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Record W1595642128

The Intertemporal Substitution and Income Effects of a VAT Rate Increase: Evidence from Japan

2011· preprint· en· W1595642128 on OpenAlexaboutno aff
David Unayama Cashin, Takashi Unayama

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsSubstitution effectConsumption (sociology)Substitution (logic)Durable goodMonetary economicsQuarter (Canadian coin)Labour economicsDemographic economicsMacroeconomicsMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

One of the biggest political issues in Japan is an increase in the rate of value added tax (VAT). In this paper, we evaluate its impact on household expenditure, using Japan’s April 1997 VAT rate increase from three to five percent as a case study. A rate increase induces price hikes, and provided this increase in price levels is anticipated, households should engage in intertemporal substitution of purchases. In addition, if households are not compensated for the rate increase, it has the potential to induce an income effects on household consumption. Based on monthly household expenditure data, we find that households spent ¥30,231 more in the quarter prior to the rate increase than they would have in its absence, while the income effect was negligible. Consistent with theoretical predictions, increased outlays on durable and storable non-durable goods and services were responsible for roughly three-quarters of the observed intertemporal substitution effects. Contrary to conventional wisdom, we find that the VAT rate increase had no impact on real household spending following its implementation, once we have accounted for intertemporal substitution, which caused a large transitory disturbance in household expenditures.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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