ABSTRACT: Evaluations of the consumer harm caused by cartels are typically partial because they do not attempt to quantify the impact of deterrence, or acknowledge that the CA does not root out all anti-competitive cases. This paper proposes a broader framework for evaluation which encompasses these unobserved impacts. Calibration of this framework is challenging because one cannot rely on estimates for cases which have been observed to make deductions about those that have not - an example of the classic sample selection problem which is endemic across much of the empirical Industrial Organisation literature. However, we show how empirical findings, already available in the existing literature, can be plugged into a Monte Carlo experiment to establish bound estimates on the magnitudes of cartel-induced consumer harm. Lower bound (i.e. cautious) estimates suggest that (i) the harm detected by the CA really is only the tip of the iceberg, accounting for only a small fraction (at most one sixth) of total potential harm; (ii) deterrence is at least twice as effective as detection as a means for removing harm; and (iii) undetected harm is at least twice as large as detected harm. Under less cautious, but very plausible, assumptions, all three effects could be much greater than this
KEYWORDS: Cartels, anti-competitive harm, deterrence, detection, selection bias, Monte Carlo simulation
CITATION: Davies, S. & Ormosi, P. (2014) "The Economic Impact of Cartels and Anti-Cartel Enforcement", CCP Working Paper 13-7 v2.
This paper was previously published as "The Impact of Competition Policy: What are the Known Unknowns?", CCP Working Paper 13-7, with a stronger focus on mergers. It has been revised with a focus on cartels rather than mergers.
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