Does employee organising power matter on wage levels in Canadian gig economy: the role of unionisation, inflation, immigration and information and communication technology
Bibliographic record
Abstract
Purpose The progressive advancement of technology and the rise of fissured workplaces have led to significant shifts in global employment structures, particularly towards the gig economy. In Canada, however, gig economy workers remain largely excluded from opportunities for unionisation. Historically, unions have demonstrated substantial organisational power, serving as critical institutions for improving workplace conditions through collective bargaining. This study, therefore, aims to examine the impact of unionisation, immigration, human capital, inflation and information and communication technology on wage determination in Canada, situating the analysis within the broader context of a rapidly evolving employment landscape. Design/methodology/approach Using Canadian time series data from 1980 to 2022, the research uses the dynamic autoregressive distributed lag approach to identify both cointegrating relationships and counterfactual effects among the variables. Additionally, the counterfactual analysis examines the effects of ±1% and ±5% shocks on the dependent variables. The robustness of these findings is confirmed through the kernel-based regularised least squares machine learning approach. Findings The findings reveal that unionisation, inflation, immigration and information and communication technology development significantly influence wages at a 1% level, while human capital at a 5% level in the long term. The robustness of these findings is further confirmed by the kernel regularised least squares machine learning algorithm. Practical implications Based on the findings, the study recommends that policymakers should implement targeted strategies to enhance union representation among gig economy workers and strengthen collective bargaining mechanisms. Additionally, addressing broader factors influencing wage dynamics, such as human capital development, immigration policies, information and communication technology advancements and inflation-indexed wage adjustments, can foster equitable and sustainable wage growth across diverse sectors. Originality/value Exploring the dynamic and cointegrating relationships between unions’ organising power and wage levels within the purview of inflation, immigration, human capital and information and communication technology development is unprecedented. Additionally, applying the kernel regularised least squares machine learning algorithm to check robustness is completely new in a study within the realm of employment relationships.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".