The trade facilitation impact of the Chinese diaspora

Martínez‐Zarzoso, Inmaculada; Rudolf, Robert Article — Published Version The trade facilitation impact of the Chinese diaspora The World Economy Provided in Cooperation with: John Wiley & Sons Suggested Citation: Martínez‐Zarzoso, Inmaculada; Rudolf, Robert (2020) : The trade facilitation impact of the Chinese diaspora, The World Economy, ISSN 1467-9701, Wiley, Hoboken, NJ, Vol. 43, Iss. 9, pp. 2411-2436, https://doi.org/10.1111/twec.12950 This Version is available at: https://hdl.handle.net/10419/230077 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ World Econ. 2020;43:2411–2436. wileyonlinelibrary.com/journal/twec | 2411 Received: 26 April 2019 | Revised: 13 November 2019 | Accepted: 25 February 2020 DOI: 10.1111/twec.12950 ORIGINAL ARTICLE The trade facilitation impact of the Chinese diaspora InmaculadaMartínez-Zarzoso1,2 | RobertRudolf3 1Department of Economics, University of Goettingen, Goettingen, Germany 2University Jaume I, Castellón de la Plana, Spain 3Division of International Studies, Korea University, Seoul, Korea Funding information Spanish Ministry of Economy and Competitiveness, Grant/Award Number: ECO2017-83255-C3-3-P and UJI-B2017-33; Korea University Research Grant, Grant/Award Number: K1717891 KEYWORDS Chinese networks, correlated random-effects Poisson pseudo-maximum likelihood, gravity model, panel data, sectoral trade 1 | INTRODUCTION With approximately US$2.3 trillion worth in exports of goods and services in 2015, the People's Republic of China (PRC) is by far the number one exporter in the world. In addition, China's merchandise imports stood at US$1.7 trillion making it the world's second largest importer closely following the United States. Understanding the determinants of Chinese bilateral trade flows thus is of vital importance given the prominent role that China plays in world trade today (Bussière & Schnatz, 2009; Caporale, Sova, & Sova, 2015; Johnston, Morgan, & Wang, 2015; Yang & Martínez-Zarzoso, 2014). At the same time, ethnic Chinese play an enormous role in global migration. Being the world's most populous nation and having witnessed large outmigration streams, in both past and present, gives mainland Chinese state and business actors access to a unique coethnic network spread around the world. The overseas Chinese with an estimated size of approximately 65 million are considered one of the largest diasporas in the world (Poston & Wong, 2016). Rising levels of migration around the world today have sparked growing public and academic interest in the social and economic impacts of migrants. Diasporas often function as an important economic link between their source and their host countries. At least three channels have been suggested through which the presence of migrants can promote trade between source and host countries (Felbermayr, Grossmann, & Kohler, 2015). First, migrant networks alleviate incomplete information. They can help overcoming informal trade barriers related to language, culture and institutions. Coethnic networks often share valuable market information and thus help in identifying business This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. The World Economy published by John Wiley & Sons Ltd 2412 | MARTÍNEZ-ZARZOSO ANd RUdOLF opportunities and creating business partnerships. Second, migrant networks reduce frictions related to asymmetric information. For instance, coethnicity can raise contract enforceability since members of the same ethnic network are less likely to cheat each other. These two mechanisms constitute the trade-cost channel. Third, via the preference channel, migrants boost imports to the host country if they derive higher utility from the consumption of goods made in the country of their ethnic origin (Aleksynska & Peri, 2014; Felbermayr & Toubal, 2012; Gould, 1994; Greif, 1993; Head & Ries, 1998; Munshi, 2003; Parsons & Vézina, 2018; Rauch, 2001). Past studies that have focused on Chinese coethnic networks have established a robust effect of overseas Chinese on the facilitation of international trade (Anderson & van Wincoop, 2004; Felbermayr, Jung, & Toubal, 2010; Rauch, 2001; Rauch & Trindade, 2002) and foreign direct investment (Gao, Liu, & Zou, 2013; Gao, 2003; Tong, 2005).1 Rauch (2001) and Rauch and Trindade (R&T) (2002) were the first to study the impact of overseas Chinese on bilateral trade flows. Using a gravity model for a sample of 60 countries, they were able to show that the product of the ethnic Chinese population shares of each two countries was positively related to these countries' bilateral trade flows in 1980 and 1990. R&T further showed that effects were stronger for differentiated than for homogeneous products, providing evidence for the hypothesis that part of the effect runs through information sharing.2 The authors attribute their findings to Chinese coethnicity helping to lower trade costs by overcoming information barriers on the one hand and raising contract enforceability on the other. As noted by Combes, Lafourcade, and Mayer (2005), ethnic networks do affect bilateral trade through yet another channel, preferences for home country goods. Since R&T's landmark study, a growing number of economists have been engaged in the study of coethnic networks in international trade. Anderson and van Wincoop (2004) estimate the ad-valorem tariff equivalent of informational costs implied by R&T's findings to be approximately 6%, a figure higher than average applied tariff rates around the world today. Felbermayr et al. (2010) re-estimate the R&T model considering multilateral resistance terms (MRT) and confirm their main findings. They also distinguish between the direct effects—involving China as a trading partner—and the