集群技术一种分析产业竞争力的方法.doc

本科毕业论文外文翻译外文题目: Cluster Techniques as a Method to Analyze Industrial Competitiveness出 处：Inernational Advances in Economic Research 作 者：Michael Pender 原 文： Cluster techniques as a method to analyze industrial competitiveness TuranSubsatIntroduction Porters influential study on the competitive advantage of nations inspired a methodologically extended work on Austrian data. In contrast to Porters analysis, competitiveness is determined endogenously by means of statistical cluster techniques. Avoiding his cut-off approach, well-and badly performing industries are the objects of analysis. The resulting cluster center constitute the typical pattern of competitiveness for the chosen trade indicators, while the classifications produce a map of Austrian industrial export performance. The results further show that: 1) clustered industries generally are rare in the case of Austria; 2) some of them are located in declining, crisis-shaken sectors; and 3) competitiveness underlines the importance of transnational links (as opposed to narrow national boundaries) for the formation of successful industries. (JEL L10)INTRODUCTIONPorters 1990 influential study on the competitive advantage of nations inspired this methodologically extended work on Austrian data. Basically building upon Marshalls 1920 insights into the regional concentration of economic activities, Porter strongly emphasizes the importance of industrial clusters, characterized by the presence of successful horizontally related firms, as well as vertically supporting industries. Dense informational structures with significant externalities, intense competition, lower transaction costs, and cooperation and greater weight in political lobbying feed a self-reinforcing process of dynamic competitive advantages and growth (for more details, see Hutschenreiter and Peneder 1994).However, a major methodological problem in Porters analysis-where no cluster techniques in their literal (i.e., statistical) meaning are applied-is its determination of competitiveness of different industries by exogenously given boundaries on performance indicators (e.g., market share x% above a countrys average). In addition, all industries performing below this level are eliminated for the rest of the analysis, with the implication that no information on badly performing industries is retained.Taking Porters cluster analysis literally, competitiveness is determined endogenously by means of statistical cluster techniques in this paper. Furthermore, avoiding his cut-off approach, well- and badly performing industries are the objects of analysis. As cluster techniques are descriptive by nature, what will be gained are better insights into the composition of competitive and noncompetitive industries within a country, whereby the multi-dimensional character of the phenomenon competitiveness is explicitly acknowledged. This paper demonstrates on behalf of Austrian data how clustering techniques can be applied to create a profile of a countrys industrial performance. Finally, conclusions about the importance of clustered industries in Austria will be drawn.ANALYZING PATTERNS OF INDUSTRIAL COMPETITIVENESSStatistical clustering techniques provide a classification scheme of individual observations, depending on their relative similarity or nearness to an array of different variables. These classifications are determined endogenously by the individual data and the chosen cluster algorithm. The basic idea is one of dividing a countrys overall performance profile into segments by creating maximum homogeneity within and maximum distance between groups of observations.Although it is a frequent objective in applied economic research, competitiveness as a concept has stayed rather vague and lacks a universally accepted definition, as well as a broad consensus on the appropriate empirical measure Bellak, 1992. The ability to earn sustainable and high incomes while at the same time being able to maintain and improve on social and environmental standards Aiginger, 1987 may be the best definition of industrial competitiveness on an abstract level, because it demands measurement that goes beyond the more quantity-based indicators like trade specialization or market shares alone. The ability to earn sustainable and high incomes depends as well on quality indicators and, accordingly, on the level of prices that can be charged.Reflecting both the quantitative as well as qualitative dimensions of competitiveness, four variables have been chosen to enter the clustering algorithm in standardized form on the basis of mean values for the years 1990-92. Their underlying symmetric structure guarantees their implicit equal weighting in