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Working from home and income inequality: risks of a 'new normal' with COVID-19 - PubMed

Working from home and income inequality: risks of a 'new normal' with COVID-19

Luca Bonacini et al. J Popul Econ. 2021.

Abstract

In the current context of the COVID-19 pandemic, working from home (WFH) became of great importance for a large share of employees since it represents the only option to both continue working and minimise the risk of virus exposure. Uncertainty about the duration of the pandemic and future contagion waves even led companies to view WFH as a 'new normal' way of working. Based on influence function regression methods, this paper explores the potential consequences in the labour income distribution related to a long-lasting increase in WFH feasibility among Italian employees. Results show that a positive shift in WFH feasibility would be associated with an increase in average labour income, but this potential benefit would not be equally distributed among employees. Specifically, an increase in the opportunity to WFH would favour male, older, high-educated, and high-paid employees. However, this 'forced innovation' would benefit more employees living in provinces have been more affected by the novel coronavirus. WFH thus risks exacerbating pre-existing inequalities in the labour market, especially if it will not be adequately regulated. As a consequence, this study suggests that policies aimed at alleviating inequality, like income support measures (in the short run) and human capital interventions (in the long run), should play a more important compensating role in the future.

Keywords: COVID-19; Inequality; Unconditional quantile regressions; Working from home.

© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

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Conflict of interest statement

Conflicts of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1

Incidence of high WFH feasibility and average labour income by activity sector. Notes: Descriptive statistics are computed with individual sample weights. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median

Fig. 2
Fig. 2

Incidence of high WFH feasibility and wage gap in favour of employees with high feasibility levels by decile of annual income. Notes: Descriptive statistics are computed with individual sample weights. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median

Fig. 3
Fig. 3

Labour income distribution by level of WFH feasibility. Notes: Descriptive statistics are computed with individual sample weights. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median

Fig. 4
Fig. 4

Unconditional effects of a positive shift in the WFH feasibility along labour income distribution. Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. high WFH feasibility) only. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median. UE estimates are based on a model specification that only includes the variable of interest, while for UPE estimates additional covariates are included in the model (see Sect. 4). Estimates by employees’ characteristics refer to the UPE specification. Complete estimates for the pooled sample are provided in Appendices 1.4 and 1.5

Fig. 5
Fig. 5

Unconditional effects of a positive shift in the WFH feasibility along labour income distribution by COVID-19 infection incidence. Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. high WFH feasibility) only. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median. UE estimates (in the left panel) are based on a model specification that only includes the variable of interest, while for UPE estimates (in the right panel) additional covariates are included in the model (see Sect. 4)

Fig. 6
Fig. 6

Unconditional effects along income distribution by item of the WFH feasibility index. Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. high feasibility) only. Employees with high feasibility level are defined, for each item, as those reporting a value of the single index over the sample median. UE estimates are based on a model specification that only includes the variable of interest, while for UPE estimates, additional covariates are included in the model (see Sect. 4)

Fig. 7
Fig. 7

Income values by decile of annual labour income. Notes: All descriptive statistics are computed with individual sample weights. Shadowed area report confidence intervals at 95% level.

Fig. 8
Fig. 8

Unconditional effects of a positive shift in the WFH feasibility along labour income distribution (relatively to the point estimates of deciles). Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients reported in Fig. 4 divided by the point estimation value for the specific decile in the specific subgroup of employees. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median. UE estimates are based on a model specification which only includes the variable of interest, while for UPE estimates, additional covariates are included in the model (see Sect. 4). Estimates by employees’ characteristics refer to the UPE specification

Fig. 9
Fig. 9

COVID-19 infection incidence by province. Notes: All descriptive statistics are computed with individual sample weights. The choropleth map is based on a quantile method, so that class breaks coincide with quartiles of COVID-19 infection incidence at provincial level in the analysis sample. Source: Elaboration of the authors on data by the Italian Civil Protection Department (2020). Accessed 5 May 2020

Fig. 10
Fig. 10

Unconditional effects along the labour income distribution considering full-time open-ended employees only (left panel) or including self-employees in the sample (right panel). Notes: Standard errors are clustered by NUTS-3 region, and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. high WFH feasibility) only. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index over the sample median (i.e. 52.2 for both samples of workers). UE estimates are based on a model specification which only includes the variable of interest, while for UPE estimates, additional covariates are included in the model (see Sect. 4)

Fig. 11
Fig. 11

Unconditional effects along the labour income distribution (variable of interest with continuous specification). Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. WFH feasibility index) only. The WFH feasibility index is a multidimensional index ranging from 0 to 100. UE estimates are based on a model specification which only includes the variable of interest, while for UPE estimates, additional covariates are included in the model (see Sect. 4). Estimates by employees’ characteristics refer to the UPE specification

Fig. 12
Fig. 12

Unconditional effects along the labour income distribution (variable of interest with other specifications). a Median of the WFH feasibility index (base). b Quintile groups of the WFH feasibility index. c Quartile groups of the WFH feasibility index. d Tertile groups of the WFH feasibility index. e Mean of the WFH feasibility index. f Sixty percent of the mean of the WFH feasibility index. Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest only, which is defined through different specifications (expressed in panel labels) of the same WFH feasibility index. UE estimates are based on a model specification which only includes the variable of interest, while for UPE estimates, additional covariates are included in the model (see Sect. 4). Estimates in (b), (c), and (d) refer to the UPE specification

Fig. 13
Fig. 13

Unconditional effects of WFH feasibility along the wage distribution (UPE2 estimates). Notes: Standard errors are clustered by NUTS-3 region and estimates are computed with individual sample weights. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. high WFH feasibility) only. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index above the relevant sample median. UE estimates are based on a model specification which only includes the variable of interest, while for UPE2 estimates, additional covariates demographic characteristics regarding individuals and their households are included in the model (see Sect. 6). Estimates by employees’ characteristics refer to the UPE2 specification. Complete estimates for the pooled sample are provided in Appendix Table 19

Fig. 14
Fig. 14

Unconditional effects of WFH feasibility along the wage distribution (with no sample weights). Notes: Standard errors are clustered by NUTS-3 region. Shadowed area reports confidence intervals at 90% level. The figures present coefficients of the variable of interest (i.e. high WFH feasibility) only. Employees with high WFH feasibility level are defined as those reporting a value of the WFH feasibility index over the (non-weighted) sample median (i.e. 53.4). UE estimates are based on a model specification which only includes the variable of interest, while for UPE estimates, additional covariates are included in the model (see Sect. 4). Estimates by employees’ characteristics refer to the UPE specification

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