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Cheng, Yu-Tong, Patricia Sauri Lavieri, and Sebastian Astroza. 2022. “How Did the COVID-19 Pandemic Impact the Location and Duration of Work Activities? A Latent Class Time-Use Study.” Findings, May. https://doi.org/10.32866/001c.35621.
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  • Figure 1. ROC curve and AUC.
  • Table 1. Results of the outside-good latent-class multiple discrete-continuous extreme value model (LC-MDCEV).
  • Table 2. Average treatment effects on time allocation (based on the probability-weighted fitted value).
  • Table A1. Sample distribution of the outcome variable and the COVID-19 incidence variable.
  • Table A2. Sample description of exogenous variables.

Abstract

We investigate the variation in location and time allocated to work during the COVID-19 pandemic. Data from the American Time Use Survey (2019-2020) was modeled using a latent-class multiple discrete-continuous model. Two main segments of individuals who suffered different impacts from the pandemic on their work arrangements were identified. Men, young adults, and less educated individuals with lower-mid-income were often unable to transfer work into a residential setting, showing a reduction in work opportunities. Women, middle-aged, highly educated, and high-income individuals were prone to an increase in total hours worked when substituting out-of-home work with at-home work, showing productivity loss.

Accepted: May 11, 2022 AEST

Appendices

Appendix A

Table A1. Sample distribution of the outcome variable and the COVID-19 incidence variable.
Year Alternative Number of individuals (%) Mean duration in minutes (std. dev)
Only spent time in one alternative Spent time in multiple alternatives Including all individuals Including only individuals who spent more than zero minutes
2019 Non-work activities 1616
(44.37)
2026
(55.63)
1219.16
(244.70)
Work
activities
Workplace 1242
(85.83)
205
(14.17)
178.19
(236.08)
439.38
(150.49)
Home 399
(64.77)
217
(35.23)
30.21
(100.89)
178.62
(183.60)
Other places 102
(43.78)
131
(56.22)
12.44
(66.59)
194.40
(184.58)
2020 Non-work activities 1853
(46.39)
2141
(53.61)
1228.52
(242.39)
Work
activities
Workplace 957
(80.22)
236
(19.78)
127.62
(215.77)
427.25
(166.83)
Home 844
(79.85)
213
(20.15)
74.95
(163.40)
283.21
(204.75)
Other places 75
(44.64)
93
(55.36)
8.91
(59.53)
211.89
(203.63)
Covid-19 variable
Variable Number of individuals (%)
2019 2020
No COVID-19 3642 (100.00) 122 (3.05)
Mild incidence COVID-19 0 (0.00) 3485 (87.26)
High incidence COVID-19 0 (0.00) 387 (9.69)
Table A2. Sample description of exogenous variables.
Variable Number of individuals (%) Chi-square
test p-valuea
2019 2020
Age
Age 18-24 242 (6.64) 258 (6.46) 0.75
Age 25-29 312 (8.57) 330 (8.26)
Age 30-39 849 (23.31) 961 (24.06)
Age 40-49 829 (22.76) 859 (21.51)
Age 50-64 1080 (29.65) 1221 (30.57)
Age > 64 330 (9.06) 365 (9.14)
Genders
Female 1796 (49.31) 1947 (48.75) 0.62
Male 1846 (50.69) 2047 (51.25)
Education
Below bachelor’s degree 1947 (53.46) 2069 (51.80) 0.12
Bachelor’s degree 965 (26.50) 1143 (28.62)
Postgraduate degree 730 (20.04) 782 (19.58)
Annual household income
Low income (< USD 35,000) 547 (15.02) 473 (11.84) 0.00
Medium income (USD 35,000-74,999) 1283 (35.23) 1405 (35.18)
High income (>= USD 75,000) 1812 (49.75) 2116 (52.98)
Employment status
Unemployment 139 (3.82) 262 (6.56) 0.00
Managers, professionals, education- and art-related employees 1173 (32.21) 1293 (32.37)
Service/manual labor, healthcare, and legal-related employees 1958 (53.76) 2026 (50.73)
Self-employed workers 372 (10.21) 413 (10.34)
Metropolitan status
Non-metropolitans 543 (14.91) 537 (13.45) 0.07
Metropolitans 3099 (85.09) 3457 (86.55)
Interactions of income and metropolitan status
Low-income x Metropolitan status
Low income metropolitans 439 (12.05) 374 (9.36) 0.00
Non-low income metropolitans 3203 (87.95) 3620 (90.64)
Medium-income x Metropolitan status
Medium income metropolitans 1043 (28.64) 1161 (29.07) 0.68
Non-medium income metropolitans 2599 (71.36) 2833 (70.93)
High-income x Metropolitan status
High income metropolitans 1617 (44.40) 1922 (48.12) 0.00
Non-high income metropolitans 2025 (55.60) 2072 (51.88)

a Contingency table chi-square test: H0 – the distribution of categories in 2020 is not different from that of 2019


Appendix B. Detailed occupations for the two occupational employee categories.

