Silviase/jgov_v1.4_full_ann huggingface dataset:
network by adding 2n potential new goods through the provision of a complementary link (say ES) to the existing links. $ ^{2} $

In typical one-way networks, the network effect is only indirect. When there are m varieties of component A and n varieties of component B as in Figure 2 (and all A-type goods are compatible with all of B-type), there are mn potential composite goods. An extra customer yields indirect externalities to other customers, by increasing the demand for components of types A and B. In the presence of economies of scale in production, the increase in demand may potentially increase the number of varieties of each component that are available in the market.

Exchange networks (financial networks such as the NYSE and NASDAQ, commodities, futures, and options exchanges as well as business to business “B2B” exchanges) also exhibit indirect network externalities. There are two ways in which these externalities arise. First, externalities arise in the act of exchanging assets or goods. Second, externalities may arise in the array of vertically related services that compose a financial transaction. These include the services of a broker, bringing the offer to the floor, matching the offer, etc. The second type of externalities are similar to other vertically-related markets. The first way in which externalities arise in financial markets is more important.

The act of exchanging goods or assets brings together a trader who is willing to sell with a trader who is willing to buy. The exchange brings together the two complementary goods, “willingness to sell at price p” (the “offer”) and “willingness to buy at price p” (the “counteroffer”) and creates a composite good, the “exchange transaction.” The two original goods were complementary and each had no value without the other one. Clearly, the availability of the counteroffer is critical for the exchange to occur. Put in terms commonly used in Finance, minimal liquidity is necessary for the transaction to occur.

Financial and business-to-business exchanges also exhibit positive size externalities in the sense that the increasing size (or thickness) of an exchange market increases the expected utility of all participants. Higher participation of traders on both sides of the market (drawn from the same distribution) decreases the variance of the expected market price and increases the expected utility of risk-averse traders. Ceteris paribus, higher liquidity increases traders’ utility. $ ^{3} $ Thus, financial exchange markets also exhibit network externalities. $ ^{4} $
As we have noted earlier, network externalities arise out of the complementarity of different network pieces. Thus, they arise naturally in both one- and two-way networks, as well as in vertically-related markets. The value of good X increases as more of the complementary good Y is sold, and vice versa. Thus, more of Y is sold as more X is sold. It follows that the value of X increases as more of it is sold. This positive feedback loop seems explosive, and indeed it would be, except for the inherent downward slope of the demand curve.

To understand this better, consider a fulfilled expectations formulation of network externalities. Let the willingness to pay for the nth unit of the good when $ n^{e} $ units are expected to be sold be $ p(n; n^{e}) $ . In this formulation, n and $ n^{e} $ are normalized so that they represent market coverage, ranging from 0 to 1, rather than absolute quantities. Willingness to pay $ p(n; n^{e}) $ is a decreasing function of its first argument because the demand slopes downward. $ p(n; n^{e}) $ increases in $ n^{e} $ ; this captures the network externalities effect, i.e., that the good is more valuable when the expected sales $ n^{e} $ are higher. At a market equilibrium of the simple single-period world, expectations are fulfilled, $ n = n^{e} $ , thus defining the fulfilled expectations demand $ p(n, n) $ .

Figure 3 shows the construction of a typical fulfilled expectations demand in a network industry. Each willingness-to-pay curve, $ p(n, n_{i}^{e}) $ , $ i = 1, 2, \ldots $ , shows the willingness to pay for a varying quantity n, given an expectation of sales $ n^{e} = n_{i}^{e} $ . At $ n = n_{i}^{e} $ , expectations are fulfilled and the point belongs to $ p(n, n) $ as $ p(n_{i}^{e}, n_{i}^{e}) $ . Thus $ p(n, n) $ is constructed as a collection of points $ p(n_{i}^{e}, n_{i}^{e}) $ . It is reasonable to impose the condition $ \lim_{n \to 1} p(n, n) = 0 $ . This means that, as the market is more and more covered, eventually we reach consumers who are willing to pay very little for the good, despite the fact that they are able to reap very large network externalities. It follows that $ p(n, n) $ is decreasing for large n. In Figure 3, the fulfilled expectations demand at quantity zero is $ p(0, 0) = 0 $ . This means that consumers think that the good has negligible value when its sales (and network effect) are zero. Although this is true for many network goods, some network goods have positive inherent value even at zero sales and no network effects. If the good has an inherent value k, $ p(0, 0) = k $ , the fulfilled expectations demand curve in Figure 3 starts at $ (0, k) $ .

