Leveraging new and existing labour force data to understand the - - PowerPoint PPT Presentation

Leveraging new and existing labour force data to understand the impact of COVID-19

• n 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: àWhat are some of those dimensions? àHow can they be measured? Data requirements and access issues? àWhat research questions? àExploratory 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 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 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. Physical proximity Exposure to infections or diseases 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 Men Women 4.5 *** 1.7 ***

• 0.8

6.5 * 3.3 ***

• 3.0 ***
• 0.2

Canada-born Immigrant 0.6 0.3

• 0.4
• 0.6

0.3 0.7 0.4 15-24 years old 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 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) Population estimates (N) Source: Census of Population (2016), and O*Net. * p<0.10; ** p<0.05; *** p<0.01 47,450 47,450 18,147,370 18,147,370 3,456 3,450 5,184 35,360 35,360 1,246,510 2,078,245 4,304,595 10,518,020 10,518,020 (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Health Educ, Gov, … Sales/Services Other NOC (1) Other NOC (2) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) All NOC (1) All NOC (2)

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 Men Women 11.9 *** 2.8 ***

• 0.3

5.5 0.0 4.8 *** 4.0 *** Canada-born Immigrant

• 0.7

0.2 0.9

• 1.3

2.0

• 1.3 *
• 0.7

15-24 years old 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 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) Population estimates (N) Source: Census of Population (2016), and O*Net. * p<0.10; ** p<0.05; *** p<0.01 10,518,020 18,147,370 18,147,370 1,246,510 2,078,245 4,304,595 10,518,020 (ref.) 47,450 47,450 3,456 3,450 5,184 35,360 35,360 (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Other NOC (2) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) All NOC (1) All NOC (2) Health Educ, Gov, … Sales/Services Other NOC (1)

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.

Ability of workers to protect self from occupational risks of exposure Occupational risk of exposure in low-income occupations

Occupational exposure risks score quartile distribution by low-income occupation status Percentage Frequency Percentage Frequency Percentage Frequency Percentage Frequency 1st quartile 28.3 4,705,120 2.9 50,795 27.6 4,592,765 5.7 98,685 2nd quartile 28.6 4,768,090 0.8 13,030 24.6 4,090,915 24.1 417,960 3rd quartile 20.8 3,462,160 46.1 797,810 23.8 3,957,260 53.5 925,495 4th quartile 22.3 3,714,385 50.2 869,200 24.1 4,008,815 16.7 288,695 Total 100.0 16,649,755 100.0 1,730,835 100.0 16,649,755 100.0 1,730,835 Source: Source: Census of Population (2016), and O*Net. Not a low-income

• ccupation

Low-income occupation Exposure to diseases or infections Physical proximity Not a low-income

• ccupation

Low-income occupation

Measuring remote work A diversity of approaches

How can we estimate the feasibility of remote work?

• O*Net approach (Dingel & Neiman 2020; Mongey et al. 2020)
• Static, does not account for possible technological innovations and evolution of norms and behaviours
• Does not account for differences in resources across firms
• Nevertheless, allows to identify the potential for transformations
• Alternative approach: previously collected self-reported survey data on place of work, aggregated at occupation level.
• Canadian Census: Question on usual place of work
• Possibly not reflective of the extent of changes in behaviours and technologies
• May be impacted by the effect of “sticky” norms quite well
• Other sources: GSS, and ATUS, ACS in the US
• Real-time surveys (April 2020 LFS Supplement)
• Captures changes in behaviours resulting from pandemic and emergency measures.
• Does not account for establishment closures where remote work would have been possible.
• May be a nice complement to occupation-based measures that allows to highlight.
• GPS data could provide some alternative real-time estimates – not really my area…

Measuring remote work The O*Net approach

O*Net variables used in Dingel and Neiman (2020) If any of the following conditions in the “Work Context” survey responses are true, the occupation is coded as one that cannot be performed at home:

• Average respondent says they use email less than once per month
• Majority of respondents say they work outdoors every day
• Average respondent says they deal with violent people at least once a week
• Average respondent says they spent majority of time wearing common or specialized protective or safety equipment
• Average respondent says they spent majority of time walking or running
• Average respondent says they are exposed to minor burns, cuts, bites, or stings at least once a week
• Average respondent says they are exposed to diseases or infection at least once a week

If any of the following conditions in the “Generalized Work Activities” survey responses are true, the occupation is coded as one that cannot be performed at home:

• Performing General Physical Activities is very important
• Handling and Moving Objects is very important
• Controlling Machines and Processes [not computers nor vehicles] is very important
• Operating Vehicles, Mechanized Devices, or Equipment is very important
• Performing for or Working Directly with the Public is very important
• Repairing and Maintaining Mechanical Equipment is very important
• Repairing and Maintaining Electronic Equipment is very important
• Inspecting Equipment, Structures, or Materials is very important

Measuring remote work Change in observed practices

Remote work measures by 1-digit NOC occupations Worked at least one hour Worked from home Worked from home Worked outside the home Absent, full week Worked at home Usual place of work No fixed workplace Total 7.4 81.0 11.6

