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Our analysis focused on the urban areas of Sydney and Melbourne during a pre-COVID period (which we use as a baseline), during the first pandemic wave in March and April 2020 (with Australia-wide transmission and mobility restrictions), and during the second wave from July 2020 (with substantial transmission and mobility restrictions in Melbourne but not in Sydney). We identify positive correlations between income security and changes to internet activity during COVID-19. These correlations are consistent with the hypothesis that higher income security is associated with more people working from home during lockdown. This hypothesis is further supported by individual-level data from the CARE survey. We observe that in Sydney this trend persists after the release of lockdown restrictions, indicating the possibility of a new normal of remote working conditions, particularly for occupations associated with higher income security. In Melbourne, we find that the role of children conducting their studies online disrupts these correlations due to an inverse relationship between income security and the proportion of families with children.

Income security is distributed spatially according to distinct patterns, with high values in the central and northeast suburbs of both Sydney and Melbourne (Fig. 2a,b). The upper 50% income security quantile (Fig. 2c) favours managerial and office-based occupations, while the lower 50% quantile (Fig. 2d) contains more service staff and other socially-oriented occupations. The frequency distributions of average income security among SA2s in Sydney and Melbourne (respectively) are provided in the Supplementary Fig. S4, which demonstrates that the distributions in the two regions are not significantly different (two sample t-test, (p = 0.552)). High resolution choropleth maps of income security by region can be found in the Supplementary Fig. S6a.

Geospatial and occupational distribution of income security. The choropleth maps in (a) and (b) demonstrate the geospatial distribution of income security in Greater Melbourne and Greater Sydney (respectively) on the scale of Statistical Area Level 2 (SA2). Areas below the median income security index of 0.1809 are colored orange while those above the median are colored purple. The histograms in (c) and (d) demonstrate the distribution of the population in these regions over the income security spectrum, with indicative occupation types for each bin shown in the bottom y-axis labels (the label corresponding to the most prevalent occupation classification for each bin is shown). For both histograms, waiters have the lowest income security, while anaesthetists have the highest.

To examine the qualitative association between income security and the ability to work from home indicated by the distributions in Fig. 2c,d, we apply the occupation classification method developed by Dingel and Neiman37. This results in a binary (0 or 1) value indicating whether or not a particular occupation type can be performed from home. We found a strong association between income security and the ability to work from home (Fig. 3). This association was observed both by occupation (Fig. 3a) and by geographic region (Fig. 3b). See the Supplementary Fig. S5 for histograms of the distributions shown in Fig. 3a as well as the distributions of the x- and y-axis variables used in Fig. 3b.

Relationship between income security and the ability to work from home. Box plots in (a) compare the distributions of log-transformed income security, grouped by the ability to work from home for each of 321 occupations classified by 4-digit ANZSCO codes. The distributions in (a) are computed from the HILDA survey, 2018. The scatter plot in (b) demonstrates the correlation between average income security for each SA2 region, and the corresponding average proportion of individuals who can work from home, computed from the occupation distribution of each SA2 in the Greater Sydney and Greater Melbourne regions released in the 2016 ABS Census. [Note: the log-transformed income security data in (a) omits 24 occupations that had a value of 0 for income security (no securely employed HILDA respondents), of these, 2 were grouped into the can work from home category and 22 into the cannot work from home category].

To quantify changes to home internet use during COVID-19 restrictions, we aggregated internet activity data from all SA2 regions within Greater Sydney and Greater Melbourne (respectively). Over the pre-COVID baseline, we averaged the per-user upload and download rates from the hours of 9 a.m. to 12 p.m. in order to capture a baseline measurement of putative remote work-related internet activity (see Methods). During the first and second waves of COVID-19 in Australia, peaks in case incidence coincided with the implementation of the most restrictive policies, and were followed by increases in total internet use, which peaked approximately 1 to 3 weeks after implementation of the tightest level of restrictions (Fig. 4).

After identifying time intervals representative of the changes induced by the first- and second-waves of restrictions, we examined spatial variation among individual SA2 regions during those periods. The grey bands in Fig. 4 show the periods over which nbn data was averaged for each individual SA2 in order to examine the spatial distribution of changes to internet activity during first and second waves of COVID-19. For visualisation of spatial trends, high-resolution choropleth maps of internet activity changes relative to baseline can be found in the Supplementary Fig. S6b,c.

