Effective competition inside an Oligopoly:
China’s e-commerce market and its welfare implications
Ruiqi Zeng
University College London
Department of Economics
March 2017
1. Introduction
China’s e-commerce market is the largest and one of the fastest growing and most developed
in the world. It surpassed US to be the largest in 2013 (Bain & Company, 2015). BCG (2015)
estimates that this market will drive 42% of all growth in Chinese consumption over next 5
years. Especially, with the rise of platforms such as Taobao, Tmall and JD.com, online
marketplaces for Consumer-to-Consumer (C2C) and Business to Consumer (B2C) have
impacted people’s consumption pattern greatly. However, there is an important element of
market structure, what I propose as two-layered market, in online marketplaces that has been
neglected in literature. Therefore, the welfare implications have not be properly explored.
Considering the economic contribution of the sector and the number of stakeholders involved,
careful analysis must be done in order to lay the ground for optimal policy.
To this end, I aim to test two hypothesis, one regarding its market structure, and the other
regarding welfare implications. The first hypothesis states that inner markets resemble effective
competition while the outer market resembles oligopoly. Inner markets are the virtual markets
facilitated by marketplace platforms. Outer markets, on the other hand, are the market for
platform providers. The second hypothesis states that two-layered market naturally have high
market concentration and this can in fact facilitate more effective competition in the inner
markets.
To test the first hypothesis, I will attempt to identify the market structure of China’s online
marketplaces by mapping the features of the relevant market to the existing descriptions of
different market structures. Using the predictions of the models of market structures identified,
I aim to estimate the outcomes in those markets and compare to the outcomes in practice using
available data. This is an approach similar to those taken by Competition Authorities when
conducting market investigation for anti-trust cases. However, the main difference is that I will
not be considering the behavior of firms. For the second hypothesis, I aim to theoretically
explain why two-layered market naturally converges to oligopoly and why such market
structure may be promote effective competition through welfare analysis. I will be combining
simple model of two-layered market with theories from two-sided markets and electronic
markets.
2. Literature review
Despite the size and development of China’s online marketplaces, there is a lack of geographic
focus on China in related literature outside China. Inevitably, online marketplaces have
attracted significant attention in mainland China, both from academic, business, and political
circles. For example, Yong (2016) from Shanghai Development Strategy Research Institute
published a paper arguing that the trend of the e-commerce marketplace providers as an
oligopoly is likely to remain in the next several years. However, most papers are narrowly
focused on outer market and failed to develop framework and models to examine the sector.
Furthermore, the existing literature are mainly theoretical models on the general two-sided
market. Two-sided market, sometimes called multi-sided market/platform, is a strand of
literature where online marketplaces are typically examined in economics. This field is said to
have originated on the papers published by Jean-Charles Rochet and Jean Tirole, which started
to circulate around 2001 (Evans, 2011). Two-sided markets are characterized by the existence
of the chicken and eggs problems. Such problems arise when a customer on one side of the
market will be willing to participate to the platform activity only if he expects a sufficient
participation from the other side (Caillaud and Jullien, 2003). While a number of concepts and
price theories have been developed, they fail to develop models online marketplaces separately
and generally lack case studies and empirical evidences. Furthermore, they do rarely
distinguish between offline and online two-sided markets.
Another strand of literature that could possibly make up for such lack of focus is on electronic
markets, especially focusing on the welfare impacts of information systems electronic markets
are built upon. “An electronic marketplace is an interorganizational information system that
allows the participating buyers and sellers to exchange information about prices and product
offerings” (Strader and Shaw, 1997). It offers online or electronic way to facilitate transactions
between buyers and sellers that could provide support for the whole process in the transaction
process (ibid). It is possible to see from the papers by Bakos (1997), Grover and Ramanlal
(1999), and Brynjolfsson et al (2003) that there is no consensus on the welfare implication.
Furthermore, there are few papers that combine the two fields of two-sided markets and
electronic markets while theories and models in two-sided markets could be applied in
electronic markets.
Lastly, there are some literature on the anti-trust analysis for two-sided markets. Evans and
Schmalensee (2013) wrote on the antitrust analysis of multi-sided platform business. They
argue that due to complexity of multi-sided platform business, it is inappropriate to apply
standard economic models used in antitrust analysis on single-sided markets. They also states
that it is impossible to have one versatile model as there are various forms in two-sided markets
with different characteristics. This view is in line with the paper published by Wright (2004)
who wrote on the six fallacies when applying one-sided logic in two-sided markets. A paper
published by OECD (2009) also emphasized the significant difficulties in applying economic
models in two-sided market cases.
