The Effect of Competitive Bidding on Engagement Planning and Pricing*
Bibliographic record
Abstract
Abstract This paper investigates how clients' choices regarding whether or not to engage in competitive bidding affect a bidding firm's decisions about planned engagement effort and pricing. Specifically, we investigate whether competitive bidding is associated with higher planned engagement effort and lower fees relative to noncompetitive bidding, and whether competitive bidding is associated with increased sensitivity of effort and fees to cost drivers and the components of service production. There is little available evidence regarding the effects of competitive versus noncompetitive bidding in the current market, and none that focuses on both quality and pricing effects associated with competitive bidding across a broad array of clients. We address these issues using data from a sample of one firm's evaluations of prospective clients, made during 1997‐98. During that period, about half of the firm's bids were competitive and half were noncompetitive, providing a unique opportunity to study how the bidding environment affects engagement planning and pricing. Our findings reveal that competitive bidding is associated with higher planned engagement effort and lower fees. In addition, we find that in competitive bidding situations there are stronger associations between cost drivers and planned engagement effort, and between the components of service production and fees.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".