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Record W7070743470

The relationship between immigration and the level of prices in Canada

2017· other· en· W7070743470 on OpenAlexaboutno aff

Bibliographic record

VenueSU+ Digital Repository (Strathmore University) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLiquationPopulationLimitingNucleofectionDerogation
DOInot available

Abstract

fetched live from OpenAlex

There has been a decline in fertility rates in Canada over the past few years. This has brought about a decrease in the supply of labour in the country. To deal with the shortfall in labour, the Canadian government has put in place policies aimed at encouraging immigration into the country. This study seeks to investigate the impact of the increased immigration on the level of prices of goods and services within the Canadian economy, which may take place through an increase in aggregate demand.
\nTo do this, the study aims at determining if there exists a relationship between immigration and prices in Canada. A VAR model is used, to examine the dynamic relationship between the two variables over the period of 1961 to 20 I 4. The main variables under study are the price levels measured by the GOP deflator and economic immigrants. An analysis of the data reveals that a sudden increase 111 immigration has a 9% positive impact on prices; likewise 19% of shocks 111 immigration can be explained by shocks to prices. This result is in contrast with other empirical studies possibly due to the fact that it concentrates on high- skilled economic immigrants as opposed to low- skilled immigrants. In conclusion it is found that increased immigration has a small but positive impact on prices. However, further research that incorporates social immigrants needs to be conducted in order to get a conclusive outlook of the relationship between immigration and prices.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.213
Teacher spread0.177 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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