Working From Home Leads to More Family-Oriented Men By

We examine how working from home (WFH) affects men’s participation in childcare and housework and their attitudes toward family. Because WFH is an endogenous decision, we apply a first­difference instrumental variable estimator, taking the degree to which one can work from home, measured at the individual level, as the instrument. We find that WFH increases the time that men spend on household chores and with family, and the fraction of men who consider life more important than work. Although WFH decreases their commuting time, we find no evidence that it reduces working hours or self­perceived productivity.


Introduction
Work-family conflict has been a major concern for modern families as the number of dual earning couples has risen.Telecommuting or working from home (WFH) has been regarded as a promising means of improving workplace flexibility, and previous research (Kelly et al., 2014; Sherman, 2020) has shown that WFH can reduce work-family conflict for women.
While the earlier studies focus on women, some scholars suggest that WFH should also increase men's engagement with their families.Under the social distancing policy that have become implemented in response to the current COVID19 pandemic, the practice of WFH has become common for many workers, 1 although the feasibility of WFH varies greatly across and within industries and occupations. 2 Alon et al. (2020) claim that because many women work in health care and other businesses considered critical, such as grocery stores and pharmacies, their husbands who can work from home inevitably become the main providers of childcare. 3  They further argue that the reallocation of household duties during the pandemic is likely to have persistent effects on men's future participation in childcare, as indicated by the literature on paternity leave policy reforms. 4However, as far as we are aware, there is a lack of causal evidence in the literature that WFH increases husbands' household work or engagement with their family more generally.
The objective of this paper is to estimate the causal effects of WFH on male workers' en gagement with their families using Japanese data.While gender gaps in unpaid domestic work exist in many OECD countries, Japan exhibits the largest inequality (Figure 1).Therefore, it is especially relevant to examine how the prevalence of WFH affects men's participation in domestic work and attitudes toward their families in a society with such entrenched traditional gender roles.
The survey asks questions on relative changes that have occurred since December 2019 (before the pandemic) in the number of days per week that men work from home and how much they engage with their family.These questions allow us to use the firstdifference estimator to avoid an omitted variable bias from timeinvariant unobserved individual characteristics.
Nevertheless, concerns may arise about an endogeneity bias caused by a possible correla tion between a growth in the frequency of WFH and that of unobserved factors.For example, if workers chose to work from home because their fear of COVID19 led them to become more family oriented, the change in their attitude toward the family is likely to increase their WFH days and participation in housework simultaneously.To address this concern, we use the fea sibility of WFH as of December 2019 (i.e., before the pandemic) to instrument the changes in the number of WFH days.Our WFH feasibility index is selfreported by each individual and, hence, varies within occupations and industries.We take this firstdifference instrumental variable (IV) estimator as our preferred specification.
We find that an additional WFH day increases male workers' engagement with their fam ilies.Specifically, an extra day of WFH per week leads to a 6.2% increase in time spent on housework and a 9.3% increase in the fraction of couples in which the husbands' share of house work rises.An additional day of WFH also increases time spent with the family by 5.6%, and raises the share of male workers reporting that they became more life oriented rather than work oriented by 11.6%.
A potential drawback of WFH is its adverse effects on workrelated outcomes such as pro ductivity.Our estimates indicate that WFH effectively reduces time spent on commuting but has no significant effect on working hours and workers' selfperceived productivity.Hence, we conclude that the practice of WFH encourages male workers to engage in their family life without sacrificing productivity.
Our main results are robust to alternative specifications.Note that a key identifying assump tion is that the WFH index is uncorrelated with the changes of unobserved factors influencing outcomes (i.e., the error term).One might argue that regional, industry, and/or spouse's job characteristics, such as the feasibility of WFH, can influence both the instrument and error term.To address this concern, we control for region and industry fixed effects, and whether the spouse works from home.Our estimates remain essentially unchanged from the main results.
Finally, we examine the heterogeneity of the treatment effects.Our estimates indicate that the effects are stronger for male workers under 45 years of age and those who have preschool children, suggesting that there is a greater increase in time spent on childcare compared with time spent on other household chores when male workers increase their WFH days.In addi tion, our estimates suggest that the estimated effects are largely driven by universityeducated male workers.Overall, our estimates indicate that WFH increases the time that men spend on domestic work and makes them more family oriented without losing productivity or reducing work hours, which will eventually promote greater gender equality within the family.This re sult suggests that policymakers may wish to promote WFH even once the pandemic ends in future.
The rest of the paper is structured as follows.Section 2 reviews the related literature.Sec tion 3 describes the data set and defines the variables.Section 4 explains our identification strategy and lays out the firstdifference IV model.In Section 5, we present the results, includ ing robustness checks and the heterogeneity analysis.In Section 6, we discuss the implications of our results in the context of the literature.We conclude in Section 7.

