Introduction

Welcome to the Vicinity Jobs Trends Navigator for Educators. This guide provides an overview of the tool's features, organized around specific reports and outputs. We'll cover how to perform various actions within the tool and provide use case examples to illustrate practical applications.

The Vicinity Jobs Trends Navigator allows users to explore labour market information based on trends found in online job postings from across Canada.

The information found in online job postings can help to track trends in work requirements such as skills, knowledge, and tools and technologies, as well as occupational demand.

The Trends Navigator is updated monthly with new data. Data from online job postings are collected from thousands of Canadian websites and job boards by Vicinity Jobs; this data is ingested into a database so that the Trends Navigator can share timely, granular LMI that can help educators and researchers across Canada explore employment trends.

To learn how to use the Trends Navigator, consult this user guide, including the FAQ. If you still can’t find the answer you’re looking for, please reach out to the Vicinity Jobs team by phone or email.

Overview of Key Features

  • Quick Search: allows you to search by skills, job titles, employers, and national occupational classification (NOC) codes. Ideal for exploring data and getting an initial sense of trends and job posting demand.
  • Advanced Search: Offers more detailed filtering options, including geographic and date ranges.

Quick Tips for Effective Use

  • Regular Updates: The tool updates monthly with new job posting data. Check back frequently for the latest trends.
  • Custom Filters: Use advanced search options to tailor your queries to specific needs.
  • Export Data: Utilize the export features to create reports and presentations for stakeholders.

Using the Tool & Detailed Reports

Quick Search: The Quick Search feature allows you to perform rapid searches based on common criteria such as skills, job titles, employers, and educational programs.

Use Case: Exploring a New Skill

  1. Scenario: Your director wants to know more about a new skill, e.g., "Artificial Intelligence".
  2. Action:
    • Navigate to the Quick Search tab.
    • Select specific skills, apply the filter, and view related job postings, top job titles, employers, and locations.

Use Case: Search for Job Titles:

  • Example: Searching for "DevOps".
  • Identify top employers hiring for this role and further filter down to specific companies or job details.

Use Case: Search for Employers

Use Case: Search by Program name

Use Case: Requested Classification of Instructional Programs (CIPs)

Use Case: Search by Occupational Titles or Categories (NOCs)

Advanced Search: The Advanced Search provides additional filtering options, including geographies and date ranges.

Use Case: Region-Specific Job Demand

  1. Scenario: You want to know about “curriculum consultant” job postings in Ontario.
  2. Action:
    • Navigate to the Advanced Search tab.
    • Select "Job Titles" and type "Curriculum Consultant".
    • Select "Ontario" in the geographic filter.
    • Select and Apply the filter your reporting date range
    • Apply the filter to see job postings specific to Ontario.

Overview of Detailed Reports

Job Posting Summaries:
  • Top Skills and Certifications: View and export the most in-demand skills and certifications for selected job titles.
Job Titles and Descriptions:
  • Wages Information: View job titles and corresponding wage data from job postings.
  • Job Descriptions: Explore detailed job descriptions by clicking on specific postings.
Skill Analysis
  1. Skill Trends:
    • Track the demand for specific skills over time.
    • Example: Monitoring the demand for "Python" and related skills.
  2. Skill Relationships:
    • Identify commonly co-occurring skills.
    • Example: Employers seeking Python skills often also require SQL skills.
Employer Analysis
  1. Employers Hiring for Specific Skills:
    • Filter job postings by employer to see which companies are hiring for particular skills.
    • Example: Exploring job postings by major banks like RBC, TD, and Scotiabank.
Labour Projections
  1. Occupational Projections:
    • View projected labour demand for specific occupations.
    • Example: Projections for "Software Engineers" across various provinces.
  2. Industry Projections:
    • Analyze projections for specific industries.
    • Example: Growth outlook for "Software Publishers".
  3. Population Projections:
    • Assess population growth projections to understand potential labour market changes.

Example Use Cases

Use Case 1: Program Development

  1. Identify Emerging Skills:
    • Search for emerging skills (e.g., "Drone Operator") to understand market demand.
    • Analyze which employers are hiring for these skills and the volume of postings.
  2. Validate Program Content:
    • Use skill relationships to ensure the curriculum covers all necessary competencies.
    • Example: A program on AI should include Python, SQL, and teamwork modules.

