Measuring Industry Contributions to Labour Productivity Change: A New Formula in a Chained Fisher Index Framework
Bibliographic record
Abstract
Canada and the United States use Fisher indexes in their input-output accounts. Existing methods for decomposing aggregate labour productivity growth into industry contributions in a Fisher index framework either leave some productivity growth unaccounted for or are poorly suited for answering relevant questions about the industry sources of productivity growth. This article derives formulas for analyzing industry contributions to productivity change that add up exactly to the aggregate change in productivity and that have useful economic interpretations. These formulas show that the manufacturing sector made a positive contribution to productivity growth in the Canada in 2000-2010 and in the United States in 1998-2012, whereas the widely used GEAD formula implies that manufacturing made a negative contribution. Methods that can be used to decompose chained Laspeyres measures of productivity growth are also developed. These methods would be applicable in countries other than Canada and the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".