Exposure to superstar firms and financial distress
Bibliographic record
Abstract
A small minority of highly successful firms (referred to as superstar firms) have captured large market shares and earned massive profits in recent decades. In this study, we examine whether superstar firms are associated with a greater likelihood of financial distress of firms that are exposed to them in product markets. Building on recent research that shows that superstar firms are associated with increasing aggregate markups, we identify superstars as firms with the highest markups in the industry and whose industry markup share is increasing over time. We then measure a focal firm’s overall product market exposure to superstars by employing product similarity scores. We document that firms with greater exposure to superstars in product markets are more likely to subsequently file for bankruptcy. We also shed light on the underlying channels through which superstar exposure is associated with bankruptcy and show that firms with the greater superstar exposure exhibit weaker financial performance and greater riskiness. Furthermore, we show that the association between superstar exposure and the likelihood of bankruptcy is stronger when superstar firms have greater market power. This association is also concentrated among focal firms that are less innovative, have lower credit financing flexibility, and those with less favorable product market attributes, as these firms are less likely to withstand the competitive pressure from superstar firms. Finally, we triangulate our primary evidence by providing suggestive evidence that sophisticated market participants account for the adverse effects of superstar exposure in their decision-making.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".