1Immigration and Canada’s Wage Structure in the First Half of the Twentieth Century
Bibliographic record
Abstract
In an earlier paper, (Green and Green (2007)), we presented evidence using Census data on movements in the Canadian wage structure between 1911 and 1941. That evidence pointed to a substantial widening in the Canadian wage structure in the early twentieth century. More specifically, between 1911 and 1921 real wages fell at both the top and bottom ends of the wage distribution while in the following decade the wages at the bottom stayed relatively constant while the wages at the top end increased dramatically. Finally, during the 1930s, the spread of the wage distribution remained relatively constant. This broadening of the distribution is in striking contrast to the description of substantial compression in the US wage distribution over this same period presented in Goldin and Katz(2001). In particular, the two countries had substantially different experiences in terms of movements in their wage structure in the 1920s, with dispersion expanding substantially for Canada but not for the US. Understanding what forces drove these very different wage trends is potentially useful as an input into a better understanding of differences in technological choices in the two countries and, thus, of the different paths of economic growth in each. We argue in our earlier paper that the difference in immigration experiences between
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".