3. Market analysis
3.1 Product and services
Online marketplaces are e-commerce platforms where consumers can purchase products and
services from sellers such as manufacturers, large and small retailers and individuals through
online stores on platforms similar to eBay and Amazon Marketplace.
The platforms take the forms of both websites and mobile apps. While the main function is to
match buyers and sellers, they also offer a wide range of services are integrated to make the
online shopping experience smooth. Such services include online payment platform, search
engines, social media, logistics, and advertising.
3.2 Two-sided platforms
In this section, I would like to explain the concepts of two-sided platforms and why they are
relevant to the market in focus. While some call it multi-sided platforms instead of two-sided,
I will be using the term two-sided platforms. As there are many stakeholders involved in online
marketplaces, I will be looking at only sellers and buyers in inner markets as consumers of
platforms, for more focused analysis.
Among the various definitions of two-sided platforms proposed by researchers, one by Evans
and Schmalensee (2007) is broader definition that focuses on the role of two-sided platforms
as economic catalysts. It states that a two-sided platform has “(a) two categories of customers;
(b) who need each other in some way, (c) but cannot capture the value from their mutual
attraction on their own; and (d) rely on the catalyst to facilitate value-creating interactions
between them”. As such, platforms can generate value that would not exist in its absence. The
new value arises from solving problem of coordination and transaction cost between the groups
of customers.”
Indeed, in China’s online marketplaces, there exist two groups of customers, sellers and buyers,
and act as the intermediaries to facilitate the matching and transactions between two groups.
Online marketplaces also possess various features of online platforms that help play the role as
economic catalysts. According to Oxera (2015), there are various benefits of online platforms
to consumers. Online platforms can improve the ease of transactions through reduced search
and transaction costs. Increased access to information through ratings and reviews facilitate
better matching and greater information transparency. With lower barriers to entry for small
and medium enterprises, increased transparency and reduced geographic barriers, it increases
seller competition. There are also greater choice or variety of product available. Online
platforms also enable sellers to expand their market and reduce cost. Therefore, online
marketplace is not only effective but efficient in solving coordination and transaction cost
problems.
The OECD report (2009) also highlighted two other characteristics of two-sided platforms.
One is that there exist indirect network externalities across two groups of consumers.
According to Rochet and Tirole (2006), such externalities comprise of usage externality and
membership externality. Usage externality occurs when two different economic agents need
each other to generate value. On the other hand, membership externality occurs when there is
positive correlation between the utility received by agents on one side and the number of agents
on the other side. In china’s online marketplaces, both types of externalities exist. Buyers and
sellers need each other to conduct transactions and make profit or purchase a product (usage
externality). The utility received by buyers on one side increases with the number of sellers,
and vice versa (membership externality).
The third characteristics is the non-neutrality of price structure. This means that transactions
level on platforms is endogenous on the price structure, which represents how price are
allocated to different consumer groups (OECD, 2009). In China’s online marketplaces,
platforms directly charge prices only to sellers as fixed (etc annual membership), variable
(transaction fees). However, these fees are passed on to buyers as part of their price paid to
sellers for goods and services purchased, unless sellers are making a loss. Therefore, if the
platforms charges greater fees to sellers, their cost function will change, marginal cost curve
shifts, which is likely to affect the price charged to consumers and affect quantity demanded.
It is important to notice, however, that online marketplaces is only one type of two-sided
markets. There are four different forms of two-sided markets (Evans and Schmalensee, 2005):
exchanges, advertiser-supported media, transaction devices, and software platforms. Online
marketplaces belong to the category of exchanges as they are characterized as platforms to
match sellers and buyers and enable them to look for the other those in the other group and
complete transactions (ibid).
3.3 Two-layered market
I would now like to propose a new concept of two-layered markets. As explained in the
literature review, the existing literature fail to recognize the existence of inner markets and the
relationship between inner and outer markets. This new term is aimed to fill this gap and enable
more solid analysis of online marketplaces.
I define two-layered market as a market structure where markets exist inside a platform. The
platform create markets in its system and facilitate transactions. It differs from standard markets
in that there are two levels of competition: competition among platform providers and
competition among sellers in inner markets. This structure is built on technology that creates
virtual platforms and markets, therefore, offline markets will not possess this market feature.
