Bibliographic record
Abstract
In a world where poor countries provide weak protection for intellectual property rights, market integration shifts technical change in favor of rich nations. Through this channel, free trade may amplify international income di¤erences. At the same time, integration with countries where intellectual property rights are weakly protected can slow down the world growth rate. A crucial implication of these results is that protection of intellectual property is most bene cial in open countries. This prediction, which is novel in the literature, is consistent with evidence from a panel of 53 countries observed in the years 1965-1990. JEL classi cation: F14, F43, O33, O34, O41. Keywords: Economic Growth, North-South Trade, Directed Technical Change, Intellectual Property Rights, Cross-Country Income Di¤erences. A previous version of this paper circulated under the title Globalization, Divergence and Stagnation. I am very grateful to Daron Acemoglu, Torsten Persson, Jaume Ventura and Fabrizio Zilibotti for advice. I also thank Philippe Aghion, Pol Antras, Alessandra Bon glioli, Paolo Epifani, Renato Flores, Omar Licandro, Paul Segerstrom, Bob Staiger, Dan Treer and seminar participants at MIT, IIES, Stockholm University, Stockholm School of Economics, University of British Columbia, Toronto, Wisconsin, Rochester, CREI, Pompeu Fabra, UCL, LSE, Bocconi, the NBER Summer Institute (2004), ESSIM (2004), the SED Annual Meeting (2004), the EEA Meetings (2004) and the European Winter Meeting of the Econometric Society (2002). The usual caveat applies. The rst draft of this paper was written while visiting the MIT Economics Department. I thank MIT for its hospitality. yCREI, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005, Barcelona (Spain). E-mail: gino.gancia@upf.edu
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".