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Inferences from Litigated Cases

Inferences from Litigated Cases. Dan Klerman & Yoon-Ho Alex Lee Conference on Empirical Legal Studies October 24, 2013. Motivating Questions. Can empirical legal scholars use the plaintiff trial win rate to draw inferences about the law ?

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Inferences from Litigated Cases

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  1. Inferences from Litigated Cases

    Dan Klerman & Yoon-Ho Alex Lee Conference on Empirical Legal Studies October 24, 2013
  2. Motivating Questions Can empirical legal scholars use the plaintiff trial win rate to draw inferences about the law? Would a change in the law lead to a predictable change in the plaintiff trial win rate?
  3. Answers in the literature NO. Plaintiffs will win 50% regardless of the legal standard. - Priest & Klein (1984) NO. If there are deviations from 50%, they are caused by asymmetric stakes or other factors unrelated to the law. - Priest & Klein (1984) Any plaintiff trial win rate is possible under asymmetric information models. - Shavell (1996)
  4. Our Answer Sometimes Under all standard settlement models, change in legal standard, under plausible assumptions, will lead to predictable changes in plaintiff trial win rate Priest-Klein divergent expectations model Bebchuk screening model Reinganum-Wilde signaling model Pro-plaintiff change in law will lead to increase in plaintiff trial win rate Good news for empirical legal scholars
  5. Priest-Klein Model: Overview DISTRIBUTION OF ALL DISPUTES (SETTLED OR LITIGATED) p WINS (BLUE) p WINS (BLUE) DEGREE OF D FAULT DEGREE OF D FAULT Distributions of litigated disputes if parties make moderate errors Distributions of litigated disputes if parties make small errors PRO-D STANDARD PRO-p STANDARD
  6. Priest-Klein Model: Overview Proposition 1 (Inferences Under the Priest-Klein Model). Under the Priest-Klein model, if the distribution of disputes has a log concave CDF, then p’s win-rate among litigated cases increasesas the decision standard becomes more pro-p. PDFs with Log-Concave CDFs: normal, generalized normal, skew normal, exponential, logistic, Laplace, chi, beta, gamma, log-normal, Weibull…
  7. Priest-Klein Model As legal standard becomes morepro-D, p’s win-rate decreases Effect varies with standard deviation of prediction error Paper presents evidence that standard deviation is large
  8. Screening Model 2 types of defendants High liability defendants 70% probability that will lose at trial Low liability defendants 30% probability that will lose at trial 50% of each kind Defendant knows type Plaintiff does not Plaintiff knows overall proportions Damages 100K Each side has litigation costs of 10K 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 10K, 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 10K, 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 10K, 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 10K, if case does not settle Plaintiff makes take it or leave it offer
  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 10K, if case does not settle Plaintiff makes take it or leave it offer
  14. 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 10K, if case does not settle Plaintiff makes take it or leave it offer
  15. 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
  16. 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
  17. 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
  18. Screening Model Proposition 2 (Inferences Under the Screening Model). The probability that pwill prevail in litigated cases is strictly higher under a more pro-plaintiff legal standard, as characterized by case distributions that satisfy the monotone likelihood ratio property. Holds whether plaintiff or defendant has informational advantage. PDF Families over [0,1] Exhibiting MLRP: uniform, exponential, binomial, Poisson, beta, rising triangle, falling triangle
  19. Extensions Signaling model Effect of different decisionmakers Republican versus Democratic judges Male versus female judges 6 or 12 person jury Whether factor affects trial outcome Race or gender of plaintiff Instate or out-of0state defendant Law firm quality
  20. Caveats Assumes that distribution of underlying behavior doesn’t change Not usually true Exceptions Retroactive legal change Uninformed defendants Advice to empiricists Worry less about settlement selection Worry more about changes in behavior Distribution of disputes (litigated & settled) Logconcave for Priest-Klein Monotone likelihood ratio property for asymmetric information
  21. Conclusions Selection effects are real But may be able to draw valid inferences from litigated cases Measure legal change Measure biases of decision makers Identify factors affecting outcomes Good news for empirical legal studies
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