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Who Are Canada’s
January 2019
Tech Workers?
Au t hor s
VIET VU CREIG LAMB
Economist Senior Policy Analyst
Viet is an Economist at Creig is a Senior Policy Analyst
the Brookfield Institute for at the Brookfield Institute
Innovation + Entrepreneurship. where he leads the Skills for
Viet is interested in how governments an Innovation-Driven Economy
and companies can intentionally design policies workstream. Creig’s research is focussed on
and markets to drive human behaviour. He is examining how technology is reshaping skills
also fascinated by how the world adapts to the demands and preparing Canadian firms and
emergence of new types of markets as legal workers for the future. Creig holds a Master of
frameworks often lag behind. Viet holds a Master Public Policy from the University of Toronto and a
of Science in Economics from the London School Bachelor of Communications from the University of
of Economics & Political Science and a Bachelor Ottawa.
of Arts in Economics with Honours from the
University of British Columbia. creig.lamb@ryerson.ca | @creiglamb
viet.vu@ryerson.ca | @vviet93
ASHER ZAFAR
Fellow, Data Science
Asher’s passion for civic The Brookfield Institute for Innovation +
innovation has led him through Entrepreneurship (BII+E) is an independent and
a career spanning technology, nonpartisan policy institute, housed within
strategy consulting, and government. Ryerson University, that is dedicated to building
Now a Data Scientist on the Facebook News team, a prosperous Canada where everyone has the
Asher spent the previous year as a consultant opportunity to thrive due to an inclusive resilient
working on production machine learning models economy. BII+E generates far-sighted insights and
and advising on public sector digital strategy and stimulates new thinking to advance actionable
data science projects. Previously, he built and innovation policy in Canada.
managed a quantitative policy analysis team with
the Ontario government, and was a public sector ISBN 978-1-926769-94-3
strategy consultant with Deloitte. Asher holds
degrees in Economics from the University of Texas For more information, visit
at Austin (B.A.) and York University (M.A.). brookfieldinstitute.ca
asherzafar.github.io | @asherzafar /BrookfieldIIE
@BrookfieldIIE
The Brookfield Institute for
Innovation + Entrepreneurship
20 Dundas St. W, Suite 921
Toronto, ON M5G 2C2
w ho a re ca na da’s tech workers?
A C K N O W L E D G E M E N TS CONTRIBUTORS Sarah Doyle, Director of Policy + Research Andrew Do, Policy Analyst Nisa Malli, Senior Policy Analyst Melissa Pogue, Manager, Program Research and Operations, Talent Development, MaRS Discovery District REVIEWERS We would like to thank the following individuals for their feedback on this report: Mark Muro and Sifan Liu from the Brookings Institution Bethany Moir from Toronto Global John Ruffolo from OMERS Ventures Sarah Saska from Feminuity w ho a re ca na da’s tech workers?
Ta b le of C o n t en t s
Introduction 1
Tech workers are diverse, but some
groups are underrepresented and
Understanding tech workers 1
earnings are not equal 26
Defining Tech Workers 2
Visible Minority Tech Workers 26
Tech Skills and Occupations 2
Similar to women, Black workers in
Toronto’s tech sector report lower levels
Glossary of Statistics Canada’s
of diversity, inclusion and belonging 30
demographic concepts for this report 4
Indigenous Peoples in Tech Occupations 34
Concepts calculated and examined 3
Immigrant Tech Workers 35
Defining Tech 3
Conclusion 37
Part 1: Tech Workers at a Glance 5
View and download the data for this
Size and Breakdown 5
report, and for your city! 37
Growth 7
Appendix A: Defining the Tech
Occupations 38
Salary 9
Aggregation methods 40
Education 10
Model Dependence 41
Age 11
Principal Components Analysis 41
Industries 11
Tech Occupations Identified 42
Cities 13
Robustness 44
Part 2:
Diversity in Tech Occupations 17
Appendix B: Decomposing
Demographic Changes 45
Women are underrepresented, and
receive lower salaries in tech occupations 17
Appendix C: Regression with
Aggregated Data 46
For the past 10 years, growth in tech
occupations has primarily been driven
Endnotes 47
by an older male cohort 19
Special Thanks 50
MaRS Diversity, Inclusion, and
Belongings survey: Women report
lower levels of diversity, inclusion and
belonging in tech 23
w ho a re ca na da’s tech workers?
I n t roduc t ion
I
n recent years, Canadian governments at all U N D E R S TA N D I N G T E C H W O R K E R S
levels have been placing some big bets on
technology to propel our economy forward. We For this report, we define tech workers as
are investing billions of dollars into groundbreaking individuals that either produce or make extensive
research in fields such as quantum computing use of technology, regardless of industry. We
and artificial intelligence, and supporting the have taken a bottom-up, skills-based approach
creation of superclusters across the country. We to identify tech occupations, which allows
are producing world-class tech companies and these definitions to evolve as technology, skills,
attracting the attention of large international occupations and industries evolve. We examine
firms such as Amazon and Google. Perhaps most who tech workers are, where they work, and what
importantly, we are also investing heavily in tech’s they earn, as well as which demographic groups
most valuable resource: people. are underrepresented in tech occupations.
As the lines between tech and the rest of the The main takeaway is that Canada is home to
economy continue to blur, tech workers are a large, growing and diverse tech workforce;
becoming critical to the success of most industries.1 ensuring its continued growth is vital for Canada’s
From aerospace engineers to video game economy. However, there are gaps in terms of pay
designers, to metallurgical engineers, tech workers and participation along gender, race, and ethnic
are employed in firms of all shapes and sizes and lines. Canada has a significant opportunity to
they encompass a wide array of skills and outputs. more fully engage it’s diverse labour market to
However, many Canadians lack obvious pathways contribute to an already vibrant tech workforce.
into tech jobs, and for those working in tech, pay
and opportunities for progression are uneven. In addition to this report, we have also released
open data sets and an interactive data visualization
This report sheds light on who Canada’s tech to allow readers to explore our data and findings in
workers are, and on diversity and equity within more detail, and to build upon them with their own
tech occupations. It recognizes the importance analysis.
of the people working in tech occupations across
Canada, while drawing attention to those who are
underrepresented.
w ho a re ca na da’s tech workers? 1
D e f ining Tec h
W o r ke r s
T
o analyze tech workers, we must first define Engineering and Technology, Programming, and
them. Our definition aims to capture the Telecommunications.
pervasiveness of tech talent across industries
and occupations. We ranked each occupation based on how
important each of these six skills is in performing
Many groups around the world have attempted the work of the occupation, as well as the mastery
to define tech occupations in the past, including one is expected to have of these skills within
the Brookings Institution, the US Bureau of Labor the occupation. We used this information to
Statistics and Economic Analysis, and academic generate a “tech ranking” for each occupation.
researchers at Carnegie Mellon University and We then defined tech occupations as those with a
elsewhere. We scanned these definitions to inform composite ranking in the top 5 percent (this cut-off
and contextualize our approach (see Appendix A). was chosen to focus on the most tech-intensive
jobs). Sensitivity tests were performed when
Our approach is founded on an assessment we relaxed this constraint, and relatively small
of the tech intensity of the work involved in employment impacts were observed.
an occupation. This allows us to explore tech
occupations across the economy. Furthermore, we distinguish between two groups
among tech occupations: digital occupations and
high-tech occupations:
T E C H S K I L L S A N D O C C U PAT I O N S 2
++ Digital occupations are those which typically
To reach our tech occupations definition, contribute to the development of computer
we analyzed the skills involved in different hardware or software solutions (i.e., software
occupations. To do this, we linked the US Bureau developers or technology architects).
of Labour Statistics’ (BLS) O*NET database3 to
Canada’s National Occupational Classification ++ High-tech occupations, on the other hand,
(NOC) and selected six skills used by O*NET require advanced technical skills in which
that clearly relate to the production or use computers are used as a means to other ends
of technology: Interacting with Computers, (i.e., engineers or scientists).
Computers and Electronics, Engineering Design,
w ho a re ca na da’s tech workers? 2
DEFINING TECH
Skills Occupations
++PCA
++Network
analysis
Tech
Digital
Skills
Occ
High-Tech
Occ Occ
“Tech
Skills Non-
Score” Skill
Data Tech Non-Tech
cut-off
Occ
Based on PCA and the network analysis of O*Net Occupations with a tech score below the
skills knowledge, and work activities, six items are aforementioned cut-off were excluded. Those above
selected as core tech capabilities. a tech score are sorted into two categories:
Science and math skills correlate with these, but are ++ Digital Occupations: Primarily contributes to
no included. These are averaged into a “tech score” the output of hardware or software.
for each occupation (4-digit NOC).
++ High-Tech Occupations: Not primarily a digital
output, but makes advanced, intrinsic use of
digital technology.
C O N C E P T S C A LC U L AT E D A N D E X A M I N E D
Participation in tech: Share of a demographic Pay in tech: Weighted average of pay in tech
group that works in a tech occupation. E.g. if occupations for the considered demographic
there were 100 male workers in the Canadian groups, where the weight placed on each
economy and 8 of those workers worked in a occupation is the number of people employed
tech occupation, the participation rate for male in that occupation.
workers would be 8 percent.
Pay in non-tech: Weighted average of pay
Share of tech workers: Share of tech workers in non-tech occupations for the considered
that belong to a specific demographic. E.g. if demographic group, where the weight placed
there were 100 tech workers in Canada and 20 on each occupation is the number of people
of them were women, we would say women employed in that occupation.
workers made up a 20 percent share of tech
workers.
w ho a re ca na da’s tech workers? 3
G L O S S A R Y O F S TAT I S T I C S C A N A D A’ S D E M O G R A P H I C C O N C E P T S
FOR THIS REPORT
This report relies on a series of statistical Visible Minority: Under the Statistics Canada’s
definitions from StatCan’s 2016 Census definition, visible minority refers to “whether
Dictionary. a person belongs to a visible minority group
as defined by the Employment Equity Act
Working Individuals: Under Statistics Canada’s and, if so, the visible minority group to which
2016 Census Dictionary definition, those the person belongs. The Employment Equity
considered working individuals were people Act defines visible minorities as ‘persons,
who worked for any amount of time during other than Aboriginal peoples, who are non-
the reference year (2015), even if only for a few Caucasian in race or non-White in colour.’
hours. Categories in the visible minority variable
include South Asian, Chinese, Black, Filipino,
Sex: Statistics Canada recently updated their Latin American, Arab, Southeast Asian, West
sex and gender variables. Under the new Asian, Korean, Japanese, Visible Minority,
definitions, “sex” refers to “sex assigned at n.i.e. (‘n.i.e.’ means ‘not included elsewhere’),
birth” which is typically “based on a person’s Multiple Visible Minorities and Not a Visible
reproductive system and other physical Minority.”
characteristics.” Gender, on the other hand,
refers to “the gender that a person internally Immigrant Status: Under Statistics Canada’s
feels (‘gender identity’ along the gender definition, immigrant status refers to whether
spectrum) and/or the gender a person publicly the person is a non-immigrant, an immigrant
expresses (‘gender expression’).” or a non-permanent resident. Immigrants are
those who have been granted the right to live
We recognize that there are important in Canada permanently, including naturalized
differences in meaning between the terms citizens.