Literature Review
Our paper contributes to the literature on the causal impacts of WFH.Reflecting difficulty of avoiding selfselection into WFH, the literature has faced a challenge in establishing causality.
Exceptions include Dutcher (2012) and Bloom et al. (2015).Dutcher (2012) conducts a labora tory experiment and shows that the productivity of telecommuting may depend on how creative the tasks are.Bloom et al. (2015) provide evidence from a field experiment that WFH increases the performance of call center employees by 13%.
As these papers mainly examine the effect of WFH on productivity, our research is more closely related to Kelly et al. (2014) and Sherman (2020), who estimate the effects of man agement practices on work-life balance.Kelly et al. (2014) conduct a randomized training intervention designed to improve supervisors' support and employees' schedule control, and show that the intervention leads to improvements in employees' work-family balance and fam ily time adequacy.Note, however, that the intervention aims to improve employees' control over when and where to work, and the support provided by supervisors.Hence, it is not clear to what extent the improved work-life balance can be attributed to remote working.Sherman (2020) focuses on the discretionary uptake of remote working and finds significant effects on familytowork conflict for mothers but not for fathers.
The above two studies treat WFH as an option that improves workplace flexibility for those suffering from work-family conflicts, presumably working mothers.However, they do not ex amine how WFH affects fathers, who typically pay less attention to their families than mothers.
The pandemic is a compelling situation in which workers who would not ordinarily prefer to work from home are strongly encouraged or required to do so.Exploiting the pandemic and preexisting variations in the feasibility of WFH as an IV, we estimate a causal and independent effect of WFH, which complements the evidence from the previous studies.
Our research also contributes to the recent emerging literature on the impacts of COVID19 on withinhousehold gender inequality.Some studies report increased participation of males in childcare during the pandemic.5However, very few studies have attempted to establish causal evidence of the effects of the increased WFH on the allocation of housework.Champeaux and Marchetta (2021) assess the effect of the lockdown policy in France on the distribution of housework and intrahousehold conflict.They find that the husband's share of housework increased only when the husband stayed at home and the wife worked away from the home.In contrast, our estimates suggest that WFH positively affects men's engagement with their family regardless of whether their spouse works from home.Moreover, unlike the studies examining the total impact of the lockdown, we attempt to isolate the effects of WFH by simulating the estimated model.

