Global production sharing and trade patterns in...

Global production sharing and trade patterns in ASEAN

Buavanh Vilavong

PhD candidate

Crawford School of Public Policy

Australian National University

Abstract

Draft 05.06.2016

This paper analyses the patterns and determinants of global production sharing

with an emphasis on ASEAN. The region’s production and trade networks are

largely concentrated in the electronics, electrical and automotive industries. The

study also examines the determinants of countries’ participation in production

networks, covering 80 economies from 2000 to 2014. The findings suggest that

the exports of products dominated by global production networks are associated

positively with countries’ output but negatively with distance, landlockedness

and trade costs. Trade costs (in particular cost to import) are found to have a

negative impact on bilateral exports; and the effect is more significant for trade

in final products and for landlocked countries.

ASEAN production networks

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1 Introduction

The world is increasingly interconnected thanks to expansive webs of international

production sharing. Trade related to global production networks (GPNs) -- the

fragmentation of production into stages located in different economies -- accounted for

almost half of global trade in 2011 as compared to only 36 per cent in 1995 (WTO 2015).1

There has also been a shift in the centre of gravity of production and trade from developed

to developing countries, reflecting the significant rise of East Asia, led by China as a hub of

global assembly linking the supply of intermediate inputs from other Northeast and

Southeast Asian countries (Athukorala & Menon 2010; ESCAP 2015).

As for the Association of Southeast Asian Nations (ASEAN), GPN-dominated products

accounted for around 80 per cent of ASEAN manufacturing exports in 2012/13. The

region’s participation in international production sharing varies between older and newer

members.2 For example, Singapore specialises in high-tech and services-oriented sectors

(e.g., medical equipment, and research and development) while Malaysia and Thailand

engage in a wide range of sectors dominated by production sharing, including electronics,

electrical and automotive production (Athukorala & Kohpaiboon 2013). Among newer

ASEAN members, Vietnam has played an increasingly significant role in international

supply chains while the involvement of Laos and Cambodia is still concentrated in standard

assembly line such as apparel production. This reflects not only individual ASEAN

countries’ diverse incomes but also their development level. Higher industrial development

has been observed in older ASEAN members, resulting from more foreign direct investment

and their earlier exposure to international production sharing (Harvie 2009).

1 GPNs have also been called ‘global value chains’ (Gereffi 1999), ‘slicing up the value chain’ (Krugman et al. 1995), ‘international fragmentation’ (Deardorff 2001), ‘vertical specialisation’ (Hummels et al. 2001), ‘trade in tasks’ (Grossman & Rossi-Hansberg 2006), ‘unbundling’ (Baldwin & Venables 2011), and ‘global production sharing’ (Athukorala & Menon 2010). 2 Older ASEAN members are Brunei Darussalam, Indonesia, Malaysia, the Philippines, Singapore, and Thailand. Newer members are Cambodia, Laos, Myanmar, and Vietnam.


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A related policy question asks why some ASEAN countries have managed to diversify or

even tap into higher value addition within international supply chains, whereas others have

not been so successful. For instance, despite being the biggest country in Southeast Asia,

Indonesia still lags behind in integrating into international production sharing. Only 14.5 per

cent of Indonesian firms participate international production sharing compared to around 40

per cent and 30 per cent in Thailand and in Vietnam, respectively (Athukorala &

Kohpaiboon 2013). In Laos, approximately 20 per cent of firms are involved in production

networks (Vilavong 2015b). Many factors are found to be important for countries’ success

in promoting production and trade networks, including a favourable business environment,

human capital development, economic links to high-income markets, sector-specific

industrial development policies, and resource endowments (Athukorala & Nasir 2012;

Nicita et al. 2013).

The existing literature examines the patterns and determinants of global production sharing

among developing countries (Athukorala & Nasir 2012; ESCAP 2015) and in East Asia

including ASEAN (Athukorala & Yamashita 2007; Thanh et al. 2009; Lim & Kimura 2010;

Athukorala & Menon 2010; Ando 2012). Even though a handful of studies focus on

ASEAN, little or none, to the best of the author’s knowledge, has covered all ASEAN

members. Therefore, the current paper tries to fill in this research gap by probing the factors

essential to countries’ integration (or lack of integration) into the GPNs of all ASEAN

countries, including smaller members such as Cambodia, Laos, and Myanmar.

This research seeks to understand the factors and mechanisms that influence participation in

international production sharing by addressing the following questions. What are the extent

and depth of ASEAN participation in GPNs? And, what are the factors that are key to

determining trade flows in products dominated by global production networks? As such, a

gravity model has been employed to evaluate the relative importance of natural and policy-

related factors so that policy implications can be drawn.

The study into these inter-related issues is important for a number of reasons. First, the

focus on ASEAN is significant given the region’s increasingly important role in

international supply chains. ASEAN has been found to be increasingly dependent on intra-

regional networked trade with broader East Asia, yet the region’s still maintains a


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substantial linkage with North America and Europe (Athukorala & Kohpaiboon 2013). The

establishment of the ASEAN Economic Community (AEC) in the beginning of 2016 also

draws the attention of policy makers and business leaders regarding the dynamism of the

regional grouping, in particular their rising role in global production sharing. Second, GPNs

have progressively become a key feature of the international economy, with intermediate

goods making up a third of the world’s networked trade. In addition, engagement in

production sharing eliminates the need for countries to specialise in all the complete

production processes, but it instead allows them to focus on certain stages commensurate

with their comparative advantage.

The rest of this study is structured as follows. The next section elaborates a theoretical

framework to explain why production can be dispersed to different countries to take

advantage of locational differences. Section 3 discusses how GPNs are measured and how

their determinants can be quantified. Section 4 reveals that there is an apparent shift in

global production sharing away from developed countries toward developing countries,

primarily driven by the growing importance of China and ASEAN. The gravity model has

been adapted in Section 5 to address possible biased estimations, taking into account trade

in intermediate goods and unobserved heterogeneity. Section 6 presents econometric results,

which suggest the significance of trade costs and landlockedness that could hamper

countries’ ability to participate in GPNs. Section 7 concludes and draws policy

recommendations.

2 Theoretical framework

It is imperative to be clear at the outset regarding the concept of GPNs. The term originates

from fragmentation theory, which is defined as a splitting up of previously integrated

production processes into segments for performing in different locations to take advantage

of cost differentials (Jones & Kierzkowski 2000; Deardorff (2001). In a broader setting, a

GPN is described by Haris (2001) as a set of links and infrastructure connecting related

production units, while Henderson et al. (2002) portray this phenomenon as a nexus of

interconnected functions through which goods are produced, distributed and consumed. The

notion of GPNs, therefore, covers a full range of activities performed to gather different

inputs so that these can be assembled into a final output.


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In the current study, GPNs capture the concept of trade linkages among countries involved

in production sharing: from the importation of intermediate inputs for processing into final

products for exportation. The terms ‘production networks’, ‘regional production networks’,

‘global production networks’ as well as ‘international production sharing’ are used

interchangeably. In any case, the focus here is on production sharing that expands across

international borders rather than within a national economy.

Production networks can be categorised as either producer- or buyer-driven (Gereffi 1999).

The former refers to those in which multinational corporations (MNCs) play a central role

in coordinating production systems, mostly in the capital- or technology-intensive

industries. These include automobile, aircraft, semi-conductor, and heavy machinery

production. The latter is characterised by a standard assembly line having become common

in the consumer-goods industries that is producing labour-intensive goods such as garments,

footwear, toys, housewares, and consumer electronics.

The expansion of GPNs driven intermediate goods exchange challenges the way that

theories based on comparative advantage are used to explain the patterns of international

trade. That is, the one-to-one relationship between the characteristics of a country and its

specialisation in a final good as customarily understood no longer holds. The chief reason is

that no single nation is considered to be the producer of a particular final product but it may

specialise in stages of production (Baldone et al. 2007). This refers to fragmentation theory

pioneered by Jones & Kierzkowski (1990; 2000) that extends traditional trade theories by

incorporating two mutual reinforcing forces: comparative advantage and increasing returns

to scale.

Production fragmentation allows the processes of production to be subject to the

comparative advantage of each participating economy. This is due to the fact that each

economy has workers with different skills required for each production stage so that the

physical dispersion of production activities can lower the marginal cost of production as in

the case of the Ricardian model. On the contrary, the stage of production may be different

from others due to the required factor proportion, enabling firms to relocate labour-intensive

stages in the country with lower labour costs as in the Heckscher-Ohlin (H-O) model

(Deardorff 2001; Athukorala & Menon 2010).