the clustering process, as is displayed in Table 1. Actually, there is a significant correlation between two pairs of variables, namely between market shares and trade specialization, as well as between comparative price advantage and relative export unit values. Both of them are no big surprise, since each pair share one common factor, which are export values and export prices, respectively. But none of them could be labeled redundant; MAS offers information about the relative importance of an industry on an international level and TSP on the national level. CPA gives analogous insights into the vertical composition of prices, while RUV does so horizontally.After the set of variables has been chosen, an optimization cluster technique, based on the minimization of within-group dispersion, is used to classify 208 product groups (SITC, three-digit) into clusters of maximum homogeneity according to these indicators of trade performance. In this first step of the analysis, the set of observations is divided by a pre-defined number of clusters. Then cluster-centers are estimated for each group, which are vectors with means of the corresponding for each variable. The optimization criterion is given by the trace of the matrix of within-group dispersion W (of dimensions x p variables), which consists of vectors xj for tenth observation in the ith group and the according cluster-centers:Minimization of trace (W) is done by iterative algorithms, where the position of the cluster centers are varied until the process converges. Convergence means that there is no additional alteration that improves the clustering criterion above a prespecified value. However, the erogeneity of the total number of clusters g that shall be obtained and has to be chosen in advance by the researcher remains a serious problem with optimization techniques. In spite of the fact that this choice of g can have a major impact on the final results, there exists no general rule for its determination. To partly overcome this difficulty, the following self-binding rule of thumb was applied in the current analysis: Choose the lowest number g that maximizes the quantity of individual clusters which include more than 5 percent of the observed cases. According to this rule, the number g = 21 clusters, producing eight clusters comprising more than 5 percent of total observations, was identified to be able to represent the underlying structure best. The resulting outcome is an endogenous classification of all observations into the given numbers of clusters.THE INTRODUCTION OF THE AUSTRIAN TRADE PERFORMANCEAnalogous to population densities in alpine regions, to locate near the top is tough and only a very small number of peak performers can afford to live there. The majority of product groups belong to the lower regions of industrial performance (Levels Ixv, N, 0, Ixp). Mountainous “peaks” to the left refer to those clusters which show extraordinarily high values in terms of export volumes (Levels Ilxv and III). Those to the right refer to high values in terms of export prices (Level Ixp). The different performance levels, which have resulted from the regrouping of the 21 clusters according to the information gained in steps two and three, are interpreted as follows: In the area of basic industries, an internationally successful cluster grew out of rich endowments of wood and covers: simply worked wood (SITC 248), particle boards (634), wood manufactures (635), and paper and paperboard (642). Another cluster of successful related and supporting industries includes steam or other (vapor-generating) boilers (711), manufactures of based metal (699), as well as structures of iron, steel, or aluminum (691). Rails and railway track construction materials of iron or steel (677), together with railway maintenance vehicles (791), form an especially competitive product group in the domain of public transport. The production of internal combustion piston engines (713) is one of the most successful areas of Austrian industrial activity. Besides the innovative capacity of some of the Austrian manufacturers, this extraordinary performance is mainly due to successful transnational links to the international automobile industry. The crisis-shaken textile industry still plays an important, but seriously diminishing role in Austrian manufacturing. Particularly successful product groups are man-made fibers for spinning (276), manufactures of leather and saddleries (612), and knitted and crocheted fabrics (655), together with tulles, laces, and the like (656). Television receivers (761) are the most successful product group in the electronics industry. Again, transnational links via foreign ownership are a