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Archives of Business Review – Vol. 8, No.7
Publication Date: July 25, 2020
DOI: 10.14738/abr.87.8770.
Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
The Impact of COVID-19 on the Canadian Economy
Sadequl Islam
Professor, Department of Economics
Laurentian University, Sudbury, Ontario, Canada.
Tahsina Tarannum
Data Analyst, Data Science,
University of Toronto, Canada.
ABSTRACT
This paper examines various types of effects of COVID-19 on Canadian
businesses and industrial sectors. Using the data from Labour Force
Surveys, the paper explores the impact of COVID-19 on the Canadian
labour market. The paper also investigates the relationship between
the intensity of lockdown measures ( stringency index) and the
unemployment rate for Canada and selected countries. The main
findings are the following: 1)The businesses which faced a high level of
decreases in demand are food &accommodation, arts, entertainment,
and recreation and retail trade; 2) Small businesses witnessed a higher
level of decrease in demand compared to large businesses; 3) The
goods- producing industries, especially motor vehicle and parts
producing industries experienced the steepest decline in growth rates;
4)The pandemic adversely affected the jobs of women, workers with
high school education, and young workers; 5) Finally, it appears that
there is a weak positive relationship between the Stringency Index and
the unemployment rate across selected countries including Canada.
Key Words: COVID-19, social distancing, stringency index, labour market.
INTRODUCTION
The coronavirus has generated triplet crises in the world: a public health crisis, an economic crisis,
and a social crisis. The world economy is witnessing a recession far worse than the recession of
2008-09. As of July 21, 2020, as reported by the Public health Agency of Canada, the total number
of COVID-19 cases and deaths in Canada were 111,697 and 8,862, respectively. According to the
database of Worldometers, among all countries, Canada ranks 21 in the total number of COVID-19
cases and 14 in the total number of deaths. The economies of major countries, including Canada,
are facing negative demand as well as supply shocks (Guerrieri, Lorenzoni, Straub, and Werning,
2020). Because of social isolation and travel restrictions, many industries in the service sector, such
as travel and tourism, sports, hotels, entertainment, retail outlets, and restaurants, have faced rapid
declines in demand. The supply shocks are induced by disruptions of supply chains in an
interdependent world economy and closure of factories.
To protect public health, Canada, like many other countries, undertook many policies such as the
closing of educational institutions and workplaces, cancellation of public events, shutting down of
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Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
public transport, restrictions on gathering of people, restrictions on internal movements,
restrictions on international travel, quarantine measures, stay-at-home orders, and public
information campaigns. These policies have had unprecedented adverse effects on employment,
incomes, and revenues of individuals and businesses. Accordingly, advanced countries, including
Canada, have been forced to respond with massive expansionary monetary policy and fiscal policy.
Central banks have reduced their policy interest rates to near -zero levels. Fiscal policies have
included billions of dollars of government expenditures to provide financial support to low-income
people, laid-off workers, and support to business companies crippled by the coronavirus. Through
the Canada Emergency Response Benefit Program, Canada has provided financial support 1) to
protect health & safety, 2) direct support measures and tax reliefs to individuals and businesses,
3) other liquidity support, and capital relief to businesses totaling $686.5 billion.
The main objectives of this paper are 1) to examine the effects of COVID-19 on Canadian businesses
and industries; 2) to investigate the effects of COVID-19 on the Canadian labour market; and 3) to
compare the effects of lockdown measures on the unemployment rate in Canada and other selected
countries.
IMPACT OF COVID-19 ON CANADIAN BUSINESSES AND INDUSTRIES
Table 1 in Panel A and Panel B reports the impacts of COVID-19 by broad groups of industries. It
reports six types of impacts and four levels of impacts. The six types of impacts are decreases in
demand, difficulties in shipping and transporting goods, cancellation of services offered by
business, cancellation of contracts, reduction of worker productivity, and customer fear. The level
of impact is categorized as none, low, medium, and high. Several points from Table 1 can be
highlighted. First, overall, 64.8% of businesses experienced a high level of decreases in demand. In
food & accommodation, 88.7% of businesses witnessed a high level of decrease in demand; in arts,
entertainment, and recreation, the relevant figure is 87.1%. In retail trade, 72.5% of businesses
faced a high level of decrease in demand. The construction, education, and mining sectors also
witnessed a higher than average level of decrease in demand. Somewhat surprisingly, in the
healthcare sector, 67.7% of businesses experienced a high level of decreases in demand. This is
likely to be the result of decreases in demand for health care services for non-COVID-19 treatments
as patients postponed treatments.
