Australia's Immigration Selection System and Labour Market Outcomes in a Family Context: Evidence from Administrative Data
Bibliographic record
Abstract
This paper examines the efficacy of the Australian points system in a family context among working‐age permanent resident immigrants who arrived between 2000 and 2011 when there was a major focus on skills selection. Sixty‐seven per cent of these immigrants were granted a skilled visa while 25 per cent hold a spousal visa (spouses of Australian citizens). More than half of the skilled visa recipients are the spouses of the primary applicants. Primary applicants among skilled visa holders are assessed for their skills in line with the Australian points system but secondary applicants, such as spouses, among skilled visa holders and spousal visa holders are not subject to any skills assessment before becoming permanent residents. We study differences in economic outcomes by permanent visa types and the role of points system factors in explaining these differences using the Personal Income Tax and Migrants Integrated Dataset and the Australian Census Longitudinal Dataset. We find that primary skilled visa holders earn at least 26–28 per cent more than spousal visa holders, and this is similar for both genders. However, spouses of primary skilled visa holders earn 13–18 per cent more than spousal visa holders. This difference is higher among females than males. Occupation differences can account for nearly half of the differences in income and can entirely capture the role of education and English proficiency. Primary skilled immigrants and their spouses have higher rates of labour force participation and employment than spousal visa holders, starting in the first year of arrival, and the gap is much higher for primary skilled visa holders, but these differences do not disappear quickly.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".