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According to the theory of fragmentation, there are three key factors contributing to the

emergence of international production sharing. First, fragmentability in production

technology enables production processes to be segmented and located in different

economies. Production processes that previously had to be performed in close geographical

proximity can now be spread out without impacting upon the efficiency or timeliness of the

supply chains (Baldwin & Venables 2011). Second, improvements in communication and

transport infrastructure have contributed to a decline in the costs of service links. This has

not only shrunk the physical distance but also facilitated service links that combine

separated fragments of production in a timely and cost-effective manner (Athukorala &

Menon 2010). Third, economic liberalisation has lowered trade and investment barriers in

both the home and host countries (De Backer & Miroudot 2014). This enables multinational

corporations to decentralise their operations overseas where labour costs are lowest in order

to take locational advantage, allowing for finer division of labour and greater efficiency

gains (Kimura & Ando 2005). The relocation of production bases to a low-wage location,

according to Lim & Kimura (2010), is economical only if the country is labor-intensive, and

the costs saved from lower wages and transport (locational advantage) outweigh the costs of

coordinating production fragments (service link costs). Service link costs are a more

important determinant than production costs in determining a country’s participation as far

as developing countries are concerned.

An empirical analysis of the determinants of production networks requires the application of

a gravity model. The model originated from the law of gravitation pioneered by Tinbergen

(1962), submitting that countries are expected to trade more the larger the mass of trading

partners becomes, and on average trade less the further they are apart. Gravity modelling

has been widely applied beyond analysing aggregate trade flows. This includes examining

the determinants of trade in production networks (Kimura et al. 2008; Athukorala et al.

2016), in intermediate goods (Baldwin & Taglioni 2011), and at the sectoral level (Eaton &

Kortum 2002; Martínez-Zarzoso et al. 2011).

3 Production sharing: measurements and determinants

Even though there is no definitive way to measure GPNs, a few approaches have been

deployed including value-added and trade-based measures. The former uses an input-output

table to capture value addition in vertical integration as implemented by Hummels et al.


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(2001), among others. This relies on imported input contents of industrial production to

measure the expansion of production sharing at the industry or country level. Alternatively,

GPN can also be measured using a trade-based approach by delineating trade in parts and

components (P&Cs) from aggregate trade reported in the United Nations Comtrade

database. This measure of trade dominated by GPN products (or networked trade) is

implemented in Ng & Yeats (2003), Athukorala (2011), and Wignaraja et al. (2013).

The current research adopts the trade-based measure as it has the advantages of covering not

only a comprehensive list of parts and components but also a wide range of countries,

including all ASEAN member countries (De Backer & Miroudot 2014). According to

Athukorala & Nasir (2012), global production sharing has been largely concentrated in the

following product categories (based on the Standard International Trade Classification:

SITC): information and communication technology (SITC 75+76+772+776), electrical

appliances (SITC 77 excluding 772 and 776), automotive (SITC 78), apparel (SITC 84),

professional equipment (SITC 84), and photographic equipment (SITC 88). Production

sharing has also be observed in other sectors, including textiles. The current research,

therefore, measures GPN-dominated products slightly broader; i.e., machinery and transport

equipment (SITC 7), other manufactured articles (SITC 8), and also textiles (SITC 65)3.

The theory of fragmentation provides a conceptual framework as regards the emergence and

expansion of production networks, which are characterised by trade in intermediate goods.

An empirical analysis of the determinants of bilateral trade requires the use of a gravity

model, which originated from the law of gravitation as pioneered by Tinbergen (1962).

Essentially, bilateral trade flows are amplified by the relative economic size of trading

partners but gravitated by their distance.

An early use of the gravity model was criticised for a lack of theoretical underpinnings as it

was empirically implemented to fit the observed features of bilateral trade. However, the

situation has changed with rigorous derivations of gravity equations led by Anderson (1979)

3 Final products are calculated from deducting P&Cs from the aggregate exports reported in Comtrade. Following Athukorala (2010), there are 325 items (SITC revision 3 at 5 digit) considered as P&Cs, of which 249 in SITC 7, 67 in SITC 8, and the rest in SITC 65. See the list of parts and components in Annex 2.


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and Bergstrand (1985). An important contribution to gravity modelling has been made by

Anderson & van Wincoop (2003) who micro-founded gravity derivations building upon the

model of Anderson (1979). Another contribution that Anderson & van Wincoop (2003)

added to the modern application of gravity modelling is the recognition of multilateral trade

resistance. Bilateral trade is not only determined by factors specific to the two trading

partners, but there is also a third-party effect (e.g., the proximity of third-party countries).

Any model specification failing to take such multilateral effects into account can result in

biased estimates, known as the ‘golden mistake’ by Baldwin & Taglioni (2006).

Modern gravity modelling tends to rely on structural equations supported by theoretically

consistent derivations rather than throwing explanatory variables in as the naïve type of

model. Structural gravity models can be derived by either demand- or supply-side

techniques. In the former, exogenous wage along with constant returns to scale (constant

markups) neutralises the supply side of the model. Examples of the demand-side gravity

derivations include the Anderson-Armington model based on a differentiated production

assumption (Anderson 1979), the Dixit-Stiglitz-Krugman (DSK) model with a monopolistic

competition assumption (Bergstrand 1985; Baier & Bergstrand 2001), and the

heterogeneous consumer model (Anderson et al. 1992).

Many empirical studies have explored factors driving countries’ participation in GPNs,

looking into the role of trade-related policies (tariffs, logistics performance, regional

integration) while controlling for natural factors such as output and distance (e.g., Yi 2003;

Feenstra & Hanson 2004; Kimura 2006; Hummels & Schaur 2013). Yi (2003) examines the

effects on trade flows of tariff rates while other studies (e.g., Kimura 2006; Hummels &

Schaur 2013) examine the role of service links such as transport and communication costs.

Yi (2003) calibrates a two-country Ricardian model with and without vertical specialisation.

Intermediate inputs are assumed to be tradable and required for production. Kimura (2006)

emphasises the essential role of service links for connecting expansive production

fragments in the efficient development of international production and distribution networks

in East Asia.

As far as transport infrastructure is concerned, Hummels & Schaur (2013) study the choices

of companies in using air and ocean cargo transport. Their key conclusion is that trade in


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intermediate goods is especially sensitive to speed. This suggests a linkage between

reduction in the relative cost of rapid transportation and the expansion in international

fragmentation. Feenstra et al. (2002) and Feenstra & Hanson (2004) analyse the role of

Hong Kong logistic hubs in the distribution of the exports from China, by adding value to

the goods through sorting, packaging, testing, marketing, and matching suppliers and

customers (Amador & Cabral 2014). The movement of goods through logistic hubs such as

Hong Kong and the Netherlands, as argued by Young (1999), is driven not only by transport

linkage, but also by the role of these hubs in the processing and marketing of products in

international supply sharing.

Nordas (2006) also explores speed as a competitive factor and concludes that simplified

customs clearance resulting from effective logistics services and trade facilitation have a

positive effect on trade, in particular participation in global production sharing. In addition,

ICT has also been found to facilitate the timely and efficient cross-border exchange of

intermediate goods. Hillberry (2011) finds that the use of the global positioning system,

coupled with efficient ICT enable firms to deliver and monitor their product shipments in an

effective and speedy manner.

Kimura & Ando (2005) suggest that the East Asian policy environment is important in

fostering integrated production sharing in the region while Escaith & Inomata (2011) cite

the interplay between technical, institutional and political changes. China’s rapid integration

into GPNs has provided greater opportunities for other countries, including ASEAN

members, to engage in different production segments commensurate with their comparative

advantages (Athukorala 2011).

Working on East Asia, Athukorala & Yamashita (2007) suggest that the region’s

fragmentation has been underpinned by advantages in relative wages, geographical

proximity, and a first-mover lead. Using gravity modelling, their findings reveal different

effects of the determinants between trade in components and final assembled goods.

Athukorala et al. (2016) suggest that there is evidence to indicate a strong global production

network bias in intra-East Asian trade. More specifically, their econometric regression analysis

finds that intra-regional exports of production sharing products are five to six times larger than

predicted by the other explanatory variables in their model. In other regions, Martínez-Zarzoso


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et al. (2011) indicate that the Central and Eastern European Countries (CEECs) have

become better integrated into regional production sharing in terms of not only increasing

trade volumes but also expanding trade varieties with the Western hemisphere of Europe.

Geographic proximity and sea access are the important determinants of intermediate trade in

the CEECs (Martínez-Zarzoso et al. 2011).

