Mapping Wage Theft in the Informal Economy: Employment Standards Violations in Residential Construction and Renovations
Bibliographic record
Abstract
This article explores the experiences of informal construction and home renovation workers with payment-related violations of employment standards. Such violations, often broadly referred to as ‘wage theft,’ can comprise an array of practices including, but not limited to, withholding workers’ wages for long periods of time, paying workers below the minimum wage, extracting illegal deductions from workers’ paycheques, and outright not paying the wages due. Drawing on twenty-two in-depth interviews with foreign-born men employed informally in residential construction and home renovations in Toronto, Ontario, the first half of the article documents the specific forms of wage theft that workers experienced in these sectors where flat daily rates and piece rates are common, but written contracts are not. I also explore the individuated and extra-legal strategies that workers adopted in trying to recoup stolen wages. In general, they framed their sustained efforts to persuade employers to adhere to the law as a form of employment standards enforcement running parallel to the tactics of state enforcement. In the second half of the article, I examine the accessibility of the more formal legal channels that exist to assist workers in recouping lost wages—specifically, claims filed through the Ontario Labour Relations Board. Through a review of recent case law on Board adjudication of employment standards complaints about wages owing to informally-employed, non-citizen workers, I highlight the document burden that informal workers in this sector must bear in order to file a robust claim with the state. Drawing on scholarship that has shown how employment standards violations are pervasive in the more sub-contractual and informal tiers of the construction industry, I pinpoint multiple interlocking conditions in informal construction and home renovations that not only increase the likelihood of wage theft for workers in these sectors, but also disproportionately burden them with the responsibility of enforcing the law or proving their employer’s non-compliance. In so doing, I show how this group of workers shoulders the responsibilities of either the state or their employer in recouping lost wages.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".