The Demographics of Automation in Canada: Who Is at Risk? IRPP Study

Abstract

The COVID-19 pandemic has exposed a new vulnerability among firms that rely on human labour. In order to comply with public health directives on physical distancing, many businesses have had to completely shut down their operations for months. Others remained functional thanks to teleworking, which many intend to prolong and
even adopt permanently. As experts contemplate the long-term repercussions of the
pandemic on the economy, many expect firms to ramp up their adoption of new technologies to better weather the post-pandemic recession and insulate themselves from future health crises.

Just a few years ago, policy-makers became concerned about the prospect of many job-related tasks being automated using advances in robotics and artificial intelligence and in particular about the projected job losses at the time. While we no longer expect entire jobs to disappear, new technologies may substantially transform jobs,
forcing workers to adjust to new requirements and prompting governments to assist them in this process.

In this study, Statistics Canada researchers Marc Frenette and Kristyn Frank are breaking new ground by examining the demographic and employment characteristics of workers facing a high risk of job transformation due to automation. To assess the potential impact of a new wave of automation on vulnerable workers, policy-makers
need to know not only what jobs are at risk but also who holds these jobs. For instance, while we know that previous waves of robotization replaced low-skilled workers and enhanced the work of those with high skills, this time around there are fears it is high-skilled workers who are at risk, given the rise of new algorithms that are increasingly
proficient at accomplishing complex cognitive tasks.

Full text

IRPP STUDY
June 2020 | No. 77
The Demographics of
Automation in Canada:
Who Is at Risk?
Marc Frenette and Kristyn Frank
THE FUTURE OF SKILLS
AND ADULT LEARNING

ABOUT THIS STUDY
This study was published as part of The Future of Skills and Adult Learning re-
search program, under the direction of Natalia Mishagina. The manuscript was
copy- edited by Madelaine Drohan, proofreading was by Robyn Packard, edi-
torial coordination was by Francesca Worrall, production was by Chantal
Létourneau, and art direction was by Anne Tremblay.
Marc Frenette is a researcher at Statistics Canada. For over two decades he has
been highlighting and explaining trends in various socio-economic areas, including
post-secondary education, skills, immigration, social assistance and income inequal-
ity. He spent two years at the Social Research and Demonstration Corporation as the
lead researcher for two of Canada’s largest randomized eld experiments in post-
secondary access (Future to Discover and BC Avid). He holds a PhD in economics from
the University of Nottingham (UK).
Kristyn Frank is a researcher at Statistics Canada. Her research focuses primarily on the
skills and labour market outcomes of post-secondary graduates, and the social and
economic integration of immigrants in Canada. Previously, she held a Social Sciences
and Humanities Research Council postdoctoral fellowship at the University of Guelph
and was a research analyst at the Higher Education Quality Council of Ontario. She
holds a PhD in sociology from the University of Waterloo.
To cite this document:
Frenette, Marc, and Kristyn Frank. 2020. The Demographics of Automation in Canada:
Who Is at Risk? IRPP Study 77. Montreal: Institute for Research on Public Policy.
The opinions expressed in this study are those of the authors and do not necessarily reect the views of the
IRPP or its Board of Directors.
IRPP Study is a refereed monographic series that is published irregularly throughout the year. Each study is
subject to rigorous internal and external peer review for academic soundness and policy relevance.
If you have questions about our publications, please contact irpp@irpp.org. If you would like to subscribe to
our newsletter, IRPP News, please go to our website, at irpp.org.
Cover photo: Shutterstock.com.
ISSN 1920-9436 (Online) ISSN 1920-9428 (Print)

CONTENTS
Summary ................................................................................................................................. 2
Résumé ....................................................................................................................................3
Automation and Jobs ............................................................................................................ 5
Methods .................................................................................................................................. 6
Who Is at Risk? ........................................................................................................................ 8
Conclusion ............................................................................................................................16
Appendix ..............................................................................................................................19
References ............................................................................................................................24

The Demographics of Automation in Canada: Who Is at Risk?
2
SUMMARY
The COVID-19 pandemic has exposed a new vulnerability among rms that rely on
human labour. In order to comply with public health directives on physical distanc-
ing, many businesses have had to completely shut down their operations for months.
Others remained functional thanks to teleworking, which many intend to prolong and
even adopt permanently. As experts contemplate the long-term repercussions of the
pandemic on the economy, many expect rms to ramp up their adoption of new tech-
nologies to better weather the post-pandemic recession and insulate themselves from
future health crises.
Just a few years ago, policy-makers became concerned about the prospect of many
job-related tasks being automated using advances in robotics and articial intelli-
gence and in particular about the projected job losses at the time. While we no longer
expect entire jobs to disappear, new technologies may substantially transform jobs,
forcing workers to adjust to new requirements and prompting governments to assist
them in this process.
In this study, Statistics Canada researchers Marc Frenette and Kristyn Frank are break-
ing new ground by examining the demographic and employment characteristics
of workers facing a high risk of job transformation due to automation. To assess the
potential impact of a new wave of automation on vulnerable workers, policy-makers
need to know not only what jobs are at risk but also who holds these jobs. For instance,
while we know that previous waves of robotization replaced low-skilled workers and
enhanced the work of those with high skills, this time around there are fears it is high-
skilled workers who are at risk, given the rise of new algorithms that are increasingly
procient at accomplishing complex cognitive tasks.
Consistent with the ndings of previous research, Frenette and Frank show that, over-
all, more than 10 percent of Canadian workers face a high risk of seeing their jobs
transformed through automation – high risk being dened as a probability of 70 per-
cent and higher. And close to 30 percent of workers face a 50-to-70 percent risk. What
the authors underscore, however, is that the extent to which the automation risk varies
is based on certain worker characteristics. For instance, more than a third of workers
without a certicate, diploma or degree face a high risk of job transformation, com-
pared with fewer than 4 percent for those with degrees. The probability of being at
high risk also decreases signicantly as employment income increases. Over a quarter
of workers in the bottom decile of the income distribution are at high risk, whereas
only 2 percent of workers in the top decile are. Also among the groups most exposed
to job transformation are older workers (aged 55 and over), those with low literacy or
numeracy prociency, the part-time employed, those working in small rms, and in
manufacturing, where about 27 percent workers are at high risk. The authors nd no
signicant differences in the risk of job transformation on the basis of gender, immi-
gration status, having a disability or being unionized.
The results from this study stand in sharp contrast with many observers’ expectations
that the new technologies could adversely affect workers previously seen as immune
to automation. It suggests that the workers at high risk of job transformation due to