Two-layered markets are two-sided by nature, as the inner markets serve two groups of
customers, sellers and buyers. Another characteristics is the existence of asymmetric
competition, also recognized in paper by (Evans and Schmalensee, 2012). By asymmetric
competition, I mean that there are competitions in different dimensions. Platforms in outer
markets can face competition with other B2C online direct retailers as well as offline large
retailers and malls. On the other hand, sellers in inner markets face competition in at least four
dimensions. The first is with other sellers on the same platform; the second is with sellers on
other platforms; the third is with online direct retailers (B2C); and the last is with offline
retailers.
3.4 Market definition
As outlined by Bishop and Walker (2010), market definition is crucial in competition analysis
as the scope of analysis depends on market definition. This paper is not aimed to investigate
the market for merger and competition issues like competition authorities do but rather to fill a
gap in academic research. However, market definition is still required to ensure the focus of
analysis is consistent. As discussed in the introduction and illustrated in figure 1, the market of
focus is China’s B2C and C2C online marketplaces. While many papers and articles do not
distinguish online marketplace and direct retailers clearly, I will focus on marketplaces as there
is no inner market in direct retailers. Cross-border marketplaces are excluded as I am only
interested in domestic consumers. Business-to-Business (B2B) marketplaces are also excluded
as I am interested in direct welfare impacts on individual consumers.
A question may be raised as to why I include both B2C and C2C markets. By general definition,
B2C and C2C marketplaces differ in that the sellers (suppliers) in B2C markets are business
while they are individuals in C2C. However in China, sellers’ differences in B2C and C2C are
not as pronounced as in other countries. While sellers in B2C and C2C are both business, those
in C2C in China are mainly small enterprises and microbusiness that lack company registration
(McKinsey, 2013). Although consumers generally distinguish them in terms of perceived
quality (eMarketer, 2017), consumers can purchase same category of goods and hence target
customers of B2C and C2C marketplace sellers can overlap. It is highly likely that they compete
with each other and this competition can be seen from a shift in share of C2C to B2C platforms
in recent years (iResearch). It is therefore possible to infer that consumers may see B2C and
C2C online marketplaces as substitutes.
Figure 1
C2C
Marketplace
B2C
B2B
Relevant market
Direct retailers
3.5 Outer market
Market size
Figure 2
China’s retail e-commerce has seen tremendous
growth in its size. In terms of gross merchandise
value, it grew to $0.6 trillion in 2015 from $0.1
trillion in 2010 (BCG, 2015). E-commerce as a
share of total private consumption also increased
from 3% to 15% and is expected to reach 24% by
2020 (ibid). As most reports include not only
marketplaces but also other online B2C retailers,
the figure is likely to overstate the market size for
C2C and B2C marketplaces. As figure 2 shows,
there has been a shift from C2C to B2C market
and currently C2C is estimated to account for
45% while B2C account for 55% (iResearch,
2016). As for market share, the figures for B2C is
likely to be overestimated as it includes B2C
online direct retailers.
Note. Retrieved from “China's Online Shopping GMV
Approached 5 Trillion Yuan in 2016”. Copyright 2016 by
iResearch Global Group.
Main players and their market share (Gross Merchandise Value)
The main players are Taobao, Tmall and JD.com. Although there is no recent figures, Taobao
had market share of nearly 90% in C2C marketplaces in 2013 (China E-Commerce Research
Center.). Tmall had 53% and JD.com 25% in 2016 for B2C counterpart (iResearch). Using the
ratio of C2C and B2C marketplaces (45% v 55%), we can estimate roughly that the market
share for Taobao, Tmall, and JD.com is 40%, 29%, and 14% respectively. 3-firm concentration
ratio, the sum of market share of largest three firms, is 83%, which is significantly high. As
these figures are from different sources and measured in different years, they are only an
approximation.
Figure 3
Market Share in China's B2C and C2C
marketplaces
Others
17%
JD.com
14%
Taobao
40%
Tmall
29%
Consumer
As this is a two-sided market, the consumers are both the sellers and buyers in inner markets.
As shown in figure 4, there are numerous sellers and buyers.
Products & Services
Products and services are generally similar in that they provide platform for sellers and buyers
to match. However, they are differentiated in certain ways. The quality of services and systems
is likely to differ. Furthermore, number of users, both buyers and sellers, differ significantly
among platforms. One important difference may be the business structure of marketplaces. For
example, 41% of JD.com gross merchandise value derive from marketplaces while 59% from
direct retailing. However, I will neglect the implications of direct retailing business for
simplicity of analysis.
