- A lack of local and granular LMI data
- The most frequently used LMI-gathering techniques are rooted in traditional methods, to the exclusion of innovation
- There is no commonly agreed methodology for projecting future skills
- LMI data is often inaccessible, unreliable or not relevant to stakeholder needs
- The needs of end-users are not being met by most current LMI tools
- The overall impact of LMI tools is not being tracked
- We lack data that represents a diversity of population groups
- The private sector is innovating, but their data are challenging to validate
- Collaborate to close gaps in LMI generation
- Prioritize innovation in LMI
- Develop, endorse, adopt and advocate for best practices and principles
- Champion open access to data and tools
- Develop impact and evaluation frameworks for LMI
- Convene the ecosystem
Table of contents
LMIC is a young organization, currently in its fourth year of operation. With the 2021 launch of its five-year strategic plan, an objective and goal is to add value to Canada’s complex labour market information (LMI) ecosystem to ensure it responds to the needs of Canadians.
The report aims to provide insight into the make-up of the current pan-Canadian LMI ecosystem. Specifically, it highlights gaps and challenges present within the current system and identifies opportunities where LMIC and others can act as leaders in Canadian LMI, creating a collective roadmap for the future.
By turning a spotlight on the challenges and opportunities within Canada’s LMI landscape, this analysis will inform recommendations on how to improve the LMI ecosystem—and in so doing, help guide LMIC’s work and that of organizations like ours.
This report is the first of a series that will outline the pan-Canadian LMI ecosystem. As LMIC works to further the assessment of the current ecosystem, subsequent reports will build on this analysis. Future reports will touch on themes such as what key stakeholders are doing and how, and international initiatives advancing LMI.
An overview of the Canadian LMI landscape
The pan-Canadian LMI landscape is a complex system made up of stakeholders from the public, private and not-for-profit sectors.
Some have missions focused solely on LMI, while others position LMI as complementary to their core objectives and activities. All have unique mandates and missions that lead them to establish different objectives and, ultimately, to create a variety of LMI with their target audiences in mind.
Respecting the diversity and nuances of this system, we have attempted to document it by analyzing the stakeholders through a few central themes, including:
- Who produces LMI?
- What types of LMI are produced in the pan-Canadian ecosystem? Who are the target audiences?
- What data collection methods and sources are used to produce LMI?
- What are some of the challenges faced by organizations in gathering, producing and sharing LMI?
Statistics Canada is the source of most of the LMI used in labour market tools and products in Canada.
A global leader in providing high-quality data, Statistics Canada establishes and maintains data toolkits characterized by transparency, relevancy, reliability and accessibility. See Statistics Canada’s Data Quality Toolkit for more information.
Most LMI stakeholders, whether or not they produce primary LMI data, make use of Statistics Canada’s data to analyze and create LMI tools and products that are, in turn, used by a variety of audiences – including students, policy-makers, job-seekers, human resources professionals and employers.
Federal, provincial and territorial governments are major contributors to generating and sharing LMI.
While their needs vary, federal, provincial and territorial governments all work to produce LMI that they can use internally (to make funding, policy and program decisions and improve their local labour force outcomes) and externally (to help individuals make informed decisions about education, training and career-related goals).
All three levels of government produce important products that allow Canadians to explore and better understand the labour market. These include online resources such as the Job Bank, occupational outlooks, labour market updates and reports, as well as a range of surveys that provide insights into labour market outcomes and trends.
Not-for-profit organizations in Canada often gather their own primary LMI and complement it with data from external sources.
Within the not-for-profit sector, some organizations incorporate LMI into their missions and core activities while others use it to support projects and initiatives – for example, those related to immigration, innovation, workforce development and other policy issues.
There is a growing need among not-for-profit organizations to access, source, compile, analyze and generate LMI data and insights to better understand these topics and develop responsive programming.
Sector councils and sector associations produce and share LMI related to their members, primarily private sector employers.
In Canada, there are more than 50 associations and councils seeking to advance their sectors by understanding employment needs, driving growth and profitability, attracting and retaining talent and identifying new skills or competencies required to maintain a competitive workforce.
