The Peculiar Economics of Online Marketswebfac/eichengreen/e191_sp12/morgan_2 … · Pricing 101 Basic pricing theory Know your marginal cost Know the elasticity of demand for your

JOHN MORGAN HAAS SCHOOL OF

BUSINESS UC BERKELEY

The Economics of Platform Competition and Online Markets

All markets are becoming online markets

Travel

Books/music/movies

Cars

Houses

Clothes…

Adventures Online

Some economic stories in the online world?

In Five Acts

Act I: No $500 Bills

Principle 1: No arbitrage

Improved Principle 1: There are no $500 bills lying on the ground

If there were, someone would pick them up.

Testing the No $500 Bill Theorem

Selling the same item on two different platforms (auction sites) should yield the same revenues

A theory of equilibrium coexistence

Ellison, Fudenberg, and Mobius (2004) propose a theory where competing online auction sites can coexist even with vastly uneven market shares

Key intuition: Two competing effects

Size effect: Platform with more users is more attractive

Market impact effect: Switching platforms increases competition

Testing Coexistence versus Tipping

Prediction 1: Expected revenue to a seller should be approximately equal across the two platforms

Prediction 2: The number of bidders attracted per seller should be approximately equal across the two platforms

…all else equal

Goal of the Experiments

Try to come as close as possible to “all else equal”

Choose markets that are relatively thick across platforms

See whether we, as sellers, have a profitable deviation

Determine why this might be the case (buyer/seller ratios)

Morgan Silver Dollars

Product: Popular collectible coins sold on both sites

Search of “Morgan Dollars (1878-1921) produces 12,559 listings on eBay and 1,209 listings on Yahoo!

Other ratios:

Antique books 2:1

Antique firearms 3:1

Beanie babies 20:1

Considerable price variation for these items

Experimental Design

Identical “batches” of Morgan silver dollars slabbed and graded.

8 coins/batch x 11 batches

Sold some batches on eBay and some on Yahoo using identical text, photos, auction characteristics

“Borrowed” reputations from coin dealers

Compare revenues and number of bidders

Dataset was generated by 88 auctions.

88 auctions

48 Yahoo! 40 eBay

32 zero 24 zero 16 positive 16 positive

16 hard 16 soft 8 hard 8 soft

site

reserve

ending rule

treatments

Add photo of coin here

Yahoo versus eBay revenue comparison.

0

20

40

60

80

100

120

Revenues

1 2 3 4 5 6 7 8

Coin

Mean Revenues by Coin

eBay

Yahoo

Yahoo versus eBay number of bidders comparison.

0

1

2

3

4

5

6

7

8

Number of

Bidders

1 2 3 4 5 6 7 8

Coin

Mean Number of Bidders by Coin

eBay

Yahoo

Regressions examining Prediction 1

revenue=β0+β1site+β2reserve+γX+ε

Site = 1 if eBay; 0 if Yahoo

Controls:

Coin dummies

Random effects

Book value

Dealer Price

Equilibrium coexistence implies β1 = 0.

Results of regressions examining Prediction 1

Site coefficient is positive and significant at the 1% level in all specifications

Baseline:

eBay premium = $13.173 (or 26.8 percent)

Baseline + Reserve Price

eBay premium = $14.90 (or 29.5 percent)

Results of regressions examining Prediction 2

Site coefficient is positive and significant at the 5% level in all specifications

Baseline:

eBay attracts 2.125 more bidders than Yahoo

Baseline + Reserve Price

eBay attracts 2.1 more bidders than Yahoo

Does the ending rule matter?

0

1020

30

4050

6070

80

90

Revenues

1 2 3 4 5 6 7 8

Coin

Mean Revenues by Coin

Soft Close

Hard Close

Does the ending rule matter?

The ending rule does not seem to matter

No significant difference in revenues

Coefficients at most 11 cents

No significant difference in number of bidders

No significant difference in the timing of bids

What’s Going On?

Market in the slow motion process of tipping to eBay

Yahoo Press Release, June 15, 2007:

“After careful consideration, we have decided to close down our Yahoo! US and Canada Auction sites to better serve our valued customers through other Yahoo! properties."

