Does the Presence of Ethnic Chinese in Trading Partner Countries Influence Bilateral Trade Flows with China?

David Greenaway Priydarshini .A. Mahabir

Chris Milner

Leverhulme Centre for Research in Globalisation and Economic Policy (GEP)

University of Nottingham

September 2007

Very Preliminary Draft

ABSTRACT This paper uses gravity modelling to investigate whether ethnic Chinese presence in host countries influences their imports from, exports to and total trade between China over the period 1990-2000. Trade flows at both aggregate and disaggregate levels are considered. In the latter analysis, commodities are grouped into homogeneous, reference priced and differentiated categories according to Rauch (1999) classification of goods. We capture ethnic Chinese presence by a dummy variable as well as other intensity measures based on absolute, relative and density criteria. Results from the Poisson pseudo-maximum likelihood estimator shows that ethnic Chinese presence affects imports, exports and trade at both levels. Network effects are higher for exports compared to imports, indicating dominance of the “transactions cost or information” effect. We find strong evidence of ethnic Chinese networking for reference priced and differentiated goods categories on the export side while only the reference priced group is affected on the import side.

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Does the Presence of Ethnic Chinese in Trading Partner Countries Influence Bilateral Trade Flows with China?

1. Introduction

Information costs associated with intra- and inter-national trade vary widely. Intra-

country trade involves relatively low information costs as buyers are able to hold as much

information on available varieties and their characteristics and sellers know better how to

reach their potential clients. In trade across borders, however, considerable costs are incurred

in gathering information. Buyers of consumer and intermediate goods need to learn about

foreign varieties available while producers have to conduct market research to locate

potential buyers of the products they have to offer and ensure their goods conform to quality

standards and other regulations. When engaging in international trade, traders often stumble

on contract enforcement problems that arise when different jurisdictions are involved. As

noted by Rodrik (2000), in the event of a breach of contract involving parties of different

nationalities, local courts may be reluctant- and international courts unable- to enforce

contracts due to discontinuities in the legal system across jurisdictions. International

transactions may thus be subject to heightened risk of opportunistic behaviour where explicit

contracting is involved.

Informal barriers such as inadequate information and weak contract enforcement can

be overcome by information-sharing networks. In broad terms, a network can be viewed as a

set of nodes (people, industries or organizations) linked together by ties. In the words of

Podolny and Page (1998, pp.59), a network is “any collection of actors that pursue repeated,

enduring exchange relations with one another”. As a result of repeated exchanges,

cooperation and collusion are sustained and ‘strong ties’ emerge. In contrasting networks to

markets, Rauch (1996) refers to the market as a “black box” which automatically matches

buyers and sellers. This reflects a degree of ‘anonymity’ between traders, which allows for

commodity arbitrage between distant and unrelated parties. Networks, on the other hand, are

characterised by personal contacts which are enhanced by factors like proximity, common

language, common culture and historical ties.

Networks can be considered as one of those less quantifiable factors that affect trade

volumes alongside standard economic influences. They have long been recognized as a fast

and reliable medium to transmit information on profitable trading opportunities and an

effective safeguard against opportunism. Transnational networks, for instance, facilitate the

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matching of agents to trade opportunities through provision of information on new styles,

market trends, expected reaction of consumers to new products and suggestions on how to

adapt products to suit consumers’ tastes. Producers can be directed to the right suppliers for

their inputs as well as distributors for final products and investors to suitable joint-venture

partners through such ties. Networks have also been found to assist trade by reinforcing trust

where contract enforcement is weak or nonexistent. Because of increased reciprocal

knowledge and number of transactions among members, networks provide a high speed

information channel to publicize any opportunistic business conduct. The threat of collective

punishment by shunning business with the deviant agent by all the other group members

deters opportunistic behaviour.

Overseas Chinese1 have the most well-known ethnic networks active in trade and direct

investment. Rauch and Trindade (2002) refer to ethnic Chinese network as “probably the

world’s largest and most internationally dispersed set of interlinked business and social

networks” (pp.116). Ethnic Chinese networks have certain peculiar traits such as deeply-

rooted instinct of trust, heavy reliance on interpersonal obligation bonding and the need to

keep a reputation of trustworthiness (Redding, 1995). Violations of agreements by

individuals or parties would preclude them from all future dealings as the entire Chinese

network will refrain from doing business with the guilty party (Weidenbaum and Hughes,

1996, pp. 51). The widespread dispersion of ethnic Chinese enables them to quickly identify

prime business opportunities (Kotkin, 1992). Business transactions, ranging from financing

to advance orders to material purchases are done through personal contacts and

recommendations by friends. Information is conveyed through an informal network rather

than through more conventional channels.

Using a gravity modelling framework, this paper explores how ethnic Chinese presence

in host countries influences their bilateral trade flows with China, both at aggregate and

disaggregate levels. We look at all three aspects of trade flows, namely, imports from,

exports to and total trade (imports + exports). We first examine whether ethnic Chinese

1 Overseas Chinese broadly comprises three categories of people: Chinese living abroad and retaining their Chinese nationality, whether citizenship of People’s Republic of China, Taiwan, Hong Kong, Macao (huaqiao);overseas Chinese who have been naturalized by their host countries (huaren); and ethnic Chinese who were born and residing outside China (huayi), e.g. Chinese-Americans or Sino-Thai. There is much ambiguity around the definition of ‘Overseas Chinese’ as the categorisation is made by governments, both Chinese and foreign, by the individuals concerned, societies in which they live and individual scholars (Poston, 2002)

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presence, captured by a dummy variable as well as other intensity measures, has any impact

on the aggregate level of bilateral trade flows. At the disaggregate level, we use Rauch

(1999) classification of goods to group commodities into homogeneous, reference priced and

differentiated, where we examine for any network effect through the Chinese presence

dummy variable. Unlike previous studies which rely on OLS, standard Tobit or the threshold

Tobit estimation strategies, we employ the Poisson pseudo maximum-likelihood method,

proposed by Santos Silva and Tenreyro (2006) to estimate our model. It is argued that this

procedure appropriately accounts for both zero value observations of the dependent variable

and the heteroskedastic nature of cross section or pooled data, yielding more consistent

estimates.

The remainder of the paper is organised as follows: Section 2 gives a brief overview of

overseas ethnic Chinese networks. Section 3 elaborates on the mechanisms through which

networks affect trade. Previous studies are briefly reviewed in section 4. Section 5 details the

empirical analysis, explaining the hypotheses, model and data sources. The estimation

strategy and key findings are discussed in Section 6, followed by concluding remarks in the

final section.

2. Ethnic Chinese Networks

The overseas Chinese community is remarkably heterogeneous. Not only do ethnic

Chinese, who fanned across continents, originate from different areas in China, they also

come from diverse countries outside China. They speak mutually unintelligible Chinese

dialects and migrated independently of one another as a result of different waves of

migration.

Networks and associations are often formed on the basis of attributes such as sharing of

the same surname, shared provenance, dialects, ideology and knowledge (Minghuan, 1998).

Given differences in culture, provenance and language, the various overseas Chinese

subgroups in host countries may have little interactions. Chain migration often means

entering an already established community and little interest in interacting with native

populations or Chinese from other areas. Within the subgroups, however, strong and reliable

informal networks prevail. Besides being responsible for the organization of social and

cultural activities, such networks nurture friendship, solidarity and trust among members.

They provide business information, employment to newcomers, support to set up new

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businesses in terms of credit allocation under a rotating credit system and assistance in

imports and exports of goods.

Ethnic Chinese networks have been strengthened by the formation of large-scale formal

overseas Chinese associations. Liu (2000) documents the large gatherings of overseas ethnic

Chinese and shows the growing trend of globalization in ethnic Chinese networks in the last

few decades. For example, since the 1960s about 100 world conventions of overseas Chinese

associations have been held. These serve as nodes for information exchange, providing

commercial information for their members and business people outside their communities.

Gao (2003) notes that many overseas Chinese associations consider establishing connections

between members and their ancestral hometowns in China. Delegates are invited to trade

fairs and business talks organised by such associations.

Minghuan (1998), who writes about voluntary Chinese associations in Europe, observes

the existence of vast but loosely-knit webs of voluntary associations around the world,

providing numerous possibilities for communications, mutual help and organised activity

among Chinese migrants. Associations differ in size and structure, from ephemeral and

informal to firmly established and tightly organised. Such associations are not necessarily

confined to one country but they unite ethnic Chinese of particular attributes across countries.

While first generation migrants tend to stay within their own groups, second and third

generations tend to be more integrated into a national or regional Chinese community. They

are more likely to transcend the cultural and language dissimilarities and interact at a more

regional or national level, whilst preserving their ethnic Chinese identity.

Continental Distribution of Overseas Chinese

About 81% of ethnic Chinese live in Asia with sizable and longstanding communities

in Indonesia, Thailand, Malaysia, Singapore, Vietnam and Philippines. The Americas has

about 11% of Chinese population, with the majority living in the United States and Canada.

