دانلود مقاله ISI انگلیسی شماره 19675
عنوان فارسی مقاله

مسائل مربوط به آنتی تراست در مقایسات بین المللی ساختار بازار

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
19675 2003 30 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Antitrust issues in international comparisons of market structure

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Econometrics, Volume 113, Issue 1, March 2003, Pages 129–158

کلمات کلیدی
شاخص هرفیندال - هیرشمن - تمرکز - تنوع - تصادفی - مواد غذایی فرآوری شده -
پیش نمایش مقاله
پیش نمایش مقاله مسائل مربوط به آنتی تراست در مقایسات بین المللی ساختار بازار

چکیده انگلیسی

The international comparison of market structure is complicated by a lack of adequate and comparable data. This paper addresses the issues encountered in the construction of international market data from financial reports, and provides a method for the comparison of market concentration and industry diversity. A firm-level data set is constructed to compute comparable measures of market concentration and industry diversity in the food industries for the U.S. and European Community. An innovation is the imputation of the distribution of sales of sub-code products by firm and the construction of nonparametric tests.

مقدمه انگلیسی

With the increasing globalization of the world's economies, antitrust concerns for industries within a given country become increasingly complex. Traditional analysis of antitrust, market definition, and market power is focused on the “structure” of industries under scrutiny. One important component of the market power puzzle is the degree of concentration within an industry. Applied economists have understood for some time that measuring market power is not a one-dimensional issue. Even before the advent of the so-called “New Empirical Industrial organization” (henceforth NEIO) in the early 1980s, economists working on these matters understood well that one could not simply look at any one aspect of industrial behavior in a given industry, and draw meaningful conclusions about the level of competitiveness therein. Bresnahan 1989 and Bresnahan 1997 refers to the traditional empirical approach to analyzing competitiveness in a given market as the structure, conduct and performance paradigm (or the SCPP). That is, the traditional approach has been to define the relevant product and geographic markets, and then to examine structure by looking at the degree of concentration in the market and to look at pricing behavior to see if the firm(s) is (are) indeed pricing their outputs close to marginal cost. The implicit assumption in this body of work is that marginal cost is observable and measurable and that a reduced form analysis of structure and performance on cross-section data is sufficient, cf. Church and Ware (1997) for an initial critique of this approach. Church and Ware (1999, p. 239) note that under the SCP strategy, knowledge of market share is an important element in ascertaining the degree of market power within a given market. The NEIO approach emphasizes the fact that in general, economic marginal cost is not observable. In addition, each industry has its own nuances which distinguish it from others and a “conduct parameter” is an unknown to be estimated, not assumed in a cross-section model, cf. Bresnahan (1989, pp. 952–953). Bresnahan further notes that the NEIO approach focuses on the use of an econometric model for an individual industry, NOT on its reduced form and using data over time. The NEIO approach has been applied in practice in several applications. Ellison (1994) builds on work by Porter (1983) to show that demand for a given product (they focus on railroads) can be assumed to be log-linear in price and takes the form View the MathML source In this model α1 is the elasticity of own price demand. The supply relationship can take the form View the MathML source where Si is the market share of the ith firm and wi is a “conduct” parameter. As in Church and Ware (1999, p. 441), let θ=Siwi in the expression above. Then, View the MathML source Thus, within the NEIO framework, the elasticity of demand for the product and a firm's market share are important determinants of conduct and, ultimately, of a given firm's market power within that industry. It should be clear that regardless of one's beliefs about how to econometrically ascertain market power, a firm's market share, and ultimately the degree of concentration in an industry, are important considerations in the determination of market power in that industry. Unfortunately, the paradigm of a firm controlled locally producing one product in a single market is rarely found and would most likely never be the case for all the firms in a market in a country that is integrated into the World Economy. In addition, even if the level of concentration is determined for the market of a particular product within a country, this would only result in a particular value for the concentration statistic. The important question for that market is: How does this compare to equivalent markets in other countries where the same technology and multinational firms compete? Thus, an important aspect of any analysis of the concentration of a particular market is the ability to make international comparisons so that the local conditions for competition can be put in a relative position. The purpose of this paper is to make international comparisons of market concentration and diversity from multiproduct firm data. Thus, we examine the measurement of concentration and diversity in a set of multinational multiproduct firms and demonstrate the comparisons that can be made between markets in different countries. In particular, we study firms in the food processing industry in the U.S. and the EC. Due to problems of data compatibility, cross-country comparisons of industrial structure are far less common (Yamawaki et al., 1989). Furthermore, the majority of market structure studies used in antitrust analysis and elsewhere, employ “establishment-level” data that do not reveal the possibility of interrelated and multinational ownership that characterizes many of the larger firms. This paper presents a strategy for using firm-level data to estimate market structure variables that can be compared across countries. Our method provides not only point estimate comparisons of various market structure measures, but also probability statements on the computed differences. One method for avoiding potential data incompatibilities is to use firm-level data, such as that collected by private investment information services, or from direct contact with the firms. This is the approach taken in Sutton (1991) for the four largest food producing firms in France, West Germany, Italy, Japan, the United Kingdom and the U.S. In contrast, the present work examines many of the larger firms, in all countries, and attempts to trace the impact of multinational ownership. Furthermore, because this is a larger group of firms, it relies exclusively on the information available from private investor data. An innovation of the present study is to employ a Monte Carlo method to simulate a firm's distribution of sales and employment over sub-product categories (SIC codes). Several statistical distributions were studied for the generation of “imputed sales/employment distributions”. These were chosen either on the basis of stylized empirical facts (e.g., log normal and Pareto), or to examine bounds and sensitivities (various normal distributions). A particularly more detailed data set (Trinet) was also employed to verify and calibrate. Log normal and Pareto receive further support from this latter exercise. The imputed shares and characteristics are then used to compute measures of industrial concentration and diversity that are subsequently compared across industry and country. Given the global nature of the data it is also possible to define markets among groups of countries, allowing comparisons between trade groups and specific nations. Due to data limitations, we examine only major firms (with total sales in all lines of production over $150 million) with at least one product in the food processing industry (SIC 20) as reported in the Dunn & Bradstreet computer data base (Dun & Bradstreet International, 1991). These data limitations also restrict the concentration indices reported here to a class of Herfindahl–Hirschman measures as opposed to measures based on shares of the total market. It should be noted that the definition of the market (i.e. the assumption of a four-digit SIC and the entire EC as a single market) may be flawed for the direct application of the conclusions from this work into a particular legal proceeding. However, we present this method of analysis for consideration as a tool for acquiring sufficient information for the allocation of resources for a more complete investigation by authorities in a particular jurisdiction. The paper is outlined as follows. In Section 1, the steps in constructing the data set are outlined. A description of the simulation analysis is given in Section 2, along with a comparison to results obtained from a similar source of these data for the U.S. alone. Then sample-size-independent (weighted) measures of industrial concentration and diversity are developed and computed using the imputed shares. The concluding sections discuss the results of the comparisons of these measures both across sectors and across countries or country groups.

