Labour Market Planning Report

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What is the Labour Market Planning Report?

Our Local Labour Market Planning Report is built on evidence-based analysis of labour market strengths and challenges within the Niagara region. Community organizations, local government, job seekers, employers, and other Niagara stakeholders can gain valuable, up-to-date understanding of the full labour market landscape from the data in this report.

We publish the Local Labour Market Planning Report annually alongside a series of municipal fact sheets. These smaller reports offer an up-to-date snapshot of population and employment patterns in Niagara’s 12 municipalities.

Working together, we believe that sound planning will lead to future successes that will continue to make Niagara an excellent place to live and do business.


Read the Current Report:

To support employers during the COVID-19 pandemic, we released a mid-year update on June 15, 2020. This document provides insights on the 2019 calendar year as well as monthly data from the first quarter of 2020. It is our hope that this document will inform recovery strategies and demonstrate measurable goals for returning Niagara residents to meaningful employment.

Download the Spring 2020 Labour Market Update in English
Download in English (.pdf) »


2019 Reports:

Download the 2019 Labour Market Planning Report in English & French
Download in English (.pdf) »Download in French (.pdf) »

Download the 2019 Municipality Snapshots in English:

Fort Erie (.pdf) » | Grimsby (.pdf) » | Lincoln (.pdf) » | Niagara Falls (.pdf) » | Niagara-on-the-Lake (.pdf) » | Pelham (.pdf) » | Port Colborne (.pdf) » | St. Catharines (.pdf) » | Thorold (.pdf) » | Wainfleet (.pdf) » | Welland (.pdf) » | West Lincoln (.pdf) »


Previous Reports:

Looking for our Labour Market Planning Report from a specific year? Have a look at our archived reports:

Archive: Labour Market Planning Report

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We are collecting data to better understand who is looking for work and what kind of opportunities jobseekers are searching for. This data is completely anonymous and non-personally identifiable.

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