Privacy, Information Acquisition, and Market Competition

Transcription

1 Privacy, Information Acquisition, and Market Competition Soo Jin Kim Michigan State University May / 19

2 Background - Facebook Ad Targeting Example 2 / 19

3 Background - Facebook Ad Targeting Example 2 / 19

4 Background - Facebook Ad Targeting Example 2 / 19

5 Research Questions 3 / 19

6 Research Questions AT&T TimeWarner Example 4 / 19

7 Main Message and Contribution Privacy concern may disproportionately benefit the incumbent and make the entrant lag far behind the incumbent. Individually optimal decision on data disclosure might not be socially optimal when aggregated. Contribution by jointly allowing for consumer heterogeneity and seller market asymmetry, provide microfoundation for agent s optimal decision expand horizon to more diverse market outcomes: how privacy concern affects data-sharing aspect of vertical integration 5 / 19

8 The Basic Model Setup N = {Consumer, Platform, Seller I, Seller E} is the set of players, 6 / 19

9 Model - Payoff τ c = P(i IS)=Total amt of aggregate detailed information. Consumer i s utility from using the platform is vi Platform v(τ c ) ψ(τ i) if disclosing (i IS) = r p 0 if not disclosing (i S) (1) where v(τ c ) is immediate benefits with v > 0, ψ(τ i ) r p denotes the nuisance cost, with ψ > 0, ψ 0, and r p is the platform s reputation. Consumer i s utility from purchasing a product from seller j is ( 1 ) u ij = V + θ i s j P j 1 {i S} γ j D j (2) }{{} mismatch cost mismatch cost information externality 7 / 19

10 Targeting and Mismatch Cost Example Figure: Targeting Example 8 / 19

11 Model - Payoff Assuming that the platform charges per unit price C for data, max C π p (C n, τ c ) = ncτ c (3) where n denotes the number of sellers who buy the data and τ c is the amount of data to sell. The profit for each seller j is defined as follows. max π j = P j X j (P j, D j γ, s) 1 buy Cτ c (4) P j,d j where P j is price that seller j charges, X j is j s market share, D j is the amount of data seller j uses, C is the price for data, and 1 buy indicates that j purchases data. 9 / 19

12 1. No Vertical Integration Timing 1. Consumer decides whether to disclose information to the platform and obtains net utility by (1). 2. The platform sets the price for data (C). 3. East seller simultaneously decides whether to buy data from the platform (D j ) and then sets P j. Also, each seller sends targeted ads to consumers. 4. Consumer decides from which seller to buy a product and obtains net utility by (2). A consumer forms a rational expectation on P(i IS) = τ c. Given the expectation, I solve for Nash Equilibrium using backward induction to obtain sequentially rational strategies. 10 / 19

13 1. No Vertical Integration (1st & 4th Stage) In the first stage in which consumer i makes info disclosure decision, any consumer i who has ψ(τ i ) r p < v(τ c ) will disclose. P(i IS) = P(τ i < ψ 1 (r p v(τ c ))) = F (ψ 1 (r p v(τ c ))) = τ c Given τ c, the amount of aggregate detailed data, by backward induction, In the last subgame, consumer decides one of the sellers. The marginal consumer (vertical) type is θ c. (under s I < s E ) Parametric Example 11 / 19

14 1. No VI - 3rd Stage (Price Decision) By solving each seller s profit maximization problem w.r.t. price, the equilibrium profit for each seller is as follows. B N B (s+ BB (1 τ c )) 2 Cτ c, (2s BB (1 τc )) 2 9s 9s (s+ NB (1 τ c )) 2 (2s, NB (1 τ c )) 2 9s 9s Cτ c (s+ BN (1 τ c )) 2 9s Cτ c (s+ NN (1 τ c )) 2 9s N Cτ c, (2s BN (1 τc )) 2 9s (2s, NN (1 τ c )) 2 9s s = s E s I (prod quality gap between I and E) = 1 1 (targeting gap between I and E) D E γd I (D j : the amt of data seller j has, τ c : privacy-insensitive consumer portion = total amt detailed data, γ : γ I γ E > 1) 12 / 19

15 1. No VI - 2nd Stage (Data Acquisition Decision) Whether to buy or not depends on thresholds on data cost C. Given E buys, I buys if C < (1 τ c )( BB NB )(2s+(1 τ c )( BB + NB )) 9sτ c C I. Given E not buys, I buys if C < (1 τc )( BN NN )(2s+(1 τ c )( BN + NN )) 9sτ c CI. Given I buys, E buys if C < (1 τ c )( BN BB )(4s (1 τ c )( BN + BB )) 9sτ c C E. Given I not buys, E buys if C < (1 τc )( NN NB )(4s (1 τ c )( NN + NB )) 9sτ c C E. Under interior solution assumptions (s 1 2 ), the ranking is Given that, the platform s profits under different level of C are If C C I, (Buy, Buy) π p( C I ) = 2 C I τ c If C C E, (Not Buy, Buy) π p( C E ) = C E τ c 13 / 19

