Bibliographic record
Abstract
The relative price of services rises with development.A standard interpretation of this fact is that productivity differences across countries are larger in manufacturing than in services.The service sector comprises heterogeneous categories.We document that many disaggregated service categories-such as transportation, communication, and finance-feature a negative income elasticity of relative prices, whereas the relative price of aggregate services is mostly driven by large expenditure categories in housing, collective government, and health that feature a positive income elasticity of relative prices.We also document a substantial reallocation of expenditures in services from categories with positive income elasticities (traditional services) to categories with negative elasticities (non-traditional services) as income rises.Using an otherwise standard multi-sector development accounting framework extended to include an input-output structure, we find that the cross-country income elasticity of sectoral productivity is large in non-traditional services (1.15), smaller in manufacturing (1.05) and much smaller in traditional services (0.67).Eliminating cross-country productivity differences in non-traditional services reduces aggregate income disparity by 58 percent, a 7.9-fold reduction in aggregate productivity differences.We also find that the heterogeneity between traditional and non-traditional services has a substantial impact on aggregate productivity and that the input-output structure is important in this assessment.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".