Inflation and the rule-of-thumb method of adjusting the discount rate for income taxes
Bibliographic record
Abstract
The rule-of-thumb method of adjusting discount rates for income taxes is (after-tax rate) = (before-tax rate) × (1 tax rate). Previous researchers have concluded that the rule-of-thumb adjustment for income taxes is accurate only when the investment alternative used to define the opportunity cost of capital has certain characteristics: if other investments are limited to land rental, taxable bonds, and bank certificates of deposit, for example. In the present article we demonstrate that inflation is also an important factor in the rule-of-thumb's accuracy in accounting for income taxes. If the unmodified rule-of-thumb adjustment for income taxes is applied to a discount rate specified in uninflated terms, the estimated discount rate may be significantly higher or lower than the actual rate. If applied to real interest rates and not corrected for the effect of inflation, the rule-of-thumb will overestimate the after-tax discount rate that is equivalent to a specific before-tax rate; it will underestimate the before-tax rate equivalent to an after-tax rate specified in real terms. Discount rates estimated by the unmodified rule-of-thumb applied in real terms can be six percentage points or more too high or too low, depending on the rate of inflation, the income tax rate, and whether a before-tax or an after-tax discount rate is being estimated. We present modified rule-of-thumb formulas to adjust real discount rates for income taxes. They include a correction factor to account for the impact of inflation. We summarize these and other formulas used to adjust discount rates for inflation and income taxes in discounted cash flow analysis.
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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 | 0.015 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".