Tax Loss Carryovers in a Competitive Environment*
Bibliographic record
Abstract
ABSTRACT The fact that incumbent firms can immediately deduct research and development (R&D) investments from taxable income is generally believed to give them a strategic advantage over new firms that cannot deduct the investment cost, but instead generate a net operating tax loss carryover. Using an analytical model, we show that this conventional wisdom need not hold in a competitive environment. We examine operating and investment decisions in a duopolistic industry in which an initial investment in R&D yields an immediate tax benefit for one firm, but creates a net operating loss carryover for the other firm. If both firms invest in R&D, the firm with the net operating loss carryover makes more aggressive capital investment decisions following successful R&D. This may deter the incumbent firm from investing in R&D despite the lower aftertax costs of this investment. Changing the tax loss carryover rules would thus not only affects start‐up or loss firms, but would also affect the investment decisions of profitable firms in the same industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".