Estimating Total Factor Productivity Growth: Canadian Freight Railways, 1986 to 2009
Bibliographic record
Abstract
Canadian railways are a vital part of the country’s transportation sector, moving goods and people across the country. We perform firm-level productivity analysis of Canadian freight railways for 1986 to 2009, focusing on the two railways which dominate the market: Canadian National (CN) and Canadian Pacific (CP). We obtain total factor productivity (TFP) estimates both by constructing productivity indices and by econometrically estimating cost functions. Driven in part by operational improvements, the strong TFP growth at both firms considerably outpaced aggregate TFP growth in Canada over the period of interest. This robust TFP growth, together with significant capital deepening, led to impressive labour productivity gains. We pay special attention to the productivity effects of the 1995 privatization of CN. While CN enjoyed much stronger productivity growth over the 1986-2009 period than CP, its performance was equally superior before and after the 1995 privatization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".