16 1. No Vertical Integration - Equilibrium Proposition 1 There exists τ NV (0, 1) such that (1) if τ c (0, τ NV ), the platform sets C = C I (lower price) so both sellers buy data. (B,B) (2) if τ c (τ NV, 1), the platform sets C = C E (higher price) so only seller E buys data whereas seller I does not. (N,B) The equilibrium τ c = P(i IS) solves F (ψ 1 (r p v(τ c ))) = τ c. }{{} total amt of detailed data implications 14 / 19

17 2. Vertical Integration - Timing Timing 1. Consumer decides whether to disclose info or not. 2. The platform first makes a vertical integration deal with one of the sellers. It also sets the price for data C.(decides whether to sell or foreclose data) 3. The unaffiliated seller decides whether to purchase data from the platform. The affiliated seller always uses data for targeted ads. Then each seller sets price. 4. Consumer decides from which seller to buy a product. τ < τ c < τ guarantees VI eqm can always emerge. The platform decides the merger partner : profit comparison between VI with I and with E. VI firm has two choices : sell or foreclose the data access 15 / 19

18 2. Vertical Integration - Equilibrium Proposition 2 (Vertical Integration Eqm on (s, τ c ) space) where τ c = P(i IS)=total amt of detailed data and s = s I s E Proposition 3 (Anticompetitive Effect of Vertical Integration) Only seller E suffers from lower profit due to vertical integration. 16 / 19

19 3. Welfare Analysis (NV vs VI) Parametric assumptions as τ i U[0, 1], ψ(τ i ) = aτi 2 where a > 2, r p = 1, and v(τ c ) = 1 + τ c and V = 2. The main focus here : how data-driven vertical integration with I affects CS and SW (τ < τ c < τ). (B,N) if τ c (τ, τ V ) }{{} privacy concern vs. (N,B) if τ c (τ V, τ) }{{} privacy concern Figure: (s, τ c ) space when γ = 2 Figure: CS a fcn of nuisance cost 17 / 19

20 3. Welfare Analysis (B,N) if τ c (τ, τ V ) }{{} privacy concern vs. (N,B) if τ c (τ V, τ) }{{} privacy concern Figure: (s, τ c ) space when γ = 2 Figure: CS a fcn of nuisance cost Proposition 4 (Welfare Comparison) Data-driven vertical integration with the incumbent makes a consumer worse off than either no vertical integration or vertical integration with the entrant if s E s I s > s. 18 / 19

21 Conclusion How does consumers privacy concerns affect market competition? - E that needs more data is disproportionately affected by a lack of data arising from greater privacy concerns. - Eventually leads to lower CS and SW due to the lack of competition arising from data foreclosure. Privacy concern not only harms the entrant in a disproportionate way but also adversely affects consumers themselves. Privacy protection policy which prohibits data acquisition might harm entrant or small sellers only. Privacy concern is trust-based if more confidence in data-usage policy, τ c (e.g.) Privacy certification program such as TRUSTe Next steps Extension : dynamic setup(entry/exit), data-driven network effect... In empirical analysis, alternative methods to control endogeneity 19 / 19

22 Appendix Supplements 1 / 17

23 Empirical Evidence Data Mobile Application Data from Google Play Store (from Oct ) App-specific information (price, category, size and so on) & permissions each app requests when downloading 2 / 17

24 Stylized Facts Among total 17 categories of permission groups, the most frequent permission groups are Identity (Identity and Contact), Location, Social (SMS and Phone), and Device & App History. Free apps ask more permissions than paid apps on average, which raises a doubt on that app developers have ulterior motives for providing free apps. Most (free) apps ask redundant permissions which do not affect apps functionality. 3 / 17

25 Stylized Facts Figure: The most frequent permissions on average 4 / 17

26 Stylized Facts Figure: The number of privacy-sensitive permissions 5 / 17

27 Summary Statistics Variable Mean Std. Dev. N ln Num Reviews ln Ipp Ige Price Game non game Avg rating Num of screen shots Num of apps for developer Dummy top developer D location D social D identity D history Total permissions Total without others Redundant permissions / 17

28 Empirical Strategy Main Variables The number of reviews of each app as a proxy for the demand measure The number of redundant permissions as threat to privacy Dummy variables for privacy related permissions (Location, Social, Identity, and Browing History) Top Developer status which Google grants to an app as a proxy for firm reputation Control variables all available app-specific characteristics (release dates, average app rating, app size, dummy for game category, the number of screenshots on app description page, the total number of distinct apps each developer provides, and so on) 7 / 17

