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Evaluating China’s Integration in World Trade with a Gravity Model Based Benchmark

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Abstract

This paper assesses China’s “natural” place in the world economy with a new set of trade integration indicators, which are used as a benchmark in order to examine whether China’s share in international trade is consistent with fundamentals such as economic size, location and other relevant factors. They constitute a better measure of trade integration that incorporates many more factors than traditional openness ratios. The model tracks international trade well and confirms that China is already well integrated in world markets, particularly with North America, several Latin American and East Asian emerging markets and most euro area countries.

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Notes

  1. This review does not include the studies that took a completely different approach to China’s trade integration and have used general equilibrium models instead; see McKibbin and Woo (2002), Yang (2003), or Prasad (2004) for a review.

  2. It seems however that the model underpredicts the acceleration of China’s trade flows after 2003.

  3. China did not only record by far the largest increase in absolute terms. What is even more noticeable is that it also recorded the largest increased in relative terms (by 108%), second only to Bosnia, whose share rose by nearly 300% mainly reflecting its very small share in 1995.

  4. For a meaningful comparison across countries, Table 1 reports the share of extra-euro area trade. The share of total (intra- plus extra-) euro area trade would be much higher as it includes trade across euro area countries (representing a little bit more than half of total trade). Detailed account and analysis of euro area trade can be found in Anderton et al. (2004).

  5. This measure uses GDP evaluated in PPP terms, i.e. converting the GDP of all countries using Purchasing Power Parity exchange rates. In nominal (US dollar) terms, China’s share also doubled but at a lower level from 2.5 to 5% of world GDP over this period, as the yuan’s current exchange rate is generally found to be lower than its PPP exchange rate.

  6. Bilateral trade data between China and Hong Kong partly reflect transit trade, see for instance Schindler and Beckett (2005) for a discussion.

  7. Crossing a border involves not only fees but also other transaction costs, implying that countries that do not have a common border may incur a higher cost of trading with each other, as they have to ship goods through third countries.

  8. See for instance Blattner (2005) and the references therein.

  9. Country-pair fixed effects are also used in Glick and Rose (2002), who use the model to assess the impact of exchange rate volatility on trade flows.

  10. Anderson and van Wincoop (2003) included a so-called multilateral trade resistance term in their cross-section analysis, which may be modelled as country dummies.

  11. The Hausman and Taylor (1981) estimator (HT) is a random-effects estimator which yields consistent and efficient estimates even if some explanatory variables are correlated with the error term. Thereby, it also better accounts for possible endogeneity between the explanatory variables and trade and allows the estimation of the coefficient of the time-invariant variables. In gravity models, the HT-estimator has been used, among others, by Egger (2003, 2004); Koukhartchouk and Maurel (2003) and Serlenga and Shin (2004).

  12. Baldwin and Taglioni (2006) point out that, in the presence of trade costs, taking the logarithm of the average trade flows—as it is common in the literature—can potentially bias the results since the sum of the logarithms of exports and imports should be the right measure. We checked both definitions of trade and found that the results are not sensitive to the use of one or the other definition. Baldwin and Taglioni (2006) also argue against deflating nominal trade values by the US aggregate price index. However, as they also mention, the difference between the two approaches is mostly offset by including time dummies in the regression, as it is done in this study.

  13. As in Micco et al. (2003), real GDP per capita is not included in the fixed effect estimation owing to the high collinearity between those dummies and the population.

  14. Most Central and Eastern European countries enter the dataset in the 1990s only, when the transition period to market economies started.

  15. As regards the USA, New York (0.48), Los Angeles (0.23), Chicago (0.17) and Houston (0.12) were considered. For China, Shanghai (0.35), Beijing (0.20), Guangzhou (0.19), Chongquing (0.13) and Tianjin (0.13) were included. The numbers in parentheses are the respective weights.

  16. One may wonder about co-linearity among the dummy variables. However, the cross-correlation coefficients between all dummy variables (available upon requests) show that the absolute value of these coefficients is always small, the highest number being 0.30 for the “language” and the “territory” dummies (e.g. the Czech and the Slovak Republics).

