The Political Economy of Economic and Productivity Growth: An Interview with Daron Acemoglu and James Robinson, Authors of "Why Nations Fail"
Bibliographic record
Abstract
In fast-growing developing countries, rapid productivity growth is largely driven by economic growth. Consequently, an understanding of the reasons for this strong productivity growth requires a broader perspective on the dynamics of the overall growth process. In early 2012 Daron Acemoglu, an economist at MIT and James A. Robinson, a political scientist and economist at Harvard University, published Why Nations Fail: The Origins of Power, Prosperity, and Poverty. With great historical detail, the book makes the case that it is man-made economic and political institutions that underlie economic success by creating incentives for wealth creation, rewarding innovation and allowing widespread participation in economic opportunities. This article is an edited transcript of an interview with the two authors on the major issues addressed in their book.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".