|Enforcement authorities and organizations|
In economics, market concentration is a function of the number of firms and their respective shares of the total production (alternatively, total capacity or total reserves) in a market. In any industry, a handful of firms that hold a significant portion of the market share and likely engage in the practice of consolidation will indicate higher market concentration within that industry. The market concentration ratio measures the concentration of the top firms in the market, this can be through various metrics such as sales, employment numbers, active users or other relevant indicators. In theory and in practice, market concentration is closely associated with market competitiveness, and therefore is important to various antitrust agencies when considering proposed mergers and other regulatory issues. Market concentration is important in determining firm market power in setting prices and quantities.
Market concentration is affected through various forces, including barriers to entry and existing competition. Market concentration ratios also allows users to more accurately determine the type of market structure they are observing, from a perfect competitive, to a monopolistic, monopoly or oligopolistic market structure.
Market concentration is related to industrial concentration, which concerns the distribution of production within an industry, as opposed to a market. In industrial organization, market concentration may be used as a measure of competition, theorized to be positively related to the rate of profit in the industry, for example in the work of Joe S. Bain.
An alternative economic interpretation is that market concentration is a criterion that can be used to rank order various distributions of firms' shares of the total production (alternatively, total capacity or total reserves) in a market.
There are various factors that affect the concentration of specific markets which include; barriers to entry(high start-up costs, high economies of scale, brand loyalty), industry size and age, product differentiation and current advertising levels. There are also firm specific factors affecting market concentration, including: research and development levels, and the human capital requirements.
Although fewer competitors doesn't always indicate high market concentration, it can be a strong indicator of the market structure and power allocation.
After determining the relevant market and firms, through defining the product and geographical parameters, various metrics can be employed to determine the market concentration. This can be quantified using the SSNIP test.
A simple measure of market concentration is to calculate 1/N where N is the number of firms in the market. A result of 1 would indicate a pure monopoly, and will decrease with the number of active firms in the market, and nonincreasing in the degree of symmetry between them.[clarification needed] This measure of concentration ignores the dispersion among the firms' shares. This measure is practically useful only if a sample of firms' market shares is believed to be random, rather than determined by the firms' inherent characteristics.
Any criterion that can be used to compare or rank distributions (e.g. probability distribution, frequency distribution or size distribution) can be used as a market concentration criterion. Examples are stochastic dominance and Gini coefficient.
The most commonly used market concentration measure is the Herfindahl–Hirschman Index (HHI or H).
Where is the market share of firm i, conventionally expressed as a percentage,  and N is the number of firms in the relevant market. HHI can range from 10000/N to 10000. In most markets, an HHI below 1500 indicates a market with low concentration. An HHI of 5000 indicates a market where one firm has at least 50% market share, and an HHI of 10000 indicates a fully monopolised market. If market shares are expressed as decimals, an HHI of 0 represents a perfectly competitive industry while an HHI index of 1 represents a monopolised industry. Regardless whether the decimal or percentage HHI is used, a higher HHI indicates higher concentration within a market.
Section 1 of the Department of Justice and the Federal Trade Commission's Horizontal Merger Guidelines is entitled "Market Definition, Measurement and Concentration" and states that the Herfindahl index is the measure of concentration that these Guidelines will use.
Another common measure is the concentration ratio (CR). This ratio simply measures the concentration of the largest firms in the form
where N is usually between 3 and 5. Although CR can provide a quick insight into the overall market concentration, it is limited in providing an accurate representation of industry competition, as this ratio does not provide a measure for the concentration within the top n, as a merger between two firms would not increase the overall CR, but would increase overall market concentration using other measures.
As a rule of thumb, when using n=5 anything over 0.6 or 60% is considered an oligopoly, whereas when N=5, anything under 0.5 or 50% can be considered competitive and lowly concentrated.
|Type of Market||CR Range||HHI Range|
|Monopoly||1||6000 - 10 000 (Depending on Region)|
|Oligopoly||0.5 - 1||2000 - 6000 (Depending on Region)|
|Competitive||0 - 0.5||0 - 2000 (Depending on Region)|
Since the introduction of the Sherman Antitrust Act of 1890, in response to growing monopolies and anti-competitive firms in the 1880s, antitrust agencies regularly use market concentration as an important metric to evaluate potential violations of competition laws. Since the passing of the act, these metrics have also been used to evaluate potential mergers' effect on overall market competition and overall consumer welfare. The first major example of the Sherman Act being imposed on a company to prevent potential consumer abuse through excessive market concentration was in the 1911 court case of Standard Oil Co. of New Jersey v. United States where after determining Standard Oil was monopolising the petroleum industry, the court-ordered remedy was the breakup into 34 smaller companies.
