Consequences of Labor Cost Reduction Practices: A Structured Literature Review*
Bibliographic record
Abstract
ABSTRACT When firms face pressures to reduce costs, evidence from the field suggests that they often reduce labor costs (i.e., wages, benefits, payroll taxes). Because of the prevalence of labor cost reduction in the field, academic research has begun to investigate the consequences of management's decisions to reduce labor costs. I provide a structured literature review on the employee‐level consequences of three labor cost reduction practices: employee downsizing, furloughs, and pay cuts. My literature review synthesizes the labor cost reduction research through a lens of a discretionary management accounting decision to reduce costs and highlights opportunities for management accounting researchers to explore the consequences of labor cost reductions on employees' attitudes and behaviors. To synthesize the literature on labor cost reduction, I develop a model that proposes that management's labor cost reduction decisions, which include features of the implementation and contextual factors, influence employees' perceptions of management's and employees' attitudes and behaviors. Consistent with my model, my synthesis of the literature shows that labor cost reduction generally has negative employee‐level consequences. However, features of management's implementation of the labor cost reduction practice and contextual factors can alter employees' perceptions and mitigate these negative consequences.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".