Methodology
This page explains the data sources, models, and assumptions behind How Poor Am I? The goal is full transparency. Every number you see in the tool can be traced back to its source and calculation.
Wealth Distribution Data
Wealth share data comes from the World Inequality Database, which publishes Distributional National Accounts (DINA) for dozens of countries. These accounts split national wealth into groups:
- Bottom 50% — The lower half of the population by net wealth
- Middle 40% — The 50th to 90th percentile, often called the "middle class"
- Top 10% — The wealthiest tenth, which in most countries holds 60-80% of total wealth
- Top 1% — A subset of the top 10%, typically holding 25-40% of total wealth
These shares define the boundaries used to place you in the distribution. The more granular the boundary data for a country, the more precise the final percentile.
Income-to-Wealth Estimation
Most people know their income but not their net wealth. To bridge this gap, the tool uses an 18-factor estimation model that adjusts the income-to-wealth ratio based on demographic and financial characteristics:
- Age brackets (younger people typically have lower wealth-to-income ratios)
- Education level (higher education correlates with higher lifetime earnings and savings)
- Employment type (self-employed vs. salaried, public vs. private)
- Savings rate and investment behavior
- Property ownership and mortgage status
- Outstanding debts (student loans, consumer debt)
Each factor narrows the uncertainty range. With no factors provided, the model carries a spread of roughly ±70%. With all 18 factors answered, uncertainty drops to approximately ±10%. The tool always shows you the confidence band alongside your estimated percentile.
Percentile Calculation
Once your estimated net wealth is computed, the tool places you in the distribution using piecewise linear interpolation between the known wealth share boundaries. For example, if the bottom 50% holds 5% of total wealth and the middle 40% holds 35%, your position between those boundaries is interpolated linearly based on your estimated share. This is an approximation. Real distributions are not perfectly linear between boundary points. But it provides a reasonable estimate given the available data.
Billionaire Comparison
The "How Long Would It Take?" mode uses the Forbes Real-Time Billionaires list. Net worth figures are bundled into the site at build time for the wealthiest individual in each country.
The "years to earn" calculation is deliberately simple: it divides the billionaire's net worth by your annual income with no adjustments for interest, compound growth, taxes, or inflation. This is intentional. The point is not financial planning but to viscerally illustrate the scale of the gap. When the answer is "4 million years," whether it accounts for a 7% return rate is beside the point.
Tax Rate Data Sources
Effective tax rates by wealth class are compiled from academic research and government statistics. Unlike wealth distribution data, these are not available through a single API and are maintained manually from the published sources below.
| Country | Source | Year |
|---|---|---|
| 🇦🇺Australia | ATO Taxation Statistics; Leigh (2009, updated); Grattan Institute distributional analysis | 2022 |
| 🇦🇹Austria | Statistik Austria, Integrierte Lohn- und Einkommensteuerstatistik (integrated wage and income tax statistics). Social contributions ~18% employee share, capped. Flat 27.5% KESt on capital income reduces effective rates for top brackets. Sub-percentile rates estimated from capital income concentration data. | 2023 |
| 🇧🇪Belgium | Statbel fiscal income statistics (decile distribution of net taxable income, total tax, and average tax rate, income year 2023). Very high social contributions (~13% employee). No general capital gains tax; 30% withholding on dividends. Sub-percentile rates estimated from OECD/WID capital income concentration. | 2023 |
| 🇧🇷Brazil | Receita Federal; Morgan (2017), WID.world; Gobetti & Orair (2017) | 2021 |
| 🇨🇦Canada | Statistics Canada; PBO Distributional Analysis 2023; OECD Tax Database 2024 | 2022 |
