The political economy of participation in IMF programs: a disaggregated empirical analysis
Bibliographic record
Abstract
What factors determine whether or not countries have programs with the International Monetary Fund (IMF)? The existing literature suggests that a number of economic and political variables are important, but there is disagreement about their relative significance. Moreover, the fit of general participation models is not particularly good. An increasingly popular view in the recent literature is that the pattern of IMF lending is politically driven and that it reflects the interests of the Fund’s leading shareholders; the US is seen as exerting a powerful influence. Using both quantitative and qualitative techniques, and based on an informal analytical framework, we examine in detail the factors that may be at work. We cover the period from 1984 to 2008. We discover considerable variation across the nature of programs (concessional and non-concessional), income levels, geographic regions, and time periods. The degree of observed variation means that it is unsafe to use one general participation model as the basis for evaluating the effects of IMF programs. It also means that the design of policy needs to reflect the nuances that the data reveal.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".