The short-term effects of COVID-19 on labour market outcomes of recent immigrants to Canada
Bibliographic record
Abstract
Purpose Given these potential negative consequences, it is important to determine how the unanticipated Covid shock affected labour market outcomes of recent immigrants, and whether it had a disproportionately negative effect relative to the Canadian-born, especially for immigrants in the low-level occupations and in the industries that are hard hit by the pandemic. That is the purpose of this analysis and is a main contribution to the literature. Design/methodology/approach We use the LFS data and a conventional Difference-in-Difference (DiD) equation to estimate the differential effects of the COVID-19 lock-down on recent immigrants’ labour market outcomes including employment, actual hours of work and wages, compared to the comparable Canadian-born. Findings Our DiD analysis indicates that Covid-19 had a disproportionately adverse effect on the employment of recent immigrants relative to the Canadian-born and this was especially the case in lower-level occupations and in industries hard hit by the pandemic. The effects of Covid on hours worked for those who remained employed were modest as were the differential effects for recent immigrants. Covid was associated with higher wages for recent immigrants who remain employed relative to their Canadian-born counterparts, and this is especially the case for recent immigrants in lower-level occupations and hard-hit industries. Research limitations/implications The substantial adverse effect of Covid on the employment of recent immigrants, both absolutely and relative to their Canadian-born counterparts, has important implications for the assimilation of immigrants into the Canadian labour market. The fact that this adverse effect is disproportionately felt by recent immigrants in lower-level occupations has equity implications. The fact that the adverse effect is disproportionately felt by recent immigrants in industries hard-hit by the pandemic highlights the double whammy of being in hard-hit sectors with above-average reductions in their employment rate and having their employment probabilities disproportionately reduced in those sectors. Practical implications In addition to recognizing foreign skills, enhancing the skills of recent immigrants can also increase their employability and earnings. Given the growth of the knowledge economy such important skills include core ones in such areas as communication, socio-emotional, digital and basic literacy and numeracy skills, as well as soft skills such as those related to interpersonal relationships, leadership, communication, conflict resolution, teamwork and time management. Assessing the skills of recent immigrants and providing timely and local labour market information (LMI) as well as mentoring, training and information on Canadian workplace cultural norms can also help match the skills of recent immigrants with employer needs. Social implications Disruption in the labour market assimilation of immigrants can inhibit them from earning their living and contributing to tax revenues and lead them to “have-nots” in receipt of transfer payments. Dependency on transfer payments can foster backlashes and the polarization and xenophobia associated with immigrants. Negative labour market experiences for immigrants can contribute to long-lasting downward career mobility and talent waste that can inhibit the ability of Canada to compete for international talent. It can lead to a legacy of longer run even intergenerational negative effects in various dimensions. Clearly this issue merits policy attention. Originality/value Our study utilizes DiD analysis to provide causal estimates of the differential impact of Covid-19 on three outcomes: employment, hours and hourly wages. Comparisons are made for recent immigrants relative to comparable Canadian-born persons prior to the pandemic, and the differential effect of the pandemic on recent immigrants relative to the Canadian-born. A main contribution to the literature is that it also does the comparisons in a separate intersectional fashion for individuals who are in lower-level and higher-level occupations as well as in industries that are low-hit and hard-hit by the pandemic.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".