Although ethnic Chinese resided in Europe for many decades, its Chinese population of

around 5%, has been half that of the America’s. Recently, fewer restrictions in asylum

applications, the Schengen agreement and easier access to educational institutions projected

Europe as an attractive alternative destination. France, UK, Germany, Netherlands, Italy and

Spain host the largest number of ethnic Chinese. In the 1990s, the emergence of market

economies in Russia and lax visa regulations in the eastern European countries led to a rapid

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increase in the number of ethnic Chinese in Russia2, the Czech Republic, Hungary and

Yugoslavia (Nyiri, 2003). Countries in Oceania have also been appealing destinations to

Chinese, with Australia and New Zealand having the highest absolute numbers. Among all

the continents, Africa has been the least attractive to Chinese migrants. About 92% of the

region’s ethnic Chinese are concentrated in four countries, namely Mauritius, Reunion, South

Africa and Madagascar, with well established communities whose roots can be traced back to

the coolie trade. The remaining 8% is unevenly spread out over more than 30 African

countries (Haugen and Carling, 2005).

Table 1. Distribution of Absolute Chinese Population by Continent Asia 27,094,752 America 3,570,150 Europe 1,662,000 Oceania 563,240 Africa 137,239

% of Total 82.0 10.8 5.0 1.7 0.4

Indonesia 7,310,000 US 2,000,000 France 300,000 Australia 454,000 Mauritius 40,000

Thailand 6,100,000 Canada 910,000 UK 250,000 N Zealand 35,000 Madagascar 30,000

Malaysia 5,280,000 Panama 150,000 Germany 111,000 Tahiti 20,000 South Africa 30,000

Singapore 2,291,100 Brazil 100,000 Italy 100,000 Marshall Is 12,000 Reunion 25,000

Philippines 2,200,,000 Cost. Rica 63,000 Netherlands 80,000 Papua N G 10,000 Seychelles 2,000

Vietnam 1,900,000 Peru 60,000 Spain 35,000 Samoa 10,000 Algeria 2,000

Russia 680,000 Venezuela 50,000 Belgium 30,000 Fiji 8,000 Nigeria 2,000

Cambodia 300,000 Argentina 30,000 Austria 20,000 Guam 5,000 Lesotho 1,000

Laos 200,000 Mexico 30,000 Sweden 20,000 Solomon Is 5,000 Mozambique 700

Japan 170,000 Jamaica 25,000 Romania 14,200 Christmas Is 2,605 Tanzania 600

India 135,000 Guyana 20,000 Switzerland 13,300 N Caledonia 1,000 Ghana 500

*Total = 32,016,458 Source: Ohio University Database

3. Channels of Effects

The mechanisms via which networks affect trade have been described in the literature by

a number of authors (eg Gould, 1994; Wagner, Head and Ries, 2002; Dunlevy, 2004). Two

main trade enhancing mechanisms have been identified: the “transaction costs or

information” effect and the “taste or preference” channel. Work by Gould (1994) and Rauch

and Casella (1998) emphasize that because of superior knowledge of home country markets

and host country characteristics, migrants help to lower transaction costs by providing

information on trading opportunities and distribution channels in both countries. Immigrants

are more connected to business networks that enable them to find customers or suppliers in

their country of origin. This has been referred to as the ‘information bridge hypothesis’ by

2 Ohio University (OU) estimates the number of Chinese in Russia as 680,000. This number may incorporate Chinese population of the Russian Far East Gelbras (2002) estimates the total number of Chinese in Russia between 200,000 and 400,000. Vitkovskaia (2000) arrives at a number between 200,000 and 500,000while stressing that most of them were commuters rather than residents. Most researchers believe that the number of Chinese in Russian Far East for 1993 to 1995 were between 50,000 and 100,000.

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Dunlevy (2004), ‘immigrant links hypothesis’ by Gould (1994) and ‘information hypothesis’

by Wagner et.al.(2002). Their understanding of home and host country languages, business

culture and regulations further provide a ‘cultural bridge’ that help to lower communication

barriers (Dunlevy, 2004). Greif (1989, 1993) promulgated the idea that coethnic networks

can promote international trade by enforcement of sanctions that deter violations of contracts

in a weak international legal environment. This is termed as ‘enforcement bridge’ by

Dunlevy. Reduction of transaction costs in these ways helps to stimulate both imports and

exports between host and home countries. Dunlevy (2004) suggests that the networks’ impact

on trade would be larger with greater variations in institutions, language and culture between

trading partners and less transparent business and political environment in the origin country.

The “preference” mechanism posits that migrants have an affinity for their homeland

products, which if not locally produced, would be acquired through importations. This

channel directly affects imports of the host country. Over time, however, as the immigrant

population grows, production of these homeland commodities may take place in the host

country, resulting in some import substitution (Dunlevy and Hutchinson, 2001; Girma and

Yu, 2000).

4. Previous studies

The empirical literature3 on the impact of networks on international trade is

concentrated on empirically identifiable information-sharing networks. This has been

captured in a number of ways including stock of immigrants, share of ethnic population in

host country and common language/ colonial ties. Many studies routinely include in their

gravity equations a dummy variable set to one when two countries share a common language

and zero otherwise as a proxy for linguistic similarity. The general finding is that language

commonality has a positive and significant impact on international trade. For example,

Frankel (1997) obtains positive estimates on the language dummy4 in the in a pooled

regression for the period 1965-92 (estimated at five-year intervals) for 63 countries. Rauch

(1999) discusses the importance of links formed by proximity (common border), common

language and colonial ties between countries in matching international buyers and sellers. He

finds the above variables to have a positive and significant effect on bilateral trade between

63 countries in Frankel’s (1997) sample for various types of goods.

3 Wagner et.al. (2002) provide a critical review of these studies. Our discussion borrows from their work. 4 He considers nine major languages, namely, English, Spanish, Chinese, Arabic, French, German, Japanese, Dutch and Portuguese.

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The network-trade link has been examined on various aspects of trade, namely

aggregate trade (Wagner et. al., 2002; Bandyopadhyay et. al., 2006; Herander and Saavedra,

2005; Combes et. al. ,2005), disaggregate trade by types of goods (Gould, 1994; Mundra,

2005; Rauch, 1999; Rauch and Trindade, 2002; Dunlevy and Hutchinson, 1999), country

(Girma and Yu, 2000; Co et. al.. ,2004; Dunlevy, 2004) and types of immigrants (Head and

Ries, 1998). The common thread running through most of these studies is the use of gravity

modelling and evidence of a positive relationship between network and trade, supporting the

notion that networks facilitate trade.

Since the basic gravity equation takes a log-linear form, it is natural for most studies

to employ log-linear specification eg Head and Ries (1998), Rauch and Trindade (2002),

Dunlevy and Hutchinson (1999, 2001), Girma and Yu (2000), Wagner, et. al. (2002),

Bandyopadhyay et. al.(2006). Some studies, however, depart from this practice by adopting a

non-linear formulation as it is argued that the constant elasticity formulation does not take

into account factors such as level of trade and number of immigrants in the country (Wagner

et. al., 2002). Gould addresses this issue by assuming that immigrants provide market

information that decreases the transaction costs to trade at a decreasing rate. Mundra(2005)

employs a partially linear (or semiparametric) formulation. Wagner et. al. (2002) also

estimate a random-opportunities model that reflects the idea that immigrants’ trading

advantages fade as the number of immigrants increases.

Studies have adopted different estimation strategies depending on their sample

characteristics. While the common practice is to estimate gravity models by Ordinary Least

Squares (OLS) as in Dunlevy and Hutchinson (1999, 2001); Girma and Yu (2000); Wagner

et. al. (2002); and Co et. al. (2004), many studies use threshold Tobit to account for

numerous zero values of the dependent variable (e.g., Dunlevy, 2004; Head and Ries,1998;

Rauch and Trindade, 2002; Heranda and Saavedra,2005).

There is mixed support as to whether the information or preference effect dominates5.

Gould (1994) for example estimates both imports and exports between the US and 47 trading

partners for the period 1970-1986. He finds that immigrant links (proxied by number of

immigrants) to their home country have a strong positive impact on both imports and exports,

5 Divergence in results may stem from differences in country coverage, time period, econometric specification and estimation methods

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with greater effects on exports across all immigrants. However, when a relatively large

number of immigrants are present, import elasticities become stronger than that of exports,

reflecting the role of immigrant preference for home country products. Girma and Yu (2000)

study the immigrant-trade relationship using bilateral export data between the UK and 48

trading partners, grouped into Commonwealth and non Commonwealth, for period 1981-

1993. They obtain smaller network effects on imports than exports.

On the other hand, Head and Ries (1998) use bilateral imports and exports between

Canada and 136 trading partners from 1980 to 1992. They find elasticities for imports to be

about three times higher than exports. They also find that immigrants contribute less to trade

flows the longer they reside in Canada. Dunlevy and Hutchinson (1999) evaluate US imports

from 17 partners from 1870-1910 (at 5 yr periods). The same model is re-estimated for

exports over the same sample in Dunlevy and Hutchinson (2002). They find that estimated

impact of migrant stock on imports to be two and a half times as strong as that on exports.

They conclude that the ethnic taste effect was particularly powerful over the sample period

for the overall regressions. For sub-group analysis of immigrants from various regions

however, immigrants have a positive effect on exports and is uniformly greater than for

imports in each geographical groupings. Taste advantages in import process is found to be

more than offset by more powerful information and trust linkages on the export side when

immigrants are grouped by place of origin.

