Presentation Transcript

1. Inferences from Litigated CasesDan KlermanUSC Law SchoolAlex LeeUSC Law SchoolSCELSUSC Law SchoolJune 7, 2013

2. Introduction Priest & Klein (1984) Most cases settle Litigated cases are non-random sample Expect 50% win rate Deviations from 50% caused by factors other than law Asymmetric stakes Can’t draw inferences about law from win rate Highly cited Closes off much empirical work Klerman & Lee Can draw inferences from litigated cases Priest-Klein prediction is limiting result Asymmetric information models
3. Priest-Klein Model Distribution of all disputes, whether settled or litigated Plaintiff victories Plaintiff victories Degree of defendant fault Degree of defendant fault Distribution of litigated disputes if parties make larger estimation errors Distribution of litigated disputes if parties make small estimation errors Pro-defendant decision standard Pro-plaintiff decision standard
4. Screening Model 2 types of defendants High liability defendants Likely to lose at trial Low liability defendants Likely to win at trial 50% of each kind Defendant knows type Plaintiff does not Plaintiff knows overall proportions Damages 100K Each side has litigation costs of 30K if case does not settle Plaintiff makes take it or leave it offer
5. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
6. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
7. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
8. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
9. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
10. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
11. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer
12. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer Pro-plaintiff shift in law
13. Screening Model 2 types: High liability defendants, low liability defendants (equal probability) Defendant knows type, but plaintiff does not (but knows distribution) Damages 100K Each side has litigation costs of 30K, if case does not settle Plaintiff makes take it or leave it offer Pro-plaintiff shift in law
14. Screening Model With Continuous Distributions Percentage of Plaintiff Wins at trial Legal Standard (α)
15. Extensions and Caveats Priest & Klein model Lots of simulations Working on analytical proof Screening model Proven analytically Signaling model Caveat Assume that distribution of underlying behavior doesn’t change Not usually true Exceptions Retroactive legal change Uninformed defendants Advice to empiricists Worry more about change in behavior Worry less about settlement selection
16. Regression Analysis of Trial Outcomes Might try to estimate effect of Factor X on trial outcomes E.g. Jury bias against out-of-state defendants Priest & Klein: Won’t work Party settlement behavior will take bias into account Expect 50% win rate against in-state defendants Expect 50% win rate against out-of state defendants Even if jury very biased Even if control for all observable factors Klerman & Lee: Regressions will work Selection will make win-rates look more similar But differences will remain Regression will under-estimate effect of jury bias So effect is larger than actually observed
17. Conclusions Selection effects are real But can draw valid inferences from litigated cases Can measure legal change Can measure factors affecting plaintiff win rates Good news for empirical studies of law