To achieve these goals, many have developed in-house capacity to gather, analyze and produce LMI. Others have opted to outsource LMI-related work or use a combination of in-house and external support.
Private sector firms are increasingly producing LMI and providing LMI-related services.
Private sector firms are relatively new entrants to the LMI landscape in Canada and are increasingly producing real-time LMI by scraping websites for job postings and resumé data while using advanced techniques to gain current and pertinent insights into labour force trends at different levels of granularity. Real-time LMI can include supply and demand trends, information on emerging occupations, current and new skill requirements, and market demand for education and certifications.
Many of these private sector firms have developed algorithms and can leverage technologies such as machine learning, allowing them to scrape information from the internet, provide intelligence on labour market demand and supply (notably related to skills), and make projections about different aspects of the labour market.
Some these firms also produce LMI resources, tools and services that other stakeholders purchase to make important policy and business decisions and to complement or augment their in-house LMI.
Challenges in Canada's LMI ecosystem
The issues identified are systemically persistent and cannot be solved by one organization or stakeholder group alone. They are due to processes, methodologies and approaches that are inherent in the system. Many are challenges which stakeholders encounter when they try to gather data and develop LMI products.
Below are the primary challenges identified that, if addressed, could transform the pan-Canadian LMI ecosystem.
Challenge 1: A lack of local and granular LMI data
LMI producers are looking for ways to acquire local and granular data1 that better describe the realities of individual labour markets.
In Canada, these types of data are not publicly available, are difficult to access, or simply do not exist.2
Part of the challenge is in the traditional methods through which most data are collected, such as surveys. Considerations like cost, sample sizes and coverage, the time needed to gather data, and response rates can all influence the localness, granularity and timeliness of data.
This often results in a lack of detailed information for regions with smaller populations and industries. Since a large part of the data used by stakeholders is gathered by Statistics Canada, which must uphold strict privacy standards, publicly accessible data are aggregated at a level that many organizations do not find useful.
Where disaggregated data does exist, it can be challenging to access. Statistics Canada has restrictions related to their research data centres and other data channels. Disaggregated data collected by the private sector are typically unavailable due to the firms’ intellectual property policies.
The result is that it is difficult, and at times impossible, to gather quality LMI about certain regions and industries, or specific to certain occupations, training and education.
This, in turn, affects the type of data used to make important decisions that influence policy, programming, funding and career paths.
These barriers to local and granular data limit the kinds of LMI tools and services that can be developed. For example, forecasting demand for talent in smaller population areas is challenging if information about the types of jobs in these regions is not available. Critically, data limitations affect the ability to answer important questions about labour and skills shortages in Canada at local and occupational levels.
Challenge 2: The most frequently used LMI-gathering techniques are rooted in traditional methods, to the exclusion of innovation
Surveys have long been the traditional method for gathering LMI data. Statistical agencies often turn to this method because it has proven effective for decades; expert teams are dedicated to designing, conducting and analyzing these research tools.
The resources and machinery behind many surveys allow for timeliness and good overall representation; they can mitigate biases; and they can offer a wide range of indicators that support socioeconomic planning and policies.
However, a significant concern in surveying as a methodology is related to non-response rates. Household survey non-response rates have been on the rise since the mid- to late 1990s. The COVID-19 pandemic has disrupted this further by hampering statistical agencies’ data collection activities, leading to unusually low response rates in many countries. Response rates in the Labour Force Survey, one of the principal sources of LMI in Canada, fell to a record 72% in June 2020 from 88% in February 2020.3 Response rates have begun to recover but the level of non-response still remains above pre-pandemic levels.
Additionally, cost concerns, accurate population representation, the length of time it takes to conduct a survey, analyze the results and arrive at insights, respondents’ willingness to participate, and the potential biases of respondents and those conducting the surveys are all factors that must be considered when selecting this research and data-gathering method.
The nature of work is evolving rapidly due to technological, social, environmental and other related factors. To keep up, new techniques to gather LMI are constantly being developed.
Web scraping and artificial intelligence (AI) technologies are among some of the most promising advances in this space. However, many stakeholders have concerns about the quality of these new methodologies, leading to their uptake being primarily among private sector organizations.
