Essays on asean trade and foreign direct investment

Trang 4 PREFACE The Association of Southeast Asian Nations ASEAN was established to enhance the cooperation among the ASEAN members and other countries in economic, political, security,

INTRODUCTION

One problem concerned in low-income countries is the export concentration The concentration of export might be on either the range of product classification or the range of destinations The product concentration shows whether a country’s export share is distributed among a large range of products or a small number of products Whereas, the destination concentration shows whether a country’s export share is distributed among a large range of countries or a small number of countries In Chapter 2 of this dissertation, we focus on the destination concentration When the export is more concentrated, these countries may be more threatened by fluctuations in export price and sudden decline (disappearing) of the export market compared with the high-income countries Thus, export diversification is a very important policy goal for all countries, especially the low-income ones Following the U-shape relationship between production and export diversification introduced by Cadot et al (2011), low-income countries should pursue export diversification instead of export concentration to reach their economic goals The majority of ASEAN members belong to low-income countries so this topic is notable to consider

ASEAN was established with the signing of the Bangkok Declaration by the governments of five countries (Indonesia, Malaysia, Philippines, Singapore and Thailand) in

1967 Then, the other countries located in Southeast Asia joined ASEAN, Brunei in 1984, followed by Vietnam in 1995, Laos and Myanmar in 1997, and Cambodia in 1999 ASEAN with five founding countries is called ASEAN-5.Since the 1980s, with only six members at that time (five founders and Brunei Darussalam), ASEAN has pursued outward-oriented

2 trade and FDI policies to expand trade and FDI inflows In 1992, the six members signed the AFTA to support trade and attract FDI Signing the AFTA becomes the requirement for joining ASEAN of the four other countries in Southeast Asia (Cambodia, Lao PDR, Myanmar and Vietnam) This confirms the objectives and motivations of ASEAN in trade expansion and attracting FDI This leads me to investigate the determinants of trade and FDI in ASEAN

This dissertation consists of a collection of three essays on trade and FDI in ASEAN These essays deal with some aspects of international trade and FDI in ASEAN including how to use the Theil index to assess the level of export market concentration, the spatial effects of the determinants of FDI and the COVID-19 impact on trade The first essay (presented in Chapter 2) is concerned with the change in export market concentration In this chapter, I address the measurement of the level of export market concentration, namely, the Theil indices The advantage of this index that makes it different compared to other indices in assessing economic equality is this index can be used itself and its decompositions to track the inequality within a subgroup and the inequality across subgroups Thus, I calculate the Theil index to analyze the level of export market concentration in ASEAN Then, I decompose the Theil indices into two components, namely, within-group and between-group components, which refer respectively to the inequality across the countries in subgroups and the inequality between subgroups that are classified by levels of income or regions By doing so, I identify which group of countries the ASEAN export focuses on and how unequal the ASEAN exports to different destination countries are

Chapter 3 introducing the second essay is about the spatial effects of the neighboring countries on the FDI inflow to the ASEAN countries The research questions are whether the FDI inflow into the members of ASEAN has any influence on each other and whether the

3 trade value in one country affects the FDI flow into other countries The method used in this chapter is the spatial Durbin model which allows me to examine the spatial effects of not only the dependent variable (FDI) but also the independent variable (trade value) Furthermore, I apply the dynamic spatial Durbin model to show the spatial effects of the one- period lagged inward FDI and trade value The results confirm the indirect impacts of neighboring countries’ trade and FDI on FDI in the ASEAN countries

Chapter 4 (the third essay) undertakes a quantification of the impacts of the COVID-

19 pandemic on export in ASEAN from January 2018 to December 2020 I apply the DID method to compare results from two groups, one is treated by COVID-19 and the other is not

I use the year 2020 as the treatment year because COVID-19 started in early 2020 In most ASEAN countries, the pandemic spread dramatically from March 2020 so I define January and February of the year as the pre-pandemic period and ten months from March to December as post-pandemic I choose the years 2018 and 2019 as control years that are unrelated to the COVID-19 pandemic In this chapter, we also use the numbers of COVID-19 cases and deaths in both the ASEAN countries and the ASEAN trade partners to measure the presence of COVID-19 in the change of the ASEAN export during the year 2020 The findings of this chapter confirm the negative effects of COVID-19 on the ASEAN export

HOW CONCENTRATED IS THE ASEAN EXPORT? AN ANALYSIS OF

Introduction

ASEAN was established in 1967 by the governments of Indonesia, Malaysia, Philippines, Singapore, and Thailand to accelerate economic growth, social progress, and cultural development in Southeast Asia Since 1999, it is composed of ten members located in Southeast Asia Cohesion between the countries in all dimensions became tighter after Cambodia, the last member, joined in 1999 In particular, in 2003, at the 9 th ASEAN Summit, the ASEAN leaders affirmed the necessity of an ASEAN Community Not only ASEAN has reached a population of 647.74 million in 2018 and covered an area of approximately 4.5 million square kilometers, but also the ASEAN economy is an important component of the global economy The ASEAN’s GDP in 2018 reached more than US$3.06 trillion (in current U.S dollar) accounting for a 3.55% share of the world GDP Its share in world exports has reached 7% in recent years (ASEAN, 2017) This statistic suggests that the ASEAN exports are an important component of global trade Figure 2.1 depicts the trade value of ASEAN for

15 years, from 2004 to 2018 The figure shows that, over this period, both the ASEAN exports and imports increased rapidly and more than doubled They dropped in 2009 because of the 2008 financial crisis In 2015-2016, a decrease in trade value was recorded However, in 2018, the ASEAN export reached over US$1,436 billion, an increase of nearly 2.5 times compared with the value in 2004 Figure 2.2 illustrates the share of export value of each

ASEAN member This figure shows that Singapore was the largest exporter, with a share of around 30% of the ASEAN total, followed by Thailand, Malaysia, and Vietnam In Vietnam, the share of export increased rapidly (4% in 2004 to 16% in 2018) The largest markets of the ASEAN exports are illustrated in Figure 2.3 Collectively, countries in ASEAN called intra-ASEAN comprise the largest market of the ASEAN exports, with a 25% share of the total export value of ASEAN The other markets of the ASEAN exports are China (10.6%), the EU (11.4%), the U.S (10.8%), Japan (9.9%) and the Republic of Korea (4%) In particular, the ASEAN exports to China during this period increased rapidly In 2018, the ASEAN exports to China reached over US$198 billion, an increase of nearly fourfold compared with the 2004 value

Figure 2.1 ASEAN trade value in the period 2004-2018 Figure 2.1 ASEAN trade value in the period 2004-2018

Figure 2.2 Shares of export value of ASEAN members in the period 2004-2018

Figure 2.3 Shares of export value by partners in the period 2004-2018

ASEAN has eight members that are low-income countries One problem that concerns the policymakers in low-income countries is “the vulnerability that arises from export concentration” (Cadot et al., 2013) When the export is more concentrated, these countries may be more threatened by fluctuations in export price and sudden decline (disappearing) of the export market compared with the high-income countries Export diversification is a very important policy goal for all countries, especially the low-income ones Many studies focus on the relationship between export concentration and economic growth, such as Imbs and Wacziarg (2003), Koren and Tenreyro (2007) and Cadot et al

(2011) They show a hump-shaped relationship between production and export diversification, which means that at the early stage of economic development, exports diversify and after the turning point they start to specialize at higher levels of development Lee and Zhang (2019) find that diversification in export may encourage economic growth and lower economic volatility in low-income countries Other studies figure out the relationship between export diversification and trade openness Haddad et al (2013) find that export diversification plays a key role in reducing the transmission of external shocks These studies suggest that low-income countries should pursue the export diversification policy to reach their economic growth goal Some indices such as the Gini index, the Herfindahl- Hirschman Index (HHI) and the Theil index are used to calculate the level of export concentration However, the Theil index has a very important feature compared with others

It is used to measure regional differences in economic development levels and to analyze the contributions of the main sources to the total differences by decomposing the regional overall difference into two parts: within-regional difference and between-regional difference

In this chapter, I attempt to contribute to the limited literature by analyzing the level of ASEAN export concentration on a range of ASEAN trade partners Furthermore, to identify which group of countries the ASEAN export focuses on and how unequal the ASEAN exports to different destination countries are, I construct the Theil indices to investigate the changes in the level of the ASEAN export concentration from 2004 to 2018 The Theil indices are decomposed into two components, namely, within-group and between- group components, which refer respectively to the inequality across the countries in subgroups and the inequality between subgroups that are classified by levels of income or regions By doing so, I can assess the within-group and between-group components of concentration after splitting the sample into distinct subgroups

This chapter is organized as follows Section 2.2 reviews the literature Section 2.3 provides details on data and methodology Section 2.4 presents the empirical results Finally, Section 2.5 concludes the chapter.

Literature Review

Export concentration is a popular topic in recent years Many studies show the relationship between export concentration and economic growth or export growth By using the decomposition of intensive and extensive margins (the variation in bilateral trade flows across countries due to the change in the average size of an existed exporter or the newly established exporters, respectively) they try to find which margin has a higher contribution in the growth The first group intimates the dominance of the intensive margin Evenett and Venables (2002) use 3-digit (SITC) trade data (around 200 product categories) of 23 developing countries for 28 years from 1970 to 1997 and conclude that the intensive margin

9 contributes about two-thirds of total export growth Helpman et al (2008) use the real trade volume data of 158 countries to show the key role of the intensive margin Besedes and Prusa

(2011) use the UN Commodity Trade Statistics from 1975 to 2003 and show the more important contribution of the intensive margin than the extensive margin to the trade growth in developed countries Brenton and Newfarmer (2007) analyze SITC 5-digit level trade data (3,078 products) of 99 countries in 20 years and find that the intensive margin accounts for about 80% of trade growth Amurgo-Pacheco and Pierola (2008) also find that extensive margin only accounts for about 14% of total export growth by analyzing the HS6 level trade data for a set of 24 countries from 1990 to 2005 For the specific country, Reis and Taglioni

(2013) use the firm-level data of Pakistan to confirm this viewpoint By contrast, the others show a higher contribution of the extensive margin to export growth Hummels and Klenow

(2005) analyze the United Nations Conference on Trade and Development (UNCTAD) cross-section trade data at the HS6 level of 126 countries for the year 1995 and find that the extensive margin explains 62% of trade growth Cadot et al (2011) find that although the intensive margin is the dominant component, the extensive margin is the component that leads to change in the export concentration

Several indices are used to measure the export concentration Meilak (2008) analyzes eight indices, namely, the Concentration Ratio, the HHI, the Hall-Tideman Index, the Rosenbluth Index, the Comprehensive Concentration Index, the Hannah and Key Index, the Entropy measure, and the Diversification Index to show the inverse relationship between export concentration and country size Imbs and Wacziarg (2003), Koren and Tenreyro

(2007), Cadot et al (2011), Cadot et al (2013) and Aditya and Acharyya (2013) use the commodity concentration index (CCI), the HHI, the Gini Index to show the U–shaped relationship between income and export concentration level Cadot et al (2011) also calculate

10 the Theil index to measure the level of export concentration and use the OLS estimation to show the hump-shaped relationship between economic development and export diversification However, these indices are used to measure the level of export concentration for each country and each year Recently, Theil’s entropy Index is widely used for measuring economic inequality because of its different properties compared with other indices stated above The Theil index can be used itself and its decompositions to track the inequality within a subgroup and the inequality across subgroups The most common issue is income inequality Bourguignon (1979) constructs the decomposable Theil indices by using two different weights (income-weight and population-weight) to trace the income inequality within a subgroup and between subgroups Duro and Esteban (1998) use the per capita income data of 120 countries in the period from 1960 to1989 and decompose the Theil indices into four components such as the productivity per employed worker, the employment rate, the active over working-age population rate, and the working-age over total population rate The results show the increasing role of inequalities in unemployment rates in the total inequality of per capita income Conceicao and Galbraith (2000) develop the Theil indices and apply the monthly data on wages and employment in Brazil from 1976 to 1995 to show the inequalities within each of 17 industries and among them Akita et al (1999) use the household expenditure data of Indonesia from three National Socio-Economic Surveys in 1987, 1990 and 1993 and analyze the Theil indices to show the inequalities within and between the regions in Indonesia Besides, Bickenbach et al (2015, 2018 and 2019) calculate the Theil indices and their decompositions to trace the concentration of the FDI inflows and the concentration of foreign aid With these papers, the advantages of Theil indices are emphasized when analyzing the levels of concentration over time

Many previous studies treat ASEAN as a group The first popular issue is the economic integration and intra-ASEAN trade Sharma and Chua (2000) estimate the gravity model by using trade data from 1980 to 1995 for ASEAN-5 to show that the ASEAN economic integration did not promote trade between ASEAN countries Thornton and Goglio

