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Record W1518684035

Labour Market Matters - June 2013

2013· preprint· en· W1518684035 on OpenAlexaboutno aff
Vivian Tran

Bibliographic record

VenueRePEc: Research Papers in Economics · 2013
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEconomicsUnemploymentPopulationPer capitaPovertyProductivityLabour economicsGovernment (linguistics)Demographic economicsImmigration policyInvestment (military)Economic growthPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Despite a history built on immigration, immigrants are among those who struggle the most in Canada. Recent research finds that the proportion of recent immigrants (in Canada for 5 years or less) who were in poverty has risen steadily from 24.6% in 1980 to 47% in 1995, before falling to 36% in 2005. Disturbingly, this increase in poverty for immigrants was occurring at the same time as poverty rates for the non-immigrant population was generally falling. At times when the ageing population is expected to impose a heavy fiscal burden through age-related programs like pensions and health care, immigration is often looked upon as a possible way to mitigate that burden. Immigration can also help break skilled labour shortages and production bottlenecks – which can expand job opportunities for domestic-born workers. Importing people through immigration to domestically produce goods and services can be a substitute to importing such goods and services from other countries. In a study entitled “Macroeconomic Impacts of Canadian Immigration: Results from a Macro-model†(CLSRN Working Paper no. 106) by CLSRN affiliates Peter Dungan (University of Toronto), Tony Fang (York University), and Morley Gunderson (University of Toronto) find that additional immigration is likely to have a positive impact on the Canadian labour market and economy in general – with positive impacts on factors such as real GDP and GDP per capita, aggregate demand, investment, productivity, and government expenditures, taxes and especially net government balances, with essentially no impact on unemployment. Older immigrants in Canada often struggle in the labour market compared to both their native-born peers and their younger counterparts. In addition to encountering difficulties with labour market issues related to assimilation and credential recognition, immigrants as a group tend to have difficulty gaining the needed years of contribution to both public and private pension plans due to the fact that a significant part of their working careers may have occurred outside of Canada. When an immigrant arrives in Canada after the age of 50, these problems are accentuated. While the socio-economic welfare of older immigrants is concerning, there is evidence that the effects of the lower incomes on the welfare of older immigrants are mitigated to a certain extent through co-residency, presumably with their younger relatives already resident in Canada. In a CLSRN study entitled “Retirement Incomes, Labour Supply and Co-residency Decisions of Older Immigrants in Canada: 1991-2006†(CLSRN Working Paper no. 116) Ted McDonald (University of New Brunswick) and Christopher Worswick (Carleton University) examine the extent to which older immigrants are able to support themselves in their retirement years, as well as analyze the extent to which older immigrants are more (or less) likely to reside with other family members relative to the Canadian-born as a way of diminishing the effects of low income on consumption.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.551
Threshold uncertainty score0.640

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.5510.414

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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

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