fragmentation in mobile payment platforms

7/30/2019 Fragmentation in Mobile Payment Platforms

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Economics of the Internet

A-Karim Lalani1

Fragmentation in Mobile Payment Platforms:

A Case Study on East Africa

The success of M-Pesa in Kenya has been celebrated as one of Africas greatestsuccess stories. The service was introduced in 2007 and within five years M-Pesa hasattracted fourteen million subscribers and in the last year processed $14.2 Billion intransactions1. M-Pesa is a mobile payment platform deployed by Kenyas largest telecomoperator, Safaricom. The payment platform functions as a mobile wallet, an individualcan deposit cash through an affiliated agent and then use their balance to pay and receivepayments from other individuals using the platform. Individuals can also withdraw cashfrom their electronic account through an agent. M-Pesa is a household name in Kenya,however, the same payment platform has struggled in a neighboring country.

The Vodafone group is a multinational firm that owns telecom operators globally.This paper is particularly interested in the fact that the firm owns 40% of Safaricom andhas a majority share in Vodacom Tanzania. In 2003, Vodafone contracted a firm todevelop what would become the M-Pesa platform and began to implement it in Kenya,Tanzania and Afghanistan post 2007. Despite the many economic and politicaldifferences between Kenya and Tanzania, we can argue that they have many similaritiesthat make them comparable target markets. Both countries have similar population sizes(41 to 43 million people), population age structures, economic composition (both haveapproximately 75% of the labor force in agriculture) and both countries have experiencedsimilar surges in mobile phone penetration rates. It is reasonable to expect the successesof M-Pesa in Kenya to be comparable to the successes of M-Pesa in Tanzania, howeverthis is not the case. In May 2009 Vodacom Tanzanias implementation of M-Pesa is saidto have attracted 280,000 users compared to Safaricoms 6.5 Million and had processed3% the amount of money in transactions that year (IFC)2.

The question that remains unanswered is: why is M-Pesa so much moresuccessful in Kenya than it is in Tanzania? Having witnessed the potential of a mobilepayment platform, many organizations are keen to replicate the success of M-Pesa inother parts of the world but it seems as though the Kenyan example is an outlier whencompared to the deployment of similar platforms in other countries. There have beenattempts to answer why M-Pesa has worked better in Kenya than in Tanzania, but thesehypotheses have been stated and not verified through empirical data nor have they beenbased on the predictions of a formal economic framework3. In this paper I present my

own hypothesis for M-Pesas user adoption, I describe a model that applies to the M-Pesain and then compare the predictions of the model to our observations in both Kenya andTanzania.

1Kenyas GDP is $70.6 Billion, their transaction volume is very high relative to GDP2The most recent statistics are less drastic and they will be evaluated later in the paper.


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Section 1: Background and Hypothesis

Mobile payment platforms as they have been developed in emerging markets arestructurally different from similar platforms in the developing world. Most payment

platforms in the United States (this includes credit cards, the Square Card Case andGoogle Wallet) are tied to an underlying bank account and all payments made on theplatform withdraw money from the account of the payer and deposit money into theaccount of the recipient. In emerging markets, payment platforms differ because theiraccounts are not connected to an underlying bank account, instead the accounts areregistered with the platform operator who takes on the responsibility of providing theuser the ability to deposit and withdraw money. Once the user has debited their platformaccount they are free to transact with other users on the platform using a virtual currencythat is backed by actual cash. The operators earn revenue on each transaction on theirplatform and the individual making the payment is charged according to a tiered paymentschedule4.

Payment platforms in East Africa employ individual agents to convert physicalcurrency to the platform-specific virtual currency and vice versa. Agents are generallyentrepreneurial individuals, shop owners, or even ATMs. These agents allow users todeposit and withdraw money from their accounts and are paid for each transaction5. Theyare vital to the success of the platform because they collect the money underlying theirvirtual currency and provide liquidity to the users who can withdraw their money at anytime. We expect that as the number of users increases there will be more trust in thevirtual currency and transactions with other users on the platform will become vastlymore popular than transacting with agents (Mas).

In Tanzania there are four different telecom operators and each has their own

mobile payment platform that is similar to M-Pesa. Each one of these platforms operatesa different virtual currency, also known as an e-money, and each virtual currency isincompatible with the others. For example, if A uses platform 1 and B uses platform 2, Acannot pay B using platform 1 and expect Bs account balance to increase by thetransaction amount. As the payment platforms have evolved, they have developedmeasures for weak compatibility, for example if A pays B who is on platform 2, B canwithdraw money from an agent of platform 1 and then deposit it their account. Truecompatibility doesnt exist among the various payment platforms in East Africa and onevirtual currency has to be converted to cash before it can be converted into another virtualcurrency.