Timeseries plots of average daytime internet use, COVID-19 case incidence, and restriction policy implementation for (a) New South Wales and (b) Victoria, Australia. Daily average upload rates per household, per 30 min interval between 9 a.m. and 12 p.m. are shown as blue dots for weekdays (blue dots, the blue line is the 7-day average). Daily case incidence is shown as black dots (the black line is the 7 day average), and dates on which restriction policies were modified are shown as vertical dashed lines for increasing (red) and decreasing (green) restriction levels. The grey bands indicate the dates over which nbn data was averaged for our analysis of first- and second-wave changes. See the Supplementary Fig. S1 for an equivalent timeseries presenting average downloads rather than uploads.

We found that during the first period of restrictions, areas with higher income security tended to exhibit larger increases in internet volume per household (Fig. 5ac). However, these trends were produced by qualitatively different changes for downloads and uploads, respectively:

During the pre-COVID baseline period, absolute download volume tended to decrease with income security, becoming uncorrelated during the first wave of restrictions (Fig. 5a). This transition produces larger increases in download volume in areas with higher average income security (Fig. 5b,c).

On the other hand, absolute upload volume shows baseline rates that are initially uncorrelated with income security and transition to an increasing trend during the first wave of restrictions (Fig. 5d). This produces changes in upload volume that have similar correlations with income security to those observed for downloads (Fig. 5e,f), but that occur due to the emergence of a positive correlation rather than the removal of a negative correlation with the onset of restrictions.

The negative baseline trend of download rates with income security may result from the activity of children. We observed a strong positive correlation between the proportion of families with children and baseline download rates ((rho ~=~0.72~95%;text {CI}~[0.68,~0.75])), and a negative correlation between the proportion of families with children and income security ((rho ~=~-0.39~95%;text {CI}~[-0.45,~-0.32])). This suggests that children engaged in online activity may establish the negative baseline correlation between download rates and income security (Fig. 5a).

Childrens activities also appear to influence the changes observed during lockdown. During the time interval selected to represent the first wave of restrictions (April 18th to April 24th), school holidays were still in effect in Greater Sydney while in Melbourne, children had returned to their studies remotely. Because regions with higher income security tend to have a lower proportion of families with children, remote learning activity weakens the positive association between upload activity and income security produced by adults working from home. Correlations between income security, the proportion of families with children, and internet activity in Sydney and Melbourne (respectively) during the first wave of COVID-19 restrictions are shown in the Supplementary Tables S2 and S3.

Changes to internet use during the 1st wave of COVID-19 restrictions in Australia, plotted against income security for each SA2 region. (a) Shows absolute average household download rates before (black dots) and during (green dots) the selected period (April 18th to April 24th, 2020). (b) Plots the absolute change in average per-household download rate during the first-wave period, and (c) plots the change in download rate relative to baseline. (d) Shows absolute upload rates before (black) and during (green) COVID-19, while (e) shows absolute changes to upload volume, and (f) shows changes to upload volume relative to baseline. In each subplot, the internet traffic quantifiers are plotted against the income security score for the corresponding SA2 region, and Pearsons correlation coefficients with 95% CI intervals are shown in the legends.

For the second wave of COVID-19 (and associated restrictions), we selected the appropriate time period using internet data from Victoria, where the second epidemic wave was concentrated. In Victoria during the second wave, internet activity peaked during the week of August 8th to August 14th. As for the first wave, this home internet activity peak immediately followed the implementation of the highest level of restrictions (Fig. 4b).

Because of the substantially different epidemiological and policy situations in Sydney (New South Wales) and Melbourne (Victoria) during the second-wave period (Fig. 4), we examined the relationship between internet traffic, lockdown policy, and income security for each city separately. Comparing the two cities provides insight regarding changes in behaviour related to the contrasting scenarios. During the second wave, Greater Sydney experienced a series of localised outbreaks with minimal social restrictions, while in Melbourne there was a large-scale epidemic with mandatory movement restrictions.

While household internet traffic declines in Sydney during the second wave relative to the first wave, the positive correlation between income security and internet activity relative to baseline remains prominent for both downloads (Fig. 6a,c), and uploads (Fig. 6d,f). This is despite the absence of formal stay-at-home orders in the Greater Sydney region at that time (though some restrictions on social gatherings remained in place). The time interval between the first and second waves was long enough to support the assertion that behavioural changes made in response to COVID-19 lockdown policies remain observable after those policies have been formally relaxed.

