Difficulties Assessing Multifactor Productivity for Canada
Why this work is in the frame
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Bibliographic record
Abstract
In 2011, Canada's business sector multifactor productivity (MFP) index, as estimated by Statistics Canada, was below that for 1977, a third of a century earlier. Over these years, public policies were enacted to try to improve Canada's productivity. Yet the nation's MFP continued to fall, relative to both the past and Canada's main trading partners. Policymakers and business decision makers need to know whether Canada's MFP statistics accurately reflect the nation's productivity. We argue that they do not. RÉSUMÉ En 2011, l'indice de la productivité multifactorielle (PMF) du secteur des entreprises du Canada tel qu'estimé par Statistique Canada en 2011 était inférieur à celui de 1977, plus d'une trentaine d'années plus tôt. Pendant des années les politiques publiques ont été développées pour améliorer la productivité du Canada. Pourtant, l'indice du PMF continue à decliner, par rapport au passé et aux principaux partenaires commerciaux du Canada. Il est crucial pour les responsables des politiques et les décideurs des milieux d'affaires de savoir si la situation reflète fidèlement le rendement du Canada sur le plan de la PMF. Nous soutenons que cela n'est pas le cas. IN 2011, CANADA’S BUSINESS SECTOR multi-factor productivity (MFP) index, as estimated by Statistics Canada, was 94.8, which is lower than the value of 97.6 for 1977. For years now, governments in Canada have sought to improve the nation's productivity. Don Drummond (2006 and 2011) and Paul Boothe and Richard Roy (2008) describe some of the policy mea-sures. Yet, as Drummond (2011:4) laments, “multifactor productivity did not grow at all.” Drummond notes that the implementation of a large number of market-oriented policies by governments in Canada over the past several decades was expected to boost productivity 1 Michael J. Harper retired in 2011 from his position as Associate Commissioner for Productivity and Technology of the U.S. Bureau of Labor Statistics (BLS) which he had held since 2006. At the BLS he was involved in developing the Bureau's measures of multifactor productivity. Alice Nakamura is a professor in the Department
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it