Bibliographic record
Abstract
Why do structural adjustment programs (SAPs) administered by the IMF and World Bank fail so often? Although different standards have been used to evaluate the impact of SAPs, almost everyone agrees they usually do not improve a country's balance of payments significantly. SAPs were intended to improve a country's ability to earn more through exports over the long run. Yet, SAPs were often unable to flip a country's balance of payments from the negative to the positive. Moreover, in cases where SAPs succeeded economically (achieving a positive balance of payments), they have often done so only in the short run. Why has trade adjustment proven so difficult, even when countries have received substantial external support during the process, provided by international financial institutions (IFIs)? In this essay, we seek to provide an answer on two grounds. First, the economic models behind SAPs rest on unrealistic assumptions about the microlevel processes of trade adjustment. Second, these microlevel dynamics have shaped the domestic politics of structural adjustment. Political opposition often has made SAPs unsustainable. To explain variation in SAP performance, we turn to both economic and political variables.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".