(2002) confirm that intra-region bilateral trade and membership in ASEAN play key roles in promoting intra-regional trade They use the trade data of ASEAN-5 as well as five other southeast Asia countries (China, Japan, Hong Kong, Korea, and Taiwan) in the period from

1976 to 1995 and apply the gravity model to show the bias that southeast Asia has displayed towards intra-regional trade Ramasamy (1995) runs an Ordinary Least Squares (OLS) regression to show the significant positive influence of establishing the ASEAN Free Trade Area on the trade diversion effect among ASEAN members by using the data of ASEAN-5 for 17 years from 1976 to 1991 Siah et al (2009) estimate the Autoregressive Distributed Lag (ARDL) framework on data ASEAN-5 from the year 1970 to the year 2001 to examine the positive effects of the ASEAN Free Trade Area on trade within the ASEAN countries Another issue concerned is the impacts of agreements between the ASEAN countries and their trade partners, such as China and the EU Most articles point out that closer integration enhances the trade between ASEAN and China (Wong and Chan, 2003; Chirathivat, 2002), or the relationship with China is the important factor affecting the bilateral trade among ASEAN countries (Devadason, 2010) Lindberg and Alvstam (2007) use the statistical analysis of trade data between the EU and ASEAN to indicate that the lack of free trade platform between the EU and ASEAN hinders the bilateral trade growth Vahalík (2014) calculates and assesses the indices of regional trade intensity and trade complementarity by using bilateral trade data between six ASEAN countries (ASEAN-5 and Vietnam) and two of their largest trade partners (EU and China) from 1995 to 2012 They show that after joining

12 the Free Trade Agreement with ASEAN countries in 2005, China passed the EU to become ASEAN’s biggest trading partner, however, the EU is an important “natural trading partner” of ASEAN countries than China, i.e EU used to import what the ASEAN countries export (the definition of “natural trading partner” is introduced by Schiff, 1999) Other common studies examine the relationship between FDI and economic growth in ASEAN (Lee and Tan, 2006; Dinh et al., 2019; Bende et al., 2001) However, only a few studies focus on ASEAN’s level of export concentration Hinlo and Arranguez (2017) use the HHI to measure the level of export concentration of ASEAN-5 and examine the causal relationship between export diversification and economic growth from 1980 to 2014 The results are different across these countries Only Malaysia has a significant causal bidirectional relationship, and other countries have not Noureen et al., (2016) also use the HHI and a dataset including export, GDP and FDI of ASEAN-5 in the period 1986-2012 to show how per capita income along with other factors affects the export concentration They state that the increase in GDP per capita led to the decrease in the ASEAN export concentration These studies stated above use data of ASEAN-5 However, the number of ASEAN countries has increased to ten since

1999 To my best knowledge, this chapter is the first one that uses the comprehensive data of all ten ASEAN members.

Data and Methodology

The ASEAN export data used in this study are collected from the ASEAN database which is available at https://data.aseanstats.org/trade-annually This database has the export value from the ASEAN to all countries from 2004 to 2018

In this chapter, the sample is divided into several sets of subgroups I use the classifications of countries by income collected from the World Bank’s World Development Indicators (WDI) database as well as by regions classified by continent The classifications of countries by income change over time, so I choose the year 2011 which is the midpoint of the sample period to make the numbers of countries in each subgroup fixed The population data used to calculate the weight of each country is also from WDI

Finally, all variables are compiled into panel data that includes export data of ASEAN to 161 destination countries in 15 years (2004-2018) Table A1 in the Appendix describes the number of countries and the import share of each subgroup from the ASEAN The list of countries is provided in Table A2 in the Appendix

I use the Theil index developed by Bickenbach et al (2018) to measure the concentration of export to the destination countries It is expressed as:

𝐼 𝑖=1 , (2.1) where 𝐼 is the number of destination countries I is the set of all countries i = 1,…, I; and 𝑥 𝑖 = 𝑋 𝑖

𝑖=1 is the share of country 𝑖 (𝑖 ∈ 𝐈) in export value to all countries from

ASEAN (𝑋 𝑖 is the export value to country 𝑖) 𝑤 𝑖 is the relative weight of country 𝑖, i.e.,

∑ 𝐼 𝑖=1 𝑤 𝑖 = 1 In this chapter, each country’s weight is calculated in two different ways: (i) The weights of all destination countries are the same (i.e 𝑤 𝑖 = 1/𝐼), thus every destination country is treated the same regardless of its size In this case, the Theil index is called the

14 absolute Theil index; (ii) The weight of each country is equal to its share of the total population 1 :

𝑤 𝑖 = 𝑃 𝑖 / ∑ 𝐼 𝑖=1 𝑃 𝑖 , (2.2) where 𝑃 𝑖 is the population of the destination country 𝑖 In this case, it is named the relative Theil index Because the population of each importer is measured in the index, this index will reveal other factors (not the size of the country) that affect the export value from the ASEAN to that country In short, for the absolute Theil index, every country has the same weight For the relative Theil index, I measure the country-specific demand for imports in terms of the per capita value of imports from ASEAN The results for both Theil indices are shown in Section 2.4 If a country’s share in total export value from ASEAN to all countries is equal to its weight 𝑥 𝑖 =𝑤 𝑖 , T 𝐈 = 0, i.e there is no concentration

In this chapter, the Theil index is used to show the inequality in export value across countries A property that makes the Theil index different from other concentration indicators such as the Gini and HHI indices, is that it can be decomposed into two components, namely, within-group and between-group components The within-group component refers to the inequality across countries within the subgroup and the between-group component shows the inequality between subgroups When I separate the overall sample into sub-samples, these sub-samples have different sizes, so we cannot compare them across the within-group components I can only compare countries within a subgroup by using within components and compare between subgroups by using the between components

1 I also use other relative Theil indices - the specific demand for import in terms of income level from ASEAN The weights of each country are equal to its share in income as follows:

𝑖=1 , where 𝐺𝐷𝑃𝑝𝑐 𝑖 and 𝐺𝐷𝑃𝑝𝑐 𝑖 are GDP per capita and GDP of destination country 𝑖, respectively The results from using the new weights are similar to those shown in this chapter and are represented in Appendix (Figures A1-A4)

For clarity, I suppose that the overall sample I is separated into several cases of two subsets (denoted by A and B), the decomposition of the Theil index can be described as:

T 𝐈 = Tw AB + Tb AB , (2.3) where Tw AB = ∑ (( ∑ 𝑖∈𝑆 𝑋 𝑖

𝑋 𝑖 𝑖∈𝐼 is the share of subset S in export value from the ASEAN of I and T 𝐒 is the Theil index of the export concentration of subset S

The total inequality T 𝐈 in export value across countries can be aggregated from the sum of the inequality within the subgroups Tw AB (within-group component) and the inequality between these subgroups Tb AB (between-group component) 𝑊 𝐴 T 𝐀 and 𝑊 𝐵 T 𝐁 refer to the contribution of groups A and B to the within-group component, respectively T 𝐀 and T 𝐵 are the Theil indices that reflect the inequality among countries in groups A and B, respectively However, as mentioned above, we cannot compare T 𝐀 and T 𝐁 to know which group has the higher level of export concentration Tb AB itself is also a Theil index and is used to measure the inequality between the subgroups

In this chapter, I separate the overall sample 2 into several sets of subgroups Firstly,

I divide 161 importing countries of the overall sample into two subgroups, namely, the high- income subgroup and the non-high-income one The second set includes four regional subgroups such as Asia and Pacific (AP), Europe (EU), America (AM), and Africa (AC) For

2 China is one of the biggest trade partners of ASEAN I exclude China from the AP subgroups, then calculate the Theil index for each set of subgroup The results in general regarding the trends and the contributions do not change and are reported in Appendix (Figures A5-A7)

16 deeper analysis, in each regional subgroup, I separate these countries into two smaller subgroups, namely, high-income and non-high-income Finally, I separate the countries in the AP group into two smaller subgroups, namely, ASEAN countries and non-ASEAN ones to trace the differences in the level of export concentration intra-ASEAN countries The results are presented in Section 2.4 below.

Empirical Results

In this part, the Theil indices are decomposed in both absolute and relative for 161 destination countries On the basis of the classification of countries by income from WDI for the year 2011, I separate all countries into two subgroups, namely, the high-income subgroup (H) and the non-high-income one (NH) Subgroup H includes 54 countries and subgroup NH has 107 countries The countries in subgroup H are denoted by “H” in Table A2

Applying the equations in Section 3.2 leads to:

T 𝐈 = Tw 𝐻−𝑁𝐻 + Tb 𝐻−𝑁𝐻 = 𝑊 𝐻 T 𝐻 + 𝑊 𝑁𝐻 T 𝑁𝐻 + Tb 𝐻−𝑁𝐻 , (2.6) where Tw 𝐻−𝑁𝐻 = 𝑊 𝑁 T 𝐻 + 𝑊 𝑁𝐻 T 𝑁𝐻 is the within-group component that refers to the inequality within the high-income and the non-high-income subgroups The between-group component, Tb 𝐻−𝑁𝐻 , is the inequality between two subgroups

Figure 2.4 shows the contributions of these components to the changes in total export concentration to 161 destination countries Panels (a) and (b) display the decomposition between two subgroups such as high-income countries and non-high-income countries The

“All” line illustrates the level of export concentration to all countries (i.e., the sum of the within- and between-group components) The “Within” line depicts the within components of concentration It is the sum of the role of two subgroups with their weights (i.e 𝑊 𝑁 T 𝐻 ,

𝑊 𝑁𝐻 T 𝑁𝐻 in Equation 2.6), that is, the contribution of high-income countries (“H-W” line)

17 and the contribution of non-high-income countries (“NH-W” line) The “Between” line depicts the between-group component Panels (c) and (d) describe the export concentration across the high-income countries (“H” line) and across the non-high-income countries (“NH” line) denoted by “T 𝐻 ” and “T 𝑁𝐻 ” in Equation 2.6, respectively

In Panel (a) the “All” line is quite close to the “Within” line, which means the absolute Theil index for total concentration has been largely influenced by the within-group component the within component accounts for about 90 percent of total concentration in this period, whereas the between-group component is very small and contributes only about 10 percent to the total concentration Comparing Panel (a) with Panel (c), we can see that after using the weight to calculate, the inequalities across countries of each subgroup (H and NH) with weight are very close (“H-W” line and “NH-W” line)

The between-group component in both the absolute and the relative Theil index is low and trends downward, which refers to the small gap in export value from ASEAN (absolute index) as well as export per capita (relative index) between the high-income and non-high-income subgroups “Between” itself is the Theil index reflecting the increasing diversification in the export of ASEAN to these two subgroups in this period

A difference is observed in the shape of the two lines (i.e., “H” line and “NH” line) between Panel (c) and Panel (d) because of the difference in calculating the weight For example, when using the population weight, some countries, such as China and India, are in the non-high-income subgroup with large populations, which makes the weight of these countries in this subgroup higher Therefore, the relative Theil index of this group is lower than the absolute Theil index The level of inequality across countries within the high-income subgroup is quite stable reflecting the difference in export from ASEAN to countries in this subgroup does not change much over time By comparison, the level of inequality across countries within the non-high-income subgroup tends to decrease

18 Figure 2.4 Decomposition between high-income and non–high-income countries

This section focuses on the differences between the levels of concentration for each region WDI provides the classification of seven regions including East Asia & Pacific, South Asia, Middle East & North Africa, Sub-Sahara Africa, Europe & Central Asia, Latin America & Caribean and North America Using this regional classification of countries and compiling countries by continent, we group all countries into four subgroups: Asia and Pacific (AP), Europe (EU), America (AM), and Africa (AF) The AP includes countries in East Asia & Pacific and South Asia The AF includes countries in Middle East & North Africa and Sub-Sahara Africa The EU includes countries in Europe & Central Asia after excluding countries that belong to Central Asia (Kazakhstan, Uzbekistan, Maldives, Mongolia and Turkey) These countries are moved to the AP subgroup The

AM includes countries in Latin America & Caribean and North America The number and list of countries in each group are given in Table A1 and Table A2 in Appendix, respectively

The total inequality of all countries is the sum of two components as below:

T 𝐈 = Tw all + Tb all = 𝑊 𝐴𝑃 T 𝐴𝑃 + 𝑊 𝐸𝑈 T 𝐸𝑈 + 𝑊 𝐴𝑀 T 𝐴𝑀 + 𝑊 𝐴𝐹 T 𝐴𝐹 + Tb 𝑎𝑙𝑙 , (2.7) where Tw all = 𝑊 𝐴𝑃 T 𝐴𝑃 + 𝑊 𝐸𝑈 T 𝐸𝑈 + 𝑊 𝐴𝑀 T 𝐴𝑀 + 𝑊 𝐴𝐹 T 𝐴𝐹 is the within-group component that refers to the inequality within each subgroup, namely, AP, EU, AM, and AF The between-group component, Tb all , is the inequality between the four subgroups

