港口的效率及国际贸易：港口的效率作为一个决定性的海洋运输成本【外文翻译】.doc

1、 外文翻译 原文 Port Efficiency and International Trade: Port Efficiency as a Determinant of Maritime Transport Costs Material Source: Maritime Economics in the case of intermediate and capital goods, this also increases the costs of local production. If exports become dearer to ship, the result is a drop

2、in earnings for the exporting country or simply the loss of a market, depending on the elasticity of demand and the availability of substitutes. Econometric estimates suggest that the doubling of an individual countrys transport costs leads to a drop in its trade of 80% or even more (Hummels, 2000;

3、Limao and Venables, 2001). Quality versus costs As with goods, the production of transport services is also subject to the impact of technological advances. With the use of new information and communication technologies, improvements in infrastructure, and by taking advantage of the growing rate of

4、containerisation, today the same freight and insurance per tonne of cargo can buy a quicker, more reliable service with less variation in delivery time than a decade ago. In addition, it is worth noting that greater commercial demands as regards speed have at the same time given rise to an increase

5、in the share of air transport as compared to maritime transport, and may entail an increase in the average cost of international transport. The fact that the average cost of freight and insurance rose worldwide in the 1990s (see Table 1) should not be interpreted as a worsening of the international

6、transport system, but rather as a reflection of greater use of air transport and improvements in other transport services. Equivalently, when interpreting the regression results presented later in this paper, improved port efficiency does not necessarily imply lower transport costs, as the user may

7、be required to pay for the improved service. Direct impacts versus indirect impacts The distance separating countries impacts on trade between them in different ways. The main models used to explain international trade flows can be described as gravitational: countries trade with one another dependi

8、ng on their patterns of production, income, and whether they belong to economic blocs, with the distance between them also having some bearing. That gives an advantage to countries located in the center of gravity, and hence the name of the model. There is an assumption of a close link between dista

9、nce and transport costs, which would explain why countries closer to one another trade more than with countries further away. In practice, distance may also have a bearing on other characteristics of countries, which leads them to trade more. For instance, countries located nearer to one another ten

10、d to have more similar histories, cultures and languages. Most importantly, geographical closeness provides scope for alternative modes of transport to sea and air, thereby boosting competition and reducing prices for services. In other words, shorter distances entail lower costs and more trade. Inc

11、reased trade in turn makes for economies of scale, leading to even further reductions in transport costs. In the case of intra-Latin American seaborne trade, a partial correlation coefficient of -0.463 is calculated between distance and the volume of bilateral trade, with a coefficient of +0.178 bet

12、ween distance and the costs of transport per ton. In other words, distance has its own bearings on trade and should not be taken only as a proxy for transport costs. Latin Americas foreign trade In terms of volume (tonnes), trade using air transport accounts for barely 0.10.6% of the foreign trade c

13、onducted by the countries of Latin America; in terms of value (USD), however, this mode represents anywhere between 8% and 21% (Table 2). The table also indicates that sea- and airborne transport are used particularly in foreign trade conducted by Argentina, Brazil, Chile, Colombia, and Peru, while

14、in Mexico (significant trade with the United States) and Uruguay (significant trade with Brazil and Argentina), the overland mode plays a relatively greater role. Air transports share is higher in long-distance trade; accordingly, although total trade decreases with distance, there is virtually zero

15、 correlation (-0.001) between distance and the volume of airborne trade (estimate for intra-Latin American trade). Transport costs of intra-Latin American trade For the 10 countries included in Table 3, Chilean exports to Uruguay have the highest transport costs as a percentage of the value of trade

16、, followed by Ecuadors exports to Uruguay and Paraguays to Ecuador. On average, the country with the highest transport costs for its imports from other Latin American countries is Ecuador, followed by Chile. Trade between Paraguay and Uruguay has the lowest transport costs, followed by that between

17、Argentina and Uruguay, and Argentina and Brazil. It is not possible, using these figures, to reach hasty conclusions about the efficiency of the respective transport services, nor to conclude that transport in one country is more expensive than in another. For example, the low density of regular shi