Mundra (2005) studies the effect of immigration into the US on imports and exports

between 47 US trading partners for the period 1973-1980. The author finds positive

coefficients for US imports but negative estimates for US exports. This result is in sharp

contrast to the study on US imports and exports undertaken by Gould. Wagner et. al. (2002)

also estimate immigrant effect for Canada using provincial data for the years 1992-1995.

They consistently find higher import elasticities in their log linear specifications.

The literature finds support for networking for trade in consumer, finished and

differentiated goods. Brand names and country specific details are important for

differentiated goods and price alone cannot provide all the information, as would be the case

with homogenous goods. Thus network effects, more specifically, through the information-

link, should be strongest for differentiated goods. As part of his analysis, Gould (1994)

estimates import and export equations for consumer and producer manufactured goods. In

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addition to export effects outweighing import effects in both cases, he finds trade in

consumer goods to be more influenced than trade in producer goods. He infers that major

effect of trade through immigration is through establishment of business contacts, with

preference for country of origin goods only having a secondary effect. At the product level,

Mundra’s (2005) results corroborate with Gould’s in that positive network effect is found for

both imports and exports of finished products. Rauch’s (1999) analysis revolves mainly

around trade in three different categories of goods which he defines as organised exchange,

reference priced and differentiated. He finds that proximity, common language and colonial

ties to be largest for differentiated products. In their study, Dunlevy and Hutchinson (1999,

2002) also find support for higher imports of finished and semi-finished goods due to

networks. Significant export elasticities are obtained only for semi-finished products such as

coloured cotton cloth, tobacco manufactures and leather, in which case they infer that quality

assurance i.e. trust rather than tastes serve as a key export-promoting vehicle.

Endoh (2002) finds contrasting evidence when he classifies goods by industry. He

argues that networking is less important for differentiated goods and more applicable to trade

in more homogeneous products. Since differentiated products are hard to substitute, countries

have to import all products, irrespective of whether the source country is distant or has no

language commonality. Using the same sample of countries as Rauch (1999) for period 1970-

95, he finds that trade in food (SITC01) commodity group is highly sensitive to common

language and ties. Less differentiated manufactures such as organic chemicals, medical and

pharmaceutical products, iron, steel and textile group (SITC56) on the other hand are

insensitive to cultural ties and highly sensitive to distance. Organised exchange and

referenced price commodities such as material (SITC24) is not much influenced by distance,

cultural or historical ties. Mineral fuel (SITC3), however, which also comprise of a high

proportion of organised exchange goods, is affected by distance and cultural proximity.

Amidst the large number of studies on network effects on trade, limited research has

been undertaken on the influence of specific co-ethnic networks on trade. Rauch and

Trindade (2002) is perhaps the most cited work in this area. They examine whether presence

of large numbers of ethnic Chinese residents in partner countries is associated with more

trade in types of goods defined by Rauch (1999) for Frankel’s (1997) sample of 63 countries

for two year 1980 and 1990. To measure the strength of ethnic Chinese network, they use

product of ethnic Chinese population shares in each trading pair (capturing the idea that if an

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individual is selected at random from each country, both will be ethnic Chinese) as well as

the product of the two countries’ ethnic Chinese populations (reflecting the number of

potential international connections). Using imports plus exports as their dependent variable,

they find that all three types of goods are influenced by ethnic Chinese networks, but the

effect gets stronger as one move from homogenous to more differentiated ones. Thus there is

partial support for the ‘enforcement hypothesis’ ethnic Chinese are found to enhance trade in

homogeneous products as well.

5. Empirical Analysis

Hypotheses

The opening up of the Chinese economy has not only enhanced the flow of goods and

services, but has also eased the movement of its people across its border. With an increasing

number of Chinese abroad and rapidly expanding Chinese trades, it is possible that ethnic

Chinese networks might influence trade flows between the host countries and China. In this

study, we specifically examine the influence of ethnic Chinese networks on bilateral trade

flows between China and its trading partners. Our analysis departs from existing studies

examining the effect of ethnic Chinese networks on trade in that we consider separately all

three aspects of trade, namely imports, exports and total trade, with China only rather than in

a multi-country setting. Furthermore, we use Poisson pseudo-maximum likelihood method, a

novel way of estimating gravity and other constant elasticity models. This method is found to

yield more consistent estimates in the presence of heteroskedasticity and appropriately deals

with zero value observations for the dependent variable.

One could expect the presence of ethnic Chinese in trading partner countries to

positively influence bilateral exports, imports and total trade in general with China under the

“preference effects” and “transaction costs” channels. The literature finds such effects to be

stronger for differentiated goods. Would the same conclusions hold in our single-country

framework? The typical networking effects found in the literature may not replicate itself as

dealings with China, which has a competitive edge in labour-intensive products, may

altogether be different from trade with other countries.

The Model

In our analysis, we employ a gravity model where we control for factors that

generally affect trade flows. The gravity model has been an empirical success in explaining

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impacts on bilateral flows including international trade, foreign investment and labour

migration. It has been extensively used as a baseline model to explore a variety of policy

issues such as currency unions (Frankel and Rose, 2000; and Rose, 2002a); national borders

(McCallum, 1995; Helliwell, 1996, 1998); linguistic distance (Hutchinson, 2003); regional

trading agreements (Sharma and Chua, 2000; Soloaga and Winters, 1999); multilateral

agreements (Rose, 2002b; Subramanian and Wei, 2003); implications of WTO accession for

current non-members (eg Lissovolik and Lissovolik, 2004; Eremenko and Mankovska,

2003); calculation of trade potentials (Nilsson, 2002; Egger, 2002); cross-border investment

(Egger and Pfaffermayr, 2004) and China’s trade displacement effect (Eichengreen et. al..,

2004). Its theoretical underpinnings have been developed in Anderson (1979), Bergstand

(1985) and Deardoff (1995) where it has been shown it can be derived from alternative trade

theories.

The basic gravity model expresses bilateral trade, imports or exports between a pair

of countries as a positive function of their economic sizes (measured by GDP, GDP per

capita or population) and inverse function of distance between them. The basic model is

usually augmented with other trade enhancing or hindering variables, specific to the issue

being explored. We define our specification as follows:

ln Tijt = β0+ β1 chpri+ β2 ln GDPit + β3 ln CAPit + β4 ln Distij + β5 lnAreapij + β6 Landlij + β7 Islandij + β8 Borderij + β9 NatLangij + β10Icorit + λt + εijt (1)

where lnTij :Log of nominal6 imports of country i from China (j) or exports of

country i to China or total trade (imports+exports) chpri :ethnic Chinese presence dummy, set to one if Chinese population is

reported and exceeds 1,000 in host countries ln GDPi :Log of GDP of host country, current international dollars ln CAPi :Log of GDP per capita of host country, current international dollars ln Distij :Log of distance between i and China ln Areapij :Log of product of land areas of country pairs in km² Landlij :Number of landlocked countries in country pair (0/1/2) Islandij :Number of island nations in country-pair (0/1/2) Borderij :Binary dummy which is unity if i and China share a land border, zero

otherwise NatLangij :Binary dummy which is unity if i and China share a common native

living language, zero otherwise Icori: :Importer’s Corruption Index λt :year dummies εt :Other omitted influences on imports.

6 Previous studies such as Rauch and Trindade (2002), Co et. al. (2004), Dunlevy (2004), use dependent variable expressed in current dollar terms. We follow this practice and use nominal flows.

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In gravity models, GDP proxies economic size of country i in terms of market (if

importing) and production capacity (if exporting). GDP per capita is included separately to

identify the effect of level of development of country i on bilateral trade7. The richer the

country, the more it will engage in international trade. Since we are considering bilateral

trade flows with China only, the latter’s GDP and per capita GDP do not vary across trading

partners. We do not explicitly model these factors as their effects are subsumed into the set of

year dummies. This approach has been followed by Girma and Yu (2000). Distance, which

captures some of the transaction costs such as freight costs and time lags, is expected to

negatively influence bilateral trade flows.

In addition to the basic gravity variables, we control for geographic size of partners

through product of land areas (Areap). Bigger country sizes are indicative of large resource

endowments and potential self-sufficiency and therefore tend to be associated with lower

trade. Border accounts for the fact that neighbouring countries tend to trade more with each

other. Landlocked countries (landl) are expected to trade less due to higher transportation

costs. Island states (island) have been found to have ambiguous effects on trade in the

literature. One can argue that since they are small, they rely heavily on trade. On the other

hand, their small population may also mean that little trade is required. We also include a

measure of host countries’ corruption (icor) as this may act as an impediment to trade. The

importance of corruption has also been highlighted in the network literature. Dunlevy (2004)

notes that networking is stronger when destination markets and societies are less transparent

and more corrupt. Omission of this variable can, therefore, bias the network coefficient. The

literature also emphasizes the role of common birth language. To isolate the effect of

common native language or dialect from the network influence, we include a dummy

variable natlang, which is set to 1 if a Chinese dialect is spoken in host countries. This is

cruder measure than the constructed language variable of Rauch and Trindade (2002)8, but

we do not have the data to replicate this.