The telecom markets in both Kenya and Tanzania are also structured differently.There is a much higher level of fragmentation in the Tanzanian telecom market than thereis in Kenya as depicted in Figures 1 and 2. Safaricom, the leading operator in Kenya, hasa market share of almost 70% whereas the leading operator in Tanzania (also owned byVodafone) has a market share of 45%. Competition is also much greater in Tanzania

4Traditionally lower transaction values have had a higher percentage cost5With the exception of ATMs that are not capable of letting the users deposit money.


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whereas in Kenya, the combined market share of Safaricoms competitors is less thanhalf their market share. The fragmentation of these markets may be a key predictor in theuser adoption rates of M-Pesa in the respective countries.

The differences in market fragmentation may significantly reduce the valueproposition to an M-Pesa subscriber in Tanzania. Assuming a users transactions aremainly domestic, an M-Pesa subscriber in Kenya can transact with 70% of the mobilephone using population whereas an M-Pesa user in Tanzania can transact with less than

17.95

4.17

2.75

1.63

Kenyan Subscribers (Millions ofPeople)

Safaricom

Airtel

Orange

yuMobile

11.625

6.993

5.45

1.524

Tanzanian Subscribers (Millions ofPeople)

Vodacom

Airtel

Tigo

Zantel

Figure 1: Kenyan Telecom Subscribers (as of Jan 2012)

Figure 2: Tanzanian Telecom Subscribers (as of March 2012)


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half of all telecom subscribers. It is more attractive to use M-Pesa in Kenya because ofthe existence of network effects.

Network effects exist in a platform when the value of using that platformincreases with the number of other people using it. This is the case in mobile paymentplatforms where the intrinsic value of the platform is lower if there is no one to transactwith. Higher fragmentation reduces the value for the payment platforms because there arefewer users on any given network. It is the level of market fragmentation that I believehas caused such a large discrepancy in M-Pesas success in the two East Africancountries.

I hypothesize that in a market for platforms, a greater degree of fragmentation

will cause lower total user adoption at a given price level and that prices will have to

drop significantly in order to attract more users.

In the next section I propose a model to test this hypothesis and analyze the extentto which this hypothesis predicts the user adoption rates and relative revenues of M-Pesa

in the two countries.

Section 2: A Model of Network Effects

As previously defined, a technology exhibits network effects when the value ofthat technology to a potential customer increases with the number of other peoplecurrently using it6. The technology this paper is concerned with, in network terminology,exhibits direct network effects and is arguably one-sided. Direct network effects refers toa simple increase in the value of a product as the number of people using it increases, andthis is present in payment platforms as a larger user base provides customers more usersto transact with. However, it is less obvious the extent to which the network is one-sided.In two-sided networks, the increase of users for one technology increases the demand foranother. This is true in mobile payment platforms with respect to agents, as a highernumber of agents can attract more users to the network. However, if we take the exampleof credit cards, which are the Western analogous of payment platforms, the two sides ofthe platforms are cardholders and merchants. Banks are the entities that support the creditcard system. Similarly, agents act as supporters to the system and both merchants andcardholders have their own accounts on the platform, making the platform one-sided forfinancial transactions. In further analysis I consider mobile payment platforms as beingone-sided.

With regards to the adoption of M-Pesa, a consumer makes the decision whetheror not to join the network. This is analogous to buying a good, but with the constraint that

6An alternate way of viewing network effects is to consider them as positive externalities. For example, thenumber of users joining a social network is a spillover benefit to current users of that network because there

is no mutually defined way of compensating the new users who are contributing to the surplus of old users.However, if the added surplus is compensated for, the network effect ceases to be an externality. I believethis is the case in payment platforms where the providers are able to charge higher prices for largernetworks and capture the consumer surplus.


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the consumer can purchase one good at most. The consumer decides to join the networkonly if the value of joining the network exceeds the cost to join it. Ifn is the number ofuser using the network, we can say the total value to the user is given by the function

!(!), where the value is dependent on n.