Use Case 2: Employer Engagement

  1. Target Potential Employers:
    • Identify top employers looking for specific skills relevant to your graduates.
    • Example: Employers hiring for "Data Analysts" with Python skills.
  2. Develop Partnerships:
    • Reach out to employers to validate program relevance and explore partnership opportunities.
    • Example: Collaborating with IBM or Royal Bank for curriculum input and student placements.

Use Case 3: Labour Market Research

  1. Trend Analysis:
    • Track changes in job demand and skill requirements over time.
    • Example: Monitor how demand for "Cybersecurity" skills evolves.
  2. Regional Insights:
    • Compare labour market demand and projections across different regions.
    • Example: Contrast job growth in Ontario vs. Alberta for specific occupations.

Using Pick Lists and Keyboard Shortcuts for Multiple Selections

The Vicinity Jobs Trends Navigator for Educators tool, built using Tableau, includes several features to help users filter and select data effectively. Among these features are pick lists, which are dropdown menus that allow users to select multiple options for filtering data. Below is a detailed guide on how to use these pick lists and keyboard shortcuts to make multiple selections.

Understanding Pick Lists

Pick lists in Tableau are dropdown menus that present users with a list of options to choose from. These options can include job titles, skills, locations, employers, and other relevant categories. Pick lists help narrow down the data displayed, allowing for more targeted analysis.

Example of Pick Lists:

  • Skills: Users can select specific skills they are interested in, such as "Artificial Intelligence" or "Data Analysis."
  • Job Titles: Users can select specific job titles like "Data Scientist" or "Software Engineer."
  • Locations: Users can filter data by geographical locations such as provinces or cities.
Steps to Use Pick Lists
  1. Accessing the Pick List:
    • Navigate to the section of the tool where you want to apply a filter (e.g., Skills, Job Titles).
    • Click on the dropdown arrow next to the filter category to open the pick list.
  2. Selecting Options:
    • Click on the options you want to select. Each click will select or deselect an option.
Using Keyboard Shortcuts for Multiple Selections

To make the selection process more efficient, you can use keyboard shortcuts to select multiple options at once. This is especially useful when dealing with long lists of options.

Keyboard Shortcuts for Multiple Selections:
  • Select a Range of Options:
    • Hold down the Shift key and click on the first option in the range.
    • While holding the Shift key, click on the last option in the range. This will select all options between the first and last clicked items.
  • Select Multiple Non-Adjacent Options:
    • Hold down the Ctrl key (Windows) or Command key (Mac).
    • Click on each option you want to select. Each clicked option will be added to the selection without deselecting the others.
Example Usage

Selecting Multiple Skills:

  1. Open the Skills pick list by clicking the dropdown arrow next to "Skills".
  2. To select a range of skills:
    • Click on the first skill in the range.
    • Hold down the Shift key and click on the last skill in the range. All skills between the first and last will be selected.
  3. To select multiple non-adjacent skills:
    • Hold down the Ctrl key (Windows) or Command key (Mac).
    • Click on each skill you want to include. Each selected skill will be highlighted.
Filtering by Location:
  1. Open the Locations pick list by clicking the dropdown arrow next to "Location".
  2. To select a range of locations:
    • Click on the first location in the range.
    • Hold down the Shift key and click on the last location in the range.
  3. To select multiple non-adjacent locations:
    • Hold down the Ctrl key (Windows) or Command key (Mac).
    • Click on each location you want to include.
Applying Selections

Once you have made your selections using the pick list and keyboard shortcuts:

  1. Click the "Apply" button at the bottom of the pick list to apply the filters to the data.
  2. The data displayed will now reflect the selections you made, allowing for a more focused analysis.

Exporting Tables or Data

Export Options Overview

The tool offers several export options to allow users to efficiently extract and utilize the data and visualizations they generate. The export functionalities are designed to provide flexibility and ease of use, enabling users to integrate the data into their reports, presentations, and further analyses seamlessly. Below are the available export options and how to use them:

  1. Downloading Visualizations and Data

    On any visualization or data view within the tool, users have the option to download the content in various formats. The download button is typically represented by a downward arrow icon, often found in the top right corner of the visualization or view.

