Do Digital Technology Firms Earn Excess Profits? Alternative Perspectives
Bibliographic record
Abstract
ABSTRACT Despite regulators’ allegations that digital technology giants misuse their market power to earn abnormal profits, there is a dearth of systematic work on (1) whether digital-tech firms in general, and tech giants in particular, earn excess profits or (2) whether their abnormal profitability, if any, is due to market power. We use two alternative measures of economic profitability in addition to accounting rate of return (ARR): internal rate of return (IRR), which equates current investments to their long-term payback, and return on invested capital (ROIC), whose numerator (profits) and denominator (invested capital) are adjusted for capitalized intangibles. Inferences based on IRRs differ from those based on ARRs and ROICs. IRRs show that the digital-tech sector is now the best-performing sector, and its gap between profitability and cost of capital has increased over time. We are unable to separate the contribution of market power and innovation to digital tech’s high IRRs. JEL Classifications: D43; L1; M21; M41.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".