Cost
I estimate that the cost structure is asymmetric in this market. Cost in e-commerce is reported
to be technology intensive. Especially, there is significant investment into the digital
infrastructure. Although marginal cost of additional user should be relatively low, the
differences in user and service portfolio must generate asymmetric structure. One example may
lie in logistics. JD has its own centralized distribution system and networks including
warehouses while Taobao and Tmall have less centralized system without their own warehouse.
Barriers to entry
There seems to be significant barriers to entry in outer market. CMA report highlight some key
barriers to entry on online platforms. The main barriers include critical mass, indirect network
effects, and economies of scale. These will be explained and discussed later in the welfare
analysis section.
Price
In this market, price is charged only to the sellers as fees. Such fees mainly include membership,
transactional, advertising. These fees are then passed onto the buyers as part of the price they
for services and products, if sellers are making profit. With high market share, major platforms
are price makers. This can be seen from sudden increase in fees for Tmall sellers in recent years
(Sina.com, 2012).
Profitability
Certainly Taobao and Tmall are making positive profit with Adjusted EBITA margin of 64%
(Alibaba, 2017). JD.com has been making some loss over the past few years, however, nonGAAP operating margin has now turned positive for 2016 3rd quarter (JD.com, 2016). Although
not directly comparable the seemingly lower profitability of JD.com mall may be due to its
large proportion of business conducted through direct retailing instead of marketplaces.
Figure 4
Summary: Comparison of main players
Type
Estimated
share
# sellers
Taobao
Tmall
JD.com
C2C
B2C
B2C
market 40%
29%
14%
Over 500，000
Over 100,000
Over 1.5 million
(according to their
websites)
# buyers
493 million (Alibaba, 2016)
Over 198.7million
(JD.com, 2016)
# product listing
Over 8 billion (2013) Over 1 billion (China 40 million
Briefing, 2015)
(China
Briefing,
2015)
Fee (Price)
Zero explicit fees
Fixed and variable Fixed and variable
Advertising fee but fees
fees
not compulsory
Profit
Adjusted EBITA margin 64% (2016 Q4, Non-GAAP
Alibaba)
operating
margin
0.4% (2016
JD.com)
Q3,
3.6 Inner markets
Market size
In terms of market size, the outer market value is the sum of all inner markets combined as
inner markets exist inside an outer market by definition. The market definition is very tricky
for inner market for two reasons. First, the categorization of products and services depend on
each platform. Second, while sellers compete with each other inside same platform, they are
also competing with sellers on other platforms, a type of asymmetric competition. It is hence
difficult to estimate market size for each inner market. However from figure 5, it is possible to
see e-commerce value penetration and adopting rate differ among categories. E-commerce
value penetration refers to the sales in e-commerce as a share of total retail sales while online
shopping adoption refer to the share of buyers who purchase the product in certain category
online. For example, consumer electronics has e-commerce value penetration and average
adoption rate while apparel has low value penetration with high shopping adoption rate. From
this we can estimate that the size of inner markets differ greatly among categories. High online
shopping adoption does not necessarily mean that the online market is large compared to the
total retail market.
Figure 5
Note. Retrieved from “China's e-tail revolution”. Copyright 2013 by McKinsey & Company
Number of sellers and buyers
The number of sellers and buyers in all inner markets combined are numerous as described in
figure 4. It is hard to estimate the actual number for each market as buyers may shop and sellers
may sell in different categories and in different platforms.
Market concentration
Market concentration is each category is estimated to be low considering the number of sellers
and buyers.
Product
Products sold in inner markets can be both homogeneous and differentiated. For example,
sellers can sell identical products sourced from same wholesaler, but they may also sell
differentiated products especially if products are customizable.
Cost:
It is likely to be asymmetric for majority of shops (sellers) as they all generally have different
product range. However, for shops with exactly same product portfolio, the cost may seem
closer to symmetric as many shops purchase from similar wholesalers such as B2B platforms.
Information transparency
Compared to most offline markets, sellers in inner markets have high level of information
transparency. The search engines built into platforms allow buyers to find and compare prices
easily. The review system also enable consumers to get a better understanding of the quality of
both products and sellers.
Barriers to entry and exit
Barriers to entry in inner markets in C2C marketplaces is close to zero while those in B2C is
relatively high due to the need for company registration and higher fees. However, the barriers
are considerably lower than in most other markets. Cost of selling via online platforms is lower
than bricks-and-mortar stores (Oxera, 2015). Therefore, barriers to exit is estimated to be low.
