Essays in Corporate Finance and Corporate Innovation
Bibliographic record
Abstract
In the first chapter I investigate whether firms grant stock options to rank-and-file employees (RFOs) for retention purposes. I find that it depends on their level of investment in intellectual property. Firms grant more RFOs if they have more knowledge capital, more patents awarded, more citations received by these patents, and more patents for “breakthrough” innovations. After the risk of poaching by rival firms is diminished by the adoption of the\nInevitable Disclosure Doctrine, I find that firms with above median level of knowledge capital reduce their RFO grants by 32% relative to control firms; the reduction is minimal for firms with below median level of knowledge capital.\n\nIn the second chapter I investigate how effective are RFO grants in retaining employees. Previous studies find that while there is an initial reduction in employee turnover, the effect is temporary and quickly reversed within three years of the grant. In this chapter, I show that the impact of RFOs is permanent. I find that there is a 36% reduction in inventor turnover in the year of grant and the impact remains over the next three years. Importantly, there is no reversal suggesting that the benefit is permanent instead of transitory. I also find that the importance of RFOs to reduce inventor turnover increases following the implementation of FAS123R when firms reduce the use of RFO.\n\nIn the final chapter I study the behavior of firms with respect to their patent applications. I find that firms engage in window dressing by timing the date of their patent applications. I find that almost 40% of firms’ yearly patents are filed in the last quarter of the calendar year accompanied by a reduction in the quality of these patents. The behavior is more prevalent among firms subject to lower external and internal monitoring, supporting the incentive to window\ndress as the underlying channel. To establish window dressing as the key mechanism, I use the adoption of America Invents Act as a quasi-natural experiment that has increased the costs of window dressing. Following the adoption of the Act, the window dressing behavior become less prevalent.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".