Survey Non-Response in COVID-19 Times: The Case of the Labour Force Survey
Bibliographic record
Abstract
During the COVID-19 pandemic, labour-force survey non-response rates have surged in many countries. We show that in the case of the Canadian Labour Force Survey (LFS), the bulk of this increase is due to the suspension of in-person interviews following the adoption of telework within Federal agencies, including Statistics Canada. Individuals with vulnerabilities to the COVID-19 economic shock have been harder to reach and have been gradually less and less represented in the LFS during the pandemic. We present evidence suggesting that the decline in employment and labour-force participation have been underestimated over the March-July 2020 period. We argue that these non-response issues are moderate when analyzing aggregate outcomes, but that researchers should exert caution when gauging the robustness of estimates for subgroups. We discuss practical implications for research based on the LFS, such as the consequences for panels and the choice of public-use versus master files of the LFS.
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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 | 0.216 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".