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  • Articles and reports: 36-28-0001202201200004
    Description: In recent years, Statistics Canada has released data and analysis on business ownership by women and persons with disabilities. Furthermore, in an effort to continue to fill the information gap on business ownership by employment equity groups and better inform policy making, Statistics Canada has released, for the first time, time series on Indigenous-owned businesses by sex of ownership and other characteristics such as age of primary owner and province of operation.
    Release date: 2022-12-22

  • Articles and reports: 11-633-X2022008
    Description:

    In recent years, Statistics Canada has made available data on business ownership by individuals in employment equity groups, namely women and persons with disabilities. However, little is known about business ownership among the other two employment equity groups, Indigenous peoples and racialized groups. This article describes the methodology used to fill the data gap on Indigenous business owners and Indigenous-owned businesses and is based on a novel linkage between the Canadian Employer–Employee Dynamics Database (2018), the Census of Population (2001, 2006, 2016) and the 2011 National Household Survey.

    Release date: 2022-11-24

  • 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.

    Release date: 2021-11-05
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Articles and reports (3)

Articles and reports (3) ((3 results))

  • Articles and reports: 36-28-0001202201200004
    Description: In recent years, Statistics Canada has released data and analysis on business ownership by women and persons with disabilities. Furthermore, in an effort to continue to fill the information gap on business ownership by employment equity groups and better inform policy making, Statistics Canada has released, for the first time, time series on Indigenous-owned businesses by sex of ownership and other characteristics such as age of primary owner and province of operation.
    Release date: 2022-12-22

  • Articles and reports: 11-633-X2022008
    Description:

    In recent years, Statistics Canada has made available data on business ownership by individuals in employment equity groups, namely women and persons with disabilities. However, little is known about business ownership among the other two employment equity groups, Indigenous peoples and racialized groups. This article describes the methodology used to fill the data gap on Indigenous business owners and Indigenous-owned businesses and is based on a novel linkage between the Canadian Employer–Employee Dynamics Database (2018), the Census of Population (2001, 2006, 2016) and the 2011 National Household Survey.

    Release date: 2022-11-24

  • 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.

    Release date: 2021-11-05
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