Organized Labor Effects on SG&A Cost Behavior*
Bibliographic record
Abstract
ABSTRACT This study examines how organized labor affects selling, general, and administrative (SG&A) cost behavior. Human capital is emerging as firms' most valuable asset, and we further the understanding of the interaction between employees and SG&A cost behavior. We predict and find that labor cost stickiness is higher for firms facing stronger unions, but the stickiness of SG&A costs is lower. This is consistent with our arguments that in firms with stronger unions, managers' discretionary decision to retain SG&A resources is negatively affected by higher labor adjustment costs that result in the retention of slack labor resources during periods of decreased demand. To assess the robustness of our main findings, we conduct an event analysis of labor union elections and find that SG&A cost stickiness decreases after firms experience new union certification. Cross‐sectional tests also show that the effect of labor union strength on SG&A cost stickiness is more pronounced for firms that are in better financial condition, have higher analyst coverage, and have higher net operating assets. We find a similar effect of union strength on discretionary spending when examining R&D costs. Overall, we contribute to the literature by showing that organized labor has a significant effect on cost behavior, which has implications for financial statement users and financial forecasting.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".