7 GDP per capita has been used interchangeably with countries’ population in gravity models with little difference in estimates. 8 They calculate a language share variable by dividing the number of mother tongue speakers by mid-year population estimates.

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Data sources

Data on disaggregated imports and exports (recorded in nominal US$ 000) are taken

from the NBER_UN World Trade Flows9, 1962-2000 database, compiled by Feenstra et. al.

(2005), which reports UN trade data classified by Standard Industrial Trade Classification

(SITC) Revision 1 for the period 1962-1983 and Revision 2 for the period 1984-2000. The

dataset gives primacy to importers’ reports as this is considered more accurate by the

authors. Where such information is not available, the corresponding exporters’ report is

used. Data reported by importer is C.I.F (cost, insurance, freight) which includes

transportation cost and that reported by exporter is F.O.B (free on board), which excludes

transportation costs. Tariffs are not included in either series.

Country specific variables, namely distance, product of land area, landlockedness,

number of islands, and border are from Rose (2002). Native language dummy is

constructed from information available on the Ethnologue website (www.ethnologue.com)

and refers to living languages spoken both in China and host countries. Corruption indices

are from the International Country Risk Guide-ICRG and range between 0 (high

corruption) and 6 (low corruption).

The dummy variable indicating Chinese presence is constructed from Overseas

Chinese population data taken from the Ohio University’s Shao Centre database. Unlike

Rauch and Trindade (2002), we are unable to proxy strength of ethnic Chinese network by

ethnic Chinese population in country i due to unavailability of precise10 and yearly Chinese

population data for our chosen sample of countries. Countries for which no data is reported

and those with an ethnic Chinese population of less than 1,000 are assigned a zero dummy

value. We additionally construct four indicators capturing absolute numbers of overseas

Chinese population (absolute measure), its share in host countries’ population in 1995

(relative measure), its density relative to land area (land area density) and its density relative

habitable11 land area (habitable area density). We categorise each measure into high, medium

and low. Although the absolute overseas Chinese population indicates the strength of

9 Downloaded from www.nber.org/data 10 Due to problems of definition and compilation, diversity of countries of origin, irregular and illegal migration, the number of overseas Chinese is only imperfectly known (Pieke, 1998; Nyiri , 2003). A first attempt to put together data on overseas Chinese population for circa 1980 and 1990 for 130 countries was made by Poston and Yu (1990).Overseas Chinese population data for a few selected countries are available on the Overseas Chinese Affairs Commission (OCAC) website 11Using information from WDI online, habitable land area is obtained by deducting forest area from total land area.

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Chinese presence, the relative measure takes into account its share in the total population. As

such a small country may have a lower ranking on the absolute scale but higher in relative

terms. The density measures are intended to capture spatial distribution and concentration of

the overseas Chinese population. One would expect bilateral trade flows with countries

having the strongest Chinese presence to be higher. (The ranking of host countries in the

sample according to each criterion is given in Appendix A)

Classification of Goods

Using Rauch’s (1999) classification12 of goods, we categorise commodities into three

groups: homogeneous, reference priced and differentiated. The ‘homogeneous’ category

consists of goods that are internationally traded in organised exchanges, with a well defined

price (e.g. live sheep and goats, unmilled durum wheat, dried, salted or smoked fish and

meat, soya beans, oils such as sunflower, peanut, petrol and gas oils, iron ores). Most

products in this category are food, food products, agricultural goods, oils and alloys.

Reference priced group includes goods that are not traded in organised exchanges but have

reference prices13 available in specialised publications such as trade journals and commodity

handbooks. Examples include butter, cheese, fish fillets, aluminium, nickel and copper ores,

petroleum jelly and mineral waxes. These goods are considered as relatively homogenous but

their lack of organised exchanges also makes them like differentiated products (Rauch,

1999). Rauch distinguishes goods sold on organised exchanges as those having specialised

traders who are informed of their prices around the world while reference priced

commodities only potentially have such traders. Differentiated products usually have

multidimensional differences in characteristics and are, therefore, neither traded on organised

exchanges nor possess reference prices. Examples include cutlery, television receivers, office

machines, garments, and so on.

Since categorizing commodities in this manner gave rise to numerous ambiguities,

Rauch adopted two broad classifications of goods: ‘conservative’ and ‘liberal’. The

conservative group minimizes the number of goods sold on organised exchanges and having

reference prices while the liberal scheme gave primacy to these categories. In our study, we

12 Obtained from Jon Haveman’s website http://www.macalester.edu/research/economics/PAGE/HAVEMAN/Trade.Resources/TradeData.html#classification 13 Rauch (1999) defines a reference price as a price that is quoted without mentioning a brand name or product identification of the good to be sold.

15

use the conservative standard as it is less stringent in the classification of goods as

differentiated14.

The following table gives the share of each category of goods in total value of

imports, exports and trade in our sample.

Table 2. Share of Commodity Categories in Total Value of Trade Flows (%), 1990-2000

Imports Exports Trade

Homogeneous 5.1 15.4 8.9

Reference Priced 11.4 30.0 18.3

Differentiated 83.5 54.6 72.8

Differentiated products account for the biggest share (83.5%) in the total value of host

countries’ imports from China while homogeneous products make up only about 5% of the

total. This pattern differs slightly for host countries’ exports to China, with exports of

homogeneous products accounting for a higher proportion (15.4%) and differentiated goods a

lower share of 55% of the total value of exports. Host countries also tend to export more of

reference priced goods (30.0%) compared to a share of 11.4% of imports. Overall, host

countries export more homogeneous and reference priced goods to China and less

differentiated products compared to their imports. Because of the relatively high proportion

of differentiated goods imports, trade (imports + exports) is concentrated in this category

with a share of 72.8%, compared to 18.3% for reference priced and 8.9% for homogeneous

goods. Rauch’s (1999) and Rauch’s and Trindade’s (2002)15 sample also display similar

features, with differentiated products accounting for the largest share, 67.1%, of total trade,

followed by 12.6% for organised exchange and 20.3% of reference priced goods in 1990.

A few examples of goods mostly traded16 in our sample include:

14 The literature finds a strong evidence of networking in differentiated goods. We maximise on the number of such goods based on the conservative scheme to examine how far this hypothesis is applicable to our sample of countries. 15 The same sample of 63 countries as in Frankel (1993, 1997) are used in the studies respectively. The commodity group shares are from Rauch (1999). 16 This refers to SITCs traded by the highest number of host countries (and may not be those with highest trading value)

16

Table 3. Goods Most Frequently Trade in our Sample

Imports Exports Homogeneous • Rice, semi milled or wholly

milled, broken rice • Edible nuts • Tea • Cotton Yarn • Tobacco, wholly or partly stripped

• Aluminium and Copper alloys, unwrought

• Petrol and crude oils from bitumen minerals

• Cotton, not carded or combed • Yarn of wool or animal hair • Sheep or lambs wool, greasy or

fleece washed • Waste and scrap metal of iron and

steel. Reference Priced • Beans, peas and lentils and other

leguminous vegetables, preserved vegetables; Spices

• Chemical products • Clay and other refractory minerals • Ferro alloys; Insecticides • Petroleum jelly and mineral waxes • Woven fabrics

• Crustaceans and Molluscs • Aluminium and Copper alloys,

worked • Iron/steel (bars and wires) • paper and paperboards • Polyethylene • Non-ferrous base metals waste and

scrap

Differentiated • Antibiotics, medicaments, pharmaceutical products

• Tyres • Footwear; Children’s Toys • Lighting fixtures and fittings;

Locksmiths wares; nails, screws, nuts and bolts

• Cotton fabrics; bed/table linen; Garments of textiles fabrics

• Porcelain tableware • Machinery and parts; television

receivers; electric power tools and parts

• Frozen Fish • Leather • Chemical Products and

Preparations • Machinery and Appliances for

specialised industries • Electrical checking and measuring

equipment • Parts and accessories of motor

vehicles • Articles of conveyance or packing

of goods

Construction of Dependent Variables

Due to financial constraints, Feenstra et. al. (2005) report only values in excess of

US$100,000 in the NBER_UN dataset. For example, if a country imports three units of a

commodity that amount to a total value of less than $100,000, this is not included in the

dataset. Despite this lower bound, the data account for 98% of world trade. Because of such

omissions, our extracted data initially contained gaps in years. This may either mean that

trade actually exists between the country pairs but because it totals less than US$100,000, it

is not included. Or it may imply that the data is missing. Given that 2% of world trade is not

accounted for by the dataset, we assume the first possibility. Where data is not included in

the dataset for a particular year, we assign a zero to account for the possibility of existence of

small trade.

6. Estimation and Analysis of Results

17

The specification of the estimating equation (1), formally derived from the gravity

theory and expressed in additive form, suggests the use of a logarithmic specification. Such

functional form, which conveniently allows elasticities to be estimated directly, has been the

most common approach to estimate gravity models. In this procedure, however, country pairs

with zero trade are automatically dropped, inducing possible sample selection bias based on

the dependent variable and inconsistent results (Head and Ries, 1998). The occurrence of

zero may be due to a number of factors: country pairs may not trade in a given period due to

high variable and fixed costs, rounding errors where small trade values are rounded off as

zeros and missing observations wrongly recorded as zeros (Santos Silva and Tenreyro, 2006).