Greater Melbourne behaves similarly in both waves with respect to changes in download traffic as a function of income security (compare Figs. 5a,c, 6a,b). However, changes to upload volumes do not mirror the correlations observed during the first wave (compare Figs. 5d,f, 6d,e). In fact, there are many areas of Melbourne with high income security that show substantial reductions in upload traffic during the second wave, relative to the first. While our data gives no immediate explanation for this counter-intuitive trend, we speculate that it may be due to alterations in work habits that occurred as the lockdown became protracted. Decreases in upload traffic without corresponding decreases in download traffic could result from individuals continuing to perform work activities from home, but participating in less face-to-face online interaction. Conversely, widespread adoption of remote schooling practices could help explain the increase in upload rates for regions in the mid-range of the income security spectrum. Such an effect is consistent with the weak but positive correlations between the proportion of families with children and changes to upload volumes ((rho = 0.15~ 95%~ text {CI}~ [0.033, 0.25]), see Supplementary Table S5). This suggestion is also consistent with the observation that daytime internet activity increases during school holidays, when children are more likely to be in the home (Fig. 1b).

We hypothesise that schooling in the home had a greater impact on internet volume in general, and upload rates in particular, than working remotely from home during the second wave of COVID-19 restrictions in Melbourne. This hypothesis is supported by a preliminary principal component analysis, summarised in the Supplementary Fig. S2. This 3-component PCA shows an increased role of children in determining upload rates in Greater Melbourne during the second-wave period. Specifically, Supplementary Fig. S2 demonstrates a qualitative change in the relationship between the proportion of families with children, income security, and second-wave changes to upload activity. Upload rate is positively associated with the proportion of families with children in the first component (explaining 55% of the variance) and positively associated with income security in the second component (explaining 32% of the variance). In both components, income security and the proportion of families with children are negatively associated. These PCA results support the suggestion that occupation-related correlations between net changes to upload activity and income security are disrupted by the activities of children in online schooling. This may be explained as a competing effect because lower average income security is associated with a higher proportion of families with children ((rho = -0.45~ 95%~ text {CI}~ [-0.53, -0.35]), Supplementary Table S5), while the capacity to work from home increases with income security ((rho = 0.90~ 95% ~text {CI}~ [0.88, 0.91]), Fig. 3b).

Changes to internet use during the second wave of COVID-19 restrictions in Australia, plotted against income security for each SA2 region. (a) Shows absolute download volumes averaged over the baseline period and second wave of COVID-19 restriction policies (August 8th to August 14th, 2020), which includes baseline (black dots), 2nd wave values for Greater Sydney (blue squares), and second wave values for Greater Melbourne (orange triangles). Plots (b) and (c) show the change in download volumes relative to pre-COVID baseline as a function of income security index for each SA2 in Sydney and Melbourne, respectively. Plots (d), (e) and (f) show the same analysis as (a), (b), and (c), respectively, for uploads rather than downloads. In each subplot, the internet traffic quantifiers are plotted against the income security score for the corresponding SA2 region, and Pearsons correlation coefficients with 95% CI intervals are shown in the legends.

To confirm that the household-level trends inferred from SA2-level aggregate variables corresponded to observations made on the individual level, we analysed representative data from Victoria collected by the CARE study. While the CARE study did not collect data on income security per-se, it did record the annual income bracket reported by each respondent.

One of the survey questions was posed as follows: Have you personally experienced a change in work environment (working from home) because of COVID-19 and the measures to prevent its spread? (yes or no). We computed the proportion of respondents who reported income above and below (or within) the median income bracket for the sample (sample median annual income was $AUD 60,000 to 69,999) who answered yes to this question. We then performed a two-tailed Fishers exact test to determine the resulting odds ratio between the two groups, and its statistical significance given the response numbers (see Table 1). The results demonstrate a strong positive relationship between income and switching to work from home, with an odds ratio of 2.15 (95% CI [1.59, 2.92], (p = 6.8times 10^{-7})) computed for the above-median income group, relative to the median-and-below income group. While the income data tabulated by the CARE survey is not an exact representation of the income security score used in our analysis of internet trends (which incorporated contract classification), this result supports the same conclusion: those with higher financial security have more capacity to change their work environments in response to COVID-19 restrictions.

In summary, our results support the hypothesis that occupational factors link the ability to work from home with income security, and clearly show how this link produces strong positive correlations between income security and increases to home internet activity during COVID-19 restrictions. These correlations are consistent with the assertion that higher income security is associated with more people working from home during lockdown. This assertion is further supported by individual-level data from the CARE survey. We observe that in Sydney this trend persists after the release of lockdown restrictions, indicating the possibility of a new normal of remote working conditions, particularly for occupations associated with higher income security. In Melbourne, we find that the role of children conducting their studies online disrupts these correlations due to an inverse relationship between income security and the proportion of families with children.

Read more:
Mapping home internet activity during COVID-19 lockdown to identify occupation related inequalities | Scientific Reports - Nature.com