Incentivizing Responses in International Organization Elite Surveys: Evidence from the World Bank
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Scholars of International Organizations (IOs) increasingly use elite surveys to study the preferences and decisions of policymakers. When designing these surveys, one central concern is low statistical power, because respondents are typically recruited from a small and inaccessible population. However, much of what we know about how to incentivize elites to participate in surveys is based on anecdotal reflections, rather than systematic evidence on which incentives work best. In this article, we study the efficacy of three incentives in a preregistered experiment with World Bank staff. These incentives were the chance to win an Amazon voucher, a donation made to a relevant charity, and a promise to provide a detailed report on the findings. We find that no incentive outperformed the control group, and the monetary incentive decreased the number of respondents on average by one-third compared to the control group (from around 8% to around 5%).
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it