Discretionary Capitalization Of R&D: Evidence On The Usefulness In An Australian And Canadian Context
Bibliographic record
Abstract
This study addresses the discretionary capitalization of R&D costs in Australia and Canada. We demonstrate, for both samples, that the discretionary capitalization of development costs (hereafter capitalized D) by the manager results in balance sheet and income numbers that are more highly associated with market value, relative to the corresponding asif numbers generated by expensing GAAP. Moreover, we show that a dollar worth of capitalized D is worth more than a dollar worth of expensed R&D, for the same firm. This points to a corroboration role for capitalization. As a caveat, our results hold only when the samples are partitioned on the materiality of capitalized D. Our results point to a potentially useful signalling role for discretionary capitalization, in Australian and Canadian capital markets. However, while the manager’s capitalized D is associated with firm value, it has at best a modest advantage over what the analyst can do, using the researchercreated capitalized R&D. Thus, the regulatory policy debate must consider the small incremental benefits from allowing discretionary capitalization compared to the costs associated with earnings management when discretion is allowed.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".