Bibliographic record
Abstract
In this paper I ask which of the multiple mechanisms suggested in the literature are quantitatively important for understanding the process of structural change. I build a model combining four forces in a common framework: (i) sector-biased technological progress, (ii) nonhomothetic tastes, (iii) international trade and (iv) changing wedges between factor costs across sectors. I calibrate the model using the data for 45 diverse countries over the period 1970–2005 and use counterfactual simulations of the model to systematically assess the relative importance of the four determinants of structural change. I find that sector-biased technological change is overall the most important mechanism and it is essential for understanding the decline of manufacturing labor share and the corresponding growth in services in developed countries. Nonhomothetic preferences are key to accounting for movement of labor out of agriculture, which matters primarily for poorer countries. International trade and changes in relative factor costs across sectors are important for individual countries but their impact on the relocation of labor is less systematic. I also show that a model with homothetic preferences would overstate the importance of agriculture in accounting for differences in aggregate productivity across countries and over time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".