indirect effects of Chinese migrants. The former are found to be sizeable, whereas the latter almost vanish in some cases, when models properly control for MRT in a cross-sectional setting. The authors further show similar trade creation effects for other ethnic networks and find particularly strong effects for Polish, Turkish, Mexican and Pakistani networks. Trade creation effects of overseas Chinese are further in line with the sociological literature, which sees diasporas “as middlemen who are active as cosmopolitan catalysts for economic transactions between global cities […] that form the backbone of the world economy” (Felbermayr et al., 2010: 41). Sociologists have further pointed out that Chinese business networks are often built on informal personal relations based on regional connections and kinship, sometimes referred to by the popular Chinese term Guanxi (Folk & Jomo, 2013; Hamilton, 1996). A recent study by Priebe and Rudolf (2015) extends the discussion of economic impacts of the Chinese diaspora to aggregate economic growth in host countries. Introducing a new, enhanced dataset on the population share of overseas Chinese covering 147 host countries in 1970, the authors find that a country's initial relative endowment with overseas Chinese is positively related to subsequent economic growth of host countries. Besides enhanced investment and general TFP effects, the authors identify greater trade openness as a major growth transmission channel. The present paper's main objective is to quantify the influence of the Chinese diaspora in explaining Chinese bilateral trade flows. Using an enhanced dataset on overseas Chinese, this study estimates 1Such effects have been observed not only between the host country and the PRC, but also between host country pairs. 2R&T define homogeneous goods as goods for which reference prices are available. In contrast, differentiated goods are defined as goods without reference prices. | 2413 MARTÍNEZ-ZARZOSO ANd RUdOLF one-side gravity models of Chinese exports and imports, respectively. Analyses are carried out also at the sectoral level to identify heterogeneity of diaspora effects across main product groups. The present study contributes to the existing literature in the following ways: first, by using a new dataset on ethnic Chinese compared to earlier studies, we are able to expand the number of countries included in the analysis from 63 (R&T and related studies) to 175. This represents a big increase in the trade flows covered. Second, to the best of our knowledge, this is the first study that focuses on the question how coethnic networks affect bilateral trade flows of a major migrant sending country. Earlier single-country studies on the nexus of migration and trade have all used the perspective of the migrant receiving country (Gould (1994) for the United States; Peri and Requena-Silvente (2010) for Spain; Bratti, Benedictis, and Santoni (2014) for Italy, among others). Third, in contrast to earlier studies that used total trade, we estimate trade facilitation effects separately for exports and imports, accounting for the facts that the preference channel should only affect Chinese exports and that coethnic business networks can be of different importance for the sending country's exporters versus importers. We further study the heterogeneity of effects by trading sector. Fourth, we analyse the role of informal coethnic networks as substitutes for formal trade agreements. Lastly, regarding our methodological approach, we control for time-invariant characteristics of countries and global time trends that can influence migration and trade by applying panel data techniques. In contrast to R&T and similar to Felbermayr et al. (2010), we depart from the traditional gravity model (GM) and use a Poisson pseudo-maximum likelihood (PPML) correlated random-effects estimator.3 Our estimation technique differs from Felbermayr et al. (2010) in that, whereas they use cross-sectional estimations (for single years: 1980, 1990, 2000), we exploit the longitudinal nature of bilateral trade data and we allow for the fact that China does not export to (import from) all countries in our dataset. In particular, we use a (PPML) correlated random-effects estimator with regional fixed effects that allows us to control for the unobserved heterogeneity that is region-specific and time-invariant. We also use a system-GMM estimator (Blundell & Bond, 1998) that addresses the endogeneity of the Chinese network variable. We further use panel fixed-effects estimations as a robustness check. Our main results indicate that Chinese coethnic networks indeed positively affect China's bilateral trade flows. We find substantial trade creation effects resulting from the presence of ethnic Chinese in the trade partner population. Diaspora impacts on Chinese imports are higher than those found for exports. Coethnic networks play a larger role as long as the partner country does not have a regional trade agreement with the PRC. Sectoral analyses suggest that, among Chinese exports, diaspora effects are strongest for the foodsector, as well as machinery and transport equipment. In regard to imports, coethnic networks matter mostly for raw materials, machinery and transport equipment, and chemicals. The remainder of this paper is organised as follows. Section 2 describes the data and the empirical strategy used in this study. Section 3 presents main results, extensions to main results and sector-specific analyses, while section 4 reports robustness checks. Conclusions are outlined in Section 5. 