Leveraging new and existing labour force data to understand the impact of COVID-19 on workers in Canada Xavier St-Denis University of Toronto ** Center on Population Dynamics Webinar May 12, 2020
Context COVID-19 and the labour market • The COVID-19 pandemic challenges the way we traditionally study employment trends and labour market stratification. • The risk of COVID-19 infection in the workplace has led employers to close establishments and consumers to change their habits. This resulted in important job losses, first in the service sector (Statistics Canada, 2020a [March LFS]). • Around mid-March, governments in Ontario, Québec and other provinces reacted by declaring states of emergency, which mandated physical distancing and the closure of most non-essential workplaces. This was followed by job losses in goods-producing sectors in addition to persistent losses in the service sector (Statistics Canada, 2020b [April LFS]).
Context Basic labour force statistics and the impact of COVID-19 on workers • In order to track the impact of COVID-19 on workers, traditional labour market indicators such as the unemployment rate are still useful (unemployment rate rose to 13.0% in April 2020). • Timely dissemination of labour force data by national statistical agencies. • A wide range of dimensions have taken a new importance, perhaps in surprising ways. Structure of the talk: à What are some of those dimensions? à How can they be measured? Data requirements and access issues? à What research questions? à Exploratory results.
The Labour Force Survey “tradition” The renewed importance of sometimes neglected indicators • Employment rate as a supplement to unemployment rate. • See also administrative data on EI claims and other benefit claims (i.e., CERB). • Other indicators not usually at the forefront of analyses of employment trends: • Actual hours worked; • Absences from work (paid or not); • T emporary layoffs (versus permanent). • Availability of relatively detailed public use monthly Labour Force Survey microdata in Canada. • Higher-frequency data collection (“real-time” surveys, job posting data).
New dimensions The need for data innovation • Health-related job and worker characteristics traditionally do not receive a lot of attention • …but see studies of the impact of health on labour supply and employment participation. • Emerging theme: How do workforce characteristics and traditional employment outcomes interact with risks of exposure to COVID-19 at work. • Health-related issues at the center of policies and practices aimed at implementing physical distancing. • …but risks of exposure to COVID-19 at work are likely to be experienced very differently across the workforce (level of risk, protection from risk, …).
Measuring risks of exposure to COVID-19 at work Leveraging occupation-level data from O*Net • Intuition: • Some jobs require closer physical interactions than others. • In some jobs, the probability of entering in contact with people with COVID-19 is higher. • Examples: health occupations, food processing, etc. • No epidemiological data collected systematically by occupation. • Detailed datasets on occupational characteristics, matched with survey microdata, can be used to infer risks. • This is NOT epidemiological data on actual work-related infection or death rates. • Crosswalk between O*Net SOC 2010 8-digit codes and 2016 NOC 4-digit code by Viet Vu, Brookfield Institute for Innovation + Entrepreneurship, Ryerson University.
Measuring occupational risks of exposure to COVID-19 Overall risk of occupational exposure (1) Distribution of O*Net work activity occupational scores, workers employed in 2015 Percent Frequency Physical proximity 0 I don't work near other people (beyond 100 ft.) 0.2 27,660 25 I work with others but not closely (e.g., private office) 1.9 357,480 50 Slightly close (e.g., shared office) 51.8 9,575,495 75 Moderately close (at arm's length) 39.0 7,216,740 100 Very close (near touching) 7.2 1,322,075 Total 100.0 18,499,450 Exposure to infections or diseases 0 Never 49.2 9,103,000 25 Once a year or more but not every month 31.6 5,845,125 50 Once a month or more but not every week 10.9 2,008,365 75 Once a week or more but not every day 4.1 761,030 100 Every day 4.2 781,930 Total 100.0 18,499,450 Source: Census of Population (2016), and O*Net. Note: Occupation score values are rounded to nearest score value cutoff. Each category includes those in a range of +/- 12.5 points around score value.
Measuring occupational risks of exposure to COVID-19 Overall risk of occupational exposure (2) Distributional statistics, occupational risks of exposure scores Mean Median 25th pctl 75th pctl Physical Proximity Total 61.1 58.0 48.0 72.3 Men 59.1 56.5 48.0 69.5 Women 63.3 64.0 48.0 76.0 Exposure to diseases or infections Total 20.8 13.3 4.0 29.0 Men 14.9 7.3 4.0 17.0 Women 27.1 17.0 5.3 40.0 Source: Census of Population (2016), and O*Net.
