Industrial Organization

1. Industrial Organization Bundling

2. Bundling • Also called “tying” or “tie-in-sales” • Typical case: different goods (or services) bundled for a single price • Under some conditions profits increase

3. Pure Bundling • Without bundling (assuming marginal cost=0): • Option 1: PA=5 , PB=5 ; π: 5(2) + 5(2) = 20 • Option 2: PA=10 , PB=15 ; π : 15 + 10 = 25 • With bundling: • Pbundle=15 ; π : 15(2) = 30 • Key assumption: consumer valuations are negatively correlated

4. Mixed Bundling • Intermediate consumer (2): medium valuation for A and B • No bundling: • Option 1: PA=3 , PB=3 ; π : 3(2) + 3(2) = 12 • Option 2: PA=4 , PB=4 ; π : 4 + 4 = 8 • Pure Bundling: • Option 1: Pbundle=6 ; π : 6 • Option 2: Pbundle=4; π : 4(3) = 12 • Mixed Bundling: • Pbundle=6;PA=4 , PB=4 ; π : 6 + 4 + 4=14

5. Generalization: No Bundling VA, PA 1 • Consumers uniformly distributed on [0,max valuation] • Consumer valuations standardized by max valuation • Maximization without bundling: P*A=0.5 PB, VB 0 P*B=0.5 1

6. Generalization: Pure Bundling PA ,VA 1 • Bundling: 1 price for both goods % of buyers Pbundle=0.5 Consumers that are indifferent PB,VB 0 1 Pbundle=0.5

7. Generalization: Pure Bundling PA 1 1-good consumers now purchase 2 goods • Profit maximization with bundling • P*bundle=0.82 ; πbundle = 0.54 > πno bundle • What happens? • Firm gains some consumers and loses some • Certain consumers now buy 2 goods instead of one P*bundle=0.82 Lost consumers New Consumers PB 0 P*bundle=0.82 1

8. Generalization: Mixed Bundling PA 1 Buy 2 • New consumers: buy 1 good • Part of old 2-good consumers stop purchasing one of the two goods Buy A PA New Consumers Buy B PB 0 PB Pbundle 1

9. Generalization: Mixed Bundling PA 1 Large profit loss • Mixed bundling is usually preferred to pure bundling, but distribution of consumers is critical PB 0 Pbundle 1

10. Generalization: Mixed Bundling PA 1 Small profit loss Many new consumers CN PB 0 1

11. Industrial Organization I “Music for a Song…”, Shiller & Waldfogel Journal of Industrial Economics “Bundle Size Pricing …”, Chu, Leslie, Sorensen American Economic Review

12. Pricingschemes – setup of 2 papers Monopolist sells A, B, C Bundle Size Pricing: P1, P2, P3 Mixed Bundling (MB) PA, PB, PC PAB, PBC ,PAC PABC Uniform Pricing (UP) P Component Pricing (CP) PA, PB, PC Pure Bundling (PB) PABC Usually: Π(UP)<Π(CP)<Π(PB)<Π(MB) Π(UP)< Π(CP)<Π(PB)<Π(BSP) )<Π(MB)

13. Introduction – S & W • Motivation: i-tunes (99 cents/song) v. Nokia (bundle pricing) • 1,000 students valuations of 100 popular songs in early 2008 and 2009 (survey) • Study different pricing schemes and compare them according to profit, welfare and DWL • Under non-discriminatory pricing, revenue was raised between 1/6 and 1/3 relative to profit maximizing uniform pricing

14. The Exercise • Consumer i has reservation price for song s,Vis • Either zero or one unit of each song purchased • Resale is impossible • Bundles evaluated by adding each song’s valuation • The seller needs to choose a vector of prices P that maximizes profit

15. The Exercise • Uniform Single Price Monopolist • Component Pricing (different price per song) • Pure Bundling (all songs for a single price) • Mixed Bundling (limited to 3 songs) • Bundle Size Pricing (“non-linear”) • Two-Part Tariff (fixed fee & per unit fee) • Person Specific; (Extreme) 3rd DegreePrice Discrimination