The number of zeros tends to increase with more disaggregated data.

To address the potential bias from dropping zero values, the Tobit or censored

regression model developed by Tobin (1958), has been adopted in many studies. The

dependent variable T is usually redefined as (T+1) prior to logarithmic transformation to

retain zeros in the sample17. The Tobit procedure is specially designed to account for

dependent variables which have a lower limit, an upper limit or both. Another commonly

used technique in the network literature is the threshold Tobit, a variant of the standard Tobit

model introduced by Eaton and Tamura (1994).

More recently, Santos Silva and Tenreyro (2006) have drawn the attention of

researchers to the fact that in the presence of heteroskedasticity, OLS estimates of log

linearized models such as gravity equations, can be biased even after controlling for fixed

effects as the basic assumption of constant variance is violated. They also argue that

estimates from log-linearized regressions can lead to misleading inferences as the Jensen’s

inequality18 is often ignored. They propose a pseudo-maximum likelihood (PML) technique

that estimates gravity equations in particular, and other constant elasticity models in general,

in their multiplicative form. This method addresses the problem of zero value observations as

the dependent variable is estimated in levels, curtailing the problem of taking log of zero.

Among the various pseudo-maximum likelihood estimators, they choose the Poisson

estimator (PPML), often used for count data, as it assumes that the conditional variance is

proportional to the conditional mean ie E[yi|x]∝V[yi|x]. The validity of this condition does

17 This practice has been criticised on the ground that it is sensitive to the units in which the dependent variable is measured (Head and Ries, 1998). 18 Jensen’s inequality implies that E(ln y) ≠ lnE(y).

18

not require the data to be Poisson distributed and can therefore be easily extended to non-

Poisson data.

To support their claim, Santos Silva and Tenreyro (2006) examine the potential bias

of elasticities in the loglinear regression models through both Monte Carlo simulations and

an empirical example using aggregate trade flows for a cross section of 136 countries in

1990. In their empirical section, they report results from various techniques, namely (i) OLS

where zero observations are dropped by using ln(dependent variable); (ii) OLS with zeros

retained through ln(dependent variable +1); (iii) Eaton’s and Tamura’s threshold Tobit; (iv)

non linear least squares; (v) Poisson using a subsample of positive trade only; and (vi)

Poisson on the whole sample (containing zero trade pairs). They find that the PPML

estimates with and without zero trade pairs are remarkably similar. Most coefficients,

however, differ from those obtained using OLS, which they attribute to heteroskedasticity

rather than truncation. OLS on ln(dependent variable +1) and threshold tobit give close

estimates for most coefficients. The authors conclude that unlike the PPML, the other

methods yield puzzling asymmetries in elasticities with respect to importer and exporter

characteristics.

Schumacher and Siliverstovs (2006) extend this work by testing the applicability of

the PPML to data disaggregated at the industry level. To enable comparisons with the

conclusions reached by Santos Silva and Tenreyro (2006), they first use aggregate bilateral

trade flows between 22 OECD countries using average annual data for years 1988-1990.

Despite divergences in the number of observations and zero trade counts in the dataset, they

find that the total income elasticities under Poisson are close to those obtained by Santos

Silva and Tenreyro. In general, these coefficients are smaller in the Poisson estimations.

Similar to Santos Silva and Tenreyro, they find a bigger role of distance as a trade deterrent

in OLS compared to Poisson. Their work find supporting evidence for the Poisson regression

over the traditional OLS approach in estimating gravity models, especially in the presence of

heteroskedasticity as found in their sample.

We first estimate our model using pooled aggregate19 data from 1990-2002 for 152

countries. 5.1%, 20.1% and 6.5% of zero observations appear in aggregate imports, exports

and total trade sub-samples respectively. Visual inspection of scatter plots indicates the 19 We do not use the reported NBER_UN aggregate flows since it aggregates a number of products that do not appear in Rauch’s Classification.

19

presence of some outlier countries. We deal with this by dropping 1% of observations are

each tail of the dependent variable distribution. Estimates for aggregate flows obtained from

this estimator are reported in the following table:

Table 4.Aggregate Flows, Pooled Poisson Estimates, 1990-2000

Imports Exports Trade chpr2 0.113 0.409*** 0.251*** (1.163) (3.307) (2.699) Lgdp 0.801*** 0.612*** 0.716*** (22.716) (18.755) (20.916) Lcap 0.633*** 0.652*** 0.680*** (11.899) (8.996) (13.610) Ldist -0.617*** -1.200*** -0.960*** (-10.895) (-13.498) (-14.796) Lareap -0.064*** 0.101*** 0.009 (-3.337) (3.418) (0.458) landl (d) -0.493*** -0.448*** -0.487*** (-7.412) (-3.746) (-6.879) island (d) 0.467*** 0.340*** 0.321*** (7.584) (3.134) (4.168) border (d) -0.987*** -0.651*** -0.844*** (-6.017) (-4.059) (-6.681) natlang (d) 0.469*** 0.333*** 0.456*** (8.021) (4.445) (7.642) Iicor -0.054** 0.029 -0.028 (-2.015) (0.823) (-1.122) y1991 0.191 0.217* 0.199 (0.892) (1.872) (1.168) y1992 0.126 0.366*** 0.341** (0.734) (3.486) (1.969) y1993 0.211 0.550*** 0.283* (1.221) (4.567) (1.903) y1994 0.244 0.678*** 0.389*** (1.352) (5.436) (2.592) y1995 0.377** 0.758*** 0.512*** (2.093) (5.694) (3.497) y1996 0.424** 0.842*** 0.555*** (2.394) (6.742) (3.800) y1997 0.492*** 0.723*** 0.586*** (2.814) (5.805) (4.087) y1998 0.456*** 0.584*** 0.543*** (2.611) (4.261) (3.852) y1999 0.488*** 0.736*** 0.610*** (2.775) (5.610) (4.388) y2000 0.622*** 1.074*** 0.817*** (3.497) (8.187) (5.813) _cons -6.655*** -2.921*** -3.899*** (-6.477) (-3.265) (-3.460) r2_p 0.906 0.825 0.903 N 1204 1204 1204

Figures reported in parenthesis are t-ratios. All regressions have robust variance estimates. *** significant at 1%, ** significant at 5%, significant at 10%.

Countries with a Chinese presence, in general, have higher exports and total trade

with China compared to those with no Chinese presence. Chinese migrants’ business contacts

20

in their homeland as well as the familiarity with Chinese culture help to foster these trade

flows with China. The dummy variable does not, however, show statistical significance for

aggregate imports. Other gravity variables have the expected signs. Higher GDP and GDP

per capita are associated with higher trade flows, while distance has a deterring effect.

Landlocked countries are found to trade less and island states engage more in trade with

China. Contrary to our expectation of contiguity having a beneficial impact, we find bilateral

trade flows between China and adjacent countries to be lower. Given the mountainous region

along some borders and deeply-rooted border conflicts between China and contiguous

countries such as India, Russia, Kazakhstan, Tajikistan, it is not surprising for such an effect

to exist. Moreover, many transactions are undertaken in black markets and therefore not

reflected in the data. Host countries where a native Chinese language is spoken have more

trade with China. More corrupted host countries also have lower imports.

Would countries with stronger ethnic Chinese presence transact more with China? To

examine this question, we use other measures of Chinese presence, namely absolute, relative

and land and habitable area density measures, classified into high, medium and low. While

the dummy variable in Table 4 shows that Chinese presence has no significant impact on

imports, the other measures indicate otherwise, as shown in Table 5 below. One may argue

that the influence of ethnic Chinese networks on imports is observed when the Chinese

population in host countries is large enough. The high category on all four measures shows

positive and significant effect on imports, exports and total trade. The stronger the Chinese

presence, the higher the impact on trade flows relative to a low Chinese presence. Except for

the relative measure, we find estimates for the Chinese presence dummy to be higher in the

export regression.

21

Table 5. Aggregate Flows, Pooled Poisson Estimates, 1990-2000 with Other Measures of Ethnic Chinese Presence

Imports Exports Trade Absolute measure Cut-off levels HIGH ≥500,000 0.417*** 0.619*** 0.370*** (7.139) (3.979) (4.750) MED <500,000; ≥50,000 -0.020 0.262** -0.076 (-0.285) (1.985) (-0.971) Relative measure HIGH ≥5% 0.707*** 0.679*** 0.548*** (6.793) (3.399) (4.074) MED <5%; ≥1% 0.277*** 0.035 0.152 (2.791) (0.229) (1.613) Land Area Measure HIGH ≥15 0.910*** 1.396*** 1.043*** (9.384) (9.375) (10.237) MED <15 ≥ 4 -0.129 0.102 -0.186 (-1.155) (0.499) (-1.524) Habitable Area measure

HIGH ≥15 0.499*** 0.830*** 0.468*** (4.677) (3.669) (3.316) MED <15; ≥5 -0.066 0.511** 0.102 (-0.377) (2.162) (0.607)

Base category: LOW. Figures reported in parenthesis are t-ratios. All regressions have robust variance estimates. *** significant at 1%, ** significant at 5%, significant at 10%.

