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The “China Effect” on EU Exports to OECD Markets: A Focus on Italy

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Abstract

This paper analyzes the indirect impact of China on the export performance of major European countries (Italy, France, Germany and Spain) in their main outlet markets (the OECD countries). Given their specialization in manufacturing, these EU countries are likely to be vulnerable to Chinese competition, especially in consumer goods. The heterogeneity of the production (and export) structures of the other EU countries makes Italy the most vulnerable, with its export concentration in the traditional sectors. Using data for the period 1995-2009, we estimate the possible displacement effect at sector level, finding considerable variation in EU countries exposure to Chinese competition, with a very strong China effect indeed in some sectors. This applies especially in the period since Chinas entry into the WTO and with respect to Italy, both in traditional and in more capital-intensive sectors.

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Notes

  1. 1.

    According to Naughton (2007), the main policy measures to promote exports were ending the foreign trade monopoly, with more trading companies allowed to trade; the establishment of the export-processing trade regime granting special status to foreign-invested enterprises (FIEs); and the devaluation of the renminbi against the dollar by 60% (a policy retained, in substance, until 2005, with the exception of an overvaluation in correspondence with the Asian financial crisis of 1997).

  2. 2.

    The SITC-7 group.

  3. 3.

    Namely SITC-8 miscellaneous manufactures and SITC-6 manufactured materials.

  4. 4.

    The chapter by Marvasi in this volume looks specifically at China’s structural transformation and provides a detailed review of the literature on some of these issues. We therefore decided not to go into detail here.

  5. 5.

    High-tech products as a group moved from 0.7 in 1987 to 2.16 in 2006, driven by automatic data processing equipment, telecommunications equipment and optical instruments.

  6. 6.

    They compare the situation in 1992 and 2005.

  7. 7.

    The Chinese gains have been greater in the middle and bottom segments of the market, often excluding Germany from tougher competition even though China has achieved an important diversification of exports and is now shipping almost as many products as Germany to the US.

  8. 8.

    For instance, with the help of gravity models, the impact of trade-related policies, such as participation in free trade agreements (Rose 2003; Fontagné and Zignano 2007) or monetary unions (Serlenga and Shin 2004), can be confidently assessed.

  9. 9.

    Though we examine four EU countries, in what follows we mainly present and comment the results on Italy and Germany, because their different structural characteristics make them the most instructive cases.

  10. 10.

    In particular, the Wu-Hausman test of endogeneity for the variable lch_v_export rejects the null hypothesis of endogeneity (p-value = 0.0000). The Kleibergen-Paap rk statistics perform an LM test, testing the rank of matrix, and reject the null of under-identification, while the Wald F version of Kleibergen-Paap rk statistics similarly reject the null of weak identification.

  11. 11.

    The complete results are not reported for reasons of space; they are available on request.

  12. 12.

    In six more sectors the coefficient is negative, albeit not significant (bringing the total to 11 out of the 26 sectors considered).

  13. 13.

    Bai and Carrion analyze the presence of multiple structural breaks when testing for the unit root hypothesis in a panel data framework. They compute the test as a weighted sum of partial sum processes so as to get rid of the break fraction parameters in the limit distributions. It is a five-step procedure: (1) differentiate the data and estimate the number and locations of structural breaks for each series; (2) given the locations of structural changes, estimate the common factors, factor loadings, and the magnitudes of changes via an iterative procedure; (3) compute the residuals for each series based on the estimated quantities in step 2 and obtain the cumulative sum of residuals; (4) compute the univariate test for each residual series; and (5) construct the panel test by pooling the individual series.

  14. 14.

    On the other hand, no significant results can be drawn for the other two countries, France and Spain, whose role as exporters in manufacturing is smaller than Germany’s and Italy’s and whose sectors of specialization are less overlapping with those of China.

  15. 15.

    The distinction between resource-based, low-, medium- and high-technology sectors follows Lall’s classification (Lall 2000).

  16. 16.

    The value reported in Figs. 8.2 and 8.3 is computed using data at the six-digit level of the HS-1992 classification. It is the average of the medians of Italian and Chinese unit values of exports of individual products at the six-digit level to all the OECD markets in the year.

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Acknowledgements

We are grateful for the comments of the participants at the workshop “The Chinese Economy” hosted by the Venice International University in November 2010, at a seminar at the Vienna Institute for International Economic Studies (Wiiw) in June 2011, at the annual conference of the Chinese Economic Association in Dublin in July 2011, and at the European Economic Association Meeting in Oslo in August 2011 and for those of an anonymous referee. Giorgia Giovannetti thanks FIRB 2007 for financial support.

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Giovannetti, G., Sanfilippo, M., Velucchi, M. (2012). The “China Effect” on EU Exports to OECD Markets: A Focus on Italy. In: Gomel, G., Marconi, D., Musu, I., Quintieri, B. (eds) The Chinese Economy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28638-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-28638-4_8

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