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1、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 advanta
2、ge 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 resul
3、ting 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
4、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
5、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 fi
6、rms, 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 mo
7、re 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 perform
8、ance 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, compet
9、itiveness 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
10、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 co
11、untrys 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 similari
12、ty 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 dist
13、ance 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
14、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 indicator
15、s 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 variable
16、s 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 share