2 | DATA AND ESTIMATION METHOD 2.1 | Overseas Chinese data Following earlier studies that use R&T-style ethnographic data, the Chinese diaspora in this study refers to the group of people that were born in or claim ancestry to China but who reside outside the People's Republic of China, Taiwan, Hong Kong and Macau (Poston, Mao, & Mei-Yu, 1994; Poston 3Law et al. (2013) also used a correlated random effects model to estimate the effects of New Zealand's diaspora on its exports and imports but allowed for zero trade by adopting a Heckman (1979) selection model instead of the PPML estimator suggested by Santos Silva and Tenreyro (2006). 2414 | MARTÍNEZ-ZARZOSO ANd RUdOLF & Wong, 2016; Priebe & Rudolf, 2015; Rauch & Trindade, 2002). These definitions therefore involve Chinese born in mainland China but living abroad (first-generation or foreign-born migrants) as well as those Chinese that were born outside mainland China and who continue to live outside of China, in certain countries already for multiple generations. Changes in the number of overseas Chinese over time can therefore be attributed to both, fertility levels among Chinese in the host country and outmigration from China to the host country or from one host country to another. The present study draws on country-level data on the number of overseas Chinese for 1970 and 1990 from the World Christian Encyclopedia (WCE; Barrett, Kurian, & Johnson, 1982, 2001), which provides one of the most comprehensive ethnographic datasets to date and has recently been used by Priebe and Rudolf (2015) for a related analysis. WCE is a highly detailed census of churches and religions around the world which, as a by-product, offers detailed ethnographic data for most countries. WCE's ethnographic data rely on countries' population census data and additional secondary data sources. World Christian Encyclopedia data have been frequently used in ethnic-related studies. Montalvo and Reynal-Querol (2005a: 298, 2005b) describe it as “one of the most detailed data for ethnolinguistic diversity.” In regard to measuring the size and distribution of the Chinese diaspora during historic periods, WCE's main advantage is that it has data available for many more countries compared to OCAC data used in earlier studies on the trade effects of the Chinese diaspora. For example, the 1981 World Christian Encyclopedia provides data on overseas Chinese for 183 countries. In contrast, OCAC data from its Overseas Chinese Economy Yearbook 1983/84 provide records for only 94 countries (OCAC, 1983). In particular, OCAC reports do not indicate whether countries that are not listed have no overseas Chinese population or are missing due to other reasons (political instability, civil war, lack of census data, etc.). The inability to distinguish missing values from zeros is the main shortcoming of OCAC data. The WCE dataset that we use in this study provides information on the number of overseas Chinese for a total of 165 countries in 1970 and 186 countries in 1990. In the following analysis, our main variable of interest is the proportion of overseas Chinese in the total population of a host country— hereafter referred to as sharechinese. Table1 shows the distribution of overseas Chinese across world regions and over time using a combination of OCAC and WCE data and our own imputations. The total size of the Chinese diaspora has been constantly growing over time and across all world regions. It should be noted that our definition of migration differs from many recent trade-migration studies that use data on first-generation migrants only (the so-called “foreign-born concept,” see, e.g., Bratti TABLE 1 Distribution of overseas Chinese across world regions and over time Variable Year Region Asia Americas Europe Africa Oceania No. of overseas Chinese (in thousands) Average share of OC across countries (%) No. of countries 1970 15,360.2 3.804 36 1,013.6 0.355 36 191.0 0.017 33 74.3 0.076 46 54.6 0.347 8 No. of overseas Chinese (in thousands) Average share of OC across countries (%) No. of countries 1990 24,296.6 2.648 44 3,118.2 0.475 39 597.0 0.046 39 118.0 0.066 49 258.3 0.678 10 No. of overseas Chinese (in thousands) Average share of OC across countries (%) No. of countries 2010 28,252.1 2.433 44 7,249.3 0.692 39 1,765.5 0.137 39 220.9 0.080 49 905.4 1.180 11 Notes: Author's calculations based on data from OCAC, WCE and authors imputations. In addition, data on the Hong Kong, Macau and Taiwan were not included in the calculations. | 2415 MARTÍNEZ-ZARZOSO ANd RUdOLF et al., 2014; Felbermayr & Toubal, 2012; Özden, Parsons, Schiff, & Walmsley, 2011; Peri & RequenaSilvente, 2010). We hypothesise that trade effects of migration go well beyond the first generation of migrants, particularly with regard to the trade-cost channel. Anecdotal evidence from the Chinese diaspora suggests that migrants active in trading often take a generation to build a functioning family business in their new country of residence. Thus, this wider definition of migration adds a new perspective on the trade-migration nexus to the literature. 