| 🇨🇱Chile | SII (Servicio de Impuestos Internos) bracket data; WID.world Chile distributional accounts; López & Figueroa (Harvard CID). Highly regressive due to 19% IVA consumption tax burden on lower incomes and favourable capital income treatment. Top 1% earn ~25% of pre-tax income (WID). Sub-percentile effective rates estimated. | 2024 |
| 🇩🇰Denmark | Danish Ministry of Taxation; OECD Tax Database 2024; WID.world Denmark series | 2022 |
| 🇫🇮Finland | Statistics Finland income distribution statistics; Verohallinto public tax data. Finland's dual income tax system: progressive earned income tax up to ~51.4%, flat 30–34% capital income tax. Sub-percentile rates estimated from capital vs labour income shares. OECD Taxing Wages 2025: average net tax rate 30.3%. | 2023 |
| 🇫🇷France | Landais, Saez & Zucman (2020); WID.world France series; EU Tax Observatory (2024) | 2022 |
| 🇩🇪Germany | Bach, Beznoska & Steiner (2020), DIW Berlin; Bundesfinanzministerium Datensammlung; OECD 2024 | 2021 |
| 🇮🇪Ireland | Revenue Commissioners income tax distribution tables; CSO Ireland's Tax Statistics 2024; Social Justice Ireland effective rate analysis (10.3% at €25K, 39.0% at €120K). Top rate = 40% income tax + 8% USC. Top 1% (>€203K) pay ~19% of personal tax. Entrepreneur relief (10% CGT) benefits top brackets. | 2024 |
| 🇮🇹Italy | Ministero dell'Economia; Acciari & Morelli (2023); EU Tax Observatory (2024) | 2022 |
| 🇯🇵Japan | National Tax Agency statistics; Moriguchi & Saez (2008, updated); OECD Tax Database 2024 | 2022 |
| 🇳🇿New Zealand | NZ Inland Revenue, High-Wealth Individuals Research Project (April 2023): median effective rate of 8.9% for 311 high-wealth individuals on economic income incl. unrealised gains, vs 20.2% for middle NZ. Lower brackets from NZ Treasury distributional analysis. No general capital gains tax. | 2023 |
| 🇳🇴Norway | Alstadsæter, Johannesen & Zucman (2019); SSB tax statistics; WID.world Norway series | 2021 |
| 🇿🇦South Africa | SARS Tax Statistics; Chatterjee, Czajka & Gethin (2022), WID.world; National Treasury | 2021 |
| 🇪🇸Spain | Agencia Tributaria; Alvaredo & Saez (2009, updated); EU Tax Observatory (2024) | 2022 |
| 🇸🇪Sweden | Waldenström (2020), IFN Stockholm; SCB tax statistics; WID.world Sweden series | 2021 |
| 🇨🇭Switzerland | Swiss Federal Tax Administration; Brülhart et al. (2022); OECD Tax Database 2024 | 2022 |
| 🇳🇱The Netherlands | CPB Netherlands Bureau; CBS income statistics; EU Tax Observatory (2024) | 2022 |
| 🇬🇧United Kingdom | Advani, Chamberlain & Summers (2023); HMRC Survey of Personal Incomes; ONS household data | 2022 |
| 🇺🇸United States | Saez & Zucman (2019), The Triumph of Injustice; updated with IRS microdata through 2018 | 2018 |
Effective tax rates include all taxes: income, payroll, corporate (allocated to shareholders), property, estate, and consumption taxes, divided by total pre-tax economic income. Sources combine academic research, government tax statistics, and the EU Tax Observatory Global Tax Evasion Report (2024). Tax rate data is not API-fetchable and is maintained manually from published government statistics and academic papers.
Limitations
- Top-wealth underestimation — Survey-based wealth data systematically underestimates the holdings of the ultra-rich, who are underrepresented in household surveys. WID partially corrects for this using tax data, but gaps remain.
- Self-reported income bias — Users enter their own income, which may not reflect total compensation (bonuses, equity, unrealized gains).
- Country-specific caveats — Data quality varies by country. Some nations have detailed tax-based wealth data; others rely on survey estimates with wider margins.
- Model approximation — The 18-factor income-to-wealth model is a statistical approximation, not personalized financial advice. Individual circumstances can diverge significantly from population averages.
Data Freshness
All data is bundled at build time and served statically. No external API calls are made when you use the tool. A single fetch script (scripts/fetch-all-data.mjs) pulls data from:
- WID.world API — wealth shares, income shares, mean/median wealth, Gini coefficients
- World Bank API — population (SP.POP.TOTL)
- ECB / Frankfurter API — exchange rates for currency conversion
- Forbes RTB API — billionaire net worth data
- OECD / FRED — wages, CPI, house price indices
Tax rate data is the exception. It comes from academic papers and is maintained manually with full source citations (see table above).