Measuring the gig economy in Canada using administrative data
Bibliographic record
Abstract
Abstract A rapidly growing literature on informal work increasingly turns to administrative data to document changes in the size of informal economy and to learn more about the characteristics of freelancers, on‐demand/platform workers and similar types of workers commonly referred to as “gig workers.” These studies have established a conceptual link between the work arrangements of gig workers and how these are reported in tax data. We contribute to this literature by introducing a method of identifying gig workers specific to the way work arrangements are reported in the Canadian tax system and estimating the size of the gig economy in Canada using administrative data. Based on our definition, the share of gig workers among all workers rose from 5.5% in 2005 to 8.2% in 2016. Some of this increase coincided with the introduction and proliferation of online platforms. Our analysis highlights gender differences in the trends and characteristics of gig workers. By linking administrative data to 2016 census microdata, we are also able to examine educational and occupational differences in the prevalence of gig workers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".