Farmers, Clerks, and Engineers: A look at how we selected the occupations informing our forecast of employment in 2030

Farmers, Clerks, and Engineers: A look at how we selected the occupations informing our forecast of employment in 2030

For our ongoing Employment in 2030 project, we want to know which skills and jobs will likely be in demand in 10–15 years. But with 500+ occupations in Canada, learn which jobs we focused on and why.
Diana Rivera
Alumni, Senior Economist
August 8, 2019
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In Employment in 2030, we are creating an employment forecast for Canadian occupations. It is driven by both the insights of the labour market experts that we gathered in workshops across the country and the skills, abilities, and knowledge requisite for each profession. Our aim is to help governments, educators, and Canadians in general, attain a clearer picture of the skills that will help to future-proof everyone in the face of complex forces, ranging from automation and offshoring to resource scarcity.

With 500 national occupations (NOCs), it would have been an unwieldy task for  experts to examine how each one would change by the year 2030. So—how did we select the occupations that would drive this study? We considered several factors, including the skills data available, the workshop design and constraints, and the information needs of the machine learning model that we will apply to expand our forecast. 

Skills Data

After scanning the available skill classifications, we selected the skills, abilities, and knowledge features from O*NET (Occupational Information Network) to represent the worker attributes that are important in each occupation. Since this database focuses on US occupation groups, the Brookfield Institute created a crosswalk that matches US codes to their Canadian counterparts. This matching approach uses both the International Standard Classification of Occupations (ISCO) and the job titles contained in each occupation to link the NOC and US codes. 


We conducted six workshops across the country with the goal of gathering quality data from expert participants to inform our machine-learning model. Having each workshop in a different city, from St. John’s to Whitehorse, had two major implications: the workshop design and delivery needed to be as consistent as possible, and we had to consider potential regional differences.

Selecting occupations to drive a forecast

We designed a data-driven and reproducible method to ensure that we included occupations important to both the model and the labour market. At each workshop, we asked experts to rate 20 occupations chosen through a two-stage approach. Fifteen were benchmark occupations presented to all participants across the country, and five were regional occupations unique to each workshop. 

Benchmark occupations

We chose these occupations because they best represent the combination of skills, abilities, and knowledge present in the Canadian job market. Our benchmarks include financial managers and electrical mechanics, among others (scroll down to see our full list). With our selection,  we aimed to inform our model, reduce its uncertainty, and allow for comparison between regional workshops.

Our starting data consisted of 485 Canadian occupations that we matched with 120 skill, ability and knowledge scores from O*NET. Our first step was to reduce these 120 variables to 21 using a single value decomposition (SVD) technique. These combine the original variables in a way that accounts for over 90 percent of the variation in O*NET scores among all the occupations. Why did we want a smaller group of variables? With this reduced data, it was easier to cluster our occupations into 15 groups based on the similarities of their skill, ability and knowledge requirements using k-means clustering. After careful review, we saw that this process created meaningful groups of occupations that share either responsibility levels  or broad sector characteristics. For example, one cluster includes most managerial codes while another is comprised of occupations in education and social service provision.

From each group, we selected a benchmark occupation that was most representative of the skills required for the occupations included in that group. In more technical terms, we selected the occupation that was closest to the centre of each cluster.  In the case of a tie between occupations, we selected the one with the highest employment. However, there were also cases where additional criteria were necessary to ensure that we gathered high-quality responses from our workshop participants. We chose the next closest occupation as the benchmark occupation when:

  1. The occupation selected though this process included jobs “not elsewhere classified” (e.g. Other professional engineers not elsewhere classified). We believe that the lack of specificity in both the occupational description and the associated skill profile would not lead to a good participant assessment.
  2. The historical employment data for the occupation selected through the process was affected by changes in the national classification. If the employment estimates were inflated because of changes in the 2011 NOC structure, the graphs provided to participants may have deterred and misguided participants. We also considered this issue with regional occupations.


Table of benchmark observations.

Regional occupations

To select these occupations, we prioritized gathering information for regionally and nationally important occupations while also relying on the local expertise of our participants. Our regional jobs included graphic designers in British Columbia, cooks in Quebec and lawyers in Ontario (scroll a little further for a full list). We explored an occupation’s regional importance by aggregating three measures:

  1. Regional employment share
    To identify professions with high employment, we calculated the percentage of employed workers in the region who are employed in each occupation.
  2. Regional quotient
    To identify occupations that are very prominent in some regions but not at the national level, we created a ratio of the regional share of employment of an occupation over its national share.
  3. Regional concentration
    To identify occupations highly concentrated in particular regions of the country, we calculated the percentage of each occupation’s employment present in each region.

Finally, to create the overall regional importance score, we calculated the percentile of each value for each occupation and weighed them to reflect our prioritization of each measure given our goals for regional selection. Employment share percentile accounted for 50 percent of the score, regional quotient for 30 percent, and regional concentration for 20 percent.

While the focus of these occupations is regional, we also considered the needs of the model. We excluded occupations that were already represented by a benchmark occupation. Or in more technical terms, any occupations that were closer to the centre of their cluster than their group average were not included in this selection process. This way, we ensured that experts rated only occupations that were substantially different from the benchmark occupations.

Table of regional occupations grouped by provinces and regions.

Bringing it together

Overall, ratings from benchmark occupations provide our model with representative information on the possible skills, abilities, and knowledge combinations in the Canadian labour market. They will also allow us to explore and potentially extract regional differences among the workshops.
At the same time, ratings from regional occupations provide us with information for regionally and nationally important occupations. Both sets of occupations will be vital in training our machine-learning model to create a picture of what employment in 2030 may look like. 

If you are curious about how we designed the workshops, what we asked experts, and the main themes of their answers, check out our last blog How to design a workshop for the future of employment and look out for our upcoming report Signs of the Times: Expert insights about employment in 2030.  For the latest information about Employment in 2030, visit our project page.

For media enquiries, please contact Nina Rafeek Dow, Marketing + Communications Specialist at the Brookfield Institute for Innovation + Entrepreneurship.

Diana Rivera
Alumni, Senior Economist
August 8, 2019
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