Second, the sectors which relatively faced less decreases in demand are agriculture, finance &
insurance, and public administration.
Third, the impact on business because of difficulties in shipping was less severe. The effects were
relatively higher in agriculture, wholesale trade, retail trade, and transportation.
Fourth, the sectors that experienced high levels of effects because of cancellation of services are
retail trade, education, healthcare, arts, entertainment, recreation, and food & accommodation.
Fifth, cancellation of contracts affected businesses unevenly. 73.3% of businesses in arts,
entertainment, and recreation experienced a high level of impact because of cancellation of
contracts. The relevant figures for food &accommodation and education are 48.1% and 44.4%,
respectively.
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Sixth, the impact on businesses because of reductions in productivity was relatively milder.
Finally, public fear causing avoidance of companies’ physical locations severely affected sectors
such as retail trade, education, healthcare, arts, entertainment, recreation, food & accommodation.
Table 1 Level of Impact of COVID-19 on Canada by Industry
Panel A
Impacts experienced by
businesses
All
industries
Agricult
ure Mining Uitilities
Construc
tion
Manufa
cturing
Wholesal
e trade
Percent
Decrease in Demand None 9.4 19.6 11.1 23.6 5.4 7.9 5.1
Low 8.2 15.4 4.5 13.5 7.7 9.3 8.9
medium 16.1 21.9 11.6 21.3 17.4 20.2 17.5
High 64.8 38.4 71.2 41.6 68.2 61.7 68.2
Difficulties in shipping None 41 25.5 24.7 42.7 30.9 16.2 13.4
Low 21 25 25.8 24.7 24.6 34.8 33.9
medium 15 20.5 19.7 21.3 19 28 25.3
High 15.8 24.8 20.7 7.9 18.6 18.6 26.7
Cancellation of services None 21.4 45.3 24.2 37.1 15.4 31.2 23.3
Low 13.9 20.7 11.6 13.5 17.1 25 25
medium 13.9 11.9 15.7 16.9 19.3 16.2 18.2
High 48.5 17.6 43.4 30.3 45.7 24.5 31.5
Cancellation of contracts None 30.8 47.9 20.7 37.1 19 29.9 29.5
Low 15.9 17.2 9.6 21.3 18.6 26 23.3
medium 14.1 11.2 17.2 15.7 20.4 16.2 20.5
High 33.7 16.8 47 22.5 36 23.4 22.9
Reduction in productivity None 38.3 60.8 27.3 31.5 36.7 31.3 29.8
Low 19.2 17.9 27.3 21.3 16.9 28.7 24
medium 17.8 9.4 16.7 28.1 16.9 21.5 23.3
High 19.8 8.3 24.7 16.9 24.1 15.6 19.9
Customer fear None 20.7 34.6 28.8 39.3 15.1 26.3 20.2
Low 14.3 20.3 23.2 19.1 16.4 23.5 23.6
medium 15.6 19.9 12.1 11.2 19.7 21.5 21.2
High 46.5 22.3 30.8 27 45.6 26.1 32.5
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URL: http://dx.doi.org/10.14738/abr.86.8770 500
Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
Panel B
Source of data: Statistics Canada, Table 33-10-0229-01
Table 2 reports the effects of COVID-19 by the six types of effects on different types of business
based on ownership. It can be easily observed that private businesses experienced decreases in
demand more severely compared to government and non-profit enterprises: 65.7% of businesses
witnessed a high level of decease in demand, compared to 58.8% of non-profit enterprises and
35.3% of government enterprises. Table 2 also reveals that cancellation of services, cancellation of
contracts, and public fear heavily affected the non-profit enterprises, compared to private and
government enterprises.