“sex” and “gender,” as well as “female/male”
and “woman/man”; however, in this report we Aboriginal Identity: Under Statistics Canada’s
use these terms interchangeably given that this definition, “Aboriginal identity refers to whether
distinction was not made in Statistics Canada’s the person reported identifying with the
last Census, which is the primary data source Aboriginal peoples of Canada. This includes
for this report. those who reported being an Aboriginal person,
that is, First Nations (North American Indian),
Age: Under Statistics Canada’s definition, Métis or Inuit and/or those who reported
age refers to the age of a person at their last Registered or Treaty Indian status, that is
birthday (or relative to a specified, well-defined registered under the Indian Act of Canada,
reference date) and/or those who reported membership in a
First Nation or Indian band.” While Statistics
Canada used the term “Aboriginal” in the last
Census, for this report we instead use the term
“Indigenous” to better represent all of the
Indigenous Peoples in Canada.
Unfortunately, due to data limitations, we were
unable to examine other critical intersections,
such as LGBTQ+ or disabled tech workers.
w ho a re ca na da’s tech workers? 4
Pa rt 1 : Tec h
W o r ke r s at a
G lance
I
n this first section, we provide an overview Of the top 10 technology occupations in Canada
of Canada’s tech workers, including: how in 2016, the top 4 occupations that employed the
many there are, what they earn, what level of most Canadians were primarily digital ones. This
education they have, what age they are, as well as included 160,000 people working as information
what cities and industries they work in. systems analysts and consultants, forming the
largest occupational group in tech; this was
followed by 104,000 people working as computer
SIZE AND BREAKDOWN programmers and interactive media developers.
The high-tech occupation with the highest
In 2016, around 935,000 Canadians were working employment was civil engineers, with nearly
in tech occupations, representing 5.1 percent of the 58,000 workers.
Canadian labour force. Of these, 681,000 were in
digital occupations while 254,000 were in high-
tech occupations.
Occupational Number of Share of
Group workers workforce
Digital 681,000 3.7%
High-Tech 254,000 1.4%
Non-Tech 18,300,000 94.9%
w ho a re ca na da’s tech workers? 5
Figure 1:
0
40,000
80,000
120,000
160,000
Figure 1
User support technicians
43,820
Electrical and electronics
engineering technologists and
technicians
44,490
Source: 2016 Canadian Census
Electrical and electronics
engineers
46,410
Software engineers and
designers
47,545
Mechanical engineers
w ho a re ca na da’s tech workers?
Digital
Top 10 Tech Occupations by Employment in Canada
54,585
Civil engineers
57,880
High−Tech
Computer and information
systems managers
63,465
Top 10 Technology Occupations by Employment in Canada
Computer network technicians
67,620
Computer programmers and
interactive media developers
104,085
Information systems analysts
and consultants
159,895
6GROWTH
Tech occupations grew relatively faster than the occupations, as defined in this report, exist across
rest of the workforce. Between 2006 and 2016, Statistics Canada’s occupational categories (2
there were 183,000 more people in the tech digit NOCs); these categories are therefore not
workforce. mutually exclusive. Even so, the fact that only
two occupational categories experienced a higher
The share of tech workers in the workforce over percentage change in employment compared
this period grew by 0.66 percentage points to to tech occupations suggests that the relative
5.1 percent. In addition, employment in tech importance of tech workers in Canada’s economy is
occupations grew by 24 percent, which was faster growing.4
than most other occupational categories. Tech
Figure 2:
Percent Change in Employment between 2006 and 2016 for 2 digits NOCs compared to tech
occupations
Figure 2
Change in Share of employment of different occupational groups
75%
50%
25%
0%
−25%
−50%
Business, finance and
administration occupations
Natural resources, agriculture
and related production
occupations
Occupations in manufacturing
Trades, transport and
Health occupations
and utilities
equipment operators and
related occupations
Occupations in art, culture,
recreation and sport
and related occupations
Tech Occupations
Occupations in education,
Sales and service occupations
government services
Natural and applied sciences
Management occupations
law and social, community and
Source: 2006, 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 7Using Employment and Social Development The share of high-tech occupations in Canada’s
Canada’s (ESDC) Canadian Occupational Projection labour market is expected to remain mostly
System (COPS)5, we forecasted future digital and unchanged over this period, at 2.3 percent, while
high-tech employment in Canada. Employment is the share of employment in digital occupations is
projected to grow by eight percent (around 45,200 expected to increase to 4.8 percent—an 8 percent
workers) in high-tech occupations from 2016 to increase in its share of the total workforce. COPS,
2026, and 18 percent (around 143,800 workers) in like other forecasts, relies on many assumptions
digital occupations, totalling 189,000 new workers about future economic conditions and the size
in tech occupations. Employment in non-tech and distribution of occupation demand. If the
occupations is expected to increase by 8.6 percent. rate of tech growth increases, these figures may
underestimate the potential growth in tech jobs.
Figure 3
Figure 3:
Projected Employment
Projected Employment Growth forGrowth for Tech Occupations:
Tech Occupations: 2016-2026 2016−2026
Digital High−Tech
750,000
Employment
500,000
250,000
0
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
Year
Source: Canadian Occupational Projection System (COPS)
w ho a re ca na da’s tech workers? 8SALARY
In 2016, tech workers were paid considerably more
Occupational Group Salary
than non-tech workers. High-tech occupations
earned the most, earning on average $45,000 more
Digital $66,000
than non-tech occupations. Digital occupations
High-Tech $90,000 earned on average nearly $21,000 more than non-
tech occupations.
Non-Tech $45,400
Pay in tech occupations is the highest amongst
engineers, in particular, those working in the
resource sector. In 2016, petroleum engineers
earned the highest salary at $175,292, followed
by engineering managers at $132,409 and mining
engineers at $126,190.
