Competition and slack: The role of tariffs on cost stickiness
Bibliographic record
Abstract
Abstract Slack exists when entities hold resources in excess of those required to support current operations. “Sticky” spending is a specific type of slack spending that occurs when a firm reduces spending by less when sales fall than it increases spending when sales rise by an equivalent amount. Changes in a firm's environment affect its propensity to engage in sticky spending. We examine how competition influences sticky spending, positing two hypotheses. First, we hypothesize that competition will be positively associated with firms' investment in sticky spending both for cost of goods manufactured (COGM) and selling, general, and administrative (SG&A). Second, we hypothesize that competition moderates the relation between sticky spending and firm performance. Utilizing variations of tariff rates as exogenous shocks to reflect competition, we measure sticky spending in the firm's COGM and SG&A spending. To empirically verify our hypotheses, we draw data from three different sources, industry‐level tariff data, Compustat, and the Hoberg–Phillips data library, for firms in the manufacturing sector from 1974 through 2017. Our regression analyses confirm that managers maintain more stickiness in spending as competition increases. The market's assessment of firm performance (as measured by Tobin's Q) is largely positively associated with sticky spending as competition increases. This expectation of higher performance is evidenced by immediate improvements in the firm's return on assets (ROA) when we consider COGM spending, but not when we consider SG&A spending.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".