Disaggregate Analysis

Before presenting our disaggregate analysis estimates, we first tentatively replicate

some of Rauch’s and Trindade’s (2002) methodology on our sample. We estimate our model

with total trade (imports + exports) as the dependent variable using cross-sectional data for

the year 1990. We shrink our sample to 60 countries, which is close to that used by Rauch

and Trindade20 and apply Tobit estimation. We also examine how this modified sample fares

with the Poisson estimation method. Coefficient estimates are as follows:

20 Rauch and Trindade used a sample of 63 countries which includes China, Hong Kong and Taiwan. These countries are not included in our sample.

22

Table 6. Tentative Comparison of Network Effects with Rauch and Trindade (2002)

a. Tobit Estimates, 1990 Dependent variable: total trade

60 countries (as in RT,2002)

152 countries (our sample)

HOM REF DIF HOM REF DIF chpr -0.043 0.843 1.503 0.319 0.336 0.390 (-0.029) (0.656) (1.371) (0.179) (0.339) (0.516) Lgdp 0.908 1.124** 0.785* 1.287* 1.053*** 0.754** (1.336) (2.095) (1.740) (1.810) (2.839) (2.601) Lcap -0.351 0.077 0.114 0.474 0.850 0.734 (-0.316) (0.089) (0.160) (0.485) (1.447) (1.637) Ldist -0.734 -1.734 -1.316 -3.547** -1.776* -0.976 (-0.552) (-1.473) (-1.327) (-2.242) (-1.915) (-1.419) Lareap 0.256 0.376 0.322 0.991* 0.326 0.100 (0.598) (1.036) (1.049) (1.887) (1.116) (0.463) landl (d) 0.931 0.427 0.814 -0.504 -0.582 0.460 (0.450) (0.291) (0.665) (-0.252) (-0.492) (0.505) Island (d) 2.728 0.765 0.250 2.114 1.338 0.168 (1.485) (0.495) (0.194) (0.939) (1.061) (0.181) border (d) -2.059 -2.936 -2.985 -2.577 -1.521 -2.038 (-0.668) (-1.108) (-1.356) (-0.657) (-0.632) (-1.144) natlang (d) 2.122 1.406 1.088 -0.381 0.747 1.156 (1.600) (1.229) (1.127) (-0.212) (0.690) (1.428) Iicor -0.143 0.137 0.256 0.455 -0.139 -0.172 (-0.265) (0.296) (0.663) (0.827) (-0.421) (-0.703) _cons -5.389 -10.349 -3.721 -22.142 -11.538 -2.253 (-0.315) (-0.688) (-0.296) (-1.090) (-0.973) (-0.258) r2_p 0.057 0.097 0.105 0.068 0.095 0.105 Chi2p 0.0832 0.0007 0.0007 0.0000 0.0000 0.0000 N 52 56 55 91 100 98

Marginal effects reported. Figures reported in parenthesis are t-ratios. N is the number of observations and r2_p the pseudo r-squared. *** significant at 1%, ** significant at 5%, significant at 10%.

23

b. Poisson Estimates, 1990

Dependent variable: total trade

60 countries 152 countries (as in RT,2002) (our sample)

Figures reported in parenthesis are t-ratios. N is the number of observations and r2_p the pseudo r-squared. All regressions have robust variance estimates. *** significant at 1%, ** significant at 5%, significant at 10%.

HOM REF DIF HOM REF DIF Chpr 0.883 0.544 0.176 0.494 0.521* -0.076 (1.434) (1.493) (0.615) (1.019) (1.798) (-0.297) Lgdp 0.636*** 0.831*** 0.901*** 0.640*** 0.785*** 0.873*** (3.812) (8.608) (14.528) (4.133) (8.682) (13.530) Lcap -0.065 0.136 1.150*** 0.081 0.254 1.182*** (-0.130) (0.511) (4.362) (0.230) (1.148) (5.174) Ldist -0.465 -0.997*** -0.719* -0.547 -1.030*** -0.673** (-0.893) (-3.203) (-1.911) (-1.086) (-3.757) (-2.158) Lareap -0.059 -0.007 0.028 -0.036 0.014 0.033 (-0.375) (-0.060) (0.252) (-0.209) (0.133) (0.331) Landl -2.261*** -0.578* 0.124 -1.412* -0.626** 0.113 (-3.106) (-1.831) (0.436) (-1.925) (-2.280) (0.452) Island 1.470*** 0.566 0.271 1.448*** 0.514 0.291 (2.861) (1.446) (0.584) (2.876) (1.471) (0.762) Border -1.149 -2.303** -1.078 -1.176 -2.187*** -0.985 (-0.876) (-2.552) (-0.901) (-1.011) (-2.796) (-0.941) Natlang 0.617* 0.409* 0.445* 0.708* 0.351 0.399* (1.782) (1.663) (1.858) (1.925) (1.437) (1.825) Iicor 0.185 0.048 -0.114 0.143 0.021 -0.158 (0.701) (0.391) (-0.846) (0.702) (0.188) (-1.430) _cons -0.490 -3.321 -16.112*** -1.492 -3.327 -15.689*** (-0.091) (-1.493) (-7.977) (-0.324) (-1.576) (-8.426) r2_p 0.752 0.910 0.964 0.785 0.923 0.964 N 52.000 56.000 55.000 91.000 100.000 98.000

While drawing conclusions, one needs to keep in mind that the two samples, although

containing almost same countries, are not directly comparable. Rauch and Trindade (2002)

consider trade among the 63 countries while our sample consists of these countries trading

with China only. Hence the number of observations and proportion of zeros are different. The

estimation method is also different in that we use the standard Tobit procedure. Nonetheless,

there is a slight indication that networking coefficient is higher (although insignificant) as

goods become more differentiated. The same pattern emerges for our sample of 152

countries. With the Poisson procedure however, although insignificant, the magnitude of the

Chinese presence dummy variable runs in the reverse order with homogeneous goods having

24

a higher impact in the 60-country sample. Almost all coefficients in the Tobit estimation are

statistically insignificant.

Below we report the pooled Poisson estimates for disaggregate trade flows:

Table 7. Disaggregate Flows, Pooled Poisson Estimates, 1990-2000 Imports Exports Trade HOM REF DIF HOM REF DIF HOM REF DIF chpr 0.094 0.440*** 0.045 -0.072 0.698*** 0.789*** -0.048 0.591*** 0.184* (0.559) (4.607) (0.466) (-0.477) (4.880) (3.201) (-0.321) (5.869) (1.821) lgdp 0.853*** 0.830*** 0.782*** 0.127** 0.509*** 0.938*** 0.297*** 0.619*** 0.838*** (12.921) (24.063) (21.007) (2.381) (12.481) (20.160) (5.916) (18.828) (22.933) lcap -0.019 0.170*** 0.800*** 0.189** 0.700*** 1.291*** 0.215*** 0.564*** 0.900*** (-0.165) (2.842) (13.821) (2.084) (8.228) (12.705) (2.667) (8.839) (17.379) ldist -1.072*** -0.971*** -0.408*** -0.657*** -1.367*** -1.610*** -0.834*** -1.292*** -0.744*** (-6.756) (-14.070) (-7.020) (-4.636) (-9.900) (-18.538) (-6.767) (-11.245) (-12.304) lareap -0.238*** -0.165*** -0.044** 0.367*** 0.261*** -0.093*** 0.185*** 0.106*** -0.043** (-5.969) (-7.678) (-2.237) (4.938) (7.872) (-2.954) (3.310) (4.208) (-2.019) landl -1.089*** -0.699*** -0.449*** -1.378*** -0.517** -0.224* -1.312*** -0.621*** -0.353*** (-6.179) (-8.528) (-6.662) (-4.936) (-2.280) (-1.816) (-5.679) (-4.385) (-5.426) island 0.482** 0.052 0.486*** 0.592*** 0.175 0.047 0.812*** 0.240** 0.308*** (2.569) (0.463) (7.592) (3.486) (1.235) (0.331) (5.308) (2.008) (4.038) border -1.278*** -1.063*** -0.911*** -0.931*** -0.771*** -0.039 -0.958*** -0.774*** -0.835*** (-4.899) (-7.471) (-5.112) (-4.180) (-3.752) (-0.170) (-4.941) (-4.965) (-6.084) natlang 1.272*** 0.493*** 0.437*** 0.886*** 0.467*** -0.009 0.845*** 0.456*** 0.376*** (9.504) (7.420) (7.650) (6.049) (4.321) (-0.087) (6.446) (5.665) (6.231) iicor -0.134** -0.078** -0.057** -0.070 -0.092** 0.150*** -0.083* -0.080** -0.017 (-2.181) (-2.501) (-2.045) (-1.354) (-2.343) (2.971) (-1.868) (-2.248) (-0.613) Time dummies

Yes yes yes yes yes yes Yes yes yes

_cons 4.072** 0.508 -10.336***

0.890 -4.843*** -10.581***

3.601** -1.930* -10.009***

(2.169) (0.527) (-11.417) (0.456) (-3.972) (-12.320) (2.463) (-1.888) (-9.098) r2_p 0.737 0.884 0.904 0.479 0.812 0.908 0.586 0.866 0.925 N 1146 1202 1204 1068 1150 1128 1035 1148 1128

Figures reported in parenthesis are t-ratios. Time dummies are included in all specifications but not reported for brevity. N is the number of observations and r2_p the pseudo r-squared. All regressions have robust variance estimates. *** significant at 1%, ** significant at 5%, significant at 10%. Some striking features stand out in the above sets of results. As in the aggregate case, ethnic

Chinese presence is found to influence exports of host countries more than their imports.

