🤖 AI Summary
Existing economic policy modeling suffers from fragmented instrument inventories, inconsistent classification criteria, and weak interpretability of policy combinations. To address these issues, this paper introduces the first taxonomy-based, hierarchical tree-structured framework for classifying economic policies. Grounded in economic theory, the framework systematically synthesizes and abstracts policy instruments, achieving exhaustive coverage and structured representation of all government-implementable macroeconomic levers. Compared with prior approaches, it enables multi-dimensional policy mapping, enhances transparency of model assumptions, and provides standardized inputs for macroeconomic simulation. Its core contributions are threefold: (1) a comprehensive, hierarchically organized policy classification system; (2) improved logical consistency and interpretability in policy package design; and (3) support for targeted, indicator-specific modeling—e.g., inflation, employment, or growth—and rigorous counterfactual analysis.
📝 Abstract
This paper proposes a framework for categorizing economic policies in a form of a tree taxonomy. The purpose of this approach is to construct an exhaustive and standardized list of actions that a governing authority has access to and can change to control an economy. This is advantageous from two perspectives: by having an exhaustive list of tools, it becomes easier to construct "complete" models (i.e., models that take in all empirical data and aim to simulate economic dynamics) of an economy and understand what the assumptions of these models are; and by knowing all available actions, economic strategies can be devised that target specific economic performance metrics with an exhaustive list of policies.