Implementing Optimal Taxation: A Constrained Optimization Framework for Tax Reform

While optimal taxation theory provides clear prescriptions for tax design, translating these insights into actual tax codes remains difficult. Existing work largely offers theoretical characterizations of optimal systems, while practical implementation methods are scarce. Bridging this gap involves designing tax rules that meet theoretical goals, while accommodating administrative, distributional, and other practical constraints that arise in real-world reform. We develop a method casting tax reform as a constrained optimization problem by parametrizing the entire income tax code as a set of piecewise linear functions mapping tax-relevant inputs into liabilities and marginal rates. This allows users to impose constraints on marginal rate schedules, limits on income swings, and objectives like revenue neutrality, efficiency, simplicity, or distributional fairness that reflect both theoretical and practical considerations. The framework is computationally tractable for complex tax codes and flexible enough to accommodate diverse constraints, welfare objectives and behavioral responses. Whereas existing tools are typically used for ex-post `what-if' analysis of specific reforms, our framework explicitly incorporates real-world reform constraints and jointly optimizes across the full tax code. We illustrate the framework in several simulated settings, including a detailed reconstruction of the Dutch income tax system. For the Dutch case, we generate a family of reforms that smooth existing spikes in marginal tax rates to any desired cap, reduce the number of rules, and impose hard caps on income losses households can experience from the reform. We also introduce \texttt{TaxSolver}, an open-source package, allowing policymakers and researchers to implement and extend the framework.