2.2 | Trade and gravity data Bilateral Chinese exports and imports from 1973 to 2013 are taken from UN-COMTRADE.4 Data on countries' GDP and population are drawn from the World Development Indicators Database (World Bank, 2016). Distances between capitals, as well as trade impeding or promoting factors such as common border or being landlocked, are taken from the CEPII database (Mayer & Zignago, 2005). RTA and WTO dummy variables are—an actualised version—from De Sousa (2012). Table2 presents summary statistics of the above variables.5 2.3 | Empirical strategy Over the past two decades, the gravity model of trade has evolved into a sophisticated tool to analyse the broad determinants of bilateral trade flows, among them a number of policy factors such as regional trade agreements (RTA), trade facilitation factors, tariffs, regulations and others (Feenstra, 2016). The gravity model has been broadly used to investigate the role played by specific policy or geographical variables in explaining bilateral trade flows. Consistent with this approach, and in order to investigate the effect of the presence of Chinese networks on Chinese trade flows, we include the variable sharechinese (in the trade partner country) as a “trade facilitator.” According to the underlying theory that has been reformulated and extended byAnderson and van Wincoop (2003), our model assumes constant elasticity of substitution and product differentiation by place of origin. In addition, prices differ among locations due to symmetric bilateral trade costs.6 The reduced form of the model is specified as: where Xijt is bilateral exports from country i to country j in year t, and Yit, Yjt and Yt W are the GDPs in the exporting country, the importing country and the world in year t, respectively. tijt denotes trade costs between the exporter and the importer in year t, and Pit and Pjt are price indices that account for the so-called multilateral resistance factors and are a function of the trade cost of a country with respect to all countries in the world. The empirical specification in log-linear form is given by: 4Data were downloaded from https://comtr ade.un.org/data/ on 13 April, 2017. 5Following earlier studies, Hong Kong, Taiwan and Macao have been excluded from the analysis. The data that support the findings of this study are available from the corresponding author upon reasonable request. 6The assumption of symmetric trade costs is not required in the empirical application. (1) X ijt =YitYjt YW t (tijt PitPjt ) 1 −𝜎 , 2416 | MARTÍNEZ-ZARZOSO ANd RUdOLF where ln denotes natural logarithms. The estimation of Equation(2) is not straightforward due to the presence of trade costs and multilateral resistance terms. The trade-cost function is assumed to be a linear function of a number of trade (2) ln X ijt =ln Y it +ln Y jt −ln Y W t +(1−𝜎)ln t ijt −(1−𝜎)ln P it −(1−𝜎)ln P jt, TABLE 2 Summary statistics Variable Obs Mean SD Min Max China's exports Ln exports 4,329 18.366 3.016 7.194 26.634 Ln Y_imp 4,329 23.456 2.461 16.839 30.451 Ln pop_imp 4,329 15.576 1.975 10.941 20.948 Ln dist 4,329 9.066 0.502 6.862 9.868 Border 4,329 0.055 0.228 0 1 Ln area_imp 4,329 11.593 2.446 3.912 16.654 Landlocked_imp 4,329 0.161 0.367 0 1 Sharechinese 1970 (ethnicity) 4,329 0.013 0.072 0 0.742 Sharechinese 1990 (ethnicity) 4,329 0.012 0.064 0 0.677 Sharechinese (foreignborn only) 4,275 0.001 0.007 0 0.092 WTO 4,329 0.383 0.486 0 1 RTA 4,329 0.022 0.147 0 1 China's imports Ln imports 3,597 17.306 3.890 0.693 25.810 Ln Y_exp 3,597 24.141 2.196 17.791 30.451 Ln pop_exp 3,597 16.045 1.784 10.878 20.948 Ln dist 3,597 9.040 0.502 6.862 9.868 Border 3,597 0.046 0.210 0 1 Ln area_exp 3,597 12.020 2.212 3.912 16.654 Landlocked_exp 3,597 0.133 0.340 0 1 Sharechinese 1970 (ethnicity) 3,597 0.017 0.087 0 0.742 Sharechinese 1990 (ethnicity) 3,597 0.014 0.066 0 0.677 Sharechinese (foreignborn only) 3,597 0.001 0.009 0 0.127 WTO 3,597 0.399 0.490 0 1 RTA 3,597 0.023 0.148 0 1 Notes: ln denotes natural logarithm; exports are in thousands of US$. Y_imp and Y_exp denote gross domestic product of exporter and importer country, respectively. Pop denotes population and area denotes the geographical area of the countries. Dist is the distance between capital cities of origin and destination countries. Border (landlocked) is a dummy variable that takes the value of 1 when the trading countries share a border (do not have an exit to the sea), and zero otherwise.WTO (RTA) is a dummy variable that takes the value of 1 when the trading countries belong to the World Trade Organization (to the same regional trade agreement), and zero otherwise. | 2417 MARTÍNEZ-ZARZOSO ANd RUdOLF barriers, namely, the time-invariant determinants of trade flows, including distance, area, common border, landlocked dummies and the time-varying RTA and WTO variables. In the recent gravity literature, multilateral resistance terms are modelled as time-varying or time-invariant country-specific dummies when a full-gravity is estimated. However, for a single exporter, some specificity applies. In our empirical application, we focus exclusively on exports from (imports to) China over time for all its trading partners for which the relevant data are available. We therefore specify a one-side gravity model to explain bilateral exports and imports, in which trade partners are indexed by j and years by t. Since we have a single country (China) as exporter (importer) to (from) any other country in the world, and the target variable is time-invariant and country-specific, we opted by including regional time-invariant dummies in the main specification a way to proxy for multilateral resistance. Regionspecific fixed effects are used in order to mitigate potential biases due to time-invariant unobserved heterogeneity due to cultural factors, for example. Asian countries may have communalities that affect bilateral trade between Asian countries in a different way as trade between countries in different regions. We explain below the panel data techniques used to control also for permanent country heterogeneity. We account for global trends in trade by adding common time dummies. After dropping the i subscript, substitution of the trade-cost function into Equation(2) and addition of regional dummies, time dummies and an idiosyncratic error term suggests estimating: where Distj denotes geographical distance from China to country j. Landlockedj takes the value of one when country j is landlocked, and zero otherwise. Borderj takes the value of one when country j shares a border with China, zero otherwise. Areaj is the geographical area of country