Overview
Our main data are taken from the 2nd Survey on Changes in Attitudes and Behaviors in Daily Life under the Influence of the Novel Coronavirus Infection,6 conducted in December 2020 by the Cabinet Office of Japan.The survey asks about the frequency of WFH, workrelated outcomes such as hours of work and commuting time, the share of housework and childcare within the household, views on work-life balance, and other questions, such as why a respon dent has changed his/her number of WFH days.Notably, the survey mainly asks respondents about changes since December 2019, prior to the COVID19 pandemic.For example, one ques tion asks, "How has the time you spend with your family changed compared with December 2019?"The format of such questions makes them suitable for our firstdifference specifica tion, as explained in Section 4. Approximately 10,000 individuals participated in the survey.
They were randomly selected from a pool of registered monitors so that the same number of individuals are included for each gender and fiveyear age group.The region of residence was selected according to the population composition, ensuring that the sample is geographically representative.
We note that the survey is retrospective, that is, respondents working in December 2020 answered the questions; therefore, the sample is conditioned on working after the outbreak of COVID19.This survey structure raises concerns because working status after the outbreak may be affected by the COVID19 outbreak.To address this issue, we restrict our sample to married male workers with children under the age of 18 years.We impose this sample restriction because the employment rate of this specific demographic group is extremely stable even during the COVID19 pandemic, as shown in Figure 2 (reproduced from Fukai et al. (2021)).The Japanese Labor Force Survey showed that from 2015 to 2020, the employment rate of this group stayed very high (98.7-99.5%).Importantly, their employment rate after the COVID19 outbreak did not decrease significantly from the prepandemic period.Therefore, we consider that any biases arising from conditioning on working after the COVID19 outbreak are negligible.

Working From Home
In the survey, respondents were asked what percentage of their total work was conducted from home in December 2019 and December 2020, selecting their response from five possible an swers: 100%, more than 50%, less than 50%, usually go to the office but work from home irregularly, and none.Taking the middle points of the intervals, we treat "more than 50%" and "less than 50%" as 75% and 25%, respectively.If respondents answered that they usually went to the office but worked from home irregularly, we assume WFH accounts for 10% of their work.Hence, the share of WFH in total work takes a value of 100%, 75%, 25%, 10%, or 0%. 7  To facilitate interpretation, we multiply this variable by the number of days worked per week. 8  The constructed variable is interpreted as the number of WFH days per week. 9 The survey also asks about the feasibility of WFH before the pandemic, as follows: "How much of your work falls into each of the following four categories?: 1. work that you can do from home without any problems, 2. work that you can do from home although productivity would be slightly lower, 3. work that can be done from home if the work procedure is appro priately altered, and 4. work that you cannot do from home.Provide your answers to each category as a percentage of your total workload.Make sure that the sum is 100%."We define the share of "work that can be done from home without any problems" as our index of WFH feasibility. 7We can construct the variable so that it is equally spaced (that is, takes values of 100%, 75%, 50%, 25%, and 0%) by assigning values of 50% and 25% to the alternatives "less than 50 %" and "work from home irregularly", respectively.However, this change does not affect the results.
8 Although we presume that most respondents work 5 days a week, some answered that they reduced their working days to 3 or 4 days a week.For those respondents, we assume that they work 3.5 days per week and construct WFH variables by multiplying the frequency of WFH by 3.5.
9 Note that we have the same series of survey data conducted in May 2020, although we do not use it in our analysis because of the lack of an IV, the feasibility of WFH.Using that data, we confirm that the percentage of workers who work from home in May 2020 in our data is close to the figure reported in Okubo (2020) and Morikawa (2020), both of whom use a survey conducted in Japan.

Engagement with Family
The survey asks several questions on how engagement with family has changed since December 2019, which are our main outcome variables.First, respondents provide answers on the per centage change in time spent on housework compared with the level in December 2019.Sec ond, respondents report the change in the shares of housework within a couple.We construct a dummy variable that takes a value of one if a respondent answers that his own housework share (the husband's share of housework) increased, and zero otherwise.If respondents answer that "the share changed but has now returned to normal," the value is zero.Third, respondents report the percentage change in time spent with family in interval terms, with possible answers including −51% or lower, −50% to −21%, −20% to −6%, −5% to 5%, 6% to 20%, 21% to 50%, and 51% or higher.We construct a variable of the change in time spent with family by taking the middle point of each interval in the original question.If respondents answered that they increased (decreased) time spent with family by 51% or more, we calculate the variable as 51 × 1.25 (−51 × 1.25)%.10Fourth, we have another dummy variable that takes a value of one if respondents answer that they became more life rather than work oriented, and zero otherwise.