Price competitiveness (measured by relative real exchange rate) is found by Athukorala et

al. (2016) not to be a key driver of GPN exports. The authors argue that because GPN

exports are predominantly ‘relation-specific’ among suppliers and producers. The findings

from Athukorala et al. (2016) also reveal that RTA membership has not helped expand

manufacturing exports as far the case study on Australian is concerned. In analysing the role of

RTAs on GPN trade, Athukorala & Yamashita (2007) find that only the ASEAN free trade

agreement has significant impact but other RTAs do not. Findings from a study by Kimura

et al. (2008) reveal that there are no concurrent effects of RTAs on trade in parts and

components. However, RTAs are found to affect trade in parts and components, and final

goods between 6 and 9 years after their formation (Kimura et al. 2008). Interestingly,

Athukorala & Nasir (2012) suggest that even though RTAs play an important role in

encouraging South-South networked trade, natural economic dynamisms associated with

growth and structural change along with the overall macroeconomic environment, and the

quality of trade-related logistics are far more significant.

A key contribution that the current research tries to make is to extend gravity modelling to

explain the factors driving GPN participation. First, the gravity model is adapted by

augmenting the mass variables to reflect the total demand for and supply of intermediate

goods in response to the critique of Baldwin & Taglioni (2011). Second, with a presumption

that natural and policy-induced factors in combination determine countries’ participation in

GPNs, this research explores which policy variables are the most relevant with special

attention on the ASEAN region. Lastly, it will also take advantage of the availability of

panel data and advanced econometric techniques to address potential estimation problems

such as unobserved heterogeneity.


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4 The patterns of ASEAN production networks

Southeast Asian economies have become increasingly and deeply entrenched in

international supply chains, especially in production sharing with China, Japan, and Korea.

However, this increasing entrenchment does not diminish the linkage of ASEAN in

supplying final goods the European and North American markets (Athukorala &

Kohpaiboon 2013). The degree of the region’s integration into production sharing varies

among individual ASEAN countries, which reflects their varying trade policy regimes and

investment climate. The involvement of ASEAN in production sharing initially started in

the electronics and garment sectors then gradually spread into such diverse industries as

automobiles, televisions and radio receivers, office equipment, power and machine tools,

cameras and watches, and printing and publishing (Athukorala 2011b).

Table 1 illustrates the patterns of GPN-dominated trade in key regions: ASEAN, Northeast

Asia (NEA), the European Union (EU 25) and North American Free Trade Area (NAFTA)

in 2000/01 and 2012/13. The world’s exports of P&Cs dominated by GPNs increased from

US$1,377 billion in 2000/01 to US$2,684 billion in 2012/13.4 Parts and components

accounted for around a third of trade (imports+exports) in GPN-dominated products in

2012/13. There has been an evident shift in global production sharing away from developed

countries toward developing countries driven by the growing importance of East Asia, in

particular China and ASEAN. In 2012/13, NEA and ASEAN made up 38.1 and 7.9 per cent

of the world’s GPN exports (P&C and final products), respectively.5 The share of the EU in

the world’ total exports of GPN-dominated products fell from 21.5 per cent in 2000/01 to

14.2 per cent in 2012/13. A similar trend had also been observed in the case of NAFTA, a

slight reduction from 34.7 per cent to 31 per cent during the same period.

[Table 1 is about here]

Within ASEAN, the share of P&C trade was relatively high among older ASEAN members

(e.g., Malaysia, Thailand, and the Philippines). However, the share of Singapore has slightly

4 Reporting country records are used due to less likelihood of double-counting and erroneous identification of trading partners. 5 Northeast Asia is referred to Mainland China, Hong Kong, Japan, Korea, and Taiwan.


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declined, reflecting the country’s shift in production sharing away from standard assembly

to high-tech and services industries such as medical equipment, oversight functions, product

design, and technology-intensive tasks (Athukorala & Menon 2010). Production sharing has

strengthened intra-regional economic interdependence in Southeast Asia, and between

ASEAN and East Asia. However, this has not lessened the dependence of ASEAN growth

dynamism on the global economy in particular with markets for their final goods in North

America and Europe (Athukorala & Kohpaiboon 2013).

In terms of product composition, machinery and transport equipment took up the lion’s

share in ASEAN manufacturing trade (See Table 2). The majority was concentrated in ICT

products, with the combined exports of P&Cs and assembled products accounting for 42.7

per cent of the region’s manufacturing exports. Electrical and automotive products made up

6.3 and 4.4 per cent of ASEAN manufacturing exports, respectively. As for miscellaneous

manufacturing products, apparel and professional equipment constituted 6.1 and 2.6 per

cent of the region’s manufacturing exports, respectively. It can be observed that export

dynamism in these products has been driven by ongoing processes of fragmentation and the

increasingly deeper integration of Southeast and Northeast Asia into GPNs.


5 Methodology and data

As previously mentioned, this paper analyses factors influencing countries’ participation in

GPNs using the gravity model. The model is a workhorse used to explain bilateral trade

driven by natural and policy-related factors such as countries’ size, distance, cultural and

language similarity, tariffs, and other non-tariff measures (Head & Mayer 2015).

Model specification

In general, the gravity equation can be formulated as

�� = ��

∅��

� (1)

where �� is exports from country i to j; �� denotes exporter-specific factors representing


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total supply; � represents importer-specific factors capturing total demand; and ∅�� and G

represent economic distance and multilateral resistance, respectively. , �, � and � are the

coefficients to be estimated.

The dependent variable (X��) is measured by P&Cs, final assembled, and total exports of

products dominated by GPNs. This follows an approach implemented by Athukorala &

Menon (2010), Saslavsky & Shepherd (2012), and Martínez-Zarzoso et al. (2011).6

As for variables on the right-hand side of equation (1), Si should be the gross production

(not value-added) of traded goods while Mj is consumption. This reflects two underlying

assumptions: the first imposes market-clearing conditions for the exporter, and the second

governs spatial allocation of the importer’s expenditure (Head & Mayer 2015). In practice,

GDP is often used to represent both mass variables, Si and Mj.7 As argued by Baldwin &

Taglioni (2011), the analysis of global production sharing should control for potential bias

in the mass variables as their effects appear to be weakened with an increasingly important

role of trade in intermediate products. This is because trade in intermediate goods is the sum

of goods whose demand depends upon the importer’s GDP (i.e., consumer goods) and

goods whose demand depends upon the total costs of the sector that buys relevant

intermediates. The current study, therefore, addresses such shortcomings by augmenting the

mass variables with the imports of parts and components. That is, the imports of

intermediate goods are added to manufacturing output (value-added) for Si. This is to

capture the direct definition of total costs to be the cost of primary inputs plus the value of

intermediate inputs. On the importing side, GDP of the importer is adjusted with the

purchases of intermediate inputs from all sources, except from itself (Baldwin & Taglioni

2011).

There is also a possibility of adding other explanatory variables. Linnemann (1966) extends

the model of Tinbergen (1962) to include other regressors to explain bilateral trade flows,

6 Another approach measures GPN by the exports of intermediate goods based on the Broad Economic Categories (BEC) classification as adopted by Baldwin & Taglioni (2011), and Florensa et al. (2011). 7 This is more appropriate only when bilateral trade relation is measured by aggregate trade and the sample covers a wide range of countries. Adjustment is needed for analysing trade in GPNs in the case where trade in intermediate goods is important to avoid biased estimates of the mass variable (Baldwin & Taglioni 2011).


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e.g., population and complementarity. Note that the theoretical derivations made by

Anderson (1979), Helpman (1987), and subsequently Deardorff (1995), do not justify the

inclusion of population in the reduced form of the gravity equation. The rationale is that its

effect is sometimes positive, and other times negative (Armstrong 2007). A positive effect

implies that an economy with a higher population trades more, while a negative effect could

suggest that an economy with more citizens would have a larger absorption effect.

The use of geographical distance is inadequate, as it constitutes only part of trade costs. A

wider measure would be economic distance: natural and man-made factors (Armstrong

2007). Physical distance is a prime example of natural causes while tariffs and trade

agreements represent policy-induced factors. In this study, economic distance (∅��) not only

covers geographical distance, but also contiguity, common language, landlockedness, trade

costs, and regional trade agreements (RTAs).

As for trade costs, this research uses cost to export and cost to import as opposed to tariffs.

The average tariff rate not only fails to capture the effects of non-tariff barriers but it is also

not a good proxy for trade costs in analysing GPNs, which is dominated by trade in

intermediate goods. As pointed out by Yi (2003), networked trade is more sensitive to

changes in trade costs since vertical specialization allows products to move across borders

many times before reaching their markets at final destination. An alternative measure of

trade costs can be the logistic performance index (LPI).8 However, such data is available

only for a very limited number of years (i.e., 2005, 2007 and 2010), which could affect our

regression using panel data. Therefore, LPI is used in the current research for robustness

checking instead.