automation, by and large, share the same characteristics as workers who have been
susceptible to poor labour market outcomes in the past. Frenette and Frank say that
more research is needed to better understand which characteristics can be interpret-
ed as risk factors. Nevertheless, by shedding light on the differential effects of automa-
tion on particular segments of the workforce, their study contributes to labour market
policy development going forward.
RÉSUMÉ
La pandémie de COVID-19 a mis en évidence la vulnérabilité des entreprises qui dé-
pendent du travail humain. Pour se conformer aux directives de distanciation phy-
sique des autorités de santé publique, beaucoup ont dû fermer leurs portes pendant
plusieurs mois. D’autres ont pu maintenir certaines activités grâce au télétravail, que
plusieurs songent à prolonger et même à pérenniser. Tandis que les experts étudient
les répercussions économiques à long terme de la pandémie, nombre d’observateurs
prévoient que les entreprises accéléreront l’adoption de nouvelles technologies pour
mieux affronter la récession attendue et se protéger de futures crises sanitaires.
Il y a quelques années, les décideurs ont commencé à s’inquiéter de l’automatisation
de nombreuses tâches suscitée par les progrès de la robotique et de l’intelligence
articielle, et surtout des pertes d’emplois que laissaient entrevoir les projections ini-
tiales. Mais si l’on n’envisage plus la disparition totale de certains métiers, les techno-
logies continueront de transformer les emplois, obligeant les travailleurs à s’adapter à
de nouvelles exigences et exhortant les gouvernements à leur venir en aide.
Les chercheurs de Statistique Canada Marc Frenette et Kristyn Frank innovent ici en exa-
minant les caractéristiques démographiques et d’emploi des travailleurs qui risquent
d’être fortement touchés par l’automatisation. Car pour mesurer l’impact d’une pro-
chaine vague d’automatisation sur les travailleurs vulnérables, les décideurs doivent
non seulement connaître les emplois menacés mais aussi ceux qui les occupent. Nous
savons ainsi que les précédentes vagues de robotisation ont remplacé des salariés
peu qualiés et proté aux travailleurs spécialisés, mais certains craignent que ces
travailleurs hautement qualiés soient à leur tour menacés par des algorithmes qui
exécutent de plus en plus efcacement des tâches cognitives complexes.
Corroborant des études antérieures, celle-ci montre que plus de 10 p. 100 des tra-
vailleurs canadiens courent un risque élevé (probabilité d’au moins 70p. 100) de voir
leur emploi transformé par l’automatisation, près de 30 p. 100 courant un risque de
50à 70p. 100. Mais ce risque d’automatisation varie selon les caractéristiques des tra-
vailleurs, soulignent les auteurs. Pour plus du tiers des salariés sans certicat ou diplôme
d’études supérieures, par exemple, la transformation des emplois présente un risque
élevé, alors que ce même risque ne touche que moins de 4 p. 100 des diplômés. Et
cette probabilité de risque élevé diminue sensiblement avec l’augmentation du revenu
d’emploi: plus du quart des travailleurs du décile inférieur de la répartition des revenus
courent ainsi un risque élevé, contre seulement 2p. 100 du décile supérieur. Et parmi
3
IRPP Study | June 2020

The Demographics of Automation in Canada: Who Is at Risk?
4
les groupes les plus menacés gurent les travailleurs âgés (55ans et plus), ceux qui ont
un faible niveau de littératie et de numératie, les employés à temps partiel ainsi que les
salariés des petites entreprises et du secteur manufacturier, dont 27p. 100 courent un
risque élevé. Aucune différence notable ne s’observe toutefois selon le sexe, le statut
d’immigration, la situation de handicap ou la présence syndicale.
Les conclusions de cette étude contrastent vivement avec les prédictions de nom-
breux observateurs, selon qui les nouvelles technologies pourraient nuire à des
travailleurs qu’on croyait à l’abri de l’automatisation. Elles indiquent plutôt que les tra-
vailleurs fortement menacés par l’automatisation présentent globalement les mêmes
caractéristiques que ceux qui risquaient déjà de se retrouver désavantagés sur le mar-
ché du travail. Les auteurs estiment qu’il faut approfondir la recherche pour mieux
comprendre quelles caractéristiques constituent des facteurs de risque. Toutefois,
leur étude constitue un solide premier pas vers l’analyse des différents effets de l’au-
tomatisation sur certains segments de la main-d’œuvre. Ce faisant, elle contribue à
l’élaboration de meilleures politiques d’adaptation au marché du travail.