j in squared km, RTAjt takes the value of one when China and the country j are members of the same regional trade agreement in year t, zero otherwise, and WTO takes the value of one when both countries are members of the WTO. Most important for the sake of this study, sharechinesej denotes the population share of ethnic Chinese in country j in 1970 or in 1990. Moreover, 𝛾t denotes a set of year dummies that proxy for time-variant common factors (globalisation) that affect Chinese trade flows to all its partners. Finally, 𝛿r denotes regional fixed effects and ujt is the error term that is assumed to be well-behaved. It should be reasonable to assume that the variable sharechinesej70 is exogenous with regard to Chinese bilateral exports and imports from 1973 to 2013. As noted in Priebe and Rudolf (2015), most Chinese outmigration took place before 1952 and then again after the Open Door policies by Deng Xiaoping in 1978. The PRC's economic isolation in combination with strict migration controls between 1952 and the end of the 1970s is reason enough to believe that overseas Chinese in 1970 were not able to foresee Chinese bilateral trade during the four decades post-1973. The use of sharechinesej90, on the other hand, is more controversial. While potentially it allows us to estimate the relationship between overseas Chinese and Chinese bilateral trade with more up-todate population figures and for a larger sample of countries, it could clearly violate the exogeneity assumption of our estimators. To remedy this problem, we use only the trade period 1991–2013 in our estimations with this variable, in combination with other approaches to establish robustness (system-GMM). As regards the techniques used to estimate the gravity model, the main novelties are reviewed by Head and Mayer (2014). The authors discuss the main trade theories supporting the model and estimation challenges involved for correct identification of trade effects of specific economic and political factors. In our choice of estimation techniques, we have to consider that we are estimating a one-side gravity and that the variables in the model cannot capture all influences on China's trade. The use of (3) ln X jt =𝛼 0 +𝛼 1 ln Y jt +𝛼 2 ln Dist j +𝛼 3 Landlocked j +𝛼 4 ln Area j +𝛼 5 Borderj +𝛼 6 sharechinese j70(90) +𝛼 7 RTA jt +𝛼 8 WTO jt +𝛿 r +𝛾 t +u jt , 2418 | MARTÍNEZ-ZARZOSO ANd RUdOLF panel data techniques enables us to control for permanent unobserved country-specific heterogeneity. In particular, we specify the gravity model as: where αj is an unobserved country-specific effect that represents the permanent cross-country heterogeneity. The model could be estimated using a random-effects approach if αj is assumed to be uncorrelated with the regressors. Since this assumption is difficult to maintain in practice,7 we adopt a correlated random-effects approach and assume that the country-specific effects are a function of the time averages of the time-variant variables: Substituting Equation(5) into Equation(4) we obtain: Equation(6) can be estimated using random effects. Since the above approach does not allow for zero trade and given that for the regressions with sectoral data an important percentage of the observations are zeros or missing, we also estimate a PPML correlated random-effects model. According to it, the dependent variable is in levels. It has also the added advantage that the model is robust to heteroscedasticity in the error term. Finally, in order to account for the potential endogeneity of the target variable (sharechinese) and to allow for dynamics in the dependent variable, we estimated a system-GMM model in which the first lag of the dependent variable is added as regressor and internal instruments (further lags of the dependent variable and lags of the other time-variant variables) are considered as instruments of the potentially endogenous variables, that is the lag-dependent variable and the sharechinese variable. 3 | RESULTS 3.1 | Chinese diaspora and China's bilateral trade The main results for Chinese bilateral exports and imports are presented in Tables3 and 4, respectively. In both tables (columns (1) to (4)), we use sharechinese in trade partner countries in 1970 (sharechinese70) for the trade period 1973–2013. In TablesA1 and A2 in the Appendix, we employ sharechinese measured in 1990 (sharechinese90) for the period 1991–2013 instead. The OLS estimates applied to the traditional specification of the gravity model with time fixed effects and regional fixed effects (OLS_TFE) are shown in column (1), country random effects are added to this specification (RE_TFE) in column (2), and the correlated random-effects estimates, resulting from adding to the RE_TFE model the averages of the time-variant variables as regressors (CRE_TFE) are shown in column (3). Finally, the results from the correlated (4) ln X jt =𝛼 j +𝛼 1 ln Y jt +𝛼 2 ln Dist j +𝛼 3 Landlocked j +𝛼 4 ln Area j +𝛼 5 Border j +𝛼 6 sharechinese j70(90) +𝛼 7 RTA jt +𝛼 8 WTO jt +𝛿 r +𝛾 t +u jt, 7It is also rejected by the regression-based Hausman test that consists on testing for the joint significance of the coefficients of the time averages of the time variant variables in Equation (6). (5) 𝛼j =𝛽0+𝛽1ln Y j .+𝛽2RTA j .+𝛽3WTO j .+∈ j. (6) ln X jt =𝛽 0 +𝛼 1 ln Y jt +𝛼 2 ln Dist j +𝛼 3 Landlocked j +𝛼 4 ln Area j +𝛼 5 Border j +𝛼 6 sharechinese j70(90 ) +𝛼7RTA jt +𝛼8WTO jt +𝛽1ln Y j .+𝛽2RTA j .+𝛽3WTO j .+𝛿 r +𝛾 t +∈ j +u jt . | 2425 MARTÍNEZ-ZARZOSO ANd RUdOLF primary commodities. In addition, raw materials are usually imported from developing countries, in which institutions are often weak and thus there might be a stronger role of informal business links as trade facilitators. 