Lastly, following Anderson & van Wincoop (2003), the gravity model estimation is

theoretically consistent if multilateral resistance (G) has been controlled for. Bilateral trade

is not only determined by factors that are specific to the two trading partners; there is also a

third-party effect, which is also known as multilateral resistance (Anderson & van Wincoop

2003), remoteness (Frankel & Wei 1997), or gravitational un-constant (Baldwin & Taglioni

2006). The third-party effect includes the size of neighbouring countries or the proximity of

8 See discussions about the advantages of using LPI by Saslavsky & Shepherd (2012).


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third-party countries (Armstrong 2009). Any model specification failing to take multilateral

resistance into account can result in biased estimates, known as the ‘golden mistake’

(Baldwin & Taglioni 2006).

It is generally difficult to observe multilateral resistance, but it can be captured in regression

analysis. One approach is to use an iterative method to obtain the estimates of the price-

raising effects of barriers to multilateral resistance (Anderson & van Wincoop 2003). This

is, however, not frequently adopted because a non-linear least square procedure is required

to calculate it. A much simpler approach is to use country fixed effects to account for

multilateral resistance and other unobserved country-specific factors (Baldwin & Taglioni

2006; Head & Mayer 2015). A year dummy is also added into the regression to account for

factors that may vary from year to year such as the effects of the global financial crisis.

The gravity equation is normally presented in a multiplicative form, which after taking

logarithm will become

�� = �� + �� + �� + �� !"�� + �#$%� �&'� (�� + �)*��&'�&"�� +

�+*��,�%!-",� + �.*��,�%!-",� + �/��$%� 012345� + �7��$%� �82345� +

��,: ∑ <=>:: + ��?5 + ��5 + @�� (2)

where �� is the exports of GPN-dominated products (parts and components, final products

or all products) from country i to j, measured at current US$ using reporter records. Si and

Mj are the mass variables measured at current US$, which capture country-specific factors

of i and j, respectively.9 �� !"�� is the relative distance of i and j. $%� �&'� (�� is a

dummy taking 1 if i and j share a common land border; zero, otherwise. *��&'�&"�� is a

dummy taking 1 if i and j share a common official language; zero, otherwise. *��,�%!-",�

or (*��,�%!-",�)is a dummy taking 1 if i (or j) is landlocked; zero, otherwise.$%� 012345�

and $%� �82345� are i’s cost-to-export and j’s cost-to-import, respectively. <=>: is a

dummy taking 1 if both i and j are RTA members; zero, otherwise. RTAs cover trade

9 For analysing P&C exports, Si is proxied by the manufacturing output (value-added) of the origin country (i), adjusted by the imports of intermediate goods, and by the GDP of the destination country (j), adjusted by the imports of intermediate goods. As for the exports of final and all products dominated by production sharing, the GDP of i and j is used.


15

agreements by ASEAN, ASEAN-Australian and New Zealand (ASEAN-AANZ), ASEAN-

China, ASEAN-India, ASEAN-Japan, ASEAN-Korea, NAFTA, and EU. See the variable

description in Table 3. In addition, ?5and�5 represent the country (exporter, importer) and

year fixed effects, used to account for multilateral resistance and other unobserved factors.

Lastly, @�� is the error term.

Equation (2) is estimated using the ordinary least squares (OLS). Given the availability of

panel data, two techniques are also used: fixed effects (FE) and random effects (RE)

models. The choice between the two models hinges upon certain assumptions. The FE

model assumes that unobserved heterogeneity is correlated with the error term. In contrast,

the RE model assumes that such heterogeneity is strictly exogenous, which does not impose

any correlation between unobserved heterogeneity (individual effects) and any explanatory

variables. Under the null hypothesis of zero correlation, the RE model is efficient. However,

if the null hypothesis is rejected, the FE model is consistent while the RE model is neither

consistent nor efficient (Wooldridge 2010). It has to be noted at the outset that time-

invariant explanatory variables such as distance and common language will be dropped

from the FE model as they are perfectly collinear with the fixed effects. This is one of a few

drawbacks of the FE model given that some important theoretically relevant variables such

distance cannot be established.

The signs for mass (Si and Mj), Contiguity, and Language variables are expected to be

positive. For the mass effects, it means that the bigger are the economies the more likely

they will trade. Likewise, language similarity and border sharing will encourage more

networked trade between trading partners given cultural closeness. The signs for Distance,

Landlocked, Cost to export, and Cost to import are expected to be negative because of their

trade-restricting effects. The sign for RTA can be positive or negative, depending on the

trade-creating or diverting nature of a given regional grouping.

Data

This study covers 80 countries/economies (all ASEAN members included) from 2000 to

2014. This covers the 77 largest exporters (average exports from 2011 to 2013) plus 3 more

from the smallest ASEAN member countries (Cambodia, Laos, and Myanmar). See Annex

1 for a complete list of the sample countries. The aggregate exports of these countries


16

account for 98 per cent of the world’s exports in 2014. Data are drawn from various

sources: bilateral trade from the UN Comtrade, macroeconomic variables (GDP,

manufacturing output, cost to export, cost to import) from the World Development Indicator

database of the World Bank, and RTAs from the World Trade Organization (WTO) and the

European Union (EU). For the remainder (distance, contiguity, language, landlocked), data

is drawn from the CEPII dataset of a French research agency.10 Table 4 provides descriptive

statistics of these data series.

6 Results and discussions

This section first presents baseline results for the aggregate exports of GPN-dominated

products. It is then followed by panel data estimations to examine whether there are

different impacts on trade flows in parts and components and final products. Robustness

tests have also been provided.

To start with, the OLS estimations have been performed based on equation (2) for the

exports of all GPN products as reported in Table 5. The first column presents pooled results

while those from the fixed effect (FE) and random effect (RE) models are provided for in

the next two columns. To account for multilateral trade resistance and other unobservable

factors, a dummy for country- (importer, exporter) and year-specific effects are included in

all regressions.11

A few diagnostic tests can be performed to check model specifications. First, the Breusch

and Pagan test indicates that an underlying assumption of no correlation between the

unobserved heterogeneity and at least one of the explanatory variables has been rejected.

This implies that the usage of the pooled estimation is not appropriate. Second, the

Hausman test suggests that the FE model is preferred to the RE model because the null

hypothesis of no systematic difference between the two models has been rejected. That is, it

is valid to control for country- and year-specific factors, and they correlate with at least one

of the explanatory variables.

10 Data for Taiwan is from Taiwanese Statistics Year Book. 11 Regressions without country- and year-specific effects have also been estimated. But the results have not been shown here and are available upon request.


17

The baseline results from Table 5 reveal that the exports of GPN products are positively

associated with GDP but are negatively related to relative distance, as expected. The results

are consistent across all models (except for distance which cannot be observed in the FE

model), even though the magnitude of their coefficients is slightly different. Sharing a

common border and language has a significant and positive impact on trade flows while the

effects of importers’ landlockedness and cost to import are negative and significant.

Interestingly, however, the effects of the exporter’s cost to export appear to be positive and

significant while the landlocked status of exporters shows alternating signs for the

estimations from the pooled and RE methods (the first versus third columns). Many RTAs

are found to have insignificant impacts on bilateral exports across different models. For

example, from the FE model (the second column), the effects of ASEAN-Korea and EU

have been significant and positive. Note that the effects of some RTAs (e.g., ASEAN and

NAFTA) cannot be observed, as they are invariant during the study period. The effects of

other RTAs are found to be not significant for the FE model in the same column.


In light of the baseline results discussed above, equation (2) has been re-estimated with the

FE and RE models to see whether there are varying effects on the exports of parts and

components and final products dominated by global production sharing. Again, all

regressions (shown in Table 6) allow for exporter, importer and year dummy to account for

unobserved heterogeneity. To improve accuracy in inferring the estimators, robust standard

errors are reported in all regressions. The estimations for parts and components are reported

in columns (1) and (2) for the FE and RE models, respectively. The third and fourth

columns present the results for final products while the last two columns report the results

for all GPN products. In performing model specification tests, the Hausman test again

suggests that the FE method is preferred; i.e., country-specific factors are correlating with at

least one of the regressors.12 So we use the FE estimators as a basis for interpreting the

econometric results.


12 The null hypothesis (difference in FE and RE coefficients not systematic) is rejected.


18

Focusing on the first, third and fifth columns of Table 6, the mass variables from the FE

estimation have the expected signs. That is, they are positive and significant, except for the

case of parts and components, which is not significant (at the 10 per cent level).13 Given the

nature of the FE model, the effects of variables that are invariant over time such as

Distance, Contiguity, Language, and Landlocked cannot be observed as earlier discussed.

