Stuck in the Middle? Structural Change and Productivity Growth in Botswana
Bibliographic record
Abstract
This paper decomposes Botswana's growth from the late 1960s through 2010 into a within-sector and a between-sector (structural change) component. We find that during the 70s and 80s Botswana's rapid economic growth was characterized by significant structural change with the share of the labor force employed in agriculture dropping from more than 80 percent to around 40 percent. Between 1990 and 2010 growth was also rapid, but structural change detracted from growth. We hypothesize that this is one of the reasons for persistent poverty and very high income inequality in Botswana today. This leaves us with the following puzzle: why is it that a country with such an impressive track record marked by good governance and prudent macroeconomic and fiscal policy is having so much trouble diversifying its economy?
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How this classification was reachedexpand
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 | 0.027 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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 teacher head, 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".