4 | ROBUSTNESS As a first robustness check, we use OCAC data on sharechinese to compare our main results obtained using WCE data. As mentioned earlier, OCAC data provide information on the number of overseas Chinese for significantly fewer countries as compared to WCE data, particularly for periods before 2000, but has been the standard source for data on overseas Chinese used in past studies on the Chinese diaspora. In practice, we estimate the same gravity models as presented in Tables3 and 4 for exports and imports and replace sharechinese1970 (WCE) with OCAC data on sharechinese for single years. Results of using the OCAC sharechinese variable for 1963 are presented in the first row of the first and second part of TableA6 in the Appendix. The population share of overseas Chinese in trade partner countries has a strong effect on both Chinese bilateral exports and imports. Effects found are higher than those found earlier in Tables3 and 4, while at the same time the number of countries covered reduced from 155 (150) using WCE data to 82 (80) using OCAC data in export (import) regressions. TableA6 shows further results from regressions using later OCAC years, and it can be observed that coefficients for sharechinese decrease systematically when using more recent years. Therefore, estimates using earlier OCAC years might have been biased upwards due to the sample selection correlated with time that is inherent in OCAC data. While having substantially fewer countries available, OCAC data have the potential advantage that it is measured regularly and thus one can try to use it in longitudinal form. We merged data for the Chinese diaspora for the years 1963, 1984, 2000, 2003, 2005 and 2010, creating a panel dataset in which sharechinese takes the value of the previous year of data available in our sample. That is, from 1963 to 1983 we assign the value of 1963, for 1984 to 1999 the value of 1984, etc. The results of estimating the gravity model with this constructed panel are reported in TableA7 in the Appendix. Three models have been estimated for comparative purposes. First, results of a correlated random-effects model (CRE-TFE) are presented in columns (1) and (4) for exports and imports, respectively. Second, a panel fixed-effect (FE) model, retaining only the within-variation in columns (2) and (5) and, third, the equivalent PPML FE model in columns (3) and (6). The estimations mostly confirm our main results, showing that an increase in the Chinese diaspora increases both exports and imports. However, when using panel methods, the magnitude of the effect is higher for exports than for imports, contrary to what we obtained in Tables3 and 4. It is worth noticing that the difference in results could be due to unobserved heterogeneity that was left uncontrolled for in the regressionspresented in Tables 3 and 4. However, it could also be due to smaller sample size, sample selection that is correlated with time, and the resulting unbalanced nature of the OCAC data panel. OCAC data are particularly lacking for many countries before the year 2000; thus, identification using within-variation only is likely to be biased towards changes in variables in later years. We further tried to estimate the gravity model using fixed-effects panel data estimations with WCE data for 1970 and 1990 exploiting the difference between the Chinese share in both periods; however, with a single change in the target variable, the results are instable and there is not enough variability to explain changes in exports/imports. Another sensitivity check, as suggested by Priebe and Rudolf (2015) in their analysis of diaspora effects on economic growth in host countries, consists of estimating the model only for the sample of countries with sharechinese≤0.05, ≤0.03 and ≤0.01. This exercise helps us to verify that results are 2426 | MARTÍNEZ-ZARZOSO ANd RUdOLF not only driven by a few countries with high population shares of overseas Chinese such as Singapore, Malaysia or Thailand. Estimating over the restricted sample using WCE data confirms our main results. In accordance with this exercise, we also checked for nonlinearities in the effect of sharechinese by splitting it into several bins and also by using a quadratic specification. Results indicate that the coefficients of different bins were not statistically different and the quadratic term was not significant; thus, the effects do not appear to be nonlinear. 5 | CONCLUDING REMARKS This article evaluates the role of the Chinese diaspora in explaining Chinese bilateral trade flows. In order to achieve this goal, we use a new dataset on the population share of overseas Chinese and estimate one-side gravity models of Chinese exports and imports, respectively. The present study contributes to the existing literature in the following ways: first, by using a new dataset on ethnic Chinese compared to earlier studies, we were able to expand the number of countries included in the analysis from 63 (R&T and related studies) to 175. Second, to the best of our knowledge, this is the first study that focuses on the question how coethnic networks affect bilateral trade flows of a major migrant sending country. Earlier single-country studies on the nexus of migration and trade have all used the perspective of the migrant receiving country. Third, in contrast to earlier studies which used total trade, we estimated trade facilitation effects separately for exports and imports, accounting for the facts that the preference channel should only affect Chinese exports and that coethnic business networks can be of different importance for the sending country's exporters versus importers. We further studied the heterogeneity of effects by trading sector. Fourth, we analyse the role of informal coethnic networks as substitutes for formal