    Available Download Formats:

    • Image: Export the current visualization as a static image file (PNG or JPEG). This is useful for including visualizations in presentations or documents.
    • Data: Export the underlying data of the visualization in CSV format. This option is ideal for further data analysis in tools like Excel or other data processing software.
    • PDF: Export the visualization as a PDF document. This format is useful for sharing static reports or documentation.
    • PowerPoint: Export the visualization directly to a PowerPoint slide. This is particularly useful for creating presentations with embedded visualizations.
  2. Exporting Specific Data Sheets

    In some views, especially those displaying tabular data, users can download specific data sheets. This feature allows users to extract detailed data tables that are presented within the tool.

    Steps to Export Specific Data Sheets:

    1. Click the download icon (downward arrow) on the bottom left of the screen.
    2. Choose the "Download Data" or "Download CSV" option.
    3. Select the specific data sheet you wish to export from the list provided.
    4. Click the "Download" button to save the data sheet as a CSV file.
  3. Exporting Filtered Data

    When users apply filters to narrow down the data, they can export the resulting filtered data set. This ensures that only the relevant subset of data is extracted.

    Steps to Export Filtered Data:

    1. Apply the desired filters to the data using the filter options available on the right-hand side of the screen.
    2. Once the data is filtered, click the download icon.
    3. Select the appropriate download format (e.g., CSV, PDF, Image).
    4. The exported file will contain only the filtered data.
  4. Exporting Job Postings

    For job postings, users can download the full set of job postings that match their search criteria. This is especially useful for detailed analysis or reporting on specific job markets.

    Steps to Export Job Postings:

    1. Navigate to the "Job Descriptions" tab after applying the necessary filters.
    2. Click the download icon to export the job postings.
    3. Choose the "CSV" format to export the job postings as a CSV file.
    4. The exported file will include all the job postings that match the selected filters and criteria.

Spotlight on Using the Job Titles and Descriptions Report

The Job Titles and Descriptions report within the Vicinity Jobs Trends Navigator for Educators tool is a powerful feature that provides detailed insights into the specific job postings and associated roles. This report is particularly useful for educators and program developers who aim to align educational offerings with current labour market demands.

Accessing the Job Titles and Descriptions Report

  1. Navigate to the Report:
    • From the main dashboard, select the Job Titles and Descriptions option from the left-hand navigation menu.
  2. Understanding the Report Structure:
    The report is divided into two main sections:
    • Job Titles and Wages: Provides information on job titles along with wage data.
    • Job Descriptions: Offers detailed job descriptions extracted from job postings.

Steps to Use the Job Titles and Descriptions Report

  1. Selecting Job Titles:
    • Use the pick list to filter by specific job titles. For example, if you are interested in "Data Scientists," select this title from the list.
    • Apply additional filters as needed, such as geographic location or educational requirements.
  2. Exploring Wage Information:
    • The “Job Titles and Wages” section will display the average, minimum, and maximum wages for the selected job titles. This can help understand the compensation trends in the industry.
  3. Viewing Detailed Job Descriptions:
    Switch to the Job Descriptions tab to view the full text of job postings. This section includes:
    • Job responsibilities
    • Required skills
    • Preferred qualifications
    • Additional details such as job type (full-time/part-time), language requirements, and more.

Using Keyboard Shortcuts for Efficient Navigation

  1. Selecting Multiple Job Titles:
    • Hold down the Shift key to select a range of job titles.
    • Hold down the Ctrl key (Windows) or Command key (Mac) to select multiple non-adjacent job titles.