The results reflecting the contributions of these components to the changes in total export concentration are shown in Figure 2.5 Panels (a) and (c), as well as panels (b) and (d), illustrate the change of the absolute Theil indices and the relative Theil indices in the period 2004-2018, respectively Panel (a) and (b) display the decomposition between four subgroups with their weights, that is, the contribution of AP countries (“AP-W” line), EU countries (“EU-W” line), AM countries (“AM-W” line), and AF countries (“AF-W” line), The level of export concentration to all countries is illustrated in the “All” line The “Within” line and the “Between” line depicts the within and between components of concentration, respectively Panels (c) and (d) describe the export concentration across the AP countries (“AP” line), EU countries (“EU” line), AM countries

(“AM” line), and AF countries (“AF” line) that interpret the terms T 𝐴𝑃 , T 𝑬𝑼 , T 𝐴𝑀 , T 𝐴𝐹 in Equation 2.7, respectively

Panels (a) and (b) show that the levels of inequality with weight across countries of each subgroup (except the AP group) are low, ranging from 0 to 0.5 over time These levels indicate that the total export value from ASEAN to countries within each subgroup after calculating with weight is quite equal Three subgroups, namely, EU, AF and AM have relatively equally contributions to the within-group component We can observe the peak of “AF-W” line, it influences within components as well as the overall concentration in 2006 However, in most years of the period from 2004 to 2018, the contribution of the AP group is the greatest to the overall concentration, reflecting the largest difference in export value between countries in this subgroup as well as the largest weight for this subgroup For the absolute Theil index (Panel (a)), the two components are nearly equivalent (although the “Within” line is slightly higher than the “Between” line) This means that the inequality across countries within the subgroup is nearly equal to the inequality between subgroups Between-group component changed a little over time (about 0.5 during the period) The levels of concentration of the AF group change the most We can observe the strong peak of the AF group in 2006 In 2007, after one year, it dropped by about 50% (Panel (c)) and 20% (Panel (d)) The reason is that export value from the ASEAN to the AF group in 2006 is nearly double compared to that in 2005, which was concentrated in some countries such as Malta, Qatar, United Arab Emirates, and Saudi Arabia (the increase in import value of these countries from the ASEAN in 2006 was double that in 2005) It makes the level of inequality across countries in this subgroup increase For the absolute Theil indices (Panels (a) and (c)), as well as the relative Theil indices (Panels (b) and (d)), the shapes of the “AM” line, the “AM-W” line, the “Within” line and the “All” line, are quite similar It reflects the influence of this group on all samples The concentration levels of ASEAN export across countries in AM group decreased in the period from

21 Figure 2.5 Decomposition among four subgroups separated by regions

This section further separates each region above into two smaller subgroups, such as high- income countries and non-high-income countries Therefore, we now have eight subgroups

The total inequality of all countries is the sum of two components as below:

+𝑊 𝐴𝑀−𝐿 T 𝐴𝑀−𝐿 + 𝑊 𝐴𝐹−𝐻 T 𝐴𝐹−𝐻 + 𝑊 𝐴𝐹−𝐿 T 𝐴𝐹−𝐿 is the within-group component that refers to the inequality within each subgroup, namely, Asia and Pacific high-income (AP-H), Asia and Pacific non-high-income (AP-L), Europe high-income (EU-H), Europe -non-high-income (EU-L), America high-income (AM-H), America non-high-income (AM-L), Africa high-income (AF-H), and Africa non-high-income (AF-L) The between-group component, Tb all , is the inequality between eight subgroups

Figure 2.6 shows the contributions of these components to the changes in total export concentration Panels (a) and (b) display the decomposition between eight subgroups after calculating it with the weight Panels (c) and (d) describe the export concentration across the countries in each subgroup

23 Figure 2.6 Decomposition among eight subgroups separated by regions and income

In Panels (c) and (d), similar to the result in the previous part, the levels of the ASEAN export concentration in the AF change the most When we separate the sample into smaller subgroups, by observing Panels (a) and (b), we find that in the AP and AF subgroups, non-high- income subgroups have more contribution to the level of export concentration in each region (𝑊 𝐴𝑃−𝐿 T 𝐴𝑃−𝐿 and 𝑊 𝐴𝐹−𝐿 T 𝐴𝐹−𝐿 are higher than 𝑊 𝐴𝑃−𝐻 T 𝐴𝑃−𝐻 and 𝑊 𝐴𝐹−𝐻 T 𝐴𝐹−𝐻 , respectively) However, in the EU, the high-income subgroup contributed more (𝑊 𝐸𝑈−𝐻 T 𝐸𝑈−𝐻 is higher than

𝑊 𝐸𝑈−𝐿 T 𝐸𝑈−𝐿 ) This can be explained by the number of countries in each subgroup In the AP and

AF, most countries are non-high-income countries (29 of 40 and 47 of 55 countries, respectively)

Conclusions

As most members of ASEAN belong to the non-high-income group and are developing countries, the export concentration is thus a concern This study aims to provide another approach for measuring export concentration Using Theil indices and their decomposition, we assess the changes in the ASEAN export concentration and in its components during the period 2004 - 2018 to show which component contributes more to the total export concentration Both the absolute Theil indices (i.e., every country in the sample is treated equally) and the relative Theil indices (i.e., every country in the sample is treated unequally and the weight of each country depends on their size measured by the population) are also calculated We apply the indices for four sets of subgroups

Four findings are drawn from our empirical results First, the results show that the total ASEAN export concentration decreased over time, and the within-group component contributed more to the total concentration Second, decomposing the concentration of export value across the countries of two subgroups, namely, high-income and non-high-income subgroups, shows that with the absolute Theil indices, the inequality across countries in each group was quite stable and the gap in import value from ASEAN between two subgroups was little Third, by focusing on four subgroups separated by regions and further dividing each region into two smaller subgroups by income, we find that the high-income countries played the key role in the volatility of the level of the ASEAN export concentration in the AF subgroup However, in the AM subgroup, the non- high-income group played that important role Fourth, the empirical results also show that the export concentration among the ASEAN members was decreasing gradually during this period The result in this chapter is reasonable and consistent with the “U-shape relationship” mentioned in Section 2.1 This study emphasizes the important role of export diversification in the ASEAN countries For the developing countries (most of the ASEAN countries), the export is diversified, and the levels of export concentration decrease Low-income countries have a tendency to diversify These countries’ exports are spread more equally across destinations over time Since most ASEAN countries depend on exports, export diversification reduces the threat of fluctuations and the vulnerability that arises from adverse external shocks as well as keeps achieving sustainable export growth In addition, we consider ten ASEAN countries as a group since all ASEAN countries are in the same supply chain The results in this chapter show a large contribution to the export concentration of high-income countries in Europe It refers that ASEAN countries still follow the comparative advantages and they are at bottom of the global value chain

FOREIGN DIRECT INVESTMENT AND TRADE IN ASEAN COUNTRIES: A SPATIAL ECONOMETRIC ANALYSIS

Introduction

Since the 1980s, FDI has grown at a dramatic rate It plays an important role in economic growth in developing countries The role of FDI in development is recognized in many aspects On the one hand, FDI channel resources of capital flow from countries where they are abundant to those where they are scarce These inflows of capital allow host economies to invest in productive activities beyond what could be achieved by domestic savings alone On the other hand, the benefits of FDI resemble those from trade, especially in sectors producing goods and services which tend not to be traded internationally It leads to many studies that figure out the factors that affect the behavior of FDI activities and attracts the concern of the policymakers

One of the Association of Southeast Asian Nations (ASEAN)’s aims is to enhance the economic cooperation among the ASEAN members and other countries In 1992 The ASEAN Free Trade Area (AFTA), a trade bloc agreement in ASEAN, was signed by six members (Brunei Darussalam, ASEAN-5) to support local trade in ASEAN and increase the ASEAN's competitive edge to attract more FDI to ASEAN The four latecomers (Vietnam, Lao PDR, Myanmar and Cambodia) were required to sign the AFTA agreement to join ASEAN In 1998, a regional investment agreement was signed among nine ASEAN members to establish the ASEAN Investment Area (AIA) 3 It aims to "attract greater and sustainable levels of FDI into the region

3 Cambodia is the last member which joined ASEAN in 1999

30 and to realize substantially increasing flows of FDI from both ASEAN and non-ASEAN sources by making ASEAN an attractive, competitive, open and liberal investment area" 4 This confirms the key role of FDI in the ASEAN countries’ economic growth and these countries’ policies are geared towards attracting FDI Although there is only a small share of total investment or employment in each ASEAN economy, FDI has been a key factor driving export-led growth (Thomsen, 1999) Recently, ASEAN has become one of the most attractive investment locations in the developing world Eight of ten members of ASEAN are developing countries Among the ASEAN countries, Singapore achieved the top of the list with US$114 billion in FDI inflows, followed by Indonesia (US$23 billion), Vietnam (US$16 billion), and Malaysia (US$7 billion) in

2019 Figure 3.1 depicts FDI inflow to the ASEAN countries in the period from the year 1999 to

2019 The total FDI inflow into ASEAN increased in this period from US$31.4 billion in 1999 to US$182 billion in 2019 Singapore contributed about 62 percent of the total amount

4 Joint Press Release, Inaugural Meeting of the ASEAN Investment Area (AIA) Council, 8th October 1998, Manila, the Philippines

Figure 3.1 FDI inflows to ASEAN in the period 1999-2019

Figure 3.2 Shares of FDI inflows to ASEAN in the period 2010-2019 Source: WDI database

Figure 3.2 illustrates the contribution of inward FDI to ASEAN by source countries This figure shows that the U.S., EU-27, Intra-ASEAN and Japan were the largest investors to the SEAN countries, with a share of around 15%, 16%, 19% and 13%, respectively of ASEAN We can observe the negative share of inward FDI to ASEAN in 2012 and 2018 The reason is the divestment of MNEs of the U.K and the U.S in the financial services sector (ASEAN, 2013&2019) For example, ASEAN (2019) reports that “FDI from the United States plummeted to just $8 billion in 2018, from $25 billion in 2017 This fall in United States investment was global The tax reforms introduced in late 2017 encouraged many American MNEs to move profits retained in their foreign subsidiaries, holding companies or regional headquarters back to the home country In Singapore, the drop in the U.S FDI was significant, from $28 billion in 2017 to just $4 billion last year the U.S MNEs in financial services divested -$11 billion in 2018 (from $24 billion in 2017).” Figure 3.3 depicts the trade value of ASEAN (in constant 2010 prices) from 1999 to

2019 The figure shows that both the ASEAN exports and imports increased rapidly In 2009, a decrease in trade value was recorded because of the global financial crisis However, in 2019, the ASEAN export reached over US$1,936 billion, an increase of nearly 3.4 times compared with the value in 1999 The largest market of ASEAN exports is intra-ASEAN, with a 25% share of the total export value of ASEAN The other markets of the ASEAN exports are China (10.6%), the EU (11.4%), the U.S (10.8%), Japan (9.9%) and the Republic of Korea (4%) In particular, the ASEAN exports to China during this period increased rapidly

Figure 3.3 ASEAN trade value in the period 1999-2019

Many studies focus on the determinants of FDI Most of these articles confirm the influences of market size, production cost, trade openness, human capital, labor productivity and infrastructure quality in both home and host countries on the FDI inflow in the host country In addition, the relationships between FDI and trade (including one-way and bi-direction) are always concerned by not only the researchers but also the policymakers The relationship between FDI and trade is considered in two aspects: complement or substitute For the first aspect, FDI and trade move in the same direction It relates to the vertical FDI that the home country invests in the lowest cost host country, produces goods in the host country and then increases the exports Kojima (1975), Martínez et al (2012) and Akadiri et al (2020) find out the complement of FDI and trade For the second aspect, it occurs when the home country establishes and managerial controls operations in

34 the host country In this case, the home country aims to seek a market, and sell the product in the host country (Markusen and Venables, 2000; Chiappini, 2016) It can be seen that in the empirical FDI literature, the impact of the FDI in proximate countries on the host country’s FDI has been ignored However, recently, this spatial interdependence has been paid attention to The development of a new method (namely spatial econometrics) opens up a new direction for the literature This method allows observing the spatial effects of the determinants of FDI According to Blonigen et al (2007), Coughlin and Segev (2000) and Baltagi et al (2007) are the two first papers that use spatial econometrics to show the spatial interactions in FDI behavior After that, using the different datasets, such as the U.S outbound FDI and Chinese outbound FDI to a set of countries located all over the world, Blonigen et al (2007), Poelhekke and Van der Ploeg (2009) and Chou et al (2011) also find out the existence of spatial effects on FDI

Although the ASEAN countries are not only in the same geographical area but also members of the same organization, each of them is still an individual country with specific economic and political policies Pointing out the determinants of FDI within a country as well as the spatial impacts of the third country are the key information to the policymakers The spatial Durbin model allows us to examine both the relationship between FDI and trade and the spatial interaction of this relationship based on the location of ten countries in ASEAN In this chapter, we attempt to contribute to the limited literature by analyzing the spatial effects of the neighboring countries on the FDI inflow to the ASEAN countries Does the FDI inflow into the members of ASEAN have any influence on each other and does the trade value in one country affect the FDI flow into other countries? Furthermore, we apply the dynamic spatial model to show the spatial effects of the one- period lagged inward FDI and trade value Our results confirm the indirect impacts of neighboring countries’ trade and FDI on FDI in the ASEAN countries

This chapter is organized as follows Section 3.2 reviews the literature Section 3.3 provides details on data and methodology Section 3.4 presents the empirical results Finally, Section 3.5 concludes the chapter.