Economides and Himmelberg (1995) show that the fulfilled expectations demand is increasing for small n if either one of three conditions hold:

(i) the utility of every consumer in a network of zero size is zero; or

(ii) there are immediate and large external benefits to network expansion for very small networks; or

(iii) there are a significant number of high-willingness-to-pay consumers who are just indifferent on joining a network of approximately zero size.
Figure 3: The fulfilled expectations demand and critical mass

The first condition is straightforward and applies directly to all two-way networks, such as the telecommunications and fax networks where the good has no value unless there is another user to connect to. The other two conditions are a bit more subtle, but commonly observed in networks and vertically-related industries. The second condition holds for networks where the addition of even few users increases the value of the network significantly. A good example of this is a newsgroup on an obscure subject, where the addition of very few users starts a discussion and increases its value significantly. The third condition is most common in software markets. A software application has value to a user even if no one else uses it. The addition of an extra user has a network benefit to other users (because they can share files or find trained workers in the specifics of the application), but this benefit is small. However, when large numbers of users are added, the network benefit can be significant.
### 3.2 Critical Mass

When the fulfilled expectations demand increases for small n, we say that the network exhibits a positive critical mass under perfect competition. This means that, if we imagine a constant marginal cost c decreasing as technology improves, the network will start at a positive and significant size $ n^{o} $ (corresponding to marginal cost $ c^{o} $ ). For each smaller marginal cost, $ c < c^{o} $ , there are three network sizes consistent with marginal cost pricing: a zero size network; an unstable network size at the first intersection of the horizontal line through c with $ p(n, n) $ ; and the Pareto optimal stable network size at the largest intersection of the horizontal line with $ p(n, n) $ . The multiplicity of equilibria is a direct result of the coordination problem that arises naturally in the typical network externalities model. In such a setting, it is natural to assume that the Pareto optimal network size will result.

The existence of an upward slopping part of the demand curve and the multiplicity of equilibria even under perfect competition also allows for a network to start with a small size and then expand significantly. Suppose, for example, that marginal cost is at $ c < c^{0} $ and a new invention creates a new product with significant network effects. Then, it is possible that the industry starts at the left intersection of the horizontal at c with $ p(n, n) $ as expectations are originally low, and later on advances suddenly and quickly to the right intersection of the horizontal at c with $ p(n, n) $ . Thus, the multiplicity of equilibria in network industries can lead to sudden significant expansions of network size.

### 3.3 Features of Markets with Network Effects

#### 3.3.1 Ability to Charge Prices on Both Sides of a Network

There are a number of fundamental properties of network industries that arise out of the existence of network effects.

First, a firm can make money from either side of the network. For example, a telecommunications services provider can charge subscribers when they originate calls or when they receive calls or for both. $ ^{5} $ When a network consists of software clients and
servers, both provided by the same firm, the firm can use the prices of the client and server software to maximize the network effect and its profits. For example it can distribute the client software at marginal cost (free) and make all its profits from the server software. In a similar vein, Adobe distributes the “Acrobat Reader” free while it makes its profits from the “Acrobat Distiller” product that allows the creation of files that can be read by the Acrobat Reader. The availability of prices on both sides of the network allows for complex pricing strategies, and, depending on the dynamics and market shares on the two sides of the market, can be used strategically to enhance and leverage a firm’s strong strategic position on one side of the network. Of course, this is not confined to high technology or software industries, and applies wherever complementary components are present. $ ^{6} $