Organizations that collect LMI data in Canada have tended to stick to traditional methods—that is, the sector relies on surveys and, to some extent, administrative data. This has meant that while the private sector is leading and innovating in this space, many other organizations and stakeholder groups are being left behind.
Challenge 3: There is no commonly agreed upon methodology for projecting future skills
Organizations and governments need future skills data so they can develop education and training programs that are aligned to the qualifications and skills that will lead to successful careers and a competitive economy.
Many of the stakeholders report a growing need for data that allow organizations to project future skills needs.
Individual Canadians who seek out LMI also express a need for future skills data. Based on surveys conducted by LMIC, approximately one quarter (24%) of Canadians feel that available LMI does not provide insight into the future. Respondents reported that information about skills is one of their top needs.4
This gap in the provision of LMI related to future skills exists, in part, because forecasting skills is a difficult task.
Developing projections for future skills is typically done by linking skills to employment forecasts and identifying which skills are associated with in-demand occupations. However, forecasting employment is challenging. This kind of economic analysis requires several assumptions that can lead to disputable projections. The challenge of linking skills information to this already complex process and arriving at valuable insights is compounded by an absence of up-to-date skills information and a scarcity of methodologies for forecasting skills.
While there is currently no established approach for forecasting skills information, stakeholders in the LMI ecosystem are aware of these challenges and are working to address them. A recent paper from LMIC and the Future Skills Centre explores approaches available to identify skills for the future.5 Employment and Social Development Canada is currently exploring one of these approaches by building an Occupational Skills and
Information System (OaSIS) to provide a framework for the Canadian context, resulting in the development and validation of profiles for almost 900 occupations.6
While initiatives like these are valuable, the challenge remains that there is currently no common standard for identifying or measuring LMI related to skills.
Challenge 4: LMI data is often inaccessible, unreliable or not relevant to stakeholder needs
The accessibility and clarity of data and methodologies is an ongoing challenge in the Canadian LMI ecosystem.
Our analysis found that, among the majority of LMI publications, tools and platforms, sources are frequently not cited properly, methodologies are unclear or undocumented, data are often shared in PDF formats that are not machine readable, and data sets are separated from LMI tools.
This means that data cannot be referenced or tracked down after publication, stakeholders cannot validate or peer-review findings, and data are difficult or impossible to use. These practices ultimately reduce the entire Canadian LMI ecosystem’s ability to access powerful data that could be used to develop labour market projects and tools and to inform public policy.
These issues generate a lack of trust in Canada’s LMI, and point to questions of reliability, relevance and accessibility — all critical components of quality data. If data and tools cannot be properly assessed or validated, they are unlikely to be trusted or used in the broader LMI ecosystem.
Challenge 5: The needs of end-users are not being met by most current LMI tools
The format in which LMI has historically been presented and disseminated does not match end users’ needs, and typically does not lend itself to informed decision-making.7
This is mostly a result of LMI tools being built for a broad audience rather than to meet specific needs. Given that end-users have diverse needs and objectives, products built with broad audiences in mind often result in individual users being unable to make informed decisions using the LMI presented to them.
This disconnect is broadly acknowledged in Canada’s LMI ecosystem, and many organizations are seeking better ways to make tools user friendly, focused and relevant.
Because interactive tools require ongoing maintenance and, often, a detailed level of data granularity to be useful to an individual, there can be some difficulty in justifying the cost of building them.
Investing in new LMI-gathering techniques that allow for localness and granularity requires a sizable investment and can present a steep learning curve for many organizations. This, in turn, creates a disincentive to develop the tools users most need.
Challenge 6: The overall impact of LMI tools is not being tracked
How do we know that LMI tools in Canada are successfully meeting the needs of users and stakeholders? The Canadian ecosystem lacks impact metrics.
Our analysis shows that most organizations limit their evaluation to how many times a web page has been visited. Very few stakeholders are investing in effective evaluation and performance management frameworks.
This means that the impact of LMI tools — for example, tools to help individuals choose a post-secondary curriculum or to guide government policy on re-skilling programs— continues to be difficult or impossible to track.