Literature Review

3.2.1 The development of the spatial econometrics

Recently, spatial econometric techniques have been widely used to examine spatial effects in many research areas Anselin (1988) is the first study introducing models and methods of spatial econometrics specifically This book presents the foundations of spatial econometrics, including the spatial effects, the spatial econometrics models as well as how to estimate and test the models Anselin (2010) takes the year 1979 as “the historical starting point for spatial econometrics” and defines spatial econometrics as “the collection of techniques that deal with the peculiarities caused by space in the statistical analysis of regional science models”

Elhorst (2014) can be considered as a very important introduction to the spatial econometric model It develops and summaries three generations of spatial econometric models: the models based on cross-sectional data, the static models based on spatial panels and the dynamic spatial panel data models, as illustrated below

Figure 3.4 The spatial dependence models for cross-sectional data Firstly, the relationship between the spatial dependence models based on cross-sectional data is summarized in Figure 3.4 5

Secondly, the spatial panel data models including the fixed-effect model, the random-effect model, the fixed-coefficient model, the random-coefficient model, and the multilevel model are summarized and expressed as follows:

Finally, the dynamic spatial panel model is constructed as:

5 𝑌 is an 𝑁x1 vector including one observation on the dependent variable for every unit in the sample (𝑖 = 1, … , 𝑁)

𝑋 represents an 𝑁x𝐾 matrix of exogenous explanatory variables, 𝑊 is a nonnegative 𝑁x𝑁 matrix illustrating the spatial arrangement of the units in the sample SAC model includes both a spatially lagged dependent variable and a spatially autocorrelated error term and the SLX (spatial lag of X) model contains exogenous interaction effects model

Many researches adopt these models introduced above to deal with the causes and consequences of economic growth, FDI and trade For example, Driffield (2006) states that the role of agglomeration and proximity is very important in models analyzing the spillover effects In this paper, the author uses the dataset stratified by industry and region in the U.K from 1984 to 1992 and develops a spatial GMM estimator to examine the local intra-industry spillovers and interregional spillover effects from FDI The local intra-industry spillovers are confirmed However, no interregional spillover effects from FDI are found The reason discussed is that the opposite directions of the national and local intra-industry spillovers from FDI on productivity might cancel off each other

Fischer and Griffith (2008) introduce a comparison between the spatial econometric approach and the eigenfunction spatial filtering approach to account for spatial autocorrelation among flow residuals This paper also uses the data of patent citations, including 12,432 knowledge flows across 112 European regions to illustrate an application of these approaches The results show that both methods are not significantly different from each other and lie within the 95 percent confidence limits of the least squares estimates

Blonigen et al (2007) adopt the spatial econometric model on a panel data of the U.S outbound FDI into 35 countries from 1983 to 1998 They find the significantly positive spatial impact of FDI and the significantly negative spatial impact of the market potential Regelink and Elhorst (2015) show that results in Blonigen et al (2007) are not consistent with any of the FDI motivations They use the same dataset as Blonigen et al (2007), but exclude countries after considering their neighbors (their sample includes 20 European countries) from 1999 to 2008 and apply the spatial Durbin model They show that there is competition in attracting U.S companies among European countries The results correspond to the pure vertical and export-platform FDI

Ho et al (2013) aim to examine the augmented Solow model by applying the dynamic spatial Durbin model with a sample of 26 OECD countries over the period 1971-2005 They find that there is a positive spillover effect of growth from one country to its trade partners

Benos et al (2015) introduce a non-linear regression model using the annual panel data for 1,273 NUTS III (Nomenclature of Units for Territorial Statistics) regions in seven EU member- states, namely the U.K., France, Germany, Sweden, Italy, Spain and the Netherlands over the 1990-

2005 period In their model, geographical, economic and technological proximity weights are analyzed to show that the existence and magnitude of interregional spillovers play a key role in the process of EU regional development

Abate (2016) applies the spatial Durbin Ramey-Ramey model by using the dataset of 78 countries for the period 1970-2010 to examine the effects of volatility on growth in the framework of spatial interactions In contrast to previous papers, the results show that the spatial effect plays an important role that influences the relationship between volatility on the growth of a particular country

Gutierrez-Portilla et al (2018) use the data of Spain's FDI outflows to the top 50 host countries for the period 1996-2014 and apply the dynamic spatial Durbin model to examine whether the Spanish FDI outflows to a host country are influenced by the economic growth and the Spanish FDI outflows of other host countries in the sample The empirical results show that in the pre-crisis period, there is a positive impact of direct investment in neighboring countries to the host country However, in the crisis period, this impact disappears

Gutierrez-Portilla et al (2019) apply the panel spatial Durbin model by using the data of FDI inflows to Spain, at both the aggregate and sectoral levels, over the period from 1996 to 2013 Their findings reveal that inward FDI in one region is complementary to that in neighbor regions

Kim (2020) adopts the dynamic spatial Durbin model for the Schumpeterian technology diffusion model by using the dataset of 11 Asian countries including China, Hong Kong, Indonesia, India, Korea, Malaysia, Philippines, Singapore, Sri Lanka, Thailand and Taiwan over the period 1970–2014 The significant and negative results of the total effect on the lagged relative income confirm the hypothesis about the conditional convergence among Asian countries

Amidi and Majidi (2020) use the dataset of 25 EU countries from 1992 to 2016 and adopt the spatial dynamic panel data model to confirm the positive effect of a country’s growth on its neighbor countries Basile (2008) aims to test for the presence of spatial externalities on the process of economic growth of the European region Based on a dataset of 155 European NUTS II regions for the period 1988-2000, this paper applies a semiparametric spatial Durbin model to show that regions surrounded by richer regions have higher expected growth rates than regions surrounded by poorer regions

Methodology and Data

Using the spatial Durbin model, the spatial lag model, including spatial lags of the dependent variable and spatial lags of the independent variable, is written as: ln(𝐹𝐷𝐼 𝑖𝑡 ) = 𝜌𝑊xln(𝐹𝐷𝐼 𝑖𝑡 ) + 𝛾ln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡 ) + 𝜃𝑊xln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡 ) + 𝛽𝑋 𝑖𝑡 + 𝜇 𝑖 + 𝜆 𝑡 + 𝜀 𝑖𝑡 , (3.1) 6 where 𝐹𝐷𝐼 𝑖𝑡 is an 𝑁x1 vector including one observation on the total FDI inflow for each ASEAN country (𝑖 = 1,…, N and N = 10) at time 𝑡 (𝑡 = 1999,…, 2019), 𝑊xln(𝐹𝐷𝐼 𝑖,𝑡 ) denotes the interaction effect of FDI in country 𝑖 with FDI in neighboring countries (country 𝑗, 𝑗 ≠ 𝑖, 𝑗

= 1,…, 9) at time 𝑡 𝑊 is spatial weight 𝑁x𝑁 non-negative inverse distance matrix

6 In this chapter, I focus on the relationship between trade and FDI I also try running the SDM for all control variables However, most coefficients reflecting the spatial effect of Xs on FDI are insignificant The results are available upon request

), where each element is calculated by the formula 𝑤 𝑖𝑗 = 1/𝑑 𝑖𝑗 for 𝑖 ≠ 𝑗 𝑑 𝑖𝑗 is the distance between country 𝑖 and 𝑗 calculated by using the longitude and latitude of the capitol of each country in ASEAN The diagonal elements of the matrix are set to zero i.e 𝑤 𝑖𝑗 =0 if 𝑖 = 𝑗 𝑊 describe the location of ten countries in ASEAN

𝑇𝑟𝑎𝑑𝑒 𝑖𝑡 , a 𝑁x1 vector, is a part of 𝑁x𝐾 matrix of the independent variables and it is separated into export value (𝐸𝑥𝑝 𝑖𝑡 ) and import value (𝐼𝑚𝑝 𝑖𝑡 ) 𝐾 is the number of independent variables (K = 7) 𝑊xln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡 ) denotes the interaction effect of trade in country 𝑖 with trade openness in neighboring countries (country 𝑗) at time 𝑡

𝑋 𝑖𝑡 denotes an 𝑁x(𝐾-2) matrix of the 𝐾-2 control variables Regelink and Elhorst (2015) mention that FDI tends to move between wealthier markets So they use the Population variable together with the GDP variable to control for the market size of country 𝑖 at time 𝑡 Therefore, we use the set of variables including 𝐺𝐷𝑃 𝑖𝑡 , 𝐿𝐴𝐵 𝑖𝑡 𝑇𝐶 𝑖𝑡 , 𝑃𝑂𝑃 𝑖𝑡 , and 𝑄𝐺 𝑖𝑡 We take the logarithm forms for 𝐺𝐷𝑃 𝑖𝑡 and 𝑃𝑂𝑃 𝑖𝑡

𝐺𝐷𝑃 𝑖𝑡 is used to measure the market size We expect to observe the positive effect of this variable on FDI 𝐿𝐴𝐵 𝑖𝑡 is the average years of schooling of people aged over 25 to measure the skill levels of the labor force 𝑇𝐶 𝑖𝑡 is the trade cost of the destination country It is a proxy variable for the trade barriers between the host and home countries In this chapter, we follow Blonigen et al (2007) and use the inverse of the openness that is calculated by dividing the trade value by GDP 𝑃𝑂𝑃 𝑖𝑡 is the population 𝑄𝐺 𝑖𝑡 , the quality of government, is calculated as the average value of three indices, including corruption, law and order, and bureaucratic quality It has a value ranging from 0 to 1 and increases if the quality of government improves

𝜇 𝑖 denotes a country-specific effect, 𝜆 𝑡 denotes a time-period specific effect and 𝜀 𝑡 is the vector of disturbance term This model can be treated by either the fixed-effect or the random- effect We use the Hausman test and the result shows applying the fixed-effect model is more appropriate than the random-effect one 7

We also apply the dynamic spatial Durbin model to examine the spatial effects of one period lagged dependent variable as well as independent variables In addition, using the dynamic model with the time lag might avoid possible endogenous problems ln(𝐹𝐷𝐼 𝑖𝑡 ) = 𝜌 1 𝑊xln(𝐹𝐷𝐼 𝑖𝑡 ) + 𝜌 2 ln(𝐹𝐷𝐼 𝑖𝑡−1 ) + 𝜌 3 𝑊xln(𝐹𝐷𝐼 𝑖𝑡−1 ) + 𝛾 1 ln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡 ) +

𝛾 2 ln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡−1 ) + 𝜃 1 𝑊xln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡 ) + 𝜃 2 𝑊xln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡−1 ) + 𝛽𝑋 𝑖𝑡 + 𝜇 𝑖 + 𝜆 𝑡 + 𝜀 𝑖𝑡 , (3.2) where 𝐹𝐷𝐼 𝑖𝑡−1 and 𝑇𝑟𝑎𝑑𝑒 𝑖𝑡−1 denote a 𝑁x1 vector that includes one observation on the total FDI inflow and trade for each ASEAN country (𝑖 = 1,…,10) at time 𝑡 (𝑡 = 1999,…, 2019), respectively 𝑊xln(𝐹𝐷𝐼 𝑖𝑡−1 ) and 𝑊xln(𝑇𝑟𝑎𝑑𝑒 𝑖𝑡−1 ) are the interaction effects of FDI and trade value in country 𝑖 with FDI and trade value in neighboring countries (country 𝑗) at time 𝑡-1, respectively In this chapter, we use the one-period lagged variables because of the expectation that the impacts of FDI and trade in neighboring countries do not happen instantaneously, but may take time to be absorbed, observed and responded to

