Do local union strategies explain the (unexpected) union pay premium in China?
Bibliographic record
Abstract
Purpose The authors investigate the extent to which differences in provincial union legislation have impacts on the union earnings premium. Design/methodology/approach Content analysis of provincial union regulations of 25 provinces is conducted to create two indices: one reflecting the degree of stringency of the local requirement that unions be established in a timely fashion and the other reflecting requirements for employers to negotiate wages with the union. The authors use individual level data from the China Family Panel Studies (CFPS) of 2010 to estimate the union earnings premium. Findings The authors find that unionised workers in China receive an earnings premium ranging from 6.4 to 9.6%, which is in range of other studies (but not all) for China that tend to find a (perhaps surprising) union wage premium in spite of the fact that unions tend to be “company unions” designed to foster stability and growth and to serve as a transmission belt for the wishes of the Party rather than bargaining for the benefit of their members. The authors also find that provincial requirements to establish unions in a timely fashion enhance the impact of unions on the earnings of their members, but provincial requirements to negotiate wages dampen the effect of unions on the earnings of their members. Reasons for these results are discussed. Originality/value Despite this lack of independence of the Chinese unions, research continuously finds that Chinese unions have effects that are surprisingly similar to those of unions in Western countries. This paper drills deeper into the underlying mechanisms to see if local union strategies, exemplified by provincial union legislation, can explain the unexpected union effects on compensation. To the authors’ knowledge, this is the first paper to do so. Moreover, the authors use individual-level data in contrast to most studies on China that use firm or provincial level aggregate data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".