Bibliographic record
Abstract
Why do living standards differ so much across countries?A consensus in the development literature is that differences in productivity are a dominant source of these differences.But what accounts for productivity differences across countries?One explanation is that frontier technologies and best practice methods are slow to diffuse to low income countries.The recent literature on misallocation offers a distinct but complementary explanation: low income countries are not as effective in allocating their factors of production to their most efficient use.We provide our perspective on three key questions.First, how important is misallocation?Second, what are the causes of misallocation?And third, beyond the direct cost of lower contemporaneous output, are there additional costs associated with misallocation?A summary of our answers is as follows.Misallocation appears to be a substantial channel in accounting for productivity differences across countries, but the measured magnitude of the effects depends on the approach and context.Researchers have not yet found a dominant source of misallocation; instead, many specific factors seem to contribute a small part of the overall effect.Beyond the static cost of misallocation, we believe that the dynamic effects of misallocation on productivity growth are significant and deserve much more attention going forward.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".