In the original model of one-sided networks, Katz and Shapiro (1985) consider amodel where the good has an intrinsic value independent of the size of the network. Inthe case of mobile payment platforms, we have to ask whether there is any value to thefirst adopter of the network given there is no one else to transact with? The platform isprovides no utility to a user if he or she cannot transact with anyone else, and hence thereis no inherent value in joining the network. This can be restated as:

! 0 = 0In making the decision to join a network, individuals can have different valuations

for the platform for a given network size. If the network size was to be held fixed, arestaurant owner may value the platform more than a civil servant because the restaurant

owner appreciates the ability to streamline a large volume of transactions whereas a civilservant who has to be frugal and makes few transactions doesnt value joining theplatform as much. In both cases, their valuations differed due to factors that wereunrelated to the size of the network. These random valuations suggest the total valuefunction is composed of two separate functions:

! ! = !(!)!(!)The first function ! ! accounts for the random variation in values for each user, similarto distributed reservation prices that users have for goods in a traditional market. The

second function !(!) describes the relationship between total value of the platform andthe size of the network.

Let us consider the nature of the relationship between total value and size ofnetwork. Adding an additional person should increase the total value of the platform, butthere will be diminishing returns for each additional person. Mathematically we can say:

!"

!"! ! > 0

!!!

!!!! ! < 0

lim!!

!"

!"! != 0

Any value of!! where 0 < ! < 1 fulfills such these three conditions7, to choose anunbiased value we choose the midpoint of that range and assign:

! ! = !!!7Other functions also fulfill the previously stated conditions but we choose nx for simplicity of calculation.


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We borrow the functional form of! ! from work done by Shapiro and Varian (1999) asfollows:

Lets take the example of the demand for a good that does not have network

effects. We index 100 people by ! = 1, ,100 and define !, where ! measures the

reservation price for a good by person!

. Then if the price of the good isp, the number ofpeople who think the good is worth at leastp is 100 !. For example, if! = 50, therewill be 50 people for which ! > 50 and hence 50 people will buy the good.

The consumer decides to buy that good if! < !(!) and if1 ! 100. In such ascenario there will be a person who is indifferent to buying the good. If this individual is

indifferent to buying the good, we know that everyone with a higher value for !(!) willwant to buy the good, so we know that:

! = 100 !(!)

Rearranging, we can define ! ! as:

! ! = 100 !Shapiro and Varian apply this same function for reservation prices to a market

with network effects to model the different values a population has for a good at a givennetwork size8. So if we consider the market of 100 people for a good that does exhibitnetwork effects, the total value to an individual is defined by:

! ! = ! ! ! ! = (100 !)!!

!

In equilibrium, the platform provider can set a price equal to the total value function suchthat:

! = (100 !)!!

!

This equation gives us the relationship between price and size of the network. For amobile payment platform a user can decide to join or not, which is analogous to buying atmost one good. Hence we can take the total number of users to be the same as totalquantities sold and the above equation represents an inverse demand curve. We see theshape of this curve in Figure 3 to be parabolic.

8Easley and Kleinberg (2010) suggest that the general function for r(x) need not look like the way definedby Shapiro and Varian, however, they go on to say that they typically expect to see a function of similarform


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Figure 3: Demand for a Mobile Payment Platform

The parabolic shape of the demand curve describes three unique equilibriumswhen the price is set to 200. The first of which is if! = 0. If there is currently no one onthe network, then all individuals will derive no utility from the joining the network andno users will choose to join the network. This equilibrium exists in all platforms withsimilar demand curves, in which case we must ask why does the first user ever join aplatform? We can answer this if we assume the user is forward looking and makes theirvaluation decisions based on their expected size of network. So the first user may expect

that at a price of 200, the platform will reach equilibrium A and expect !!

number ofusers when making their valuation decision. This is a self-fulfilling expectations

equilibrium where if!!

number of users expect as many people to join, they all join andfulfill their expectation.

Once the platform gets to equilibrium A, the platform will have reached a critical

mass. We see on the graph that for the !!+ 1 user, his willingness to pay is higher than

the price; hence this user will join the network. We see this is true for many users whowill continue to join the network until we reach equilibrium B. In that sense, A is not astable equilibrium because once a platform reaches equilibrium A, there will be upwardpressure for the size of the network to reach equilibrium B. It is important to note thatthe process of moving from A to B is not instantaneous and requires time.

In the example we have considered so far, we assume there is only one mobilepayment platform in a market of 100 potential consumers. If we were to assume there isfragmentation, the demand curve would look different. Figure 4 describes the demand

curves for a single mobile payment platform where there are 1, 2 and 4 total platforms inthe market, each with equal market share.

We observe that fragmenting the market reduces prices. As most markets, weexpect competition to decrease profit margins for firms and increase welfare, however in


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a market with network externalities, welfare may decrease with competition for identicalplatforms9.

