Further Evidence on the Contribution of Services Outsourcing to the Decline in Manufacturing’s Employment Share in Canada
Bibliographic record
Abstract
In October 2015, the Centre for the Study of Living Standards released a report examining how outsourcing of work from the manufacturing sector to the services sector contributed to the recorded decline in Canadian manufacturing employment over the past four decades. The evidence was mixed. An examination of the input-output structure of the economy suggested that the effect of services outsourcing was very small while a decomposition of employment growth by industry and occupation suggested that the effect may have been substantial. This report revisits these results using new custom data products provided by Statistics Canada. In particular, the earlier work examined an input-output structure based on current dollar data which may have skewed the results due to large price swings, particularly in the oil and gas sector. This report uses chained dollar estimates to avoid this problem. Similarly, the employment decomposition used highly aggregated occupational data which may have overstated the contribution of outsourcing to manufacturing’s declining employment share. We use more detailed occupational data from the Census / National Household Survey. We find that the results regarding the contribution of services outsourcing are fairly robust to the choice of data. Furthermore, we are able to reconcile the differing estimates of the importance of services outsourcing between the input-output and occupational decomposition methodologies by noting that much of the decline in manufacturing employment in services occupations might be expected to occur if the manufacturing sector shrank for reasons unrelated to services outsourcing. In particular, the expected share of the decline associated with service occupations in response to a negative shock to the manufacturing sector should be roughly equal to the share of service occupations in total manufacturing employment. Adjusting for this, we find that both exercises suggest the contribution of services outsourcing to the decline of manufacturing’s employment share was quite small, explaining no more than 8.3 per cent.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".