A Test of Hirschman’s Hiding Hand Principle in World Bank-Financed Hydropower Projects
Bibliographic record
Abstract
Abstract This study is an attempt to determine whether the need to get hydropower project appraisals perfectly right during the pre-construction phase, so as to prevent significant overruns along with benefit shortfalls, should supersede the need to deliver projects at the earliest possible time so as to meet the needs of the people. To achieve the study objective, we test whether the Hiding Hand principle is predominantly benevolent or malevolent. We argue that if the Hiding Hand is benevolent, then project stakeholders are better off focusing on the quick delivery of power projects; however, if it is malevolent, then more attention should be given to perfecting project appraisals. It transpires from the statistical analysis that the Benevolent Hiding Hand dominates the Malevolent Hiding Hand in the selected World Bank-financed hydropower projects (33% v. 21%), and that ultimately, 75% of the projects were even more successful than anticipated—while 25% of the projects failed. Our findings further show that while a total loss of 2.335 billion USD in the sampled dams was caused by the Malevolent Hiding Hand, 11.259 billion USD was gained as a result of the Benevolent Hiding Hand. The predominance of the Benevolent Hiding Hand justifies placing some weight on proceeding with hydropower projects that show significant promise even if all the implantation risks are not fully quantified at the appraisal stage, especially in developing countries.
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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 | 0.034 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".