of exporting and importing countries are positively correlated with the trade volume. Ornegi et al. (2018) analyze the impacts of logistics performance on the international trade of the European Union and Middle East and North Africa countries. They confirm that logistics performance could be one of the fundamental determinants for the competition among countries. Gani (2017) conclude that the overall logistics performance is positively and statistically significant correlated with exports and imports. Bensassi et al. (2015) estimate the impacts of logistics and transport infrastructure on bilateral exports from 19 Spanish regions to 64 destinations. Their findings show that logistics are important for the analysis of trade flows of goods in terms of number, size and quality of logistics facilities. Ojala and Çelebi (2015) indicate that promoting of the logistics performance may improve countries’ ability to trade competition in international markets. Marti et al. (2014) examines some sub-dimensions of the performance logistics (customs procedures, logistics costs and the quality of transport infrastructure) on the trade. They reveal that amelioration in any of the aforementioned sub-dimensions could take to significant growth in a country’s trade flows. Puertas et al. (2019) study how the development of logistics performance has affected the European Union exports. Findings show that logistics is more important for exporting nations than importing nations. Hausman et al. (2012) examine the effects of logistics performance in global bilateral trade among 80 countries. They concluded that logistics performance is statistically significant related to the volume of bilateral trade. Korinek and Sourdin (2011) confirm that trade flows depended on certain infrastructures, customs management, the maturity of the private sector in terms of the supply of services on behalf of ocean carriers, the role of experienced shipping agents and the incorporation of ICTs into logistics chain services. Nguyen and Tongzon (2010) explore the relationship between
3. 974 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI Australia - China trade and the development of the Australian transport and logistics sector. Their findings indicate that growth in Australia’s trade with China causes the development of the Australian logistics sector (especially the transport) but not the other way around. Also, they extend their study to allow the effect of Australia’s trade with the US, Japan, the rest of the world and other variables. Australia’s trade with its other main trading partners, Japan and the US, also causes the growth in its transport sector. Tahar (2020) analyzes the relationship between logistics performance and maritime exports in Tunisia. Findings confirm that only Logistics Performance Index sub-dimension related to the quality of infrastructure (e.g., seaports, railroads, roads, information technology) has a significant and positive impact on Tunisian maritime exportation. For the case of Vietnam, after conducting a survey, the authors find no articles which examine the relationship between the LPI and exports of the country. To ensure the originality and significance of the research, this article will employ a gravity model to check the possible the relationship between the LPI and exports of Vietnam. 3. THE ECONOMETRIC MODEL SPECIFICATION AND DATA SOURCES The gravity model in international economics, similar to other gravity models in social science, can be employed to predict bilateral trade flows based on the sizes of the economy (often using the Gross Domestic Product (GDP) measurements, GDP per capita, Gross National Product (GNP), and GNP per capita), and the distance between two trade partners. The model was first used by Tinbergen in 1962. It was given the name “gravity model” for its analogy with the Newton Law of universal gravitation which also takes into consideration the distance and physical size between two objects. The basic theoretical model for trade flows between two countries i and j takes the following formula: Fij = G(MiMj)/Di j (1) In which: Fij is the bilateral trade flow between country i and country j Mi is the economic mass of country i (often using GDP, GNP measurements) Mj is the economic mass of country j (often using GDP, GNP measurements) Dij is the distance between country i and country j, and G is a constant. There are several estimation techniques, while OLS can lead to a significant bias, the RE model assumes exogeneity of all the regressors and the random individual effects. In contrast, the FE model allows for endogeneity of all the regressors and the individual effects. This all or nothing choice of correlation between the individual effects and the regressors prompted Hausman and Taylor (1981) to propose a model where some of the regressors are correlated with the individual effects. The resulting estimator is called the HT estimator. The Hausman- Taylor (1981) estimator is basically a hybrid of the fixed-effects and the random-effects models and takes the following formula: yit = β1x’1it + β2x’2it + α1z’1i + α2z’2i + ɛit + ui (2) In which, yit reflects the dependent variable for country i in period/time/year t; x’1it denotes variables that are time varying and uncorrelated with the error term in the random-
4. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 975 effects model (ui); x’2it refers to a set of variables that are time varying and correlated with ui; z’1i represents the time invariant variables that are uncorrelated with ui; z’2i describes the time invariant variables that are correlated with ui; βi and αi are the vectors of coefficients associated with the covariates; and ɛit is the random error. Accordingly, one of the main assumptions of the Hausman-Taylor (1981) estimator is that the explanatory variables that are correlated with ui can be identified. Our benchmark specification model takes the following formula: LnEXPjt = β 20 + β 21LnDISVNj + β 22LnGDPVNt + β 23LnGDPjt + β 24LnFDIjt + β 25LnIMPjt + β 26LnEXRUSD/ VNDt + β27Ln(insVNt*insjt) + γ21BothinWTOVNjt + γ22ACFTA + γ23AEC + γ24AIFTA + γ25AKFTA + γ26AJCEP + γ27USBTA + γ28AANZFTA + γ29VKORFTA + γ210JVCEP + γ211EAEU + γ212BORVNj + γ213CRISIS2008 + LnLPIvnt + ε2VNj (3) In which: • EXPjt is the export value of Vietnam to country j at year t (USD). • DISVNj is the distance between Vietnam and country j (km) - taken from the CEPII. • GDPVNt is the nominal GDP of Vietnam at year t (USD). • GDPjt is the nominal GDP of country j at year t (USD). • FDIjt is the approved FDI capital at year t of country j in Vietnam (USD). • IMPjt is the value of Vietnam’s imports from country j at year t (USD). • EXRUSD/VNDt is the average exchange rate between USD and VND at year t. • Regarding the exchange rate, theoretically, if EXRUSD/VND increases, the VND has devaluation, will stimulate exports of Vietnam to international market because at this time, Vietnamese goods will be relatively cheap in the international market. • insVNt is a measure of the effectiveness of the Vietnamese government at year t (Government effectiveness) provided by the World Bank with a value ranging from 0 to 100. A higher value indicates high government efficiency and vice versa. • insjt is a measure of the effectiveness of the partner government j at year t. • insVNt*insjt reflects the quality of institutional interaction between Vietnam and partner j at year t. If insVNt*insjt is higher, it proves that Vietnam and partner have higher institutional quality. This will promote exports of Vietnam to partner j and vice versa. • BothinWTOVNjt is a binary dummy variable which is 1 if Vietnam and country j are WTO members at year t and otherwise is 0. • ACFTA is a binary dummy variable which is 1 if Vietnam and country j are members of the ASEAN-China Free Trade Area at year t and otherwise is 0. • AEC is a binary dummy variable which is 1 if Vietnam and country j are members of the ASEAN economic community at year t and vice versa. • AIFTA is a binary dummy variable which is 1 if Vietnam and country j are members of the ASEAN-India Free Trade Agreement at year t and otherwise is 0. • AKFTA is a binary dummy variable which is 1 if Vietnam and country j are members of the ASEAN-Korea Free Trade Agreement at year t and otherwise is 0.
5. 976 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI • AJCEP is a binary dummy variable which is 1 if Vietnam and country j are members of the ASEAN-Japan Comprehensive Economic Partnership Agreement at year t and otherwise is 0. • USBTA is a binary dummy variable which is 1 after the years Vietnam and the United States signed a bilateral trade agreement (BTA) and vice versa is zero for the years before that. • AANZFTA is a binary dummy variable which is 1 if Vietnam and country j are members of the ASEAN-Australia-New Zealand Free Trade Agreement at year t and otherwise is 0. • VKORFTA is a binary dummy variable which is 1 after Vietnam and Korea signed the Vietnam-Korea Free Trade Agreement and zero for the previous years. • JVCEP is a binary dummy variable which is 1 after Vietnam and Japan signed the Japan- Vietnam Comprehensive Economic Partnership Agreement and zero for the previous years. • EAEU is a binary dummy variable which is 1 if Vietnam and country j are members of the Eurasian Economic Union at year t and otherwise is 0. • BORVNj is a binary dummy variable which is 1 if Vietnam and country j share a border and otherwise is 0. • CRISIS2008 is a binary dummy variable which is 1 if country j is affected by the 2008 crisis and vice versa is 0. As we know the Global crisis 2008 affects most countries in the world. In this study, the variable CRISIS2008 has a value of 1 in the period from 2008 to 2012 and zero in the remaining years. • LPIvnt (Logistics Performance Index): Logistics Performance Index provided by the World Bank (WB). This variable reflects the quality and performance of Vietnam’s logistics operations at year t. This is one of the most important variables of this model. Because it evaluates the impact of the development of logistics services/quality of logistics activities in Vietnam on the country’s exports. • ε2VNj is random error where E(ε2VNj) = 0. All quantitative variables will use the natural logarithm form (Ln) except for binary dummy variables in the model. Data Sources For data sources, the authors use a panel data set of 19 most important trade partners of Vietnam including: Australia, Belgium, Brazil, Canada, China, France, Germany, Hong Kong, India, Japan, Malaysia, Netherlands, Philippines, Russia, Singapore, Korea, Thailand, UK, and the USA from 2005 to 2018. These 19 partners account for about 80% of the total exports of Vietnam recently. Data is collected from many reliable sources such as: General Statistics Office of Vietnam (GSO), WTO Center, World Bank (WB) and the World Trade Organization (WTO). If partner J at year t does not have data on FDI, export, import to/with Vietnam, the author will add 1 USD to the dataset to address “zero trade” or “Zero FDI”. The next section will present the estimation results and discussion.
6. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 977 4. THE ECONOMETRIC ESTIMATION RESULTS AND DISCUSSION 4.1. The Econometric Estimation Results Table 1. Model estimation results using FE, RE methods and Stata 11 software. Dependent Variables Independent Variables LnEXPjt (RE) LnEXPjt (FE) -0,062 LnDISVNj -0,18 LnGDPVNt -0,01 -0,01 LnGDPjt 0,43* 0,67* LnFDIjt 0,003 -0,002 LnIMPjt 0,22* 0,22* LnEXRUSD/VNDt 3,7* 3,7* Ln(insVNt*insjt) 0,26 -0,26 BothinWTOVNjt 0,15 0,12 ACFTA 0,94* Omitted AEC -0,54* -0,66* AIFTA 0,39 0,53* AKFTA 0,14 0,2 AJCEP 0,15 0,13 USBTA 0,9 Omitted AANZFTA -0,98* -1,14* VKORFTA 0,24 0,22 JVCEP -0,48 -0,54* EAEU -0,18 -0,00 BORVNj -0,73 Omitted CRISIS2008 -0,11 -0,14* LPI 1,3 0,04 Const -34,18* -36,18* Within = 0,86 Within = 0,87 R2 Between = 0,73 Between = 0,27 Overall = 0,78 Overall = 0,45 Note: * is statistically significant at the 1% level; is statistically significant at the 5% level; is statistically significant at the 10% level. After estimating the FE and RE models, the author uses Hausman Test to choose between FE or RE model. The Hausman Test results suggest choosing the RE model because it cannot reject the null hypothesis (Ho): the difference in coefficients between the FE and RE models is not systematic. Table 2. Model estimation results using the Hausman-Taylor and Stata 11 software. Dependent Variable Independent Variables LnEXPjt Time-varying exogenous variables and uncorrelated with ui (x’1it)
7. 978 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI Dependent Variable Independent Variables LnEXPjt LnDISVNj -0,12 LnEXRUSD/VNDt 3,7* Ln(insVNt*insjt) -0,02 BothinWTOVNjt 0,13 AEC -0,60* AIFTA 0,46 AKFTA 0,18 AJCEP 0,13 AANZFTA -1,08* VKORFTA 0,22 JVCEP -0,53* EAEU -0,09 CRISIS2008 -0,12* LPI 0,69 Time-varying endogenous and correlated with ui (x’2it) LnGDPVNt -0,01 LnGDPjt 0,58* LnFDIjt 0,00 LnIMPjt 0,23* Time invariant exogenous and uncorrelated ui (z’1i) ACFTA 1,26 USBTA 0,57 BOR -1,34 Const/Hằng số -35,83* Note: * is statistically significant at the 1% level; is statistically significant at the 5% level; is statistically significant at the 10% level. Table 3. Descriptive statistics for the variables in the model 3. Variables Mean Std. dev. Min Max LnEXPjt 21,61 1,145 17.290 24,58 LnDISVNj 8,43 0,888 6.76 9,75 LnGDPVNt 25,51 1,63 0 26,22 LnGDPjt 27,85 1,23 25,35 30,65 LnFDIjt 17,03 6,56 0 23,42 LnIMPjt 21,36 1,34 18,14 24,90 LnEXRUSD/VNDt 9,86 ,13 9,67 10,02 Ln(insVNt*insjt) 8,24 0,27 7,48 8,61 BothinWTOVNjt - 0,36 0 1 ACFTA - 0,44 0 1 AEC - 0,23 0 1
8. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 979 Variables Mean Std. dev. Min Max AIFTA - 0,26 0 1 AKFTA - 0,39 0 1 AJCEP - 0,40 0 1 USBTA - 0,22 0 1 AANZFTA - 0,37 0 1 VKORFTA - 0,12 0 1 JVCEP - 0,19 0 1 EAEU - 0,10 0 1 BOR - 0,22 0 1 CRISIS2008 - 0,48 0 1 LnLPI 1,28 0,13 0,86 1,44 4.2. Discussion of the Estimation Results The estimation results of Model 3 - LnEXPjt are summarized in Table 1, Table 2 and Table 3 above. The coefficients of the following variables: LnDISVNj, Ln(insVNt*insjt), AKFTA, AJCEP, VKORFTA, EAEU, LnGDPVNt, LnFDIjt, USBTA and BOR are not statistically significant in one or both Random Effects and Haussman-Taylor models. So, we have no conclusion on these variables. The coefficients of the AEC, AANZFTA and JVCEP are negative and statistically significant at 10% or 1% level in both RE and Hausman-Taylor models. Therefore, the authors have evidence to say that joining AEC, AANZFTA and JVCEP have decreased Vietnam’s exports to this region. The coefficient of LnGDPjt, the traditional variable of the gravity model, is positive and statistically significant at 1% level in both RE and Hausman-Taylor models. Therefore, we can conclude that when the GDP of the partner increases will promote Vietnam’s exports. This is consistent with the assumptions in the model and the author’s expectations. Because when the partner’s GDP increases, people’ income will increase resulting in increasing the demand for imported goods from Vietnam. Therefore, it will increase Vietnam’s exports to those partners. The coefficient of LnIMPjt variable is positive and statistically significant at 1% in both RE and Hausman-Taylor models. This means that Vietnam’s exports depend on the