WorkRelated Outcomes
The survey asks about the change in commuting time, working hours, and selfperceived pro ductivity relative to December 2019.As for the change in commuting time and working hours, respondents answer by choosing an interval, with the same selection of responses as for the question concerning time spent with family.The method for construction of the continuous variable is also the same.Turning to selfperceived productivity, as with the question on time spent with family, respondents provide answers on the percentage change compared with the level in December 2019.

Descriptive Statistics
Table 1 reports the descriptive statistics.Our sample consists of 984 married male workers with children under the age of 18 years.The average household size is 3.873.The proportion of workers who have a preschool child is 53.9%.
On average, WFH days increased by 0.499 days per week from December 2019 to Decem ber 2020.In December 2019, 12.5% of respondents worked from home at least once.The proportion rose to 28.5% in December 2020.On average, it was possible to do 22.185% of work from home in December 2019.
Turning to familyrelated outcomes, the time spent with family increased by 9.649%, while that spent on housework increased by 1.511%.In 14.9% of the sample households, the husband increased his share of housework.The respondents' family values ware also affected, with 40.6% of respondents reporting the importance of life over work increased.
Commuting time and working hours decreased by 7.572% and 2.540%, respectively, from December 2019 to December 2020.Respondents also reported that their productivity declined by 4.163% on average.Because respondents include both those who do and those who do not work from home, the figure does not necessarily reflect WFH productivity.

Econometric Model
This section details the econometric model used to estimate the causal effect of WFH on the outcomes.In subsection 4.1, we set up the firstdifference specification as a baseline model, which examines the correlation between the change of the outcomes and the change of the WFH days.In subsection 4.2, we introduce an IV regression as our preferred specification.We instrument the change in the WFH days by the feasibility of WFH in December 2019.

Baseline Model
Because our data are for two periods, December 2019 and December 2020, we begin with the following firstdifference regression to estimate the effect of WFH: where

Identification with the Instrumental Variable
Although a correlation between the level of the WFH days and that of the error term does not bias our estimates from the firstdifference model, we are concerned that changes of the WFH days may be correlated with those of the error term.For example, the fear of COVID19 may affect both the WFH days and an individual's family orientedness.If this is the case, unobserved changes in the fear of COVID19 bias our estimates from Equation 1.
Although the pandemic meant that people were urged to stay at home more strongly than ever before, there are reasons to believe that workers and firms had some discretion about whether to adopt WFH.Japan's stayathome restrictions are substantially less stringent than those adopted in other countries, 11  and Canada (67.13). 12In fact, in the survey, some respondents report that they reduced WFH days between December 2019 and December 2020 because their preferences for WFH changed.
To address the potential endogeneity bias, we use the feasibility of WFH in December 2019 as an IV denoted by z i . 13This feasibility index for WFH can be considered to reflect the nature of the respondent's job tasks.For example, workers in the IT industry may be able to work from home because they can perform most of their tasks anywhere with a computer and an Internet connection.In contrast, WFH is infeasible for supermarket clerks because facetoface service is necessary.Even in the same occupation, the feasibility of WFH may vary depending on job tasks.We expect that the feasibility of WFH is likely to affect the actual change in the WFH days apart from the workers' preference for WFH.
The firststage regression equation is: (2) In Section 5, we confirm that the feasibility of WFH is strongly correlated with changes in the WFH days.
Our instrument must satisfy the following exclusion restriction: The exclusion restriction requires that after controlling individual characteristics X i , the feasi bility of WFH in December 2019 be not correlated with the changes in the error term.In other words, the feasibility of WFH affects outcomes only through changes in the WFH days.Note that we allow for the correlation between the instrument and the level of the error term.For example, even if workers in the IT industry tend to contribute more to housework than other workers, the exclusion restriction is not violated because the IV is correlated only with the lev els of the outcome.In contrast, if they tended to change the amount of time spent on housework between December 2019 and December 2020, then that would invalidate the exclusion restric tion.In Section 5.3, we discuss the potential threat to the exclusion restriction and examine the validity of our instrument.