29 Empirical Strategy Top Dev ln reviews i =α + βd i + β 5 Redundant i + γdi Redundant i + δprice i + θx i + ɛ i (5) where i denotes each app, D i denotes dummy variables for privacy related permissions (for location, social, identity, and browsing history), Redundant i means the total number of redundant permissions, γ is the coefficient of interest which shows the interaction effect of top developer and redundant permissions, X i is a set of app-specific characteristics as control variables. 8 / 17

30 Results (1) (2) (3) VARIABLES Free Paid Full Sample D Price *** ln Ipp Ige 1.151*** 0.363*** 0.604*** Num of apps for dev *** ** *** Game non game 0.990*** 0.516*** 0.635*** Avg rating 1.874*** 1.245*** 1.487*** Number of screenshots *** *** *** D location *** 0.225* D Social D Identity 0.535** 0.665** 0.742*** D History 0.747*** 0.309** 0.577*** Redundant ** * *** Top Dev Redundant *** 0.276*** 0.154*** Constant *** ** 0.674** Observations 3,639 2,130 5,769 R-squared *** p<0.01, ** p<0.05, * p<0.1 9 / 17

31 AT&T and TimeWarner Example Back to Research Question. 10 / 17

32 Mismatch Cost A consumer does not have a preference for a specific brand(seller) over the other but has different needs for a specific type of products which both sellers sell. If each seller has the most recent information about potential customers, he is able to make more attractive targeted ad to consumers. Consumers could spend less time on finding the most suitable product to himself because the consumer obtains the relevant information immediately from targeted ads. Thus, the mismatch cost does not arise from comparing one seller to another but from comparing types of products which each retailer sells: if one seller suggests the most suitable product which fits my current need, the suggestion would be a good match for my need. Back to consumer. 11 / 17

33 Mismatch Cost - Example Consumer in his 20s and have small kids so in need of mini van. Both of Honda and Subaru have basic information about him (his age and gender). Assume that Honda purchases additional information while Subaru does not (family status). Then, Honda would make a minivan ad but Subaru would make a sports car ad from the guess that men in their 20s prefer sports car in general. Then, he is likely to go Honda than Subaru because at Honda, as opposed to at Subaru, he knows exactly what features to look for in the car type he wants, whereas at Subaru, he still has to look up additional details. The transaction is cheaper at Honda. Back to consumer. 12 / 17

34 Information Externality Here, consumers even with providing basic information only due to great concerns for invasion of privacy would receive some amount of promotion ads. This is plausible scenario considering information externality. Firms can predict information about privacy sensitive consumers based on other information obtained by other consumers who provide some personal data in the network. (e.g.) Firms can categorize consumers into some subgroups based on gender and age. Among each consumer category, some people provide much information about themselves while others provide nothing. Then, the information can be passed along to peer group, so that consumers who do not provide any further personal information are likely to receive some promotion s. Back to consumer. 13 / 17

35 Intuition for Data Strategic Substitutes Data acquisition is strategic substitute since 2 π j D I D E = 2(1 τ)2 9sD 2 I D2 E γ < 0. The intuition is the following. The data can be used to differentiate products in some sense because better targeted ads from more available data are able to attract more consumers. Thus, consumer information which is used for better targeted ads increase the production differentiation which softens price competition. Back to main. 14 / 17

36 Intuition for Ranking on Threshold C C I < C I < C E < C I C E > C I (or C E > C I ) means worse targeting/higher quality guy(seller E) is more likely to buy data than seller I given the rival also buys. (or given the rival does not buy data.) E likely to buy if quality gap increases (s ) I likely to buy if there are more privacy sensitive consumers (τ ) or higher mismatch costs for sensitive consumers (α ) [Quality Effect] dominates [Privacy Sensitivity Effect] Back to main. 15 / 17

37 Parametric Example of τ c A parametric example Let τ i U[0, 1], ψ(τ i ) = aτi 2 where a > 2, and v(τ c ) = 1 + τ c. In this case, τ c is the solution to τ c = ψ 1 (r p (1 + τ c r )) = p(τ c +1) a. Thus, τ c rp(4a+rp)+r p = 2a. Back to main. 16 / 17

38 More Implications on NV Equilibrium Corollary 1 E enjoys higher market share and revenue from (N,B) but profit effect uncertain due to high data cost. (π BB E > π NB E if τ c > τ NV where τ NV < τ NV ) Because of the high cost required to obtain a monopoly on data, (N,B) may lead to lower profit than (B,B), except for τ NV < τ c < τ NV. Back to main. 17 / 17