Indicators and targets: A diverse and highly skilled workforce

 Jobs and innovation: Tracking progress and results

People and skills

Target: Increase the share of science and technology-related jobs to 40% by 2025

Figure 1.1: Professional, science and technology-related jobs as a share of total employment

Professional, science and technology-related jobs accounted for 34.1% of all jobs in 2018, an increase of over 3 percentage points since 2011. The share of science, technology, engineering, and mathematics (STEM) jobs also increased over the same period.

Description of Figure 1.1
Figure 1.1: Professional, Science and Technology-related JobsFootnote * as Share of Total Employment in Canada
Year STEM jobs  Other professional, S&T-related jobs Total
2000 6.7% 21.3% 28.0%
2001 6.8% 21.7% 28.5%
2002 6.8% 21.8% 28.6%
2003 6.6% 21.6% 28.2%
2004 6.6% 21.7% 28.2%
2005 6.8% 22.3% 29.1%
2006 6.8% 22.3% 29.1%
2007 7.0% 22.5% 29.5%
2008 6.9% 23.0% 29.9%
2009 6.9% 23.6% 30.5%
2010 7.2% 23.8% 31.0%
2011 7.2% 23.7% 30.9%
2012 7.2% 23.7% 31.0%
2013 7.4% 24.3% 31.7%
2014 7.6% 24.3% 31.8%
2015 7.8% 24.9% 32.7%
2016 7.8% 26.2% 34.0%
2017 7.8% 26.2% 34.0%
2018 7.9% 26.1% 34.1%
Target
2025 - - 40.0%

Sources:

Figure 1.2: Professional, Science and Technology-related Jobs, OECD Countries (2011)

In 2011 (latest international comparison), Canada ranked 21st among OECD countries, with 30% of total employment being professional, science and tech-related occupations.

Description of Figure 1.2
Figure 1.2: Professional, Science and Technology-related Jobs, OECD Countries (2011)
Country Percent

Sources:

Luxembourg 56.0%
Sweden 41.5%
Switzerland 41.1%
Denmark 40.6%
Netherlands 38.5%
Iceland 38.7%
Germany 37.3%
France 37.0%
United States 35.4%
Italy 30.6%
New Zealand 30.4%
Canada 30.0%
United Kingdom 28.1%
Spain 25.8%
Japan 14.9%
Average Top 5 OECD 43.6%
Target
Canada 40.0%

Figure 1.3: Adult Participation Rates in Formal and/or Non-formal Education, Age 25-64 (2012)

In an innovative economy, citizens have the opportunity to pursue lifelong learning and on-the job training.

Adult participation in on-the-job training is an important measure of how well Canadians are supported in developing new professional skills.

Description of Figure 1.3
Figure 1.3: Adult Participation Rates in Formal and/or Non-formal Education, Age 25-64 (2012)
Countries Participation rate in formal and/or non-formal education

Sources:

Finland 66.5%
Denmark 66.3%
Sweden 65.9%
Norway 64.3%
Netherlands 64.2%
United States 59.3%
Canada 58.3%
England/N. Ireland (UK) 56.0%
Australia 55.5%
Germany 53.0%
Estonia 52.7%
Average 51.2%
Ireland 50.8%
Korea 50.1%
Czech Republic 49.6%
Flanders (Belgium) 48.9%
Austria 48.4%
Spain 46.5%
Japan 41.9%
France 35.7%
Poland 35.4%
Slovak Republic 33.1%
Italy 24.9%
Russian Federation 19.6%

Figure 1.4: Number of Mitacs Internships

The Government has renewed and expanded federal funding for Mitacs, a not-for-profit organization that builds partnerships between industry and educational institutions, to provide additional work-integrated leaning opportunities for post-secondary students. Mitacs is on track to meet its ambitious goal of providing 10,000 work-integrated learning placements to post-secondary students and graduates each year by 2021/22.

Description of Figure 1.4
Figure 1.4: Number of Mitacs Internships
Fiscal Year Number of Internships

Sources:

2012/13 1,710
2013/14 1,762
2014/15 2,445
2015/16 4,622
2016/17 5,099
2017/18 8,096
Corporate Plan Target
2018/19 8,190
Overall Target
2021/22 10,000

Figure 1.5: Number of graduates in selected STEM fields, by sex

While women are generally underrepresented in STEM fields of study, their representation has been steadily increasing.

