Public contracting for private innovation: Government capabilities, decision rights, and performance outcomes
Bibliographic record
Abstract
Research Abstract We examine how the US Federal Government governs R&D contracts with private‐sector firms. The government chooses between two contractual forms: grants and cooperative agreements. The latter provides the government substantially greater discretion over, and monitoring of, project progress. Using novel data on R&D contracts and on the technical expertise available in specific government bureau locations, we test implications from the organizational economics and capabilities literatures. We find that cooperative agreements are more likely to be used for early‐stage projects and those for which local government scientific personnel have relevant technical expertise; in turn, cooperative agreements yield greater innovative output as measured by patents, controlling for endogeneity of contract form. The results are consistent with multitask agency and transaction‐cost approaches that emphasize decision rights and monitoring. Managerial Abstract When one private firm outsources an R&D project to another, it can use a range of sophisticated contractual provisions to elicit proper innovative effort. However, government entities are often constrained from employing such provisions due to legal and regulatory restrictions. Policymakers thus face a difficult challenge when contracting with private firms for innovation. We study the US Federal government's R&D contracts, which are restricted to two contractual types: “grants,” which offer little in‐process oversight, and “cooperative agreements,” which provide decision rights during the project. We demonstrate that policymakers can enhance outcomes by using cooperative agreements for earlier‐stage, higher‐uncertainty projects, but only when government scientists with relevant expertise are located near the firm's R&D site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".