  17. The coefficients for the time-invariant variables could be estimated by using a random effect (RE-) model, which assumes that explanatory variables are uncorrelated with random effects. However the standard Hausman-test strongly suggests that this assumption is violated in the present case.

  18. This finding is in some contrast with Bayoumi and Eichengreen (1997), who find a significant trade creating effect of EU membership in the 1980s. At the same time, the finding of a significant dummy variable in our second step regression suggests, in line with their findings, that EU members trade “…significantly more among themselves than would be expected on the basis of their observable characteristics”.

  19. The marginal effect of the dummy variables can be calculated by taking the exponential of the estimated coefficient minus one: a coefficient of 0.5 means that when the dummy is equal to 1, trade increases—ceteris paribus—by 65% (e 0.5 − 1 = 0.6487) and a coefficient of 0.25 implies a 28% increase.

  20. In this specification, several variables used in the full model drop out as there are no relevant observations (e.g. Mercosur, Cefta or common territory).

  21. Bootstrapping increases in some cases the standard errors, but the conclusions on the significance of the variables at standard levels is unaffected (this was done with 1000 replications).

  22. Bayoumi and Eichengreen (1997) use the model to estimate the impact of a policy variable on the dependent variable. We use the model with a different purpose, as our primary objective is to retrieve and analyse the idiosyncratic effects.

  23. The predicted values correspond to the projected values of the first stage fixed effect estimation of column 1, Table 1. To compute the ratios, both actual and predicted trade values have been “unlogged”.

  24. Exceptions are Luxembourg and Greece which appear to face a somewhat higher level of overall trade resistance which in the case of Luxembourg may be due to the specific structure of the economy.

  25. This may partly reflect strong intra-regional integration and a relatively low domestic value-added in their exports.

  26. Source: CEIC; see http://www.ceicdata.com.

  27. See also CEIC.

  28. However, the effect on the indicators may be complex: Rodrik (2006) also argues that China’s export composition has contributed to stimulate domestic growth, which should be picked up by the right-hand side variables of our first stage regression. It remains, however, that China’s specialisation pattern has played a key role in its rapid integration into world markets.

  29. On China’s WTO accession see in particular Prasad et al. (2004), pp 12–13.

  30. However, it does not mean of course that the trade integration of China is neutral to the welfare of the other countries. For instance, one key issue, not tackled in this paper, is the fact that China’s trade integration is not even across sectors (see e.g. Rodrik 2006), which may imply a necessary reallocation of resources across sectors.

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Authors and Affiliations

  1. European Central Bank, Kaiser Strasse 29, 60311, Frankfurt am Main, Germany

    Matthieu Bussière & Bernd Schnatz

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  1. Matthieu Bussière
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  2. Bernd Schnatz
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Correspondence to Matthieu Bussière.

Additional information

Data Sources

Countries included: Albania, Algeria, Argentina, Australia, Austria, Belarus, Belgium, Bosnia, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Czech Republic, Denmark, Ecuador, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Macedonia, Malaysia, Malta, Mexico, Moldova, Morocco, Netherlands, New Zealand, Norway, Peru, Philippines, Poland, Portugal, Romania, Russia, Singapore, Slovak Republic, Slovenia, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, Ukraine, Uruguay, USA.

Trade data: IMF DOTS.

GDP: IFS line 99b. For Ecuador data from WDI. Data for Greece up to 1994 from WDI. Date for Turkey up to 1985 from WDI. If there was a large discrepancy between World Bank and IMF data, observations have been dropped. This includes Argentina (1980–84), Bulgaria (1985–92), China (1980–1993), Estonia, Latvia, Lithuania (each 1993–95), Moldova (1995), Russia (1993–94), Ukraine (1993–95), for Albania, Bosnia and Herzegovina, Moldova and Macedonia data from EBRD.

Distance: Great circle distances based on MS Encarta World Atlas software.