Description of Figure 1.5
Figure 1.5 : Number of graduates in selected STEM fields, by sex
Year Field of study Sex Number of graduates
2000 Physical and life sciences and technologies Males 10,992
2000 Physical and life sciences and technologies Females 12,636
2000 Physical and life sciences and technologies Sex unknown 0
2000 Mathematics, computer and information sciences Males 11,697
2000 Mathematics, computer and information sciences Females 5,805
2000 Mathematics, computer and information sciences Sex unknown 18
2000 Architecture, engineering and related technologies Males 26,301
2000 Architecture, engineering and related technologies Females 6,498
2000 Architecture, engineering and related technologies Sex unknown 27
2000 Total Males 48,990
2000 Total Females 24,939
2000 Total Sex unknown 45
2008 Physical and life sciences and technologies Males 12,123
2008 Physical and life sciences and technologies Females 15,390
2008 Physical and life sciences and technologies Sex unknown 6
2008 Mathematics, computer and information sciences Males 8,379
2008 Mathematics, computer and information sciences Females 3,552
2008 Mathematics, computer and information sciences Sex unknown 72
2008 Architecture, engineering and related technologies Males 34,830
2008 Architecture, engineering and related technologies Females 7,554
2008 Architecture, engineering and related technologies Sex unknown 72
2008 Total Males 55,332
2008 Total Females 26,496
2008 Total Sex unknown 150
2012 Physical and life sciences and technologies Males 13,716
2012 Physical and life sciences and technologies Females 16,044
2012 Physical and life sciences and technologies Sex unknown 6
2012 Mathematics, computer and information sciences Males 9,111
2012 Mathematics, computer and information sciences Females 3,663
2012 Mathematics, computer and information sciences Sex unknown 9
2012 Architecture, engineering and related technologies Males 45,345
2012 Architecture, engineering and related technologies Females 8,913
2012 Architecture, engineering and related technologies Sex unknown 54
2012 Total Males 68,172
2012 Total Females 28,620
2012 Total Sex unknown 69
2014 Physical and life sciences and technologies Males 14,550
2014 Physical and life sciences and technologies Females 17,292
2014 Physical and life sciences and technologies Sex unknown 6
2014 Mathematics, computer and information sciences Males 10,164
2014 Mathematics, computer and information sciences Females 4,125
2014 Mathematics, computer and information sciences Sex unknown 12
2014 Architecture, engineering and related technologies Males 51,198
2014 Architecture, engineering and related technologies Females 10,503
2014 Architecture, engineering and related technologies Sex unknown 45
2014 Total Males 75,912
2014 Total Females 31,920
2014 Total Sex unknown 63
2016 Physical and life sciences and technologies Males 14,688
2016 Physical and life sciences and technologies Females 18,513
2016 Physical and life sciences and technologies Sex unknown 9
2016 Mathematics, computer and information sciences Males 12,039
2016 Mathematics, computer and information sciences Females 4,752
2016 Mathematics, computer and information sciences Sex unknown 24
2016 Architecture, engineering and related technologies Males 58,893
2016 Architecture, engineering and related technologies Females 12,777
2016 Architecture, engineering and related technologies Sex unknown 96
2016 Total Males 85,620
2016 Total Females 36,042
2016 Total Sex unknown 129

Sources:

Statistics Canada, Table 37-10-0020-01

Notes:

Graduates include individuals who have successfully completed an education program at either college or university from the post-secondary non-tertiary to Doctoral or equivalent level

UNESCO's International Standard Classification of Education (ISCED) is the reference classification for organising education programmes and related qualifications by education levels and fields.

Figure 1.6: Employment rates of Indigenous (off-reserve) and non-Indigenous populations by educational attainment, Age 25–54

The Indigenous population in Canada has historically had lower labour force participation and employment rates, and a higher unemployment rate, than the non-Indigenous population. However, there has been some improvement in recent years. For example, the difference in the employment rate of Indigenous persons living on reserve who have completed postsecondary education compared to the non-Indigenous population was 5 percentage points in 2018, down from 7.5 percentage points in 2015. 

Description of Figure 1.6
Figure 1.6: Employment rates of Indigenous (living off-reserve) and non-Indigenous populations by educational attainment, Age 25–54
Years Indigenous population Non-Indigenous population Employment rate gap
Completed post-secondary education
2014 80.1% 85.8% 5.7
2015 78.4% 85.9% 7.5
2016 79.2% 85.7% 6.5
2017 79.9% 86.4% 6.5
2018 81.6% 86.6% 5.0
High school graduate or some post-secondary
2014 67.0% 76.9% 9.9
2015 63.9% 76.3% 12.4
2016 65.1% 76.2% 11.1
2017 67.3% 77.2% 9.9
2018 66.1% 77.3% 11.2
All education levels
2014 69.3% 81.6% 12.3
2015 67.5% 81.8% 14.3
2016 69.1% 81.7% 12.6
2017 70.3% 82.7% 12.4
2018 71.3% 83.1% 11.8

Sources:

Statistics Canada, Labour Force Survey, Table 14-10-0359-01

Notes:

The term "Indigenous Peoples," as used by Statistics Canada, refers to whether the person reported being an Indigenous person, i.e. First Nations (North American Indian), Métis or Inuk (Inuit).

The Labour Force Survey target population does not include the population living on Indian reserves and settlements.

Date modified: