Is Development Path Dependent or Political? A Reinterpretation of Mineral-Dependent Development in Botswana
Bibliographic record
Abstract
Poor management of earnings from valuable natural resources results in a syndrome known as Dutch Disease, characterised by real exchange rate appreciation, high labour costs, and structural imbalances in economic development. Often a product of rentier politics, Dutch Disease undermines long-term economic performance in resource dependent economies resulting in a ‘resource curse’. The conventional wisdom argues that institutions and state development at the time of a resource boom lock countries into divergent developmental trajectories. I argue that political coalitions lay the foundation for development of state and other institutions, and that changes in coalitions drive changes in policy responses to resource booms. Botswana's experience illustrates the argument. Botswana has not entirely avoided symptoms of Dutch Disease, but has kept them largely in check despite the fragility of state institutions when diamonds were discovered. A broad and stable political coalition during the first decades of independence encouraged adoption of pro-growth policies and institutions. Rather than lock the country into a persistent development trajectory, these institutions left room for changes in political coalitions. As political coalitions change, economic policies and performance are also likely to change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".