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.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".