Price
Although there is no objective price comparison of goods sold offline in traditional retailers
and in online marketplaces, some studies such as (Yaobin et al, 2007) estimated that online
prices are significantly lower than offline prices. A report by KMPG (2016) also show that
41.6% of Chinese consumers listed lower price as one of top reasons for shopping online. This
consumer perception may be a better indicator than the actual price comparison as consumers
may not always be able to compare price objectively. Furthermore, Bain & Company estimates
that 38% of online products are on promotion while the corresponding figure for offline
products stand at only 14%. Therefore prices online are estimated to be lower than prices offline.
Information transparency, availability of substitutable products, ease of comparison with search
engines and rating systems are likely to promote price competition in inner markets. Combined
with the significant number of sellers available, sellers are more likely to be price takers rather
than price makers.
Profitability
It is extremely hard to estimate, however, there has been reports on media that only 5% of
sellers are making profits (Guancha.cn, 2015). Financial Times (2014) also reported that the
profitability of sellers are extremely low, quoting one source in the industry saying that only 310% of sellers make a profit.
3.7 Assessment of market structure and outcomes
I would now like to identify the appropriate model for both outer and inner markets and
compare the predictions of the models with perceived outcomes in reality. All the
characteristics highlighted in discussion are summarized in figure 6. The outer market shares
various characteristics of Oligopoly along with product differentiation, asymmetric cost, and
significant barriers to entry. By Oligopoly, I indicate a market with limited competition
dominated by a few firms. Typically there are high barriers to entry and firms take into account
of rivals’ behavior. Although marketplace providers do choose price as the fees charged to
sellers, it is more of a dynamic competition rather than pure price competition as they also
differentiate themselves in factors such as logistics. Such model generally predict positive
profitability of platforms, with price higher than marginal cost. As Alibaba that owns Taobao
and Tmall as well as JD.com report positive profitability as shown in figure 4, the model
prediction seems to be aligned to the outcomes in practice.
Inner markets seem to have effective competition. According to Bishop and Walker (2010),
effective competition is where “Firms in the market are constrained over time by rivals in the
market, potential entrants in the market, consumers, which prevents them from being able to
raise price for a sustained period of time to a level that is detrimental to consumer welfare.”
According to analysis presented earlier, there are numerous buyers and sellers. Sellers seem to
be price takers and market concentration is estimated to be low. There are high level of
information transparency, and low-medium barriers to entry. I also estimate there a high level
of price competition due to information transparency and the large number of sellers selling
substitutable or homogeneous products. Theoretically, the prediction of effective competition
should be that price is not raised to the point that may harm consumer welfare. As the available
data seem to suggest that price is relatively low at least compared to offline shops and many
sellers do not seem to be making profit, the predictions seem to be aligned with market
outcomes.
Figure 6
Outer market
Inner markets
# sellers and buyers
Mainly 3
Numerous
Market concentration
High (~83%)
Low
Price
Price-maker
Price-taker
Cost
Asymmetric
Asymmetric
Information transparency
High
High
Barriers to entry and exit
High
Low-medium
Profitability
High
Majority low
Model of market structure
Oligopoly
Effective competition
4
Welfare Analysis
4.1 Problems with welfare analysis
In standard economic theory, perfect competition is considered the ideal as it maximizes social
welfare while oligopoly is not when ignoring the behavior of the firms. However, such
straightforward analysis may not apply to two-layered market.
OECD report states that there are at least three reasons why welfare analysis in two-sided
markets can be extremely difficult. One reasons is that price variations may not lead to welfare
variations as in one-sided market due to interdependent demand between two groups of
consumers. Therefore, it is required to measure welfare directly instead of measuring price
variations as proxies. Another reason is that welfare of all parties need to be considered. In onesided market, competition authorities tend to prioritize consumer welfare over producer welfare.
For example, EU competition law set consumer welfare as its goal (Bishop and Walker, 2010).
However, it is not so straightforward in two-sided markets as sellers and buyers are the
consumers to platforms while buyer is the consumer to seller. Due to this complexity, it is not
possible to prioritize one party or generalize social welfare from one party. Last reason is that
the welfare maximizing conditions for this type of market can more complex than in one-sided
market. Therefore I would like to examine the welfare of this market using theory and concepts
developed for two-sided markets as well as those in electric markets.
