The World Bank Perspective on Productivity: A Review Article on "Productivity Revisited: Shifting Paradigms in Analysis and Policy"
Bibliographic record
Abstract
In 'Productivity Revisited', the World Bank turns its ongoing productivity research program to the issue of the apparent failure of productivity in developing countries to converge to the higher productivity in advanced economies. The World Bank asserts, but provides little evidence, that converge is not taking place The analysis is grounded in the so-called second wave of productivity research which uses firm-level data to disaggregate productivity into gains within firms, across firms through research allocation and through market entry and exit. The disaggregations are found to differ across countries, suggesting convergence policies may need to be shaped to local circumstances rather than generalized across developing countries. A common question arising throughout is why firms, sectors and economies do not do more to emulate the behaviours of the more productive counterparts in advanced economies. Considerable emphasis is placed on managers and entrepreneurs in developing countries not having the right skill set as they have inadequate education and are risk averse. Despite claims that second-wave analysis puts into question traditional policy prescriptions, the World Bank advocates a traditional set of policy recommendations involving creating favourable business conditions, reducing distortions and improving human capital.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".