Why the Job Titles and Descriptions Report is Useful for Program Development

  1. Identifying In-Demand Skills:
    • By analyzing job descriptions, educators can identify the most frequently mentioned skills and qualifications. This helps in tailoring curriculum content to ensure students acquire relevant competencies.
  2. Aligning Curriculum with Industry Needs:
    • Understanding the specific responsibilities and requirements for various job titles allows program developers to design courses that directly address these needs. This alignment increases the employability of graduates.
  3. Benchmarking Wage Data:
    • The wage information provided helps in benchmarking salary expectations for different roles. This can be valuable for career counseling and for setting realistic job expectations for students.
  4. Staying Updated with Market Trends:
    • Regularly reviewing job descriptions and titles keeps educators informed about emerging trends and new roles in the industry. This proactive approach ensures that educational programs remain current and competitive.
  5. Supporting Stakeholder Engagement:
    • Detailed job descriptions and data can be used to engage with industry stakeholders. Educators can validate program content with employers to ensure it meets their needs, facilitating stronger partnerships and potential placement opportunities for students.

Practical Example

A university is considering launching a new program in Artificial Intelligence and Machine Learning.

Using the Report:

  1. The program developers use the Job Titles and Descriptions report to filter for job titles such as "Machine Learning Engineer" and "AI Specialist".
  2. They review the job descriptions to identify key skills such as proficiency in Python, experience with TensorFlow, and knowledge of neural networks.
  3. Wage data helps them understand the earning potential for these roles, which is shared with prospective students during informational sessions.
  4. The detailed job descriptions are used to engage with tech companies to ensure the program's curriculum aligns with industry expectations.
  5. The program is designed to include courses and modules that teach the identified skills, thereby increasing the relevance and appeal of the program to both students and employers.

Data interpretation: caveats and limitations

There are some important caveats and limitations to be mindful of when using and interpreting data.

Not all job postings are posted online

Not all job postings are advertised online. Some employers may search for staff before there is a formal job opening. This search will help create a pool of applicants from which to hire.

Additionally, many open jobs are never posted online in the first place. These jobs are likely filled internally or by word-of-mouth recruitment.

Not all work requirements are the same

The list of work requirements shown does not indicate how important the requirement is for that occupation. Some work requirements are not mandatory, but this is not specified in the job posting.

Alternatively, a work requirement might be expected for that job posting but not listed.

While we cannot determine the relative importance of work requirements for a job posting, we can find out how often work requirements appear in similar job postings.

Data interpretation: job postings vs job vacancies

The dashboard presents a sample of online job postings. While this sample is large, it is only a subset of all job postings. Though many employers actively recruit online, job postings do not precisely represent job vacancies.

Job vacancies refers to the number of available job openings that an employer wants to fill. The primary data source for measuring vacancies in Canada is Statistics Canada’s Job Vacancy and Wage Survey (JVWS). It defines a vacancy as a job that is or will become vacant during the upcoming month, for which the employer is actively recruiting outside the organization.

There are three important caveats when considering job postings in the context of job vacancies:

  1. Not all vacancies are posted online.
  2. A count of job postings (online and offline) may underestimate the number of actual vacancies because employers may seek to fill multiple vacancies via a single job posting.
  3. Counting job postings may overestimate vacancies if, for example, the employer does not take down a job posting that they are not currently seeking to fill (in which case the posting does not technically represent a vacancy as defined in the JVWS).

In general, the number of job postings in our dashboard will differ from a complete count of job vacancies across Canada.

Data interpretation: gross vs net changes in employment demand

When evaluating a growing sector or occupation, we think about the net change in employment demand.

For example, “How many new web design jobs will there be this year?” is a question about net employment changes.

Conversely, gross changes in employment demand include these new jobs plus turnover (where the previous person left the position).

This distinction matters because online job postings can only be used, with some caution, as proxies for gross changes in employment demand — not net changes.

With online job postings, there is no way to know if the position results from the organization’s growth or a need to fill an existing position that is vacant.

As such, the economic health of different occupations should not be estimated simply from the growth in the number of online job postings — growth here might reflect either economic dynamism or particularly high turnover.

Data interpretation: work requirement frequencies

Work requirement frequencies do not necessarily indicate their importance.

Every week, job postings across Canada are collected, cleaned and structured, extracting key details such as occupation, location and work requirements.

Since these data are pulled from individual online job postings, we should note that the language that is used by employers does not follow any commonly agreed upon vocabulary.

This real-world use of language should be interpreted with caution.