نتیجه گیری انگلیسی

This research demonstrates the quality of the inferences available from a data set that is solely constructed from financial report data supplemented with a set of market participation ordered by importance. The concentration differences can be made a function of other variables that capture the taste and technological aspects of the SICs. The inclusion of all the countries in the EC in a single market may not be very reasonable for a number of industries—such as 2051 (bread, cakes and related products), 2082 (malt beverages) and 2084 (wines, brandy, and brandy spirits)—where individual EC countries have long histories of special tastes for these products. However, the EC is moving to develop true integration among these markets. Furthermore, some of the EC/U.S. comparisons may not be very meaningful due to the limit of the size of the firm included in the sample. In a number of cases the $150 million limit mean that a large proportion of firms (especially for the EC) were excluded. This will result in an over-statement of the concentration. This may well be the reason for the high relative concentration of SIC=2082 (malt beverages) and SIC=2064 (candy and other confection products) of the EC over the equivalent U.S. data. The malt beverage concentration may reflect the presence of only the large UK brewers which dominate the market as constructed because the smaller firms in the German market are not included. Careful attention should be paid to many of the comparisons made here. However, under the objective to study the potential for U.S. firms competing abroad and for EC firms competing in the U.S., the limitation to only large firms may prove to be very useful. If an argument can be made that scale economy is needed to consider competition in foreign markets, then limiting the analysis to large firms may be reasonable. However, the argument that concentration translates into potential ability to compete abroad may not be a viable argument, especially in light of the highly concentrated U.S. car market and the relatively low propensity for U.S. food producers to export (see Handy and Henderson, 1992). Future directions for this research include the verification of these results using simulated data for smaller firms that would be sampled under the $150 million sales level. Another future topic would be to differentiate the diversity measures to account for upward and downward vertical integration as well as other horizontal integration by region. Furthermore, the simulations used here could be extended to include simulations of data used in a second level econometric analysis. This could involve the use of the simulated data along with other information in regression analysis. A first step in this direction was the interregional correlation of the concentration measures.

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