Figure 4
Figure 4: Top 10 Technology Occupations by Income in Canada
Top 10 Tech Occupations by Average Earnings in Canada, 2016
Digital High−Tech
$200,000
$175,292
$150,000
$132,409
$126,190
$118,009
$109,681 $109,975
$99,521 $99,545
$100,000 $94,629 $97,434
$50,000
$0
Mathematicians, statisticians
and actuaries
Electrical and electronics
engineers
Telecommunication carriers
managers
Metallurgical and materials
engineers
Computer and information
systems managers
Geological engineers
Chemical engineers
Mining engineers
Engineering managers
Petroleum engineers
Source: 2016 Canadian Census
w ho a re ca na da’s tech workers? 9E D U C AT I O N
Tech workers have higher levels of formal held no degree or diploma. Workers in non-tech
education on average than non-tech workers. The occupations, on the other hand, were less likely to
majority of tech5 workers (57.8 percent) held at least
Figure hold at least a Bachelor’s degree (25.7 percent), and
a Bachelor’s degree in 2016, and only a minimal 38.9 percent had either no degree or held only a
Educational Composition of Tech Occupations
number (0.8 percent or around 14,000 people) secondary school diploma.
Figure 5:
Educational Composition of Tech Workers in Canada, 2016
100%
No Degree
Secondary School
Apperenticeship and Trade Schools
College, CEGEP
75%
University Degree Below Bachelors
Bachelors
Above Bachelors
50%
25%
0%
Not Tech Occupation Tech Occupation
Source: 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 10AGE
Nearly 53 percent of tech workers in 2016 were
between the ages of 25 and 44, while over 38
percent were between 45 and 64.
Age # of Tech Share of Tech Participation Pay Pay in non-Tech
Group Workers Workforce in Tech in Tech Occupations
15 – 24 57,000 5.9% 2% $26,400 $15,500
25 – 44 514,000 52.8% 6.5% $72,100 $45,300
45 – 64 373,000 38.3% 4.9% $92,000 $52,300
65 and over 28,000 2.9% 2.6% $67,900 $38,000
INDUSTRIES
Figure 6
Figure 6:
Number of Employment
Tech Workersof Tech Workers
Employed by Industry
by Industry GroupsGroups
Digital High−Tech
300,000
200,000
Employment
100,000
0
Professional, scientific and
technical services
Information and cultural
industries
Manufacturing
Public administration
Finance and insurance
Wholesale trade
Construction
Educational services
Utilities
Retail trade
Source: 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 11Among industries, the greatest number of tech Information and Cultural Industries have the
workers are in Professional, Scientific, and Technical highest concentration of tech workers at 28
Services, distantly followed by Information and percent, primarily digital. Utilities had the highest
Cultural Industries. The makeup of tech workers concentration of high-tech workers at 9 percent,
varies by industry. For instance, Manufacturing while the Finance and Insurance sector’s tech
employs a large number of engineers and other workforce is almost entirely digital.
high-tech workers. Meanwhile, the relatively large
number of tech workers in Public Administration
and Finance is driven by their large digital
workforce, particularly Information Systems
Analysts and Consultants, which accounted for
about 21,000 workers in each industry.
Figure 7: Figure 7
Share ofShare
Tech Workers by Industry
of Tech Workers Groups Groups
by Industry
Digital High−Tech
30 %
Share of Industry Employment
20 %
10 %
0%
Information and cultural
industries
Professional, scientific and
technical services
Utilities
Management of companies and
enterprises
Mining, quarrying, and oil and
gas extraction
Finance and insurance
Public administration
Manufacturing
Wholesale trade
Construction
Source: 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 12CITIES
The top five cities by tech worker employment Between 2006 and 2016, Toronto and Montréal saw
were Toronto with 238,000, Montréal with 140,000, the largest absolute increase in the number of tech
Vancouver with 82,000, Ottawa with 69,000, and workers, with the cities adding 53,000 and 33,000
Calgary with 63,000. tech workers over the 10-year period, respectively.
Meanwhile, Kitchener-Waterloo and Fredericton
The cities across Canada with the highest saw the largest increase in the concentration
concentration (proportion of the labour force of tech workers over the same 10-year period.
occupied by tech workers) were Ottawa with 9.8 Kitchener’s tech employment grew from 5.5%
percent, Calgary with 7.9 percent, Toronto with 7.6 of their total workforce to 7 percent, while
percent, Fredericton with 7.2 percent, and Waterloo Fredericton’s grew from 6 percent to 7.2 percent.
Region with 7 percent. Digital workers make up the
majority of tech workers in these cities; however, Learn more about your city’s tech workforce with
Calgary also has a large share of high-tech workers, our data visualization for every city in Canada.
presumably the result of a large number of
engineers working in the region’s resource sectors.
Figure 8: Figure 8
Concentration of TechConcentration
Geographical Workers by Cities
(%) in
ofCanada
Technology Occupations, 2016 Canada
Digital High−Tech
10 %
8%
5%
2%
0%
St. John's
Vancouver
Montréal
Québec
Kitchener − Cambridge −
Waterloo
Carleton Place
Fredericton
Toronto
Calgary
Ottawa − Gatineau
Source: 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 13Figure 9: Figure 9
Tech Occupations Employment
Geographical by Canadian
Distribution Cities Occupations, Canada
of Technology
Digital High−Tech
250,000
200,000
150,000
100,000
50,000
0
Winnipeg
Hamilton
Kitchener − Cambridge −
Waterloo
Québec
Edmonton
Calgary
Ottawa − Gatineau
Vancouver
Montréal
Toronto
Source: 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 14Figure 10:
10 Years Change in Tech
FigureOccupations
10 Employment for Canadian Cities, 2006-2016
10 Years Change in Absolute Number of Tech Workers by Canadian Cities
In 2006 In 2016
Toronto 185,360 237,885
Montreal 107,645 140,240
Vancouver 61,685 81,535
Calgary 49,300 62,975
Ottawa − Gatineau 61,655 69,435
Edmonton 27,300 34,360
Quebec 22,735 29,210
Kitchener − Cambridge −
13,785 19,875
Waterloo
Hamilton 14,500 18,205
Winnipeg 15,575 18,080
0 50,000 100,000 150,000 200,000 250,000
Source: 2016, 2006 Canadian Census
w ho a re ca na da’s tech workers? 15Figure 11:
Figure
10 Years Change in Share of11Employment for Canadian Cities
10 Years Change in Relative Number of Tech Workers by Canadian Cities
Kitchener − Cambridge −
Waterloo
Fredericton
Quebec
Montreal
Toronto
Vancouver
St. John's
Calgary
Ottawa − Gatineau
5% 6% 7% 8% 9% 10 %
Source: 2016, 2006 Canadian Census
w ho a re ca na da’s tech workers? 16Pa rt 2:
Di v er si t y in Tec h
Occ u pa t i o n s
I
n this section we examine diversity among tech Women in tech occupations are more likely to
workers, looking specifically at the earnings and hold a Bachelor’s degree or higher. However,
participation of women, visible minority groups, when comparing women and men in tech with a
immigrants and Indigenous Peoples. Bachelor’s degree or higher, the simple pay gap is
much higher at $19,570. The pay gap between men
and women is greater for older workers, which
WO MEN ARE UN D ERREPRE S EN TED, might indicate that pay differentials increase as
AND RECEIVE LOWER SALARIES IN careers progress or might reflect an improvement
T E C H O C C U PAT I O N S in pay equity in recent years.