Exchange rate: IFS line rf. Exchange rates for individual euro area countries were chain-lined with the euro exchange rate upon EMU entry.

Consumer prices: IFS line 64. For Belarus, China, Russia and the Ukraine, inflation rates (IFS line 64.xx) were transformed into price indices.

Industrial producer price: IFS line 63a for the USA.

Real exchange rate: Product of the US dollar exchange rate and the ratio of domestic and foreign consumer prices.

Exchange rate volatility: Standard deviation of the month-on-month log changes in the bilateral nominal exchange rate within a year.

Common language: Based on a matrix including the following languages: English (Australia, Canada, India, Ireland, Hong Kong, Malta, New Zealand, Philippines, Singapore, United Kingdom and the USA), Spanish (Argentina, Chile, Colombia, Ecuador, Mexico, Peru, Spain, Uruguay, Venezuela), French (Algeria, Belgium, Canada, France, Luxembourg, Morocco, Switzerland), German (Austria, Germany, Luxembourg, Switzerland), Chinese (China, Hong Kong, Singapore), Russian (Belarus, Estonia, Latvia, Lithuania, Moldova, Russia, Ukraine), Dutch (Belgium, Netherlands), Greek (Greece, Cyprus), Arabic (Algeria, Morocco), Serbo-Croatian (Bosnia, Croatia, Slovenia), Portuguese (Brazil, Portugal), Swedish (Sweden, Finland), Albanian (Albania, Macedonia), Malay (Malaysia, Singapore).

Free-trade agreement: ASEAN Association of South East Asian Nations (1967): Brunei (1984), Cambodia (1999), Indonesia, Laos (1997), Malaysia, Myanmar (1997), Philippines, Singapore, Thailand, Vietnam (1995), CEFTA Central European Free Trade Agreement (1994): Bulgaria (1999), Czech Republic, Hungary, Poland, Romania, Slovak Republic, Slovenia (1997), European Union (EU15): Austria (1995), Belgium, Denmark, Finland (1995), France, Germany, Greece (1981), Ireland, Italy, Luxembourg, Netherlands, Portugal (1986), Spain (1986), Sweden (1995), UK, European Union (EU15) and Customs Unions: EU15, Cyprus, Malta, Turkey (1996); MERCOSUR Southern Common Market (1993): Argentina Brazil Paraguay Uruguay, NAFTA North American Free Trade Agreement: Canada (1988), Mexico (1993), USA (1988).

Common territory includes countries which constituted in the past 20 years at some point a common country. They include a) former Czechoslovakia (the Czech Republic and the Slovak Republic), b) countries of the former Soviet Union (Belarus, Estonia, Latvia, Lithuania, Moldova, Russia and the Ukraine, and c) countries of former Yugoslavia (Bosnia, Croatia, Macedonia, Slovenia).

We have benefited from very valuable comments during the Far Eastern Meeting of the Econometric Society in Beijing in July 2006, the 10th International Conference on Macroeconomic Analysis and International Finance in Rethymno in May 2006, the Conference on “Globalisation and Regionalism” in Sydney in December 2005 and at the European Central Bank in June 2005, especially from our discussants K.-K. Tang and Mardi Dungey. Without implication, we would like to thank Mike Artis, Richard Baldwin, Anindya Banerjee, Agnès Bénassy-Quéré, Jarko Fidrmuc, Peter Egger, Françoise Lemoine, Warwick McKibbin, Adrian Pagan and Alvaro Santos-Rivera for their useful advice. In addition, we would like to thank George Tavlas and an anonymous referee for their very constructive comments. The views expressed in this paper are those of the authors and do not necessarily represent those of the European Central Bank.

Appendix

Appendix

Fig. 5
figure 5

Residuals by country pair, fixed-effect specification

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Fig. 6
figure 6

Residuals by country pair, fixed-effect specification

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Bussière, M., Schnatz, B. Evaluating China’s Integration in World Trade with a Gravity Model Based Benchmark. Open Econ Rev 20, 85–111 (2009). https://doi.org/10.1007/s11079-007-9061-5

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