Democracy, Technology, and Growth
Bibliographic record
Abstract
We explore the question of how political institutions and particularly democracy affect economic growth. Although empirical evidence of a positive effect of democracy on economic performance in the aggregate is weak, we provide evidence that democracy influences productivity growth in different sectors differently and that this differential effect may be one of the reasons of the ambiguity of the aggregate results. We provide evidence that political rights are conducive to growth in more advanced sectors of an economy, while they do not matter or have a negative effect on growth in sectors far away from the technological frontier. One channel of explanation goes through the beneficial effects of democracy and political rights on the freedom of entry in markets. Overall, democracies tend to have much lower entry barriers than autocracies, because political accountability reduces the protection of vested interests, and entry in turn is known to be generally more growth-enhancing in sectors that are closer to the technological frontier. We present empirical evidence that supports this entry explanation. JEL Classification codes: H7 Acknowledgments: The authors are particularly grateful to Daron Acemoglu for his comments. We also benefitted from discussions with Matilde Bombardini, Elhanan Helpman Guido Tabellini and seminar participants at a CIAR meeting in Toronto for comments, and we thank Daron Acemoglu, Simon Johnson, Jim Robinson, and Pierre Yared for sharing their data. 1 Andrea Asoni provided excellent research assistance. Trebbi kindly acknowledges financial support from the Initiative on Global
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".