Impacts experienced by
businesses
Retail
trade
Transp
ortatio
n
Finance
&
insuran
ce
Educati
on
Health
care
Arts,ent
ertainm
ent&rec
reation
Accomm
odation
&Food
Public
Administr
ation
Decrease in Demand None 7 7 17.5 11.8 13.1 4.7 1.8 14.3
Low 5.5 9.9 18 6.5 5.6 2.3 1.5 21.4
medium 14.3 21.1 29.9 8.9 12.1 5 6.8 32.1
High 72.5 60.8 33.6 71 67.7 87.1 88.7 28.6
Difficulties in Shipping None 19.5 18.5 70.1 57.4 54.2 51 47.5 50
Low 24.3 25.1 15.8 14.8 16.3 15 17.7 25
medium 22.4 24.3 4.4 5.9 10.1 7.8 10.7 7.1
High 28.5 26.4 4.6 8.6 11.4 16.2 11.9 7.1
Cancellation of services None 14.6 23.5 40.9 9.2 9.2 6.5 7.1 14.3
Low 14.7 20.4 22.4 4.7 8 2.5 5.7 7.1
medium 16.2 17.8 18.5 11.8 10.8 3.6 9.4 35.7
High 52.2 33.7 15.8 71.6 71.7 86 76.2 42.9
Cancellation of contracts None 36.8 28.5 49.1 18.6 46.7 10.8 25.6 42.9
Low 18.3 19.6 22.4 17.5 13.7 4.9 10.6 17.9
medium 13.2 16.2 12.4 12.7 7.7 9.2 9.1 25
High 24.2 31.3 13.4 44.4 22.4 73.3 48.1 7.1
Reduction in productivity None 44.7 42.3 24.6 23.7 34.8 28.9 59.1 3.6
Low 15.6 27.4 26.3 19.2 17 14.4 11.4 50
medium 11.7 16.2 30.7 24.3 13.3 22.9 8.2 21.4
High 20.3 8.4 17.5 27.5 29.2 28.2 11.8 25
Customer fear None 7.2 22.7 26.8 14.2 10.6 14.2 4.4 7.1
Low 11.7 17.5 21.9 11.5 10.5 7 2.6 17.9
medium 16.5 19.6 18 9.8 15.2 7.7 11.8 35.7
High 61.8 37.3 31.4 58.6 60.1 66.8 79.6 35.7
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Table 2: Impact of COVID-19 by Business Ownership
Source of data: Statistics Canada, Table 33-10-0229-01
Table 3 presents the six effects of COVID-19 by company size measured by the number of
employees. Several points deserve attention. First, the decrease in demand appears to fall more on
small businesses. More than 60% of businesses in categories 1-4, 5 -19, and 20-99 have
experienced a high level of decrease in demand, compared to only about 48% for companies with
250-499 employees and over 500 employees. Second, cancellation of services and cancellation of
contracts also affected small companies more severely than big companies. Finally, public fear
shows a similar pattern of the burden falling more on small companies.
Impacts experienced by
businesses Level of impact All
Business
Government Private Non-profit
Percent
Decrease in demand None 9.4 26.3 8.6 16
Low 8.2 12.8 7.9 10
Medium 16.1 21.8 16.3 13.8
High 64.8 35.3 65.7 58.8
Difficulties in Shipping None 41 48.9 39.2 60.1
Low 21 21.1 21.5 15.3
Medium 15 9.8 15.9 6.4
High 15.8 9 16.5 9.6
Cancellation of Services None 21.4 19.5 22.3 10.7
Low 13.9 15 14.5 7.6
Medium 13.9 20.3 14 12.5
High 48.5 42.1 46.8 67.8
Cancellation of contracts None 30.8 35.3 31.1 26.5
Low 15.9 18 16 14
Medium 14.1 16.5 14 14.2
High 33.7 23.3 33.2 41.2
reduction in productivity None 38.3 12.8 40.3 19.5
Low 19.2 32.3 18.4 26.6
Medium 17.8 33.1 16.2 32.6
High 19.8 18.8 19.8 19.1
Public fear None 20.7 15.8 20.9 18.8
Low 14.3 21.1 14.2 13.6
Medium 15.6 21.1 15.9 11.5
High 46.5 36.1 46.1 51.6
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Table 4 GDP Growth Rates by Industries
Source: Statistics Canada, Table 36-10-0434-02
Table 5 shows the growth rate of GDP for selected sub-sectors of manufacturing sectors. It is
evident that the motor vehicle manufacturing and motor vehicle parts manufacturing experienced
the steepest decline: the growth rate of GDP in motor vehicle manufacturing was -97.7% during
North American Industry Classification System (NAICS) March 2020 to April 2020 April 2019 to April 2020
Percentage change
All Industries -11.6 -17.1
Goods Producing Industries -17 -21.5
Service Producing Industries -9.7 -15.5
Business Sector Industries -13 -18.3
Non-Business Industries -5.9 -11.6
Industrial Production -16.1 -22.5
Non-Durable manufascturing indudstries -15.4 -18.4
Durable Manufacturing Industries -29.2 -38.8
Information and Cultural industries 0.4 2.2
Energy Sector -5.6 -9.7
public Sector -7.7 -14.9
Agriculture,Forestry & Fishing -1 0.4
mining -9.4 -17.7
Utilities -1.8 -1.8
Construction -22.9 -24.4
manufacturing -22.5 -29.4
Wholesale trade -17.9 -21.4
Retail Trade -22.9 -29.8
Transportation -23.1 -33.5
Finance & Insurance -1 1.7
Real Estate -3.5 -1.7
Professional,Scientific &technical Services -1.3 -5.8
Educational services -8.6 -20.8
Health Care & Social Assistance -10.4 -17.5
Arts,Entertainment & Recreation -25.6 -55.4
Accommodation & Food -42.4 -63.6
Public Administration -4.3 -7.6
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Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
March-April 2020 and -98.5% during April2019-April 2020. For motor vehicle parts
manufacturing, the relevant figures are -86.4% and -90%, respectively. This huge decline is most
likely caused by disruptions in supply chains and the closure of the Canada-US border because of
the COVID19. Plastic & rubber products, fabricated metal products, and furniture & related
products also experienced a sharp decline in GDP.