FamilyRelated Outcomes
Table 4 presents the estimates for the outcomes related to engagement with the family.Columns 1, 4, 7, and 10 report estimates from the firstdifference specification defined by Equation 1.
Overall, an increase in the WFH days improves all four outcomes.An additional day of WFH increases time spent on housework by 5.461% and increases by 8.3% the fraction of male work ers whose share of housework rose.In addition, time spent with family increased by 5.226% and the fraction of male workers who became more life oriented than before increased by 6.1%.
the endogeneity bias caused by timevarying unobserved variables.We address this problem by employing the IV specification.The estimation result for the first stage (Equation 2) is reported in Table 3.The coefficient of WFH feasibility is significantly positive, whereas other characteristics of workers are not predictive of the growth in WFH days.The Fvalue is 165.012,confirming that the feasibility of WFH serves as a strong instrument for the growth of the WFH days.
In Table 4, Columns 5, 8, 11, and 14 report the reducedform estimates, and Columns 6, 9, 12, and 15 report the IV estimates using the feasibility of WFH as an instrument.All the estimates from the reduced form and the IV regressions are significantly positive.Focusing on the IV results, our preferred specification, an additional WFH day increases time spent on housework by 6.150%.Similarly, an extra day of WFH increases the fraction of men who increased their share of housework in the family by 9.3%.It also increases time spent with family by 5.550% and the fraction of men who became more life oriented than before by 11.6%.
The IV estimates are greater than the firstdifference estimates for all four outcomes; how ever, using the Hausman test, we can reject the hypothesis that the two estimates are the same only for the life oriented indicator.We note that the discrepancy in the estimates could be explained by the fact that the IV estimator identifies the effects of WFH on a different subpop ulation from the one for which the firstdifference estimator identifies the effects.Whereas the firstdifference estimates reflect the change in outcomes for all treated workers, the IV estimator identifies local average treatment effects for workers induced to work from home because of their high feasibility of WFH.

WorkRelated Outcomes
Although WFH increases workers' engagement with their families, a concern is that it may po tentially have adverse effects on work performance.To examine whether WFH lowers work productivity, we conduct the same estimation exercise as in Section 5.1 for workrelated out comes.The results are reported in Table 5.
We find that an additional day of WFH reduces commuting time by 12.388%.Because most workers work five days a week and the effect of WFH on the change in commuting hours is expected to be around 20%, the effect appears underestimated.Nevertheless, this difference is likely to arise from a rounding error.As discussed in Section 3, we take the middle point of each interval to construct the variable.Accordingly, for the respondents whose commuting time decreased by 20%, our variable is −13%, almost the same as our estimate.
While WFH effectively reduces commuting time, we find no significant effect of WFH on working hours or productivity in all specifications.Thus, taking this together with the results in Section 5.1, we conclude that WFH promoted greater family engagement by male workers without sacrificing their productivity at work.

Validity of the Exclusion Restriction
As discussed in Section 4, the exclusion restriction is a crucial assumption to identify the causal effect of WFH.Although we argue that the exclusion restriction holds-that is, the percentage of work that workers can do from home is not correlated with changes in the error termconcerns may remain about its validity.This section considers possible pathways other than WFH through which the IV affects the outcome, i.e., the possible threats to our identification strategy.Then, we examine whether controlling such variables changes the results.

