Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how managerial risk-taking incentives affect the sensitivity of R&D investments to the availability of a firm’s internal finance. Design/methodology/approach The author studies a large panel sample of US firms from 1992 to 2013 using a dynamic structural model and estimates a system GMM estimator that accounts for unobserved firm-specific effects, and that allows the author to address the potential endogeneity of all of the financial and executive compensation variables. Findings Managerial risk-taking incentives, in particular CEO portfolio vega, have a significantly positive impact on the financial constraints that bind R&D investments. Moreover, the author finds that CEO portfolio vega has stronger impacts on the investment-cash flow sensitivity of R&D in firms that are more likely to face binding financial constraints. Originality/value Prior studies on the financial constraints of R&D investments do not consider the potential impact of executive compensation on R&D investments. The author complements this stream of literature by providing novel results showing that managerial risk-taking incentives have a significant impact on the severity of the financial constraints on R&D investments.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".