Regulatory Enforcement with Dynamic Mechanisms Including a Targeting Tournament
*Scott Gilpatric, University of Tennessee Economics
Building on models of regulatory enforcement employing dynamic targeted audit mechanisms, we develop two mechanisms for regulating firms based on self-reported actions and demonstrate both theoretically and through laboratory experiments that they achieve significant leverage over random audits. One of these mechanisms characterizes a dynamic tournament in which firms compete to avoid being targeted. In this model firms transition between a targeted group which faces a high audit probability and a non-targeted group where the audit probability is lower. The size of these groups is fixed as are the number transitioned each period, and firms compete to avoid being transitioned to the targeted group and to be transitioned out of this group. This model captures the strategic interaction among firms that are regulated through a dynamic targeting process and also represents a new type of dynamic tournament model. We also develop a standard (non-strategic) dynamic targeting mechanism for enforcement of regulations requiring self-reporting. Both mechanisms are studied in the laboratory, where we find that both achieve significant leverage gains relative to a random audit mechanism. The comparative statics of the tournament mechanism are broadly confirmed, but this is not the case for the standard mechanism.
Back to Abstract