Regional Characteristics
Kawaguchi and Motegi (2020) provide evidence that the proportion of people working from home is quite high in Tokyo, suggesting that WFH is more feasible because whitecollar oc cupations are concentrated in the metropolitan area.We confirm their finding in our data; the average feasibility of WFH in Tokyo is 35%, whereas it is 21% in other regions.In general, WFH is more feasible in larger cities.
On the other hand, the numbers of COVID19 cases and deaths vary substantially by prefec ture, and Tokyo has the largest number of cases per population in Japan in almost every period that we study. 14In general, large cities tend to have more COVID19 cases.More COVID19 14 Although there were some periods when Okinawa had the largest number of COVID19 cases per population in Japan, even during those periods, Tokyo had the second largest number.cases and more deaths may make people more family oriented out of fear, leading them to spend more time with their family and, hence, contribute more to housework.
If this is the case, workers in large cities are more likely to have a job with high WFH feasibility and to become more family oriented because of the more intense COVID19 situation compared with other cities, which implies a correlation between the instrument and the changes of the error term.
We include prefecture fixed effects in Equation 1 and estimate the model with the IV to address this concern.By including the prefecture fixed effects, our identification relies on the variation of the WFH feasibility within the prefecture rather than across prefectures.The esti mates are reported in Columns 1, 4, 7, and 10 in Table 6 and Columns 1, 4, and 7 in Table 7.
The results are essentially the same as the main results in Tables 4 and 5. Thus, we consider that regional differences in the spread of COVID19 do not invalidate our exclusion restriction.

Industry Characteristics
Another potential threat to the validity of the exclusion restriction lies in industry characteris tics.For example, under the COVID19 pandemic, the IT industry has been increasing profits, whereas the food service industry has experienced a significant drop in sales.Such differences in business performance by industry may affect workers' perceptions regarding work-life bal ance and change their roles in the household.For example, workers in the food service industry may increase their contribution to housework to compensate for the reduction in their salary or to make use of the reduction in their working hours.Because the feasibility of WFH varies by industry, our instrument may be correlated with changes in the error term in Equation 1 through industry characteristics, which would bias our estimates.
To avoid the endogeneity bias, we additionally control for the industry (for example, manu facturing, retail business, and transportation), the job category (for example, sales, accounting, and human resources), and the number of employees of the firm in Equation 1 and estimate the model with the IV.Columns 2, 5, 8, and 11 in Table 6 and Columns 2, 5, and 8 in Table 7 report the estimates from these regressions.Again, the estimates are similar to the main results and do not change our conclusion.The results ensure that industry and other job characteristics are not pathways through which our IV is correlated with changes in the error terms.

Spouse's WFH Status
A final concern is that the feasibility of WFH is associated with the spouse's feasibility of WFH, and that this correlation may lead to the violation of the exclusion restriction.According to Malkov (2020), in the US, teleworkabilitybased occupational sorting occurs; in about 60% of couples, both spouses work in either teleworkable or nonteleworkable occupations.If the wife's feasibility of WFH is positively correlated with her time spent on housework, this can reduce the time that the husband spends on housework.Moreover, this is more likely to occur in couples where the husband's feasibility of WFH is high, which is a potential source of bias in our case.
Because our data do not contain information on spouses' feasibility of WFH, we directly control whether the spouse works from home, assuming that the husband's WFH days do not affect the wife's WFH status.Columns 3, 6, 9, and 12 in Table 6 and Columns 3, 6, and 9 in Table 7 report the results of the IV regressions.All estimates are comparable with those from the main specification, which reassures us about the validity of our IV.
However, some may argue that the husband's WFH status directly influences whether his spouse works from home.If that is the case, we should not directly control the spouse's WFH status in Equation 1 because it is affected by our treatment variable, the husband's WFH days.
As we mentioned in Section 4, workers in Japan have their own discretion regarding whether to work from home.Thus, a husband and a wife may jointly decide on their WFH days.Nonethe less, because the husband's feasibility of WFH tends to be positively correlated with the spouse's feasibility of WFH, and because the wife's feasibility of WFH is negatively correlated with the outcomes concerning the husband's involvement with the family, our estimates from the speci fication without controlling the spouse's WFH status (Tables 4 and 5) can be regarded as a lower bound of the effects of WFH on the outcome.Therefore, even if our estimates are biased by omitting variables related to the spouse's feasibility of WFH, our conclusion does not change or would be even stronger.