Table 5 GDP Growth Rates by Industrial Sub-sectors
Source: Statistics Canada, Table 36-10-0434-05
THE IMPACT OF COVID-19 ON THE LABOUR MARKET
Because of the negative supply and demand shocks, the unemployment rate in Canada increased
sharply from 5.6% in February 2020 to 13.7% in May 2020. The unemployment rate, which is
calculated as the number of employed as % of the labour force, doesn’t, however, presents the
entire picture of the dynamics of the labour market.
North American Industry Classification System (NAICS) March 2020 to April 2020 April 2019 to April 2020
Percentage change
Food Manufacturing -12.8 -8.8
Textile, Clothing, and Leather -13 -36.6
Wood Products -17.7 -27.2
Petroleum and coal Products -23.7 -27
Chemical Manufacturing -6 -10.5
Pharmaceutical & Medicine products -3.5 -0.7
Plastic & Rubber products -32.9 -42.9
Non-metallic mineral Products -32 -31.7
Cement & Concrete Products -42 -40.5
Primary Metal Products -13.9 -22.5
Fabricated Metal Products -27.7 -31.1
Machinery Manufacturing -17.2 -32.7
Computer and Electrical Products -17.3 -26.2
Household appliances -19 -28.7
Electronic Equipment -5.9 -18.8
Motor vehicle Manufacturing -97.7 -98.5
Motor Vehicle Parts manufacturing -86.4 -90
Aerospace Products and Parts -10.6 -13.3
Furniture and Related Products -39.2 -51.6
Medical Equipment & Supplies -26.6 -36.9
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Accordingly, it will be useful to examine the labour force status of respondents by their
characteristics. The labour force status includes employed at work, employed but absent from
work, unemployed, and not in the labour force. Table 6 and Table 7 display two-way frequency
tables involving labour force status and gender for February 2020 and May 2020. Several points
can be highlighted. First, the employment ratio ( the employed as % of working-age population) in
February 2020 was 58.14% for males, compared to 51.18% for females. The employment ratio for
males dropped to 49.76% in May 2020, while for females, the employment ratio dropped from
51.18% to 39.78%. Second, in February 2020, the ratio of unemployment for males was 5.01%
compared to 3.07% for females. In May 2020, the unemployment ratio increased to 8.32% for
males and 6.94% for females.
Third, for both males and females, the proportion of employed, but absent from work increased in
May 2020. Finally, the propotion of males out of the labour force increased from 32.69% in
February 2020 to 34.89% in May 2020; the relevant figures for females are 40.65% and 44.22%
(The increase in the proportion of workers out of the labour force suggests decreases in the
participation rate. Some evidence for the United States is given by Coibion, Gorodnichenko and
Weber (2020).). It appears that COVID-19 has adversely affected the jobs of females compared to
male workers. The Pearson Chi-Square test has a p-value of zero in Table 6 and Table 7, which
rejects the null hypothesis of independence of gender and labour force status.