#### 3.3.2 Externalities Internalized or Not

Second, in network industries, often the additional subscriber /user is not rewarded for the benefit that he/she brings to others by subscribing. Hence typically there are “externalities,” i.e., benefits not fully intermediated by the market. However, firms can use price discrimination to provide favorable terms to large users to maximize their network effect contribution to the market. For example, a large customer in a financial market can be given a very low price to be compensated for the positive network effect it brings to the market. $ ^{7} $

#### 3.3.3 Fast Network Expansion

Third, generally, the pace of market penetration (network expansion) is much faster in network industries than in non-network industries. In the earlier discussion on critical mass, we saw that, in a one-period model, as unit cost decreases, the network starts with significant market coverage. In the presence of frictions and not perfectly elastic supply, the network expansion is not instantaneous from 0 to $ n^{0} $ but rather is a rapid expansion following an S-shaped curve, as seen in Figure 4. This figure compares the market share expansion of a new good (diffusion) in presence (delta = 1) and absence (delta = 0) of network effects as a function of time. The self-reinforcing nature of network effects leads to a much faster expansion when they are present. $ ^{8} $
Figure 4: Equilibrium Diffusion with and without network effects

#### 3.3.4 Inequality of Market Shares and Profits

Fourth, markets with strong network effects where firms can choose their own technical standards are “winner-take-most” markets. In these markets, there is extreme market share and profits inequality. The market share of the largest firm can easily be a multiple of the market share of the second largest, the second largest firm’s market share can be a multiple of the market share of the third, and so on. This geometric sequence of market shares implies that, even for a small number of firms n, the $ n^{th} $ firm’s market share is tiny. In equilibrium, there is extreme market share and profits inequality. $ ^{9} $

The reason for the inequality is straightforward. A firm with a large market share has higher sales of complementary goods and therefore its good is more valuable to consumers. This feeds back resulting in even higher sales. Conversely, a firm with small market share has lower sales of complementary goods, and the feedback results in even lower sales. However, the low sales firm is not necessarily driven out of business because that would require too low a price by the high sales firm. In the absence of fixed costs, an infinite number of firms can survive, but there is tremendous inequality in market shares, prices, and profits among them. Good examples of this market structure are the PC operating systems market and many software applications markets.

To understand the extent of market share, price, and profits inequality in network industries, we provide results from Economides and Flyer (2000). As a benchmark, they assume that all firms produce identical products, except for whatever quality is added to them by network externalities. Also assume that no firm has any technical advantage in
production over any other with respect to any particular platform and that there are no production costs. We consider here only the extreme case of “pure network goods” where there is no value to the good in the absence of network externalities. The summary of the equilibria under total incompatibility (which can be enforced when firms have proprietary standards) is in the following tables. Firm #1 has the largest sales, firm #2 is the second largest, etc.

Table 1: Quantities, Market Coverage, and Prices Under Incompatibility $ ^{10} $

Number of firms I	Sales of largest firm $ q_{1} $	Sales of second firm $ q_{2} $	Sales of third firm $ q_{3} $	Market coverage $ \Sigma_{j=1}^{I} q_{j} $	Price of largest firm $ p_{1} $	Price of second firm $ p_{2} $	Price of third firm $ p_{3} $	Price of smallest firm $ p_{1} $
1	0.6666	0.6666	0.222222	2.222e-1
2	0.6357	0.2428	0.8785	0.172604	0.0294	2.948e-2
3	0.6340	0.2326	0.0888	0.9555	0.170007	0.0231	0.0035	3.508e-3
4	0.6339	0.2320	0.0851	0.9837	0.169881	0.0227	0.0030	4.533e-4
5	0.6339	0.2320	0.0849	0.9940	0.169873	0.0227	0.0030	7.086e-5
6	0.6339	0.2320	0.0849	0.9999	0.169873	0.0227	0.0030	9.88e-11
7	0.6339	0.2320	0.0849	0.9999	0.169873	0.0227	0.0030	0

Even with no fixed costs and an infinite number of firms, the Herfindahl-Hirschman index is HHI = 0.464, which corresponds to between two and three firms of equal size.

