Disaggregate Productivity Comparisons: Sectoral Convergence in OECD Countries
Bibliographic record
Abstract
Abstract International comparisons of productivity have used exchange rates or purchasing power parity (PPP) to make output comparable across countries. While aggregate PPP holds well in the long run, sectoral deviations are persistent. It raises the need for a currency conversion factor at the same level of aggregation as the output that is compared. Mapping prices from household expenditure surveys into the industrial classification of sectors and adjusting for taxes and international trade, I obtain an expenditure-based sector-specific PPP. Using detailed price data for up to 8 years between 1970 and 1999, I test whether the sectoral PPPs adequately capture differential changes in relative prices between countries. They work well for agriculture and the majority of industrial sectors, but not for most service sectors and for manufacturing sectors that produce differentiated products. Using the most appropriate conversion factor for each industry, produc-tivity convergence is found to be taking place in all but a few industries for a group of 14 OECD countries. The latter results are robust to the base year used for the currency conversion.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".