In Quantifying Legislative Uncertainty: A Case Study in Tax Policy, Jason Oh (UCLA) and Christopher Tausanovitch (UCLA--Poli Sci) set out to articulate (and test) an empirical model designed to quantify when a legislative body will (or will not) enact law. The paper nests this difficult conceptual task in the federal income tax context. More specifically, the paper presents a "model of legislator preferences on tax rates and show that the political process can be well understood in terms of the preferences of key legislators. We use our statistical model to quantify the uncertainty of tax rates and forecast the direction of likely rate changes in the future."
The paper--and argument--stimulate an array of implications and take-aways. "First, quantifying legislative uncertainty offers insight into the behavioral effects of the law. How people respond to the law depends on their perception of the law’s future trajectory. Second, our analysis allows us to explore the stability of major legislative reform. Our methodology allows us to demonstrate that reforms are sometimes predictably unstable. Such reforms can have the perverse result of increasing future legislative uncertainty."