Table 2: Profits, Consumers’ and Total Surplus Under Incompatibility

Number of firms I	Π1	Π2	Π3	Profits of Last Firm Π1	Total Industry Profits Σ1j=1 Πj	Consumers’ surplus CS	Total Surplus TS
1	0.1481	0.1481	0.1481	0.148197	0.29629651
2	0.1097	7.159e-3	7.159e-3	0.1168	0.173219	0.29001881
3	0.1077	5.377e-3	3.508e-4	3.508e-4	0.1135	0.175288	0.28878819
4	0.1077	5.285e-3	3.096e-4	1.474e-5	0.1132	0.175483	0.28868321
5	0.1077	5.281e-3	2.592e-4	8.44e-7	0.1132	0.175478	0.28867817
6	0.1077	5.281e-3	2.589e-4	1.18e-14	0.1132	0.175478	0.28867799
7	0.1077	5.281e-3	2.589e-4	0	0.1132	0.175478	0.28867799

The market equilibria exhibit extreme inequality. The ratio of outputs of consecutive firms is over 2.6. Ratios of prices of consecutive firms is at least 7. The ratio of profits of consecutive firms is about 20. This means that a firm that has about 38% of the sales of the immediately larger firm, can charge only 15% of the price of the
next larger firm, and receives only 5% of the profits of the immediately larger firm. Entry after the third firm has practically no influence on the output, prices, and profits of the top three firms as well as the consumers' and producers' surplus. From the fourth one on, firms are so small that their entry hardly influences the market.

#### 3.3.5 Monopoly May Maximize Total Surplus

Fifth, in industries with significant network externalities, under conditions of incompatibility between competing platforms, monopoly may maximize social surplus. This is because, when strong network effects are present, a very large market share of one platform creates significant network benefits for this platform, which contribute to large consumers' and producers' surpluses. It is possible to have situations where a breakup of a monopoly into two competing firms of incompatible standards reduces rather than increases social surplus because network externalities benefits are reduced. This is because de facto standardization is valuable, even if done by a monopolist.

In the Economides-Flyer model, although consumers’ surplus is increasing in the number of active firms, total surplus is decreasing in the number of firms. That is, the more firms in the market, the lower is total welfare. This remarkable result comes from the fact that when there are fewer firms in the market there is more coordination and the network effects are larger. As the number of firms decreases, the positive network effects increase more than the dead weight loss, so that total surplus is maximized in a monopoly! Total surplus is highest while consumers’ surplus is lowest in a monopoly. This poses an interesting dilemma for antitrust authorities. Should they intervene or not? In non-network industries, typically both consumers’ and total surplus are lowest in a monopoly. In this network model, maximizing consumer’s surplus would imply minimizing total surplus.

Compared to the market equilibrium under compatibility, the incompatibility equilibrium is deficient along many dimensions. Consumers’ and total surplus are higher under compatibility; the profits of all except the highest production firm are higher under incompatibility; and prices are lower under compatibility except possibly in a duopoly.

#### 3.3.6 No Anti-Competitive Acts are Necessary to Create Market Inequality

A sixth implication of network effects is that, because inequality is natural in the market structure of network industries, there should be no presumption that anti-competitive actions are responsible for the creation of market share inequality or very high profitability of a top firm. Thus, no anti-competitive acts are necessary to create this inequality. The “but for” benchmark against which anti-competitive actions in network industries are to be judged should not be “perfect competition” but an environment of significant inequality and profits.
#### 3.3.7 In Network Industries, Free Entry Does Not Lead to Perfect Competition

A seventh implication of network effects is that, in network industries, free entry does not lead to perfect competition. In a market with strong network effects, once few firms are in operation, the addition of new competitors, even under conditions of free entry, does not change the market structure in any significant way. Although eliminating barriers to entry can encourage competition, the resulting competition may not significantly affect market structure. This implies that, in markets with strong network effects, antitrust authorities may not be able to significantly affect market structure by eliminating barriers to entry. See the earlier example where the addition of the fifth firm hardly changes the output of the first four firms.