major determinant of export performance. Other successful product groups are baby carriages, toys, games, and sporting goods(894), glassware (665), lighting fixtures and fittings (813), monofilament (583), medicinal and pharmaceutical products (541), and non-alcoholic beverages (111).CONCLUSIONCareful examination of the listing above (results are displayed in more detail in Peneder 1994) leads to the conclusion that national clusters as proposed by Porter-and defined by a strong network of related and supporting industries at the same time showing above average performance-are relatively rare in the case of Austria, especially in comparison with other countries like Germany van derLinde, 1992 or Switzerland Borner et al., 1991. One exception is the highly competitive wood processing industry, where simply worked wood, particle boards, wood manufactures, as well as paper, paperboard, and articles of paper form the international spearheads of a broad national cluster based on the common primary resource and acquired skills. Another example is located in the sector of public transportation, with the production of rails and railway track construction materials on the one hand, and railway maintenance vehicles on the other.The results also indicate at least two main reasons for the lack of dynamic and successful national industrial clusters in Austria. First, some of the most successful Austrian product groups belong to old industries going through deep structural crisis, e.g., textiles and, to a lesser extent, steel production. Their example is a warning as to how successful clusters in the present can form the crisis-shaken industrial conglomerates of the future. For these industries, many factors directly affected by their characteristics as clusters (e.g., strong influence on political decision making and endowment with specialized factors) can inhibit necessary structural change and, therefore, dampen prospects of overall industrial development.In addition, for a small, open economy like Austria, national boundaries can be too narrow to establish broad cluster relationships. Globalization is an at least equally important factor for success, as are clusters of related and supporting industries Rugman and Verbeke, 1992. Some of the most successful industries in Austria can be interpreted as parts of bigger transnational clusters, like the highly competitive production of internal combustion piston engines.集群技术，一种分析产业竞争力的方法 TuranSubsat引言波特在国家竞争优势影响力的研究启发了一次对奥地利资料的进行方法论扩展。按照波特的分析，竞争力是由各项数据集群技术所决定的。避开他的“中断”方法不说，产业运营的好坏是这次分析的主体。集群中心组成了所选的贸易指标的典型竞争力形式，其中的各项分类组成了奥地利出口表现的蓝图。研究结果进一步显示了以下三点：1）集群产业在奥地利非常罕见；2）一些集群产业属于处在下滑状态，并且危机四伏的产业；3）竞争力强调了跨国联系（与缩小国界相反）对形成成功的产业的重要性。介 绍波特（1990）在国家竞争优势影响力的研究启发了一次对奥地利资料的进行方法论扩展。基于马歇尔（1920）对区域集团化经济行为的深刻见解，波特着重强调了产业集群的重要性，指出了成功水平联系着的企业和与其垂直关系的支持性产业的存在。但有着明显的外在性，激烈的竞争和低成本交易等特征的复杂信息结构，与合作和逐渐增加的政治游说一起促进了动态竞争力优势和竞争力增长的自我巩固。就波特的集群分析来看，竞争力是由本文中所涉及的数据集群技术所根本决定的。另外，避开他的“中断”方法不说，产业运营的好坏是分析的主体。群集技术是可描述性的，能够获得什么优势最好将一个国家的竞争性和非竞争性产业结合起来分析，这样从多方面来综合定义 “竞争力”才能明确。基于奥地利的资料，这篇文章说明了怎么应用集群技术来建立一个国家的产业表现的档案。最后，文章将给出关于产业集群在奥地利的重要性的结论。经济研究-分析产业竞争力的模式根据他们的相似性或大量不同数组的接近性，统计聚类技术提供了一个个人的观察分类方案，。这些分类由内生的个人数据和所选择的聚类运算法则确定。最基本的理念是通过在其中创造最大限度的同质性和观察群体之间的最大距离，将一个国家整体性能切割成段。尽管在应用经济学研究中，这是一个明确的目标，竞争力作为一个概念一直是一个相当模糊的和缺少大众公认的定义，同时在适当的经验性估量上 Bellak，1992也很难达成广泛的共识。“获得持续高收入同时在社会和环境标准中能够保持和完善的能力”，1987年的Aiginger也许是抽象水平上对产业竞争力的最好定义。因为它需要超越更多以数位基础的指标，像贸易专业化或单独的市场份额。“获得持续高收入的能力”同样也依赖于质量指标。因此，在这个水平上的价格可以控制。为了反映竞争力定量和定性的尺度，基于1990-92的平均值，被选中的四个变量作为标准化形式代入聚类算法。他们潜在的对称结构保证它们聚类过程中的隐含平等加权，如表1所示。事实上， 在两对变量之间有一种重要的关联，即在市场份额和贸易专业化之间，同时存在于相对价格优势和相关出口单位价值中。他们都没有太大不同，因为每一对都来享有一个共同的因素，分别是出口价值和出口价格。但他们中没有一个能被重复标记；MAS在国际水平上提供了关于一个行业的相对重要性的信息和TSP在国家水平上也提到了CPA给了关于垂直组成的价格的类似见解，同时RUV在水平上也这么做了。在被选择的变量固定后，基于群体内分散性的最小化，一种优化聚类技术，被用于将208产品集团（SITC， three-digit） 进行分类，根据这些贸易性能的指标使之进入最高同质性的集群。在这个分析的第一步，观察结果的设定被一个g群集的预定义数组除。然后数集中心为各小组进行估计，就是那些为每一个变量配有相应方法的向量。集群之间的散布W（变量x、 p的大小）的矩形痕迹给出了优化准则，在Xij集群的观察数据由向量x - j组成和相应的集群中心：通过迭代算法对微量(W)进行计算，群中心的位置是多样化的，直到过程汇聚一点。汇聚意味着没有可以在指定的价值上改进聚类标准的额外变更。然而， 集群g的总数的外生性应当被获得经号，运用最优化技术，通过研究者遗留的严重问题，必须提前做出选择。尽管这种g选择可能对最终结果带来了极大的影响是事实，但对它的决定不存在的一般规则。为了在一定程度上克服这个困难，以下自我粘合的拇指规则被应用于当前分析：“选择最低数组g，将个人集群的数量最大化，包括超过观察到的情况的5%。”根据这一规则，这个数字集群g = 21个集群，生产8个集群，包含超过5%的总数观察，被确认为能够最好的呈现的基底结构。结果是一个所有观察的内生分类，到给定的数量的集群。从单独聚类过程的第一步结果中，很难来解释潜在的性能模式。因此， 簇聚过程的第二步被执行，在这个过程中，第一步的21个集群的结果进入一个层次聚类算法，作为他们相符的集群中心的观察和价值。层次技术是基于二次矩阵D(n维的x 、n观察)，它包含选定的措施相对距离dij之间的任何对n观察根据他们的属性。距离可以用不同的计算方法。最常用的措施同时也应用于此分析是欧氏距离平方，测量两种观察数据i 和j之间的不同点，关于P变量的选择固定。在接下来层次聚类分析的会集算法中，所有的观察最初都是被当作独立的单一集群。所以有许多集群观察。在反复迭代的过程中，对所有成对的距离进行了比较和那些最小距离聚起来，形成一种常见的簇。一般连结，通过在一个外部观察和每个集群中的观察之间的每对单一对子的平均距离，测量了新成立的凝块之间的距离。在任何一步迭代中，新距离矩阵失去了一列和一栏，所以，它的新维度是（n 1）。这个过程是一直持续的，直到所有观察达到一个大型的、单聚集的凝块的时候。这个时候运算法则找到了它的自然终结。重要的是要注意，与优化方法相反，层次聚类的过程是不可逆转的