Table 6 Labour Force Status by Gender, February 2020
Frequency
Column Percentage
Gender of respondent
Labour force status | Male Female | Total
----------------------+----------------------+----------
Employed, at work | 28,188 25,948 | 54,136
| 58.14 51.18 | 54.59
----------------------+----------------------+----------
Employed, absent from | 2,016 2,584 | 4,600
work 4.16 5.10 | 4.64
----------------------+----------------------+----------
Unemployed | 2,427 1,555 | 3,982
| 5.01 3.07 | 4.02
----------------------+----------------------+----------
Not in labour force | 15,848 20,608 | 36,456
| 32.69 40.65 | 36.76
----------------------+----------------------+----------
Total | 48,479 50,695 | 99,174
| 100.00 100.00 | 100.00
Pearson chi2(3) = 926.2283 Pr = 0.000
Note:
Source: Compiled from Statistics Canada, Labour Force Survey, February 2020
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Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
Table 7. Labour Force Status by Gender, May 2020
Frequency
Column Percentage
| Gender of respondent
Labour force status | Male Female | Total
----------------------+----------------------+----------
Employed, at work | 21,602 18,173 | 39,775
| 49.76 39.78 | 44.64
----------------------+----------------------+----------
Employed, absent from | 3,053 4,138 | 7,191
work 7.03 9.06 | 8.07
----------------------+----------------------+----------
Unemployed | 3,614 3,171 | 6,785
| 8.32 6.94 | 7.61
----------------------+----------------------+----------
Not in labour force | 15,146 20,205 | 35,351
| 34.89 44.22 | 39.67
----------------------+----------------------+----------
Total | 43,415 45,687 | 89,102
| 100.00 100.00 | 100.00
Pearson chi2(3) = 1.2e+03 Pr = 0.000
Source: Compiled from Statistics Canada, Labour Force Survey, May 2020
Table 8 and Table 9 report two-way frequency tables of labour force status and educational
attainment for February 2020 and May 2020. The findings from Table 8 and Table 9 can be
summarized as follows. First, it is evident from Table 8 that the employment ratio is directly
related to years of education: for workers with 0 to 8 years of education in February 2020, it was
only 16.94%, compared to 68.83% for workers with above bachelor’s degree education. In May
2020, the employment ratio dropped across all education levels.
However, it appears that the employment ratio dropped less for workers at the lowest and highest
levels of education, compared to other levels of education. Second, the unemployment ratio was
higher for workers with some high school and high school education compared to other groups of
workers. Furthermore, the unemployment ratio sharply increased for workers with some post- secondary education- from 4.68% in February 2020 to 13.8% in May 2020.
In contrast, the unemployment ratio increased the least for workers with the lowest and highest
levels of education. The evidence suggests that during the COVID-19 pandemic, many workers with
low levels of education have worked in essential sectors. Furthermore, in many sectors such as
finance & insurance, public administration, the demand for workers with high levels of education
has contracted less.
The Pearson Chi-Squares in both Table 8 and Table 9 have a zero p-value. This suggests that
labour force status and education level are statistically dependent.
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Table 8 Labour Force Status by Educational Attainment, February 2020
Frequency
Row percentage
Highest educational | Labour force status
attainment | Employed, Employed, Unemployed Not in Total
absent from work labour force
----------------------+--------------------------------------------+----------
0 to 8 years | 828 84 133 3,843 | 4,888
| 16.94 1.72 2.72 78.62 | 100.00
----------------------+--------------------------------------------+----------
Some high school | 4,013 327 607 7,056 | 12,003
| 33.43 2.72 5.06 58.79 | 100.00
----------------------+--------------------------------------------+----------
High school graduate | 10,494 863 1,008 7,657 | 20,022
| 52.41 4.31 5.03 38.24 | 100.00
