Optimal Legal Standards and Accuracy in Antitrust Enforcement Giovanni Immordino Universit di Salerno and CSEF Michele Polo Universit Bocconi and IGIER June 2, 2011 Abstract: This paper analyzes optimal legal standards and accuracy in antitrust enforce- ment. The enforcer chooses the legal standard, the ne schedule and, if the decision rule entails errors, the level of accuracy. The policy problem is applied to traditional industries, where rms choose within a given set of feasible practices, and innovative industries, where the rm can enlarge the set of practices by investing in research. Marginal deterrence is central in traditional environments, while in innovative ones also the e/ect of enforcement on research investment - average deterrence - matters. For unlimited nes a discriminating rule implements the rst best in traditional industries opting for minimum accuracy. Instead, for innovative industries the need to sustain research leads to select a per-se legality rule when the probability of social harm is low; the optimal legal standard becomes, when harm is more likely, a discriminating rule and type-I accuracy is improved to limit over-deterrence and sustain research. When nes are limited, in traditional industries a discriminating rule is initially adopted together with type-II accuracy to improve marginal deterrence, while per-se illegality is optimal when the social damage is more likely. Finally, limited nes in an innovative industry leads  for increasing likelihood of social harm  rst to per-se legality, then to a discriminating rule and a more and more symmetric level of accuracy, and nally to per-se illegality. Keywords: legal standards, accuracy, antitrust, innovative activity, enforcement. JEL classication: D73, K21, K42, L51. Acknowledgments: Giovanni Immordino Universit di Salerno and CSEF, 84084 Fis- ciano (SA), Italy, giimmo@tin.it. Michele Polo, Universit Bocconi, Via Sarfatti 25, 20136 Milan, Italy, michele.polo@unibocconi.it. We are indebted to Nuno Garoupa, Yannis Kat- soulakos, Dilip Mookherjee, Massimo Motta, Marco Pagano, Patrick Rey, Matteo Rizzolli, Lars-Hendrik Rller, Maarten Schinkel and Giancarlo Spagnolo for helpful discussions. All usual disclaimers apply.