Household Portfolio Allocations: Evidence on Risk Preferences from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey Using Tobit Models
Bibliographic record
Abstract
This study investigates intrahousehold risk preferences in household portfolio decision-making. Most household finance data are collected at the household level, and it is challenging to come up with an explanation of risk-taking decisions and have a direction on the within-household bargaining mechanisms. We provide these challenging pieces of evidence by applying a Tobit model on panel data taken from waves 2 to 6 of HILDA surveys. Overall, the results indicate that the risk-taking attitude of partners matters in household portfolio allocations. Risk-averse males and their female counterparts invest less in risky assets. Compared with the no-conflict (identical risk preferences) group, male partners with risk-loving behaviour tend to invest more in risky assets. Further, individual risk preferences are sensitive to fluctuations in equity and housing markets in Australia. Taken together, one of the crucial implications of our findings for future research is that household-bargaining models should, perhaps, give more bargaining power to risk-loving males, offering an additional explanation for the determinants of risk-taking behaviour of households. Understanding the risk-taking attitudes of households is important for future work to understand the fraction of households that end up with a negative net worth in recessions or crisis conditions, such as financial crises, pandemics, and wars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".