Figure 4: Demand Curves in a Fragmented Market

Given the mobile payment platforms we consider in East Africa, the potentialmarket share of each platform is given before the platforms compete. Telecom operatorswere already selling voice and text message services to their subscribers before theybegan selling financial services. If we assume there is a significantly high cost ofswitching between telecom operators because a customer would have to change theirphone number, ask their network to update their contact information and potentially misscalls that were directed to their old number, we can assume that mobile paymentplatforms do not compete with each other with prices. So we can think of each platformprovider as a profit-maximizing monopolist given within the subscribers of their telecom

services. We assume no costs for simplicity, so the monopolist solves the profit function:

! = !" = ! !

!

!(! !)Where n is the number of users that join the platform,p is the price they are charged and

! is the total size of their telecom subscriber base. We solve the profit function when

! = 100 to find ! = 60. This example corresponds to percentages, so the monopolistwould want to charge a price at which 60% of his potential consumers join the platform.

This model of network effects has several powerful predictions about the EastAfrican market for mobile payment platforms that shed light on the current state of theindustry. The predictions are described as follows:

1. In a market with network effects, where there exist multiple firms who do notcompete on prices and have no costs, the aggregate number of subscribers will be

the same regardless of the number of competitors.

9We assume the different platforms are identical for this to be true. Farrell and Saloner (1985) show this isnot the case for differentiated products and that welfare can potentially increase if the dominant product isinferior to a competitive product.


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If! is the total size of the market, and there are m number of firms, if we assume they allhave equal market share then each firm has ! ! potential customers. The firm maximizes

! = ! !

!

!( !!

!)

We find the maximum of this function when:

0.6!

!

= !

If all firms m maximize their profits, and they all have equal market share, then theaggregate number of users is equal to:

0.6!

!

! = 0.6!

This result still applies when firms dont have equal market share but only in an industry

where all the platforms are identical and dont compete on price.

The implications for this is that in both Tanzania and Kenya we would expecttelecom operators to have 60% of their total subscriber base join their networks if theyare profit-maximizing. However, the assumption of all operators being profit maximizingmay not be reasonable. Introducing a new product to a new market is challenging for amonopolist who yet to learn the preferences of their potential customers and it takesmuch learning by doing to learn their what the demand curve looks like a new market.Hence if we assume that platform providers have the same pricing strategy, which meansthat they set their prices to sell a certain quantity, we would expect they would attract thesame proportion of their total subscriber base. We see that this is empirically the case forM-Pesa in both Kenya and Tanzania.

Figure 5: 2009 Price Schedules for M-Pesa in Kenya and Tanzania


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Figure 5 shows the 2009 price schedules for M-Pesa in both Kenya and Tanzaniaand we see they are very similar. Due to the differences in market share, we would expectsignificantly lower users of M-Pesa Tanzania as a proportion of Vodacoms customers. In2009 Safaricom had converted 50% of their subscriber base to M-Pesa users whereasVodacom had converted only 5% (IFC). These price schedules have changed over time

and in early 2012 Vodacom recently reduced M-Pesa prices by 75%. If we assume overtime M-Pesa has adopted a unified pricing strategy to induct the profit maximizingnumber of users across countries, then we would expect the conversion rate ofsubscribers to M-Pesa users to be the same in both countries. In December 2011Safaricom reported that approximately 81% of its 22 Million subscribers at the time hadcreated accounts on M-Pesa. Later in February 2012 Vodacom reported that roughly 9 oftheir 11.6 Million users had created accounts on M-Pesa (though they did claim only 3Million were active users). That means Vodacom had attracted close to 78% of their totaluser base to subscribe to M-Pesa, which is comparable to their Kenyan counterpart10.

2. If there are two firms offering identical platforms and arent competing withprices, then the firm with lower market share will have to charge a lower profit-

maximizing price. This can be restated as, in a market for platforms the greater

the degree of fragmentation the lower the prices charged will be.

In Figure 4 we see that when a firm has lower market share it shifts the firmsdemand curve downward so the firm is forced to reduce prices. We can see this moresimply when:

! = !

!

!( !!

!)

As ! is fixed in a market, only m can increase which causes prices to decrease (as

long as !/! is greater than !). This shows that as the number of firms increase, whichmeans there is a greater degree of market fragmentation, the prices charged by a platformprovider will decrease11. This result is the basis for the next prediction that revenues alsofall with fragmentation.

3. In a market where firms offer identical platforms and arent competing with eachother through prices, a greater degree of fragmentation will lower the aggregate

revenues earned in that market.

