The Intergenerational Transmission of Housing Wealth
Bibliographic record
Abstract
Rising wealth inequality has spurred an increased interest in understanding how and why wealth is correlated across generations.We exploit plausibly exogenous variation in housing wealth driven by home price changes in different areas to isolate the causal impact of parental housing wealth during different childhood periods on children's long-run wealth accumulation.Using population-level Danish administrative data, we find that 27% and 25% of each Krone of parental housing wealth change during early-childhood is transmitted to children's overall and housing wealth in adulthood, respectively.The corresponding transmission rates for parental housing wealth changes during middle-childhood are 25% and 15%, with a transmission to non-housing wealth of 10%.There is little evidence of transmission of parental housing wealth changes that occur during the teenage years.Examining mechanisms, we find that parental housing wealth changes in early and middle-childhood lead to modest increases in adult children's home ownership, educational attainment, and earnings.However, earnings and education can explain only 20-30% of the intergenerational transmission of parental wealth gains during these periods.We argue that the transmission of parental housing wealth changes in childhood are driven in large part by changes to unobserved household environment and parental behaviors that are passed on to children and shape their savings behavior in adulthood.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".