Precautionary Savings and Wealth Inequality: a Global Sensitivity Analysis
Bibliographic record
Abstract
This paper applies Canova JAE 1994 methodology to perform a thorough sensitivity analysis for the Aiyagari QJE 1994 economy. This is a calibrated GE model with incomplete markets and uninsurable income risk, designed to quantify the size of precautionary savings and the degree of wealth inequality. The results of this global robustness analysis are broadly consistent with Aiyagari’s findings. Even when considering priors for the parameters uncertainty which are highly dispersed, the size of the precautionary savings is modest: at most, they account for an 11% increase in the saving rate. However, the results show that the parameter representing the exogenous borrowing limit seems to lead to relatively large changes in measures of wealth inequality. The Gini index increases by 15 points when considering values of the borrowing limits that lead to empirically plausible shares of households with a negative net worth. The parameters that quantitatively have the largest effects on determining the wealth Gini index are the capital share, the borrowing limit, and the depreciation rate. The parameters affecting most significantly precautionary savings are the risk aversion and the standard deviation of the income shocks.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".