----------------------+--------------------------------------------+----------
Some postsecondary | 3,245 263 294 2,486 | 6,288
| 51.61 4.18 4.68 39.54 | 100.00
----------------------+--------------------------------------------+----------
Postsecondary certifi | 20,387 1,855 1,321 10,094 | 33,657
| 60.57 5.51 3.92 29.99 | 100.00
----------------------+--------------------------------------------+----------
Bachelor's degree | 10,240 800 422 3,693 | 15,155
| 67.57 5.28 2.78 24.37 | 100.00
----------------------+--------------------------------------------+----------
Above bachelor's degr | 4,929 408 197 1,627 | 7,161
| 68.83 5.70 2.75 22.72 | 100.00
----------------------+--------------------------------------------+----------
Total | 54,136 4,600 3,982 36,456 | 99,174
| 54.59 4.64 4.02 36.76 | 100.00
Pearson chi2(18) = 9.1e+03 Pr = 0.00
Source: Compiled from Statistics Canada, Labour Force Survey, February 2020
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Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
Table 9: Labour Force Status by Educational Attainment, May 2020
Frequency
Row percentage
Highest educational | Labour force status
attainment | Employed, Employed, Unemployed Not in Total
absent from work labour force
----------------------+--------------------------------------------+----------
0 to 8 years | 601 130 159 3,503 | 4,393
| 13.68 2.96 3.62 79.74 | 100.00
----------------------+--------------------------------------------+----------
Some high school | 2,667 586 873 6,304 | 10,430
| 25.57 5.62 8.37 60.44 | 100.00
----------------------+--------------------------------------------+----------
High school graduate | 7,120 1,436 1,594 7,481 | 17,631
| 40.38 8.14 9.04 42.43 | 100.00
----------------------+--------------------------------------------+----------
Some postsecondary | 2,217 436 775 2,187 | 5,615
| 39.48 7.76 13.80 38.95 | 100.00
----------------------+--------------------------------------------+----------
Postsecondary certif | 14,988 2,953 2,307 10,400 | 30,648
| 48.90 9.64 7.53 33.93 | 100.00
----------------------+--------------------------------------------+----------
Bachelor's degree | 8,111 1,163 808 3,788 | 13,870
| 58.48 8.39 5.83 27.31 | 100.00
----------------------+--------------------------------------------+----------
Above bachelor's degr | 4,071 487 269 1,688 | 6,515
| 62.49 7.48 4.13 25.91 | 100.00
----------------------+--------------------------------------------+----------
Total | 39,775 7,191 6,785 35,351 | 89,102
| 44.64 8.07 7.61 39.67 | 100.00
Pearson chi2(18) = 8.0e+03 Pr = 0.000
Source: Compiled from Statistics Canada, Labour Force Survey, May 2020
It is instructive to explore the impact of COVID-19 on labour force status by the age of workers.
Table 10 and Table 11 show two-way frequency tables of labour force status and age of workers.
Several points are noteworthy. First, the employment ratio in February 2020 displays an “ inverted
U” pattern: rising from 37.53% for the age group 15-19 to 79.51% for the age group 40-44 and then
falling for other age groups. The employment ratio in May 2020 sharply fell for young workers in
age groups, 15-19, 20-24, and 25-29, compared to other groups. Second, the unemployment ratio
is inversely related to age: higher for young workers compared to older workers. The
unemployment ratio increased substantially for young workers in may 2020. For example, the
unemployment ratio for workers aged 20 to 24 increased from 7.38% in February 2020 to 20.88%
in May 2020. Finally, workers not in the labour force as a percentage of the working-age
population reveals a “ U-shaped” pattern: as evident from Table 10, falling from 53.75% for the
age group 15-19 to 11.36% for the age group 40-44 and then rising for older groups.
It can be observed from Table 10 and Table 11 that for young workers in the age groups 15-19 and
20-24, the proportion of workers outside the labour force remained stable in May 2020 compared
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to February 2020. In contrast, the proportion increased for age groups such as 25-29, 30-34, 35-
39, 40-44, 45-49.