18、pping services (liner services), together with the natural barrier of the Andes, appear to be part of the reason why transport between countries on the west and east coasts of South America tends to be more expensive than transport along the same coast. It should be noted that the figures in Table 3

19、 are averages that cover all modes of transport and many different types of goods. The remainder of this paper will now look in more detail into the determinants of the maritime transport costs, with a special emphasis on the impact of port efficiency indicators for containerised cargo. Measuring po

20、rt efficiency Port of shipment efficiency was measured by using direct information gathered by way of extensive questionnaires as a part of this research. A number of potentially explanatory variables on port efficiency were measured, which were then grouped through the principal component analysis.

21、 Through the survey, we obtained information about port activity for the year 1999. The questionnaires were sent to 55 port terminals. Responses were received from 41 port terminals mainly handling general containerised cargoes. These terminals handle over 90% of the containers exported from their r

22、espective countries. The responses corresponding to bulk items and pallet break-bulks were insufficient as regards statistical purposes, and had to be discarded. Other terminals had to be excluded due to insufficiently complete responses. Table 4 lists the 19 ports that were included in this study.

23、To avoid concerns about overparameterisation and spurious correlations between multiple variables, a PCA was conducted. The identified factors, which retained the patterns of the original variables, were to be introduced as new variables into the latter regression model. The correlations between the

24、 obtained port activity variables ranged from -0.295 to 0.969 (Table 5). As anticipated, most of the nine variables are heavily correlated. The first three, out of nine components, account for more than 70% of the intrinsic variance of the data fulfilling the Kaiser criterion with eigenvalues over 1

25、. The KaiserMeyerOlkin (KMO) overall statistic delivers 0.625 as a result for the sample of port efficiency variables, which indicates the sampling adequacy of the chosen variables. The PCA extracted three factors (Table 6). The first component, which accounted for more than 40% of the total varianc

26、e, incorporates the bureaucratic turnaround of a container, the terminal turnaround for loading and unloading of a container, the average waiting time for ships during congestion time, the average waiting time for ships without congestion in the port, and the time of port congestion during the year.

27、 Together, these variables could be interpreted as representing ports time efficiency. The loading and unloading rate per hour, the handling capacity, and the average number of containers per ship handled in the terminals loaded high in the second component, which can be referred to as the productiv

28、ity of the terminal. Finally, the average port stay of the ships was found as a single variable loaded on the third component. The original, unrotated principal components solution maximises the sum of squared factors loadings, efficiently creating the set of factors in the table above. However, unr

29、otated solutions are hard to interpret because variables tend to load on multiple factors. Using the Varimax rotation realises an orthogonal rotation of the factor axes to maximise the variance of the squared factor loadings of a factor on all the variables. References： 1. Australian Productivity Co

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31、 costs and port efficiency. Mimeo, World Bank, February. 4. Coto, P, Baos, J and Rodriguez, A. 2000: Economic efficiency in Spanish ports: some empirical evidence. Maritime Policy and Management 27: 2. 5. ECLAC. 2002: Globalization and development. Santiago, April. 6. Estache, A, Gonzlez, M and Truj

32、illo, L. 2001: Efficiency gains from port reform and the potential for yardstick competition. Mimeo. World Bank, Washington DC. 7. Fink, C, Mattoo, A and Neagu, IC. 2000: Trade in international maritime services: how much does policy matter?. Mimeo, World Bank, Washington, DC. 8. Fuchsluger, J. 2000

33、: Maritime transport costs in South America. Masters Theses, University of Karlsruhe, Karlsruhe. 9. Gorman, M. 2002: Revisiting the JIT paradigm. Ascet 4: 104. 10. Gosh, B and De, P. 2000: Impact of performance indicators and labour endowment on traffic: empirical evidence from Indian ports. Interna