Management occupations

53.0 43.7 38.8 17.5 14.0 79.0 6.9

Business, finance, and administration occupations

60.7 52.1 33.7 14.2 9.3 86.9 3.9

Natural and applied sciences and related occupations

76.1 68.8 21.6 9.5 9.1 82.4 8.5

Health occupations

9.6 7.2 68.2 24.6 2.5 91.2 6.3

Occupations in education, law and social, community and government

63.8 49.6 28.1 22.3 7.6 84.8 7.5

Occupations in art, culture, recreation and sport

80.2 49.2 12.1 38.7 22.8 63.5 13.6

Sales and service occupations

23.1 16.7 55.4 27.9 5.0 87.0 8.0

Trades, transport and equipment operators

4.5 3.3 70.8 25.9 2.8 61.2 36.0

Natural ressources, agriculture, and related production

22.5 17.5 60.4 22.1 14.8 53.4 31.8

Occupations in manufacturing and utilities

8.8 6.8 70.6 22.6 1.8 92.4 5.8 Note: Main work location in April 2020 LFS Supplement is defined as the place of work where the respondent worked the most hours. Source: Labour Force Survey and April 2020 Supplement, 2016 Census of Population, Statistics Canada Catalogue no. 98-400-X2016320, and O*Net. Labour Force Survey April 2020 Supplement Main work location of workers age 15-69, week of April 12, 2020 All employed Census of Population, Place of work of employed labour force age 15 or more, 2016 All employed

Measuring remote work Association with occupational risks of exposure to COVID-19 (1)

0 Management occupations 1 Business, finance and administration occupations 2 Natural and applied sciences and related occupations 3 Health occupations 4 Occupations in education, law and social, community and government services 5 Occupations in art, culture, recreation and sport 6 Sales and service occupations 7 Trades, transport and equipment operators and related occupations 8 Natural resources, agriculture and related production occupations 9 Occupations in manufacturing and utilities

20 40 60 80 Work from home 50 60 70 80 90 Physical proximity

0 Management occupations 1 Business, finance and administration occupations 2 Natural and applied sciences and related occupations 3 Health occupations 4 Occupations in education, law and social, community and government services 5 Occupations in art, culture, recreation and sport 6 Sales and service occupations 7 Trades, transport and equipment operators and related occupations 8 Natural resources, agriculture and related production occupations 9 Occupations in manufacturing and utilities

20 40 60 80 Absent 50 60 70 80 90 Physical proximity

Measuring remote work Association with occupational risks of exposure to COVID-19 (2)

0 Management occupations 1 Business, finance and administration occupations 2 Natural and applied sciences and related occupations 3 Health o 4 Occupations in education, law and social, community and government services 5 Occupations in art, culture, recreation and sport 6 Sales and service occupations 7 Trades, transport and equipment operators and related occupations 8 Natural resources, agriculture and related production occupations 9 Occupations in manufacturing and utilities

20 40 60 80 Absent 20 40 60 80 Exposure to diseases or infections

0 Management occupations 1 Business, finance and administration occupations 2 Natural and applied sciences and related occupations 3 Health o 4 Occupations in education, law and social, community and government services 5 Occupations in art, culture, recreation and sport 6 Sales and service occupations 7 Trades, transport and equipment operators and related occupations 8 Natural resources, agriculture and related production occupations 9 Occupations in manufacturing and utilities

20 40 60 80 Work from home 20 40 60 80 Exposure to diseases or infections

Indirect risk factors and epidemiological risk factors Combining health and labour statistics

with Emmanuelle Arpin, Institute for Health Policy Management and Evaluation, University of Toronto.

• Occupational risk of exposure and feasibility of remote work as “indirect” risk factors.
• “Direct” epidemiological risk factors include respiratory diseases, cardiovascular diseases, diabetes:
• Common pre-existing conditions among infections, hospitalizations, ICU admissions and deaths (Public Health

Agency of Canada, April 19, 2020 [Coronavirus Disease 2019 (COVID-19) Daily Epidemiological Update]).

• Are workers with chronic health conditions more likely to be in jobs with higher occupational risks of exposure?
• Finding “safe jobs” for workers with one or more risk factor becomes crucial in the longer term in order to ensure

that any recovery benefits to all equally (“safe matching” and labour reallocation – see Basso et al. 2020).

• Educational-health gradient (Zajacova and Lawrence 2018).
• Currently available Canadian data? Limited landscape...
• Exploratory analysis with US data from the National Health Interviews Survey (NHIS).

Indirect risk factors and epidemiological risk factors Insights from US occupations

Chronic health issues and occupational risk of exposure to COVID-19, US adults 1 2 3 4 Total Physical proximity Told had chronic bronchitis, past 12 months No 26.1 24.7 25.2 23.9 100.0 Yes 21.8 26.8 25.4 26.1 100.0 Ever told had asthma, still has asthma No 26.1 24.9 25.2 23.8 100.0 Yes 24.1 23.3 25.6 27.0 100.0 Exposure to infections or diseases Told had chronic bronchitis, past 12 months No 28.0 22.7 24.7 24.6 100.0 Yes 22.2 23.9 26.4 27.5 100.0 Ever told had asthma, still has asthma No 28.1 22.7 24.7 24.5 100.0 Yes 23.7 22.4 26.0 27.9 100.0 Source: National Health Interviews Survey, 2017, and O*Net Occupational exposure risk score quartile

Concluding remarks Towards lifting emergency measures and economic recovery

In summary, two new perspectives on labour market analysis to understand the impact of COVID-19 on workers: 1. A new interest for some less used labour force data variables, mostly from traditional sources; 2. New, health-related dimensions that require some level of data development. Long-term outlook:

• Interplay between occupational risk of exposure and remote work becomes important as emergency measures are

progressively lifted but COVID-19 remains present and physical distancing and other measures remain in place.

• Findings also relevant for other epidemics and infectious diseases (influenza, etc.)

Additional questions:

• Which occupations and sectors are vulnerable to workplace transmission and likely to be disrupted in the long-term?
• Data development: identifying and coding which sectors and occupations are essential or critical.
• Migrant workers, international students and global labour markets
• Feasibility of remote work as an increased source of inequality in international/global labour markets if international

travel even more limited for low-skill migrants?

Thank you!

Questions? Contact xavier.stdenis@utoronto.ca Paper in progress: St-Denis, X. 2020. Sociodemographic determinants of occupational risks of exposure to COVID-19 in Canada (under review)