Table 10 Labour Force Status by Age, February 2020
Frequency
Row percentage
Five-year age
group of Labour force status
respondent Employed, Employed, Unemployed Not in labour Total
absent from work force
---------------+--------------------------------------------+----------
15 to 19 years | 2,479 153 423 3,551 | 6,606
| 37.53 2.32 6.40 53.75 | 100.00
---------------+--------------------------------------------+----------
20 to 24 years | 3,942 243 467 1,679 | 6,331
| 62.27 3.84 7.38 26.52 | 100.00
---------------+--------------------------------------------+----------
25 to 29 years | 5,055 439 418 1,072 | 6,984
| 72.38 6.29 5.99 15.35 | 100.00
---------------+--------------------------------------------+----------
30 to 34 years | 5,552 645 371 1,034 | 7,602
| 73.03 8.48 4.88 13.60 | 100.00
---------------+--------------------------------------------+----------
35 to 39 years | 6,179 482 377 926 | 7,964
| 77.59 6.05 4.73 11.63 | 100.00
---------------+--------------------------------------------+----------
40 to 44 years | 6,048 380 315 864 | 7,607
| 79.51 5.00 4.14 11.36 | 100.00
---------------+--------------------------------------------+----------
45 to 49 years | 5,944 419 262 884 | 7,509
| 79.16 5.58 3.49 11.77 | 100.00
---------------+--------------------------------------------+----------
50 to 54 years | 5,991 503 362 1,186 | 8,042
| 74.50 6.25 4.50 14.75 | 100.00
---------------+--------------------------------------------+----------
55 to 59 years | 5,946 564 445 2,333 | 9,288
| 64.02 6.07 4.79 25.12 | 100.00
---------------+--------------------------------------------+----------
60 to 64 years | 4,076 418 349 3,783 | 8,626
| 47.25 4.85 4.05 43.86 | 100.00
---------------+--------------------------------------------+----------
65 to 69 years | 1,796 206 154 5,465 | 7,621
| 23.57 2.70 2.02 71.71 | 100.00
---------------+--------------------------------------------+----------
70 and over | 1,128 148 39 13,679 | 14,994
| 7.52 0.99 0.26 91.23 | 100.00
---------------+--------------------------------------------+----------
Total | 54,136 4,600 3,982 36,456 | 99,174
| 54.59 4.64 4.02 36.76 | 100.00
Pearson chi2(33) = 3.7e+04 Pr = 0.000
Source: Compiled from Statistics Canada, Labour Force Survey, February 2020
Page 14 of 16
URL: http://dx.doi.org/10.14738/abr.86.8770 510
Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
Table 11 Labour Force Status by Age, May 2020
Frequency
Row percentage
Five-year age |
group of | Labour force status
respondent | Employed, Employed, Unemployed Not in labour Total
absent from work force
---------------+--------------------------------------------+----------
15 to 19 years | 1,561 291 831 3,042 | 5,725
| 27.27 5.08 14.52 53.14 | 100.00
---------------+--------------------------------------------+----------
20 to 24 years | 2,275 426 1,081 1,395 | 5,177
| 43.94 8.23 20.88 26.95 | 100.00
---------------+--------------------------------------------+----------
25 to 29 years | 3,319 675 797 1,073 | 5,864
| 56.60 11.51 13.59 18.30 | 100.00
---------------+--------------------------------------------+----------
30 to 34 years | 3,971 845 609 1,047 | 6,472
| 61.36 13.06 9.41 16.18 | 100.00
---------------+--------------------------------------------+----------
35 to 39 years | 4,514 833 610 1,015 | 6,972
| 64.74 11.95 8.75 14.56 | 100.00
---------------+--------------------------------------------+----------
40 to 44 years | 4,541 624 502 934 | 6,601
| 68.79 9.45 7.60 14.15 | 100.00
---------------+--------------------------------------------+----------
45 to 49 years | 4,562 687 467 961 | 6,677
| 68.32 10.29 6.99 14.39 | 100.00
---------------+--------------------------------------------+----------
50 to 54 years | 4,843 693 527 1,172 | 7,235
| 66.94 9.58 7.28 16.20 | 100.00
---------------+--------------------------------------------+----------
55 to 59 years | 4,740 782 600 2,239 | 8,361
| 56.69 9.35 7.18 26.78 | 100.00
---------------+--------------------------------------------+----------
60 to 64 years | 3,193 698 486 3,758 | 8,135
| 39.25 8.58 5.97 46.20 | 100.00
---------------+--------------------------------------------+----------
65 to 69 years | 1,326 386 211 5,426 | 7,349
| 18.04 5.25 2.87 73.83 | 100.00
---------------+--------------------------------------------+----------
70 and over | 930 251 64 13,289 | 14,534
| 6.40 1.73 0.44 91.43 | 100.00
---------------+--------------------------------------------+----------
Total | 39,775 7,191 6,785 35,351 | 89,102
| 44.64 8.07 7.61 39.67 | 100.00
Pearson chi2(33) = 3.4e+04 Pr = 0.000
Source: Compiled from Statistics Canada, Labour Force Survey, May 2020
Page 15 of 16
Archives of Business Research (ABR) Vol.8, Issue 7, July-2020
511
LOCKDOWN MEASURES AND UNEMPLOYMENT RATES IN CANADA AND SELECTED
COUNTRIES
Sharp rises in the unemployment rate in Canada, and other countries have been caused by various
measures of lockdowns and social distancing ( Rojas et al., 2020). However, different countries
have responded differently in terms of lockdown and social distancing policies, income support,
and job protection policies. This section explores the relationship between the intensity of
lockdown and social distancing measures and the unemployment rate in Canada and selected
countries. The intensity of lockdown and social distancing measures is represented by the
Stringency Index. This index has been computed by the Blavatnik School of Government at the
University of Oxford for many countries. The index is based on seven indicators of stringency:
school closings, workplace closings, cancellation of public events, shuting down of public transport,
public information campaigns, restrictions on internal movement, and controls on international
travel. The index ranges from zero to 100. It is hypothesized that there is a direct relationship
between the Stringency Index and the unemployment rate.
