Dynamic history-dependent tax and environmental compliance monitoring of risk-averse firms
研究了基于企业近期合规历史来调整处罚或监控频率的动态监控方法，发现即使监控率低于最优静态水平，动态监控也优于静态监控，并用IRS 2010年税收数据验证了模型。
Abstract Firms may misreport income or fail to comply with environmental regulations. This study contributes to the growing literature that analyzes dynamic history-dependent compliance monitoring, under which penalties or monitoring frequency are selected on the basis of recent compliance history. The current study develops methods for evaluating and comparing explicit solutions under given monitoring costs and income distributions, using a commonplace utility-penalty scenario under which firms never comply fully with regulations if statically monitored (regardless of their income distribution), but find it to their benefit, if dynamically monitored, to comply fully when their income is sufficiently high. In most examples tried, dynamic monitoring is superior even when constrained to monitor all firms at rates below the optimal static rate. The model is applied to actual IRS 2010 tax-report monitoring and compliance data partitioned by income bracket. This allows, in particular, to deduce degrees of risk aversion.