  1. Managers, professionals, education and art-related employees
    (1) Management occupations
    (2) Business and financial operations occupations
    (3) Computer and mathematical science occupations
    (4) Architecture and engineering occupations
    (5) Education, training, and library occupations
    (6) Arts, design, entertainment, sports, and media occupations
    (7) Life, physical and social science occupations
  2. Service/manual labor, healthcare, and legal-related employees
    (1) Sales and related occupations
    (2) Office and administrative support occupations
    (3) Food preparation and serving related occupations
    (4) Building and grounds cleaning and maintenance occupations
    (5) Farming, fishing, and forestry occupations
    (6) Construction and extraction occupations
    (7) Installation, maintenance, and repair occupations
    (8) Production occupations
    (9) Transportation and material moving occupations
    (10) Personal care and service occupations
    (11) Healthcare practitioner and technical occupations
    (12) Healthcare support occupations
    (13) Community and social service occupations
    (14) Legal occupations
    (15) Protective service occupations

Appendix C. Methodology to calculate the average treatment effects and probability-weighted average treatment effects for the latent-class multiple discrete-continuous extreme value model.

Average treatment effects (ATE) are used to compare differences between a pair of outcomes (from a model or experiment), where one is assumed to undergo treatment and the other does not receive any treatment. In our study, mild and high incidence of COVID-19 were considered as the treatments affecting people’s time allocated to work. In this context, this appendix first describes the method of calculating traditional ATE based on situations that all observations have an equal weight (arithmetic average) and then outlines why and how one may calculate probability-weighted average treatment effects (WATE) when using estimates from latent-class discrete-continuous extreme value (MDCEV) models.

To investigate the ATE based on the estimates from the latent-class MDCEV model, we first used 200 random draws to generate fitted values (Yk,nc,s) of time allocated to each one of the k alternatives (k =1, 2, 3, 4; non-work activities, work at workplace, work at home, and work at other places, respectively) in (s) scenarios: (1) without COVID-19 (control), (2) and with mild (treatment 1), and (3) high (treatment 2) COVID-19 incidences. Then, to characterise differences in effects based on class membership, we assigned individuals to the class that they had the highest probability of belonging (c = 1 if Class 1 or c = 2 if Class 2). For example, if an individual (n) had a 0.32 and 0.68 probability of belonging to Class 1 and Class 2, respectively, they were assigned to Class 2 (n2). Treatment effects were then computed within each class as the relative differences between the fitted values in the treatment and control situations.

Treatment 1: ATE for all individuals=1NcNcnc=1(Yk,nc,2Yk,nc,1)Treatment 2: ATE for all individuals=1NcNcnc=1(Yk,nc,3Yk,nc,1)

where Nc is the total number of individuals in each class. Effects were also stratified based on socio-demographic groups (g):

Treatment 1: ATE for each socio-demographic group=1NgcNgcnc=1(Yk,nc,2Yk,nc,1)Treatment 2: ATE for each socio-demographic group=1NgcNgcnc=1(Yk,nc,3Yk,nc,1)

where Ngc represents the total number of individuals in each socio-demographic group in each class.

The computation of ATE using an arithmetic average (as shown in equations 1 and 2) does not accommodate for the fact that in latent class models, the probability of belonging to a class varies across individuals. For example, in the computation above, an individual with a 0.51 and 0.49 probability of belonging to Class 1 and Class 2, respectively, and an individual with a 0.99 and 0.01 probability of belonging to Class 1 and Class 2, respectively, will both be assigned to Class 1 and contribute equally to the ATE if they have the same fitted values. However, the latter should have a more substantial contribution to the aggregate effect. Therefore, to account for this variation in the probability of belonging to a class, a probability-weighted average treatment effect (WATE) can be calculated.

To compute the weights for each individual (Wnc), we first calculated the differences between their probability of belonging to class c (either Class 1 or 2) and the average probability of belonging to this class for all class members. Then, we added one to this difference to avoid negative values. Wnc=1+(Pnc1ncNcnc=1Pnc) was then applied to weigh the fitted values:

WYk,nc,s=Yk,nc,s(1+(Pnc1ncNcnc=1Pnc) )

where WYk,nc,s is the probability-weighted fitted values. Moreover, before computing the WATE, to ensure that each individual’s total available time would remain 1,440 minutes per day after adding weights, WYk,nc,swas adjusted by the ratio of 1,440 min to the total weighted fitted values of time allocated across all alternatives:

WYk,nc,s=WYk,nc,s1440Kk=1WYk,nc,s

Weighted treatment effects were then computed within each class by calculating the difference of the effects in treatment and control situations.

Treatment 1: WATE for all individuals=1NcNcnc=1(WYk,nc,2WYk,nc,1)Treatment 2: WATE for all individuals=1NcNcnc=1(WYk,nc,3WYk,nc,1)

Additionally, probability-weighted effects were also stratified based on socio-demographic groups:

Treatment 1: WATE for each socio-demographic group=1NgcNgcnc=1(WYk,nc,2WYk,nc,1)Treatment 2: WATE for each socio-demographic group=1NgcNgcnc=1(WYk,nc,3WYk,nc,1)