Labor Productivity and Wage Inequality in the Gig Economy: Evidence from Bangladesh and Canada
Bibliographic record
Abstract
We investigate the pattern of labor productivity and wage inequality in two gig economy models: Bangladesh and Canada. Re: Gig workers in global value chains and the rise of task-based services Facilitated through a combination of survey and secondary research, we compare gig worker earnings and productivity for each country with a particular focus on gender wage inequality. Our findings also point out that the Canadian gig workers earn relatively higher payoffs and endogenously exhibit superior work productivity when compared to their Bangladeshi counterparts. Canadian urban workers read more job ads online on an average day than their rural compatriots, but both nations are challenged in terms of productivity for the rural workforce as a result of access to platforms and infrastructure. Additionally, findings from the results show that gender wage discrimination is a serious problem in both countries, as female gig workers are under-compensated compared to male gig workers. But the margin is higher in Bangladesh (20%) than in Canada (15%); this also tells us about the kind of cultural and infrastructural obstacles women have to face in Bangladesh. The work also points out regional variation, such as urban workers in both countries who are much more productive and earn higher wages than those in the countryside. This study provides valuable lessons for the analysis of gig economy participation in developing and developed contexts and may be suggestive that regulatory changes in Bangladesh to improve the infrastructure protection of workers could address this inequality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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".