Similarly, the effects of RTAs (e.g., ASEAN, and NAFTA) already entered into force

before 2000 also cannot be reported in the FE regressions. In other words, we can observe

only the effects of some RTAs (e.g., ASEAN-Japan and ASEAN-Korea). Note that these

dummy variables cannot tell the whole picture about trade creating or diverting effects of

trade agreements. To do that, additional dummy variables are required to capture intra-

regional trade (both the exporter and importer are RTA members), or extra-regional trade

(either the exporter or importer is part of that RTA).14

To test for sensitivity of the findings against different model specifications and data

measurements, other proxies for trade costs (average tariff rate, logistics performance

index) are used to re-estimate equation (2) using the fixed effect OLS method. Additional

variables have also been experimented with. These include using difference in GDP per

capita for testing complementarity (capital-labour (K/L) ratio) among trading partners as

portrayed by H-O theory, and relative real effective exchange rate to capture the notion of

competitiveness. The estimations from these robustness tests show varying results as

regards the signs and significant level of our key variables of interests.15

Lastly, to test for robustness of the OLS estimation, the Poisson models have also been

performed based on the FE method. The results in Table 7 indicate that our main results do

suffer from zero trade flows as the number of observations from the Poisson estimations

does not differ much from that reported in Table 5. In addition, our conclusion drawn from

the OLS fixed effect model (e.g., as regards the effect of the mass and trade cost variables)

13 For the exporter, the mass variables are measured by GDP for the equations for final and aggregate exports while they are measured by manufacturing output adjusted by the imports of intermediate goods for the P&C equation. As for the importer, GDP is used for the final and aggregate exports regressions whereas GDP has been augmented with the imports of intermediate goods for the equation for parts and components. 14 The net effects of trade creation can then be calculated by comparing the magnitude and signs of these coefficients. This would not only make our analysis more complex but it is also out of the scope of this research. 15 Results have not been shown here but available upon request.


19

still holds because the significance level and signs of the regressors from Poisson

estimations do depart from those obtained from Table 5.


One key implication to be drawn from this study is the importance of trade costs, especially

for importing countries, which can lower bilateral flows. The effect appears particularly

pronounced in the case of final goods. Specifically, a one per cent increase in cost to import

will lower the exports of final goods dominated by GPN by 0.25 per cent, all other things

held constant (as shown in the third column of Table 5). This is due to the fact that

international production sharing often involves multi-border crossings of inputs for them to

be able to be assembled also final products (Yi 2003; Athukorala 2011a). Therefore,

countries, including ASEAN, that wish to better integrate into global production sharing

need to look into factors that are considered bottlenecks to the expansion of GPNs,

including trade costs. In a similar line, landlocked countries appear to be disadvantaged as

can be seen from the significance of the landlocked dummy in some model specifications.

These countries therefore have to make greater efforts to overcome their geographical

disadvantages.

Another implication is the role of regional economic integration. The effects of the majority

of RTAs (except for ASEAN-Korea and the EU) are found to be insignificant. This appears

to be consistent with findings from some studies (Athukorala & Yamashita 2007; Hayakawa

& Yamashita 2011). One possible explanation for this could be attributed to rules of origin

(ROO), which have been found to be restrictive in many RTAs. This imposes administrative

burden for traders, which can negate possible benefits from preferential tariffs (Hayakawa

& Yamashita 2011; Athukorala & Nasir 2012). This implies that unilateral liberalisation

and across-the-board reforms may be the best policy option.

7 Conclusions

This research has analysed the extent and depth of ASEAN participation in international

production sharing. It also examines the factors and mechanisms influencing countries’

participation in GPNs, focusing on the impacts on trade in P&Cs, and final products. The

gravity model has been adapted, aiming to obtain more rigorous estimates to account for

factors affecting trade in intermediate goods and multilateral resistance. The key findings


20

are that even though ASEAN has been well integrated into international supply chains

within the region and with East Asia, they still maintain substantial links in terms of trade in

final goods with the markets in North America and Europe. In addition, ASEAN production

sharing is essentially concentrated in the electronics, electrical and automotive industries.

As regards the determinants of GPNs, regression analysis based on the fixed effect model

suggests that trade costs (in particular cost to import) have a negative impact on bilateral

exports. The impact is more significant for trade in final products dominated by GPNs and

for landlocked countries. This suggests that trade costs play an important role in shaping the

patterns of international trade. Given the growing significance of trade in intermediate

products, location through its impact on input costs appears to be important in determining

specialisation and comparative advantage. Countries, including ASEAN nations, wishing to

better integrate into GPNs need to look into how trade costs can be curtained and undertake

reforms unilaterally and across the board.

On a final note, there are some limitations that are worth noting. The first limitation relates

to the fact that this paper looks into GPNs in a broad context. Further research should

examine the factors influencing production sharing at a more detailed level. It is interesting

to see whether the effects on trade in automotive or electronics products, which are

producer-driven GPNs, are different for the case of buyer-driven production sharing such as

apparel production. Another dimension worth examining is to assess whether the effects

will differ among countries with different income levels. As argued by ESCAP (2015),

participants in global production networks mostly tend to be high- and middle-income

countries while those in the low-income group have been left out. Their findings suggest

that exports from Asian and Pacific economies are dominated by electronics products from

the upper-middle income economies. The region is also predominant in the exports of other

product categories, except for automobiles whereby the high-income economies are the

major exporters. Further research should therefore examine these dimensions to better

understand the implications so that policy recommendations can be properly formulated.


21

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Table 1 Share in the world trade of GPN-dominated products, 2000/01 and 2012/13 Parts and

Components Final

products Total P&Cs in

networked trade

Exports 00/01 12/13 00/01 12/13 00/01 12/13 00/01 12/13

ASEAN 9.2 7.8 6.8 8.0 7.7 7.9 45.7 33.6

Brunei 0.0 0.0 0.0 0.0 0.0 0.0 1.1 27.6

Indonesia 0.5 0.5 0.8 0.8 0.7 0.7 28.0 25.4

Cambodia 0.0 0.0 0.1 0.2 0.0 0.1 0.2 2.0

Laos 0.0 0.0 0.0 0.0 0.0 0.0 0.9 8.1

Malaysia 3.0 1.9 2.0 2.1 2.4 2.1 48.2 31.9

Myanmar 0.0 0.0 0.0 0.0 0.0 0.0 2.7 3.3

Philippines 1.2 0.9 1.1 0.6 1.1 0.7 40.1 42.8

Singapore 2.9 1.3 1.3 1.6 1.9 1.5 58.7 29.7

Thailand 1.5 2.0 1.2 1.4 1.3 1.6 44.0 42.3

Vietnam 0.1 1.1 0.3 1.2 0.2 1.2 14.3 32.4

Northeast Asia 26.3 36.2 30.5 39.1 29.0 38.1 38.1 35.3

China 6.5 20.6 11.3 22.5 9.5 21.8 26.3 32.4

Hong Kong 0.9 0.5 1.5 0.5 1.3 0.5 28.2 31.3

Japan 11.8 8.1 10.8 7.2 11.2 7.5 40.5 36.9

Korea 3.5 4.2 3.3 4.8 3.4 4.6 39.1 31.3

Taiwan 3.6 2.8 3.6 4.1 3.6 3.7 38.3 26.4

EU (25) 34.9 33.1 34.5 29.9 34.7 31.0 38.5 36.6

NAFTA 24.9 16.4 19.4 13.1 21.5 14.2 44.3 39.5

World 100.0 100.0 100.0 100.0 100.0 100.0 38.3 34.3

Imports 00/01 12/13 00/01 12/13 00/01 12/13 00/01 12/13

ASEAN 7.6 7.8 5.5 7.0 6.3 7.3 46.7 35.6

Brunei 0.0 0.0 0.0 0.1 0.0 0.0 37.6 22.1

Indonesia 0.5 1.3 0.4 1.0 0.4 1.1 48.0 40.6

Cambodia 0.0 0.1 0.0 0.1 0.0 0.1 19.1 22.2

Laos 0.0 0.0 0.0 0.0 0.0 0.0 26.8 21.3

Malaysia 2.0 1.4 1.4 1.3 1.6 1.3 48.0 35.2

Myanmar 0.0 0.1 0.0 0.1 0.0 0.1 27.2 24.3

Philippines 0.9 0.6 0.8 0.6 0.8 0.6 40.8 33.5

Singapore 2.8 1.9 2.0 1.9 2.3 1.9 47.2 32.4

Thailand 1.2 1.7 0.7 1.0 0.9 1.2 52.5 47.1

Vietnam 0.2 0.7 0.2 0.9 0.2 0.9 33.5 26.8

Northeast Asia 14.5 20.1 13.8 19.6 14.2 19.7 43.5 34.8

China 2.8 6.9 1.9 5.9 2.3 6.2 47.7 36.4

Hong Kong 3.6 6.3 3.7 6.8 3.7 6.6 38.4 31.4

Japan 4.1 3.3 4.5 3.4 4.3 3.4 36.7 32.3

Korea 1.9 2.4 1.8 2.1 1.9 2.2 39.6 35.5

Taiwan 2.1 1.2 1.9 1.4 2.0 1.3 40.8 29.6 EU 25 37.9 31.3 38.0 29.9 38.0 30.4 38.7 34.1 NAFTA 27.6 21.3 27.2 19.1 27.3 19.8 39.1 35.5 World 100.0 100.0 100.0 100.0 100.0 100.0 38.8 33.1

Source: UN Comtrade (reporter records), calculated using two-year average to avoid annual fluctuations.


