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Record W3095404939 · doi:10.1111/caje.12477

Immigrants’ net direct fiscal contribution: How does it change over their lifetime?

2020· article· en· W3095404939 on OpenAlexaffvenueabout
Haozhen Zhang, Jianwei Zhong, Cédric de Chardon

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsEngineers Without Borders Canada
Fundersnot available
KeywordsImmigrationDemographic economicsEntitlement (fair division)DemographySocial securitySurvey of Income and Program ParticipationEconomicsGeographySociology

Abstract

fetched live from OpenAlex

Abstract Life‐cycle direct public fiscal contributions and transfers are studied using longitudinal income tax data from 1982 to 2016 and administrative files for immigrants landed in Canada from 1980 to 2016. Relative to a comparison group comprising the Canadian‐born and immigrants landed before 1980, immigrants since 1980 have a lower average net direct fiscal contribution (NDFC) during their working years due to their lower taxes and social security contributions but a higher average NDFC after 65 years of age because of reduced public pension eligibility and entitlement. Immigrants who landed at younger than 19 years old have much higher direct fiscal contributions than other age‐at‐arrival groups and reach their peak of contributions around 10 years earlier in life than other age‐at‐arrival groups. Immigrants whose age at arrival is above 65 have a less negative average NDFC than other age‐at‐arrival groups over the above‐65 life cycle. These life‐cycle age𠄁at‐arrival trajectories are stable for immigrants in different landing cohorts. We apply the life‐cycle estimates to project the present discounted value of lifetime NDFCs for immigrants who landed in 2016. For each landing age group, refugees and family class immigrants have negative or zero average present values of life‐cycle NDFCs, much below that of economic immigrants.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.189
GPT teacher head0.273
Teacher spread0.084 · 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 designNot applicable
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

Citations5
Published2020
Admission routes3
Has abstractyes

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