34、tional Journal of Maritime Economics, II: 259281. 11. Hoffmann, J. 2001: Latin American ports: results and determinants of private sector participation. International Journal of Maritime Economics 3: 221230. | Article | 12. Hoffmann, J. 2002: The cost of international transport, and integration and

35、competitiveness in Latin America and the Caribbean. ECLAC FAL Bulletin 191: 3. 13. Hummels, D. 2000: Have international transportation costs declined?. Mimeo, Chicago. 14. Kumar, S and Hoffmann, J. 2002: Globalization: The Maritime Nexus. Maritime business and economics. LLP: London, November. 15. L

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39、 20. Redding, S and Venables, J. 2001: Economic geography and international inequality. Mimeo, London. 21. Roll, Y and Hayuth, Y. 1993: Port performance comparison applying data envelopment analysis (DEA). Maritime Policy and Management, 20: 195217. 22. Tongzon, J. 2001: Efficiency measurement of se

40、lected Australian and other international ports using data envelopment analysis. Transportation Research Part A: Policy and Practice 35: 314. 23. UNCTAD. 2002: Review of maritime transport. Geneva. 24. Valentine, VF and Gray, R. 2001: The measurement of port efficiency using data envelopment analysi

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42、Panama, November. 译文 港口的效率及国际贸易：港口的效率作为一个决定性的海洋运输成本 资料 来源： Maritime Economics 因此 ,即使贸易总额因距离而减小 ,事实上距离和空中贸易几乎零相关 (-0.001)（拉丁美洲内部贸易的估计数）。 对拉丁美洲内部贸易的运输成本 在表 3 中的 10 个国家，智利出口到乌拉圭的作为贸易价值百分比的运输成本是最高的，其次是厄瓜多尔到乌拉圭和巴拉圭的出口。平均而言，从其他拉丁美洲国家进口运输成本最高的国家是厄瓜多尔，其次是智利。巴 拉圭和乌拉圭之间的贸易具有最低的运输成本，其次阿根廷和乌拉圭，阿根廷和巴西。 用这些数据得出一个

43、关于现在运输服务的效率的草率结论以及总结在一个国家的运输比在另一个国家的运输要贵是不可能的。例如，定期航运服务（班轮服务），以及安第斯山脉的天然屏障似乎成为南美的西部和东部沿海国之间的运输会比同一个海岸更贵的部分原因。应该指出的是，表 3 中的数字是平均值，涵盖货物运输和许多不同类型的所有模式。 衡量港口的效率 装运港效率是通过广泛的问卷调查方式，利用直接测量所收集的信息来衡量的从而作为这个研究的一部分。许多潜在的 解释变量在港口效率中被测量，然后将其主成分分析分组。 通过考察，我们获得有关 1999 年港口活动的一些信息。问卷被送到 55 个港口码头，共收到来自 41 个主要处理一般货物的集

44、装箱港口码头的反馈信息，超过 90%终端设备处理容器输出到各自的国家，相应的响应大宗物品和托盘突破块材不足方面统计的目的，并已被丢弃，由于不够完整的反应其他终端数量将会被移除。 为了避免对参数化的关注和多个变量之间的相关性杂散，对一个主成分进行了分析研究。保留了原始变量的模式作为确定的因素，被作为新的变量引入回归，所得到的港口之间的关联 ,范围从 -0.295 活动变量 0.969(表 5)。正如预期 , 在 9 个变量中大多数都是紧密相关的。前三，超过了九个组成部分，占 70%的履行与标准数据的内在特征值超过 1。 每小时装卸率，处理能力，以及在货柜码头处理的平均每船装载高，在第二个组成部分，它可以被称为终端的生产力。 最后 ,平均港口停留的船只被发现作为一个单独的变量装上在第三部分。 原来，未旋转主成分最大限度地解决了负荷平方因素的总和，有效地创造了在上表中的因素集。然而，未旋转的解决方案是很难解释，因为变量往往负载多种因素。使用 Varimax 旋转实现了正交旋转轴 的因素最大限度地对所有的变量的因素负荷量的平方的一个因素方差。