Liu, Xingxin (2025) Structural Inequality in Workload Allocation among Gig Drivers: Residency Status, Language Proficiency, and Human Capital in Quebec’s Last-Mile E-commerce Delivery Sector. Masters thesis, Concordia University.
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Abstract
E-commerce’s rapid growth has intensified demand for last-mile delivery. In response, many firms have shifted from direct employment to multi-tiered outsourcing models that rely heavily on gig workers. In Quebec’s bilingual labour market, this fissured workforce can amplify inequities in work allocation. We examine how residency status (permanent resident, study visa, work visa) and proficiency in French and English shape weekly and daily workload allocation and delivery performance (delivery success and customer complaint rates). We also test whether returns to human capital—job-specific skill capital (prior parcel-delivery experience), job-specific physical capital (vehicle-ownership tenure), and general human capital (driver age)—vary by residency status and language proficiency.
Drawing on operational records from a last-mile delivery company in Montreal, we analyse 129 drivers: 56.2% hold work visas, 22.6% are permanent residents, and 21.3% hold study visas; none report French or English as a first language. Most report intermediate French proficiency (64%), followed by advanced French (16%), beginner French (10%), and intermediate English (10%).
Findings show persistent structural inequalities in delivery workload allocation: drivers on work visas received significantly fewer weekly and daily allocations than permanent residents, while study-visa drivers do not differ on average. Lower language proficiency consistently reduces assigned workloads. Prior delivery experience and longer vehicle ownership increase allocations, whereas age has no main effect. Returns to human capital are heterogeneous—larger for study-visa drivers but attenuated or even negative at low language proficiency. Delivery performance mirrors these patterns: study-visa drivers exhibit slightly lower success rates, and limited-proficiency drivers face higher complaint rates, particularly as experience rises.
Overall, residency status and language proficiency generate structural inequalities in delivery work allocation and human capital only partially offsets. Managerially, language training, same-language mentoring, fairness-aware dispatch algorithms, and skill-matched route allocations can mitigate disparities. Policy recommendations include incorporating fairness principles in contracts, facilitating preferred-language use at work, and expanding support for skill development.
| Divisions: | Concordia University > John Molson School of Business > Supply Chain and Business Technology Management |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Liu, Xingxin |
| Institution: | Concordia University |
| Degree Name: | M.A. |
| Program: | Supply Chain Management |
| Date: | August 2025 |
| Thesis Supervisor(s): | Pan, Xiaodan |
| Keywords: | last-mile delivery; fissured workforce; gig drivers, workload allocation; immigrant and temporary workers; language proficiency; human capital; delivery performance |
| ID Code: | 996115 |
| Deposited By: | Xingxin Liu |
| Deposited On: | 04 Nov 2025 17:52 |
| Last Modified: | 04 Nov 2025 17:52 |
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