Challenge 7: We lack data that represents the diversity of population groups
There is a growing and long-standing need for LMI and related data for diverse population groups. Stakeholders highlight a particular need for more and better data for Indigenous Peoples, visible minorities and the LGBTQ2S+ community.
These requirements are primarily based on the need to better understand evolving workforce development trends, to assess the quality of work opportunities and to provide equity analysis.
Thematic areas of interest ranged from improved demographic data and employment-related indicators to better data about people’s behaviour over the course of their careers.
This need was particularly significant during the COVID-19 pandemic, which affected certain demographic groups far more than others. Unfortunately, because of a lack of data, more detailed information about the full nature of these impacts is limited.
To address some of these gaps, Statistics Canada made changes to the Labour Force Survey in July 2020.8 By asking respondents which population group they identified with and developing new statistical methods to gain insights into group characteristics, Statistics Canada is working to address the need for additional data on population groups.
This is a step in the right direction, but many gaps remain, and continued efforts from Statistics Canada and others are essential.
Challenge 8: The private sector is innovating, but their data are challenging to validate
Private sector representation in the LMI space has grown dramatically over the past decade. Organizations like Vicinity Jobs, Emsi-Burning Glass , Gartner Talent Neuron , and LinkedIn have joined the Canadian LMI space to address gaps and the needs of users.
These firms provide an array of solutions that include tools to augment HR processes, like internal skills assessments and gaps, training and upskilling plans and recruitment efforts.
In many instances, these firms are leveraging AI and proprietary data to address social and economic issues.
For example, Emsi-Burning Glass has developed a database said to include more than a billion current and historical job postings and more than 300 million resumes.9 By using AI to gather and analyze job postings, the company can deliver real-time data and planning tools that inform careers, define academic programs, develop career pathways for jobseekers and help shape workforces.
However, because these firms are private, the technologies they leverage to acquire real-time data are difficult to assess. Likewise, due to its proprietary nature, the data are not easily accessible. Most of this LMI sits behind paywalls or is offered through proprietary products and platforms.
The sources of real-time data in the private sector are often unclear. This creates questions about transparency and the ability to validate how representative, accurate, local and granular these data may be.
In addition, many of these firms rely on their own occupation and skill taxonomies. While taxonomies are developed with international and national classification standards in mind, it is not always possible to link insights from private firms’ datasets to official categorization standards.
Recommendations for LMI in Canada
Despite several persistent and emerging challenges in the pan-Canadian LMI ecosystem that hinder the overall quality, accessibility and availability of LMI, awareness of these challenges leads to opportunities to improve LMI and our collective practices.
The following recommendations are intended for all stakeholders who gather, produce and share LMI in Canada, including LMIC. They are meant as actionable points for organizations, firms and the system as a whole.
Recommendation 1: Collaborate to close gaps in LMI generation
The most common challenges identified in this analysis are aligned to data gaps. The scale of the challenge related to needing more, better quality, granular, local and timely data is too significant for any one organization to resolve on its own.
However, all stakeholders across the LMI ecosystem in Canada have acknowledged these needs. Recognition is the first step; collaboration is the next. LMI stakeholders must collaborate and coordinate to identify, prioritize and close LMI gaps.
Efforts must be made to develop partnerships that leverage the full capabilities within the pan-Canadian ecosystem. For example, there is much for the public and not-for-profit sector to learn from private sector firms that are innovating, and there is much to be gained by the private sector in collaborating with other LMI stakeholders in Canada to validate their work and efforts.
Recommendation 2: Prioritize innovation in LMI
Innovation is about creative response to change. Organizations can innovate by adjusting existing processes, adopting new processes and generating new ideas.
To bridge gaps in Canada’s LMI landscape, innovation must include integrating traditional methods of data collection with new techniques that allow for the use of real-time LMI. Collaboration is key in this area: continuing to function in silos will hinder technology transfer and the ability to gather the data that is needed.
