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  • Articles and reports: 11-522-X202100100004
    Description:

    With labour market uncertainty increasing across Canada, there is a need for innovative ways to help displaced workers to re-skill/up-skill and potentially pivot to in-demand occupations. In our study, we present a unique approach to bridge the gap between the displaced and in-demand occupations and provide a machine learning framework that may be able to forecast employment by NAICS for 6 months. We have combined the monthly employment data from Statistics Canada’s Survey of Employment and Payroll Hours, and the monthly job ads counts from Burning Glass to achieve our goal. Our approach consists of three steps: 1.        Finding the displaced occupations in Alberta over the last 7 years based on the integrated actual employment and job ads count data. Step. 2. Using the list of displaced occupations, a unique pivot graph is developed to map a displaced occupation to a list of in-demand occupations which have skills similar to the chosen displaced occupation. Step 3.  Applying SARIMA and SARIMAX models to forecast employment for 6 months. The above approaches are aimed at assisting public policy and planning

    Key Words: Employment; Labour Market; Job Ads; Skills; Time Series Analysis; Forecasting.

    Release date: 2021-10-15
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Articles and reports (1)

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  • Articles and reports: 11-522-X202100100004
    Description:

    With labour market uncertainty increasing across Canada, there is a need for innovative ways to help displaced workers to re-skill/up-skill and potentially pivot to in-demand occupations. In our study, we present a unique approach to bridge the gap between the displaced and in-demand occupations and provide a machine learning framework that may be able to forecast employment by NAICS for 6 months. We have combined the monthly employment data from Statistics Canada’s Survey of Employment and Payroll Hours, and the monthly job ads counts from Burning Glass to achieve our goal. Our approach consists of three steps: 1.        Finding the displaced occupations in Alberta over the last 7 years based on the integrated actual employment and job ads count data. Step. 2. Using the list of displaced occupations, a unique pivot graph is developed to map a displaced occupation to a list of in-demand occupations which have skills similar to the chosen displaced occupation. Step 3.  Applying SARIMA and SARIMAX models to forecast employment for 6 months. The above approaches are aimed at assisting public policy and planning

    Key Words: Employment; Labour Market; Job Ads; Skills; Time Series Analysis; Forecasting.

    Release date: 2021-10-15
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