Improving Job Vacancy Estimates Using Online Job Postings
A new LMIC demonstration report applies machine learning and statistical benchmarking to improve the accuracy of job vacancy estimates using online job postings.
What you'll learn:
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Why online job postings often misrepresent real labour market demand
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How benchmarking online job postings to Statistics Canada’s Job Vacancy and Wage Survey (JVWS) enhances the accuracy of vacancy estimates
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The role of machine learning and statistical methods in reducing prediction errors
A New Approach to Interpreting Online Job Postings Data
Online job postings (OJP) are a widely used source of labour market information across Canada. But despite their growing popularity, OJP data lack the structured methodologies that underlie traditional survey-based vacancy data, creating risks of misrepresentation when interpreting trends.
This new LMIC demonstration report evaluates how job vacancy estimates from online job postings can be improved through benchmarking against Statistics Canada’s Job Vacancy and Wage Survey (JVWS). Using a combination of machine learning models, statistical weighting, and robust regression, the authors show how key refinements can reduce prediction errors by up to 32%.
This report is designed for policy analysts, labour economists, researchers, and decision-makers who rely on high-frequency labour market data. The findings help clarify how to interpret OJP data accurately and demonstrate how emergent LMI sources can complement existing surveys to inform workforce planning, skills strategies, and economic development.