This finding, consistent with Gould (1994) and Girma and Yu (2000), reflects the dominance

of the “transaction costs or information” effect over the “taste or preference” effect of

networks. Given the numerous difficulties such as language barriers, lack of transparent

regulations, prevalence of corruption, experienced by traders while conducting business in

25

China, Chinese migrants’ knowledge of their homeland and their interactions with host

natives help to locate business opportunities in China and lower transaction costs. The

various difficulties encountered by foreign traders in China and vital role played by the

bamboo network in fostering business links is described in Weidenbaum and Hughes (1996).

We find evidence of networking in the export of both reference priced and differentiated

products to China, but no such evidence emerges for the homogeneous category. As noted by

Rauch (1999), reference priced commodities may or may not have specialised traders aware

of prevailing prices around the world while differentiated goods have specific characteristics,

types and quality such that trade cannot be made on prices alone. Hence, the matching of

buyers and sellers is made easier through networks. Ethnic Chinese presence, on average,

influences exports of differentiated products more than reference priced goods. In line with

findings in the literature, our results also confirm the growing importance of networking as

goods become more differentiated.

This pattern, however, is not apparent in our disaggregate import regressions where

we find evidence of networking for reference priced commodities only. This commodity

group consists of some food items such as preserved vegetables and spices as well as ferro

alloys and chemical products like fertilizers. Where no specialised traders exist, networks

help to promote trade. Ethnic Chinese presence is found not to influence imports of

homogeneous and differentiated goods. In our sample, hosts’ imports of homogeneous goods

from China account for only 5% of total value and consist of commodities such as rice, nuts,

tea and tobacco. The absence of a “preference” effect for home country product and a

relatively low proportion of goods imported in this category may be indicative of import

substituting activities or higher consumption of local products by overseas Chinese.

Differentiated products, by contrast, account for about 84% of host countries’ imports. The

absence of network effects on such imports may suggest the prevalence of other determining

factors. China demarcates itself from many other exporting countries in its ability to produce

goods at a relatively cheaper cost. It has a comparative advantage in labour-intensive light

manufacturing which includes goods such as toys, footwear, textiles and clothing, knitted

garments. As noted by Lall and Albaladejo(2004), China is also moving into medium-

technology (process industries and machinery) as well as some high-tech sectors such as

electronics and pharmaceuticals, all of which are differentiated products. One can thus argue

that network effects are largely outweighed by China’s competitive edge in this product

category.

26

Estimates of the trade equations portray the effect when imports and exports are

combined. We find statistically significant effect of ethnic Chinese presence on host

countries’ trade in reference priced and differentiated products as in the exports case. The

magnitude of effect and significance are, however, much lower for differentiated products,

reflecting the low impact on the import side overall.

7. Concluding Remarks

In this paper, we have applied gravity modelling to investigate whether ethnic

Chinese presence in host countries influences their imports from, exports to and total trade

between China. The analysis is undertaken for aggregate trade flows as well as those at

commodity levels grouped into homogeneous, reference priced and differentiated categories

over the period 1990-2000. Ethnic Chinese presence is captured by both dummy variable and

other intensity measures based on absolute, relative and density criteria. Using the Poisson

pseudo-maximum likelihood technique, we find that ethnic Chinese presence affects imports,

exports and total trade at the aggregate level. The disaggregated analysis further reveals that

these effects emerge for reference priced and differentiated goods category on the export

side, with a higher coefficient in the latter group. On the import side, only reference priced

group seems to be influenced. In general, exports coefficients are higher than those in the

import regressions, indicating dominance of the “transactions cost or information” effect.

While our analysis corroborates with some previous findings in the literature, it also differs

in other ways, reflecting the use of a different sample of countries and estimation strategy.

27

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Appendix A. List on Countries Included in Analysis, Classified by Intensity Measures

Absolute Relative % Land Area Density per km2

Habitable Area Density per km2

1 INDONESIA 7310000 SINGAPORE 64.9773 SINGAPORE 3419.5522 SINGAPORE 3524.7692 2 THAILAND 6100000 MALAYSIA 25.9302 MAURITIUS 19.7044 MALAYSIA 48.5674 3 MALAYSIA 5280000 THAILAND 10.4567 MALAYSIA 16.0706 SEYCHELLES 33.3333 4 SINGAPORE 2291100 PANAMA 5.6171 THAILAND 11.9399 MAURITIUS 24.3161 5 PHILIPPINES 2200000 LAO PDR 4.2681 PHILIPPINES 7.3783 THAILAND 17.0871 6 USA 2000000 INDONESIA 3.7925 VIETNAM 5.8374 PHILIPPINES 10.7027 7 VIETNAM 1900000 MAURITIUS 3.5651 SEYCHELLES 4.3478 INDONESIA 9.8854 8 CANADA 910000 PHILIPPINES 3.2166 INDONESIA 4.0352 VIETNAM 8.6344 9 RUSSIA 680000 SURINAME 3.1355 TRINIDAD 3.8986 TRINIDAD 7.1048

10 AUSTRALIA 454000 CANADA 3.1001 NETHERLANDS 2.3613 CAMBODIA 5.5468 11 CAMBODIA 300000 GUYANA 2.7310 JAMAICA 2.3084 PANAMA 4.8364 12 FRANCE 300000 SEYCHELLES 2.6559 PANAMA 2.0153 JAMAICA 3.3784 13 UK 250000 CAMBODIA 2.6390 CAMBODIA 1.6995 LAO PDR 3.2483 14 LAO PDR 200000 VIETNAM 2.6035 COSTA RICA 1.2338 NETHERLANDS 2.6355 15 JAPAN 170000 AUSTRALIA 2.5122 UK 1.0334 COSTA RICA 2.3900 16 PANAMA 150000 COSTA RICA 1.8130 BELGIUM 0.9141 SURINAME 1.5777 17 INDIA 135000 TRINIDAD 1.5882 LAO PDR 0.8666 JAPAN 1.4735 18 GERMANY 111000 FIJI 1.0418 FRANCE 0.5454 UK 1.1633 19 BRAZIL 100000 JAMAICA 1.0081 JAPAN 0.4664 BELGIUM 1.1494 20 ITALY 100000 NEW ZEALAND 0.9528 FIJI 0.4379 FIJI 0.9552 21 NETHERLANDS 80000 USA 0.7511 HUNGARY 0.4343 KOREA 0.8481 22 COSTA RICA 63000 BELIZE 0.6928 ITALY 0.3400 FRANCE 0.7488 23 PERU 60000 FRANCE 0.5186 SWITZERLAND 0.3322 HUNGARY 0.5437 24 TURKEY 60000 NETHERLANDS 0.5175 GERMANY 0.3181 ITALY 0.4879 25 VENEZUELA 50000 RUSSIA 0.4590 DOMINIC. REP 0.3100 SWITZERLAND 0.4706 26 SAUDI ARABIA 45000 UK 0.4292 KOREA 0.3039 GERMANY 0.4628 27 HUNGARY 40000 HUNGARY 0.3873 AUSTRIA 0.2426 AUSTRIA 0.4507 28 MAURITIUS 40000 BELGIUM 0.2960 USA 0.2184 GUYANA 0.4366 29 NEW ZEALAND 35000 PERU 0.2517 NEPAL 0.1423 DOMINIC. REP 0.4333 30 SPAIN 35000 AUSTRIA 0.2515 DENMARK 0.1414 USA 0.3250 31 ARGENTINA 30000 SAUDI ARABIA 0.2409 NEW ZEALAND 0.1306 BELIZE 0.2389 32 BELGIUM 30000 VENEZUELA 0.2268 GUATEMALA 0.1291 GUATEMALA 0.2200 33 KOREA 30000 SWEDEN 0.2265 CZECH REP 0.1165 NEPAL 0.2047 34 MADAGASCAR 30000 MADAGASCAR 0.2151 PORTUGAL 0.1093 NEW ZEALAND 0.1859 35 MEXICO 30000 P. NEW GUINEA 0.2133 GUYANA 0.1016 CZECH REP 0.1767 36 SOUTH AFRICA 30000 PARAGUAY 0.2071 CANADA 0.1001 PORTUGAL 0.1721 37 JAMAICA 25000 DOMINIC. REP 0.1955 SURINAME 0.0833 DENMARK 0.1588 38 NEPAL 20348 SWITZERLAND 0.1887 TURKEY 0.0780 CANADA 0.1519 39 AUSTRIA 20000 MONGOLIA 0.1758 CYPRUS 0.0779 SWEDEN 0.1469 40 ECUADOR 20000 ECUADOR 0.1755 IRELAND 0.0726 ECUADOR 0.1346 41 GUYANA 20000 ITALY 0.1748 EL SALVADOR 0.0724 VENEZUELA 0.1329 42 SWEDEN 20000 BOLIVIA 0.1604 ECUADOR 0.0722 PERU 0.1029 43 TRINIDAD 20000 GUATEMALA 0.1404 SPAIN 0.0701 SPAIN 0.1002 44 DOMINIC. REP 15000 IRELAND 0.1388 BAHRAIN 0.0686 CYPRUS 0.0951 45 ROMANIA 14200 GERMANY 0.1360 BELIZE 0.0658 TURKEY 0.0894 46 GUATEMALA 14000 JAPAN 0.1355 ROMANIA 0.0619 EL SALVADOR 0.0871 47 SWITZERLAND 13286 DENMARK 0.1148 AUSTRALIA 0.0591 ROMANIA 0.0855 48 SURINAME 13000 PORTUGAL 0.0997 VENEZUELA 0.0567 SRI LANKA 0.0824 49 BOLIVIA 12000 CYPRUS 0.0985 SRI LANKA 0.0542 RUSSIA 0.0820 50 P. N. GUINEA 10000 TURKEY 0.0972 MADAGASCAR 0.0516 IRELAND 0.0786 51 PARAGUAY 10000 NEPAL 0.0938 SWEDEN 0.0487 AUSTRALIA 0.0754 52 PORTUGAL 10000 SPAIN 0.0893 MALTA 0.0469 PAPUA NG 0.0692 53 CZECH REP 9000 CZECH REP 0.0871 PERU 0.0469 BAHRAIN 0.0686

