Economic links and the wealth effects of layoff announcements along the supply chain
Bibliographic record
Abstract
Purpose This paper investigates the effects of layoff announcement by customers on the valuation and operating performance of their supply chain partners. Design/methodology/approach The authors collect corporate layoff announcements from 8-K filings submitted by US publicly-traded firms from 2004 to 2017. Using event study methodology, they examine the information externality of corporate layoffs on announcing firms' suppliers. Findings Results show that suppliers, on average, experience a negative stock price reaction around their major customers' layoff announcements. The negative price effect is exacerbated when industry rivals of layoff-announcing customers also suffer from negative intra-industry contagion effects. Additionally, supply chain spillover effects are asymmetric, with only “bad news” layoff announcements causing significant value implications for suppliers, but not “good news” announcements. Supplier firms also reduce their investments in and sales dependence on layoff-announcing customers in subsequent years. Practical implications This study shows that layoff decisions, often aimed at improving firms' efficiency and effectiveness, create uncertainty for the suppliers' operation and cause negative value implications on firms' upstream partners. Findings should be useful to corporate decision-makers in making layoff decisions. Originality/value This paper is one of the first to address the value implications of corporate layoffs on announcing firms' suppliers. It provides a more comprehensive picture of the economy-wide impact of achieving efficiency through employee layoffs.
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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 | 0.023 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".