We can reinterpret a greater degree of fragmentation as a downward shift of thedemand curve. However, with a parabolic demand curve the drop in maximum profits isdisproportionately greater. In the example so far, if we assume each firm maximizes

profits then the total revenues for each firm have been depicted in Figure 6. We see thatdoubling market share for a firm increases revenues by 566% and quadrupling market

10There was no credible resource suggesting that the countries have adopted a uniform pricing strategy andthis was just a hypothesis. However, it is a reasonable hypothesis that after several years of learning bydoing the parent company has issued its subsidiaries to implement the best practices they have learned. Iwas unable to collect data on the most recent price schedules and platform user statistics for M-Pesa andother mobile payment platforms at the time of writing this paper to make further comparisons.11The decrease in prices charged by Vodacom for M-Pesa Tanzania supports this prediction.


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share increases revenues by 3200%. This general result of disproportionate changes inrevenue for a linear increase in quantity sold should hold for any firm with a parabolicdemand function. The difference is quite large at an aggregate level, for example amarket with four firms with equal market share will earn in aggregate 232412 units ofrevenue whereas that same market with just a single firm will earn 18590 units of

revenue, approximately 8 times as much.

Figure 6: Revenue for Firms in Fragmented Markets

We can begin to compare this prediction to what we observe in the mobilepayment markets. Due to a scarcity of comparable revenue data for M-Pesa in bothKenya and Tanzania, we consider transaction amounts processed as a proxy for revenues.In December 2011 M-Pesa in Kenya had reported processing $14.2 Billion intransactions. M-Pesa Tanzania has less reported data but has claimed to process over

$400 Million in December 2011, if we assume they had similar transaction volumes foreach month then they would have managed to process $4.8 Billion in transactions, almosta third of their transaction volume while they have 25% less market share. This leads to akey prediction for incompatible networks:

4. In a fragmented market where firms offer identical platforms and that arentcompatible, the firms would generate higher aggregate revenues if they operated

a compatible platform.

We observe in Figure 6, aggregate revenues are greater in a market with one paymentplatform rather than a fragmented market. Similarly, a fragmented market can choose tomake their platforms compatible. The effect of compatibility is that consumer will make

their decision to join a platform if ! ! !!

!> ! where ! is the total number of users in

the market and it is constant regardless of the number of platforms that arise. The marketoperates as if there is effectively a single payment platform provider and the variousplatform owners can share the profits fairly. For example, in a market where there are

12This is because each individual firm earns 581 units and there are four firms, so the aggregate revenue is581 * 4 = 2324 units


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four platforms with equal market share, making them compatible with each other willgenerate 8 times the aggregate revenue and each firm should rationally be willing toswitch to such a platform if they receive a share of the increased revenue.

Conclusion and Further Analysis

This paper tried to evaluate the initial hypothesis that a fragmented market forplatforms reduces the total user adoption rates for a give price level and that for aplatform to induce a higher number of adopters they will have to lower prices. The modelof network effects put forward is applied to mobile payment platforms in both Kenya andTanzania, and we see the claim of lower user adoption rates being invalidated in theorybut we still see that firms in fragmented markets have to significantly reduce prices toattract users. The model seems to predict many of the empirical observations in thegrowth of M-Pesa in both Kenya and Tanzania, however there are many predictions thatcould have been tested more rigorously with more data.

For further analysis, I would like to have specific data on the user growth of eachEast African mobile payment platform with respect to the total subscriber base of thetelecom operator since its inception. Ideally the dataset would include all similar mobilepayment platforms globally. With such data we could test whether firms that operated inmultiple countries yet had the same pricing policy achieved similar user adoption rates asa percentage of the total subscriber base. If I could also get the price schedules of eachfirm I could test whether the average price is lower for networks with fewer subscribersin a specific country. And if I could also get the monthly revenue data I would comparethe total revenue numbers for firms with larger market share with those with lowermarket share and see if the former firms have disproportionately larger revenues.

One of the most significant predictions of the model was that of compatibility. Itsuggests that the payment platforms in East Africa, or any other fragmented market,develop a compatible payment platform and share the profits fairly. Such a change in thepayment platforms could potentially have a significant impact on the GDP as more usersdiscover the value of using an electronic payment platform instead of just transactingwith cash. Several other areas on enquiry would be how exactly mobile paymentplatforms add value to a consumer. For example, Id like to begin to look at how theseplatforms provide individuals with a measure for accountability and how it can furtherenhance accountability in countries with few transaction records.


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