How Much Did the 2009 Fiscal Stimulus Boost Spending? Evidence from a Household Survey
Bibliographic record
Abstract
Using survey evidence, I estimate the impact of a $12 billion package of household payments delivered in Australia between March and May 2009. Forty percent of households who said that they received the payment reported having spent it. This is approximately twice the spending rate that has been recorded in surveys assessing the 2001 and 2008 tax rebates in the United States. One possible explanation for this is that individuals are more likely to spend ―bonuses ‖ (as the Australian payments were described) than ―rebates ‖ (as the US payments were described). Using an approach for converting spending rates into an aggregate marginal propensity to consume (MPC), the Australian results are consistent with an aggregate MPC of 0.41−0.42. Since this estimate is based only on first-quarter spending, it may be an underestimate of the longer-run impact of the package on consumer expenditure.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".