Our findings Context
There are serious participation and earnings These findings unfortunately do not come as a
disparities between men and women in tech. surprise. It has long been the case that gender
representation and earnings in tech occupations
Men are four times more likely than women to be are far from equal. A significant body of research
in a tech job; and over the past 10 years, growth suggests that barriers to entering tech roles
in the number of tech workers has been primarily begin early in life for women: influences from
driven by an increase in the share of male tech families, teachers, role models, and cultural
workers between the ages of 45 and 64. There is stereotypes can impact women’s decisions to
also a stark pay gap between men and women in engage in subjects that set them up for tech roles
tech occupations, with women earning on average later in life. There is also evidence pointing to a
$7,300 less than their male counterparts.6 male-dominated culture in science, technology,
w ho a re ca na da’s tech workers? 17engineering and mathematics (STEM) education, Gender participation in tech occupations
and to discrimination in hiring or on the job.
These barriers can steer women away from STEM Labour force participation among women in
majors, and impact their career opportunities Canada has been steadily increasing. In 1983, 65.2
and trajectories in tech. While women have long percent of Canadian women between 25 and 54
surpassed men in attaining a bachelor’s degree participated in the labour market. By 2015, this
or higher, they remain underrepresented in STEM figure had rose to 82 percent. Canada now has
education programs.7 These trends continue the lowest gender participation gap of all G-7
into the labour market in the form of lower countries. In 2016, women made up 48 percent of
participation in science and tech occupations. the labour market, compared to 45 percent in 1991.
Previous studies have also highlighted that
women tend to be paid less, both within the same Despite these trends, in 2016 there were 584,000
occupations and across occupations. Furthermore, more men in tech occupations than women. Men
the gender pay gap grows as careers progress and were almost four times more likely than women
salaries increase, resulting in particularly stark to work in a tech occupation.
differences at the top of the wage distribution.
Table 1:
Tech Workers by Gender
Gender # of Tech Workers Share of Tech Workforce Participation in Tech
Men 778,000 80% 7.8%
Women 194,000 20% 2.1%
w ho a re ca na da’s tech workers? 18F O R T H E PA S T 1 0 Y E A R S , G R O W T H I N T E C H O C C U PAT I O N S H A S P R I M A R I LY
BEEN DRIVEN BY AN OLDER MALE COHORT
Women have dramatically increased their male cohort (see full methodology in Appendix
participation in the labour force writ large. But B). Tech workers between the ages of 45 and 64
the participation rate among women in tech years old accounted for nearly 90 percent of the
occupations was much lower than men across all 189,000 person increase in tech workers across the
age groups. Canadian economy. Men in this age range were
responsible for 79 percent of the total growth,
As a result, growth in the number of tech workers adding nearly 129,000 tech workers.
from 2006 to 2016 was primarily driven by an older
Women participate at lower rates in tech, for all age groups
*MKYVI
Figure 12:
Employment in Tech Occupations by Age and Sex, 2016
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1EPI *IQEPI
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7SYVGI 'EREHMER'IRWYW&--)%REP]WMW
2SXI )EGLTSMRXVITVIWIRXWTISTPI
w ho a re ca na da’s tech workers? 19Table 2:
Age and gender contribution to tech job growth, 2006 to 2016
Age
Sex 15-24 Years 25-34 Years 35-44 Years 45-54 Years 55-64 Years 65-74 Years Total effect
Male -7% 5.4% 13.1% 33.9% 36.5% 7.6% 89.5%
(-12,800 (9,900 (24,000 (62,000 (66,800 (13,900
workers) workers) workers) workers) workers) workers)
Female -1.4% -2.8% -5.6% 7.9% 11.3% 1% 10.5%
(-2,600 (-5,100 (-10,200 (14,500 (20,700 (1,800
workers) workers) workers) workers) workers) workers)
Total -8.4% 2.6% 7.5% 41.8% 47.8% 8.6%
effect – Age
The largest differences in participation among Men earn significantly more than women
men and women in tech occupations were for in tech occupations and this pattern is
those aged 25 to 44. While a large cohort of consistent across different demographic
younger workers are entering tech occupations, groups
women between the ages of 25 and 44 saw an
overall decrease in their share of tech occupations Men are not only much more likely to work in a
from 2006 to 2016. During this period, the total tech occupation than women; they also earn higher
number of women in the labour market aged salaries than their female counterparts. With an
25 to 34 increased, but without a corresponding average salary of $76,200, men in tech occupations
increase in the number of women working in tech earn on average $7,300 more than women in tech
occupations. occupations.
Further research is needed to explain these Table 3:
trends. Are fewer younger workers entering tech Gender differences in pay for tech occupations
occupations? Or is this simply reflective of broader
demographic trends, in particular, an aging Pay in Pay in non-Tech
Sex
Tech Occupations
population?
Male $76,200 $49,500
Female $68,900 $39,400
However, women in tech occupations experienced
a higher tech pay premium, earning 74.6 percent
or $29,500 more on average than women in non-
tech occupations. This compared to men in tech
occupations who earned 54 percent or $26,700
more than men in non-tech occupations. On
average, the pay gap between men and women
in tech occupations is smaller, by approximately
$3,000 per year, compared to the pay gap in non-
tech occupations.
w ho a re ca na da’s tech workers? 20The average pay gap between men and Differences in education for women and men in
women in tech occupations gets larger the tech occupations
more education a worker has
There are two critical differences between men
Within tech occupations, there are some notable and women in tech occupations when it comes
gender differences when it comes to educational to education. First, a higher number of men (34.5
attainment and fields of study. However, percent compared to 23.4 percent of women) in
preliminary analysis suggests the gender pay gap tech occupations received their education through
gets larger with more education. colleges, apprenticeships or trade schools. Women
are more likely to hold a Bachelor’s degree or
Figure 13 higher (61.5 percent compared to 56.9 percent of
men),Occupations
Educational Composition by Sex − Technology which is consistent with broader trends in
higher education enrolment.
Figure 13:
Educational Composition by Sex, Tech Occupations, 2016
100%
No Degree
Secondary School
Apperenticeship and Trade Schools
College, CEGEP
75%
University Degree Below Bachelors
Bachelors
Above Bachelors
50%
25%
0%
Female Male
Source: 2016 Canadian Census, BII+E Analysis
w ho a re ca na da’s tech workers? 21Second, men and women tend to specialize in We use a regression framework (see Appendix C)
different fields. Looking at the top three areas that draws on aggregated-level data to separate
that tech workers have majored in highlights the effect of education and sex on pay and explore
these differences. 43.9 percent of men in tech how they interact with each other. While this by no
occupations majored in Architecture, Engineering, means constitutes a full exploration of the gender
and Related Technologies, compared to 25.3 percent pay gap in tech occupations, it illuminates an
of women. In contrast, Business, Management, interesting dimension of this gap.
Marketing, and Related Studies is a more popular
area of concentration among women in tech The simple pay gap between male and female
occupations, with just over 15 percent majoring tech workers without a bachelor’s degree is
in these fields, compared to 10 percent of men in about $7,500. For those with a bachelor’s degree
tech occupations. Interestingly, the share of men or higher, however, the pay gap grows to about
and women in tech occupations who majored in $19,600. Additionally, a man with a bachelor’s
“mathematics, computer science and informational degree or higher earned $27,400 more than a man
sciences” is roughly equivalent. without a bachelor’s. By comparison, women with
a bachelor degree or higher earned only $15,000
Differences in educational attainment do not more than women without a bachelor’s.
explain the simple gender pay gap in tech
occupations Table 4:
Pay by gender and degree
Despite differences in educational attainment
between men and women in tech occupations, the Below Bachelor
simple pay gap is, in fact, larger for tech workers bachelor’s degree and above
with a bachelor’s degree or higher. Male $67,600 $95,100
Female $60,200 $75,500
Table 5:
Does education explain the simple gender pay gap?
Estimate
Parameter (without standard error)
β0 Earnings for men without a bachelor’s in tech occupation $67,600
β1 Earnings difference between men and women in tech without a bachelor’s -$7,500
Earnings for women without a Bachelors in tech $60,200
β2 Difference in earnings for men in a tech occupation with a bachelor’s, compared to
$27,400
men in a tech occupation without a bachelor’s
Earnings for men with a bachelor’s degree or higher in a tech occupation $95,100
β3 Difference in the bachelor’s premium for women compared to men -$12,100
Earnings for women with a bachelor’s degree or higher in a tech occupation $75,500
Earnings difference between men and women in tech with a bachelor’s degree or
-$19,600
higher
w ho a re ca na da’s tech workers? 22The simple gender pay gap also gets larger individuals progress through their careers, gaining
the longer workers are in tech occupations experience and in some cases seniority. However, it
could also indicate that the simple pay gap in tech
Similar to participation rates, the simple pay gap occupations is shrinking over time, with younger
between men and women is larger for older tech tech workers experiencing smaller pay gaps than
workers (45 to 64 years old), at $11,600, while their older counterparts. Further investigation is
for younger tech workers (25 to 44 years old) it is needed to understand this relationship.
$8,600. This could signal, consistent with other
studies, that the gender pay gap increases as
M a R S D I V E R S I T Y, I N C L U S I O N , A N D B E L O N G I N G S S U R V E Y : W O M E N R E P O R T
L O W E R L E V E L S O F D I V E R S I T Y, I N C L U S I O N A N D B E L O N G I N G I N T E C H
In 2018, MaRS, Feminuity, and Fortay conducted from this report’s focus on tech workers across
a survey to examine diversity, inclusion, and Canada’s economy, the results of this survey
belonging in Toronto’s tech sector. While its help to illuminate some of the challenges
focus on workers in Toronto’s tech sector differs facing women in tech.
