Bibliographic record
Abstract
The paper studies the decision of firms to expense or capitalize R&D. In a two-period model, a monopolist decides on how much to invest in R&D and how much of that investment to capitalize, and then on each period's output. It is found that the firm has an incentive to mismatch the benefits and costs of R&D, expensing a larger portion of R&D when the benefits occur mainly in the long run, and capitalizing a larger portion when the benefits occur mainly in the short run. Smaller firms are more likely to expense a larger portion of their R&D expenditures. A faster reaction of markets to the accounting policy reduces expensing. An increase in the discount factor, although having a negative direct effect on expensing, has a net ambiguous effect, because of its effect on R&D. Given that R&D is endogenous to the model, the effects of these changes on innovation and their interactions with expensing are also derived. One major result of the model is that there is strategic substitutability berween R&D and expensing. It is argued that accounting standards, market evaluation of capitalization, and firms' accounting policies can have real effects on innovation. Overall, the model suggests that looser accounting standards, and a less conservative market evaluation of capitalization, can favor innovation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".