The Economics of Platform Competition and Online Markets BUSINESS

Transcription

1 The Economics of Platform Competition and Online Markets JOHN MORGAN HAAS SCHOOL OF BUSINESS UC BERKELEY

2 All markets are becoming online markets Travel Books/music/movies Cars Houses Clothes

3 Adventures Online Some economic stories in the online world? In Five Acts

4 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.

5 Testing the No $500 Bill Theorem Selling the same item on two different platforms (auction sites) should yield the same revenues

6 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

7 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

8 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)

9 Morgan Silver Dollars Product: Popular collectible coins sold on both sites Search of Morgan Dollars ( ) 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

10 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

11 Dataset was generated by 88 auctions. treatments 88 auctions site 48 Yahoo! 40 ebay reserve 32 zero 16 positive 24 zero 16 positive ending rule 16 hard 16 soft 8 hard 8 soft

12 Add photo of coin here

13

14 Yahoo versus ebay revenue comparison. Mean Revenues by Coin Revenues ebay Yahoo Coin

15 Yahoo versus ebay number of bidders comparison. Mean Number of Bidders by Coin Number of Bidders ebay Yahoo Coin

16 Regressions examining Prediction 1 revenue=β 0 +β 1 site+β 2 reserve+γx+ε Site = 1 if ebay; 0 if Yahoo Controls: Coin dummies Random effects Book value Dealer Price Equilibrium coexistence implies β 1 = 0.

17 Results of regressions examining Prediction 1 Site coefficient is positive and significant at the 1% level in all specifications Baseline: ebay premium = $ (or 26.8 percent) Baseline + Reserve Price ebay premium = $14.90 (or 29.5 percent)

18 Results of regressions examining Prediction 2 Site coefficient is positive and significant at the 5% level in all specifications Baseline: ebay attracts more bidders than Yahoo Baseline + Reserve Price ebay attracts 2.1 more bidders than Yahoo

19 Does the ending rule matter? Mean Revenues by Coin Revenues Soft Close Hard Close Coin

20 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

21 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."

22 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

23 Testing the Law of One Price Price comparison site Lots of merchants Identical item Manufacturer warranty

24 How E-Retail is different from Speed of price changes conventional retail Median duration of a price quote on Shopper.com = 1 day Speed of location changes Price comparison sites

25 Some Stylized Facts About Price Competition Online Ubiquitous and persistent price dispersion No single low-price firm Market structure matters Theoretical rationales

26 Some Stylized Facts About Price Competition Online Ubiquitous and persistent price dispersion Source: Nash-equilibrium.com, July 16, 2007

27 Value of Price Information Source: Nash-equilibrium.com, July 16, 2007

28 Mean Coefficient of Variation (Percent) Price Dispersion Online Coefficient of Variation Figure 1: Time Series of the Coefficient of Variation /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/ /01/2001 Collection Date

29 Some Stylized Facts About Price Competition Online Ubiquitous and persistent price dispersion No single low-price firm

30 Some Stylized Facts About Price Competition Online Ubiquitous and persistent price dispersion No single low-price firm Market structure matters

31 Average Price Gap (Percent) Market Structure Matters Figure 5: Price Gap by Number of Firms Number of Firms Listing Prices Source: Baye and Morgan (2004)

32 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!

33 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

34 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)

35 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

36 But still, how elastic is demand online? Answer 1: Amazingly elastic Ellison and Ellison (2004) estimate elasticities of computer memory Answer 2: Not that elastic -25 to -40 for Chevalier and Goolsbee (2003) estimate Amazon s demand elasticity (for books) to be Why such a difference?

37 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

38 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.

39 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 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 to Sept

40 What s it like to shop at Kelkoo?

41 % of all leads When do people click? At work Proportion of leads by hour Hour of Day

42 % of all leads How do people choose what firm to click on? Both price and screen location seem to matter Price rank Screen Location Price rank / Screen Location

43 What determines leads? Information on Kelkoo Site (X) Information on Sellers Sites (Z) Christmas Weekend Date Restocking & Return Policy Look & Feel Ease of Use Position on Screen Information on firm s site Price # of Competitors CLICKS CONVERSIONS Product Popularity Information on rivals sites Accumulated Brand Equity Offline Presence SALES

44 Determining Leads Preliminaries: Q ijt 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[Q ijt X] = exp[x β]

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

46 Where does demand come from: Suppose that the expected number of leads to firm i are E[Q i X] Suppose the conversion rate of firm i s clicks is Pr[Buy X, Z] Then firm i s expected demand is E[D i ] = Pr[Buy X, Z] x E[Q i X]

47 Relating Leads to Demand Proposition: Suppose that the convert rate is independent of some firm-specific variable x i listed on the Kelkoo site, then the elasticity of demand with respect to x i can be estimated solely with clicks data. Main application: Suppose x i = p i, firm i s price, then demand elasticity can be estimated from clicks alone

