Cross-country Conversion Factors for Sectoral Productivity Comparisons
Bibliographic record
Abstract
International comparisons of the level of labor or total factor productivity have used exchange rates or purchasing power parity (PPP) to make output and capital comparable across countries.Recent evidence suggests that aggregate PPP holds rather well in the long run, making it a good basis for comparison.At the same time, sectoral deviations from PPP are very persistent, raising the need for disaggregate price measures to make disaggregate productivity comparisons.Sectoral differences in the importance of nontradables make it even more important to work with sectoral prices when country-comparisons are made at the sectoral level.Mapping prices from household expenditure surveys into the industrial classification of sectors and adjusting for taxes and international trade, I obtain a sector-specific PPP measure.The few previous studies that used sectoral prices only had conversion factors available for a single year.With price data for 1985, 1990, 1993, and 1996, I am the first to test whether the constructed conversion factors adequately capture differential changes in relative prices between countries.For some industries--Agriculture, Mining, and less sophisticated manufacturing sectors--the indices prove adequate.For most other industries, aggregate PPP is a superior currency conversion factor.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".