The Determinants and Implications of Firms' Workforce Composition: The Case of Home Health
Bibliographic record
Abstract
This paper presents and tests a new model that highlights the role of reputation in determining firms' workforce composition and strategy. Facing demand uncertainty, firms in labor-intensive service industries, such as health care, often rely on temporary workers. Past research has shown that firms that employ more temporary workers when facing greater demand fluctuations. However, this strategy is challenged by accumulating evidence that permanent and temporary workers are not perfectly interchangeable in the production of quality. This paper examines the strategies of firms facing this trade-off: temporary workers provide flexibility in responding to demand fluctuations but can lower reputation through a decline in quality. Through a model where demand is stochastic and linked to firms' reputation for quality, this paper predicts that firms' workforce composition depends on their reputation. Using novel and rich data from a large multi-state US home health provider, I provide evidence consistent with the theory. First patients visited more by permanent nurses were less likely to be rehospitalized. I use patient's differential distances to the nearest proportion of permanent nurse visits. Second, measuring firms' reputation by the establishment of a strong referral base, I find that low-reputation firms, such as new firms, decreased the share of temporary nurses with demand fluctuations. These results imply that low-reputation firms forgo short-term profitability in favor of long-term reputation gains through improvements to service quality.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".