  1. There is no guarantee that employers explicitly state all work requirements in job postings. In many cases, they may assume that certain requirements are obvious to prospective job candidates and leave the requirements out of the posting.
  2. There is no way to tell which work requirements are critical for the position, or the proficiency that is needed to successfully perform in the job. The data only allows us to observe which requirements are more frequently posted by employers.

For example, Microsoft Excel was associated with 22% of online job postings for economists and economic policy researchers and analysts in 2019.

Does this mean that the other 78% do not need Microsoft Excel experience to succeed in their job? Probably not, but we are unable to confirm that through just job postings data.

Data collection

Representativeness/bias

In processing the content of online job postings, data may be skewed towards certain industries, occupations, regions, firm sizes and education level requirements. While Vicinity Jobs strives to capture all verifiable online job postings, this cannot be guaranteed.

Hidden job market

Many employers hire internally or through informal means such as word of mouth. These sources of information about employment demand cannot be captured in online job posting data nor vacancy survey data.

Data quality

Data are collected from online job postings via scanning algorithms that seek to deliver a comprehensive set of job postings information.

Organizations that collect data in this manner typically develop proprietary algorithms to clean and structure raw data. While the data are acquired from the same set of online job postings, the information is structured differently across different providers.

Despite these differences, quality assurance remains an essential part of the process. To this end, Vicinity Jobs frequently tests and revises its algorithms for collecting, cleaning and structuring the raw data, based both on internal quality assurance checks and ongoing feedback from partners.

Duplicate job postings

One major data-quality issue associated with online job postings is that many employers post the same job on many different websites — in fact, an estimated 80% of job postings are duplicates.

To avoid multiple counting of job postings, de-duplication — removing job postings that appear on multiple websites — is essential.

The process, however, is not perfect. Vicinity Jobs removes an estimated 95% of duplicate job postings each month. But small differences in the same posting, including website layout, means that a few are still missed.

Job Posting Collection and Reporting Methodology

Job Postings

  • The most sought-out type of data from online job postings are work requirements and job titles.
  • To provide users with their primary data of interest, the analysis should link job postings to an occupation (or job title) and to a set of work requirements. The process for doing so can be divided into four broad steps:
    1. Data collection
    2. Data cleaning
    3. Data structuring
    4. Data extraction
  • Although the four steps proceed in sequence, refining and optimizing each step often requires reviewing the results of one part while iteratively testing another part.
  • For example, steps 3 and 4 (structuring and extracting) may produce valuable information that can be used to increase the accuracy of de-duplication algorithms used in step 2 (cleaning).
  • The four-step method to transform online job postings into usable labour market information and insights are described below.

Step 1: Data collection

  • Data collection refers to the process of acquiring the text from online job postings. These job postings can be extract from job boards, job aggregator websites or directly from corporate websites.
  • Data collection typically involves a variety of approaches, including web scraping (downloading raw text from websites) and accessing website content through the host’s dedicated API connection.
  • In all cases, the data collected are publicly and freely available.
  • The technique often employed for this purpose is called Document Object Model (DOM) parsing. This allows a user to extract portions of a web page based on its underlying HTML structure.
  • Specifically, data collection programs can retrieve the dynamic content of a web page by referencing, for example, the Cascading Style Sheet (CSS) selectors associated with specific parts of the page.
  • Importantly, quality assurance protocols are implemented even in this initial step through the selective identification of which websites should be monitored.
  • Since websites vary widely in terms of the reliability of jobs posted, Vicinity Jobs selects their job posting sources through careful review and vetting prior to starting the data collection process.

Step 2: Data cleaning

  • After the raw text is downloaded, it must be processed into a form conducive to further statistical analysis. This process is known as feature extraction.
  • With respect to text data, this process includes tasks such as simplifying punctuation, converting words from plural to singular, replacing abbreviations and collapsing to lower case letters.
  • Another phase of the cleaning process focuses on removing duplicate or fake job postings.
  • A solution used by Vicinity Jobs to address job postings duplication is de-duplication, a process to remove duplicate job postings. This is achieved through a natural language processing algorithm that trains statistical models to predict whether two postings refer to the same position. Approximately half of all raw job postings are identified as duplicates and are removed.