Act II: The Law of One Price

Principle 2: In markets for identical products, merchants should offer the same price

If they didn’t, the ones offering the higher price would have no sales and go out of business

Testing the Law of One Price

Price comparison site

Lots of merchants

Identical item

Manufacturer warranty

How E-Retail is different from conventional retail

Speed of price changes

Median duration of a price quote on Shopper.com = 1 day

Speed of location changes

Price comparison sites

Some Stylized Facts About Price Competition Online

Ubiquitous and persistent price dispersion

No single “low-price” firm

Market structure matters

Theoretical rationales



Source: Nash-equilibrium.com, July 16, 2007

Value of Price Information

Source: Nash-equilibrium.com, July 16, 2007

Price Dispersion Online – Coefficient of Variation

Figure 1: Time Series of the Coefficient of Variation

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

07/01/2001

09/01/2001

11/01/2001

01/01/2002

03/01/2002

05/01/2002

07/01/2002

09/01/2002

11/01/2002

01/01/2003

03/01/2003

05/01/2003

07/01/2003

09/01/2003

11/01/2003

01/01/2004

03/01/2004

05/01/2004

07/01/2004

09/01/2004

11/01/2004

01/01/2005

03/01/2005

05/01/2005

07/01/2005

Collection Date

Mean

Co

eff

icie

nt

of

Vari

ati

on

(Perc

en

t)







Market structure matters

Market Structure Matters

Figure 5: Price Gap by Number of Firms

0

5

10

15

20

25

30

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60

Number of Firms Listing Prices

Avera

ge P

rice G

ap

(Perc

en

t)

Source: Baye and Morgan (2004)

Theoretical Rationales

Clearinghouse models of price dispersion e.g., Varian (1980), Rosenthal (1980), Narasimhan (1988)

Tradeoff between selling to “loyals” and attracting “shoppers” combined with comparison sites Cutting prices attracts shoppers but loses margin on loyals Keeping prices high to loyals gives up on price conscious market

Leads to “hit and run” pricing strategies Fun fact: The economics mostly predates e-retail!

Information “Gatekeepers”

Baye and Morgan (2001)

The platform (information gatekeeper) plays an important role in competition Optimal pricing by “gatekeeper” induces price dispersion

Inducing too much price competition is bad for the platform

Price dispersion persists even when: All consumers are “shoppers”

The product market is “competitive”

Pricing 101

Basic pricing theory

Know your marginal cost

Know the elasticity of demand for your consumers

Use simple MR = MC condition

Complications

Many customer segments

Rivals’ pricing strategies

Dashboard for online pricing

Baye, Gatti, Kattuman, and Morgan (2007)

How elastic is firm demand online?

Key difficulty: Hard to see actual demand easier to see clicks

Most leading comparison site price per click compared to per conversion

Clicks versus purchases

Time delay in clicks v purchases

Oxford Lasers

But still, how elastic is demand online?

Answer 1: Amazingly elastic

Ellison and Ellison (2004) estimate elasticities of -25 to -40 for computer memory

Answer 2: Not that elastic

Chevalier and Goolsbee (2003) estimate Amazon’s demand elasticity (for books) to be -0.6.

Why such a difference?

Market Structure Matters

Books sold online:

Concentrated market

Heavy branding activity

Direct sales

Repeat customers

Computer memory sold online:

Fragmented market

Little branding among retailers

Comparison site sales

Sophisticated consumers

Who cares what the elasticity of demand online is anyway?

Competitiveness of online markets:

“The explosive growth of the Internet promises a new age of perfectly competitive markets. With perfect information about prices and products at their fingertips, consumers can quickly and easily find the best deals. In this brave new world, retailers' profit margins will be competed away, as they are all forced to price at cost.”

The Economist, November 20, 1999, p. 112.

Our Dataset

Data from Kelkoo’s leads log

Every time a ‘lead’ is generated, record taken of: time, cookie, merchant, product, price, location on the screen.

Results presented today

18 pda’s over period Sept 2003 – Jan 2004.

19 merchants gained over 34,000 ‘leads’ from over 18,000 ‘cookies’.

