Why Firms Exist According to Coase’s Theory of The Firm

According to Coase’s theory of the firm, why do firms exist? How do firms contribute to the efficiency of the market economy in ways that ‘networks of independent contractors’ do not? How are the boundaries of the firm best established?

According to Coase’s theorem, with no transaction costs, the industry structure is unrelated to efficiency and profit maximization. Firms within an industry and their customers and suppliers can realize all possible efficiency gains through contracting and side payments. Exploiting profit opportunities would not require ownership changes or changes in firm size, as they could be realized by contracting between independent parties, regardless of the structure. Industry structure would accordingly be indeterminate, since no particular industry structure is better than another. Industry structure would also be uninteresting, because it would not impact profit or efficiency. However, transaction costs are pervasive, and therefore industry structure is interesting. Industry structure evolves as a function of three key processes: entry, exit, and market-share dynamics.

Organizational economics is relevant for the evolution of industry structure to the degree that it is relevant for understanding these three processes. Each of these three broad processes can further be subdivided into finer categories. Entry may occurs either through diversification by established firms or through the formation of new firms. Exit may occur via bankruptcy, closure, or divesture. Market share changes happen organically or via the market for corporate control. To understand industry dynamics, then, we must focus on entry, exit, growth, and decline by existing and new firms. This is the link between diversification and industry structure.

Diversifying entrants enter at a bigger scale and are more likely to survive and grow than de novo entrants. Consequently, diversifying entrants pose a bigger threat, in increasing rivalry and challenging incumbents’ market share, than de novo entrants. An important is therefore how vulnerable a given industry is to diversifying entry. Another is that diversification patterns are not random. While the performance effect of related diversification is controversial, the broad tendency of firms to diversify in a related manner is not. Indeed, in terms of predicting which industries a diversifying entrant chooses to enter, relatedness seems by far the most important determinant. From an industry perspective we may also note that some industries are not very closely related to any other industries, while still other industries are closely related to several. By means of an analogy; some industries have several close neighbors, and others do not. Also, some industries are related to fragmented industries, while other industries are related to concentrated industries.

Conditions such as these are key determinants of how likely a given industry is to become the target of diversifying entry. All of them are inextricably linked to the notion of relatedness. But as argued above, the notion of relatedness is itself inextricably linked to transaction costs. We focus on the consequences of diversification for industry structure. Note that within the received literature it has been more common to study the opposite, namely how industry structure affects diversification. The relatedness or performance link in industry characteristics: Controlling for such characteristics largely eliminated a positive relatedness or performance link. However the industry characteristics as exogenously given, whereas our point of departure is that industry structure is to a considerable extent an outcome of relatedness and diversification decisions. One way to determine how related various industries are to each other is to consider how often a pair of industries are combined inside a firm, compared to what one would expect if diversification patterns were random.

A pair of industries are related to the extent that this difference is positive, and unrelated to the extent that it is negative. Note also that such a survivor-based measure of relatedness will incorporate transaction costs considerations to the extent that these considerations are reflected in firms’ actual diversification decisions. This measure allows one to calculate the relatedness between a focal industry and all other industries in the economy, and subsequently the same can be repeated using each industry in the economy as the focal one.

The pattern that emerges if this is done is that industries differ significantly in how closely related they are to their closest neighboring industries for each industry one may calculate the sum of the relatedness scores for the four closest related industries. The mean of this sum will then describe how close the average industry has its four closest neighboring industries. This suggests that some industries are closer to their neighboring industries than others. It will probably depend on the level of concentration within those industries, for the following reasons.

First, if the industries close to the focal industry are concentrated, there is a smaller pool of potential diversifying entrants (ceteris paribus). In other words, the threat of direct entry from such an industry is smaller, weakening an important mechanism that may otherwise contribute to reduce concentration. Second, concentrated neighboring industries are themselves likely to be difficult to enter, reducing the number of entrants that can enter the focal industry indirectly, that is, using neighboring industries as stepping stones. Third, high levels of economies of scope or positive spillovers between neighboring industries can create an entry barrier that is shared between the industries, facilitating concentration in both the focal industry and its neighboring industries.

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