Labour adjustment by employee type when sales change
Bibliographic record
Abstract
We examine how companies in China manage labour resources through sales upturns and downturns. We argue that managers make implicit commitments to retain some employees through downturns based on the nature of activities the employees engage in. We predict higher commitment in contracting (more stickiness) for employees who accumulate intangible asset value and engage in other long horizon activities. We associate employees with three primary business activities: sales and marketing (S&M), accounting and financial management (A&F) and production and operations (P&O). Employees in S&M acquire product knowledge and build relations with customers that benefit the firm over time. Employees in A&F combine professional skills with knowledge of the firm to support current operations and plan for future demand. Employees in P&O apply general and firm-specific skills to service current production and sales. We discriminate between state-owned enterprises (SOEs) and non-SOEs in our analysis. For SOEs, there is stickiness in labour adjustment across all activities, consistent with political employment objectives of SOEs. For non-SOEs, firms add more employees for S&M and A&F when sales increase than they remove when sales decrease but adjustments to labour for P&O activities are symmetric with respect to increases and decreases in sales.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".