IRPP Study | June 2020
5
AUTOMATION AND JOBS
Recent advances in the development of driverless vehicles, robo-writers and comput-
er-aided medical diagnostics have led many to speculate that new technologies will
trigger widespread adoption of new forms of automation in the workplace, with major
implications for jobs. As they see it, there may be little that humans do that cannot be
replicated at a lower cost by robots or algorithms. These concerns may have been fur-
ther fuelled by early estimates produced by Frey and Osborne (2013) suggesting that
about half of all jobs in the United States could face a high risk of automation within
10 to 20 years. Although they were careful not to interpret their ndings as job loss
estimates, the same could not be said for the popular press.
Since then, conventional thinking on the topic of automation and related job loss has
evolved considerably. First, the general recognition that jobs are made up of vari-
ous tasks has led to changes in the standard methodology for estimating automation
risk. In recent years, researchers have moved away from Frey and Osborne’s occupa-
tion-based approach and adopted more task-based approaches (Arntz, Gregory and
Zierahn 2016; Manyika et al. 2017). Second, whereas initial estimates of automation
risk were generally interpreted as the risk of job loss, task-based approaches have
underscored the point that jobs facing a high automation risk are not necessarily at
risk of being lost. Humans will still be required to work with new technologies. Thus,
the more recent interpretation of automation risk focuses on job transformation, rather
than on job loss. Third, it is also more widely acknowledged that the process and the
timing of adoption of new forms of automation are contingent on a host of factors
beyond technological feasibility. There may be obstacles to overcome, such as rms’
nancial capacity to acquire new technologies, legal constraints (will robots become
licensed medical doctors?), institutional factors such as union contracts, as well as
consumer acceptance (will the public accept driverless city buses?). The social and
economic contexts also have a bearing. For example, the COVID-19 pandemic may
prompt some rms to accelerate investments in automation-related technology to re-
duce their reliance on human workers.
Regardless of the timelines involved, the potential for these new technologies to
fundamentally transform labour markets has captured the attention of many policy-
makers worldwide. They are particularly interested in gauging the potential impacts
on groups of workers susceptible to adverse employment and earnings outcomes
and what, if anything, this could mean for policy. While there is now ample research
on the risk of occupations, jobs or tasks being affected by automation, little is known
about the workers who hold these jobs. Given the targeted nature of many employ-
ment-support programs, understanding who is at risk will inform discussions on policy
development, both now and in the future.
This study seeks to advance our knowledge of automation risk in the Canadian work-
place by examining which groups of workers face greater risk. It is likely that different
types of workers will be affected as technology improves and more tasks are auto-
mated. For instance, manual labourers were the workers most affected when the

The Demographics of Automation in Canada: Who Is at Risk?
6
earliest forms of technology were being implemented. But workers performing more
cognitive tasks may be affected as digitization increasingly underpins most new forms
of technological advancements.
To answer the main research question, our study builds on the work of Arntz, Gregory
and Zierahn (2016) and applies a task-based approach to more recent Canadian data.
Two earlier studies estimated the degree of automation risk for Canadian jobs. They
were based either on the Frey and Osborne approach (Lamb 2016) or the slightly
updated and modied version by Oschinski and Wyonch (2017). In line with Frey and
Osborne’s estimates, the two studies concluded that a signicant proportion of jobs
are at a high risk of automation. Lamb estimated 42 percent. Oschinski and Wyonch
estimated 35 percent. Using a more comprehensive list of tasks in their adjustment,
Arntz, Gregory and Zierahn nd a much lower estimate for Canada — at 9 percent.1
Our study, while similar to that of Arntz, Gregory and Zierahn in methodology, goes
beyond estimating the proportion of jobs at risk and examines differences in the risk
of job transformation due to automation across several worker and rm characteris-
tics. These include workers’ age and education and the size of the rm they work for.
It is important to note that new technologies may create as many new opportunities
for some workers as they remove for others. For example, previous research demon-
strated that advances in digital technology in the 1980s and 1990s replaced certain
jobs associated with routine calculations, such as bookkeepers. But these advances
also created considerably more new jobs in areas that complement digital technology,
such as computer programmers (Autor, Levy and Murnane 2003; Graetz and Michaels
2018). The focus of our study is on the risk — not necessarily of job loss, but of job
transformation — for existing groups of workers. No attempts are made to estimate
new job opportunities for these workers.
METHODS
We used an approach similar to that of Arntz, Gregory and Zierahn, who adjusted
the initial automation risk estimates provided by Frey and Osborne in 2013. Frey and
Osborne assigned a probability of automation over the next 10 or 20 years to occupa-
tions in the 2010 US Standard Occupational Classication (SOC) on the basis of input
from articial intelligence experts.2 They rst presented the experts with a subset of 70
occupations and their task descriptions from the Occupational Information Network
(O*NET) database, and asked them, “Can the tasks of this job be sufciently specied,
conditional on the availability of big data, to be performed by state-of-the-art com-
puter-controlled equipment?” They then modelled the probabilities of automation for
the rest of the occupations on the basis of the experts’ responses. Frey and Osborne
1 Since it is more appropriate to interpret the ndings in these studies as the risk of job transformation, as
opposed to job loss, readers should be careful not to draw any comparisons to the more established litera-
ture on the risk of job loss and post-displacement outcomes (Morissette and Qiu 2020; Morissette and Qiu
2020 forthcoming).
2 The methods are described in considerable detail in the jointly released study by Frenette and Frank
(2020). Only a brief overview is provided here.

IRPP Study | June 2020
7
also used nine task variables that capture three skill categories they call “engineering
bottlenecks to computerization”: perception and manipulation, creativity, and social
intelligence. Using these probabilities, they calculated the share of the US workforce
facing a high risk of automation, which they dened as a probability of 70 percent or
higher.
Arntz, Gregory and Zierahn adjusted Frey and Osborne’s estimates to account for a
more complete list of tasks (25 in total), as well as several individual and workplace
characteristics available in the 2012 Programme for the International Assessment of
Adult Competencies (PIAAC). Their estimates varied not only by occupation, but also
by worker characteristics within occupations to the extent that tasks and other charac-
teristics vary within occupations.
Our approach is similar, but not identical, to that of Arntz, Gregory and Zierahn. As a
rst step, we assigned Frey and Osborne’s automation risk probabilities to workers in
the 2016 Longitudinal and International Study of Adults (LISA), on the basis of their
occupations.3 To make Frey and Osborne’s data compatible with the Canadian data,
we established a concordance between the 2010 SOC used in the United States and
the 2011 National Occupational Classication (NOC) used in Canada, on the basis of
the similarity of the occupational titles.4
Once we assigned the risk estimates to the NOC codes, we matched them to the 2016
LISA data le. The nal sample was limited to paid workers aged 18 or above with
valid responses for all the variables used in our analysis (these are described below).
Self-employed workers were excluded.5 This resulted in a sample of 2,267 individuals.
Next, we adjusted the automation risk estimates from Frey and Osborne using the 25
task variables in LISA, which are almost identical to the ones used by Arntz, Gregory
and Zierahn. Our approach differs from that of Arntz and her co-authors in that we
adjusted the automation risk probabilities only for tasks, and not for individual and
workplace characteristics. This provides a conceptually clear measure of automation
risk based solely on technological feasibility.
We then estimated the automation-related risk of job transformation for various categor-
ies of individual and workplace characteristics. Table A1 (see appendix) provides sample
statistics for many of the variables used to derive these subsamples. They include sex,
age, highest level of completed education, eld of study (among post-secondary gradu-
ates), literacy and numeracy scores, immigration and disability statuses, work hours,
union membership or collective bargaining agreement coverage and rm size. We also
break down the results by occupation, industry and percentile of employment income.
3 Frey and Osborne estimated automation probabilities using data from 2010. Arntz, Gregory, and Zierahn
estimated adjusted automation probabilities using data from 2012.
4 The efforts of Joe He in producing this concordance are greatly appreciated.
5 Self-employed individuals generally face a very low risk of automation (less than 2 percent). This is largely
because they are more likely to be involved in consulting services, which are still, for the most part, the
domain of humans. More generally, self-employed workers may have more exibility to leave (or avoid)
occupations facing a high risk of automation.

