TaxSolver: A methodology to realize optimal income tax reform
Mark Verhagen, Menno Schellekens, Michael Garstka
TL;DR
TaxSolver reframes income tax reform as a constrained optimization problem, solving for an optimal set of tax rates under explicit fiscal guarantees and policy objectives. By modeling the entire income tax code as a sum of simple, piecewise-linear tax rules within tax groups, it guarantees mathematical optimality within the defined constraints and enables rapid generation of reform proposals. The approach recovers existing systems when guarantees are tight and demonstrates multiple reform use cases, including scale and complexity comparable to real-world tax codes, while remaining open-source for broad adoption. This methodology offers a principled, transparent alternative to ad-hoc tinkering, potentially accelerating reform cycles and enabling policymakers to compare alternatives based on explicit goals and limitations.
Abstract
Across the globe there are growing calls to streamline and improve ever more complex income tax codes. Executing reform has proven difficult. Even when the desired outcomes are clear, the tools to design fitting reforms are lacking. To remedy this, we developed \texttt{TaxSolver}: a methodology to help policymakers realize optimal tax reform. \texttt{TaxSolver} allows policymakers to focus solely on what they aim to achieve with a reform -- like redistributing wealth, incentivizing labor market participation or reducing complexity -- and the guarantees within which reform is acceptable -- like limited fluctuations in taxpayer incomes or shocks to overall tax revenue. Given these goals and fiscal guarantees, \texttt{TaxSolver} finds the optimal set of tax rules that satisfies all the criteria or shows that the set of demands are not mathematically feasible. We illustrate \texttt{TaxSolver} by reforming various simulated examples of tax codes, including some that reflect the complexity and size of a real-world tax system.