Wish list:

Larger, cleaner dataset using leads logs

Extended analysis generated for 50+ pda’s and 100+ digital cameras over period Sept. 2003 to Sept. 2004.

What’s it like to shop at Kelkoo?

When do people click?

At work

Proportion of leads by hour

0

2

4

6

8

10

1 3 5 7 9

11

13

15

17

19

21

23

Hour of Day

% o

f a

ll le

ad

s

How do people choose what firm to click on?

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10

Price rank / Screen Location

% o

f all

lead

s

Price rank

Screen Location

Both price and screen location seem to matter

What determines leads?

SALES

CLICKS

Christmas

Date

Weekend

Position

on Screen

Price

# of

Competitors

Product

Popularity

Accumulated

Brand Equity

Restocking &

Return Policy

CONVERSIONS

Information on

firm’s site

Information on

rivals’ sites

Ease of UseLook & Feel

Offline

Presence

Information on Kelkoo Site (X) Information on Sellers’ Sites (Z)

Determining Leads

Preliminaries: Qijt denotes the number of leads for firm i, product j, on date t.

Since over half of all leads are zeros or ones, count data is appropriate

We use the Pseudo-maximum likelihood (PML) approach (Gourieroux, et al. 1984) and assume: E[Qijt | X] = exp[X β]

How do I estimate demand from clicks?

Let X denote the information available from the Kelkoo site Includes all competing firm’s prices

As well as some reputation information about all firms

Let Zi denote the additional information a consumer learns about retailer i from clicking through to its site

Where does demand come from:

Suppose that the expected number of leads to firm i are

E[Qi | X]

Suppose the “conversion rate” of firm i’s clicks is

Pr[Buy | X, Z]

Then firm i’s expected demand is

E[Di] = Pr[Buy | X, Z] x E[Qi | X]

Relating Leads to Demand

Proposition:

Suppose that the convert rate is independent of some firm-specific variable xi listed on the Kelkoo site, then the elasticity of demand with respect to xi can be estimated solely with clicks data.

Main application:

Suppose xi = pi, firm i’s price, then demand elasticity can be estimated from clicks alone

Demand elasticities for each product?

Suppose that demand is given by: E[Qijt | Xijt] = βj x ln[priceijt] + γj X1,ijt

Controls: Screen location

Month

Weekend

Note: Demand is assumed to be continuous---no jumps

What accounts for the elasticity differences?

-15

-13

-11

-9

-7

-5

-3

-1

1

1 2 3 4 5 6 7 8 9

Average Number of Firms Listing a Price

Ela

sti

cit

y

Coefficient Estimate Significant at the 1% Level

Coefficient Estimate Not Significant at any Conventional Level

How to explain the “elasticity paradox”

The larger the number of competing firms, the closer the degree of substitution Hence the more elastic is firm demand

Formal “toy” model N identical capacity constrained firms compete in a market

where goods are substitutes

Firm elasticity = N x Market elasticity

Estimating the elasticity of demand pooled over all PDAs

We estimate: E[Qijt | Xijt] = [β0 + (njt -1) β1] x ln[priceijt] + β2 njt + γ X1,ijt

njt denotes number of competing firms

Controls: Same as above, plus

PDA model x month

Bricks and clicks retailer

Results

Model 1 Model 2

Likelihood Specification for Clicks Poisson PML Poisson PML

Log Total Price -4.61 -3.761(8.91)** (7.45)**

Log Total Price x (Number of Sellers ? 1) -0.288(4.14)**

Number of Sellers 1.593(4.05)**

Position on Screen -0.186 -0.175(4.54)** (4.47)**

Bricks and Clicks Retailer 0.262 0.236(1.58) (1.67)

Weekend -0.242 -0.265(10.82)** (11.46)**

Product Dummies 17 17

Month Dummies 4 4

Product x Month Dummies 55 55

Robust Standard Errors Clustered by Firm Yes Yes

Observations 6151 6151

Overdispersion Test Test Statistic 2656.46 2488.77

P-Value 0 0

Table 3: Continuous Clicks Specifications

Note: Robust z statistics in parentheses. * Significant at 5%; ** Significant at 1%

How should I interpret these results?

