Can we identify the highest priority industry & county combinations for the Department of Labor to proactively and most efficiently investigate potential wage violators with its limited resources?

1. Predicting Wage Violations
for the Department of Labor
Xtreme Aquatic Rhinos
Aaron Keys*
Ankit Jain
Nick Handel
Samantha Fernandez
* Our weird team name was his idea

2. Billion $ Problem of Wage Theft
Sources: Working “Off the Clock” – How Employers Steal Wages
* Based off of 2009 study of an average of 4,000 low wage workers in New York City, Los Angeles and Chicago
15% of earned
annual wages lost*
ADVERSELY AFFECTS THE POOR
IMPACTS THEIR SPENDING ON VITAL SERVICES
DIFFICULT TO IDENTIFY AND ENFORCE
Low Investigation Likelihood
1 in 1,250 chance
for a garment factory
$2,634 lost of a $17,616
annual salary
~1,000 Dept. of Labor
investigators

3. Real Life Examples
Noe
Line Cook
Hilda
Garment Worker
Felipe
Car Wash Worker
Source: Working “Off the Clock” – How Employers Steal Wages

4. Can we
identify the highest priority
industry & county combos
for the DoL to proactively
and most efficiently
investigate potential wage
violators with
its limited resources?

5. Prediction Methodology
• Data Set:
– Wage & Hour Compliance Action Data from 1989 – 2013 featuring
~188,000 complaint investigations
• Methodology:
– Calculate Net Monetary Impact (Total Backwages + Total Civil
Monetary Penalties) for each wage investigation
– For each particular investigation evenly distribute the NMI across the
years the NMI was incurred
– Split data by State + Industry
– NMI split by Year for each Company
– Accumulate by State + Industry
– For a given State + Industry, use Time Series Analysis to Forecast NMI for
next year (2015)
– Distribute the predicted NMI of a State to Counties by weighting % of
working population
– Map Forecasted NMI

6. Visualization
• http://askeys.github.io/bayes_impact_
hackathon_2014/index.html

7. Key Predictions:
Top Areas for Investigation
• Construction in CA and FL
• Waste Management in NY, CA, MD and FL
• Transportation and Warehousing in CA
• Manufacturing in CA
• Health Care and Social Assistance in FL
• Oil & Gas Extraction in TX

8. Potential Future Analyses
• Future analysis on the employer level
• Explore additional data to better
understand the nature of the risks
– For example is the risk related to underpayment,
medical screening violations, discrimination or
other?
• When violations occur
– For example, do wage violations increase during
recessions or after natural disasters?

9. Appendix

10. Code
• GitHub Link:
https://github.com/askeys/bayes_impact_hackatho
n_2014/blob/master/DoL_forecast.r

11. Other Resources
• Impact Video:
https://www.youtube.com/watch?v=SBSLF6Inb-k