trade agreements. Our findings suggest substantial trade creation effects resulting from the presence of ethnic Chinese in the trade partner population. Among export sectors, effects found were strongest for food, as well as for machinery and transport equipment. In regard to imports, the largest effects were found for raw materials, machinery and transport equipment, and chemicals. Interestingly, for food products we find a higher effect for exports than for imports, indicating that for this specific sector the preference effect could play an important role. It is also worth noting that for raw materials, machinery and chemicals, the trade creation effects are in general higher for imports than for exports, supporting the importance of the trade-cost channel for these sectors. Putting our results into perspective, trade creation effects found are consistent with those of earlier studies such as Felbermayr et al. (2010). We are able to distinguish between Chinese export creation and import creation effects and find a trade creation of 2.4–2.6% and 4.8–7.1% respectively, equivalent to an AVT reduction of 0.34–0.37 and 0.67–0.98 percentage points. Diaspora impacts on Chinese imports are in general higher than those found for exports. This result supports a strong trade facilitation mechanism in place particularly for Chinese imports. The Chinese diaspora can be expected to have a better idea of the Chinese bureaucracy apparatus, contract law enforcement issues, or even the importance of bribes and being able to trust key partners in China may prove to be very valuable, as uncertainty is reduced. However, this mechanism could be less relevant for Chinese exports since the bulk of Chinese exports goes to developed countries with sound institutions, and hence, the trade-cost channel is probably less important. Relatively higher trade creation effects for imports (compared to exports) of the sending country stand in contrast to findings from earlier studies (Genc, Gheasi, Nijkamp, & Poot, 2012) who more often find the opposite to be true. A couple of explanations can be thought of. First, in contrast to the present study, earlier studies were limited by the use of first-generation migrant data. It is likely—and | 2427 MARTÍNEZ-ZARZOSO ANd RUdOLF in line with anecdotal evidence—that first-generation migrants contribute relatively more to imports from their source country than to exports to the same, indicating that the preference channel plays a larger role for first-generation migrants. Moreover, as it takes time to build up a functioning business and to be able to contribute to exports of the host country, it could be expected that the trade-cost channel takes more time to be effective. If that was the case, migrants could be expected to contribute relatively more to their host countries' net bilateral exports with their country of origin as time passes. Second, overseas Chinese are often characterised by a comparatively strong emphasis on family economic success, including long working hours, and thrifty and dynamic family businesses (Bolt, 1996; Folk & Jomo, 2013; Gomez, Hsiao, & Xiao, 2003). Thus, for the Chinese diaspora the trade-cost channel might play a more important role than the preference channel compared to other diasporas. The fact that the diaspora trade creation effect is lower for Chinese exports does not support the existence of a strong preference effect in aggregate exports. Although we cannot separate the preference effect from the trade-cost channel, a strong preference effect should lead to a higher trade creation effect on Chinese exports in comparison with Chinese imports and we find the opposite. 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A panel data analysis of trade creation and trade diversion effects: The case of ASEAN-China free trade area. China Economic Review, 29, 138–151. How to cite this article: Martínez-Zarzoso I, Rudolf R. The trade facilitation impact of the Chinese diaspora. World Econ. 2020;43:2411–2436. https://doi.org/10.1111/twec.12950 2430 | MARTÍNEZ-ZARZOSO ANd RUdOLF APPENDIX TABLE A1 Chinese diaspora in 1990 and Chinese bilateral exports Method: (1) (2) (3) (4) OLS_TFE RE_TFE CRE_TFE CRE_PPML Dependent var: Ln Exports Exports Explanatory var: Sharechinese 1990 (ethnicity) Sharechinese 3.112*** 3.856*** 2.740*** 1.595* [0.506] [0.589] [0.555] [0.884] Ln GDP importer 0.881*** 0.810*** 0.754*** 0.985*** [0.0500] [0.0802] [0.111] [0.121] Ln distance −0.493*** −0.574*** −0.478*** −0.209 [0.177] [0.184] [0.184] [0.303] Common border 1.042*** 0.745* 1.117*** 1.508*** [0.399] [0.436] [0.415] [0.464] Ln area importer 0.0642 0.118** 0.0526 −0.0370 [0.0414] [0.0575] [0.0426] [0.0643] Landlocked importer −0.734*** −0.793*** −0.722*** −0.889*** [0.208] [0.211] [0.203] [0.240] RTA −0.268* −0.370*** −0.367*** −0.116 [0.160] [0.0955] [0.0967] [0.0830] WTO 0.195 −0.218 −0.247 0.0156 [0.226] [0.186] [0.192] [0.0555] Europe and C. Asia −0.681** −0.513** −0.726** −0.738 [0.263] [0.257] [0.314] [0.609] LA and Caribbean −0.299 −0.155 −0.342 −0.789 [0.320] [0.300] [0.350] [0.804] MENA −0.0327 −0.00644 −0.00697 −0.712 [0.282] [0.263] [0.316] [0.607] North America −0.458 −0.235 −0.410 −0.299 [0.351] [0.376] [0.379] [0.849] South Asia −1.003** −0.882* −1.063** −1.466** [0.500] [0.489] [0.494] [0.590] Sub-Saharan Africa −0.0265 −0.0837 −0.0398 −0.503 [0.280] [0.299] [0.304] [0.790] Observations 3,868 3,868 3,868 3,868 R-squared 0.859    Number of id  175 175 175 Notes: Robust standard errors in brackets, cluster by country-pair. Average of the time-variant variables and time dummies are omitted to save space. East Asia and Pacific are the default regions. *p<.1. **p<.05. ***p<.01. | 2431 MARTÍNEZ-ZARZOSO ANd RUdOLF TABLE A2 Chinese diaspora in 1990 and