Globalisation and the provinces of China: the role of domestic versus international trade integration

Abstract

Regional disparities are particularly high in some larger emerging economies, such as Russia and China, and inter-provincial inequality in China has increased rapidly after 1990. In the literature, a statistical link between globalisation and increased regional disparities in China has been established, but not the precise mechanisms and causal relationships. In the paper, we therefore simulate a world trade model with 166 countries and regions in order to shed light on these mechanisms and the future development of regional disparities in China. Our results suggest that the faster growth in coastal regions may be caused by the role of these regions as transport hubs for international trade. However, uneven growth could also be caused by domestic trade disintegration, as suggested by some other research. In the paper, we demonstrate these mechanisms, but we are not able to draw firm conclusions about the relative role each of them play empirically. The weaker development for some peripheral regions can be reversed by stronger domestic trade integration or better cross-border infrastructure.

Keywords:

• trade
• globalisation
• regional development
• economic integration

Acknowledgements

The author thanks the Research Council of Norway for the funding provided under project No. 177974/I10. I thank in particular Haiyan Song, Gary Jefferson and an anonymous referee for their useful comments to earlier drafts. Thanks also for the comments from participants at the conference ‘China's Three Decades of Economic Reform (1978–2008)’, 20–21 September 2008 at Zhejiang University, Hangzhou, China, and the international workshop on ‘Technology and Trade: China and the World Economy’, at the Norwegian Institute of International Affairs, Oslo, Norway, 17–18 December 2008. As usual, the responsibility for any remaining errors stays with the author.

Notes

Notes

1. See official web page of the Congress on http://www.china.org.cn/english/news/229718.htm

2. We show results using current prices for 1992–2007, and by using annual indexes we have also constructed data using fixed 1992 prices, for 1991–2007. Data are from National Bureau of Statistics: China Statistical Yearbook, various issues.

3. Own calculations based on trade data from WITS/COMTRADE and GDP data from World Bank: World Development Indicators (online data). Trade = exports + imports.

4. For merged country groups we use population-weighted averages of the coordinates. Latitude and longitude data are from the ‘Global cities’ database. For a few missing cases, data from Wikipedia were used.

5. Europe could possibly have been more aggregated, but we have used country-level data due to the economic weight of many European countries.

6. In the analysis, we technically allow for the possibility that only some of the trade is gated (with weights that are common or vary by region). This is a possible extension of the analysis.

7. We assume that international (non-spatial) trade costs are lower within the following groups or blocs: China–Hong Kong; Australia–New Zealand; ASEAN; Commonwealth of Independent States (CIS); and in Europe between EU-15, EU-10 (the new members from 2004), Bulgaria and Romania, EEA/EFTA and Turkey. The depth of integration is assumed to vary between different cases. Details can be provided upon request.

8. We consider it simpler in terms of notation to express trade costs as a mark-up on marginal costs rather than the usual iceberg formulation where goods melt away in transport. The results are similar.

9. In one case (autarky) this is more difficult since the ‘link’ between China and the world is broken so there is no unique solution for all countries. Here we retain a minimal amount of trade between China and others and accept a slightly higher F value (F = 0.0322) in order to obtain results on how autarky in China affects all countries, and not only China itself. In the other scenarios, F is between 0.00054 and 2.88E-09.

10. Other parameter values used are σ = 5, α = 0.9, β = 0.5 and a = 0.6. Hence, the traded sector is K-intensive so poor regions have a supply-side constraint due to their small capital stock.

11. Country-level population and GDP figures are values for 2004 from World Bank: World Development Indicators 2007, supplemented with values from CIA World Factbook for a few missing cases. Regional GRP (gross regional product) and population data are obtained from the national statistical agencies of China, India, Russia and the USA. Regional GRP data are not used directly but only to allocate the World Bank country-level data across regions for these four countries.

12. Note: Sichuan and Chongqing are merged in the simulation analysis, in order to facilitate comparisons with actual data for years before 1996 (when Chongqing was not separate).