The Effect of Discretion on Procurement Performance
Bibliographic record
Abstract
We run a regression discontinuity design analysis to document the causal effect of increasing buyers’ discretion on procurement outcomes in a large database for public works in Italy. Works with a value above a given threshold have to be awarded through an open auction. Works below this threshold can be more easily awarded through a restricted auction, where the buyer has some discretion in terms of who (not) to invite to bid. Our main result is that discretion increases the probability that the same firm wins repeatedly, and it does not deteriorate (and may improve) the procurement outcomes we observe. The effects of discretion persist when we repeat the analysis controlling for the geographical location, corruption, social capital, and judicial efficiency in the region of the public buyers running the auctions. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2016.2628 . This paper was accepted by John List, behavioral economics.
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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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.002 |
| 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".