48 Demand elasticities for each product? Suppose that demand is given by: E[Q ijt X ijt ] = β j x ln[price ijt ] + γ j X 1,ijt Controls: Screen location Month Weekend Note: Demand is assumed to be continuous---no jumps

49 Elasticity What accounts for the elasticity differences? Average Number of Firms Listing a Price Coefficient Estimate Significant at the 1% Level Coefficient Estimate Not Significant at any Conventional Level

50 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

51 Estimating the elasticity of demand pooled over all PDAs We estimate: E[Q ijt X ijt ] = [β 0 + (n jt -1) β 1 ] x ln[price ijt ] + β 2 n jt + γ X 1,ijt n jt denotes number of competing firms Controls: Same as above, plus PDA model x month Bricks and clicks retailer

52 Results Table 3: Continuous Clicks Specifications Model 1 Model 2 Likelihood Specification for Clicks Poisson PML Poisson PML Log Total Price (8.91)** (7.45)** Log Total Price x (Number of Sellers? 1) (4.14)** Number of Sellers (4.05)** Position on Screen (4.54)** (4.47)** Bricks and Clicks Retailer (1.58) (1.67) Weekend (10.82)** (11.46)** Product Dummies Month Dummies 4 4 Product x Month Dummies Robust Standard Errors Clustered by Firm Yes Yes Observations Overdispersion Test Test Statistic P-Value 0 0 Note: Robust z statistics in parentheses. * Significant at 5%; ** Significant at 1%

53 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 = Ten firm price elasticity =

54 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%

55 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

56 Misspecified demand leads to estimates that are too elastic Ln(Price) Best Price Firm i s Estimated Demand Firm i s Actual Demand Shoppers Ln(Quantity)

57 How we estimated discontinuous demand 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 j j group is given by: Q p e x ' β

58 More on estimating discontinuous demand q q Q L Q S p L L (1 D) p e e 1 where D 0 ' x β ' x β D p if p S p e min otherwise x β where ' s L This yields the estimating equation: ' E[ Qijt X] exp[( 0 +(njt -1) 1)ln pijt + 2n jt D γx1, ijt ]

59 Results Likelihood Specification for Clicks Model 1 Poisson PML Log Total Price (9.11)** Log Total Price x (Number of Sellers? 1) (4.60)** Jump from Shoppers (7.11)** Number of Sellers (4.52)** Position on Screen (4.37)** Bricks and Clicks Retailer (2.41)* Weekend (13.79)** Product Dummies 17 Month Dummies 4 Product x Month Dummies 55 Robust Standard Errors Clustered by Firm Controls for Unobserved Firm Heterogeneity Yes No Observations 6151 Overdispersion Test Test Statistic P-Value 0

60 The Value of Unpredictability Source: A Dashboard for Online Pricing by Baye, Gatti, Kattuman, and Morgan

61 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

62 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 = Southwest

63 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

64 Why Does BMIBaby Do This? Strategically decompose and shroud prices to profit from naïve consumers

65 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

66 Central Question Does these practices work? If so, when? How big an effect?

67 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

68 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

69 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

70 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

71 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.

72 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

73 Field Experiments 2006 Taiwan 36 ipods of various models 2008 Ireland 40 ipods of various models Describe Taiwan in detail, Ireland similar

74 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

75 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

76 Taiwan - Treatments

77 Screenshot Disclosed

78 Screenshot Shrouded

79 Decomposition Effects Disclosed Treatment Item DL DR ipod nano 1G black 4,380 4,530 ipod nano 1G white 4,330 4,630 ipod nano 2G black 5,430 5,480 ipod nano 2G white 5,430 5,580 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

80 Decomposition Effects: Shrouded Treatment Item SL SR ipod nano 1G black 4,080 4,530 ipod nano 1G white 4,330 4,480 ipod nano 2G black 5,230 5,500 ipod nano 2G white 5,230 5,480 ipod shuffle 1G 3,080 3,280 ipod shuffle 512m 1,860 1,980 Opening Price High Low Shipping Fee Low High Shrouding Yes Yes

81 Tentative Conclusions Price decompositions matter whether prices are shrouded or not

82 Shrouding Effects High Opening, Low Shipping Item DL SL ipod nano 1G black 4,380 4,080 ipod nano 1G white 4,330 4,330 ipod nano 2G black 5,430 5,230 ipod nano 2G white 5,430 5,230 ipod shuffle 1G 3,130 3,080 ipod shuffle 512m 2,190 1,860 Opening Price High High Shipping Fee Low Low Shrouding No Yes

83 Shrouding Effects Low Opening, High Shipping Item DR SR ipod nano 1G black 4,530 4,530 ipod nano 1G white 4,630 4,480 ipod nano 2G black 5,480 5,500 ipod nano 2G white 5,580 5,480 ipod shuffle 1G 3,100 3,280 ipod shuffle 512m 2,210 1,980 Opening Price Low Low Shipping Fee High High Shrouding No Yes

84 Shrouding Effects High Opening, High Shipping Item DH SH ipod nano 1G black 4,580 4,080 ipod nano 1G white 4,480 4,480 ipod nano 2G black 5,380 5,380 ipod nano 2G white 5,980 5,580 ipod shuffle 1G 3,380 3,280 ipod shuffle 512m 2,160 2,180 Opening Price High High Shipping Fee High High Shrouding No Yes

85 Ireland Experiments Key Differences Less product mix: 2G ipod shuffles in various colors Less extreme shipping 25/75 th percentile Minimal opening bid Within week treatment differences Colors alternated treatments each week.

