Competition and Cost Accounting: Adapting to Changing Markets*
Bibliographic record
Abstract
Abstract The relation of competition and cost accounting has been the subject of conflicting prescriptions, theories, and empirical evidence. Practitioner literature and textbooks argue that higher competition generally requires more accurate product costing. Theoretical economic analysis, in contrast, predicts that the optimal level of product‐costing accuracy is sometimes higher at lower levels of competition. Results of survey research are inconsistent, suggesting a need for further identification of conditions under which higher competition leads to more accurate product costing. This study shows experimentally that individuals' choices of the level of product‐costing accuracy depend not only on the current level of competition but also on the previous level of competition — that is, on an interaction between market structure (monopoly, duopoly, and four‐firm competition) and market history (increasing versus decreasing competition). In the experiment, subjects decide on the quantity of data to collect at a pre‐set price per datum to support more accurate product‐cost estimates. Subjects collect the most cost data (i.e., choose the most accurate product costing) in monopoly, collect the least in duopoly, and an intermediate amount in the four‐firm market, consistent with the pattern of optimal cost‐data collection in Hansen's 1998 model. The process of convergence to the optimum differs significantly across market types and market histories, however. Subjects who begin in four‐firm competition adapt more successfully to change than those who begin in monopoly. The lowest levels of decision performance occur when ex‐monopolists face their first competitor: they overreact to this first encounter with competition and overspend on cost data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".