Modern regulatory bodies state that an increase in market concentration can inhibit innovation, and have detrimental effects on overall consumer welfare.
The United States Department of Justice determined that any merger that increases the HHI by more than 200 proposes a legitimate concern to antitrust laws and consumer welfare . Therefore, when considering potential mergers, especially in horizontal integration applications, antitrust agencies will consider the whether the increase in efficiency is worth the potential decrease in consumer welfare, through increased costs or reduction in quantity produced.
Whereas the European Commission is unlikely to contest any horizontal integration, which post merger HHI is under 2000 (except in special circumstances).
Modern examples of market concentration being utilised to protect consumer welfare include:
The relationship between market concentration and profitability can be divided into two arguments: greater market concentration increases the likelihood of collusion between firms which, resulting in higher pricing. In contrast, market concentration occurs as a result of the efficiency obtained in the course of being a large firm, which is more profitable in comparison to smaller firms and their lack of efficiency.
There are game theoretic models of market interaction (e.g. among oligopolists) that predict that an increase in market concentration will result in higher prices and lower consumer welfare even when collusion in the sense of cartelization (i.e. explicit collusion) is absent. Examples are Cournot oligopoly, and Bertrand oligopoly for differentiated products. Bain's (1956) original concern with market concentration was based on an intuitive relationship between high concentration and collusion which led to Bain's finding that firms in concentrated markets should be earning supra-competitive profits. Collins and Preston (1969) shared a similar view to Bain with focus on the reduced competitive impact of smaller firms upon larger firms. Demsetz held an alternative view where he found a positive relationship between the margins of specifically the largest firms within a concentrated industry and collusion as to pricing.
Although theoretical models predict a strong correlation between market concentration and collusion, there is little empirical evidence linking market concentration to the level of collusion in an industry. In the scenario of a merger, some studies have also shown that the asymmetric market structure produced by a merger will negatively affect collusion despite the increased concentration of the market that occurs post-merger.
As an economic tool market concentration is useful because it reflects the degree of competition in the market. Understanding the market concentration is important for firms when deciding their marketing strategy. As well, empirical evidence shows that there exists an inverse relationship between market concentration and efficiency, such that firms display an increase in efficiency when their relevant market concentration decreases. The above positions of Bain (1956) as well as Collins and Preston (1969) are not only supportive of collusion but also of the efficiency-profitability hypothesis: profits are higher for bigger firms within a greater concentrated market as this concentration signifies greater efficiency through mass production. In particular, economies of scale was the greatest kind of efficiency that large firms could achieve in influencing their costs, granting them greater market share. Notably however, Rosenbaum (1994) observed that most studies assumed the relationship between actual market share and observed profitability by following the implication that large firms hold greater market share due to their efficiency, demonstrating that the relationship between these efficiency and market share is not clearly defined.
Schumpeter (1950) first recognised the relationship between market concentration and innovation in that a higher concentrated market would facilitate innovation. He reasoned that firms with the greatest market share have the greatest opportunity to benefit from their innovations, particularly through investment into R&D. This can be contrasted with the position taken by Arrow (1962) that a greater market concentration will decrease incentive to innovate because a firm within a monopoly or monopolistic market would have already reached profit levels that greatly exceed costs.
In practice, there are complications in observing the direct correlation between market concentration and its effect on. In collecting empirical evidence, issues have also arisen as to how innovation, a firm's control and gaps between R&D and firm size are measured. There has also been a lack of consensus. For example, a negative correlation was established by Connelly and Hirschey (1984) who explained that the correlation evidenced a decreased expenditure on R&D by oligopolistic firms to benefit from greater monopolised profits. However, Blundell et al. observed a positive correlation by tallying the patents lodged by firms. This general observation was also shared by Aghion et al. in 2005.
Schumpeter also failed to distinguish between the different technologies that contribute to innovation and did not properly define “creative destruction”. Petit and Teece (2021) argued that technological opportunities, a variable which Schumpeter and Arrow did not include during their time, would be included in this definition as it enables new entrants to make a “breakthrough” into the industry.
Research presented by Aghion et al. (2005) suggested an inverted U-shape model that represents the relationship between market concentration and innovation. Delbono and Lambertini modelled empirical evidence onto a graph and found that the pattern demonstrated by the data supported the existence of a U-shaped relationship between these two variables.
Although, not as common as the Herfindahl–Hirschman Index or Concentration Ratio metrics, various alternative measures of market concentration can also be used.
(a) The U Index (Davies, 1980):
(b) The Linda index (1976)
(c) Comprehensive concentration index (Horwath 1970):
(d) The Rosenbluth (1961) index (also Hall and Tideman, 1967):
(e) The Gini coefficient (1912)
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