value of Vietnam’s imports. Because, currently, Vietnam does not have subsidy industries to produce domestic input raw materials and fuels for manufacturing the exporting products. Two-thirds of the cost of product are imported from the world market including China-a very important import source of Vietnam. Among the FTAs ​​that Vietnam participates in, according to the estimation results, only ACFTA and AIFTA are the two FTAs ​​that promote Vietnam’s exports. Once again, the WTO accession still promotes Vietnam’s exports when the coefficient of the BothinWTOVNjt variable is positive and statistically significant in both the RE and Hausman-Taylor models. Although the USBTA is not statistically significant in the Hausman-Taylor model, it cannot be denied that the United States is currently the Vietnam’s largest export market, reaching 61.3 billion USD in 2019.
9. 980 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI The coefficient of CRISIS2008 variable is negative and statistically significant in both RE and Hausman-Taylor models. This proves that the global financial and economic crisis in 2008 reduced Vietnam’s exports. The 2007-2008 financial crisis, also known as the Global Financial Crisis (GFC), was a severe worldwide economic crisis. Before the COVID-19 recession in 2020, it was considered by many economists to be the most severe financial crisis since the Great Depression of the 1930s. Financial institutions worldwide suffered severe damage, culminating in the bankruptcy of Lehman Brothers on September 15, 2008 and the ensuing international banking crisis. Severe consequences spread to the world through two channels: finance and foreign trade. Now we discuss the most important variable in this research, the coefficient of LnLPIvnt variable is positive and statistically significant in both RE and Hausman-Taylor models. So we can conclude that the significant improvement in logistics capacity and performance of Vietnam is a factor that has helped increasing exports of Vietnam to trade partners recently. This is completely in line with the author’s expectations and predictions. 6. CONCLUDING REMARKS AND RECOMMENDATIONS Infrastructure is an integral part of any country’s logistics development along with other factors such as business environment, institutions, human resources, technology and training. Logistics performance became an important variable to take into account when the analyses concern international trade. In Vietnam, logistics infrastructure, including transport and warehousing infrastructure as well as sea ports system, has been improved recently. By employing the gravity model and Hausman-Taylor estimator with the robust check of Fixed Effects and Random Effects estimation techniques, the authors find that the improvement of the logistics performance index of Vietnam is one of the important factors that promotes the country’s exports recently. Other factors that induce the exports of Vietnam are the trade openness within the WTO, ACFTA, AIFTA. The factors that prevent Vietnam’s exports are 2008 global crisis, joining the AEC, AANZFTA and JVCEP. This implies that the improvement of LPI will contribute for the trade expansion of developing country. In the coming times, Vietnam should continue to perfect logistics infrastructure by focusing on building sea ports, express ways, international airports as well as rail way systems. Besides that, training a good labor force for logistics industry is also important for promoting the country’s exports in the coming times. Thus, the country should enjoy the benefits resulting from trade liberalization under the trade block within free trade agreement like the WTO and other regional FTA. REFERENCES 1. Bensassi, S., Márquez-Ramos, L., Martínez-Zarzoso, I., (2015). Relationship between logistics infrastructure and trade: Evidence from Spanish regional exports. Transportation Research Part A, 72, 47–61. 2. Çelebi, D. (2019). The role of logistics performance in promoting trade. Maritime Economics and Logistics, 21(3), 307-323. 3. Gani, A. (2017). The Logistics Performance Effect in International Trade. The Asian Journal of Shipping and Logistics, 334, 279-288.
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