The remarkable property of the incompatibility equilibrium is the extreme inequality in market shares and profits that is sustained under conditions of free entry. Antitrust and competition law have placed a tremendous amount of hope on the ability of free entry to spur competition, reduce prices, and ultimately eliminate profits. In network industries, free entry brings into the industry an infinity of firms, but it fails miserably to reduce inequality in market shares, prices and profits. Entry does not eliminate the profits of the high production firms. And, it is worth noting that, at the equilibrium of this market, there is no anti-competitive behavior. Firms do not reach their high output and market domination by exclusion, coercion, tying, erecting barriers to entry, or any other anti-competitive behavior. The extreme inequality is a natural feature of the market equilibrium.

At the long run equilibrium of this model with free entry, an infinity of firms have entered yet the equilibrium is far from competitive. No anti-competitive activity has led firms to this equilibrium. Traditional antitrust intervention cannot accomplish anything because the conditions that such intervention seeks to establish already exist in this market. Unfortunately, the desired competitive outcome is not.

Can there be an improvement over the market incompatibility equilibrium? Yes, a switch to the compatibility equilibrium which has higher consumers' and total surpluses for any number of firms. Is it within the scope of competition law to impose such a change? It depends. Firms may have a legally protected intellectual property right that arises from their creation of the design of the platform. Only if anti-competitive behavior was involved, can the antitrust authorities clearly intervene.

#### 3.3.8 Imposing a "Competitive" Market Structure is Likely to Be Counterproductive

The eighth implication of network effects is that antitrust interventions may be futile. Because “winner takes most” is the natural equilibrium in these markets, attempting to superimpose a different market structure (say, one in which all firms have approximately equal market shares) may be both futile and counterproductive.
#### 3.3.9 Nature of Competition is Different in Network Industries

A ninth implication of the network effects is that competition for the market takes precedence over competition in the market. The fact that the natural equilibrium market structure in network industries is winner-take-most with very significant market inequality does not imply that competition is weak. Competition on which firm will create the predominant (top) platform and reap most of the benefits is, in fact, often intense. In network industries, there is typically an intense race to be the dominant firm. In network industries, we often observe Schumpeterian races for market dominance.

A good recent example of Schumpeterian competition is the competition among dot-coms in 1999-2000. As explained earlier, economic models imply a high valuation of the dominant firm compared to other firms in the same network industry. The same perception prevailed on Wall Street. During that period, dot-com firms advertised very intensely and subsidized consumers so as to be able to achieve the coveted dominant position in the market. The easy availability of capital for dot-coms at the time facilitated this behavior as firms “burned” almost all the cash they had in their attempts to get the top market share. Many of the dot-coms failed because demand for their services was much lower than predicted or because of flaws in their business models. However, all the successful dot-coms, such as eBay, Amazon, and Yahoo, also followed this strategy.

Generally, in network industries, the costs of entry may be higher, but the rewards of success may also be higher compared to non-network industries.

#### 3.3.10 Path Dependence

A tenth implication of network effects is the importance of path-dependence. Path-dependence is the dependence of a system or network on past decisions of producers and consumers. For example, the price at which a VHS player can be sold today is path dependent because it depends on the number of VHS players sold earlier (the installed base of VHS players). The existence of an installed base of consumers favors an incumbent. However, competitors with significant product advantages or a better pricing strategy can overcome the advantage of an installed base.

For example, in the market for video players, VHS overcame Beta after six years of higher installed base by Beta. This was an implication of

(i) Sony’s mistake in disregarding network externalities and not licensing the Beta format;

(ii) Matsushita’s widespread licensing of VHS;

(iii) The fact that one low-end, low-priced VHS player can contribute as much to the network effect as a high-end, high-priced Beta player.

In the Beta/VHS case, it is clear that Sony mistakenly ignored the network effects that arose from the availability of rental tapes of pre-recorded movies. The main function