In addition, for these models, we can observe the direct and indirect effects of each variable The direct effect is the effect of the change within a country (i.e the average change in FDI in country 𝑖 caused by a one-unit change in country 𝑖’s explanatory variable) The indirect effect is the spillover effect (i.e the aggregate impact of changing an explanatory variable in country 𝑖 on the FDI inflow to the neighboring countries) For 𝑡𝑟𝑎𝑑𝑒 variable, the direct effect equals the parameter measuring the impact of this variable 𝛾 and the indirect effect equals (𝛾 + +𝜃𝑊)(1 +

7 The Hausman test result has a 𝜒 2 value of 48.16 and the probability greater than the 𝜒 2 value is 0.000.

𝜌𝑊 + (𝜌𝑊) 2 + ⋯ ) For 𝑋 variables, the direct effect equals the parameter measuring the impact of these variables 𝛽 and the indirect effect equals 𝛽(1 + 𝜌𝑊 + (𝜌𝑊) 2 + ⋯ )

The ASEAN trade, FDI, GDP and population data used are collected from World Bank’s World Development Indicators (WDI) database They are available at https://data.worldbank.org/indicator This database provides the export value from the ASEAN countries to the world, the import value of the ASEAN countries from the world and the FDI inflow of the ASEAN countries from 1999 to 2019

The distance between countries used to construct the spatial weight matrix is calculated by using the longitude and latitude of the capitol of each country in ASEAN We derive it from the website https://thematicmapping.org/

Following Barro and Lee (2013), we use the average years of schooling of people aged over

25 to measure the skill levels of the labor force This data is available from the website http://www.barrolee.com/ This dataset provides educational attainment data at the 5-year intervals from 1950 to 2015 for hundreds of countries We apply data of one year in the dataset to the following four years For example, the average years of schooling of people aged over 25 in 2016,

2017, 2018 and 2019 are the same and equal to that in 2015 The quality of government variable is taken from the website https://www.gu.se/en/quality-government/qog-data

In this chapter, we take the logarithm forms of these variables except the trade cost, the skill levels and the quality of government variables There are five negative values of FDI in the dataset After taking the logarithm form, the negative values of FDI become missing values I replace it with zero and take the logarithm form of FDI plus 1 Table 3.1 below shows the summary statistics of variables and Table 3.2 describes the definitions and sources of variables we use in this chapter

Table 3.1 Descriptive statistics Variable Unit Number of observations

Table 3.2 Variable descriptions and sources

𝐹𝐷𝐼 𝑖,𝑡 The total FDI inflows of country 𝑖 at time 𝑡

𝐸𝑥𝑝 𝑖,𝑡 Export value of country 𝑖 at time 𝑡

𝐼𝑚𝑝 𝑖,𝑡 Import value of country 𝑖 at time 𝑡 (in constant 2010

𝐺𝐷𝑃 𝑖,𝑡 GDP of country 𝑖 at time 𝑡 (in constant 2010 US$) WDI

𝑃𝑂𝑃 𝑖,𝑡 Population of country 𝑖 at time 𝑡 WDI

𝑇𝐶 𝑖,𝑡 The trade cost of country 𝑖 at time 𝑡 Authors’ calculation

𝑄𝐺 𝑖,𝑡 The quality of government of country 𝑖 at time 𝑡 https://www.gu.se/en/quality- government/qog-data 𝐿𝐴𝐵 𝑖,𝑡 The average years of schooling of people aged over

Barro and Lee (2013) http://www.barrolee.com/

Empirical Results

Firstly, we test for the presence of spatial autocorrelation by applying the Moran’s I test with the null hypothesis of spatial randomization based on Cliff and Ord (1970) and Anselin (1995) We implement this diagnostic test in STATA for panel data Table 3.3 shows the test results for the FDI variable every year The test results reject the null hypothesis in 20 out of 21 years (except

2014) It confirms the spatial autocorrelation and it is reasonable to apply the spatial econometric models for this sample

Table 3.3 Moran’s I test for spatial autocorrelation of FDI

Note: ***,**,* denote 1%, 5%, 10% levels of significance, respectively

We also do the model selection test using the STATA package that is provided by Belloti et al (2017) First, the test results for SAR or SEM have a 𝜒 2 value of 11.99 and 13.2, and the probability greater than the 𝜒 2 value is 0.0025 and 0.014, respectively In these cases, the null hypothesis that either SAR or SEM is a better model than SDM is rejected Then, we use the criteria to test whether the most appropriate model for the data is the SAC or SDM The Akaike's

48 information criterion (AIC) and Bayesian information criterion (BIC) for SAC (999.5 and 1030) are larger than those for SDM (990.7 and 1027.5) So we fit the SDM for the data at hand

Then, we apply the spatial Durbin model with time fixed-effects and country fixed-effects (Equation 3.1) for three samples: the full sample with ten ASEAN countries, the subsample of eight developing countries in ASEAN, and the subsample of ASEAN-5 plus Vietnam We also show the results for OLS regression to compare the models with and without spatial effects Table 3.4 illustrates the results of both regressions The SDM results show that the size of the host country, the quality of government, the average years of schooling of people aged over 25 and the trade cost have significant positive impacts on FDI inflow to the ASEAN countries The larger the market size, the higher the level of the quality of government and the longer the average years of schooling of people aged over 25 of the host country increases the inward FDI of ASEAN The higher the trade cost, the higher the barrier for import has been replaced by the FDI inflow to the ASEAN countries This result suggests the existence of horizontal FDI in ASEAN The home country invests in ASEAN to seek market and sell the output in host countries In addition, the significantly positive impact of the export value on FDI inflow to ASEAN implies that there might be the presence of the form of export-platform FDI The home country invests in ASEAN not only to seek market and sell the output in host countries but also to use ASEAN countries to export products to other countries Within a country, export and FDI are complements The results for OLS regression show the negative coefficients of import value for the two subsamples and the negative coefficients of export value for the full samples It refers to a substitute relationship between import and FDI and is also not consistent with the export-platform FDI Since all ASEAN countries are in the same supply chain, the results for SDM are more reasonable

The significant spatial effects are observed in this model The coefficient of the spatial interaction of FDI inflow is negative, implying that the FDI inflow to the ASEAN countries

49 depends negatively on the FDI of neighbor countries in ASEAN These results are consistent with the discussions in Regelink and Elhorst (2015) about the competition among countries that are members in the same area in attracting FDI A one-percentage point change in one ASEAN country’s FDI reduces 0.8 percentage points in the FDI of other ASEAN countries The coefficient of the spatial impact of export value is huge and positive (64.74), indicating that the export values of an ASEAN country not only increase the FDI inflow to itself but also promote the FDI inflow to the neighboring countries in ASEAN This refers to the key role of export in the export-platform FDI both within a country and to its neighbor countries The negative significant spatial effect of import value infers the relevance of the competition effect among ASEAN countries in attracting FDI The increase in import value and FDI inflow in the neighboring countries decreases FDI inflow to an ASEAN country When we apply the SDM for the subsample excluding the two developed countries in ASEAN (Brunei Darussalam and Singapore) or ASEAN-5 plus Vietnam, the spatial effects are insignificant It can be explained that the results are quite sensitive to the countries in the sample Singapore contributes about 60 percent to ASEAN’s FDI inflow, so it is outstanding in attracting FDI among ASEAN countries Singapore’s export value also accounts for

35 percent of ASEAN’s export value in total Whereas, four countries (Brunei Darussalam, Cambodia, Lao PDR and Myanmar) excluded from ASEAN-5 plus Vietnam subgroup only had a share of about 3.6 percent of the ASEAN’s FDI inflow and 2.1 percent of the ASEAN’s export value (in the average of this research period) The significant spatial effects of FDI and trade can only be observed in the full sample

Table 3.4 Results of the spatial Durbin model

ASEAN-5 plus Vietnam Dependent variable: lnFDI lnExp -5.07**

0.22 Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Standard errors are in parentheses

Table 3.5 shows the average impacts of the changes in the factors reported It separates into the direct and indirect effects The former refers to the effect of the change within a country without spillover effects The latter is the spillover effect STATA provides the average effects from the recursive process Based on the results reported in Table 3.5, most variables in this model have significant direct and indirect effects For the export value, we find the positive effects A one- percentage point increase in export value increases the FDI inflow within an ASEAN host country by 8.5 percentage points The increase in the FDI inflow causes negative spillover effects on the FDI inflow to neighbor countries However, the spatial impact of export value on the FDI inflow

51 to neighbor countries is positive The total spillover effect of a one-percentage point increase in export value is a 32 percentage point increase in FDI inflow to an ASEAN host country The variables representing the size of the host country and quality of government have the same sign in the direct (positive sign) as well as indirect (negative sign) impacts on FDI Within the host country, these factors help attract FDI, however, the negative indirect effects are also significant This can be explained by the inverse spatial impact of FDI in the third country on FDI inflow to the ASEAN countries Similar to the results in Table 3.4, the indirect effects of both export value and import value are insignificant.

Table 3.5 The average impacts from the recursive process for SDM

Variable ASEAN Eight developing countries ASEAN-5 plus Vietnam

Total Direct Indirect Total Direct Indirect Total Direct Indirect

Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Standard errors are in parentheses

3 Time and country fixed effects are included.

We continue to estimate the impacts of these variables by applying the dynamic spatial Durbin model (Equation 3.2) In this model, we examine whether the one-period lagged variables have any influence on the FDI inflow to the ASEAN countries The results for the full sample and the two subsamples (defined above) are shown in Table 3.6 We find the significantly positive spatial effects of one-period lagged FDI and the significantly negative spatial effects of one-period lagged export value on FDI inflow to the ASEAN host countries for the full sample It means that the FDI inflow to a host country might be influenced by a one-percentage change in FDI and export value of the previous year with spillover effects from other countries in ASEAN The one-period lagged FDI increases the inward FDI in neighbor countries However, the one-period lagged export value decreases the inward FDI in neighbor countries We can observe the same direction of the spatial effect of one-period lagged FDI and the significantly negative spatial effects of one-period lagged import value on FDI inflow to the ASEAN host countries when excluding Singapore and Brunei Darussalam from the full sample For the subsample of ASEAN-5 plus Vietnam, all the spatial impacts are insignificant Based on the values of R 2 reported in Table 3.6, the subsample of eight developing countries fits the regression model the most

Table 3.7 shows the direct and indirect effects of one-period lagged variables in the dynamic spatial Durbin model The total effect equals the sum of the direct and indirect effects The table reports that FDI in the host country may depend on the FDI and trade in neighbor countries (including the one-period lagged variables) A one-percentage point change in the one-period laggedFDI increases the inward FDI directly in the country itself by 1.24 percentage points Totally, the impact of the one-period lagged FDI on the FDI inflow is positive (2.34 percentage points) We can observe the insignificant direct and indirect effects of the change in the export value last year on FDI flow to the host country and to neighbor countries However, the total impact of a change in the export value in the previous year increases the FDI to the host country by 22.7 percentage points

Table 3.6 Results of the dynamic spatial Durbin model

Variable ASEAN Eight developing countries

ASEAN-5 plus Vietnam Dependent variable: lnFDI lnFDI (one-period lagged) 1.04***

120 0.01 Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Standard errors are in parentheses

Table 3.7 The average impacts from the recursive process for DSDM 8

Variable ASEAN Eight developing countries

Total Direct Indirect Total Direct Indirect Dependent variable: lnFDI lnFDI (one-period lagged) 2.34***

Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Standard errors are in parentheses

3 Time and country fixed effects are included.

8 The coefficient of variable LAB for ASEAN-5 plus Vietnam in Table 3.6 is omitted so I could not calculate the results of direct and indirect effects for this subsample Table 3.5 shows only the results of direct and indirect effects for the full sample and the subsample of eight developing countries

Conclusions

The key role of FDI in developing countries cannot be overstated Eight of the ten ASEAN members are developing countries, so the priority of ASEAN is promoting and attracting FDI to develop its members’ economies A better understanding of the factors attracting FDI is important to the policymakers in these countries This chapter investigates the spatially lagged impacts of FDI and trade value on FDI inflows to ASEAN host countries by adopting the spatial econometric model from 1999, the year that ASEAN had ten members, to 2019 The empirical results confirm both the direct and indirect effects (i.e spillover effect) of these variables

In this chapter, we apply the spatial Durbin model that allows us to observe the spatial effects of not only the dependent variable (FDI) but also the independent variable (trade value) on FDI inflow to the ASEAN countries We also try to examine the spatial lagged and time lagged impacts of these variables at the same time by applying the dynamic spatial Durbin model The results confirm the roles of determinants of FDI such as GDP, population, trade cost, the skill levels of labor force and the quality of government More importantly, the results show that FDI to the host country is influenced negatively by FDI and positively by the export value of the neighbor countries in ASEAN These effects are also confirmed inversely for the one-period lagged variables The results confirm the spatial effects, meaning that the characteristics (i.e FDI inflow and export) of each individual country not only affect the FDI inflow of this country itself but also have a significant influence on its neighboring countries’ FDI inflow The negative spatial impact of FDI and the positive spatial effect of export imply that there is a conflict between the ASEAN aims and reality The aim of enhancing economic integration among the ASEAN countries is to attract FDI to the region continuously However, both of the competition effect and the positive influence in attracting FDI among the ASEAN members coexist.

