What Is USW-Tax-Analyzer?

USW-Tax-Analyzer is a microsimulation model for analysis of US wealth taxation policy.

It is based on wealth distribution data developed by Thomas Piketty, Emmanuel Saez, and Gabriel Zucman. The data have been made publicly available in an Open Policy Analysis project by the Berkeley Initiative for Transparency in the Social Sciences (BITSS). That project incorporates analysis of Senator Elizabeth Warren's 2019 wealth tax proposal conducted by Emmanuel Saez and Gabriel Zucman.

USW-Tax-Analyzer was built to test the ability to do rapid development with minimal coding of a tax microsimulation model using the open-source Tax Analyzer Framework. That test was a success: the model was able to replicate the 2019 BITSS results after about a day's work. The documentation of the command line interface (CLI) to the model includes an extended comparison of 2019 and ten-year results from the two models.

And the microsimulation approach to analyzing wealth tax reforms was found to provide an easy path to enhancements of basic capabilities that allow: assuming non-uniform avoidance rates that depend on the marginal tax rate structure of a reform, extrapolating 2019 data and policy to future years, and specifying time-varying policy reforms (including inflation-indexed wealth tax brackets and the reversion of reform provisions in future years).

USW-Tax-Analyzer Releases

Source code for the current release can be downloaded in several compressed formats. A summary of the changes in each release can be viewed in the change log.

Information for USW-Tax-Analyzer Users

Both users of the USW-Tax-Analyzer model and developers should be familiar with the following information about how to install and use the model:

Information for Developers

Those who only use the model for tax analysis do not need to read the following information. Those who are using USW-Tax-Analyzer as a template to create a new model derived from the Tax-Analyzer-Framework, should first read the information for USW-Tax-Analyzer users and then read about creating a new model.