TR229: Predicting, Analyzing, and Educating on Wage Theft with Machine Learning Tools
Abstract/Contents
- Abstract
- Could novel data science approaches to wage theft assist both public policymakers and enforcement investigators? Wage theft reduces the resources available to the workforce, that results in reduced health of both the wage earner and their dependents. There are wages that society has agreed on as living wages. Wage theft undercuts living wages. Detecting wage theft has proven difficult. To-date, wage theft organizing has focused on public policy by advocating for ordinances. Wage theft is a value proposition, like any business decision, decisions follow a test if income exceeds expenses. Therefore, those least able to resist wage theft become victims. Pragmatically that has been low-income workers. Vulnerable populations feel a disproportionate impact of wage theft due to their already low wages. As a measure of success, this research uses a pragmatic review by a wage theft investigator. The data science solutions presented here are novel in that they are first data science solutions. This pilot found a value in data science for policymakers and enforcement investigators. The authors recommend continuing research on data science to support public policy and investigation. They recommend a focus on data features of the context to support machine learning. With this, pilot implementations with partner organizations can proceed.
Description
| Type of resource | text |
|---|---|
| Date created | October 2018 |
Creators/Contributors
| Author | Johnson, Tessa | |
|---|---|---|
| Author | Peterson, Forest | |
| Author | Myers, Michael | |
| Author | Silver Taube, Ruth | |
| Author | Fischer, Martin |
Subjects
| Subject | Education |
|---|---|
| Subject | Labor |
| Subject | Machine learning |
| Subject | Public Policy |
| Subject | Public Works |
| Subject | Wage Theft |
| Genre | Technical report |
Bibliographic information
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- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
Preferred citation
- Preferred Citation
- Johnson, Tessa and Peterson, Forest and Myers, Michael and Silver Taube, Ruth and Fischer, Martin. (2018). TR229: Predicting, Analyzing, and Educating on Wage Theft with Machine Learning Tools. Stanford Digital Repository. Available at: https://purl.stanford.edu/mx396wr3611
Collection
CIFE Publications
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- cife-email@stanford.edu
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