Firm Unionization and Disruptions in Customer Relationships*
Bibliographic record
Abstract
ABSTRACT Relationships with major customers may be advantageous to suppliers due to economies of scale and reputational benefits. In this study, we investigate whether unionization leads to disruptions in a firm's relationships with its customers. Our goal is to provide insights regarding the impact unionization has on a firm's sales relationships with its major customers. We predict that major customers will shift purchases away from suppliers that unionize to avoid potential disruptions. Using a difference‐in‐differences research design, our results show a negative association between supplier unionization and sales to major customers. Our findings are robust to addressing endogeneity concerns through a propensity score matched analysis and regression discontinuity research design. In addition, we find that supplier firm performance declines subsequent to unionization. We also find that suppliers experience significant increases in their cost of goods sold and the number of employees after supplier unionization, suggestive of higher input prices driving the disruption with major customers. Finally, we provide evidence that higher switching costs mitigate the decline in sales to major customers. Overall, our findings suggest that employee unionization can adversely affect a firm's relationships with their major customers.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".