Chinese bilateral imports  (1) (2) (3) (4) Method: OLS_TFE RE_TFE CRE_TFE CRE_PPML Dependent var: Ln imports Imports Explanatory var: Sharechinese 1990 (ethnicity) Sharechinese 6.847*** 7.807*** 5.697*** 4.560*** [0.768] [1.032] [1.142] [1.372] Ln GDP importer 1.245*** 1.223*** 0.993*** 0.668*** [0.0669] [0.0958] [0.224] [0.151] Ln distance −0.960*** −1.022** −0.921** −0.493 [0.345] [0.424] [0.364] [0.401] Common border 1.114** 0.616 1.087** 0.153 [0.476] [0.588] [0.543] [0.489] Ln area importer 0.338*** 0.419*** 0.371*** 0.464*** [0.0641] [0.0746] [0.0719] [0.0641] Landlocked importer −0.266 −0.396 −0.367 −0.0737 [0.286] [0.309] [0.305] [0.322] RTA 0.0606 −0.604*** −0.610*** 0.00968 [0.387] [0.168] [0.159] [0.115] WTO 0.828** 0.0107 −0.251 0.0660 [0.363] [0.306] [0.335] [0.0805] Europe and C. Asia −0.456 −0.566 −0.493 −1.123 [0.420] [0.526] [0.529] [0.740] LA and Caribbean −0.510 −0.660 −0.677 −1.442 [0.602] [0.743] [0.676] [0.937] MENA 0.258 −0.472 0.0127 0.144 [0.556] [0.729] [0.683] [0.777] North America −1.864*** −2.609*** −2.305*** −3.283*** [0.591] [0.837] [0.707] [0.920] South Asia −2.555*** −2.849*** −2.754*** −2.804*** [0.548] [0.709] [0.671] [0.632] Sub-Saharan Africa −0.209 −0.455 −0.219 −0.412 [0.519] [0.652] [0.632] [0.924] Observations 2,813 2,813 2,813 2,813 R-squared 0.794    Number of id  165 165 165 Notes: Robust standard errors in brackets, cluster by country-pair. Average of the time-variant variables and time dummies are omitted to save space. East Asia and Pacific are the default regions. *p<.1. **p<.05. ***p<.01. 2432 | MARTÍNEZ-ZARZOSO ANd RUdOLF TABLE A3 System-GMM estimations for exports Dep. Variable: Ln Exports (1) (2) (3) (4) sh70_GMM sh90_GMM sh70_GMM sh90_GMM Sharechinese long run 1.988 3.890 3.551 4.588 Sharechinese 1.732** 1.595*** 2.031*** 2.285*** [0.703] [0.563] [0.538] [0.529] RTA×sharechinese −0.695** −0.394 −0.865*** −0.863*** [0.320] [0.337] [0.261] [0.317] Ln exports (t−1) 0.412*** 0.590*** 0.428*** 0.502*** [0.0794] [0.0968] [0.0774] [0.0876] Ln GDP importer 0.449*** 0.319*** 0.435*** 0.387*** [0.0591] [0.0801] [0.0599] [0.0746] Ln distance −0.391*** −0.198* −0.338*** −0.237** [0.117] [0.101] [0.116] [0.107] Common border 0.0823 0.335 0.0178 0.266 [0.234] [0.205] [0.267] [0.208] Ln area importer 0.0548** 0.0387 0.0468* 0.0558** [0.0263] [0.0252] [0.0254] [0.0256] Landlocked importer −0.376** −0.278** −0.392*** −0.396*** [0.147] [0.139] [0.136] [0.137] RTA 0.0690 0.0330 0.104 0.113 [0.113] [0.0949] [0.105] [0.100] WTO 0.0873 0.0378 0.174 0.100 [0.0927] [0.103] [0.106] [0.109] Observations 4,181 3,702 4,181 3,702 Number of countries 155 175 155 175 Number of instruments 58 44 83 72 AR2 Test probability 0.109 0.00307 0.111 0.00698 Hansen probability 0.0338 0.0317 0.193 0.135 Notes: Robust standard error in brackets. GMM-style instruments for lagged exports in columns (1) and (2) and for lagged exports and sharechinese in columns (3) and (4). The AR2 test results indicate that there is autocorrelation of second order when sharechinese in 1990 is used in columns (2) and (4), alternative specifications using farther lags of exports did not solve the problem. Hansen test of overidentification does not pass in column (2) in TableA3 and column (1) in TableA4. *p<.1. **p<.05. ***p<.01. | 2433 MARTÍNEZ-ZARZOSO ANd RUdOLF TABLE A4 System-GMM estimations for imports Dep. variable: Ln imports (1) (2) (3) (4) sh70_GMM sh90_GMM sh70_GMM sh90_GMM Sharechinese long run 7.059 6.993 7.762 8.332 Sharechinese 5.908*** 6.993*** 6.054*** 7.074*** [0.618] [0.985] [0.791] [0.997] RTA×sharechinese −1.563** −1.830* −2.042** −2.248** [0.717] [0.983] [0.917] [0.952] Ln imports (t−1) 0.163** 0.125 0.220*** 0.151* [0.0754] [0.0950] [0.0715] [0.0852] Ln GDP exporter 0.896*** 1.001*** 0.836*** 0.927*** [0.0893] [0.129] [0.0924] [0.115] Ln distance −0.825*** −0.866*** −0.822*** −0.874*** [0.248] [0.220] [0.231] [0.217] Common border −0.759* 0.0241 −0.433 −0.151 [0.429] [0.463] [0.535] [0.442] Ln area exporter 0.342*** 0.303*** 0.325*** 0.334*** [0.0593] [0.0638] [0.0634] [0.0702] Landlocked exporter 0.157 0.00530 0.107 −0.0880 [0.238] [0.262] [0.283] [0.272] RTA 0.767** 0.470 0.658** 0.607* [0.335] [0.346] [0.319] [0.321] WTO 0.487 0.469* 0.551 0.493* [0.296] [0.274] [0.340] [0.280] Observations 3,151 2,516 3,151 2,516 Number of countries 147 158 147 158 Number of instruments 86 53 120 73 AR2 Test probability 0.732 0.214 0.515 0.177 Hansen probability 0.0907 0.479 0.242 0.645 Notes: Robust standard error in brackets. GMM-style instruments for lagged exports in columns (1) and (2) and for lagged exports and sharechinese in columns (3) and (4). *p<.1. **p<.05.***p<.01. 2434 | MARTÍNEZ-ZARZOSO ANd RUdOLF TABLE A5 Comparison of results to Felbermayr et al. (2010)  Trade period No. of countries Trade measure Trade creation (per cent) AVT equivalent (percentage points) Sharechinese variable Ethnic Chinese data source Felbermayr et al. (2010) 1980 63 Total trade 4.7 0.67 Population share of ethnic Chinese around 1980 Rauch and Trindade (2002, largely based on OCAC data) 1990 63 Total trade 8.2 1.15 Population share of ethnic Chinese around 1990 2000 63 Total trade 2.4 0.34 Population share of foreign-born Chinese 2000 (first-generation migrants) World Bank international bilateral migration stock database (Parsons, Skeldon, Walmsley, & Winters, 2007) Present study 1973–2013 155 Exports 2.6 0.37 Population share of ethnic Chinese around 1970 World Christian encyclopedia (Barrettet al., 1982, 2001) 1973–2013 150 Imports 4.8 0.67 Population share of ethnic Chinese around 1970 1991–2013 175 Exports 2.4 0.34 Population share of ethnic Chinese around 1990 World Christian encyclopedia (Barrettet al., 1982, 2001) 1991–2013 165 Imports 7.1 0.98 Population share of ethnic Chinese around 1990