Friday, July 18, 2014
Leigh Osofsky (Miami), Concentrated Enforcement in a Best-Case Tax Enforcement Regime, 2014 IRS Research Bulletin:
In this Article, I set forth a theory (“concentrated enforcement”) for allocating scarce enforcement resources within a low compliance tax sector. The intuition behind concentrated enforcement is that, under a number of different circumstances, there may be increasing marginal returns to enforcement resources and psychological factors that support concentration. This Article begins by setting forth the notion of a best-case tax enforcement regime, which would allocate scarce tax enforcement resources to maximize the combination of direct revenue and voluntary compliance. The Article then examines some empirical evidence from the criminology context which suggests that, under certain circumstances, concentration of enforcement may be critical to voluntary compliance. The bulk of the Article draws on a number of different disciplines to set forth the conditions under which concentrated enforcement may increase voluntary compliance and explore how it might work in the particularly problematic cash business tax sector. The question of when concentrated enforcement can increase compliance is not merely theoretical. As I explain in this Article, concentrated tax enforcement, in the form of project-based enforcement, already exists in practice. By exploring the conditions under which concentrated enforcement can increase compliance, this Article can help explain and improve existing practice, as well as guide future research. While ultimately determining when concentrated enforcement does increase voluntary compliance requires experimental application and evaluation, examining the conditions under which concentrated enforcement is likely to increase voluntary compliance and the evidence of such conditions is the first step toward such experimentation. This Article takes this first, necessary step toward thinking about concentrated enforcement as part of a best-case tax enforcement regime.