Labor Adjustment Costs and Asymmetric Cost Behavior: An Extension
Bibliographic record
Abstract
The issue of asymmetric cost behavior has attracted significant interest in the managerial accounting literature. The literature has hypothesized that adjustment costs, particularly labor adjustment costs, play a significant and central role in driving empirically observed cost behavior patterns. Recent studies attempt to empirically test this hypothesis, albeit with distinct limitations. Using a new proxy for labor adjustment costs in a different population of firms, our study takes a fresh look at this hypothesis. We test the robustness of results documented in prior studies to help substantiate the credibility, reliability, and stability of prior findings. Our proxy for labor adjustment costs captures the reliance on skilled labor across industries in a population of US public firms. Prior studies argue that skilled labor is associated with higher adjustment costs than unskilled labor due to greater hiring and firing costs associated with skilled labor. Based on the theoretical underpinnings of asymmetric cost behavior, we expect that a higher reliance on skilled labor will be associated with greater cost asymmetry. Our empirical results support this proposition. In additional subsample tests, we also find that the effect of labor adjustment costs on cost asymmetry is more pronounced when unemployment rates are low, for firms located in high Wrongful Discharge Laws (WDL) states, and for firms situated in low-hiring credit states. Together, these results provide compelling evidence that validates the consequential role of labor adjustment costs in determining asymmetric cost behavior.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".