Recall that in books, there are effectively fewer than five firms competing

In computer memory hundreds are competing

We find:

Monopoly firm price elasticity = -3.761

Ten firm price elasticity = -6.641

Additional Interpretations

Adding one firm charging the average price costs a firm about 4% of its clicks

Moving a firm down one screen location costs it about 17.5% of its clicks

Having a bricks and clicks presence raises clicks by 25%

What about the jump in demand?

Many theoretical models (Varian, 1980; Rosenthal, 1980; Narasimhan, 1984; Baye and Morgan, 2000) suggest a “jump” in demand at the lowest price

But the previous specifications mostly ignored this

This has the potential to bias the elasticity estimates

Ln(Price)

Ln(Quantity)

Best Price

‘Shoppers’

Firm i’s

Estimated

Demand Firm i’s Actual

Demand

Misspecified demand leads to estimates

that are “too elastic”

How we estimated discontinuous demand

βx'

epQ jj

Suppose there are two types of consumers: Shoppers (S), who buy at the lowest price, and Loyals (L) who do not

Assume that the number of clicks from each group is given by:

More on estimating discontinuous demand

L

sL

SLSL

epDq

ppD

epDepQQq

ere wh)1(

otherwise 0

if 1 where

min

βx

βxβx

'

''

]+p)ln 1)-(n+[(e]|[ ,12ijt1jt0

'γXX ijtjtijt DnxpQE

This yields the estimating equation:

Results Model 1

Likelihood Specification for Clicks Poisson PML

Log Total Price -2.459(9.11)**

Log Total Price x (Number of Sellers ? 1) -0.252(4.60)**

Jump from Shoppers 0.603(7.11)**

Number of Sellers 1.415(4.52)**

Position on Screen -0.175(4.37)**

Bricks and Clicks Retailer 0.321(2.41)*

Weekend -0.268(13.79)**

Product Dummies 17

Month Dummies 4

Product x Month Dummies 55

Robust Standard Errors Clustered by Firm Yes

Controls for Unobserved Firm Heterogeneity No

Observations 6151

Overdispersion Test Test Statistic 1942.27

P-Value 0

The Value of Unpredictability

Source: “A Dashboard for Online Pricing” by Baye, Gatti, Kattuman, and Morgan

Act III: Plus Shipping and Handling

Principle 3: The total price, including all add-ons, extras, etc. determines demand

Reason: All the dollars are just as green

Hidden Fees

BMI Baby

List price for flight from Nottingham to Edinburgh: Listed Price = ₤4.99

What wasn’t mentioned in the offer price: “Taxes, fees, and charges”

Checked bag fee

Credit card fee

Lots of other fees

What I actually paid: Real Price = ₤29.84

Southwest

Why Does BMIBaby Do This?

Price components reflect incremental services and costs

Allows consumers to select what they want and opt out of what they don’t want

Why Does BMIBaby Do This?

Strategically decompose and shroud prices to profit from “naïve” consumers

Other Examples of Price Decomposition and Price Shrouding

Hotels – room rate, tax, mini bar pricing

…Plus shipping and handling on TV offers

Hidden fees at banks, car rental companies, restaurants

“Energy” fees on airlines

Central Question

Does these practices work?

If so, when?

How big an effect?

Theories

Milgrom, Jovanovic Non-disclosure “unravels” with sophisticated

consumers

Gabaix and Laibson (2006) Competition for sophisticated and naïve consumers

leads to shrouding Ellison (2005)

Consumer brand and quality preferences create opportunity for “add-on” shrouded pricing

Kahneman and Tversky (1984), Thaler (1985) Separate mental accounts create incentives for differing

price decompositions

Our Approach

Run field experiments at online auction sites around the world

Vary the decomposition and shrouding

Study a natural experiment on eBay US

Rules change at eBay made shipping charges less shrouded

Price Decomposition

Online auctions offer a natural venue for examining price decomposition

The reserve price in an auction is equal to: Minimum opening bid + shipping charge

Strategic equivalence implies that only the reserve price should affect revenues in the auction

Price Decomposition (2)

Varying the shipping and minimum opening bid allows us to examine price decomposition effects in a setting where such changes are, theoretically, revenue neutral

Shrouding

Online auctions also offer a natural venue for shrouding Shipping is typically less salient than the current

auction price

disclosed: Disclose shipping in the header of the listing

Shrouded: Disclose shipping in the body of the listing.