Measuring occupational risks of exposure to COVID-19 Overall risk of occupational exposure (3) Regression of physiclal proximity risk scores on labour force characteristics All NOC (1) All NOC (2) Health Educ, Gov, … Sales/Services Other NOC (1) Other NOC (2) Men (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Women 4.5 *** 1.7 *** -0.8 6.5 * 3.3 *** -3.0 *** -0.2 Canada-born (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Immigrant 0.6 0.3 -0.4 -0.6 0.3 0.7 0.4 15-24 years old (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) 25-54 years old -5.4 *** -3.2 *** 0.3 1.9 -5.1 *** -3.6 *** -2.5 ** 55-64 years old -6.8 *** -4.2 *** -0.3 0.5 -7.6 *** -4.3 *** -2.9 *** 65 or more years old -6.6 *** -4.3 *** 0.1 -1.3 -6.3 *** -4.6 *** -3.0 *** No certificate, diploma or degree (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Secondary (high) school diploma or equivalency certificate -0.9 0.4 -1.1 -5.0 * 0.6 -1.0 0.7 Apprenticeship or trades certificate or diploma 2.4 * 1.8 0.9 -2.7 5.2 * 0.8 1.0 College, CEGEP or other non-university certificate or diploma 0.3 0.2 0.3 -5.9 * 1.1 -2.7 * 0.0 University certificate or diploma below bachelor level -0.9 -0.6 0.1 -8.3 *** 0.8 -4.0 *** -0.7 University certificate, diploma or degree at bachelor level or above -2.7 * -2.8 ** -1.0 -13.3 *** 0.0 -6.2 *** -2.4 * Occupation dummies (1-digit) Yes Yes Constant 64.45 *** 67.9 *** 87.36 *** 70.0 *** 67.56 *** 61.8 *** 52.8 *** R-squared 0.05 0.40 0.01 0.12 0.08 0.07 0.16 Number of observations (n) 47,450 47,450 3,456 3,450 5,184 35,360 35,360 Population estimates (N) 18,147,370 18,147,370 1,246,510 2,078,245 4,304,595 10,518,020 10,518,020 Source: Census of Population (2016), and O*Net. * p<0.10; ** p<0.05; *** p<0.01
Measuring occupational risks of exposure to COVID-19 Overall risk of occupational exposure (4) Regression of exposure to diseases or infections risk scores on labour force characteristics All NOC (1) All NOC (2) Health Educ, Gov, … Sales/Services Other NOC (1) Other NOC (2) Men (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Women 11.9 *** 2.8 *** -0.3 5.5 0.0 4.8 *** 4.0 *** Canada-born (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Immigrant -0.7 0.2 0.9 -1.3 2.0 -1.3 * -0.7 15-24 years old (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) 25-54 years old 0.4 0.7 1.7 6.0 2.4 -2.4 ** -2.1 * 55-64 years old 0.8 1.3 3.0 3.9 4.1 * -1.5 -1.3 65 or more years old 1.6 1.4 4.0 * 2.6 2.7 -0.3 -0.5 No certificate, diploma or degree (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Secondary (high) school diploma or equivalency certificate -1.0 -1.4 -2.6 * -5.8 * -3.5 0.6 0.5 Apprenticeship or trades certificate or diploma 2.6 -0.6 -0.3 -1.9 0.4 1.0 0.1 College, CEGEP or other non-university certificate or diploma 4.9 ** -1.7 1.1 -4.5 -5.3 * 0.2 0.2 University certificate or diploma below bachelor level 3.7 * -2.9 ** 2.0 -8.7 ** -6.4 ** -0.8 -0.8 University certificate, diploma or degree at bachelor level or above 3.2 -5.5 *** 1.1 -16.1 *** -9.2 *** -2.4 ** -2.0 * Occupation dummies (1-digit) Yes Yes Constant 12.6 *** 19.4 *** 79.59 *** 36.4 *** 21.17 *** 12.1 *** 14.2 *** R-squared 0.05 0.40 0.01 0.12 0.08 0.07 0.16 Number of observations (n) 47,450 47,450 3,456 3,450 5,184 35,360 35,360 Population estimates (N) 18,147,370 18,147,370 1,246,510 2,078,245 4,304,595 10,518,020 10,518,020 Source: Census of Population (2016), and O*Net. * p<0.10; ** p<0.05; *** p<0.01
Ability of workers to protect self from occupational risks of exposure Job conditions and quality • Workers in high-risk occupations may not all be able to protect themselves from occupational risks of exposure. • May be consequential at the individual level (infection); also risk cluster emergence in workplaces. • Two main dimensions: • The possibility of absence from work without income or job loss. • The feasibility of remote work.
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