The Demographics of Automation in Canada: Who Is at Risk?
8
Again, it should be noted that workers affected by automation are not necessarily
facing job loss as an outcome. In many instances, new technology may change
rather than eliminate their jobs. For example, it may help them achieve the same
or new goals more efficiently. With repetitive tasks delegated to robots and com-
puters, human employees can also spend more time performing tasks not yet
achievable by technology alone. It is for these reasons that the risk we refer to in
this study is the risk of job transformation related to automation. Thus, while it may
be tempting to compare our results with those in previous literature on job dis-
placement, it is important to keep in mind that our results refer to a very different
type of outcome.
WHO IS AT RISK?
As gure 1 shows, the vast majority of Canadian workers face at least some risk of
job transformation due to automation. The predicted risk is at least 10 percent for
98.2 percent of the paid workforce. However, only 10.6 percent of workers face a
high risk of 70 percent or higher. This is the same threshold for high risk used by Frey
and Osborne and Arntz, Gregory and Zierahn. About one-quarter, or 29.1 percent, of
workers face a moderate risk of 50 to 70 percent.6
6 Arntz, Gregory, and Zierahn (2016) found 9 percent for Canada using 2012 data. When we estimated
a model that was almost identical to theirs on the data we use (from 2016), our ndings were virtually
unchanged. Thus, differences between our ndings and theirs are largely related to the timing of the data
(i.e., changes in the occupational distribution of Canadian workers between 2012 and 2016).
Figure 1. Distribution of the predicted risk of job transformation due to automation,
Canada, 2016
Source: Authors’ calculations based on Statistics Canada, Longitudinal and International Study of Adults
(LISA), Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
0
20
40
60
80
100
90%
or more
80%
or more
70%
or more
60%
or more
50%
or more
40%
or more
30%
or more
20%
or more
10%
or more
Predicted risk
%

IRPP Study | June 2020
9
The focus of this study is on the share of workers in different socio-demographic
groups who face a high risk of job transformation from automation.7 It is important to
note that the differences between groups do not represent risk factors, as we do not
account for a broad spectrum of covariates. For example, the risk faced by younger
and older workers may differ due, in part, to differences in other characteristics, such
as educational attainment or even sex. We do not account for the combined effect of
all of these other characteristics in our analysis. Identifying the broad (or uncondition-
al) differences, as we do in this study, is a rst step to understanding which workers
are at risk. Future research based on these differences could delve deeper into under-
standing the gaps identied here.
The subgroup analysis begins with occupations. Although other factors come into play,
occupations largely determine automation risk and provide good context for the results
to follow. As gure 2 indicates, the group with the greatest concentration of workers
facing a high risk is ofce support occupations, at 35.7 percent, which is almost twice as
high as any other occupation.8 This group mainly consists of various types of clerks and
receptionists. It is followed by service supervisors and specialized service occupations,
at 20 percent. This group includes food service supervisors, chefs, butchers, hairstylists,
tailors and cobblers. The next group is workers in industrial, electrical and construction
trades, at 19.7 percent. Also facing an above-average risk are sales representatives and
salespersons in wholesale and retail trade, at 14.7 percent. Next in the list are service
representatives and other customer and personal service occupations, at 13.7 percent.
This group includes those providing food and beverage service and travel and accom-
modation service, as well as security guards and customer service representatives. The
share of workers facing a high risk in maintenance and equipment operation trades is
13.2 percent. Thus, workers facing an above-average risk of job transformation due to
automation (overall, 10.6 percent of workers face a high risk) are concentrated in occu-
pations associated with nonprofessional administrative functions or in various trades,
ranging from personal services to heavy industrial work.
It should be noted that many of the personal service jobs listed above might entail
having friendly conversations with customers, who may not accept a robot substitute
for this task. It may be the case that the articial intelligence experts involved in Frey
and Osborne’s original work did not consider this implicit part of these jobs. They were
only asked about the technical feasibility of automating the job, not the commercial
viability of doing so. Furthermore, the 25 task variables used by Arntz, Gregory and
Zierahn and in our study do not include a measure of conversing with customers (al-
though other forms of communication are included in both studies, such as advising,
making speeches, persuading and negotiating). If friendly conversations with a human
service provider are indeed an important part of certain service jobs, then the risk
faced by these workers may be lower than what is presented here.
7 Although it would also be informative to document the entire risk distribution for each type of worker, the
present study focuses on documenting the share of workers in jobs facing a high risk of automation (in line
with the literature). Future work could adopt a broader distributional approach in a more fulsome investiga-
tion of the differences in the risks faced by various types of workers.
8 Figure 2 shows shares of workers at high risk by occupation, based on two-digit NOC 2011 codes, in des-
cending order of risk. Only occupations with a sample size of 50 or more are shown.

10
Figure 2. Estimated share of workers facing a high risk of job transformation due to automation, by occupation, Canada, 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA), Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/house-
hold/5144
Notes: High risk is dened as a 70 percent or higher probability of job transformation as a result of automation. Occupations are based on two-digit codes from the National
Occupational Classication 2011. Some occupations are not shown due to small sample size.
0 5 10 15 20 25 30 35 40
Professional occupations in law and social, community and government services
Professional occupations in education services
Specialized middle-management occupations in administrative, financial and business,
and communication services (except broadcasting)
Professional occupations in business and finance
Professional occupations in natural and applied sciences
Retail sales supervisors and specialized sales supervisors
Technical occupations related to natural and applied sciences
Paraprofessional occupations in legal, social, community and education services
Technical occupations in health
Administrative and financial supervisors and administrative occupations
Maintenance and equipment operation trades
Service representatives and other customer and personal service occupations
Sales representatives and salespersons — wholesale and retail trade
Industrial, electrical and construction trades
Service supervisors and specialized service occupations
Office support occupations
Percent

IRPP Study | June 2020
11
At the other end of the spectrum are several occupations in which few workers
face a high risk of job transformation. For professional occupations in law and so-
cial, community and government services; education services; and specialized
middle-management occupations in administrative services, nancial and business
services, and communications, the share is zero. Professional occupations in busi-
ness and nance (0.8 percent) and in natural and applied sciences (0.9 percent) are
also at low risk.
Table A2 shows the estimated share of workers at high risk of job transformation due
to automation along various dimensions (some of these are shown in gures 3 to 7 to
follow). Looking at the risk with respect to gender, men and women are equally likely
to face a high risk (10.7 and 10.6 percent, respectively). This nding is interesting in that
women are far more likely to be in ofce-support occupations, which face the highest
risk. Indeed, 7.8 percent of women in the analytical sample worked in those types of jobs,
compared with only 0.9 percent of men. Counterbalancing this to some extent is the fact
that the occupational group facing the third-highest level of risk is industrial, electrical
and construction trades, which is male-dominated (4.1 percent of men in the sample
worked in such occupations, compared with only 0.3 percent of women).9
Being at risk of job transformation due to automation varies more by age group than by
gender. Specically, 13.3 percent of workers between the ages of 18 and 24 and 14.6
percent of those aged 55 and over are in jobs at high risk. In contrast, 7.6 percent of those
aged 25 to 34 and 10.1 percent of those aged 35 to 54 face a high risk (see gure 3).
The difference between these two middle-aged groups and the 55-and-over age group
is statistically signicant at 5 percent.10 However, differences between the 18-to-24 age
group and the 35-to-54 age group are not statistically signicant. Although the difference
between the 18-to-24 age group and the 25-to-34 age group is only signicant at 10
percent, the U-shaped nature of this gure is perhaps not surprising. Very young workers
who have not completed their education may well land part-time jobs in low-paying in-
dustries, performing routine tasks that are highly susceptible to automation. At the other
end of the spectrum, older workers have generally been out of school for quite some
time, so they may not have had the opportunity to train for jobs that are less susceptible
to automation. Firms may prefer to transform jobs through automation only after their
older workers retire, at which point they can train younger workers who can work along-
side the new technology for years to come.
There are also large differences in the probability of facing a high risk of job trans-
formation due to automation depending on the level of education attained (gure
4). Generally, more highly educated workers are less likely to face a high risk. While
33.4 percent of workers with no certicate, diploma or degree and 24.1 percent with
a high school diploma face a high risk, this is true of only 3.6 percent of those with a
bachelor’s degree and 1.3 percent with a master’s degree.11 The differences between
9 See Casey and Nzau (2019) for a discussion of existing work on gender differences in automation risk.
10 The bootstrap standard errors used in determining the statistical signicance of differences reported in this
study are shown in table A3.
11 Results for university transfer program and doctoral graduates are not shown due to small sample sizes.

12
Figure 3. Estimated share of workers facing a high risk of job transformation due to
automation, by age group, Canada, 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA),
Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Note: High risk is dened as a 70 percent or higher probability of job transformation as a result of automation.
Age group
%
0
4
8
12
16
20
55 and over35-5425-3418-24
Figure 4. Estimated share of workers facing a high risk of job transformation due to
automation, by highest level of completed education, Canada 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA),
Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Notes: High risk is dened as a 70 percent or higher probability of job transformation as a result of automa-
tion. PhD and university transfer program graduates are not shown due to small sample size.
0 5 10 15 20 25 30 35
No certificate, diploma or degree
High school diploma or equivalent
Trade or apprenticeship certificate
College or CÉGEP certificate or diploma
University certificate or diploma
below a bachelor's degree
Bachelor's degree
University certificate or diploma
above a bachelor's degree
First professional degree
Master's degree
Highest completed level
Percent

IRPP Study | June 2020
13
individuals with a high school diploma or less and those with a bachelor’s or a master’s
degree are statistically signicant at 0.1 percent. Highly educated workers are more
likely to be employed in professional occupations such as teachers, accountants and
lawyers. As we have shown, workers in these occupations face a lower risk of job trans-
formation due to automation (see gure 2).
Although some results could be generated by eld of study, this was limited by
small sample sizes (fewer than 50 in certain cases; see table A2 in the appendix).
Nevertheless, some interesting insights emerge. For example, among those with
post-secondary certicates or diplomas, mathematics, computer and information
sciences graduates; and personal, protective and transportation services graduates
are the least likely to face a high risk of job transformation due to automation (under
7 percent). At the opposite end of the spectrum, business, management and public
administration graduates; and graduates in health and related elds are the most
likely to face a high risk (over 12 percent).
Among workers with a post-secondary degree,12 those least likely to be at high
risk are graduates of education (1 percent); health and related fields (1.8 percent);
and business, management and public administration (2.2 percent). Graduates of
physical and life sciences and technologies (4.7 percent) and the humanities (4.6
percent) are the most likely to be at high risk. Note, however, that graduates from
every discipline that could be examined at the post-secondary level face a lower
risk than average.
Another interesting nding is that graduates in business, management and public admin-
istration and those in health and related elds face the highest risk if they hold a post-sec-
ondary certicate or a diploma but the lowest risk if they hold a post-secondary degree.
This might reect differences in the share who land jobs related to their education. It could
also reect differences in the types of jobs that are related to the programs. For example,
college business programs include ofce administration. This is part of the ofce-sup-
port-occupations category, which ranks highest in automation risk (see gure 2).13
Literacy and numeracy are also important factors in the risk of job transformation due to
automation.14 Since both are so highly correlated with the level of educational attainment,
12 Note that the vast majority of post-secondary degree holders obtained their credentials from a university
rather than a college bachelor’s degree program (Frenette 2019).
13 Due to sample size limitations, it is not possible to examine the risk of automation for most detailed
categories of occupations. However, it is possible to do so using the automatability index created by Frey
and Osborne (2013). While this index is not adjusted for a broad range of tasks, it is calculated for a large
set of very specic occupations (702 in total), given that it is constructed from the relatively large Quarterly
Census of Employment and Wages. The index suggests that many occupations associated with college
business programs, such as ofce administration, bookkeeping and nancial service sales, face a very high
risk of automation. Colleges also offer several health-related programs that face an above-average risk
according to the index. These include veterinary assistant, dental hygienist and dental assistant.
14 Note that a subsample of the respondents to the PIAAC was selected to participate in the LISA. Therefore,
their scores in literacy and numeracy tests that were assessed by the PIAAC are also available in the LISA
data set. For both measures, the maximum score was 500. From these scores, individuals are given a pro-
ciency level, ranging from ve to less than one. In the analysis to follow, individuals are grouped into two
categories: those with a prociency level of three or above (corresponding to a score of 276 or above) and
those with a prociency level below three.

The Demographics of Automation in Canada: Who Is at Risk?
14
the risk is reported by level of education (table A3). For all three educational levels (no
post-secondary qualications, post-secondary certicate or diploma and post-secondary
degree), those with a literacy or numeracy prociency level of three or above (out of a
maximum level of ve) are considerably less likely to face a high risk of job transformation
due to automation than those with lower prociency levels, although the results are not
always statistically signicant. Specically, higher levels of numeracy are associated with a
lower risk among holders of a post-secondary certicate, diploma or degree, and higher
levels of literacy are associated with a lower risk among those with no post-secondary
credentials and those with a post-secondary certicate or diploma.
The probability of job transformation is reported in table A3 by immigration status,
disability status and union membership (or coverage by a collective bargaining agree-
ment). The differences in the share of workers at high risk between these categories
are small and are not statistically signicant at the 10 percent level.
The other work-related characteristics shown in table A3 reveal some interesting dif-
ferences. For example, 25.7 percent of part-time workers face a high risk of job trans-
formation due to automation, compared with only 8.7 percent among full-time work-
ers. The difference is signicant at the 0.1 percent level.
Workers facing a high automation risk are more likely to earn low employment in-
come. Over one-quarter (26.8 percent) of those in the bottom 10 percent of the
Figure 5. Estimated share of workers facing a high risk of job transformation due to
automation, by percentile of employment income, Canada, 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA),
Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Note: High risk is dened as a 70 percent or higher probability of job transformation as a result of automation.
%
0
6
12
18
24
30
90th or above 75th to below
90th
50th to below
75th
25th to below
50th
10th to below
25th
Below 10th
Percentile

IRPP Study | June 2020
15
income distribution face a high risk. In contrast, only 2.1 percent of workers in the
top 10 percent of the income distribution face a high risk. In fact, as employment
income increases, the probability of facing a high risk of job transformation due to
automation systematically decreases (gure 5). All differences between those in the
bottom 10 percent of the distribution and the other groups are statistically signi-
cant at the 1 percent level, except for those in the second bottom group (between
the 10th and 25th percentiles of the distribution). In that case, the difference is sta-
tistically signicant at the 5 percent level.
Adopting automation-enabled technology in the workplace may require considerable
nancial investment by rms. Since larger rms may have an advantage in securing the
capital stock required, automation may already be in place in those rms, leaving human
workers to perform nonautomatable tasks. However, many other factors may also come
into play, such as the industry concerned or the types of skills used by its employees.
The results in gure 6 are consistent with this hypothesis regarding rm size to some
extent. Among workers in small rms (10 or fewer employees), 14.9 percent face a
high risk of job transformation, compared with only 8.3 percent of those in large rms
(1,000 or more employees). This difference is statistically signicant at the 5 percent
level. However, if rms with 10 or fewer employees are excluded, there is little to no
relationship between rm size and the probability of facing a high risk of job trans-
formation due to automation. Thus, workers in small rms (fewer than 10) face a higher
risk than those in larger rms (10 or more employees).
Figure 6. Estimated share of workers facing a high risk of job transformation due to
automation, by rm size, Canada, 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA),
Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Note: High risk is dened as a 70 percent or higher probability of job transformation as a result of automation.
%
0
5
10
15
20
Over 1,000251-1,00051-25011-501-10
Number of employees

The Demographics of Automation in Canada: Who Is at Risk?
16
The differences in the share of workers in various industries facing a high risk of job
transformation are also considerable (gure 7). For example, 26.6 percent of manu-
facturing workers face a high risk, making them the most exposed to automation risk.
This is considerably more than the share of workers at risk in all other industries and
is statistically signicant at the 5 percent level. The next highest proportion of workers
at risk is in accommodation and food services at 15.4 percent (which is signicantly
different from that in manufacturing at the 10 percent level). At the opposite end of
the spectrum are several sectors in which fewer than 5 percent of workers face a high
risk of job transformation. These are information and cultural industries (2.8 percent);
public administration (3.7 percent); educational services (4.2 percent); and nance
and insurance, real estate and rental (4.8 percent).
CONCLUSION
New technologies have the potential to affect industries and jobs that were thought
to be immune to automation. This has led some observers to predict these technolo-
gies will have a greater negative impact on workers than previously experienced. The
early forms of robotization disproportionately affected low- and middle-skill workers
by taking over routine physical tasks. But new algorithms are expected to have an
impact on high-skill professionals like radiologists and lawyers by automating fairly
complex cognitive tasks. Moreover, new technologies may affect some workers more
Figure 7. Estimated share of workers facing a high risk of job transformation due to
automation, by industry, Canada, 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA),
Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Note: High risk is dened as a 70 percent or higher probability of job transformation as a result of automation.
0510 15 20 25 30
Information and cultural industries
Public administration
Educational services
Finance and insurance, real estate
and rental
Other services
Professional, scientific and
technical services
Construction
Health care and social assistance
Wholesale and retail trade
Transportation and warehousing
Accomodation and food services
Manufacturing
Percent