Why Online Auctions?

Hide in the crowd:

Large number of ongoing auctions of similar items

Variation in selling practices

Natural manipulation price components and shrouding

Bidders are familiar with the rules

A transparent price discovery process

Bids convey information about willingness to pay

Field Experiments

2006 – Taiwan

36 iPods of various models

2008 – Ireland

40 iPods of various models

Describe Taiwan in detail, Ireland similar

Taiwan Experiments: Design

One “seller” who is not a newcomer

Identical auction rules except for opening bid and shipping fee

Identical descriptions for each item

Two sets of treatments with “shrouded” and “disclosed” shipping fee

Within each set, three treatments varying the opening price and shipping fee combinations

Taiwan Experiments: Objects

We chose goods where 1 shot private value assumptions are a reasonable approximation

Not “expertise” goods

Resale does not play a large role

Bidders have unit demand

Multiple identical products available

Large number of buyers and sellers

Chose 6 different iPod models

Taiwan - Treatments

Screenshot – Disclosed

Screenshot – Shrouded

Decomposition Effects Disclosed Treatment

Item DL DR

iPod nano 1G black 4,380 4,530

iPod nano 1G white 4,330 4,630



iPod shuffle 1G 3,130 3,100

iPod shuffle 512m 2,190 2,210

Opening Price High Low

Shipping Fee Low High

Shrouding No No

Decomposition Effects: Shrouded Treatment

Item SL SR







Opening Price High Low

Shipping Fee Low High

Shrouding Yes Yes

Tentative Conclusions

Price decompositions matter whether prices are shrouded or not

Shrouding Effects High Opening, Low Shipping

Item DL SL







Opening Price High High

Shipping Fee Low Low

Shrouding No Yes

Shrouding Effects Low Opening, High Shipping

Item DR SR







Opening Price Low Low

Shipping Fee High High

Shrouding No Yes

Shrouding Effects High Opening, High Shipping

Item DH SH







Opening Price High High

Shipping Fee High High

Shrouding No Yes

Ireland Experiments – Key Differences

Less product mix:

2G ipod shuffles in various colors

Less extreme shipping

25/75th percentile

Minimal opening bid

Within week treatment differences

Colors alternated treatments each week.

Ireland Experiments - Treatments

Summary Statistics

Pooled Results

Conclusion

Transferring a larger portion of the price to shipping fees increase revenue, market competition does not eliminate the framing effect

Ignoring shrouded prices seem to be more important than mental accounting

The impact of this framing effect can be removed by small institutional changes that make all price attributes transparent

When possibility of shrouding is eliminated, revenue seems to increase

Results hold across cultures and markets

Act IV: What’s in a Name?

Principle 4: Reputation signals are valuable when they’re cheaper for the good guys to acquire than for the bad guys

If not, the bad guys would have the same “reputation” as the good guys

Studying Reputation

EBay’s reputation system key to competitive advantage

What does it cost good guys versus bad guys to gain reputation?

A Market for Feedback

Advice from the eBook

“Look on eBay for items that cost next to nothing. You can

find the eBay search feature to find items which cost

anywhere from .01 to $1.00. Try this. ... Now bid on 100

items. If you want to speed things up a bit, try and find

auctions with the "Buy It Now" option. If the seller offers

PayPal as a form of payment, go right away and pay for the

item. ... If you do this with a hundred different sellers you

should be able to get your feedback score up to 100 in just a

few days.”

The Strategy of thelandseller

Create reputation through penny transactions in the market for feedback

thelandseller reputation = 598

Cost of reputation: $360

The New Strategy of thelandseller

Sell “lakefront property in Texas”

Benefit from reputation: Raise price by up to 5%

Limits to Trust?

Big ticket items are key growth driver for eBay

Trust is crucial for these items

But benefit of pretending to be a reputable seller is also great in these markets

Key challenge: Can the value of reputation be sustained in these markets?