86 Ireland Experiments - Treatments

87 Summary Statistics

88 Pooled Results

89 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

90 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

91 Studying Reputation EBay s reputation system key to competitive advantage What does it cost good guys versus bad guys to gain reputation?

92 A Market for Feedback

93 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.

94 The Strategy of thelandseller Create reputation through penny transactions in the market for feedback thelandseller reputation = 598 Cost of reputation: $360

95 The New Strategy of thelandseller Sell lakefront property in Texas Benefit from reputation: Raise price by up to 5%

96 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?

97 Act V: First Mover Advantage Principle 5: In network markets, the first mover wins even if it is worse Famous Example: QWERTY versus DSK

98 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

99 What are platforms? Online auctions Display advertising exchanges Financial exchanges Dating sites Gaming consoles Search engines

100 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

101 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

102 Market Structure of Platforms Market Concentration Less tipped More tipped

103 What Accounts for these differences? Market impact effects Price differences Differentiation Path Dependence

104 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 The subscription fees are, p A = 4 and p B = 2

105 Tipping is an Equilibrium 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 The subscription fees are, p A = 4 and p B = 2

106 Coexistence is an Equilibrium 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 The subscription fees are, p A = 4 and p B = 2

107 An Amended Platform Game: Reduced Market Impact 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 The subscription fees are, p A = 4 and p B = 2

108 An Amended Platform Game: No Coexistence 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 The subscription fees are, p A = 4 and p B = 2

109 Key Features Coexistence turns on: Size of market impact effects Price differences Differentiation of platforms

110 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?

111 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

112 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

113 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

114 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

115 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

116 Market Share of the Cheaper Platform Homogeneous Setting: Session-level Results Time Series of Platform Choice Throughout the Sessions 110% 100% 90% 80% NTNT Sessions TNTN Sessions 70% 60% Period

117 Homogeneous Setting: Market-level Results Homogeneous Markets Percent of Markets Tipped 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Set Other Pareto Dominant

118 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

119 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) The subscription fees are, p A = 5 and p B = 2

120 Key Features Platform B is less efficient Tipping to B is Pareto dominant (owing to cheaper access fees) In N treatment, market share is also an equilibrium.

121 Differentiated Setting: Market-level Results Differentiated Percent of Markets Tipped 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Set Other Pareto Dominant

122 Potential Confound Pareto dominant platform is also the cheaper platform Heuristic strategy: Go to the cheaper platform Produces same predictions

123 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?

124 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) The subscription fees are, p A = 3 and p B = 2

125 Differentiated-Cheap Setting: Session-level Results Market Share of the More Expensive Platform Pareto Dominant Platform Choice in the Differentiated- Cheap Treatment 110% 100% 90% 80% 70% 60% 50% Period NTNT Sessions TNTN Sessions

126 Differentiated Cheap Setting: Market-level Results Differentiated-Cheap Percent of Markets Tipped 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Set Cheap Pareto Dominant

127 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

128 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

129 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

130 Treatment 6: Differentiated First Mover Replicate differentiated treatment But make Pareto dominant platform second mover

131 Treatment 6: Results

132 Treatment 7: Differentiated Cheap First Mover Replicate differentiated cheap treatment Pareto dominant platform second mover

133 Treatment 7: Results

134 Alternative Explanation Novelty heuristic Subjects coordinate on the new platform in period 6 Test: First mover is always the cheaper platform

135 Treatment 8: Incumbent is cheaper but Pareto Superior

136 Is First-Mover Advantage Worth Anything? Compare to treatments where neither platform has a head start

137 Head Start: Cheaper Platform is Pareto Superior

138 Head Start: Cheaper Platform is Pareto Inferior

139 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

140 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

141 Percent of Markets Tipped Heterogeneous Agents: Market-level Results for PD Tipping Under PD 100.0% 80.0% 60.0% 40.0% 20.0% Platform # Platform % 0.0% Set

142 Percent of Markets Tipped Heterogeneous Agents: Market-level Results for ND Tipping Under ND 100.0% 80.0% 60.0% 40.0% 20.0% Platform # Platform % 0.0% Set

143 Percent of Markets Coexisting Heterogeneous Agents: Market-level Results Efficiently Coexisting Platforms 100.0% 80.0% 60.0% 40.0% 20.0% Under PD Under ND 0.0% Set

144 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

145 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