Step 3: Data structuring

  • Once data from the job postings have been collected and cleaned, they are ready to be structured and organized into a specific set of classifications or categories.
  • One particularly important exercise is the classification of job postings into a standard taxonomy of occupations. Examples of taxonomies include the Canadian National Occupational Classification (NOC) and the American Standard Occupational Classification (SOC) systems.
  • Most algorithms that create this mapping between raw text and occupational classifications do so via examining the job title in the posting.

Step 4: Data extracting

  • The final step in the process is to extract insights from the resulting structured data. For example, this can take the form of calculating the most frequently requested work requirements by occupation and geography.
  • Since work requirements are a broad category, it is informative to further organize these into a hierarchical classification.
  • To that end, we categorize work requirements into four categories based on ESDC’s Skills and Competencies Taxonomy: skills, knowledge, tools and technology, and other.
  • We collapsed the remaining categories of interests, personal abilities and attributes, work activities, and work context into other.

Vicinity Jobs’ approach for linking job postings to NOC

  • Vicinity Jobs uses an iterative process that first attempts to match job postings to NOC based on their job titles. The allocation is further refined by using additional anchors from the content of each job posting and known information about the employer.
  • This process relies on Vicinity Jobs’ proprietary knowledgebase that encapsulates tens of thousands of job titles.
  • This knowledgebase was originally compiled from available NOC specification sources (for example, ESDC’s occupational profiles), then refined using historic job postings. Further refinements in linking the job posting to a NOC are then made by incorporating contextual knowledge (for certain job titles can only be allocated to certain occupations in certain specific contexts).
  • To identify the appropriate contexts, Vicinity Jobs’ algorithm attempts to allocate postings to industries. It does so, in part, by matching employer data (such as employer name and URL) to known employer profiles stored in a vast, up-to-date database of known Canadian employers.
  • After postings have been organized into Career Handbook occupations, a set of unassigned job postings remains. The content and job titles of these postings are not sufficiently specific, or the nature of the jobs does not allow for an allocation to a single Career Handbook Occupation.
  • Instead of forcing or guessing, the algorithm attempts to assign as many of them as possible to the less specific broad occupational category (1-digit NOC).

Vicinity Jobs’ skills taxonomy

  • Vicinity Jobs works to ensure their work requirements and certifications reporting meet their quality standards while remaining representative of the unique aspects of Canadian labour markets.
  • They have established a taxonomy for work requirements that combines extensive input from their clients and partners with information from publicly available sources such as the O*NET taxonomy.
  • Data from publicly available sources contains tools and technologies, structured skills keywords published by certain websites, and other work requirements data assembled from Canadian online job postings and research.
  • After compiling an initial version of their work requirements taxonomy, Vicinity Jobs uses a proprietary algorithm to enrich and refine their knowledge about work requirements and the linguistic constructs that represent them.
  • This process involves a sequence of iterative steps. In each iteration, the knowledge base is testing against a broad set of non-structured job postings content. The job postings content is then used to identify the work requirements.
  • The resulting data are manually reviewed and provide feedback to fine-tune the algorithms.
  • The resulting knowledgebase encompasses tens of thousands of work requirements and certifications. The knowledgebase includes labels and identifying keyword combinations, and a complex framework of underlying interdependencies and context definitions needed to ensure accurate identification of work requirements.
  • For example, the system knows that certain work requirements and certifications could apply to any job, while others are only relevant to specific occupations.

Population and Employment Projections Methodology

Overview: metroeconomics Employment Projections Methodology

metroeconomics is an economic consulting firm specializing in assessing historical trends and modeling the economic and demographic futures of various regions, including countries, provinces, states, metropolitan areas, and individual communities. Their customized investigations measure and project local area population, dwellings, employment, labour supply and demand, industrial and consumer market conditions, and socioeconomic progress. These projections support government land use plans, labour market strategies, economic development strategies, and private developers' project evaluations.

