MétaCan
Menu
Back to cohort
Record W2951973704 · doi:10.32721/ctj.2019.67.2.berger

An Empirical Analysis of the Displacement Effect of TFSAs on RRSPs

2019· article· en· W2951973704 on OpenAlexaffvenueabout
L. L. Berger, Jonathan Farrar, Lu Zhang

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsToronto Metropolitan UniversityWilfrid Laurier University
Fundersnot available
KeywordsSample (material)Government (linguistics)EconomicsRevenueEmpirical evidencePublic economicsEmpirical researchTax revenueActuarial scienceDemographic economicsEconometricsAccountingStatistics

Abstract

fetched live from OpenAlex

The tax-free savings account (TFSA), introduced in 2009, was intended by the Canadian government to provide an alternative catchment for savings in addition to registered retirement savings plans (RRSPs). However, little empirical evidence exists regarding the impact of saving in TFSAs on saving in RRSPs. To investigate this issue, we conduct empirical analysis, using data from Statistics Canada's Longitudinal Administrative Databank, which contains annual TFSA and RRSP contributions for a sample of 20 percent of all Canadian taxfilers. We find evidence of a displacement effect of TFSAs on RRSPs: every 1 percent increase in a TFSA contribution reduces an RRSP contribution by approximately 0.4 percent. Our findings have implications for Canadians' ability to self-fund their retirement, as well as for the Canadian government's ability to generate future tax revenues.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.272
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Tax Journal/Revue fiscale canadienneSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207