IRPP Study | June 2020
17
than others if they transform occupations predominantly held by a particular demo-
graphic group, such as truck drivers who are mostly men.
While extensive research has estimated the number and types of jobs that could be
affected by new technological advances, little is known about the workers who hold
these jobs. Understanding who is at risk can shed more light on the nature of antici-
pated labour market transformations and help inform policy development. So, who
are the workers more at risk this time around and are some groups of workers more at
risk than others?
The goal of this study was to identify the characteristics of Canadian workers facing
a high risk of job transformation due to automation. We used an approach similar to
the one developed by Frey and Osborne (2013) and rened by Arntz, Gregory and
Zierahn (2016). Using 2016 data (the most recent Canadian data available), we nd
that 10.6 percent of Canadian workers face a high risk of job transformation due to
automation (a probability of 70 percent or higher), with another 29.1 percent facing a
moderate risk (a probability of 50 to 70 percent). The groups most at risk of job trans-
formation include older workers (aged 55 and over), those with no post-secondary
education, those with low literacy or numeracy prociency and those earning a low
income. By and large, these are the same groups that are vulnerable to unfavourable
labour market conditions and are the focus of targeted labour market policies. Other
workers who are more exposed to a high risk are those employed part time and those
working in small rms and in the manufacturing sector. We nd no signicant differ-
ences in the risk of job transformation on the basis of gender, immigration status,
having a disability or being unionized.
When interpreting these ndings, it is important to keep in mind that the automation
risk estimates reported in this study represent the overall, or unconditional, risk faced
by workers with varying characteristics. Therefore, workers’ characteristics associat-
ed with high risk should not be interpreted as risk factors. To illustrate, our research
showed that part-time workers are more likely to be at a high risk of job transforma-
tion than full-time workers. But having part-time work is not necessarily a risk factor in
itself, because the risk may be due to workers’ ages or education. While this study has
taken a rst look at the demographic characteristics of workers at risk, explaining these
group differences would be a worthwhile avenue for future research.
Moreover, since the estimates of risk of job transformation we have presented are
based solely on the technical feasibility of adoption, it is far from clear how our re-
sults relate to the risk of job loss. While some jobs may be fully automatable from a
technical point of view, rms could still face nancial, legal or societal constraints that
might slow the adoption of new technologies and thus limit job loss. The COVID-19
pandemic could have the opposite effect. Some businesses may respond by investing
more heavily in new technologies in an effort to reduce the reliance on human work-
ers in case of future pandemics. A useful next step for research would be to estimate
the extent to which workers classied as being at risk of job transformation were
subsequently displaced from their jobs.

The Demographics of Automation in Canada: Who Is at Risk?
18
Finally, as they have in the past, new technologies also generate new employment
opportunities, both by creating new jobs and by modifying existing ones. These
new opportunities may mitigate any negative effects of automation. Further re-
search is needed to investigate the role played by advances in articial intelligence
and other new technologies in creating new occupations, shifting the task compos-
itions of existing occupations and changing the nature of tasks performed by work-
ers. Such research would also help inform policy development related to labour
market adjustment.

19
APPENDIX
Table A1: Sample statistics for individual and workplace characteristics, Canada,
2016
Source: Statistics Canada, Longitudinal and International Study of Adults, Wave 3 (2016).
https://www.statcan.gc.ca/eng/survey/household/5144
Characteristics Percent
Gender
Women 51.3
Men 48.7
Age
18-24 6.0
25-34 21.2
35-54 53.1
55 and over 19.6
Highest level of completed education
No certicate, diploma or degree 2.8
High school diploma or equivalent 16.6
Trade or apprenticeship certicate 9.0
College or CEGEP certicate or diploma 26.9
University transfer program 0.2
University certicate or diploma below a bachelor's degree 4.3
Bachelor's degree 23.3
University certicate or diploma above a bachelor's degree 4.5
First professional degree 2.2
Master's degree 9.1
PhD 1.1
Competence (mean score)
Literacy 293.3
Numeracy 285.6
Immigration status
Canadian-born 79.4
Long-term immigrant (10 or more years in Canada) 13.7
Recent immigrant (fewer than 10 years in Canada) 6.9
Workplace status
Part time 11.6
Full time 88.4
Union member or covered by a collective bargaining agreement
Yes 29.3
No 70.7
Disabled
Yes 14.3
No 85.7
Firm size
1-10 employees 20.2
11-50 employees 31.0
51-250 employees 25.4
251-1,000 employees 13.7
More than 1,000 employees 9.7
Sample size (N)2,267

20
Table A2: Estimated share of workers facing a high risk of job transformation due to
automation, by occupation, Canada, 2016
Source: Authors’ estimates based on Statistics Canada, Longitudinal and International Study of Adults (LISA),
Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Notes: Standard errors were obtained using bootstrapping, a computational resampling technique. High risk is
dened as a 70 percent or higher probability of job transformation due to automation.
Occupation Percent Standard error
Ofce-support occupations 35.7 6.1
Service supervisors and specialized service occupations 20.0 7.8
Industrial, electrical and construction trades 19.7 7.9
Sales representatives and salespersons — wholesale and retail trade 14.7 4.1
Service representatives and other customer and personal service
occupations 13.7 4.1
Maintenance and equipment operation trades 13.2 4.8
Administrative and nancial supervisors and administrative
occupations 11.3 2.6
Technical occupations in health 8.2 3.4
Paraprofessional occupations in legal, social, community and edu-
cation services 6.4 3.2
Technical occupations related to natural and applied sciences 4.4 2.0
Retail sales supervisors and specialized sales supervisors 1.8 1.3
Professional occupations in natural and applied sciences 0.9 0.9
Professional occupations in business and nance 0.8 0.7
Specialized middle-management occupations in administrative,
nancial, business, and communication services (except broadcasting) 0.0 0.0
Professional occupations in education services 0.0 0.0
Professional occupations in law and social, community and
government services 0.0 0.0