Methodology for Projections:

  1. Projections for the US:

    Population Projections:
    • Uses an age-cohort model considering fertility rates, mortality rates, and net immigration.
    • Results in age and gender projections.
    Labour Force Projections:
    • Derived from age and gender projections based on assumed future labour market participation rates.
    Employment Projections:
    • Translated from the potential labour force into future total employment projections.
    GDP by Industry:
    • Future real GDP is projected from total employment, based on past growth trends by industry.
    • GDP by industry at the national level is translated into state-level projections.
    State and Metropolitan Projections:
    • State-level GDP projections inform future employment and migration patterns.
    • Population projections by state are generated using migration patterns and age-cohort models.
    • Metropolitan area growth is allocated based on historical and expected future shares of state growth.

  2. Projections for Canada:

    US Influence:
    • Canadian projections are driven by the US economy due to strong trade ties.
    Total Employment:
    • Projected from Canadian GDP growth, incorporating labour productivity assumptions.
    Population Projections:
    • Employment growth informs a national age-cohort population model.
    • Future net in-migration is adjusted based on labour market requirements to balance supply and demand.
    GDP by Industry and Province:
    • Similar to the US, GDP by industry is projected based on historical trends.
    • Projections by province are derived from national GDP by industry.
    • Future employment projections by province inform migration patterns and population projections.
    • Metropolitan growth within provinces is allocated based on historical and expected future growth shares.
    Detailed Steps:
    1. Population and Labour Force:
      • Age-cohort models are used with assumptions on fertility, mortality, and migration rates.
      • Labour force projections consider participation rates by age and gender.
    2. Employment and GDP:
      • Employment projections are derived from the potential labour force.
      • Employment informs GDP projections by industry and region.
      • Historical growth trends guide the translation of national GDP to regional levels.
    3. Migration and Growth Patterns:
      • Future migration patterns are based on employment trends.
      • State and metropolitan growth projections use historical and expected future shares.
    Estimating Labour Market Trends at the Census Division Level in Canada:

    metroeconomics addresses the lack of detailed labour force data at the Census Division (CD) level by:

    • Using Labour Force Survey (LFS) data for Economic Regions (ERs) from Statistics Canada.
    • Apportioning ER level results to CDs using employment insurance recipients data and population estimates.
    • Adjusting estimates to reduce noise and ensure consistency with provincial data.
    • Estimating labour force, unemployment, and employment rates for each CD.

    This process provides timely labour market estimates at the CD level, valuable for workforce development boards and economic development agencies.

    Senior Manager occupations (NOC 00011-00015) are not included in the projections due to the projections combining them into a singular NOC.

Frequently Asked Questions:

  • The Trends Navigator draws on jobs posting data that we have ingested into Vicinity Jobs Datawarehouse.
  • Our jobs postings data is sourced from Vicinity Jobs. LMIC economists carefully evaluate the quality and relevance of data included in the Data Hub.
  • Vicinity Jobs collects data from a variety of websites and job boards (for example, Indeed and Job Bank).
  • However, not all online job postings are captured.
  • We cannot guarantee that every website with job postings is identified for data collection.
  • Certain websites use technologies that make it impossible for third parties to monitor their content.

Are all job postings associated with a geographic location?

  • Our Trends Navigator contains data only for job postings that are reliably identified with a geographic location.
  • Not all job postings can be reliably matched to a geographic location. In some cases, the employer does not list geographic information in a job posting. In other cases, the information cannot be classified or is contradictory.
  • Approximately 95% of job postings captured by our data partner are matched to a specific geographic location within a province or territory.
  • The Trends Navigator is updated monthly with a daily granularity. Vicinity Jobs scrapes, cleans and structures data from online job postings gathered from employer and job websites, and that data is ingested into the Vicinity Jobs Datawarehouse.
  • Part of the cleaning process involves removing duplicate postings from previous weeks. This means that each week shows a unique set of new job postings. The final data is updated every Monday afternoon, with updates up to the previous Wednesday.
  • Users can compare trends over time within the Trends Navigator.

How long do the job postings stay active?

  • No information is collected about the duration of a job posting. The data does not reflect when or why a job posting closes.
  • Often postings go offline on an automated schedule and not when the job vacancy is filled. The expiry of a job posting, therefore, is not a reliable indicator of how long it takes for an employer to fill a vacancy.