Heterogeneous Effects of Working From Home
In this subsection, we explore the heterogeneity of the effect of WFH.The results reported in Tables 8 and 9 show that the estimates for university graduates are similar to those obtained for the whole sample (Tables 4 and 5), suggesting that university graduates largely drive the results for the whole sample.For workers with lower education levels, all the estimates except the change in life orientation are insignificant.Note, however, that none of the differences between the two groups is statistically significant.
Turning to other workers' characteristics, the estimates in Tables 10-15 show interesting patterns: the effects of WFH on housework tend to be greater for those who are younger, whose child is younger, and whose household size is larger.In particular, workers under 45 years of age and workers with preschool children are more likely to increase their share of housework than are others at the 5% significance level.A possible explanation for the difference is that fathers of young children increase their time spent on caring for children at home rather than time spent on other household chores, as suggested by Champeaux and Marchetta (2021).
Another issue is whether the extent to which male workers increase their participation in household chores varies by whether their wives can work from home.Alon et al. (2020) expect the largest effects for families in which the father is able or forced to work from home while the mother is not.Champeaux and Marchetta (2021) show that under the lockdown in France, fathers effectively increased their contribution to housework only when the mother was the sole household member working outside the home.
As discussed in Section 5.3, although wives' feasibility of WFH is not available from our data, their actual WFH status is available, as men report whether their spouse worked from home, did not work from home, or did not have a paid job.We understand that the estimation controlling for the actual WFH status may not be valid because the spouses' WFH status may be endogenous.That being said, it is informative to estimate the IV regressions by splitting the sample by whether the wife worked outside the home or did not (i.e., in the latter case, she worked from home or did not have a paid job).The results are reported in Tables 17 and 18 in the Appendix.An additional WFH day has consistently positive effects on the familyrelated outcomes regardless of whether the wives stay at home.The differences in the effects between the two groups are insignificant for all the outcomes.This suggests that WFH effectively en courages the reallocation of housework for couples who both stay at home and for couples in which only the husband stays at home.

Discussion
We relate our results to the literature on the impact of COVID19 on families and worker pro ductivity in Section 6.1.Furthermore, it is natural to ask whether WFH played a primary role in changing workers' attitudes and behaviors during the pandemic because COVID19 affected lives across many dimensions beyond working style.Section 6.2 examines how much WFH contributed to the overall change in the various outcomes between December 2019 and 2020.

Relation to Previous Literature
Our results indicate that WFH promotes men's participation in household chores without re ducing work productivity, which provides empirical evidence for the argument by Alon et al. (2020) and Hupkau and Petrongolo (2020) who argue that the increased work flexibility for men during the COVID19 outbreak may encourage them to contribute more to housework and childcare.In contrast to many comparative studies investigating the consequences of COVID 19 confinement policies on families, our study establishes causal evidence for the impacts of WFH on families during the COVID19 pandemic.
Further, our subsample analysis suggests that WFH leads to the redistribution of housework regardless of whether the spouse works from home.This is in contrast to the previous arguments in the literature.Alon et al. (2020) argue that the increased participation of men in housework is likely to be driven by telecommuters whose spouses work outside the home.Champeaux and Marchetta (2021) show that the redistribution of housework induced by the lockdown in France is effective only for families in which the mother works outside the home while the father works from home.The difference in the results may arise from the difference in the pre existing gender disparity in domestic work.Japanese fathers may have a lower baseline and more room to increase their contribution to domestic work when working from home than do French fathers.
Regarding the estimates for work productivity, our results appear to contradict Morikawa (2020) and Kitagawa et al. (2021), who report negative effects of WFH on productivity using a survey conducted in Japan.This discrepancy can be attributed to the fact that they used different estimators and different study periods compared with our study.Morikawa (2020) asks survey participants who have adopted WFH about their productivity in WFH relative to working at the office.Kitagawa et al. (2021) estimate an average effect on employees who have experienced WFH by using a firstdifference model similar to Equation 1 in our paper.It is important to note that both Morikawa (2020) and Kitagawa et al. (2021) estimate the effect of WFH from April to June in 2020.During that period, many workers were strongly urged to work from home even if they knew their productivity would decrease as a result of WFH.
In contrast, the survey we use asks about productivity in December 2020.As discussed in Section 4, firms and workers had considerable discretion at this point in deciding whether to work from home.Workers who would not suffer a productivity decline are likely to be selected into WFH. 15Our IV estimator identifies the local average treatment effect on workers induced to work from home because of their high WFH feasibility; therefore, the estimate indicates a null effect on productivity.In addition, given that we focus on December 2020, it is important to note that over time, as the pandemic continued, firms invested in IT equipment to improve the effectiveness of WFH, and many workers became more accustomed to WFH by December 2020, meaning that there was no longer a negative impact on their productivity because of WFH. 16ur findings have important implications for considering a new working style.Some studies suggest that even after the COVID19 pandemic, a large fraction of workers prefer to continue WFH (Kitagawa et al., 2021) and, hence, the practice of WFH will continue (Barrero, Bloom, andDavis, 2021; Bick, Blandin, andMertens, 2020).By showing that WFH helps promote gender equality within households without sacrificing productivity at work, our results provide another reason to argue that policymakers should promote WFH options even after the pan demic.

