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The existing literature has established the importance of industry concentration in explaining firm performance and information environments. However, little is known about whether and how industry concentration affects investors’ ability to anticipate future earnings. This paper aims to investigate this query by identifying and testing two channels, product market power and intra-industry information transfer, through which industry concentration affects the informativeness of stock returns about future earnings.

The paper measures the informativeness of stock returns about future earnings by the future earnings response coefficient (FERC)). This study estimates the FERC using a firm-level sample from 38 economies.

The authors find that industry concentration significantly enhances investors’ ability to predict future earnings. Further tests show that both product market power and intra-industry information transfer contribute to explaining the positive association between industry concentration and the FERC, with the former playing a more salient role. Finally, the authors show that a country’s effective competition law attenuates the positive impact of industry concentration on the FERC by weakening the economic impact of the two underlying channels.

This study contributes to the growing literature on the price-leading-earnings relation, industry concentration and international corporate governance.

The purpose of this paper is to examine the effect of a client's potentially conflicting needs for privacy and industry expertise on auditor concentration.

The method examines the distribution of auditor concentration assuming that auditor choice occurs randomly. It then examines the observed distribution within the random assignment to consider whether the privacy or expertise motive dominates.

The main finding is that at the client industry level, clients in a dominant firm industry structure prefer privacy; whereas clients in a competitive market structure prefer expertise.

An implication of this paper is that an increase in the number of large auditors may change auditor concentration in client industries that prefer privacy, an auditor different from its competitor.

Policy makers should note that auditor concentration is not only a function of the number of auditors, but also of the client industry structure.

The results provide a new measure for industry specialists and provide additional insight regarding auditor choice when privacy is relevant.

This study develops a systematic analysis of the concentration and inequality levels of 20 Spanish industries over the period 1990‐2001. The methodology traced is based on the use of indices both for evaluating the inequality (Gini,MRD&Coefficient of Variation), and for studying the concentration (Herfindahl‐Hirchman, Theil & Hannah‐Kay). This article adopts a dynamic approach, through the Distributional Change Index. The analysis confirms the different behaviour within the durable and non‐durable goods groups of Spain’s industries. Significant differences also appear with respect to the characterisation of the sectors. These characteristics are centred on the intensity of capital and skill, the capacity for technological development and the intensive use of agricultural inputs. Another of the most relevant conclusions is that referring to the increase competition of certain industries in Spain, particularly those belonging to the non‐durable goods group.

Location decisions are among the most costly decisions that organizations make. This research aims to examine location decisions from a macro perspective and to utilize findings for the development of a typology.

County level source information from the US Census Bureau, the United States (US) Department of Commerce: Bureau of Economic Analysis (BEA), National Association of Counties (NACO), and Fedstats is used in this analysis. Discriminant analysis as a profile analysis is utilized as an objective assessment of differences between the cluster concentrations.

The resulting typology of clusters concentrations is based on four constructs identified in the literature: innovation, specialization, complementariness and transfer of knowledge. This typology can serve as an aid in making these critical location decisions for practitioners as well as identifying future research topics for academia.

The research is an exploratory study and limited by its nature; therefore cause and effect cannot be definitively stated. Variables such as politics, environment, geography and cultural differences could have confounding effects on the study. The generalizability of the study could be affected because of the geographic location in relationship to national differences based on these and other variables.

This typology of cluster concentrations can be used as a tool for managers when making crucial location decisions.

The research is original in that it takes a more holistic approach to developing a typology of cluster concentrations. Rather than looking at specific industries and focusing on industry clusters, the research focuses on concentrations of industry clusters.

The strategic thinking behind numerous M&As is: “bigger is more important than better”. Such logic is also followed in the growth strategies of many players who believe that increasing scale via M&As will bring sustainable competitive advantage and generate above‐average financial performance and value creation for their shareholders. But numerous failed M&As and the modest financial performance of many large players compared to smaller ones question the universality of this logic. The aim of this article is to emphasize how important it is in strategic decision‐making to correctly understand and interpret contemporary concepts such as scale‐based completion, industry consolidation, and M&As.

The discussion is guided by three leading questions which need to be answered in any strategy formulation process: does scale matter, and where? What are the implications of industry concentration? What should be the strategic logic behind M&As? These questions are addressed according to the key assumptions and premises of the merger and industry consolidation theory and the Rule of Three concept, and the discussion is enriched by several cases and examples from various industries.

