Productivity and Pay in the US and Canada
Bibliographic record
Abstract
We study the productivity-pay relationship in the United States and Canada along two dimensions. The first is divergence: the degree to which the levels of productivity and pay have diverged. The second is delinkage: the degree to which incremental increases in the rate of productivity growth translate into incremental increases in the rate of growth of pay, holding all else equal. We show that in both countries the pay of typical workers has diverged substantially from average labor productivity over recent decades, driven by both rising labor income inequality and a declining labor share of income. Even as the levels of productivity and pay have grown further apart, we find evidence for some linkage between productivity and pay in both countries: a one percentage point increase in the rate of productivity growth is associated with a positive increase in the rate of pay growth, holding all else equal. This linkage appears stronger in the US than in Canada. Overall, our findings lead us to tentatively conclude that policies or trends which lead to incremental increases in productivity growth, particularly in large relatively closed economies like the USA, will tend to raise middle class incomes. At the same time, other factors orthogonal to productivity growth have been driving productivity and typical pay further apart, emphasizing that much of the evolution in middle class living standards will depend on measures bearing on relative incomes.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".