1. Public Policy in Private Markets Collusion

2. Announcements HW:  HW 1, graded – can pick up at the end of class  HW 2, due 3/1; HW 3 due 3/6 3/6: first debate – group presenters: video is due to me by 3/2 (this Friday) 3/8: midterm #1 (review sheet posted) – material will be reviewed on 3/1

3. Collusive Restraints of Trade Practices covered by Section 1:  Direct Agreements To fix price To Allocate markets  Geographically  By type of customer  Other Collusive restraints Gray area (circumstantial evidence) Conscious parallelism, trade associations, non-profit organizations

4. Industrial Organization Weak Case of Price Fixing: School Milk

5. Ohio v. Trauth Schools: Bid solicitation for annual supply of milk (sealed bid auction) > 600 school districts Solicitation: menu of milk types, sometimes with other requirements such as napkins, coolers. Local diaries supplied milk  Costs similar across diaries  Distance is the key factor

6. Market concentration

7. Ohio v. Trauth  Homogeneous product  Similar technology across processors  Costly transportation (competition is localized)  High barrier to entry (no one builds a plant solely for selling milk to schools)  Inelastic demand  Infrequent demand  Information available (schools posted info)  Easy allocation of markets

8. Ohio v. Trauth Methods involved bid rotation & complementary bidding: artificially raised the price for schools Case where direct evidence was not enough:  Additional economic (statistical) evidence was needed  Would a “control” group behave the way defendants behaved? Closeness should increase probability of submitting bid Conditional on submitting a bid, bid level should increase with distance  Are bids correlated? (complementary bidding)

9. Ohio v. Trauth Control group behavior Accused firms behavior

10. Ohio v. Trauth Accused firms behavior

11. Ohio v. Trauth Aftermath: Settled out of court in 1996 (even though statistical evidence was strong). Problem: DOJ lost a federal case in 1995 (due to unreliable confessions) Collusion is frequent in school milk auctions 130+ criminal cases filed

12. Industrial Organization Collusion and Non-Profits: MIT & Ivy League schools case

13. MIT Financial Aid (DOJ, 1991)  MIT, Brown, Columbia, Princeton, U Penn, Yale, Dartmouth, Cornell, Harvard The controversial activity:  “Overlapping” student athletes (1950’s)  No aid beyond financial needs (agreement)  It then extends to non-athletes  Aid package + family contribution (fixed across schools) Important elements in case:  Do antitrust laws apply to not-for-profit organizations? (what do they maximize?)  Per se vs. rule of reason approach

14. MIT Financial Aid Example: Family contribution = $10,000 across all schools

15. MIT Financial Aid Government:  Tuition is commercial activity (section 1 applies) Recall NCAA case re broadcasting of games  Practice aimed at increasing tuition and revenue  Some consumers harmed: wealthy and smart  Per se rule: no room for justifications

16. MIT Financial Aid MIT:  No trade or commerce (outside of section 1)  Tuition < cost  Court did not have experience with not-for- profit organizations (hence rule of reason)  Not-for-profits maximize something else  Agreements helped the needy (in line with government’s objectives)  No evidence of increased revenue

17. MIT Financial Aid Statistical analysis  How does tuition in Ivy league schools compare to similar schools?  Regress tuition/student on many variables, including indicator of whether school is Ivy league  Further studies: Look at whether tuition increased after Overlap group practices were eliminated

18. Merger Law

19. The Trial 8 Ivy League schools signed consent decrees MIT refused and went to trial Sept ‘92: MIT found guilty of violating Section 1 of Sherman Act (under rule of reason) Court of Appeals upheld the District Court’s ruling but disagreed on several points Case ended in a Settlement in 1993: MIT could participate in overlap practices, but only in general, not on specific students

20. Statistical analyses in antitrust Bottom line in statistical analyses is to compare behavior of suspect firms:  With control group (Ohio, MIT)  During conspiracy v. outside conspiracy (ADM, MIT)