4.2 Definition of welfare
There seems to exist various definition and types of welfare. In economics, welfare typically
indicate social welfare. On the other hand, welfare in other disciplines are usually broader,
including economic growth and innovation. In this paper, I will focus on the private welfare of
consumer, producer, and platforms due to the complexity. In this case, the standard consumer
welfare indicate buyer welfare while producer welfare refers to seller welfare. Also, by private,
I mean that externalities will be excluded from focus.
4.3 Simple model of two-layered markets
I would like to propose a simple model of two-layered markets that may give some intuition of
relationship between inner market and outer market. In this context, I assume that there is price
competition in inner markets. Grover and Ramanlal (1999) suggested four factors that indicate
movement towards price competition in electronic markets. IT utilized in electronic markets
reduces transaction costs, perceived complexity of products and asset specificity while
increasing free information flow. Reducing asset specificity means that products can be
increasingly used in alternative ways. IT can change some of information attributes at very low
cost and allow greater range of consumers to get more “customized” products (Malone, 1987).
These factors are likely to be true in practice as explained in market descriptions. Therefore, I
assume that there is price competition in inner markets.
The models requires various assumptions. There is X number of sellers, Y number of buyers,
and Z number of platforms. These numbers are fixed and there is no such effect as network
externality. Also for simplicity, the platforms are homogeneous and sellers and buyers only use
one platform, therefore sellers and buyers are spread equally. In such case, the number of sellers
and buyer per platform would be X/Z and Y/Z. If we also ignore the behavior of platforms,
sellers and buyers as well as transaction costs, and apply the logic of price competition,
theoretically less platform leads to more effective competition with lower price, higher
consumer welfare, lower producer welfare. This is the case if the competition intensifies with
the number of suppliers. Obviously this is the simplest model one can think of, and there are
various assumptions that are violated in reality. Therefore, I will now attempt to relax some of
assumptions and incorporate the feature of two-sided and two-layered markets.
4.4 Theories
In this section, I aim to explain some of concepts that may explain the market concentration
and welfare in the market. Unless stated otherwise, I assume ceteris paribus.
Critical mass and platform viability
Critical mass is defined as the minimal level of demand that platforms must have on their each
side (Evans, 2010). Due to interdependent demand, buyers only perceive value when there is
sufficient number of sellers on a platform and sellers only perceive value when there are enough
buyers. For a platform to be feasible, it must have enough of both groups as it increases the
chance and quality of matches. Considering sellers and consumers do not use all platforms in
the market in reality, not all platforms will gain critical mass. Therefore, the need to reach
critical mass for platform viability is likely to lead to less players in the market.
Indirect network externalities
As explained before, indirect network externalities exist in two-sided markets. Even if there
are imbalances between the number of participants on each side, a greater number on one side
is likely to attract more on the other side, which in turn will attract even more on the first side.
This should be particularly true when the number of participants on one side on one platform
is greater than those of competitors. As such, there is a first mover advantage as the platform
that reaches the critical mass and gains the lead are more likely to widen the lead as a result of
positive feedback effect (Evans and Schmalensee, 2012). In this sense, indirect network
externalities are likely to lead to higher market concentration. These network externalities may
be considered positive for welfare in general as it improves matching by increasing the options
of buyers, sellers, and products available, hence increasing the competition. However, once
there are too many sellers and competition becomes too intense, producer surplus may decrease
due to lower price and profitability.
Economies of scales
A majority of two-sided markets require high fixed costs (OECD, 2009). Online marketplaces
should not be an exception. Developing a software platform entails substantial fixed cost with
a low marginal cost of providing the service to end-users and developers (ibid). Online
marketplaces should be similar. Platform providers need to invest substantially in order to build
the system and networks. However, the marginal cost of additional seller and buyer should be
low as explained by (Grover and Ramanlal, 1999). Therefore, as the number of sellers and
buyers, the average cost must decrease.
Furthermore, if this increase in efficiency lowers the fees sellers face, this may in turn lead to
lower prices faced by buyers, increasing consumer surplus. However, this seller behavior is
hard to test in reality as they may keep the price for larger profits. This is likely to depend on
other sellers’ behavior and the type of products. Furthermore, platforms may not reflect their
low average cost by low fees charges to sellers if they have market power. Therefore,
economies of scale is likely to lead to less platforms but the effect on competition is unclear
theoretically.
Dr Richard Hill, of the Association for Proper Internet Governance was quoted in House of
Lords report on Online Platforms and the Digital Single Market (2016) that the combination of
indirect network externality and economies of scale are likely to create natural monopolies.
