Does housing capital contribute to inequality? A comment on Thomas Piketty’s Capital in the 21st Century
Bibliographic record
Abstract
In his book, Capital in the 21st Century,Thomas Piketty highlights the risk of an explosion of wealth inequality because capital is accumulating faster than income in several countries including the US and European countries such as France. Our work challenges the conclusions of the author in three steps.First, the author’s result is based on the rise of only one of the components of capital, namely housing capital,and due to housing prices. In fact, housing prices have risen faster than rent and income in many countries.It is worth noting that “productive” capital, excluding housing, has only risen weakly relative to income over the last few decades. Over the longer run, the “productive” capital/income ratio has not increased at all.Second, rent, not housing prices, should matter for the dynamics of wealth inequality, because rent represents both the actual income of housing capital for landlords and the dwelling costs saved by “owner-occupiers” (people living in their own houses). Logically, to properly measure capital, the value of housing capital must be corrected by measuring it on actual rental price, and not housing prices.Third, when we apply this change, we find that the capital/income ratio is actually stable or only mildly higher in the countries analyzed (France, the US, the UK, and Canada) except for Germany where it rose.These conclusions are exactly opposite to those found by Thomas Piketty. However, this does not mean that housing prices do not contribute to other forms of inequality. When housing prices rise, owners of the housing capital hold a higher value that can be transformed into consumption. It is also more difficult for young adults to become homeowners. Housing incomes of owners however do not necessarily increase which casts serious doubt on Piketty’s conclusion of a potential explosive dynamics of inequality based on these trends.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".