25

Table 2 Production networks in ASEAN manufacturing trade, 2012/13 Exports Imports

P&Cs Total P&Cs Total Machinery & transport equipment 26.4 59.5 26.8 55.2 ICT (SICT 75+76+772+776) 17.5 42.7 9.5 23.8 Electrical appliances (SITC 77-772-776) 2.5 6.3 2.7 6.7 Automotive (SICT 78) 1.6 4.4 2.9 6.1

Miscellaneous manufacturing 1.2 18.1 1.6 12.3 Apparel (SITC 84) 2.0 6.1 0.0 0.0 Professional equipment (SITC 87) 0.5 2.6 0.8 3.2 Photographic equipment (SITC 88) 0.3 0.3 0.2 0.2

Source: UN Comtrade (reporter records), calculated using two-year average.

Note: Manufacturing covers chemicals (SITC 5), resource-based products (SITC 6 excluding 68), machinery and transport equipment (SITC 7) and miscellaneous manufactured articles (SITC 8).


26

Table 3 Variable description Label Description Data sources Exports

Exports (reported by trading partners) in US$ at current prices for parts and components, final products# and all products

United Nations Comtrade

Si

Exporter’s specific factors For final and all products equations: - GDP in US$ at current prices For parts and components equation: - Manufacturing output (value-added) of the exporter adjusted with the imports of P&Cs from all trading partners, except for from the corresponding importer (to avoid putting exports in both sides of the regression)

World Bank’s World Development Indicator (WDI) and UN Comtrade

Mj

Importer’s specific factors For final and all products equations: - GDP in US$ at current prices For parts and components equation: - GDP of the importer adjusted with the imports of P&Cs from all trading partners except for itself

WDI and Comtrade

Distance

Relative distance between the most populated cities

French Institute for Research on the International Economy (CEPII)

Contiguity

Dummy taking unity if i and j share a common land border, 0 otherwise

CEPII

Language

Dummy taking unity if i and j have a common official language, 0 otherwise

CEPII

Landlocked

Dummy taking unity if i or j is landlocked, 0 otherwise

CEPII

Cost to export

Cost to export in US$ per container

WDI

Cost to import

Cost to importer in US$ per container

WDI

RTAs

Dummy taking unity if both i and j are members of RTAs (ASEAN, ASEAN-Australian & New Zealand, ASEAN-China, ASEAN-India, ASEAN-Japan, ASEAN-Korea, EU, or NAFTA)

World Trade Organization (WTO) and the European Union (EU)


27

Table 4 Descriptive statistics

Variables Observations Mean Std. Dev. Min Max

Log exports (total) 85,179 16.6529 3.7704 0 26.7407 Log exports (P&Cs) 80,999 15.3954 3.9168 0 25.5501 Log exports (final prod.) 80,013 16.5815 3.4886 0 26.3783 Log exporter GDP 91,392 26.0492 1.8027 21.2721 38.7041 Log importer GDP 91,468 26.0549 1.6835 22.0191 38.7041 Log exporter augmented manufacturing output 74,310 25.9648 1.5551 20.1367 29.5338 Log importer augmented GDP 80,823 26.7553 1.4697 22.8169 33.0433 Log distance 91,830 8.5004 0.9384 4.0879 9.8940 Contiguity 91,830 0.0361 0.1865 0 1 Language 91,830 0.0766 0.2660 0 1 Landlocked exporter 91,830 0.1112 0.3144 0 1 Landlocked importer 91,830 0.1026 0.3034 0 1 Log cost to export 60,440 6.8946 0.4534 5.9661 8.5726 Log cost to import 60,256 6.9856 0.4725 5.7589 8.5688 ASEAN 91,830 0.0127 0.1122 0 1 ASEAN-ANZ 91,830 0.0064 0.0799 0 1 ASEAN-China 91,830 0.0158 0.1249 0 1 ASEAN-India 91,830 0.0106 0.1022 0 1 ASEAN-Japan 91,830 0.0074 0.0857 0 1 ASEAN-Korea 91,830 0.0053 0.0725 0 1 NAFTA 91,830 0.075792 0.264667 0 1 EU 91,830 0.073375 0.260752 0 1

Note: The exports of final products (by deducing P&Cs from aggregate exports reported in Comtrade) have been adjusted to zero for the value that is negative.


28

Table 5 Baseline results from OLS estimations Variables Pooled FE RE

(1) (2) (3) Log exporter GDP 0.198*** 0.232*** 0.229***

(0.0432) (0.0261) (0.0261) Log importer GDP 0.847*** 0.905*** 0.898***

(0.0446) (0.0269) (0.0268) Log distance -1.141*** -1.129***

(0.0121) (0.0304) Contiguity 0.422*** 0.440***

(0.0385) (0.103) Language 1.023*** 1.088***

(0.0268) (0.0716) Landlocked exporter -5.168*** 0.820***

(0.160) (0.212) Landlocked importer -0.879*** -1.104***

(0.0898) (0.208) Log exporter cost to export 0.0875* 0.115*** 0.114***

(0.0497) (0.0299) (0.0299) Log importer cost to import -0.302*** -0.297*** -0.297***

(0.0520) (0.0313) (0.0312) ASEAN -0.247 -0.0598

(0.153) (0.360) ASEAN-ANZ -0.0540 -0.106 -0.106

(0.0993) (0.0813) (0.0793) ASEAN-China -0.376*** -0.325

(0.121) (0.323) ASEAN-Japan 0.480*** -0.0299 0.0168

(0.106) (0.0835) (0.0819) ASEAN-Korea 0.0700 0.332*** 0.310***

(0.119) (0.0937) (0.0916) NAFTA -0.0353 0.00844

(0.194) (0.536) EU -0.137*** 0.132** 0.0226

(0.0310) (0.0592) (0.0492) Constant 1.308 -11.67*** -0.974

(1.599) (0.962) (1.042) Country FE Yes Yes Yes Year FE Yes Yes Yes Observations 55,274 55,274 55,274 R-squared 0.849 0.095

Note: Dependent variable is the log exports of all GPN products. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.


29

Table 6 Main results based on panel estimations P&Cs Final products All products

Variables FE RE FE RE FE RE

(1) (2) (3) (4) (5) (6)

Log exporter Si 0.0893 -0.0222 0.193*** 0.189*** 0.232*** 0.229*** (0.0973) (0.0414) (0.0399) (0.0398) (0.0429) (0.0428)

Log importer Mj 0.705*** 0.749*** 1.084*** 1.081*** 0.905*** 0.898*** (0.0819) (0.0754) (0.0419) (0.0420) (0.0428) (0.0428)

Log distance -1.286*** -1.153*** -1.129*** (0.0343) (0.0345) (0.0337)

Contiguity 0.419*** 0.458*** 0.440*** (0.148) (0.136) (0.135)

Language 0.856*** 1.101*** 1.088*** (0.0874) (0.0800) (0.0778)

Landlocked exporter 0.220 0.742*** 0.820*** (0.232) (0.226) (0.219)

Landlocked importer -0.211 -1.204*** -1.104*** (0.211) (0.178) (0.179)

Log exporter cost to export 0.230*** 0.228*** -0.000190 -0.00467 0.115*** 0.114***

(0.0566) (0.0569) (0.0464) (0.0465) (0.0436) (0.0437) Log importer cost to import -0.0863 -0.0818 -0.249*** -0.253*** -0.297*** -0.297***