21
Table A3: Estimated share of workers facing a high risk of job transformation due to
automation, selected characteristics, Canada, 2016
Individual or workplace characteristic Percent Standard error
Sex
Male 10.7 1.2
Female 10.6 1.1
Age
18-24 13.3 2.9
25-34 7.6 1.4
35-54 10.1 1.1
55 and over 14.6 2.0
Highest level of completed education
No certicate, diploma or degree 33.4 7.5
High school diploma or equivalent 24.1 2.9
Trade or apprenticeship certicate 15.4 2.7
College or CEGEP certicate or diploma 9.9 1.5
University transfer program — —
University certicate or diploma below a bachelor's degree 6.7 2.6
Bachelor's degree 3.6 0.7
University certicate or diploma above a bachelor's degree 5.5 3.3
First professional degree 6.1 3.5
Master's degree 1.3 1.0
PhD — —
Field of study (post-secondary certicate or diploma holders)
Personal improvement — —
Education — —
Visual and performing arts, and communications technologies — —
Humanities — —
Social and behavioural sciences and law 10.0 4.3
Business, management and public administration 13.2 2.6
Physical and life sciences and technologies — —
Mathematics, computer and information sciences 3.1 2.0
Architecture, engineering, and related technologies 9.6 2.1
Agriculture, natural resources and conservation — —
Health and related elds 12.9 4.4
Personal, protective and transportation services 6.6 3.0
Other/not stated — —
Field of study (post-secondary-degree holders)
Personal improvement — —
Education 1.0 0.8
Visual and performing arts, and communications technologies — —
Humanities 4.6 2.2
Social and behavioural sciences and law 3.9 1.6
Business, management and public administration 2.2 1.0
Physical and life sciences and technologies 4.7 2.6
Mathematics, computer and information sciences — —

22
Table A3: Estimated share of workers facing a high risk of job transformation due to
automation, selected characteristics, Canada, 2016 (cont.)
Individual or workplace characteristic Percent Standard error
Field of study (post-secondary-degree holders) (cont.)
Architecture, engineering, and related technologies 3.1 1.8
Agriculture, natural resources and conservation — —
Health and related elds 1.8 1.9
Personal, protective and transportation services — —
Other
No post-secondary qualications
Literacy prociency level below 3 32.4 3.8
Literacy prociency level 3 or above 18.4 3.6
Numeracy prociency level below 3 28.7 3.3
Numeracy prociency level 3 or above 20.5 4.7
Post-secondary certicate or diploma
Literacy prociency level below 3 14.8 2.3
Literacy prociency level 3 or above 8.1 1.2
Numeracy prociency level below 3 15.0 2.1
Numeracy prociency level 3 or above 7.1 1.1
Post-secondary degree
Literacy prociency level below 3 4.5 1.7
Literacy prociency level 3 or above 2.8 0.6
Numeracy prociency level below 3 7.7 2.3
Numeracy prociency level 3 or above 1.9 0.5
Immigration status
Canadian-born 10.7 0.9
Long-term immigrant (10 or more years in Canada) 10.3 2.1
Recent immigrant (fewer than 10 years in Canada) 10.5 3.1
Disabled
Yes 10.5 0.8
No 11.4 2.1
Hours worked per week
Below 30, but above 0 (part time) 25.7 3.4
30 or more (full time) 8.7 0.8
Union member or covered by a collective bargaining agreement
Yes 11.9 1.5
No 10.1 0.9
Employment income percentile
Below 10th 26.8 3.9
10th to below 25th 16.6 2.6
25th to below 50th 13.7 1.8
50th to below 75th 5.5 1.0
75th to below 90th 3.1 0.9
90th or above 2.1 1.0

23
Table A3: Estimated share of workers facing a high risk of job transformation due to
automation, selected characteristics, Canada, 2016 (cont.)
Individual or workplace characteristic Percent Standard error
Firm size
1-10 employees 14.9 1.9
11-50 employees 8.6 1.1
51-250 employees 11.2 1.6
251-1,000 employees 9.8 2.3
More than 1,000 employees 8.3 2.1
Industry
Construction 8.4 3.8
Manufacturing 26.6 3.8
Wholesale and retail trade 13.4 2.1
Transportation and warehousing 14.5 4.8
Finance and insurance, real estate and rental 4.8 1.6
Professional, scientic, and technical services 7.2 2.3
Educational services 4.2 1.5
Health care and social assistance 12.0 2.4
Information and cultural industries 2.8 1.4
Accommodation and food services 15.4 5.5
Other services 5.6 3.1
Public administration 3.7 1.0
Source: Authors’ estimates using a probit fractional response model based on Statistics Canada, Longitudinal
and International Study of Adults, Wave 3 (2016). https://www.statcan.gc.ca/eng/survey/household/5144
Notes: Standard errors were obtained using bootstrapping, a computational resampling technique. High risk
is dened as a 70 percent or higher probability of job transformation due to automation.
— Results not shown due to small sample size.

The Demographics of Automation in Canada: Who Is at Risk?
24
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Fondé en 1972, l’Institut de recherche en politiques publiques est un organisme cana-
dien indépendant, bilingue et sans but lucratif. Sa mission consiste à améliorer les poli-
tiques publiques en produisant des recherches, en proposant de nouvelles idées et en
éclairant les débats sur les grands enjeux publics auxquels font face les Canadiens et
leurs gouvernements.
L’indépendance de l’Institut est assurée par un fonds de dotation établi au début des
années 1970 grâce aux contributions des gouvernements fédéral et provinciaux ainsi
que du secteur privé.