CPS OTC Prediction
Description
The BLS productivity office publishes measures of hours worked by major sector and industry. As part of the calculation for this measure, BLS uses Current Employment Statistics (CES) data on hours paid is used to estimate hours for payroll workers. Additional adjustments are made to this measure, such as removing paid time off (PTO), adding off-the-clock (OTC) hours, and adding in hours worked by self-employed and unpaid family workers. BLS uses the Current Population Survey (CPS) dataset to identify the amount of hours that are worked off-the-clock (OTC) by workers. There are some workers in the CPS dataset that do not report whether their time off was paid, or whether they get paid hourly or not. This data needs to be imputed to calculate the ratio of total hours worked to paid hours worked. A random forest model is used to predict the responses for workers that did not report this information by training it on characteristics (such as industry, occupation, education level, age, etc.) of respondents that have reported that information.
Detailed example
worker's time off was paid, or whether they get paid hourly or not
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Efficiency and data quality improvements
Audit / financial statement impact
Used for survey processing for statistical purposes
Controls / human review
ATO: Yes; PIA: N/A
Data needed
Current Population Survey (CPS)