(0.0608) (0.0608) (0.0466) (0.0465) (0.0473) (0.0473) ASEAN 0.280 -0.0393 -0.0598

(0.386) (0.367) (0.362) ASEAN-ANZ -0.163 -0.114 -0.0818 -0.0931 -0.106 -0.106

(0.125) (0.122) (0.0835) (0.0791) (0.0870) (0.0840) ASEAN-China -0.262 -0.395 -0.325

(0.345) (0.297) (0.295) ASEAN-Japan 0.0459 0.121 -0.0689 -0.0343 -0.0299 0.0168

(0.108) (0.105) (0.0919) (0.0922) (0.105) (0.105) ASEAN-Korea 0.623*** 0.552*** 0.250** 0.243** 0.332*** 0.310***

(0.173) (0.169) (0.104) (0.100) (0.119) (0.116) NAFTA -0.0202 0.118 0.00844

(0.685) (0.687) (0.683) EU 0.262*** 0.0324 0.0445 0.00200 0.132*** 0.0226

(0.0688) (0.0549) (0.0462) (0.0397) (0.0423) (0.0382) Constant -6.421** 5.582*** -15.09*** -4.405*** -11.67*** -0.974

(2.507) (2.148) (1.518) (1.603) (1.591) (1.682) Country FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes


30

Observations 49,145 49,145 52,643 52,643 55,274 55,274 R-squared 0.053 0.119 0.095

Note: Dependent variable is the log exports of P&C, final and all products dominated by GPNs. Country and year dummies added in all regressions. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Table 7 Robustness testing based on the fixed effect Poisson estimation Variables P&Cs Final products All products

(1) (2) (3)

Log exporter Si -0.427*** 0.434*** 0.448*** (0.128) (0.0435) (0.0390)

Log importer Mj 0.907*** 0.751*** 0.646*** (0.105) (0.0610) (0.0555)

Log exporter cost to export 0.116* 0.00619 0.0532 (0.0615) (0.0508) (0.0470)

Log importer cost to import 0.462*** -0.0449 -0.0233 (0.0773) (0.0598) (0.0527)

ASEAN-ANZ -0.215* -0.224** -0.248*** (0.110) (0.0923) (0.0914)

ASEAN-Japan 0.0242 -0.0656 -0.0876 (0.0740) (0.0703) (0.0644)

ASEAN-Korea 0.163 0.0771 0.0612 (0.113) (0.0927) (0.0944)

EU 0.384*** -0.0285 0.122** (0.0819) (0.0541) (0.0537)

Country FE Yes Yes Yes Year FE Yes Yes Yes

Observations 49,082 53,034 55,265 Note: Dependent variable is the exports (in level) of P&C, final products and all products dominated by GPNs. Country and year dummies added in all regressions. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.


31

Annex 1 List of sample countries

1 Argentina 41 Kazakhstan 2 Australia 42 Cambodia 3 Austria 43 Korea, South 4 Azerbaijan 44 Kuwait 5 Belgium 45 Laos 6 Bangladesh 46 Lithuania 7 Bulgaria 47 Luxembourg 8 Bahrain 48 Latvia 9 Belarus 49 Morocco 10 Brazil 50 Mexico 11 Brunei 51 Myanmar 12 Canada 52 Malaysia 13 Switzerland 53 Nigeria 14 Chile 54 Netherlands 15 China 55 Norway 16 Colombia 56 New Zealand 17 Czech Republic 57 Oman 18 Germany 58 Pakistan 19 Denmark 59 Peru 20 Algeria 60 Philippines 21 Ecuador 61 Poland 22 Egypt 62 Portugal 23 Spain 63 Qatar 24 Estonia 64 Romania 25 Finland 65 Russian Federation 26 France 66 Saudi Arabia 27 United Kingdom 67 Serbia (Serbia & Montenegro) 28 Ghana 68 Singapore 29 Greece 69 Slovak Republic 30 Hong Kong 70 Slovenia 31 Croatia 71 Sweden 32 Hungary 72 Taiwan 33 Indonesia 73 Thailand 34 India 74 Tunisia 35 Ireland 75 Turkey 36 Iran 76 Ukraine 37 Iraq 77 United States 38 Israel 78 Venezuela 39 Italy 79 Vietnam 40 Japan 80 South Africa


32

Annex 2 List of parts and components

No SITC Description No SITC Description 1 71191 Pts nes of boilers 711.1 46 72492 Textile machinry pts nes

2 71192 Pts nes boiler equ 711.2 47 72591 Paper manuf machine pts

3 71280 Stm turbine(712.1)parts 48 72599 Paper product mach parts

4 71311 Aircraft piston engines 49 72635 Printing type,plates,etc

5 71319 Pts nes a/c piston engs 50 72689 Pts nes of bookbind mchn

6 71321 Recip piston engs<1000cc 51 72691 Type-setting machn parts

7 71322 Recip piston engs>1000cc 52 72699 Printing press parts

8 71323 Diesel etc engines 53 72719 Cereal/dry legm mach pts

9 71332 Marine spark-ign eng nes 54 72729 Indus food proc mach pts

10 71333 Marine diesel engines 55 72839 Pts nes of machy of 7283

11 71381 Spark-ign piston eng nes 56 72847 Isotopic separators

12 71382 Diesel engines nes 57 72851 Glass-working machy part

13 71391 Parts nes spark-ign engs 58 72852 Plastic/rubber mach part

14 71392 Parts nes diesel engines 59 72853 Tobacco machinery parts

15 71441 Turbo-jets 60 72855 Parts nes, machines 7284

16 71449 Reaction engines nes 61 73511 Tool holder/slf-open die

17 71481 Turbo-propellers 62 73513 Metal mch-tl work holder

18 71489 Other gas turbines nes 63 73515 Dividing head/spec atach

19 71491 Parts nes turbo-jet/prop 64 73591 Pts nes metal rmvl tools

20 71499 Parts nes gas turbines 65 73595 Pts nes mtl nonrmvl tool

21 71610 Electric motors <37.5w 66 73719 Foundry machine parts

22 71620 Dc motor(>37w)/generator 67 73729 Roll-mill pts nes, rolls

23 71631 Ac,ac/dc motors >37.5w 68 73739 Mtl weld/solder eq parts

24 71632 Ac generators 69 73749 Parts gas welders etc.

25 71651 Gen sets with pistn engs 70 74128 Furnace burner parts

26 71690 Pts nes motors/generator 71 74135 Elect furnace/oven parts

27 71819 Parts nes hydraul turbin 72 74139 Parts ind non-el furn/ov

28 71878 Nuclear reactor parts 73 74149 Pts nes indus refrig equ

29 71899 Parts nes of engines nes 74 74155 Air-conditioners nes

30 72119 Agric machine(7211)parts 75 74159 Air-conditioner parts

31 72129 Pts nes of machy of 7212 76 74172 Water proc gas gen parts

32 72139 Pts nes dairy machinery 77 74190 Parts indus heat/cool eq

33 72198 Parts wine/etc machines 78 74220 Piston eng fuel/wtr pump

34 72199 Pts nes agric machines 79 74291 Pump parts

35 72391 E-m bucket/grab/shovels 80 74295 Liquid elevator parts

36 72392 Bulldozer etc blades 81 74363 Engine oil/petrol filter

37 72393 Boring/sink machry parts 82 74364 Engine air filters

38 72399 Pts nes earth-movg mach 83 74391 Parts for centrifuges

39 72439 Sew mch needles/furn/pts 84 74395 Parts filters/purifiers

40 72449 Pts nes textile machines 85 74419 Pts nes of work trucks

41 72461 Auxil weave/knit machine 86 74443 Jacks/hoists nes hydraul

42 72467 Weaving loom parts/acces 87 74491 Parts for winches/hoists

43 72468 Loom/knitter etc pts/acc 88 74492 Lift truck parts

44 72488 Parts for leather machns 89 74493 Lift/skip h/escalat part

45 72491 Washing machine parts 90 74494 Lifting equip parts nes


33

No SITC Description No SITC Description

91 74519 Pts nes of tool of 7451 138 76499 Parts etc of sound equip

92 74529 Packing etc mchy pts nes 139 77111 Liquid dielec transfrmrs

93 74539 Weighng mach wts,pts nes 140 77119 Other elec transformers

94 74568 Spraying machinery parts 141 77125 Inductors nes

95 74593 Rolling machine parts 142 77129 Pts nes elec power mach.

