Operational Restructuring Charges and Post‐Restructuring Performance*
Bibliographic record
Abstract
Abstract Firms incur restructuring charges as a result of actions intended to improve their operating performance. However, there is little evidence on whether restructuring charges are associated with improved performance. We examine a sample of firms reporting restructuring in 1991‐93 and find that the restructuring firms' earnings increase over the levels immediately before restructuring. Compared with a control sample of firms that report no restructuring, the restructuring firms improve their earnings and operating income, but evidence for improvements in cash flow from operations is mixed. In regression analysis, we find that restructuring charges are significantly positively associated with post‐restructuring changes in earnings relative to the restructuring year, but this association is largely driven by firms with multiple restructurings and firms reporting losses in the restructuring year. We find no association between restructuring charges and post‐restructuring changes in earnings relative to the year before restructuring. Restructuring charges are significantly positively associated with post‐restructuring changes in operating income and cash flow from operations for firms with multiple restructurings. In summary, restructuring charges are associated with improved earnings, but our results suggest that restructuring in the early 1990s did not necessarily guarantee improved operating performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".