However, there are certainly other factors that may undermine this straightforward conclusion.
Multihoming
Multihoming is a phenomenon proposed by Rochet and Tirole (2003). It occurs when a number
of end users on one or both sides use more than one platform. Specifically, in online
marketplaces, multihoming means that sellers and/or buyers use more than one platform to
make transactions.
Excluding all other behavioral factors in the market, this is likely to increase competition even
in the case of high market concentration in outer markets because buyers can compare prices
of substitutable products across different platforms. This also imply that sellers are competing
with other sellers across platforms. However, this depends on the degree of multihoming,
differentiation of platforms, and how end-users use the platform. If the end users are registered
to different platforms but do not use all of them frequently and compare the price and products,
multihoming is unlikely to affect competition. Likewise, if platforms are differentiated, for
example, in that one is specialized in selling high-quality brand products while another is
specialized in selling low-quality commodities, consumers are not likely to use them as
substitutes, hence not increasing competition. Lastly, if end users are not rational and hence
compare the price or product as they are entitled to, this may not increase competition.
Product Differentiation
According to Evans and Schmalensee (2012), there are two types of product differentiation,
horizontal and vertical. Horizontal differentiation refers to differences in product attributes
while vertical differentiation points to the willingness or ability of consumers to pay for quality.
In online marketplaces, vertical differentiation may be certain features of platforms such as
website design, payment system, and the number of sellers for different categories. Horizontal
differentiation may be the difference between B2C and C2C marketplaces as sellers on C2C
marketplaces tend to offer higher quality products but at higher price.
If consumers are heterogeneous, product differentiation is likely to attract different sets of
consumers. Wealthier buyers who care about quality is likely to choose B2C marketplace
instead of C2C marketplace. This may also depend on the product type. Consumers may also
multihome and use each platform for purchase of different products. They may use B2C
marketplace to buy brand bag but use C2C to buy toilet papers, for example. Therefore,
platforms can make decisions to differentiate themselves from competitors and avoid
competing directly. Indeed, it is possible to find product differentiations among the dominant
platforms in the market. Taobao is C2C platform with cheap but relatively low quality product.
Tmall is B2C platform with more expensive but higher quality product. As Taobao and Tmall
are operated by the same business group, Alibaba, buyers do not need to use two accounts and
easy to multihome as they have the systems such as those for payment. On the other hand,
JD.com has both direct retailing and marketplaces on single platform (B2C), have stronger
portfolio for consumer electronics and faster delivery.
Therefore, product differentiation is unlikely to increase market concentration and even in
presence of market concentration, consumer welfare may not be negatively affected as
customers have different choices suited for different purposes. This is in line with Evans and
Schmalensee (ibid) who argue that “Product differentiation is a key reason why many industries
with multi-sided platforms have multiple competitors even though indirect network effects and
sometimes economies of scale would seem to propel them to monopolies.”
Asymmetric competition
As outlined in market descriptions, both platforms and sellers face asymmetric competition.
Therefore, high market concentration in outer market does not necessarily mean there is little
competition. Asymmetric competition is likely to increase competition at least in the inner
markets. Consumers can still purchase at and compare products and prices with shops outside
B2C and C2C online marketplaces. As long as there are competitors outside inner markets and
consumers use both shops inside and outside inner markers, there is an element of competition
that market concentration do not necessarily capture.
Congestion
Congestion is likely to lead to lower market concentration (Evans and Schmalensee, 2012).
This happens when the number of users reaches a point that decreases the quality of matching
transactions. However, that should occur only when the system slows down the transaction and
the function. Personally, I find the online marketplaces in focus have great search engines
where you can filter and rank by various factors. There is little glitches in the system. Therefore,
this effect is unlikely to be relevant in this market.
Barriers
Critical mass, indirect network effects, economies of scale can all be considered as barriers to
entry. However, these may not be the only barriers that we need to consider. Another areas to
consider is the barriers to switching and multi-homing. According to CMA report, such barriers
include data, reputation, contractual restrictions, inertia. If platforms have proprietary data that
competitors cannot access, it gives them competitive advantage. In online marketplaces, data
can come from tracking buyer activities so that they can offer recommendations tailored to
each buyer. In terms of reputation, many buyers and sellers may choose most reputable
platform as the safest choice, for example. Contractual restrictions occur when platforms try to
bar sellers from selling on other platforms. Alibaba, the group that owns Taobao and Tmall
have in fact been accused of such behaviour (Reuters, 2015). Inertia in this case refers to the
sellers and buyers unwilling to switch to other platforms due to limited abilities or low
incentives. This could be because they consider certain process of switching to other platforms
such as registration and learning to use the systems on new platforms to be bothersome or
unnecessary. Unless users have problems with the current platforms, they may not be promoted
to consider switching to another platform.