16. The Exercise • Vik, individual i’s valuation of bundle k • Consumer’s Problem: max Vik – Pk • Where k can be an individual song, a bundle of all songs, a bundle of some songs or can represent a two-part tariff

17. Data • Two Surveys, 500 Wharton School Undergraduates • January 2008: 23,150 observations from 463 students (top 50 songs) • January 2009: 21,650 observations from 433 students (top 25 songs + 25 not so top). • Highest valuations were greater than $2.00 • Lowest valuations below $0.70 • Similar data between 2008 & 2009

18. Fit the valuation data to a parametric distribution for smoother revenue functions • Used a “zero-inflated multivariate lognormal” model

19. Characterizing the Valuation Distributions • Valuations are often rounded • This could cause song valuations to be higher/lower than the true value • This leads to a change in the spikes of the demand functions and revenue functions

20. Finding profit maximizing solutions • Uniform pricing: benchmark to compare pricing schemes • Component Pricing: 50 different exercises • Pure Bundling: 1 exercise, summing valuations • Mixed Bundling (limited to 3 songs): grid search • Bundle Size Pricing (“non-linear”): 50 exercises • Two-Part Tariff: grid search • Person Specific; Extreme 3rd DegreePrice Discrimination: as many exercises as people

21. Results Using Different Pricing Schemes • Marginal cost of digital music is zero • Artists, labels, and retailers are assumed to be working collectively • Maximum surplus: entire area under the demand curve • 2008: $60.34 per person • 2009: $43.56 per person

22. Results Using Different Pricing Schemes

23. Optimal prices • Uniform: 2008: $2.30; 2009: $1.46 • Component: 2008 median is $2.28;2009 median is $1.20 • Pure bundling: 2008 is $74.25; 2009 is $36.84 • Two-part: 2008 is ($52.31, $0.48); 2009 is ($21.19, $0.37)

24. Comparing Mixed Bundling with Alternatives

25. The problem and thequestion (C, L & S) • Problem: multiproduct firm pricing decision • Mixed bundling is known to be the superior pricing scheme • BUT, it is hard: • N products: 2N-1 possible bundles • Firms seem to avoid complex schemes • e.g. theaters, rental cars • Can we have a “simple” approximation?

26. Pricingschemes – setup of 2 papers Monopolist sells A, B, C Bundle Size Pricing: P1, P2, P3 Mixed Bundling (MB) PA, PB, PC PAB, PBC ,PAC PABC Uniform Pricing (UP) P Component Pricing (CP) PA, PB, PC Pure Bundling (PB) PABC Usually: Π(UP)<Π(CP)<Π(PB)<Π(MB) Π(UP)< Π(CP)<Π(PB)<Π(BSP) )<Π(MB)

27. How authors prove their point • Numerical analysis (“experiments”, “fake data”) • Analytical solution is not possible • Try different distribution of tastes, costs parameters, etc. • Empirical example • Estimate distribution of tastes for 8 plays • Counterfactuals of alternative pricing schemes

28. The experiments

29. The experiments • K varies from 2 to 5 • Allow for cost variations (zero, positive, or capacity constraint) • A consumer i has valuation for product j: Vij • Vij’s are drawn from several distributions • 6 distributions, 3 covariance structures, 4 MC assumptions, 220 parameters: 71,360 examples.

30. The experiments

31. Results: # of goods • As K increases, bundle pricing is favored • Heterogeneity is reduced as K increases • Avg price of 2-good bundle in MB is close to 2-good bundle price in BSP

32. Other results • Robust to parametric family • Robust to cost specification (with some caveats) • Robust to demand asymmetry (with some caveats) • Welfare

33. Results: welfare

34. Conclusion • Simple strategies might be as good • Intuition: • Opportunity for profit stems from consumer heterogeneity • Mixed bundling sets different prices to exploit this heterogeneity, but • As K goes up, the heterogeneity disappears, making fewer prices almost as good.