Tax-Cruncher calculates federal tax liabilities from individual data under different policy proposals.
Tax-Cruncher accepts inputs similar to NBER's TAXSIM Version 27, converts those inputs to a format usable by Tax-Calculator, an open-source microsimulation model of federal individual income and payroll tax law, and uses Tax-Calculator capabilities to analyze the user-specified inputs under various tax policy proposals.
Tax-Cruncher's web application is hosted on Compute Studio. The code that powers the web application can be found in this repository in the cs-config directory.
Tax-Cruncher can analyze individual data from one filer or multiple filers.
To analyze individual data from one filer:
- The easiest way to analyze one tax filer is with the web application hosted on Compute Studio.
To analyze individual data from multiple filers:
For a more complete demo of Tax-Cruncher's multi-filer capabalities, explore this Jupyter Notebook.
-
First, define your inputs in a csv file. Each row of the file represents one filing unit. Instructions on how to construct a csv input file can be found here.
-
Second, initiate the
Batch
class found in thetaxcrunch/multi_cruncher
module. Initiate the Batch class withb = Batch('INPUT_DATA_FILE_PATH')
. -
Third, analyze your data. You can analyze your data under current law or under a policy reform using the
create_table()
method. If you do not pass an argument to the method, Tax-Cruncher will analyze your data under current law. To analyze your data under a policy reform, pass a JSON reform file, a reform dictionary, or a preset reform from the Tax-Calculator reforms folder to thecreate_table()
method.
# initiate Batch class
b = Batch('FILE_PATH')
# liabilities under current law
b.create_table()
# liabilities under reform
b.create_table('REFORM_FILE_PATH')
Install with conda:
conda install -c pslmodels taxcrunch
Install from source:
git clone https://github.com/PSLmodels/Tax-Cruncher
cd Tax-Cruncher
conda env create
conda activate taxcrunch-env
pip install -e .
Please cite the source of your analysis as "Tax-Cruncher release #.#.#, author's calculations." If you would like to link to Tax-Cruncher, please use https://github.com/PSLmodels/Tax-Cruncher
.