THE IMPACTS OF THE COVID-19 PANDEMIC ON EXPORT IN THE

Introduction

The COVID-19 pandemic started in early 2020 as a health crisis However, it soon spread worldwide and affected most of the world economies The WTO has estimated that in 2020 world trade is expected to fall by between 13% and 32% (WTO, 2020) At the beginning of the pandemic, the International Monetary Fund (IMF) estimated its impact to be only a 0.1 percentage point cut of the projected global growth, but then they announced that “the global economy will face a recession” (IMF, 2020) The Organization for Economic Co-operation and Development (OECD) also predicted a reduction in the global growth for 2020 because of the impacts of the COVID-19 pandemic, from the original forecast of 2.9% to 2.4%, or to 1.5% in case of a prolonged global outbreak (OECD, 2020)

Six of ten ASEAN countries confirmed the first case right after the beginning of the pandemic The number of new confirmed cases and deaths increase rapidly from March of 2020 Figure 4.1 and Figure 4.2 show the number of confirmed cases and deaths of COVID-19 in 2020 We can observe that COVID-19 spread rapidly, especially in Indonesia, Philippines, Malaysia, and Myanmar In these countries, the new confirmed cases and deaths rise uncontrollably Reported by the end of 2020, Indonesia was affected severely by the pandemic with 743,198 confirmed cases and 22,138 deaths, followed by Philippines with 474,064 and 9,244, respectively Some countries such as Lao PDR and Cambodia seemed to be under control (no deaths reported by 2020) “Without a doubt, the COVID-19 pandemic will have a negative impact on ASEAN’s economy and that of the rest of the world in 2020” (ASEAN, 2020)

Source: JHU CSSE COVID-19 database

Figure 4.1 The number of new COVID-19 cases in ten ASEAN countries in 2020

Source: JHU CSSE COVID-19 database

Figure 4.2 The number of COVID-19 deaths in ten ASEAN countries in 2020

In addition, China is the ASEAN’s biggest trade partner In 2018, it had a share of 17.1% of ASEAN’s total trade, followed by the U.S (12%), also the main trade partner of ASEAN These countries are directly affected by the pandemic China reported 93,153 confirmed cases and 3,327 deaths Meanwhile, the U.S confirmed 20,191 new cases and 316 349,938 deaths Thus, the overall impact of COVID-19 on ASEAN would likely be broad and deep Figure 4.3 and Figure 4.4 depict the export value of ten ASEAN countries and ASEAN in total in the period from 2018 to 2020 It reports a decrease in export value in the period from March to May of 2020 in most ASEAN countries For these reasons above, we place our focus on ASEAN and assess the impact of the pandemic on the ASEAN trade

Figure 4.3 The export value of ten ASEAN countries in the period 2018-2020

Figure 4.4 The export value of ASEAN in the period 2018-2020

There are many papers paying attention to this topic, however, it is still too early to collect sufficient data Thus, they construct the assuming scenarios by using the experiences from previous crises or pandemics or using the data of the pre-COVID-19 period Most papers in 2020 and 2021 are illustrative and cannot make an informed assessment of the full impact of the pandemic

In this chapter, we use the difference-in-differences (DID) approach to quantify the impacts of COVID-19 on trade in the ASEAN countries by using the monthly trade value data from ten ASEAN countries to their trade partners from January 2018 to December 2020 We also use the numbers of COVID-19 cases and deaths in both the ASEAN countries and the ASEAN trade

61 partners to measure the presence of COVID-19 in the change of the ASEAN trade during the year

2020 The findings of this chapter confirm the negative effects of COVID-19 on the ASEAN trade The chapter is organized as follows Section 4.2 provides a brief review of the determinants of trade in ASEAN and the impacts of the COVID-19 outbreak Section 4.3 introduces the analytical strategy and the data used Section 4.4 describes the empirical results The final section concludes the research.

Literature Review

Although COVID-19 is a health crisis, it affects all aspects of society worldwide Recognizing the effects of this pandemic, the literature on the economic impacts of COVID-19 is increasing rapidly Related to the response of the international trade to COVID-19, there are many remarkable researches

Baldwin and Weder di Maurio (2020) use the statistics and the experiences from the previous crisis (i.e global financial crisis in 2008) to assume some scenarios and for each scenario, they evaluate the economic impacts of COVID-19 They discuss clearly the influences of COVID-19 on trade flows by demand shocks (purchases fall) and supply shocks (production falls) This might be the earliest study on this topic

George et al (2021)use the inter-country input-output (ICIO) database of the OECD from

2002 to 2015 and focus on linkages among 36 sectors in China and eight ASEAN economies (Brunei Darussalam, Indonesia, Cambodia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) The authors choose 2015 as the base year of COVID-19 and apply the susceptible- infected-recovered macroeconomic (SIR-macro) framework to a general equilibrium model of production networks to show the impacts of COVID-19 The results confirm that the role of China

62 in global value chains is associated with significant economic impacts, within China and the ASEAN region

Guan et al (2020) assume a set of idealized lockdown scenarios (including stay-at-home orders, curfews, quarantines, and similar social restrictions) They suggest that imposing lockdowns from the beginning of COVID-19 makes the supply-chain losses not only more dependent on the number of countries imposing restrictions but also more sensitive to the duration of a lockdown than its strictness Thus, they propose that stricter and shorter lockdowns can minimize overall losses

Verschuur et al (2021) respond to Guan et al (2020) by showing the empirical evidence about the differences between models and reality They use the real-time shipping data from the Automatic Identification System (AIS) and show that it takes time to implement a commercial transaction It means that the forward propagation of shocks would be lagged in reality

Maliszewska et al (2020) use a standard global computable general equilibrium (CGE)model to assume the potential impact of COVID-19 on gross domestic product and trade In any scenario assuming, there is a decline in GDP and trade because of the impacts of COVID-19

Liu et al (2021) apply a gravity-like approach to China’s monthly export data at the HS 8- digit level over January 2019-December 2020 to examine the impacts of COVID-19 deaths and lockdown policies on the import value of China’s partners during 2020 The results suggest a negative influence of a country’s COVID-19 situation on its imports from China

Rose et al (2021) use the data on lockdown policies in the U.S between March and June of

2020 and apply the CGE model to analyze the impact of the pandemic on GDP through international trade They show that the effects of international trade linkages are significant in the context of COVID-19 Some countries/ regions that are weak in domestic production might be affected negatively because of shutdown or restriction policies in the pandemic

Hayakawa and Mukunoki (2021) focus on trade in finished machinery products from 35 reporting countries to 185 partner countries, over the period from January to June in both 2019 and

2020 Using the numbers of COVID-19 cases and deaths and the Poisson pseudo maximum likelihood (PPML) method, they measure the impact of the pandemic on value chains in three objects including importing countries, exporting countries and the countries that export machinery parts to exporting countries They show that the largest negative impacts were from exporting countries

Evenett et al (2022) use a unique Global Trade Alert (GTA) database that provides the weekly changes in interventions in two sectors (the medical goods and medicines sector and the agricultural and food products sector) from January to October 2020 This dataset also includes

701 policy measures covering 135 customs territories The results show that trade policy activism increases rapidly together with the rise in COVID-19 cases and suggest the extensive heterogeneity across countries in both their use of trade policy and the types of measures used de Lucio et al (2022) use Spanish firm-level monthly trade data with country-level COVID-

19 from February 2020 to July 2020 They confirm that the export value in destinations applying strict containment measures decreases more, whereas the import value remains unaffected Together with the studies on determinants of trade in ASEAN (Bun et al., 2009, Kien, 2009, Truong et al., 2019, Hoang et al., 2020, Beckman et al., 2021), in the context of COVID-19, there are some specialized researches focusing on the impact of pandemics on economic aspects of ASEAN

Beh and Lin (2021) apply the vector autoregressive (VAR) model to the 13-month data from July 2019 to July 2020 in seven ASEAN countries to examine the impacts of COVID-19 on the tourism industry The results show the two-way causality between COVID-19 and tourist arrivals

64 at the 95% significance level as well as reveal the serious impacts of the COVID- 19 outbreak on international tourism

Chong et al (2021)examine the economic impact of COVID- 19 in the ASEAN countries separately by using and analyzing their respective economic figures in the first two quarters of

2020 They conclude that three significant risk factors that the ASEAN countries facing are the slowing growth, the sluggish recovery of trade and the cross-country transmission of unemployment

Most of the papers in 2020 and 2021, because it is still too early to collect sufficient data, are illustrative and cannot make an informed assessment of the full impact of the pandemic We try to fill the gap in the literature by providing empirical evidence of the impacts of COVID-19 on the ASEAN countries.

Methodology and Data

Firstly, we apply the dynamic difference-in-differences method presented by Chen et al

(2022) We use the year 2020 as the treated year because COVID-19 started in early 2020 Since the years 2018 and 2019 are unrelated to the COVID-19 pandemic, we choose these years as control years that help us to track the change in monthly export value from the pre-pandemic period to the post-pandemic in 2020, relative to this change in the control years (2018 and 2019) For most countries in ASEAN, the pandemic spread dramatically from March of 2020 so we define January and February of the year as the pre-pandemic and ten months from March to December as the post- pandemic The dynamic DID estimation equation is expressed as follows:

65 where 𝐸𝑥𝑝 𝑖𝑗𝑚𝑡 is bilateral export value between country 𝑖 (the ASEAN countries) and country

𝑗 (the ASEAN trade partners) in month 𝑚 of the year 𝑡 𝑇𝑟𝑒𝑎𝑡𝑒𝑑 2020 is a dummy variable taking the value of 1 for the year 2020 (i.e the treatment year) and 0 for years 2018 and 2019 (i.e control year or untreated year) 𝑇 𝑘 is the event time dummy variable Because we choose March as the time that COVID-19 spread in ASEAN, we denote March of year with 𝑘 = 0, 𝑘 runs from -2 to 9 that are relative to March The event time that is denoted by 𝑇 −1 is omitted to avoid perfect multicollinearity The coefficients of this interaction term 𝛽 𝑘 show the difference in export value between the pre- and post-COVID-19 for 2020, relative to the change in the control years 𝑋 𝑖𝑚𝑡 denotes the set of control variables in the gravity model including gross domestic product per capita, population, the openness to trade, the exchange rate, the tariff rate in both exporters and importers, and other variables (the distance, the common language shared between the exporting country and importing country, WTO membership, and the pair of countries that have the same RTA) 𝜇 𝑖𝑗 , 𝛾 𝑚 , and 𝜆 𝑡 denote country-pair fixed-effect, month fixed-effect, and year fixed-effect, respectively

In ASEAN, the levels of pandemic spread are different among countries In some countries (Indonesia, Philippines, Malaysia, and Myanmar), COVID-19 spread dramatically and the number of new confirmed cases and deaths rise uncontrollably However, other countries (Lao PDR, Cambodia, and Vietnam) seemed to be under control and only a few new cases and no deaths were reported by 2020 Thus, we try to investigate the change in export value in some ASEAN countries that seems to be unable to control the spread of COVID-19, relative to the change in the other ASEAN countries Based on the number of new confirmed COVID-19 cases per million population in each country (see Table 4.1), we separate ten ASEAN members into two groups: one is greatly influenced by the pandemic (treatment group) and the other is assumed that the pandemic is under

66 control The treatment group includes five countries (Indonesia, Malaysia, Myanmar, Singapore, and Philippines) These countries reported at least 2,291 new cases per million people in 2020 The control group includes five countries (Brunei Darussalam, Cambodia, Lao PDR, Thailand, and Vietnam) These countries confirmed less than 358 new cases per million people in 2020 As mentioned before, we define the period from January 2018 to February 2020 as the pre-pandemic and ten months from March 2020 to December 2020 as the post-pandemic period

The DID regression is specified as follows:

