Adjustment by Working from Home

The greater probability of working for males/high educated/non-recent-immigrants may arise because of another adjustment mechanism that has risen to relevance in this par-ticular recession: working from home. To establish the importance of this adjustment mechanism and who is able to access it, we estimate regressions using the proportion of workers in a given occupation who reported working from home as a response to the pandemic. In particular, in April, the LFS introduced a question asking workers if they worked from home in the reference week but usually worked outside the home. In April, May and June, only one of the six rotations was asked this question, while in July, five of the six rotations were asked.¹⁵ The work from home variable is restricted to workers who were employed and not absent from work.

In Figure 16, we present the proportion of those who are employed/working who report that they are working at home but don’t usually do so. Overall, in April through July, 2002, about one-fifth of these workers are newly working from home but there are substantial differences across groups. Women are more likely to have shifted their work to home. This may suggest an advantage to women in terms of having access to this adjustment mechanism but likely reflects a greater tendency for women relative to men to work from home in order to take care of children who can no longer go to school.

In that sense, it would suggest an added work disadvantage for women to the extent that working from home with children present creates issues for productivity and career progress.

The biggest difference in working from home is between the low and high educated groups. A quarter of the high educated were newly working from home after the onset of Covid but only 6% of the low educated made this change. One way to put this difference in context is to do a simple counterfactual exercise in which we ask what the June employment level would have been for the low educated if their rate of working from home had increased to the same extent as the high educated. In doing this, we assume that the increase in people working from home would be drawn entirely from the set of non-workers rather than just a conversion from those who are working but not from home in June. In this sense, our counterfactual is an upper bound on what increasing the option of working from home would do to employment for the low educated. The counterfactual exercise yields an estimate of an increase by approximately 100,000 in the number of low educated employees in June, 2020. Going back to Figure 4, this

15While we do not use the July LFS for the rest of the analysis, due to the limited sample available for April to June, we included it for the work at home section augment the sample size.

Figure 16: Probability of Newly Working from Home

20.6 23.1

25.1 6.3

18.3 23.9 20.8

0 3 6 9 12 15 18 21 24 27

Domestic Born/Established Imm.

Recent Immigrants High-Educated Low-Educated Males Females Everyone

Notes: Proportions of employees who are employed and at work who report that they are working from home but do not usually do so. The data is from 1 rotation in the April, May, and June LFS and 5 rotations in the July LFS.

represents about a quarter of the total job loss for this group between February and June. In percentage terms, it would imply a decline of just over 6% instead of 9%. This is the same percentage decline experienced by the high educated. That is, according to our rough counterfactual, we can completely account for the extra job loss for the low educated through their lower access to the ‘work from home’ adjustment mechanism.

Table3shows results from a linear probability model in which the dependent variable is a dummy variable equal to one for people who report that they are working from home but did not do so in the past. The samples are restricted to people who are employed and at work in the survey week. Panel A shows results for all workers, while Panels B, C and D focus on the proportion of low-educated, female and immigrant workers who report working from home. Strikingly, greater riskiness of an occupation is associated with lower work from home rates. In fact, results in Column 2 suggest that the very factors that increase risk, in particular Proximity with others and Dealing with the Public, are the factors associated with a lower propensity of transitioning to work-from-home. Turning to Column 3, it is notable that the negative correlation of work-from-home with O^∗NET occupational risk almost entirely disappears when we account for census-based, at-home worker risk characteristics.¹⁶ This fits with our observation that there is a substantial difference in working from home by education level, recalling that education level and characteristics such as living in a crowded dwelling are strongly correlated. Thus, lower

16Note that the set of home condition risk characteristics includes the proportion of people in the occupation who work from home in the 2016 Census. This does not enter as a significant predictor of the dependent variable because it refers to increased rates of working from home.

educated workers do not have lower work from home rates because they tend to be in jobs such as those done in close proximity to others that cannot be shifted to home but because their home situations do not accommodate working from home.¹⁷ Putting these results together, the implication is that lower educated workers faced greater employment losses than their higher educated counterparts not because of the nature of their conditions at work but because of their living arrangements with more people living in a given number of rooms. In essence, in the face of a pandemic, inequalities in living conditions have generated inequalities in employment outcomes.

Looking at the propensity of different groups to work from home, we note that the relationship between the VSE Risk Index (and the individual risk factors) and work-from-home is similar across all three groups, relative to their baseline propensity. In other words, while certain groups were more likely to transition to work-from-home, their response to viral risk was similar.

17Recall that lower educated workers did not face higher probabilities of being in high risk occupations such as those with greater proximity to others.

Table3:RegressionofProportionWorkingfromHomeonRiskIndexandRiskComponents IndependentVariableWorkfromHome allfemaleloweducimmigr. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12) VSERiskIndex(Factor)-0.0733*-0.0879*-0.0613*-0.0694* (0.0312)(0.0352)(0.0290)(0.0322) O∗NETMeasures Proximity-0.0888***-0.00465-0.0896***0.00273-0.0597***0.00375-0.0903***-0.00248 (0.0207)(0.0158)(0.0214)(0.0152)(0.0159)(0.0130)(0.0240)(0.0211) Disease0.01670.007050.01280.00004410.01120.004800.01910.00871 (0.0403)(0.0192)(0.0402)(0.0187)(0.0313)(0.0174)(0.0471)(0.0261) Outexposed-0.0647***-0.0128-0.0530*-0.00808-0.0569**-0.0107-0.0635***-0.00921 (0.0172)(0.0106)(0.0201)(0.0164)(0.0192)(0.0132)(0.0172)(0.0116) Contact0.0860***0.0002540.0934***-0.001860.0642***-0.008060.0920***0.00206 (0.0204)(0.0110)(0.0228)(0.0152)(0.0174)(0.0120)(0.0228)(0.0152) Public-0.0700**-0.0313*-0.0930***-0.0482**-0.0707**-0.0384*-0.0687*-0.0273 (0.0235)(0.0126)(0.0261)(0.0149)(0.0259)(0.0180)(0.0267)(0.0167) SharefromCensus LivingwithHealthWorker-0.0710**-0.0536-0.0676**-0.0691* (0.0231)(0.0277)(0.0230)(0.0302) LivinginUnsuitableDwelling-0.128***-0.145***-0.100***-0.134*** (0.0161)(0.0175)(0.0168)(0.0169) UsingPublicTransitforWork0.0974***0.101***0.0799***0.103*** (0.0112)(0.0140)(0.0191)(0.0133) WorkingfromHome-0.0110-0.0147-0.0177-0.0115 (0.0126)(0.0150)(0.0104)(0.0140) Constant0.200***0.179***0.197***0.222***0.204***0.231***0.144***0.132***0.145***0.210***0.187***0.207*** (0.0329)(0.0281)(0.0191)(0.0348)(0.0311)(0.0240)(0.0270)(0.0236)(0.0204)(0.0342)(0.0294)(0.0201) N454454454408408408411411411405405405 Notes: [1]Tableshowstheestimatedlinearregressioncoefficientsoftheproportionworkingfromhomeonstandardizedmeasuresofviraltransmission risk,whereoneobservationcorrespondstoa4-digitoccupation. [2]Standarderrorsareinparenthesesandareclusteredatthe2-digitoccupation-level. [3]Statisticalsignificanceisdenotedby:***at1%level,**at5%level,*at10%level. [4]Healthoccupationsareexcludedfromthesample. [5]Regressionsareweightedbytheshareof2019employmentinagiven4-digitoccupation.