Act V: First Mover Advantage

Principle 5: In network markets, the first mover wins even if it is worse

Famous Example:

QWERTY versus DSK

Properties of Network Markets

Platforms (Two-sided networks)

2 types of users

Matching technology

Scale improves match efficiency

Users of one type benefit from more of the other type

Users of one type are harmed by more of the same type

What are platforms?

Online auctions

Display advertising exchanges

Financial exchanges

Dating sites

Gaming consoles

Search engines

Forces Driving Consolidation

Scale and Size Effects

Sellers benefit from more buyers

Buyers benefit from more sellers

Both sides benefit from scale

Platform competition leads to monopoly

Forces Opposing Consolidation

Market Impact Effect Provides Checking Force

Sellers hurt by more sellers

Buyers hurt by more buyers

Platforms can coexist in equilibrium

Compensating price differences across platforms

Market Structure of Platforms

Market Concentration

More tipped Less tipped

What Accounts for these differences?

Market impact effects

Price differences

Differentiation

Path Dependence

A Simple Platform Game

Number of players of the player's

own type (including herself) in the

platform she joined

1 2

Number of

players of the

opposite type

in the platform

the player

joined

0 5 5

1 9 6

2 12 11

The subscription fees are, pA = 4 and pB = 2

Tipping is an Equilibrium



platform she joined

1 2

Number of

players of the

opposite type

in the platform

the player

joined

0 5 5

1 9 6

2 12 11


Coexistence is an Equilibrium



platform she joined

1 2

Number of

players of the

opposite type

in the platform

the player

joined

0 5 5

1 9 6

2 12 11


An Amended Platform Game: Reduced Market Impact



platform she joined

1 2

Number of

players of the

opposite type

in the platform

the player

joined

0 5 5

1 9 8

2 12 11


An Amended Platform Game: No Coexistence



platform she joined

1 2

Number of

players of the

opposite type

in the platform

the player

joined

0 5 5

1 9 8

2 12 11


Key Features

Coexistence turns on:

Size of market impact effects

Price differences

Differentiation of platforms

Key Questions

Does the size of the market impact effect explain market structure?

If tipping occurs, can we predict the winning platform?

What are the dynamics of platform competition?

Experimental Design

A “market” consists of 4 subjects

Subjects are assigned a “type”

Two are “squares” and two are “triangles”

Two competing platforms

Named “firm #” and “firm %”

Platform competition lasts 15 periods

Subjects simultaneously select a platform in each period

Experimental Design (2)

After 15 periods, subjects are randomly re-matched into new “markets”

Repeat platform competition game

Each 15 period block constitutes a “set”

Four sets per treatment

Feedback

Subjects know:

Payoff matrix for each platform

Access fees for each platform

Result of the previous round of the platform competition game including market outcome and realized gross and net payoffs

Treatments

Varied:

The size of the market impact effect

Platform payoff matrices

Order

Typical session:

Set 1: Non-tipped equilibrium (N)

Set 2: Only tipped equilibria (T)

Set 3 = Set 1

Set 4 = Set 2

Treatment 1: Homogeneous Platforms

Platforms have the same match technology (payoff matrices)

Differ in access fees (so there is a Pareto ranking)

Vary market impact effect to turn on and off coexistence

Homogeneous Setting: Session-level Results

Time Series of Platform Choice Throughout the Sessions

60%

70%

80%

90%

100%

110%

1 5 9 13 17 21 25 29 33 37

Period

Mark

et

Sh

are

of

the C

heap

er

Pla

tfo

rm NTNT Sessions

TNTN Sessions

Homogeneous Setting: Market-level Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4

Other

Pareto Dominant

Homogeneous Markets

Set

Percent

of

Markets

Tipped

Platforms are Not Created Equal

Perhaps coordination is easy because platforms differ only in their access fees

Test: Create “differentiated” platforms by varying payoff matrices

Platforms vary in matching efficiency as well as access fee

Net payoff is relevant for efficiency

Treatment 3: Vertically Differentiated Platforms

Number of players of the player's own type

(including herself)

1 2

Number of

players of

the

opposite

type

0 (6, 3) (6, 3)

1 (10, 9) [(7, 6)] {(9, 8)}

2 (13, 12) (12, 11)


Key Features

Platform B is less efficient

Tipping to B is Pareto dominant (owing to cheaper access fees)

In N treatment, 50-50 market share is also an equilibrium.

