Need for Speed: Using fastText (Machine Learning) to Code the Labour Force Survey

Articles and reports: 11-522-X202100100013
Description: Statistics Canada’s Labour Force Survey (LFS) plays a fundamental role in the mandate of Statistics Canada. The labour market information provided by the LFS is among the most timely and important measures of the Canadian economy’s overall performance. An integral part of the LFS monthly data processing is the coding of respondent’s industry according to the North American Industrial Classification System (NAICS), occupation according to the National Occupational Classification System (NOC) and the Primary Class of Workers (PCOW). Each month, up to 20,000 records are coded manually. In 2020, Statistics Canada worked on developing Machine Learning models using fastText to code responses to the LFS questionnaire according to the three classifications mentioned previously. This article will provide an overview on the methodology developed and results obtained from a potential application of the use of fastText into the LFS coding process. 

Key Words: Machine Learning; Labour Force Survey; Text classification; fastText.

Issue Number: 2021001
Author(s): Oyarzun, Javier; Evans, Justin
Main Product: Statistics Canada International Symposium Series: Proceedings
Format Release date More information
PDF November 5, 2021

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