4.5 Theoretical implications
In terms of welfare, it is quite hard to estimate as it also depends on the behavior of different
parties, platforms, sellers and buyers. While there are both positive and negative effects on
market concentration outlined in the previous section, the positive effects seem to be greater.
Market concentration reflect the extent of competition. However, as outlined in figure 7, it is
more probable that greater concentration in outer market can facilitate effective competition in
inner markets. Critical mass, indirect network effects and economies of scale are likely to lead
to higher market concentration and may also increase welfare through positive feedback effects
and increasing seller competition. Product differentiation and multihoming are likely to reduce
market concentration in outer market, however, their effects are unlikely to be greater than the
combined effects of critical mass, indirect network effects and economies of scale.
Multihoming and asymmetric competition may increase competition in inner markets.
Congestion is also unlikely to be relevant to overall welfare as the online marketplaces do not
display such problem.
Figure 7
Cause
Effect on size/concentration in Effect on competition in
outer market
inner markets
Critical mass
viability
&
platform +
+(-)
Indirect network effects
+
+(-)
Economies of scale
+
+(-)
Product Differentiation
-
+
Congestion
-
-
Multi-homing
+
+
and +
-
Barriers
to
switching
multihoming
Note. + indicates increase, - indicates decrease and +(-) indicates that the effects may be
positive or negative.
4.6 Implications
The theory seems to predict high market concentration in outer market and effective
competition in inner market. It is quite hard to predict the welfare for platforms, sellers and
buyers. It is likely that high market concentration provides major platforms with some sort of
market power that allows them to raise rice above marginal cost. In regard to sellers, while they
benefit from lower transaction cost and greater number of buyers through indirect network
effects, their welfare is likely to be low or could even be negative due to intense competition
in inner markets predicted by various factors. Lastly, buyer welfare is likely to be high primarily
as greater competition in inner markets can possibly lower price, increasing their consumer
surplus. Overall, total welfare is likely to be large. Although counter intuitive, oligopoly
structure may be natural in this two-layered market and indeed be ideal for facilitating effective
competition.
5. Limitations and suggestions
One of major limitation of this paper is the assumptions made. I assumed that ceteris paribus
in most occasions. Therefore interactions of different effects are neglected. I also largely treated
platforms as single entity while their actual owners are platform business groups which various
business that are interlinked to one another. For example, Alibaba also own and operate online
payment platform used in Tmall and Taobao, as well as logistics system and financing for
SMEs. Proper estimate of market power and welfare should ideally take this corporate structure
into account. Furthermore, I treated JD.com as purely online marketplace most of time while a
large part of their business is via online retailing.
I also ignored the trust fraud problem by assuming that three is high level of transparency with
good review systems. Trust fraud problem is when prevalent especially in China’s C2C
marketplaces where sellers manipulate their reputation through fake review systems (Zhang et
al., 2013). Lastly, I mainly focused on structural factors not the behavioral factors. Ideas
neglected in paper but should be incorporated include exclusionary behavior such as tying and
bundling, exclusive dealing and predatory pricing as well as coordinate behavior.
Another aspect of limitation is data. There is lack of appropriate data inner markets and
comparable data for outer markets. Furthermore, most of data are short term, partly due to the
short history of this industry. These limitations all makes it harder to conduct objective market
analysis based on facts.
The last aspect is the methodology and model used. There is no clear methodology to measure
welfare in two-sided and not to mention of two-layered markets. There is also no established
models as in one-sided markets. Therefore, I could only rely on theory and estimation that are
likely to be subject to bias.
6. Conclusion
To conclude, there is a critical gap between theory and reality in China’s online marketplaces.
Analyzing online marketplaces as a two-layered market is a first step in filling the gap by
recognizing inner and outer markets separately and trying to account for the relationship
between them. From market analysis, China’s C2C and B2C marketplaces has a
counterintuitive structure of outer market resembling Oligopoly and inner markets resembling
effective competition. From the available information and theory, it seems very likely that the
nature of the markets are likely to lead to oligopoly in outer market and this may in fact
facilitate effective competition in inner markets and increase welfare.
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