Oversight and Efficiency in Public Projects: A Regression Discontinuity Analysis
Bibliographic record
Abstract
In the United States, 42% of public infrastructure projects report delays or cost overruns. To mitigate this problem, regulators scrutinize project operations. We study the effect of oversight on delays and overruns with 262,857 projects spanning 71 federal agencies and 54,739 contractors. We identify our results using a federal bylaw: if the project’s budget is above a cutoff, procurement officers actively oversee the contractor’s operations; otherwise, most operational checks are waived. We find that oversight increases delays by 6.1%–13.8% and overruns by 1.4%–1.6%. We also show that oversight is most obstructive when the contractor has no experience in public projects, is paid with a fixed-fee contract with performance-based incentives, or performs a labor-intensive task. Oversight is least obstructive—or even beneficial—when the contractor is experienced, paid with a time-and-materials contract, or conducts a machine-intensive task. This paper was accepted by Serguei Netessine, operations management.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".