Labour Shortages and Wages. An Examination of Varying Bargaining Power among Workers
Bibliographic record
Abstract
Labour shortages have become increasingly widespread across Europe and other advanced economies since the post-2008 recovery, due to rising demand and structural labour market transitions—digital, green and demographic. They further worsened during the COVID-19 pandemic, through shifts in worker preferences, and again during the post-pandemic economic rebound. While policymakers and academics often attribute the shortages to skill gaps, which may be reduced via training or increased migration, there is growing recognition that unattractive wages and poor working conditions are also hindering recruitment. We argue that labour shortages, while economically disruptive, can improve the bargaining position of workers, particularly those with historically limited power. Drawing on both aggregate and individual-level evidence, we examine the interrelationships that encompass labour shortages, individual or collective bargaining positions and wages. We find that industries facing more acute shortages tend to see stronger wage growth, especially among new hires. Wages increase especially among women, migrants and younger workers—groups with less institutional support or weaker collective representation. We thus show how labour shortages can act as a corrective force by partially offsetting decades-long declines in workers’ bargaining power. These quantitative findings are supported by a qualitative survey of union representatives in the EU construction and woodworking industry, which is greatly affected by shortages. The survey responses reveal a complex relationship between shortages and working conditions: 1) shortages lead not only to some wage growth but also to intensification of work; and 2) most strategies do not include the unions and are, for instance, more focused on domestic or international recruitment. Our research supports the view that labour shortages provide workers with opportunities, but there may also be dangers: in the short run, individual bargaining may greatly increase wages while weakening collective bargaining, thus limiting the ability of all workers to achieve lasting gains.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".