For Canada and selected countries, the maximum values of the Stringency Index have been taken,
which occurred in April. Accordingly, data on Stringency Index(SI) and the unemployment rate in
April (URApril) have been used for analysis. Figure 1 displays the relationship between SI and
URApril. It is evident from Figure 1 that there is a positive relationship between SI and URApril, as
hypothesized; however, the relationship doesn’t appear to be a strong one. The scatter plot reveals
the existence of several clusters. Canada (Can), USA Dey and Loewenstein (2020) examine the
labour market impacts of COVID-19 in shutdown sectors in the United States.) and Spain(Spa)
exhibit high values of SI and URApril. In contrast, Denmark (Den), Australia (Aus), Germany (Ger),
Norway (Nor), Netherlands (Net), and South Korea (Kor) represent a different cluster with SI
values similar to those of Canada and USA, but have low rates of unemployment. Portugal (Por),
Italy (Ita), France (Fra), and Ireland (Ire) represent another cluster with very high values of SI and
low values of the unemployment rate. Finally, Sweden (Swe), Japan (Jap), and Finland (Fin) have
low values of SI and also low values of the unemployment rate. Canada, USA, and Spain have
experienced very high rates of unemployment compared to countries such as Germany, Norway,
Finland, Denmark, and the Netherlands, because of job protection policies in these European
countries. In Canada and the USA, the policy responses have taken the form of income support and
unemployment benefits.
Figure 1. Stringency Index and the Unemployment Rate: Canada and Selected countries.
Aus
Can
Den
Fin
Fra
Ger
Ire
Ita
Jap
Kor Net
Nor
Por
Spa
Swe
USA
0 5 15 URApril
50 60 70 80 90
SI
URApril Fitted values
Stringency Index (SI) and Unemployment Rate (URApril)
Page 16 of 16
URL: http://dx.doi.org/10.14738/abr.86.8770 512
Islam, S., & Tarannum, T. (2020). The Impact of COVID-19 on the Canadian Economy. Archives of Business Research, 8(7). 497-512.
Source of data: Data on SI are obtained from the database of the Blavatnik School of government at
Oxford University while data on the unemployment rate (URApril) are obtained from the
Organization for Economic Cooperation and Development (OECD).
CONCLUSION
The Canadian economy has been severely affected by COVID-19 through supply as well as demand
shocks. The supply shocks were induced by quarantine and social distancing measures, closure of
factories and businesses. The demand shocks were generated by increases in unemployment,
reductions in exports, and social distancing measures. The adverse effects of COVID-19 have varied
across industries, businesses, and demographic groups. It appears that the burden of CoVID-19
has fallen disproportionately on small businesses, young workers, and businesses, which involve
face-to-face contacts such as food & accommodation, arts, entertainment, and recreation, and retail
trade.
The impact of lockdown measures on the labour markets has varied across countries because of
variations in policy responses concerning income support, wage subsidies, and job protection.
While the unemployment rate has sharply risen in Canada and the United States, increases in the
unemployment rate in most European countries have been modest.
References
Coibion,O., Gorodnichenko,Y., and Weber,M. 2020. ” Labor markets During the COVID-19 Crisis: A Preliminary View”,
NBER Working paper No. 27017
Dey,M., and Loewenstein,M. 2020. ” How many workers are employed in sectors directly affected by COVID-19
shutdowns, where do they work, and how much do they earn? Monthly Labor Review, April.
Guerrieri, V.,Lorenzoni,G., Straub,Ludwig, Werning,I. 2020.” Macroeconomic Implications of COVID-19: Can Negative
Supply Shocks Cause Demand Shortages?”, NBER Working Paper No. 26918.
Rojas,F.,Jiang,X.,Montenovo,L.,Simon,K., Wing,C. 2020. “ Is the Cure Worse than the Problem Itself? Immediate Labor
Market Effects of COVID-19 Case Rates and School Closures in the U.S”, NBER Working Paper No. 27127.