The COVID-19 pandemic caused many countries to implement social distancing measures that prevented people from attending their workplaces. While some individuals were able to transfer their work activities to their residences, others had work hours reduced or even lost their jobs. By investigating the observed variation in the location and the time allocated to work during the pandemic by different segments of the workforce, the current study adds to the growing body of literature on COVID-19 effects on work activities (for example, Astroza et al. 2020; Beck and Hensher 2022) and complements previous work that classified occupations and socio-demographic characteristics associated with the feasibility of working from home (WFH) (Dingel and Neiman 2020; Mongey, Pilossoph, and Weinberg 2020). In specific, we identify what types of individuals (1) decreased/increased the total hours worked, (2) substituted out-of-home work (OHW) by WFH, (3) whether individuals’ productivity was affected, and (4) how these changes varied between American states with mild- and high-incidence of COVID-19.
Firstly, we extracted individual socio-demographic characteristics, work location, and time use information from the American Time Use Survey (ATUS) in a pre-pandemic (2019) and pandemic (2020) context (US-BLS 2021). The final sample included 7636 individuals in the workforce and is described in Appendix A and B. Since the data from both years are not panel, we conducted chi-squared tests across all socio-demographic variables to evaluate whether the samples from 2019 and 2020 were comparable. We concluded that the comparison was valid as the only statistically significant differences observed were the widespread increase in unemployment, and an increase in the number of high-income individuals living in metropolitan areas in 2020, which both are likely associated with the pandemic.
Secondly, we matched the ATUS observations with the Centers for Disease Control and Prevention (CDCP 2021) data, which showed the temporal distribution of COVID-19 cases throughout 2020. The daily number of confirmed cases at the state level 7, 14, 21, and 28 days prior to the ATUS diary recording date was used to compute COVID-19 incidence by population size. Three categories of incidence severity were established: (1) no-COVID-19 (2019 observations and 2020 observations in places with zero cases); (2) mild incidence (incidence ≤ 90th percentile of all individuals in the non-zero incidence places) and (3) high incidence (incidence > 90th percentile).
Thirdly, a latent class multiple discrete-continuous model was estimated to identify subclasses of individuals in the workforce with differences in the time allocated to work at three locations: (1) workplace, (2) home, and (3) other places. The time allocated to non-work activities was considered the base category to enable the identification of overall increases and reductions in time dedicated to work activities. Readers can refer to Bhat et al. (2008) and Hess and Palma (2019) for the methodology of the latent-class MDCEV model with outside goods used in this study. We used the R package Apollo (version 0.2.5) for the estimation (Hess and Palma 2021).
Finally, to facilitate the interpretation of our model results, we calculated the odds ratio (OR) associated with the class membership variables and the average treatment effects (ATE) of COVID-19 incidence on each class stratified by socio-demographic groups (Sarrias and Daziano 2018; Etzioni et al. 2021). Average fitted values for socio-demographic groups under each class were calculated considering that all individuals were in no (control), mild (treatment 1), and high (treatment 2) COVID-19 incidence situations. ATE are then extracted from the relative comparison between the fitted values in control and treatment situations. Since ATE are aggregate statistics and the probability of belonging to a class varies across individuals, instead of using an arithmetic average of the observations in each group, we computed a weighted-average based on the class-probability to obtain the final treatment effects, as described in detail in Appendix C.
The bottom half of Table 1 shows that two latent classes of workers were identified in the final model specification, with Class 1 being twice the size of Class 2. To evaluate the quality of the classification, a receiver operating characteristics (ROC) curve was fitted and the area under the curve (AUC) measured, as illustrated in Figure 1 (see Hosmer and Lemeshow 2020, for method details). The AUC suggests the latent class classification has a 79.5% chance of accurately distinguishing an individual between two classes. As a rule of thumb, an 80% classification performance is considered excellent (Hosmer and Lemeshow 2020).
Young males with lower levels of education and low to medium income are more likely to belong to Class 1. In contrast, middle-aged women with tertiary education and high-income are more likely to belong to Class 2. The contribution of each one of the socio-demographic characteristics to the likelihood of belonging to one of these classes is demonstrated in the OR column. The strongest distinction between the two classes is regarding the level of education, as individuals who have higher education degrees are four times more likely to belong to Class 2 than those who do not have degrees.
The top half of Table 1 shows the estimated MDCEV coefficients, while Table 2 shows the average treatment effects calculated based on these coefficients. We observe that individuals in Class 1 decreased their total work time during the pandemic, with service/manual labor, healthcare, and legal employees in both low-mid and high incidence places showing the greatest reduction.
Both Class 1 and 2 present some level of substitution of OHW by WFH; however, the magnitude of the transference of work to the residence is significantly higher for Class 2. These results support Mongey, Pilossoph, and Weinberg (2020) finding that older and more educated individuals (Class 2 profile) have more autonomy in their jobs. Managers, professionals, education, and art-related employees and self-employed workers reveal a greater propensity to WFH than others (in both classes and for both COVID-19 incidence levels), which is likely associated with the nature of their work tasks (Elldér 2020).
In general, Class 2 presents an increase in hours worked, showing that the transference of work to the residential setting may have decreased their productivity. Productivity losses seem higher among individuals living in metropolitan areas compared to regional areas, indicating that non-city residents were less affected by the pandemic, as also observed by Chauhan et al. (2021).
For Class 1, reductions in time spent in the workplace were similar for both low and high COVID-19 incidence states (except for self-employed workers, who were more affected in high-incidence locations). For Class 2, on the other hand, there were greater reductions in time spent in the workplace for mild-low incidence places, which may be associated with stricter work-related social distancing measures. However, the proportional increase in hours worked at home is also greater in high-incidence areas, showing either a more significant productivity loss or an increase in workload, especially for self-employed individuals.
In conclusion, our findings suggest the need to consider a combination of assistance measures when formulating policies to support workers during pandemic situations that require social distancing. These measures should take into consideration occupation and socio-demographic characteristics and address both differences in lost work opportunity and productivity changes. For example, policies should consider that middle-aged professional women are more likely to have an increase in hours worked and a decrease in productivity than other groups, and thus, additional support to prevent this decrease in productivity may be required.