Figure 14
Figure
Toronto14
Tech sector DIB Scores by Respondent Gender
Figure 14:
Toronto Tech sector
Toronto Tech sector DIB Scores by DIB Scores
Respondent by Respondent Gender
Gender
Gender Women Men
Gender Women Men
3.47***
Overall inclusion score 3.47***
Overall inclusion score 3.72
3.72
3.74***
Overall diversity score 3.74***
Overall diversity score 3.98
3.98
3.75**
Overall belonging Score 3.75**
Overall belonging Score 3.96
3.96
0 1 2 3 4 5
0 Average
1 Response Scores
2 (1=Strongly Disagree;
3 5=Strongly agree)
4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
** at the 5% level; * at the 10% level; N = 425
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 23Overall, women in Toronto’s tech sector if it means failing, and feeling a sense of
reported lower levels of diversity, inclusion and belonging even if something negative happens.
belonging compared to men.8
Additionally, women in Toronto’s tech sector
This lower sense of belonging among women also feel less engaged in decision-making
in Toronto’s tech sector includes feeling less processes at work and are more likely to believe
comfortable being their authentic self, voicing that the division of labour and the distribution
an opinion (in particular one that differs from of salaries and benefits are unfair.
the group consensus), being innovative even
Figure 15:
Figure 15
Toronto Tech Sector Belonging Scores by Respondent Gender
Toronto Tech sector Belonging Scores by Respondent Gender
Gender Women Men
I feel comfortable to voice my 3.65***
opinion, even when it differs
from the group opinion 3.95
3.77*
I feel comfortable to be my
authentic self at work
3.94
I am encouraged to be 3.84**
innovative even though some of
the things I try may fail 4.02
Even when something negative 3.76**
happens, I still feel like I
belong at my company 3.93
0 1 2 3 4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 24Figure 16: Figure 16
Toronto Tech Sector Inclusion Scores
Toronto Techby Respondent
sector Gender
Inclusion Scores by Respondent Gender
Gender Women Men
When tasks that no one person 3.21***
is responsible for need to
get done the tasks are divided
fairly 3.65
My company enables me to 3.76
balance my personal and
professional life 3.92
I believe that my total salary 3.35**
and benefits are fair when
compared to the employees in
similar roles at my company 3.56
3.54*
I am part of the decision−
making process at work
3.74
0 1 2 3 4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 25TECH WORKERS ARE DIVERSE, Our findings also reflect Canada’s digital divide,
BUT SOME GROUPS ARE which is reinforced by uneven access to technology
UNDERREPRESENTED AND EARNINGS and training. In particular, many rural and remote
ARE NOT EQUAL communities, including Indigenous communities,
lack consistent access to the training programs,
high speed and reliable internet, and digital tools
Our findings that are vital to building and maintaining digital
literacy and the advanced skills needed to be
Diversity in Canada’s tech occupations is, in competitive in tech fields.
general, high relative to the Canadian labour
market as a whole; however certain groups are
underrepresented and receive less pay. Visible VISIBLE MINORITY TECH WORKERS
minorities made up 31.9 percent of Canada’s
tech workers and were more likely to work in Visible minorities are more likely than non-
tech occupations than non-visible minorities. In visible minorities to work in tech occupations.
addition, 37.6 percent of Canada’s tech workforce 7.6 percent of all visible minorities participated
are immigrants, and immigrants are twice as in tech occupations, collectively representing
likely to work in tech careers compared with approximately 294,000 people, compared to 4.4
non-immigrants. However, participation rates for percent of non-visible minorities, representing
Black, Filipino, and Indigenous populations are 641,000 people. Those identifying as Chinese,
low. There is also a significant pay gap for most West Asian, Arab, and South Asian were the
visible minority groups—particularly for Black tech most likely to work in tech occupations out of all
workers—relative to White and non-Indigenous visible minority groups. On the other hand, those
tech workers. identifying as Filipino or Black had the lowest
participation rates in tech occupations.
Context For most visible minority groups in tech
occupations, however, average pay is much lower
Our findings align with existing, predominantly US- than for non-visible minority tech workers. This
focused, research on diversity in tech occupations, difference in pay is particularly stark for Black
which has highlighted that there are significant tech workers.
barriers faced by certain demographic groups, in
particular, Black and Hispanic workers.9 Studies Average pay across all visible minorities in tech
have shown, for example, that teachers have lower occupations was $76,300, which is more than
expectations of Black students, particularly when $37,000 higher than the average pay that visible
it comes to math, and many underrepresented minorities received in non-tech occupations.
minorities are less likely to have strong beliefs in However, it was $3,100 lower than for non-visible
their mathematical abilities.10 Even when Black and minorities in tech occupations. Black tech workers
Hispanic students major in tech-oriented degrees, were the lowest paid out of all visible minority
they are less likely than their White and Asian groups. Their average salary was $63,000 in 2016,
counterparts to pursue a career in tech.11 Some over $13,000 less than the average across all visible
suggest this is the result of biases in recruiting, minority groups in tech occupations, and over
negative perceptions of the work culture, and $16,000 lower than non-visible minorities in tech
encounters with racism on the job. In a study of occupations.
individuals who voluntarily left tech occupations,
“men of colour” were most likely to leave because
of perceived unfairness, and nearly one quarter of
underrepresented “men and women of colour”
who left tech jobs experienced stereotyping, twice
the rate of their White and Asian counterparts.
w ho a re ca na da’s tech workers? 26Table 6:
Visible Minorities in Tech Occupations
Visible # of Tech Share of Tech Participation Pay in Pay in non-Tech
Minority Workers Workforce12 in Tech Tech Occupations
Not a Visible
641,000 68.6% 4.37% $79,400 $46,800
Minority
All Visible
294,000 31.4% 7.65% $76,300 $38,700
Minorities
South Asian 79,000 9.2% 8.92% $74,000 $40,100
Chinese 91,000 9.8% 11.94% $79,700 $42,700
Black 24,000 2.6% 4.27% $63,000 $35,900
Filipino 16,000 1.7% 3.4% $69,000 $37,400
Latin American 16,000 1.7% 6.08% $72,900 $35,700
Arab 19,000 2% 9.14% $70,000 $36,000
Southeast Asian 10,000 1.1% 6.06% $72,300 $35,900
West Asian 13,000 1.4% 10.14% $69,000 $33,300
Korean 6,000 0.6% 6.39% $68,100 $34,700
Japanese 3,000 0.3% 6.37% $84,400 $45,300
w ho a re ca na da’s tech workers? 27Visible minority women in tech (average salary $58, 550), and Filipino (average
salary $59, 620) earn the least in tech occupations.
