引用 1995 至 2014 年期間的年度數據,本論文分析中國和歐元區之 間的貿易。該分析是基於亞當·斯密關於勞動力市場和規模的分工經 濟思想為引力模型的估計。本論文推測,擴大出口可以推動經濟增 長。因此,市場規模與 GDP 的相對大小應密切相關。貿易數據嵌板, 並均以當前美元一位 SITC 數據。有三種基本的解釋變數 - 中國和 個別歐元區國家的名義 GDP (nominal GDP)和混成貿易成本變數。 此外,本論文考慮到四個虛擬變數。其中第一個變數是中國加入 WTO,這發生在 2001 年。我們從經濟和政治觀點分析了世界貿易 組織對中國的影響,也密切注意學者們普遍斷言。第二個變數是 2008 年的經濟危機,對於世界各地的貿易影響力極劇。本論文將討論中 國和歐元區的貿易,在這段時間產生什麼變化。第三個和第四個變 數都與這兩大區域之間舉行的會議和協議相關。雙邊領袖峰會始於 1998 年,目前正每年舉行一次,解決雙邊關係中的關鍵問題。最後 一個變數是中國與歐盟之間從 2003 年開始的全面戰略夥伴關係。如 同事先預測一般,統計分析證實了經濟對貿易相對規模的重要性。 貿易成本,可以解釋貿易在統計學上顯著表現。然而,近幾年出現 這樣貿易成本,並沒有比在早期樣本減少的部分多。由貿易結構顯 示,超過 80%的交易過程僅在三個不同的商品類別(按材料,機械 和運輸設備,雜項製品主要分類的製成品)。

Using annual data for the periods 1995-2014, this thesis analyzes trade between China and the Eurozone. The analysis is based on an estimation of a gravity model steeped in the economic ideas of Adam Smith concerning the division of labour and scale of the market. Our derivation rests on the idea that expanding exports can drive growth and therefore should be closely related to the scale of markets and the relative sizes of GDP. The trade data are panelled and are one digit SITC data denominated in current US dollars. There are three basic explanatory variables - the nominal GDPs of China and individual Eurozone countries and a portmanteau trade cost variable. Additionally, the thesis considers four dummy variables. The first of these concerns China’s accession to the WTO, which happened in 2001. We analyse the influence of the WTO from economic and political perspectives and we pay close attention to what scholars have generally asserted. Our second variable is the economic crisis of 2008 that had huge influence on trade around the world. We will discuss what happened to China and Eurozone trade during that time. The third and fourth variables have to do with meeting and agreements that started happening between these two big regions of trade. Summit meetings began in 1998 and are now being held annually, addressing key issues of bilateral relations. The last variable is the Comprehensive and Strategic Partnership between China and EU that started in 2003. The statistical analysis confirms the importance
of the relative size of economy on trade as predicted. Trade costs are also statistically significant in explaining trade, however in recent years it appears such costs have not decreased much more than in the earlier part of the sample. The structure of trade shows that more than 80 percent of trade is done only in three different commodity groups (manufactured goods classified chiefly by material, machinery and transport equipment, miscellaneous manufactured articles).

Table of content

Figures and Tables	V
1.	Introduction	1
1.1	Research Topic	1
1.2	Research Motivation	2
1.3	Research Methodology	5
1.4	Research Constraints	7
1.5	General Results	8
2.	Literature Review	10
3.	Evolution of China-EU Economic Relation	22
3.1 Trade and Economic Cooperation Agreement in 1985	24
3.2 China-EU Comprehensive Strategic Partnership	26
3.3 World Trade Organization (WTO)	28
3.4 Global Financial and Economic Crisis	31
4.	Theoretical Aspect of the Gravity Model of Trade	34
4.1 The Baseline Gravity Model of Trade with Reference	34
to Adam Smith	34
4.2 Trade and Growth	42
4.3 Conclusions to the Theoretical Analysis	47
5.	Empirical Analysis and Findings	49
5.1 Data Sources and Limitations	49
5.2 Econometric Model and Estimation Method	54
5.3 Pooled OLS Analysis	55
5.4 Fixed Effects Analysis	63
5.5 Random Effects Analysis	69
5.6 Trade Costs Analysis	71
6.	Conclusion	73
7.	Bibliography	79
8.	Appendix	82

Figures and Tables

Table 2.1: Literature Review	20
Figure 4.1	44
Figure 4.2	47
Table 5.1: SITC One-Digit Categories of Traded Goods	50
Table 5.2: Eurozone Countries (partner countries to China) included in Data Set	51
Table A.1: Bilateral Trade – All Goods (Pooled OLS)	82
Table A.2: Pooled OLS – Shading Denotes HAC significance at 10% level at least	83
Table A.3: Bilateral Trade – All Goods (Fixed Effects)	84
Table A.4: Fixed-effects – Shading Denotes HAC significance at 10% level at least	85
Table A.5: Bilateral Trade – All Goods (Random Effects)	86
Table A.6: Random Effects	87
Figure A-1: Austria trade cost	88
Figure A-2: Belgium trade cost	88
Figure A-3: Cyprus trade cost	89
Figure A-4: Finland trade cost	89
Figure A-5: France trade cost	90
Figure A-6: Germany trade cost	90
Figure A-7: Greece trade cost	91
Figure A-9: Ireland trade cost	91
Figure A-10: Italy trade cost	92
Figure A-11: Latvia trade cost	92
Figure A-12: The Netherland trade cost	93
Figure A-13: Portugal trade cost	93
Figure A-14: Slovakia trade cost	94
Figure A-15: Slovenia trade cost	94
Figure A-16: Spain trade cost	95

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