Tradeoffs Between Specific Investment and Optimal Resource Allocation: A Comparison of Different Transfer Pricing Policies
Bibliographic record
Abstract
This paper uses a principal-agent framework to analyze the tension between incentives for specific investment by the agent, and resource allocation that is optimal from the principal’s perspective. The analysis considers a decentralized firm in which central management can institute different transfer pricing policies to motivate divisional managers to undertake investment and production decisions. Some well-known properties of the methods are identified: a transfer price that uses a markup over and above actual costs can provide investment incentives but leads to sub-optimal resource allocation; negotiated transfer pricing suffers from the problem of under-investment even though its ex post performance is optimal; and standard cost-based transfer pricing entails over-reporting of standards, which results in inefficient levels of trade as well as low investment. The paper establishes a clear ranking amongst the three methods studied. It is shown that the overall performance of actual cost-based transfer pricing is superior if the buying division’s investments are important, while negotiated transfer pricing dominates if those of the selling division are important. The overall performance of standard cost-based method is inferior to that of the actual cost-based method, even though the latter has weaker investment incentives.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".