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Tax Reform: A Case Using Data Analytics

Tax Reform: A Case Using Data Analytics

Authored by Roby Sawyers and Tom Dow, NC State University and Lynn Jones, Georgia State University

Introduce your tax students to data analytics through Van-Griner Learning’s new tax analytics case. Students will interact with a US government data set to understand how the Tax Cuts and Jobs Act of 2017 (TCJA) has impacted constituents depending on their zip codes. This case can be incorporated after the Itemized Deductions chapter and takes approximately three hours for a student to complete. Teaching notes are available for instructors.

The TCJA limits itemized deductions for state and local income taxes, real and personal property taxes, and sales taxes to a combined $10,000. State and local tax regimes vary significantly from state to state and across counties within a state. Taxpayers who are most likely to be negatively affected by the change live in states with high income tax rates and counties with high property tax rates. Of course, higher income taxpayers typically have higher income taxes and property taxes, so taxpayers living in areas of the country with high income levels are more likely to be affected in a negative way as well.

Students will learn how to:

  • Apply an ETL (Extract, Transform, and Load) process to develop a data set appropriate for analysis.
  • Extract data from multiple sources, including IRS SOI Stats data and census data.
  • Transform data into an organized data set suitable for analysis. This includes cleaning, merging, filtering, and aggregating the data.
  • Provide visualizations of the final data set to increase understanding of impact of the TCJA on taxpayers.


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