Funders and policy-makers must lead the way by adjusting their funding structures to create incentives that prioritize innovation as a desirable and worthwhile endeavour. Other stakeholders must propose and prioritize forward-thinking innovation in methodologies, approaches and technologies, and advocate for stronger support and endorsement of these innovations within the broader ecosystem.
Recommendation 3: Develop, endorse, adopt and advocate for best practices and principles
The LMI ecosystem in Canada is unregulated, without a governing body to establish best practices and principles.
There are opportunities in this, however. It means that private sector firms, for example, can innovate and develop tools and new approaches. They are free to lead the way in developing and applying leading techniques to produce relevant and needed LMI.
But it also means that the private sector is not incentivised to disclose information like data sources or methodologies that could help to validate and build trust in their work.
There is an opportunity for stakeholders to come together to establish a set of principles and best practices around methodologies, data products, openness and transparency in a way that supports private sector innovation while also respecting the guardrails of intellectual property policies.
It is important for publicly funded organizations to establish and incorporate best practices and principles around data quality and data management that can be endorsed and adopted by other stakeholders. Upholding principles such as transparency around methodologies and the traceability of data from source to destination would make it possible to attest to the integrity and trustworthiness of products. Among publicly funded organizations, principles of transparency and access must be held to stricter and higher standards, as should methodologies and data quality.
Education is a vital component in the adoption of best practices. There is an important role for stakeholders to play in educating LMI consumers about trends, data quality and principles in data management so that when faced with new products or terminologies, they can draw their own conclusions about quality and reliability.
Endorsing, adopting and advocating for best practices that have been developed and tested by others will be instrumental across the LMI ecosystem.
Recommendation 4: Champion open access to data and tools
Making data and tools available more broadly is an important first step towards building a culture of innovation within Canada’s LMI ecosystem. There is an important role for all stakeholders to play in championing and integrating open access to data and tools.
Open data principles typically include ensuring that data is complete, granular, timely, accessible, machine processable, non-discriminatory, non-proprietary and license-free.10
Emerging from our consultations with stakeholders is a specific need to make available the data behind LMI tools and platforms to encourage the production of new insights, spur innovation and to support LMI intermediaries who do not have the time or capacity to develop their own tools.11
Here, the pan-Canadian LMI ecosystem must work to create solutions for the organizations that are integral to delivering quality LMI to Canadians.
Creating incentives for LMI producers to develop tools for LMI intermediaries needs to be a concerted effort. An additional layer to this solution involves creating opportunities for LMI intermediaries and partners to further develop their LMI skills.
With that in mind, LMIC’s Data Hub initiative is designed to separate LMI data from its purpose and give partners the capacity to develop their own user-focused tools and resources.12 By opening data beyond its intended use on one platform, initiatives such as this will make it possible for a diversity of stakeholders to conduct analyses and develop tools.
Recommendation 5: Develop impact and evaluation frameworks for LMI
There is scarce evidence of evaluation frameworks to assess the impact of LMI and LMI tools in Canada.
Understanding how to reach a target audience and whether that reach has the intended impact is an activity that all stakeholders in the LMI ecosystem should be integrating into their work and activities.
Strategies and metrics grounded in a common framework are needed not only to build trust in LMI and stakeholders in the ecosystem, but also to demonstrate the value of these data and the socioeconomic impact of information and tools.
Recommendation 6: Convene the LMI ecosystem
Across the pan-Canadian LMI landscape, many stakeholders are innovating in data collection and analysis methodologies, tool development, best practices development and evaluation. These are all important areas that must be matured to ensure the impact of LMI in Canada.
There is currently a lack of coordination between organizations, sectors and stakeholders. In our pan-Canadian consultations, desires to collaborate are frequently expressed with the purpose of sharing information, exchanging knowledge and learning from others.
At LMIC, we see this as a welcome opportunity to convene the LMI ecosystem in Canada to foster dialogue and innovation.
The way forward
The world of work is changing rapidly. With this reality comes the need to find ways to ensure that Canadian labour markets can adjust and respond – in real-time, and in the future.
Canada has a complex LMI ecosystem with diverse stakeholders. To strengthen our collective work and impact, we must address common challenges and build an efficient, cohesive system for better LMI. This work will be reflected in the policies and programs of tomorrow.
