MétaCan
Menu
Back to cohort
Record W2016838485 · doi:10.1515/1935-1690.2035

How Much Did the 2009 Australian Fiscal Stimulus Boost Demand? Evidence from Household-Reported Spending Effects

2012· article· en· W2016838485 on OpenAlex
Andrew Leigh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe B E Journal of Macroeconomics · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMarginal propensity to consumePaymentConsumer spendingQuarter (Canadian coin)Stimulus (psychology)Consumer Expenditure SurveyAggregate demandMonetary economicsDemographic economicsAggregate expenditurePublic economicsMacroeconomicsFinanceMonetary policyRecessionGeography

Abstract

fetched live from OpenAlex

Using survey evidence, I estimate the impact of $21 billion in household payments delivered in Australia between December 2008 and May 2009. Forty percent of households who said that they received a payment reported having spent it. This is a higher spending rate than 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 largely on first-quarter spending, it may understate the longer-run impact of the package on consumer expenditure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.248
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it