Express Entry as a Hybird Model of Economic Immigration Policy: Lessons from Australia's SkillSelect
Bibliographic record
Abstract
Competition for skilled immigrants is increasing as OECD countries experience ageing demographics and increased competition from countries out of which skilled individuals would have traditionally emigrated. Under these conditions, the importance of establishing effective selection criteria for selecting candidates for immigration has increased and settlement countries are beginning to converge around a similar hybrid immigration model. The move to a hybrid demand-supply immigration model by settlement countries assumes that that the selection criteria are effective predictors for positive labour market outcomes for immigrants. However, despite policy changes to improve the labour market outcomes for immigrants through better selection criteria, outcomes have consistently been poorer than for the Canadian-born population. Canada’s recent implementation of its Express Entry Expression of Interest process marks the culmination of its shift in economic immigration policy to a hybrid demand-supply model. The heavily weighted selection criteria of an arranged offer of employment or a provincial nomination now outweighs the impact of the score in the points based system for assessing human capital that most immigrants are likely to achieve. Its weighting reflects those factors that are considered most effective at ensuring positive labour market outcomes for immigrants while also meeting employer demand for specific skills shortages. As Express Entry was partly modelled on Australia’s SkillSelect (implemented in 2012) this paper uses a comparative case study to examine the selection criteria and policy levers of SkillSelect and the associated labour market outcomes of immigrants. Using data from two cohorts of the Continuous Survey of Australia’s Migrants (CSAM) from 2011 and 2013, it uses a tabular difference-in-difference analysis to estimate the effect of the policy on employment rates and median earnings. The findings show that there appears to be a positive effect on rates of highly-skilled employment among immigrants subject to SkillSelect selection criteria. Although these findings require considerably more analysis, they do suggest some important policy implications for Canada. Australia collects detailed survey data about migrants that includes the individual’s visa class, data that has been used to assess the success of the SkillSelect selection criteria. For Canada to take full advantage of the flexibility and responsiveness built into Express Entry it also needs regularly conduct surveys that relates an immigrant’s immigration stream to their labour market outcomes. Measuring the economic success of immigrants and distinguishing between the immigration stream under which they entered would allow for a meaningful evaluation of the effectiveness of the Express Entry selection criteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".