There is considerable work to be done, but there is also great interest among all stakeholders and sectors to make advances. Our hope is that the challenges and recommendations documented in this analysis provide a starting point for collaboration, and serve as a foundation for driving new, successful initiatives in the coming years.
At LMIC, we look forward to supporting and catalyzing this important work to improve LMI in Canada.
Scope and methodology
This LMI Insight Report was prepared by Lorena Camargo.
We would like to thank David Ticoll for his time and support; his feedback and constructive comments were instrumental in guiding this work.
For more information about this report, please contact Lorena Camargo, at firstname.lastname@example.org, or Tony Bonen, director of research, data and analytics, at email@example.com.
1 “Localness” is defined as the smallest geographic level, while granularity refers to the number and detail of categories for which data can be grouped (such as age, education level, immigration status, and so on). The definition of LMI criteria is found in an LMIC report titled Search for the LMI Grail: Local, Granular, Frequent, and Timely Data. See LMI Insights Report no. 15 (https://lmic-cimt.ca/publications-all/lmi-insights-report-no-15-search-for-the-lmi-grail-local-granular-frequent-and-timely-data/).
2 LMIC & Statistics Canada (2019). Search for the LMI Grail: Local, Granular, Frequent and Timely Data. LMI Insights, no. 15. https://lmic-cimt.ca/publications-all/lmi-insights-report-no-15-search-for-the-lmi-grail-local-granular-frequent-and-timely-data/.
3 Brochu, Pierre (2021). A Researcher’s Guide to the Labour Force Survey. Canadian Public Policy. http://aix1.uottawa.ca/~pbrochu/Brochu_working%20paper.pdf
4 LMIC and POR research. (2020, March). Socio-Demographic Differences in Labour Market Information Use, Sources and Challenges. LMI Insights, no. 28. https://lmic-cimt.ca/publications-all/lmi-insight-report-no-28-socio-demographic-differences-in-labour-market-information-use-sources-and-challenges/.
5 Bonen, T., N Loree, J. (2021). How to forecast skills in demand: a primer. Future Skills Centre. https://fsc-ccf.ca/research/how-to-forecast-skills-in-demand-a-primer/.
6 LMIC. (2021, June). Searching for an OaSiS in the World of Skills and Occupation Mapping. LMI Insights, no. 43. https://lmic-cimt.ca/publications-all/lmi-insight-report-43/.
7 LMIC. (2018, September). Taking Stock of Past Labour Market Information Assessments: LMI Insights, no. 1. https://lmic-cimt.ca/publications-all/lmi-insights-report-no-1-taking-stock-of-past-labour-market-information-assessments/.
8 Statistics Canada, Labour Force Survey. (2020). https://www150.statcan.gc.ca/n1/daily-quotidien/200807/dq200807a-eng.htm.
9 Burning Glass Technologies. (2020). Seven Papers at Economics Conference Draw on Burning Glass Data. https://www.burning-glass.eu/en/seven-papers-at-economics-conference-draw-on-burning-glass-data/.
10 Open Data Charter. https://opendatacharter.net/principles/.
11 These research findings will be shared in forthcoming work on Mapping the Career Development System in Canada.
12 Future Skills Centre. (2020 December 14). New project aims to equip front-line organizations with tools and insights needed to help Canadians navigate their career choices [Press release]. https://fsc-ccf.ca/engage/future-skills-centre-and-labour-market-information-council-announce-3-million-partnership-for-creation-of-front-line-career-guidance-tools/.
13 Studies included:
- Advisory Panel on Labour Market Information (APLMI). (2009). Working together to build a better labour market information system for Canada: Final report. https://publications.gc.ca/collections/collection_2011/rhdcc-hrsdc/HS18-24-2009-eng.pdf.
- Drummond, D. (2014). Wanted: Good Canadian labour market information. IRPP Insight, No. 6. http://irpp.org/wp-content/uploads/2014/06/insight-no6.pdf.
14 Recognizing the unique nature of the LMI sector council system, we thought it important to gain a deeper understanding from a subject matter expert.