𝐸𝑥𝑝 𝑖𝑗𝑝 = 𝛽 1 𝑡𝑖𝑚𝑒 𝑝 ∗ 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 𝑖 + 𝛽𝑋 𝑖𝑝 + 𝜇 𝑖𝑗 + 𝛾 𝑝 + 𝜀 𝑖𝑗𝑝 , (4.2) where 𝐸𝑥𝑝 𝑖𝑗𝑝 is the bilateral export value between country 𝑖 and country 𝑗 in time 𝑝 𝑡𝑖𝑚𝑒 𝑝 ∗ 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 𝑖 is the interaction term of 𝑡𝑖𝑚𝑒 𝑝 and 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 𝑖 The coefficient of this interaction term shows the immediate effect of the presence of COVID-19 on export value in the treatment group relative to the control group 𝑡𝑖𝑚𝑒 𝑝 is a dummy variable and is set to 1 for months from March 2020 to December 2020 and 0, otherwise 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 𝑖 is a dummy variable taking the value of 1 for five ASEAN countries in the treatment group (defined above) and 0 for the five other ASEAN countries (i.e control group) The set of control variables 𝑋 is defined as the same as Equation 4.1 𝜇 𝑖𝑗 and 𝛾 𝑝 denote country-pair fixed-effect and time fixed-effect, respectively

We also run a regression for 12 months of the year 2020 to examine the effects of the spread of the COVID-19 pandemic on trade in the ASEAN countries as below:

𝐸𝑥𝑝 𝑖𝑗𝑝 = 𝛽 1 𝐶𝑜𝑣𝑖𝑑𝑐 𝑖𝑝 + 𝛽 2 𝐶𝑜𝑣𝑖𝑑𝑐 𝑗𝑝 + 𝛽 3 𝐶𝑜𝑣𝑖𝑑𝑑 𝑖𝑝 + 𝛽 4 𝐶𝑜𝑣𝑖𝑑𝑑 𝑗𝑝 +𝛽𝑋 𝑖𝑝 + 𝜇 𝑖𝑗 + 𝛾 𝑝 + 𝜀 𝑖𝑗𝑝 , (4.3) where 𝐶𝑜𝑣𝑖𝑑𝑐, 𝐶𝑜𝑣𝑖𝑑𝑑 are the number of new confirmed COVID-19 cases and deaths at time 𝑝 in 2020 of either country 𝑖 or country 𝑗, respectively We sum up the numbers of new confirmed

67 cases and deaths for each country by month Then we can quantify the impacts of the COVID-19 situation in both the ASEAN countries (exporters) and the ASEAN trade partners (importers) on ASEAN trade

Table 4.1 The number of new confirmed COVID-19 cases per million population in 2020

The number of COVID-19 cases

Population The number of COVID-19 cases per million population

We collect monthly bilateral trade data of 10 ASEAN countries from IMF’s Direction of Trade Statistic for the 36 months from January 2018 to December 2020 9 The partners and the number of partners in each ASEAN country are different When we compile it into a panel dataset, we choose 112 biggest trade partners of ASEAN that account for approximately 99.6% of the export value of ASEAN in total 10 It reduces the missing value in the dataset We separate the sample into several subgroups: ten individual countries subgroups in ASEAN and the subgroup

9 The data for Taiwan is not available on these sources mentioned above However, Taiwan is one of the biggest importers of the ASEAN countries We collect data of Taiwan from Central Bank of the Republic of China website and National Statistic R.O.C (Taiwan) website

10 Each ASEAN country excludes itself from the list of partners 112 partners of ASEAN are listed in Table A4, Section Appendix

68 including the data of export value from ASEAN to two biggest partners (the U.S and China) GDP, exchange rate and tariff rate data is taken from IMF’s International Financial Statistics

The data on openness to trade is collected from World Bank’s World Development Indicators (WDI) database They are available at https://data.worldbank.org/indicator

The distance between countries, a common language shared, WTO members and the pair countries have an RTA data used in the gravity model are derived from the Centre d'Études Prospectives et d'Informations Internationales (CEPII) Gravity Database and updated to 2020 by using the information from the WTO website

This website “https://github.com/owid/covid-19-data/tree/master/public/data” provides the data on the number of new confirmed COVID-19 cases and deaths This dataset is maintained by a team at its Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) This data is sourced from governments across the world and updated every day

In this chapter, we take the logarithm forms of these variables except the dummy variables and the openness of trade variable The descriptions of these variables are detailed in Table 4.2 and the summary statistics of variables are shown in Table 4.3

𝐸𝑥𝑝 𝑖𝑗𝑚𝑡 Bilateral export value between country 𝑖 and country 𝑗 in month 𝑚 of year 𝑡 (in current U.S.$)

𝐺𝐷𝑃𝑝𝑐 𝑖𝑡 GDP of country 𝑖 at time 𝑡 (in current U.S.$) IMF

𝑃𝑂𝑃 𝑖𝑡 Population of country 𝑖 at time 𝑡 WDI

𝐿𝑎𝑛𝑔 𝑖𝑗 A dummy variable It takes the value of 1 if country 𝑖 and country 𝑗 share a common official or primary language

𝐷𝑖𝑠𝑡 𝑖𝑗 Distance between the capitols of country 𝑖 and country 𝑗

𝑂𝑝𝑒𝑛 𝑖𝑡 Openness to trade of country 𝑖 at time 𝑡 WDI

𝑅𝑇𝐴 𝑖𝑗𝑡 A dummy variable It takes the value of 1 if the pair of countries are in the same RTA

𝑊𝑇𝑂 𝑖𝑡 A dummy variable It takes the value of 1 if country 𝑖 is currently a WTO member

𝐸𝑥𝑟 𝑖𝑡 Exchange rate of country 𝑖 in month 𝑚 at time 𝑡 (domestic currency per U.S dollar)

𝑇𝑎𝑓 𝑖𝑡 Tariff rate of country 𝑖 at time 𝑡 IMF

𝐶𝑜𝑣𝑖𝑑𝑐 𝑖𝑝 The number of confirmed COVID-19 cases of country 𝑖 at time

JHU CSSE COVID-19 database 𝐶𝑜𝑣𝑖𝑑𝑑 𝑖𝑝 The number of COVID-19 deaths of country 𝑖 at time 𝑝

Variable Unit Number of observations

Empirical Results

Before applying the DID model, we test the validity of using this model for the whole sample and the subsamples mentioned in Section 4.3.2 Figure 4.5 shows the trends of the means of export value for twelve months including pre-pandemic (January and February) and post-pandemic (from March to December) of the control group (the years 2018 and 2019) and the treatment group (the year 2020) We compare the trends of export value in both the control year and the treatment year before COVID-19 If they have similar trends, it suggests that the assumption of using DID model is valid and we can use the DID model In the case of the individual countries, we can observe similar trends in some specific countries (Brunei Darussalam, Malaysia, Singapore, and Thailand) The same results are shown for the whole sample and the subsamples including the two largest partners (the U.S and China) Figure 4.6 shows the trends of the means of export value for 36 months including pre-pandemic (from January 2018 to February 2020) and post-pandemic (from March 2020 to December 2020) of the control group and treatment group (follow Equation 4.2)

We can observe the similar trends in both cases of the whole sample and the subsample including the two biggest trade partners

ASEAN ASEAN export value to the U.S and China

Figure 4.5 Trends of export value of control year and treatment year

ASEAN ASEAN export value to the U.S and China

Figure 4.6 Trends of export value of the control and treatment groups

We first run the dynamic DID model (Equation 4.1) The results are illustrated in Figure 4.7 Panel (a) in Figure 4.7 describes the results for the whole dataset, bilateral export from 10 ASEAN countries to 112 trade partners all over the world Panel (b) describes the results for bilateral export from 10 ASEAN countries to the two biggest trade partners (the U.S and China) that account for approximately 40% of the ASEAN value Panels (c)-(f) depict the results for bilateral export from Brunei Darussalam, Malaysia, Singapore and Thailand to their trade partners In these panels, the vertical axis displays the estimated coefficients 𝛽 𝑘 and the corresponding 95% confidence intervals The horizontal axis shows the months from January to December of a year In all Panels, the small and insignificant coefficients at 𝑘=-2 mean that before COVID-19, the trends of the treatment group and the control group are similar It confirms the validity of applying DID model Panel (a) shows the negative effect of the pandemic on trade in the ASEAN countries In two months right after the spread of COVID-19, the export value of ASEAN to the world decreases by 0.45 percentage points on average relative to the same period of control years (2018 and 2019) This negative effect weakens from June 2020 and remains significant until December 2020 In Panel (b), it reveals a significant negative effect of COVID-19 on the export value of ASEAN to

74 the U.S and China in April and May 2020 In the other months of 2020, the effects observed are negative but insignificant When we run the dynamic DID regression for four countries that meet the validity of this method, the results also show the significant negative impact of the pandemic on three of four countries (Malaysia, Singapore, and Thailand) in the first two months after the pandemic In Malaysia and Thailand, the significant negative effects of the pandemic on trade are confirmed for the other months of 2020 In Singapore, the export value of April and May 2020 to the world decreases by 0.48 percentage points on average relative to the same period of control years (2018 and 2019) However, in the last four months of 2020, the export value of Singapore increases by 0.55 percentage points on average relative to the same period of control years Table 4.4 shows the results for the DID regression (Equation 4.2) We can observe a negative but insignificant coefficient (-0.094) of the interaction term 𝑡𝑖𝑚𝑒 𝑝 ∗ 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 𝑖 that shows the effects of COVID-19 on the export of the treatment group compared to that of the control group

It refers that there is no significant difference on the impact of COVID-19 on export between the treatment and control groups The reason might be that all countries in both treatment and control groups are affected by COVID-19 Some countries might report a few new COVID-19 cases or deaths However, it does not mean these countries are unaffected by COVID-19 The COVID-19 situation of trade partners might also impact their export values

(a) ASEAN (b) ASEAN export value to the U.S and China

Figure 4.7 The effect of COVID-19 on the ASEAN export

Table 4.4 Results of DID model

Variable ASEAN ASEAN to the U.S and China

Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Robust standard errors adjusted for clusters are in parentheses

3 (a) and (b) denote the number of observations in pre-pandemic and post-pandemic, respectively

4 The results that show the impacts of some independent variables (𝐿𝑎𝑛𝑔, 𝐷𝑖𝑠𝑡 , 𝑊𝑇𝑂) are dropped because they are omitted

We then estimate Equation 4.1 for these two subgroups that are separated by the number of new cases per million population (the treatment and control groups in Equation 4.2) The results are shown in Figure 4.8 In Table 4.5, we present the estimated coefficients 𝛽 𝑘 We can observe the significant negative impact for both subgroups in April and May 2020, the two months right after the beginning of COVID-19 in ASEAN The coefficients for the treatment group including Indonesia, Malaysia, Myanmar, Singapore and Philippines are larger than for the control group, and more significant results are shown for the treatment group than the other It infers that although the levels of impact on both subgroups are different, there is the presence of the impacts of COVID-

19 on all ASEAN countries’ export It helps to explain the insignificant results presented in Table 4.4

Figure 4.8 The impacts of COVID-19 on the export of the treatment and control groups

Table 4.5 Results of the dynamic DID model for the treatment and control groups

8,071 0.94 Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Robust standard errors adjusted for clusters are in parentheses

Then, we run the regression for Equation 4.3 with time fixed-effect and country-pair fixed- effect and the results are reported in Table 4.6 We only present the coefficients representing the impacts of COVID-19 and drop the coefficients of the control variables in the equation

We can observe the negative impacts of the number of confirmed COVID-19 cases in ASEAN and the number of deaths in the ASEAN trade partners It suggests that the impacts come from both the supply and demand aspects (exporting countries and importing countries, respectively) In the exporting countries, A one-percentage point increase in the number of new confirmed cases causes a decrease in the workforce and a fall in production Consequently, it

79 decreases the export value by 0.03 percentage points In the importing countries, the spread of COVID-19 contracts production and A one-percentage point change in the number of COVID-19 deaths causes a lower in demand, thus, the import value from the ASEAN countries decreases by 0.016 percentage points

The results reported in Column “ASEAN to the U.S and China”show that the rapid spread of COVID-19 in China and the U.S might not influence the export from ASEAN to these countries When we run the regression for each individual country in ASEAN, the results show a significant impact from the supply side on trade in only four countries (Indonesia, Malaysia, Myanmar and Singapore) Only in Malaysia, the trivial effects of its trade partners’ COVID-19 situation is shown The positive signs can be observed in the results for these countries It might be impacted by the possible omitted variable problem based on the country's characteristics or the specific policies in each country In the forthcoming research, I will try to figure out other variables like patient age, health care quality, lagged period impacts, or lock-down policy in each country to have a better explanation.