Differentiated Setting: Market-level Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4

Other

Pareto Dominant

Differentiated

Set

Percent

of

Markets

Tipped

Potential Confound

Pareto dominant platform is also the cheaper platform

Heuristic strategy: Go to the cheaper platform

Produces same predictions

Treatment 4: Differentiated – Cheap

Change payoff matrices so that the Pareto dominant platform is also more expensive

Will subjects learn that “paying for quality” is optimal?

Differentiated-Cheap Setting: Payoff Matrices

Number of players of the player's own type

(including herself)

1 2

Number of

players of

the opposite

type

0 (4, 4) (4, 4)

1 (11, 8) [(8, 6)] {(10, 6)}

2 (13, 11) (12, 10)


Differentiated-Cheap Setting: Session-level Results

Pareto Dominant Platform Choice in the Differentiated-

Cheap Treatment

50%

60%

70%

80%

90%

100%

110%

1 6 11 16 21 26 31 36 41 46 51 56

Period

Ma

rke

t S

ha

re o

f th

e

Mo

re E

xp

en

siv

e

Pla

tfo

rm NTNT Sessions

TNTN Sessions

Differentiated–Cheap Setting: Market-level Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4

Cheap

Pareto Dominant

Differentiated-Cheap

Set

Percent

of

Markets

Tipped

Path Dependence

Expectational lock-in

First to market has an advantage in creating expectations

Positive feedback cycle

Durable advantage for the first-mover platform…

Even if it is inferior

First-Mover as a Business Strategy

Amazon

“Get big fast.” – Jeff Bezos

Dot-com Biz Plans

Eyeballs rather than profits as success metric

Vaporware

Microsoft “version 1.0” products

First-Mover Advantage

In first 5 periods, only one platform is active

Subjects can only choose the active platform during this time.

Starting in period 6, an “entrant” platform arrives and subjects can choose either platform

Treatment 6: Differentiated – First Mover

Replicate differentiated treatment

But make Pareto dominant platform second mover

Treatment 6: Results

Treatment 7: Differentiated Cheap – First Mover

Replicate differentiated cheap treatment

Pareto dominant platform second mover

Treatment 7: Results

Alternative Explanation

Novelty heuristic

Subjects coordinate on the “new” platform in period 6

Test: First mover is always the cheaper platform

Treatment 8: Incumbent is cheaper but Pareto Superior

Is First-Mover Advantage Worth Anything?

Compare to treatments where neither platform has a head start

Head Start: Cheaper Platform is Pareto Superior

Head Start: Cheaper Platform is Pareto Inferior

Tentative Conclusions (2)

There is no evidence for QWERTY

The (Pareto) inferior platform triumphs 0% of the time

There is no evidence for expectational lock-in

Mild evidence for first-mover disadvantage

Surplus is what counts

100% of markets converge to the platform offering the higher surplus

Horizontal Differentiation

We consider a market where agents have different preferences for the platforms

Two platforms with different gross payoff matrices

Access fees are different for different agents

A pair of square and triangle players have the same set of access fees and another pair of square and triangle players have another set of access fees

Heterogeneous Agents: Market-level Results for PD

Tipping Under PD

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Heterogeneous Agents: Market-level Results for ND

Tipping Under ND

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Heterogeneous Agents: Market-level Results

Efficiently Coexisting Platforms

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Under PD Under ND

Conclusions

When platforms are undifferentiated or mainly vertically differentiated:

Markets mainly tip to Pareto dominant platform

Theoretical coexistence is behaviorally irrelevant

First mover advantage is worthless

When platforms are mainly horizontally differentiated

Coexistence is the norm

Epilogue

The internet offers the world’s biggest and best laboratory

Ask yourself why

Do it yourself

Be open to new possibilities

You can never have enough data

Documents

The Peculiar Economics of Online Marketswebfac/eichengreen/e191_sp12/morgan_2 … · Pricing 101 Basic pricing theory Know your marginal cost Know the elasticity of demand for your