Are French language job postings captured?

  • In June 2020, Vicinity Jobs, in partnership with LMIC, developed an algorithm for cleaning and categorizing French language job postings – including the de-duplication of jobs posted separately in both French and English.
  • Note that, because job postings are deduplicated across languages, there is no way to separate English language job postings from French language job postings.

What are "work requirements"?

  • “Work requirements” are the skills, knowledge, tools and technology, and other descriptors identified by the employer.
  • Our data partner organizes job posting text into its work requirements taxonomy (which includes over 40,000 unique items, although only ~2,500 appear with significant regularity).

Are job postings and vacancies the same thing?

  • Job postings are related to job vacancies, but the two are not the same.
  • Job vacancies may or may not be advertised online and refer specifically to actual unfilled positions.
  • However, a single job posting might reflect multiple vacancies, just one vacancy or no vacancies — for example, if an employer posts a job advertisement for which there are no current openings.
  • Online job postings offer a general view of job vacancies, but there are important caveats and limitations to interpreting this information:
    1. Many jobs are filled without being advertised.
    2. Certain segments of the job market (for example, urban locations and service-oriented jobs) are more likely to be posted, potentially skewing online job postings away from the total number (across regions and occupations) of job vacancies.

Who defines job titles?

  • Job titles are taken from Canada’s National Occupational Classification (NOC) system.
  • The NOC system is used to group occupations based on the type of work performed (for example tasks, duties, responsibilities).
  • The data from job postings in the Vicinity Jobs Trends Navigator is matched to NOC codes so that you can explore trends from job postings based on commonly used categories of occupations.
  • However, not all job postings can be categorized. Approximately 15% of online job postings are associated with broad occupational categories only (1-digit NOC codes). A further 10–15% cannot be reliably associated with any NOC.
  • The Trends Navigator contains data only from job postings that can be reliably grouped into a detailed occupational category, which is about 70% of all online job postings.

How are job postings linked to occupations?

  • Job postings are linked to occupations using Vicinity Jobs’ proprietary machine learning algorithms, which maps them to detailed occupational categories (see methodology).
  • If the algorithm is unable to categorize a job posting into a detailed occupational category (4-digit NOC), it will attempt to use the broad occupational category (1-digit NOC).
  • Typically, 10–15% of online job postings lack enough detail that they cannot be associated even with a broad occupational category.
  • The dashboard contains data only for those job postings reliably identified with a 4-digit NOC (about 70% of all postings).
  • The process of collecting online job posting information from various websites is both legal and ethical.
  • All information is found on publicly available websites with no restrictions on who can access the data.
  • The information is analyzed but not reproduced, so it does not violate the author’s copyright. Because none of the data collected contains information about individual Canadians (only generic employer information), the data does not violate privacy.

Glossary of terms

Job Categories

Job categories refer to the ten broad occupational categories (that is, 1-digit NOC) based on the 4-tier hierarchical arrangement of Canadian occupational groups. Each broad category has a unique 1-digit code (0 to 9).

National Occupation Classification (NOC)

The NOC system is the organizational framework of occupations in the Canadian labour market. It is used to classify information from statistical surveys and to compile, analyze and communicate information about occupations. The first digit of the code represents the broad occupational category and the first layer of the structure. Each digit following that first one helps a NOC user delve into the NOC structure at a more detailed level and further specify the nature and characteristics associated with a given occupation.

Web Scraping

Web scraping is the process of collecting data from public websites. It usually refers to using automated software to extract large amounts of raw text and data from a variety of websites. However, the term may be used more broadly to encompass any process of gathering and copying information from the internet, whether automated or manual.

Read more on web scraping in LMIC’s LMI Insight Report no. 32, Through the Looking Glass: Assessing Skills Measures Using 21st Century Technologies.

Work Requirements

In the case of this dashboard, work requirements are defined by the proprietary taxonomy of Vicinity Jobs. Using natural language processing of the job description, the raw text from each job posting is associated to as many work requirements as possible. Work requirements are grouped into four categories: skills, knowledge, tools and technologies, and other.