Inequality: An Open Investigation

What do the data actually say about inequality, well-being, and the distribution of income?

What is this?

This is an open research project that uses publicly available data to investigate income and wealth inequality. It started as a personal attempt to understand the empirical landscape — what’s well-established, what’s contested, and where the data run out.

The analysis spans nine notebooks, each tackling a different question:

Notebook	Question
01: Measuring Inequality	How do we measure inequality, and do different measures tell different stories?
02: Predistribution vs. Redistribution	Is inequality driven by market outcomes or by tax/transfer policy?
03: Absolute vs. Relative	Are the poor getting poorer, or just falling behind?
04: Wealth vs. Income	How does wealth inequality compare to income inequality?
05: Inequality and Society	Does inequality damage health and social outcomes?
06: Synthesis	What can we conclude from the full picture?
07: Well-being Across Distributions	How does well-being vary across the global income spectrum?
08: The Easterlin Paradox	As countries get richer over time, do they get happier?
09: Labor Force & Working Hours	Do people in unequal countries work longer hours? What does this buy?

Data sources

All data are drawn from public sources:

World Inequality Database (WID) — Income and wealth distributions from tax and survey data, harmonized across countries.
SWIID — Standardized World Income Inequality Database, providing comparable Gini coefficients.
World Bank — Health, education, poverty, and national accounts indicators.
Our World in Data — Subjective well-being (Cantril Ladder / World Happiness Report) and working hours per worker (Huberman & Minns + Penn World Table).

Countries covered

The core analysis compares seven rich countries with different policy regimes: US, UK, France, Germany, Sweden, Denmark, Norway.

Notebook 07 expands to ~24 countries spanning the global income spectrum, from Norway and the US to Ethiopia and Bangladesh.

How to read this

If you’re not technical: Click any notebook link above. The code is folded by default — you’ll just see the text, charts, and analysis. Click “Show code” on any chart if you want to see how it was made.

If you are technical: The full source code is on GitHub. Clone the repo, install requirements.txt, and run the notebooks yourself. Every chart is reproducible.

Feedback and critique

This is a learning project, not a finished product. If you think the analysis is wrong, misleading, or missing something important — please say so. You can leave comments on any page using the comment box at the bottom, or open an issue on GitHub.

I’m especially interested in:

Methodological objections (Am I using the right measures? The right comparisons?)
Missing context (What important literature am I ignoring?)
Data limitations I haven’t acknowledged
Alternative interpretations of the same data

--- title: "Inequality: An Open Investigation" subtitle: "What do the data actually say about inequality, well-being, and the distribution of income?" --- ## What is this? This is an open research project that uses publicly available data to investigate income and wealth inequality. It started as a personal attempt to understand the empirical landscape — what's well-established, what's contested, and where the data run out. The analysis spans nine notebooks, each tackling a different question: | Notebook | Question | |----------|----------| | [01: Measuring Inequality](notebooks/01_measuring_inequality.ipynb) | How do we measure inequality, and do different measures tell different stories? | | [02: Predistribution vs. Redistribution](notebooks/02_predistribution_vs_redistribution.ipynb) | Is inequality driven by market outcomes or by tax/transfer policy? | | [03: Absolute vs. Relative](notebooks/03_absolute_vs_relative.ipynb) | Are the poor getting poorer, or just falling behind? | | [04: Wealth vs. Income](notebooks/04_wealth_vs_income.ipynb) | How does wealth inequality compare to income inequality? | | [05: Inequality and Society](notebooks/05_inequality_and_society.ipynb) | Does inequality damage health and social outcomes? | | [06: Synthesis](notebooks/06_synthesis.ipynb) | What can we conclude from the full picture? | | [07: Well-being Across Distributions](notebooks/07_wellbeing_across_distributions_executed.ipynb) | How does well-being vary across the global income spectrum? | | [08: The Easterlin Paradox](notebooks/08_easterlin_paradox.ipynb) | As countries get richer over time, do they get happier? | | [09: Labor Force & Working Hours](notebooks/09_labor_force_and_working_hours.ipynb) | Do people in unequal countries work longer hours? What does this buy? | ## Data sources All data are drawn from public sources: - **[World Inequality Database (WID)](https://wid.world/)** — Income and wealth distributions from tax and survey data, harmonized across countries. - **[SWIID](https://fsolt.org/swiid/)** — Standardized World Income Inequality Database, providing comparable Gini coefficients. - **[World Bank](https://data.worldbank.org/)** — Health, education, poverty, and national accounts indicators. - **[Our World in Data](https://ourworldindata.org/)** — Subjective well-being (Cantril Ladder / World Happiness Report) and working hours per worker (Huberman & Minns + Penn World Table). ## Countries covered The core analysis compares seven rich countries with different policy regimes: **US, UK, France, Germany, Sweden, Denmark, Norway**. Notebook 07 expands to ~24 countries spanning the global income spectrum, from Norway and the US to Ethiopia and Bangladesh. ## How to read this **If you're not technical:** Click any notebook link above. The code is folded by default — you'll just see the text, charts, and analysis. Click "Show code" on any chart if you want to see how it was made. **If you are technical:** The full source code is on [GitHub](https://github.com/clayborneo/inequality-inquiry). Clone the repo, install `requirements.txt`, and run the notebooks yourself. Every chart is reproducible. ## Feedback and critique This is a learning project, not a finished product. If you think the analysis is wrong, misleading, or missing something important — **please say so.** You can leave comments on any page using the comment box at the bottom, or open an issue on GitHub. I'm especially interested in: - Methodological objections (Am I using the right measures? The right comparisons?) - Missing context (What important literature am I ignoring?) - Data limitations I haven't acknowledged - Alternative interpretations of the same data