Catch-Up Growth and Inter-industry Productivity Spillovers: Evidence from Trade Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Where and when does export-led growth work? This paper estimates the importance of inter-industry productivity spillovers for the export-led growth of developing countries. My empirical strategy is based on a standard quantitative trade model that features sector-level gravity in trade flows. Applying the framework to four decades of trade data, I find clear evidence of spillovers, which are larger for skill-intensive sectors. The estimates imply that patterns of sectoral specialization play a quantitatively important role in accounting for the slow convergence of labor productivity in tradable sectors. Quantitative exercises suggest that export-led growth works for poorer countries with an initial comparative advantage in manufacturing, as these countries can use foreign demand from richer countries to reallocate labor towards sectors with high spillovers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it