Topic: Federal Income Tax Fairness in the U.S.

Multifaceted Research Process

To research the fairness of the U.S. federal income tax system, I took a layered, critical approach: I began with the question: Is the U.S. tax system fair across income groups? I refined this into sub-questions about effective tax rates, perceptions of fairness, and the role of other taxes (like payroll and sales taxes). I used sources from: Primary government agencies (CBO, IRS) Academic economists (Saez & Zucman) Nonpartisan research groups (Pew, Tax Foundation) Data aggregators (Statista) I evaluated each source for: Credibility (Is it academic or official?) Relevance (Does it directly address federal income tax fairness?) Currency (Recent data, usually 2019–2024) Bias/Purpose (Is it data or opinion?) By cross-checking numbers and arguments, I developed a balanced understanding of federal income tax progressivity and its limitations.

Trials and Tribulations

Some challenges I faced: Overload of repetitive stats: Many sources repeated “top 1% pays X%” without explaining context. Inconsistent definitions: Terms like “effective tax rate” weren’t always defined the same way. Perception vs reality: Many people feel the system is unfair even when the data shows progressivity. I had to explore why — such as the role of payroll and consumption taxes not included in income tax data. Data misinterpretation: At first, I misread “effective tax rate” as including all taxes, not just federal income tax — I corrected this after deeper reading.

MLA Annotation (with explanation)

MLA Citation: Congressional Budget Office. The Distribution of Household Income and Federal Taxes, 2019. Congressional Budget Office, 2022, https://www.cbo.gov . Annotation: This CBO report presents effective federal tax rates across income levels. It shows the top 1% paid ~25.6%, while the bottom 20% paid ~0.9%, after tax credits like the EITC. This source is highly credible, uses IRS/Census data, and is key to showing that while the system is progressive, fairness perceptions may differ. I chose it because it provides a factual foundation that I can compare with more critical views.

Why it’s wonderful:

-MLA format is correct

-Clear structure: summary + evaluation + reflection

-Directly supports my thesis

-Comes from a nonpartisan government source

Numeracy in Action

“According to the IRS 2022 data, taxpayers earning under $50,000 contributed only 5% of total income tax revenue, while those earning above $500,000 contributed nearly 40%.”

Why this matters:

-It illustrates the progressive nature of the federal income tax system.

-It supports the argument that higher earners carry the largest tax burden, despite public perceptions.

-I used this alongside effective tax rates (CBO data) to triangulate both rate and share.

-I also reflected on limits: income tax isn’t the full picture — sales and payroll taxes hit lower-income groups harder.

Differential thinking:

I compared rate vs revenue share, across multiple income brackets and over time (Statista visuals). This helped me translate raw data into meaningful argument.

My Research Journey

-Topic discovery: Began with general interest in fairness > narrowed to tax fairness

-Source discovery: Used JPL access + public data (IRS, CBO, Statista, etc.)

-Numeracy stage: Downloaded data tables, built visual comparisons, interpreted statistical meaning

-Synthesis & annotation: Wrote MLA-style entries, considered source bias, purpose, and audience

-Self-correction: Learned to refine definitions and correct early misinterpretations

-Reflection writing: I’m now organizing and posting this reflection to Padlet to complete the "Observant Researcher" badge.

-Biggest insight: Tax fairness depends not only on numbers, but how those numbers are presented and perceived.

Final Thoughts

This assignment made me a more observant researcher. I practiced:

-Critical thinking (challenging narratives)

-Empirical skills (interpreting real data)

-Responsibility (citing properly, reflecting honestly)

-Communication (translating complex data clearly)