96 74597 Automatic vending machs 143 77220 Printed circuits

97 74610 Ball bearings 144 77231 Fixed carbon resistors

98 74620 Tapered roller bearings 145 77232 Fixed resistors nes

99 74630 Spherical roller bearing 146 77233 Wirewound var resistors

100 74640 Needle roller bearings 147 77235 Variable resistors nes

101 74650 Cyl roller bearings nes 148 77238 Elect resistor parts

102 74680 Ball/roller bearings nes 149 77241 High voltage fuses

103 74691 Bearing ball/needle/roll 150 77242 Auto circuit breakr<72kv

104 74699 Ball etc bearng part nes 151 77243 Other auto circuit brkrs

105 74710 Pressure reducing valves 152 77244 Hi-volt isolating switch

106 74720 Pneumat/hydraulic valves 153 77245 Limiter/surge prtect etc

107 74730 Check valves 154 77249 Hi-volt equipment nes

108 74740 Safety/relief valves 155 77251 Fuses (electrical)

109 74780 Taps/cocks/valves nes 156 77252 Automatic circuit breakr

110 74790 Tap/cock/valve parts 157 77253 Circuit protect equi nes

111 74821 Ball/roll bearing housng 158 77254 Relays (electrical)

112 74822 Bearing housings nes 159 77255 Other switches

113 74839 Iron,stl a-l chain parts 160 77257 Lamp holders

114 74840 Gears and gearing 161 77258 Plugs and sockets

115 74850 Flywheels/pulleys/etc 162 77259 El connect equ nes<1000v

116 74860 Clutches/sh coupling/etc 163 77261 Switchboards etc <1000v

117 74890 Gear/flywheel/cltch part 164 77262 Switchboards etc >1000v

118 74920 Metal clad gaskets 165 77281 Switchboards etc unequip

119 74991 Ships propellers/blades 166 77282 Switchgear parts nes

120 74999 Mach parts nonelec nes 167 77311 Winding wire

121 75230 Digital processing units 168 77312 Co-axial cables

122 75260 Adp peripheral units 169 77313 Vehicle etc ignition wir

123 75270 Adp storage units 170 77314 Elect conductor nes <80v

124 75290 Adp equipment nes 171 77315 El conductor nes 80-1000

125 75991 Typewrtr parts,acces nes 172 77317 El conductor nes >1000v

126 75993 Dupl/addr mach parts etc 173 77318 Optical fibre cables

127 75995 Calculator parts/access. 174 77322 Glass electric insulator

128 75997 Adp equip parts/access. 175 77323 Ceramic elect insulators

129 76211 Mtr vehc radio/player 176 77324 Other electrc insulators

130 76212 Mtr vehc radio rec only 177 77326 Ceram elec insul fit nes

131 76281 Other radio/record/play 178 77328 Plastic el insul fit nes

132 76282 Clock radio receivers 179 77329 Other elec insul fit nes

133 76289 Radio receivers nes 180 77423 X-ray tubes

134 76432 Radio transceivers 181 77429 X-ray etc parts/access.

135 76491 Telephone system parts 182 77549 Electr shaver/etc parts

136 76492 Sound reprod equip parts 183 77579 Parts dom elect equipmnt

137 76493 Telecomm equipmt pts nes 184 77589 Domest el-therm app part


34

No SITC Description No SITC Description

185 77611 Tv picture tubes colour 232 78421 Motor car bodies 186 77612 Tv picture tubes monochr 233 78425 Motor vehicle bodies nes 187 77621 Tv camera tubes etc 234 78431 Motor vehicle bumpers 188 77623 Cathode-ray tubes nes 235 78432 Motor veh body parts nes 189 77625 Microwave tubes 236 78433 Motor vehicle brake/part

190 77627 Electronic tubes nes 237 78434 Motor vehicle gear boxes

191 77629 Electrnic tube parts nes 238 78435 Motor veh drive axle etc

192 77631 Diodes exc photo-diodes 239 78439 Other motor vehcl parts

193 77632 Transistors <1watt 240 78535 Parts/access motorcycles

194 77633 Transistors >1watt 241 78536 Parts/acces inv carriage

195 77635 Thyristors/diacs/triacs 242 78537 Parts,acces cycles etc

196 77637 Photo-active semi-conds 243 78689 Trailer/semi-trailer pts

197 77639 Semi-conductors nes 244 79199 Rail/tram parts nes

198 77649 Integrated circuits nes 245 79283 Aircraft launchers etc

199 77681 Piezo-elec crystals,mntd 246 79291 Aircraft props/rotors

200 77688 Piezo-elec assmbly parts 247 79293 Aircraft under-carriages

201 77689 Electrnic compon pts nes 248 79295 Aircraft/helic parts nes

202 77812 Electric accumulators 249 79297 Air/space craft part nes

203 77817 Primary batt/cell parts 250 81211 Radiators, parts thereof

204 77819 Elec accumulator parts 251 81215 Air heat/distrib equipmt

205 77821 Elec filament lamps nes 252 81219 Parts for c-heat boilers

206 77822 Elec discharge lamps nes 253 81380 Portable lamp parts

207 77823 Sealed beam lamp units 254 81391 Glass lighting parts

208 77824 Ultra-v/infra-r/arc lamp 255 81392 Plastic lighting parts

209 77829 Pts nes of lamps of 7782 256 81399 Lighting parts nes

210 77831 Ignition/starting equipm 257 82111 Aircraft seats

211 77833 Ignition/starting parts 258 82112 Motor vehicle seats

212 77834 Veh elect light/etc equ. 259 82113 Bamboo/etc seats/chairs

213 77835 Veh elect light/etc part 260 82119 Parts of chairs/seats

214 77861 Fixed power capacitors 261 82180 Furniture parts

215 77862 Tantalum fixd capacitors 262 84552 Girdles/corsets/braces..

216 77863 Alum electrolyte capacit 263 84842 Headgear plaited

217 77864 Ceram-diel capacit sngle 264 84848 Parts for headgear

218 77865 Ceram-diel capacit multi 265 87119 Binoc/telescope part/acc

219 77866 Paper/plastic capacitor 266 87139 Electron/etc diffr parts

220 77867 Fixed capacitors nes 267 87149 Microscopes parts/access

221 77868 Variable/adj capacitors 268 87199 Parts/access for 8719

222 77869 Electrical capacitr part 269 87319 Gas/liq/elec meter parts

223 77871 Particle accelerators 270 87325 Speed etc indicators

224 77879 Parts el equip of 778.7 271 87329 Meter/counter parts/acc.

225 77881 Electro-magnets/devices 272 87412 Navigation inst part/acc

226 77882 Elec traffic control equ 273 87414 Survey instr parts/acc.

227 77883 Elec traffic control pts 274 87424 Pts nes of inst of 8742

228 77885 Electric alarm parts 275 87426 Meas/check instr part/ac

229 77886 Electrical carbons 276 87439 Fluid instrum parts/acc

230 77889 Elec parts of machy nes 277 87454 Mech tester parts/accs

231 78410 Motor veh chassis+engine 278 87456 Thermometer etc part/acc


35

No SITC Description No SITC Description 279 87461 Thermostats 317 65621 Woven textile labels etc

280 87463 Pressure regulators/etc 318 65629 Non-woven text label etc 281 87469 Regul/cntrl inst part/ac 319 65720 Non-woven fabrics nes 282 87479 Elec/rad meter parts/acc 320 65751 Twine/cordage/rope/cable 283 87490 Instrument part/acc nes 321 65752 Knotted rope/twine nets 284 88113 Photo flashlight equipmt 322 65771 Textile wadding nes etc 285 88114 Camera parts/accessories 323 65773 Industrial textiles nes 286 88115 Flashlight parts/access 324 65791 Textile hosepipping etc 287 88123 Movie camera parts/acc. 325 65792 Machinery belts etc 288 88124 Movie projector part/acc

289 88134 Photo equip nes part/acc

290 88136 Photo,cine lab equip ne

291 88422 Spectacle frame parts

292 88431 Camera/etc objectiv lens

293 88432 Objective lenses nes

294 88433 Optical filters

295 88439 Mounted opt elements nes

296 88571 Instr panel clocks/etc

297 88579 Clocks nes

298 88591 Watch cases,case parts

299 88592 Watch straps/bands metal

300 88593 Watch strap/band non-mtl

301 88597 Clock cases,case parts

302 88598 Clock/watch mmnts unass

303 88599 Clock/watch parts nes

304 89111 Armoured tanks/etc

305 89129 War munitions/parts

306 89191 Pistol parts/accessories

307 89195 Shotgun/rifle parts nes

308 89199 Military weapon part nes

309 89281 Labels paper,paperboard

310 89395 Plastc furniture fittngs

311 89890 Musical instr parts/acc.

312 89935 Cig lighter parts/access

313 89949 Parts nes umbrella/canes

314 89983 Buttons/studs/snaps/etc

315 89985 Slide fasteners

316 89986 Slide fastener parts

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