Inequalities in household wealth across OECD countries
Bibliographic record
Abstract
This paper describes how household wealth is distributed in 28 OECD countries, based on evidence from the second wave of the OECD Wealth Distribution Database. A number of general patterns emerge from these data. First, wealth concentration is twice the level of income inequality: across the 28 OECD countries covered, the wealthiest 10% of households hold, on average, 52% of total household wealth, while the 60% least wealthy households own little over 12%. Second, up to a quarter of all households report negative net worth (i.e. liabilities exceeding the value of their assets) in a number of countries. In addition, some countries feature large shares of households with high levels of debt relative to both their incomes and the assets that they hold; this potentially exposes such households to significant risks in the event of changes in asset prices or falls of their income. Third, more than one in three people are economically vulnerable, as they lack liquid financial assets to maintain a poverty-level living standard for at least three months. Fourth, one in three households has received some gift or bequest in their life, with this share being considerably larger among high income and high wealth households. The paper also describes changes in wealth distribution since the Great Recession among the sub-set of countries for which repeated observations are available in the OECD Wealth Distribution Database. Finally, the paper discusses a number of methodological challenges, notably on how to better account for the top end of the wealth distribution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".