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54 FIJI 8000 ARGENTINA 0.0861 INDIA 0.0454 MADAGASCAR 0.0670 55 COLOMBIA 7000 SOUTH AFRICA 0.0767 BULGARIA 0.0452 BULGARIA 0.0648 56 UKRAINE 7000 BAHAMAS 0.0717 RUSSIA 0.0415 INDIA 0.0583 57 DENMARK 6000 NICARAGUA 0.0670 PARAGUAY 0.0252 PARAGUAY 0.0514 58 BULGARIA 5000 KOREA 0.0665 NICARAGUA 0.0247 NICARAGUA 0.0492 59 CHILE 5000 ROMANIA 0.0626 SOUTH AFRICA 0.0247 MALTA 0.0469 60 IRELAND 5000 BRAZIL 0.0620 PAPUA NG 0.0221 BAHAMAS 0.0412 61 MONGOLIA 4000 BULGARIA 0.0595 SAUDI ARABIA 0.0209 HONDURAS 0.0314 62 PAKISTAN 3600 ICELAND 0.0373 BAHAMAS 0.0200 BRAZIL 0.0295 63 SRI LANKA 3500 CHILE 0.0347 MEXICO 0.0157 SOUTH AFRICA 0.0267 64 BELARUS 3000 MEXICO 0.0329 BELARUS 0.0145 BOLIVIA 0.0255 65 NICARAGUA 3000 BELARUS 0.0294 HONDURAS 0.0134 MEXICO 0.0243 66 ALGERIA 2000 HONDURAS 0.0267 UKRAINE 0.0121 BELARUS 0.0228 67 NIGERIA 2000 EL SALVADOR 0.0265 BRAZIL 0.0118 SAUDI ARABIA 0.0212 68 SEYCHELLES 2000 NORWAY 0.0229 KUWAIT 0.0112 COLUMBIA 0.0164 69 BELIZE 1500 FINLAND 0.0196 BOLIVIA 0.0111 UKRAINE 0.0144 70 EL SALVADOR 1500 SRI LANKA 0.0193 ARGENTINA 0.0110 ARGENTINA 0.0125 71 HONDURAS 1500 COLUMBIA 0.0182 ISRAEL 0.0104 FINLAND 0.0123 72 FINLAND 1000 INDIA 0.0145 COLUMBIA 0.0067 KUWAIT 0.0112 73 NORWAY 1000 UKRAINE 0.0136 CHILE 0.0067 ISRAEL 0.0112 74 CYPRUS 720 KUWAIT 0.0111 BANGLADESH 0.0054 CHILE 0.0084 75 BANGLADESH 700 LIBYA 0.0104 PAKISTAN 0.0047 BANGLADESH 0.0058 76 MOZAMBIQUE 700 URUGUAY 0.0093 FINLAND 0.0033 PAKISTAN 0.0048 77 TANZANIA 600 GABON 0.0089 NORWAY 0.0033 NORWAY 0.0047 78 ANGOLA 500 BAHRAIN 0.0082 MONGOLIA 0.0026 GHANA 0.0031 79 GHANA 500 ALGERIA 0.0071 JORDAN 0.0023 MONGOLIA 0.0027 80 LIBYA 500 UAE 0.0062 GHANA 0.0022 NIGERIA 0.0026 81 POLAND 420 LIBERIA 0.0056 NIGERIA 0.0022 GABON 0.0026 82 URUGUAY 300 JORDAN 0.0048 UAE 0.0018 GREECE 0.0024 83 ZIMBABWE 300 MOZAMBIQUE 0.0044 GREECE 0.0018 JORDAN 0.0023 84 GREECE 229 ANGOLA 0.0041 URUGUAY 0.0017 LIBERIA 0.0020 85 ISRAEL 225 ISRAEL 0.0041 POLAND 0.0014 POLAND 0.0020 86 BAHAMAS 200 MALTA 0.0040 LIBERIA 0.0012 UAE 0.0019 87 CONGO DEM 200 OMAN 0.0037 LEBANON 0.0012 URUGUAY 0.0018 88 IRAN 200 PAKISTAN 0.0029 ICELAND 0.0010 ZIMBABWE 0.0017 89 IVORY COAST 200 GHANA 0.0028 MOZAMBIQUE 0.0009 LEBANON 0.0013 90 JORDAN 200 ZIMBABWE 0.0025 ALGERIA 0.0008 TANZANIA 0.0012 91 KUWAIT 200 GREECE 0.0022 ZIMBABWE 0.0008 MOZAMBIQUE 0.0012 92 KENYA 190 TANZANIA 0.0019 TANZANIA 0.0007 ICELAND 0.0010 93 UAE 150 NIGERIA 0.0019 IVORY COAST 0.0006 IVORY COAST 0.0009 94 ZAMBIA 150 ZAMBIA 0.0016 TOGO 0.0006 MALAWI 0.0009 95 LIBERIA 120 IVORY COAST 0.0014 MALAWI 0.0005 ALGERIA 0.0008 96 EGYPT 110 POLAND 0.0011 UGANDA 0.0005 ANGOLA 0.0008 97 ETHIOPIA 100 KENYA 0.0007 ANGOLA 0.0004 UGANDA 0.0007 98 GABON 100 CONGO REP 0.0007 GABON 0.0004 TOGO 0.0006 99 ICELAND 100 TOGO 0.0007 KENYA 0.0003 ZAMBIA 0.0005

100 IRAQ 100 BANGLADESH 0.0006 LIBYA 0.0003 KENYA 0.0004 101 UGANDA 100 MALAWI 0.0005 OMAN 0.0003 LIBYA 0.0003 102 OMAN 80 UGANDA 0.0005 IRAQ 0.0002 OMAN 0.0003 103 CAMEROON 50 IRAQ 0.0005 ZAMBIA 0.0002 IRAQ 0.0002 104 MALAWI 50 CONGO DEM 0.0004 IRAN 0.0001 CONGO DEM 0.0002 105 MOROCCO 50 LEBANON 0.0004 MOROCCO 0.0001 CAMEROON 0.0002 106 BAHRAIN 48 CAMEROON 0.0004 EGYPT 0.0001 CONGO REP 0.0002 107 SUDAN 45 IRAN 0.0003 CAMEROON 0.0001 IRAN 0.0001 108 TOGO 30 NIGER 0.0002 ETHIOPIA 0.0001 MOROCCO 0.0001 109 CONGO REP 20 MOROCCO 0.0002 CONGO DEM 0.0001 ETHIOPIA 0.0001 110 NIGER 20 EGYPT 0.0002 CONGO REP 0.0001 EGYPT 0.0001 111 MALTA 15 ETHIOPIA 0.0002 SUDAN 0.0000 SUDAN 0.0000 112 LEBANON 12 SUDAN 0.0002 NIGER 0.0000 NIGER 0.0000

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Countries with no Chinese presence ALBANIA DJIBOUTI LATVIA SLOVENIA ARMENIA EQUATORIAL GUINEA LITHUANIA SOMALIA AZERBAIJAN ESTONIA MACEDONIA ST.KITT BENIN GAMBIA MALI SYRIA BERMUDA GEORGIA MAURITANIA TAJIKISTAN BURKINA GUINEA MOLDOVA TUNISIA BURUNDI GUINEA-BISSAU QATAR TURKMENISTAN CENTRAL KAZAKHSTAN RWANDA UZBEKISTAN CHAD KIRIBATI SENEGAL YEMEN CROATIA KYRGYZ REP SLOVAK REP YUGOSLAVIA No. of countries=40

34