Scale does not necessarily bring competitive advantage, improve financial performance, or generate scale‐related synergies per se. To develop sustainable competitive advantage, executives must understand the true nature and mechanisms which drive the industry concentration process, value creation through M&As and the consequent advantages of increasing company scale.

The article discloses key strategic elements to be considered when formulating effective corporate strategies in a complex business environment and a context of rapidly concentrating industries. The discussion also raises awareness regarding the importance of strategic logic, correct interpretation of strategic concepts and sensitivity toward differences in industries in avoiding false conclusions and poorly‐formulated strategies.

In an earlier paper (House, 1973) the structural characteristics of manufacturing industries, as indicated by the number of competitors in relation to the size of the market, were related to their performance in Kenya for 1963. The index of concentration was constructed so that account was taken of the influence of foreign competition in the home market. This factor is relatively large in some markets of a developing country and could not be ignored as it has been in most other studies of the developed world. The results showed both a positive and continuous relationship between a measure of performance and the index of concentration. In addition, it proved impossible to establish any independent influence on performance of a proxy measure for the capital requirements barrier to entry, which has been found to be important in other studies (Bain, 1951; Mann, 1966).

The purpose of this paper is to investigate the relation between industrial concentration and technical inefficiency in the Indonesian food and beverages industry using a dynamic performance measure (dynamic technical inefficiency) that accounts for the presence of adjustment costs.

This research uses panel data of 44 subsectors in the Indonesian food and beverages industry for the period 1980-2014. The dynamic input directional distance function is applied to estimate the dynamic technical inefficiency. Further, the Granger causality between industrial concentration and dynamic technical inefficiency is tested using a dynamic panel data model. A bootstrap truncated regression model is finally applied to estimate the relation between industrial concentration and dynamic technical inefficiency based on the results from the Granger causality test.

The results show that the Indonesian food and beverages industry has a high dynamic technical inefficiency. Investigation of the causality of the relation shows that industrial concentration has a positive effect on dynamic technical inefficiency at the subsector level, with no reversed causality. The results suggest that the quiet life hypothesis applies to the Indonesian food and beverages industry.

The literature investigating the relation between industrial concentration and performance relies on static measures of performance, such as technical efficiency. Static measures provide an incorrect metric of the firms’ performance in the presence of adjustment costs associated with investment. Therefore, this research has a contribution in measuring dynamic technical inefficiency that accounts for the presence of the adjustment cost as well as its relation with industrial concentration in the Indonesian food and beverages industry.

The purpose of this paper is to further the knowledge of how industries perform, and sheds light on how the relative positions of industry change over time.

Using rank-order listings, histograms, and linear regressions, the comparisons of firms in the Fortune 1000 yield four results, two that are confirmatory, and two that are new.

As expected, industries differ widely in performance, regardless of the financial metric used, and there is a dramatic difference between within-industry variance (high) and between-industry variance (low). In fact, high-performing firms in less profitable industries often outperform low-performing firms in more profitable industries. Contrary to previous research, the paper shows that industries shift relative position over time: the industries with the highest return on equity in one year often are not the highest in subsequent years; and, contrary to IO theory, the paper finds that concentration is not a reliable predictor of profitability. Although certain industries may show increased profitability after undergoing concentration, there is no consistent relationship between an industry's concentration ratio and that industry's average profitability.

While the research is limited to its use of visual (such as histogram) and qualitative (such as rank-order) observations of only large (Fortune 1000) US-based, public firms, the results suggest that researchers should decompose the elements of industry structure and firm strategies to understand what, specifically, contributes to variation in firm performance.

For executives, the research confirms that the quality of their business strategies is more important than the initial choice of industries within which they choose to compete. Simply competing in an industry with high average profitability does not guarantee success.

This research shows how industries vary significantly in relative profit rankings over time, a finding that differs from prior research where time coefficients are found to be small. In addition, the research challenges the traditional IO notion that industry concentration leads to superior performance.

The purpose of this paper is to theorize how the industry life cycle unfolds differently across places and how economic agglomeration varies over time.

The paper relies on literature review and conceptual analysis.

It generates a dynamic geographic concentration model (i.e. an industry’s degree of geographic concentration drops in the growth stage, rises in the mature stage, and drops again in the new growth stage) and a localized industry life-cycle model (i.e. temporal dynamics differ between the center and the periphery).

It makes contribution by theorizing that the extent to which an industry is geographically concentrated changes over time, and by demonstrating how an industry’s center and periphery may experience different temporal dynamics.