Disparities in pay are even starker for women tech
workers belonging to visible minority groups. For However, for both men and women across visible
the most part, women receive lower compensation minority groups, there is a pay premium for
than men across all visible minority groups, working in tech occupations that on average 20.6
receiving, on average, $10,900 less than their male percent higher than the pay received by each group
counterparts in tech occupations. However, non- in non-tech occupations.13
visible minority and Chinese women, with average
salaries of $71,480 and $73,430 respectively, do With the exception of Chinese women, all women
earn more than many visible minority men in tech, from visible minority groups participated in tech
notably Black, West Asian, and Korean men. occupations at rates lower than men from the
same visible minority groups. Participation rates
Amongst women in tech occupations, visible are highly correlated with the average salary for
minority women earn less than all non-visible men and women across visible minority groups, as
minority women. Women who identify as Korean shown in Figure 18.
(average salary $50,150), West Asian (average salary
$58,880), Black (average salary $58,480), Arab
Figure 17: *MKYVI
Pay Difference between Tech and Non-Tech Occupations by Visible Minority Identities and Sex
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w ho a re ca na da’s tech workers? 28Figure 18:
Figure 22
Pay and Participation by Visible Minority and Sex
Pay and Participation by Visible Minority and Sex
Sex
Female Male
20 %
Chinese
Participation Rate in Tech Occupations
15 % West Asian
Arab South Asian
Korean Japanese
10 % Southeast Asian
Latin American
Black Not a visible minority
West Asian Filipino
Chinese
5%
South Asian
Arab
Korean Southeast Asian
Japanese
Latin American
Black Not a visible minority
Filipino
0%
$0 $25,000 $50,000 $75,000 $100,000
Average pay in Tech Occupations
Source: 2016 Canadian Census
Note: Each Point Represents a Visible Minority − Sex pair
w ho a re ca na da’s tech workers? 29SIMILAR TO WOMEN, BLACK WORKERS IN TORONTO’S TECH SECTOR
R E P O R T L O W E R L E V E L S O F D I V E R S I T Y, I N C L U S I O N A N D B E L O N G I N G
Once again, drawing upon the survey conducted who are different can thrive at their company
by Feminuity, MaRS, and Fortay, we see similar compared to White, Asian, and other visible
trends. Black workers in Toronto’s tech sector minorities. They also reported feeling less
reported lower levels of diversity, inclusion and involved in the decision-making process
belonging. at work; and in line with our findings, they
were more likely to feel that their salaries
Of those surveyed, Black workers in Toronto’s and benefits are unfair compared to other
tech sector were less likely to feel that those employees in similar roles.
Figure 19:
Figure 17
Toronto Tech Sector Dib Scores By Repondent Race
Toronto Tech sector DIBS Scores by Respondent Race
Gender White Non−White Black Asian
3.62
3.53
Overall inclusion score
3.3**
3.61
3.85
3.85
Overall diversity score
3.53*
3.84
3.88
3.81
Overall belonging Score
3.56**
3.85
0 1 2 3 4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 30Similar to women, surveyed Black workers in Black workers in Toronto’s tech sector were also
Toronto’s tech sector feel less of a sense of less likely to feel that their company comprised
belonging than their White, Asian and Non- of a diverse workforce and provided equal
White counterparts. They feel less comfortable opportunities for all workers.
being their authentic self at work, and feel less
like they belong when a negative situation arises.
Figure 20:
Figure 18
Toronto Tech Sector Inclusion Scores By Respondent Race
Toronto Tech sector Belonging Scores by Respondent Race
Gender White Non−White Black Asian
When tasks that no one person 3.39
is responsible for need to 3.44
get done the tasks are divided 3.35
fairly
3.44
3.86
My company enables me to 3.79
balance my personal and
professional life 3.6
3.97
I believe that my total salary 3.5
and benefits are fair when 3.39
compared to the employees in 3.09**
similar roles at my company
3.48
3.73
I am part of the decision− 3.5**
making process at work 3.15***
3.54
0 1 2 3 4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 31Figure 21:
Figure 19
Toronto Tech Sector Belonging Scores By Respondent Race
Toronto Tech sector Belonging Scores by Respondent Race
Gender White Non−White Black Asian
3.82
I feel comfortable to voice my 3.76
opinion, even when it differs
from the group opinion 3.56
3.78
3.85
I feel comfortable to be my 3.84
authentic self at work 3.52*
3.92
3.97
I am encouraged to be 3.84
innovative even though some of
the things I try may fail 3.74
3.84
3.87
Even when something negative 3.79
happens, I still feel like I
belong at my company 3.41***
3.85
0 1 2 3 4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 32Figure 22:
Figure 20
Toronto Tech Sector Diversity Scores By Respondent Race
Toronto Tech sector Belonging Scores by Respondent Race
Gender White Non−White Black Asian
People who look, feel, and 3.88
think differently have equal 3.88
opportunities to thrive at my 3.36**
company
3.92
4.02
My company values the 4.05
differences of individuals 3.82
3.98
3.88
My company represents a 3.82
diverse group of talent 3.48**
3.79
3.61
My company invests time and 3.64
energy in making our company
diverse 3.45
3.65
0 1 2 3 4 5
Average Response Scores (1=Strongly Disagree; 5=Strongly agree)
Source:MaRS Discovery District analysis using survey dataset powered by Fortay and Feminuity
Note: *** denotes statistically different from men score at the 1% level;
** at the 5% level; * at the 10% level; N = 425
w ho a re ca na da’s tech workers? 33You can also read
