International Scrutiny and Pre-Electoral Fiscal Manipulation in Developing Countries
Bibliographic record
Abstract
Pre-electoral fiscal manipulation—spending more or taxing less prior to an election—is an important tool that governments possess to enhance their chances for reelection. Existing explanations of pre-electoral fiscal manipulation focus primarily on domestic characteristics. We extend this line of inquiry by examining international influences on governments ’ decisions to engage in pre-electoral fiscal manipulation. We find that international scrutiny of the economy and international scrutiny of elections affect pre-electoral fiscal manipulation in cross-cutting ways. Using data from 1990 to 2004 for 94 developing countries, we show that pre-electoral fiscal manipulation is more likely when international election monitors make direct election manipulation more difficult, and it is less likely when governments are subject to international economic scrutiny resulting from an IMF agreement. P re-electoral fiscal manipulation—spending more or taxing less prior to an election—is an important tool that governments may use to enhance their chances for reelection.1 Recent studies document that it is employed most often in new
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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 | 0.001 | 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 | 0.001 |
| 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".