Table 4.6 The results of the fixed-effect model

ASEAN to the U.S and China

Brunei Darussalam Cambodia Lao PDR Indonesia Malaysia Myanmar Philippines Singapore Thailand Vietnam

Note: 1 ***,**,* denote 1%, 5%, 10% levels of significance, respectively

2 Robust standard errors adjusted for clusters are in parentheses

Conclusions

The COVID-19 pandemic has affected international trade in several aspects On one hand, the dramatic increase in the number of new COVID-19 cases causes a production cut, while the lockdown policies disrupting foreign production and transport networks cause the lack of foreign inputs Demand for most products has fallen sharply as a result of the economic crisis caused by the pandemic On the other hand, global demand for some kinds of goods and services such as certain medical products has increased substantially

We try to investigate the impacts of COVID-19 on the ASEAN export by applying the DID approach to monthly export data of ten ASEAN countries to 112 trade partners in the period from January 2018 to December 2020 Based on the test, it is valid to choose the years 2018 and 2019 as control years and the year 2020 as the treatment year The results confirm the significant negative impacts of COVID-19 on the ASEAN export When separating ten ASEAN countries into the treatment group (including Indonesia, Malaysia, Myanmar, Philippines, and Singapore) and the control group (including the five other ASEAN countries), we find that there is no significant change in the treatment group between the pre-pandemic and post-pandemic period relative to the change in the control group We also utilize the data of the number of confirmed COVID-19 cases and the number of COVID-19 deaths in both the ASEAN countries and the ASEAN trade partners in 12 months of the year 2020 to examine the impacts of the COVID-19 situation in both ten ASEAN countries and the ASEAN trade partners on the ASEAN export The results show the significant impacts from both supply and demand sides The increase in the number of new confirmed cases in the ASEAN countries as well as the increase in the number of COVID-19 deaths in the ASEAN trade partners lowers the ASEAN export value

The results show that the magnitude of ASEAN export reduction because of the COVID-19 impacts reached its trough in April of 2020 After that, the negative impacts decrease gradually It infers the short-lived impacts of COVID-19 on ASEAN export The governments in ASEAN countries seem to have effective policies to respond immediately to the impacts of the pandemic on their economies.

SUMMARY AND FUTURE RESEARCH

Most ASEAN members are low-income countries, so attracting FDI is one of the key policies of these countries as well as a common strategic objective of ASEAN This dissertation focuses on some concerns of the policymakers in these countries, namely the change in the level of export concentration and the determinants of trade and FDI in ASEAN By the empirical results introduced in Chapters 2 to 4, we review briefly some of the main findings

In Chapter 2, we calculate and use the Theil index and its components to show the change in ASEAN export concentration First, the ASEAN export concentration decreased over time, and the within-group component contributed more to the total concentration Second, by separating the whole sample into several sets of subgroups, we find that with the absolute Theil indices, the export concentration across countries in either the high-income or the non-high-income subgroup, was quite stable and the gap in import value from ASEAN between the two subgroups was small We also find that the high-income countries played a key role in the volatility of the level of the ASEAN export concentration in Africa However, in America, the non-high-income group played a more important role The empirical results also show that the export concentration among the ASEAN members was decreasing gradually during the sample period

In Chapter 3, we examine the spatial effects of FDI and trade on FDI inflow to the ASEAN countries by applying SDM and DSDM The negative impacts of FDI inflow and the positive impact of export in the neighbor country on ASEAN’s FDI are confirmed These effects are also confirmed inversely for the one-period lagged variables

In Chapter 4, we investigate the impacts of COVID-19 on the ASEAN export by applying the DID method The results confirm the significant negative impacts of COVID-19 on the ASEAN

84 export and show the significant impacts from both supply and demand sides The increase in the number of new confirmed cases in the ASEAN countries as well as in the number of deaths in the ASEAN partners lowers the export value

This dissertation attempts to fill the gap in the literature related to ASEAN trade and the relationship between trade and FDI However, the dissertation still has some limitations First, the methodology used in Chapter 2 focuses on how to measure the Theil indices and how to trace the export concentration level by using this index and its decomposition This study does not offer in- depth analysis to point out the factors that cause the changes in the level of ASEAN export concentration In the forthcoming research, I will try to improve the paper by applying a suitable model to figure out the determinants of the ASEAN export concentration Second, in Chapter 4, we can see the different results when we deal with the aggregated level data and country-level data Although the significant results for the whole sample that combines data from ten ASEAN members are found, for each individual country, the significant impacts are only shown in a few countries Some results in Table 4.6 showing the positive impacts for the individual country might be explained by unobserved factors In the forthcoming research, I will try to figure out other variables like lock-down policy in each country to have a clear explanation for this

Regarding further research, based on this dissertation, I may consider the new topics noteworthy in the area of ASEAN international trade In reality, international trade is more complicated, not only exporting the final products abroad Traditional international trade accounts for approximately 30% of all trade in goods and services compared with the new approach, namely global value chains (GVCs) that account for 70% It involves complex interactions among a variety of domestic and foreign suppliers when exporting from one country to another country So, applying the GVCs approach might provide more insightful results

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Share of population Share of GDP per capita Share of GDP

Figure A1 Comparison of different weights between two subgroups separated by income

Share of population Share of GDP per capita Share of GDP

Figure A2 Comparison of different weights among four subgroups separated by regions

Share of population Share of GDP per capita Share of GDP

Figure A3 Comparison of different weights among eight subgroups separated by regions and income

Share of population Share of GDP per capita Share of GDP

Figure A4 Comparison of different weights between ASEAN and non-ASEAN subgroups

100 Figure A5 Decomposition among four subgroups separated by regions, excluding China

101 Figure A6 Decomposition among eight subgroups separated by regions and income, excluding China

102 Figure A7 Decomposition between ASEAN and non-ASEAN subgroups, excluding China

Table A1 ASEAN export value to different subgroups

Table A2 List of economies by region

Asia and Pacific America Europe Africa

Afghanistan Papua New Guinea Antigua and Barbuda Bermuda (H) Albania Algeria Malta (H)

Australia (H) Philippines Argentina Canada (H) Austria (H) Angola Mauritania

Bangladesh Samoa Aruba (H) United states (H) Belarus Bahrain (H) Mauritius

Bhutan Singapore (H) Bahamas (H) Belgium (H) Benin Morocco

Brunei Darussalam (H) Solomon Islands Bolivia Bulgaria Cameroon Mozambique

Cambodia Sri Lanka Brazil Croatia (H) Cape-Verde Namibia

China Taiwan (H) Chile Cyprus (H) Chad Niger

Fiji Thailand Colombia Czech Republic(H) Comoros Nigeria

French Polynesia (H) Turkey Costa Rica Denmark (H) Congo Oman (H)

Hong Kong (H) Uzbekistan Cuba Estonia (H) Djibouti Qatar (H)

India Vanuatu Dominican Rep Finland (H) Egypt Saudi Arabia (H)

Indonesia Vietnam Ecuador France (H) Ethiopia Senegal

Japan (H) El Salvador Georgia Gabon Seychelles

Kazakhstan Guatemala Germany (H) Ghana Sierra Leone

Korea, Dem People's Rep of Guyana Greece (H) Guinea Somalia

Korea, Republic of (H) Honduras Greenland (H) Guinea-Bissau South Africa

Lao PDR Jamaica Hungary (H) Iran, Islamic Rep of Sudan

Macao (H) Mexico Iceland (H) Iraq Swaziland

Malaysia Nicaragua Ireland (H) Israel (H) Syrian Arab Republic

Maldives Panama Italy (H) Jordan Tanzania, United Rep.of

Marshall Islands Paraguay Europe Latvia Kenya Togo

Mongolia Peru Slovakia (H) Lithuania Kuwait (H) Tunisia

Myanmar Puerto Rico (H) Slovenia (H) Luxembourg (H) Lebanon Uganda

Nepal St Vincent and the Grenadines Spain (H) Netherlands (H) Liberia United Arab Emirates (H)

New Caledonia (H) Suriname Sweden (H) Norway (H) Libyan Arab Jamahiriya Yemen

New Zealand (H) Trinidad and Tobago (H) Switzerland (H) Poland (H) Madagascar Zambia

Northern Mariana Islands Uruguay Ukraine Portugal (H) Malawi Zimbabwe

Pakistan Venezuela United Kingdom (H) Russian Federation Mali

Source: WDI regional classification adjusted by continental classification

Table A3 Absolute Theil indices and its components of eight subgroups

AP-L 1.2451 1.2255 1.2303 1.1994 1.1833 1.1729 1.1663 1.1579 1.1643 1.1488 1.1385 1.1362 1.1361 1.1752 1.1582 AP-H 0.7552 0.7369 0.7321 0.7455 0.7338 0.7245 0.6952 0.6983 0.6939 0.6884 0.6773 0.6901 0.6987 0.6950 0.6899 AF-L 1.1730 1.1925 1.1580 0.9073 0.9929 0.8136 0.8750 0.9732 0.9124 0.8768 0.8661 0.8605 0.8253 0.8167 0.8466 AF-H 0.8229 0.8932 1.1803 0.6642 0.6958 0.6335 0.6747 0.6767 0.6561 0.7237 0.7127 0.7501 0.7383 0.6456 0.5959 EU-L 1.6363 1.6025 1.0599 0.9013 0.9861 0.8190 1.0336 1.0324 1.0102 1.0471 1.1111 1.0914 1.1272 1.1339 1.0541 EU-H 1.0635 1.0201 0.9550 0.8987 0.9214 0.8691 0.8433 0.8315 0.8425 0.8422 0.8301 0.8578 0.8437 0.8331 0.8258 AM-L 1.4133 1.3589 1.3012 1.1680 1.3495 1.3163 1.1724 1.2517 1.2485 1.2740 1.1898 1.1517 1.1561 1.2015 1.1799 AM-H 1.6948 1.7471 1.7089 1.6830 1.6504 1.6420 1.6444 1.6157 1.5949 1.6100 1.5891 1.6158 1.6492 1.6661 1.6494 AP-L-W 0.3898 0.3992 0.4132 0.4079 0.4268 0.4118 0.4185 0.4341 0.4393 0.4460 0.4314 0.4290 0.4239 0.4578 0.4569 AP-H-W 0.2746 0.2622 0.2521 0.2417 0.2536 0.2419 0.2367 0.2338 0.2336 0.2244 0.2203 0.2129 0.2119 0.2058 0.2070 AF-L-W 0.0181 0.0178 0.0189 0.0202 0.0224 0.0225 0.0243 0.0296 0.0263 0.0243 0.0239 0.0213 0.0171 0.0167 0.0170 AF-H-W 0.0109 0.0146 0.0326 0.0150 0.0131 0.0161 0.0151 0.0148 0.0159 0.0192 0.0204 0.0220 0.0188 0.0149 0.0125 EU-L-W 0.0040 0.0044 0.0035 0.0037 0.0050 0.0034 0.0051 0.0058 0.0058 0.0063 0.0063 0.0055 0.0063 0.0067 0.0061 EU-H-W 0.1259 0.1140 0.1083 0.1195 0.0974 0.1071 0.0993 0.0933 0.0892 0.0849 0.0877 0.0973 0.1048 0.1041 0.0953 AM-L-W 0.0176 0.0175 0.0193 0.0218 0.0274 0.0291 0.0292 0.0350 0.0342 0.0352 0.0305 0.0288 0.0290 0.0296 0.0269 AM-H-W 0.2737 0.2792 0.2467 0.2274 0.2002 0.1846 0.1692 0.1491 0.1494 0.1564 0.1630 0.1880 0.2021 0.1922 0.1984 Within 1.1145 1.1088 1.0945 1.0572 1.0458 1.0165 0.9974 0.9954 0.9939 0.9967 0.9836 1.0048 1.0139 1.0278 1.0202 Between 0.8411 0.8574 0.8076 0.7401 0.7801 0.7199 0.7240 0.7102 0.7212 0.7203 0.7164 0.7098 0.7117 0.7115 0.7362

All 1.9556 1.9662 1.9021 1.7973 1.8260 1.7364 1.7214 1.7056 1.7151 1.7170 1.6999 1.7146 1.7256 1.7393 1.7563 Note: “All” illustrates the level of export concentration to all countries (i.e the sum of within- and between-group components) “Within” depicts the within components of concentration that is the sum of the contributions of nine groups with their weights, i.e the contribution of AP non-high-income countries (AP-L-W), AP high-income countries (AP-H-W), AF non-high-income countries (AF-L-W), AF high-income countries (AF-L-W), EU non-high-income countries (EU-L-W), EU high-income countries (EU-H-W), AM non high- income countries (AM-L-W), AM high-income countries (AM-H-W) “Between” depicts the between-group component “AP-L”, “AP-H”, “AF-L”, “AF-H”, “EU-L”, “EU-H”,

“AM-L”, and “AM-H” describe the export concentration across countries in each subgroup listed above, respectively.