Bibliographic record
Abstract
The McKinsey Global Institute (MGI) is a think tank based in Washington, D.C. founded in 1990 with the objective of analyzing international productivity levels from both economic and management perspectives. MGI uses microeconomic analysis on a sector-by-sector level to study the effects that industry decisions ultimately have on national productivity. For the most part the productivity drivers identified by MGI can be grouped into three broad areas: competitive factors (concentration, trade protection, deregulation, minimum wages, work rules, and zoning laws); managerial factors (best practice, human capital, capital intensity, and information technology); and demand factors (average income, cyclical factors, and consumer preferences). This paper examines these factors in an attempt to shed light on the causes of Canada-U.S. productivity differences at the industry level. Competitive factors may explain the poor productivity performance of the Canadian financial and cultural service industries relative to their U.S. counterparts, and likewise may explain the high productivity levels of some natural resource industries in Canada relative to the United States. Managerial factors, especially the implementation of new technologies and related processes, may be important in explaining the poor productivity growth in Canada relative to the United States in service industries such as retail trade. Given the similarities between Canada and the United States, the findings of the MGI studies cannot be indiscriminately applied to Canada-U.S. productivity differences at the industry level. However, the MGI studies do put forward a number of useful working hypotheses for analyzing these differences.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.032 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".