How Much Does WFH Contribute to the Overall Changes?
We have confirmed that WFH has causal effects on outcomes related to engagement with family and commuting time, but to what extent does WFH account for the changes in attitude toward family under the COVID19 pandemic?That is, because the pandemic has dramatically im pacted our perceptions and behavior, WFH may play a little role relative to the role of the pan demic itself.We examine how much WFH contributed to the overall change in the outcomes between December 2019 and 2020.
Using the estimates obtained from Equation 1 with the IV, β0 , β1 , and β2 , the sample mean of our dependent variable, ∆Y , can be written as follows: where ∆D and X are the sample averages of ∆D and X, respectively.To quantify the contribution of WFH, we define the counterfactual mean of the change in outcome, ∆Y CF , as the value when no respondents change the number of WFH days, or by setting ∆D = 0: Then, we define the percentage contribution of WFH as For example, ∆Y = 1 and ∆Y CF = 0.6 indicates that 40% of the overall change is contributed by WFH.
Table 16 reports the actual sample mean, counterfactual mean, and WFH contribution.For the outcome variables related to involvement with family, WFH contributes to 14% to 33% of the change in these outcomes from December 2019 to December 2020, with the exception of time spent on housework.Because our estimates predict that the average married male worker who does not change the number of WFH days will decrease his time spent on housework, the contribution calculated for WFH exceeds 100%.Overall, the contribution of WFH to the change in engagement with family is large even compared with other effects, including the pandemic itself.
As for workrelated outcomes, it is worth noting that the contribution of WFH to the change in commuting time is 87%, and it is not statistically significantly different from 100%.This estimate implies that WFH is the only major path through which commuting time decreases between December 2019 and December 2020, which we find plausible.

Conclusion
In this paper, we study the impacts of WFH on male workers' participation in household chores and attitude toward their families.Our estimates indicate that WFH leads men to spend more time on housework and with their family, and makes it more likely that they will take a larger share of housework and value their life relative to work.Regarding workrelated outcomes, we find no significant effect on the workers' selfperceived productivity and hours worked.
Therefore, our estimates indicate that WFH encourages male workers to contribute more to household chores without sacrificing their performance at work.This paper contributes to the literature on WFH by showing that the practice of WFH im proves men's work-life balance.Although several studies have established that WFH reduces women's work-family conflict, scant attention has been paid to the impact of WFH on men.
This lack of evidence for men may be attributed to the difficulty of avoiding selfselection into WFH in "normal" nonpandemic times or in an experiment that allows workers' discretion about whether to work from home.Exploiting the preexisting variation in the feasibility of WFH as an instrument and the pandemic as a compelling situation in which many male workers are mo tivated to work from home, we show that WFH increases fathers' engagement with the family.
This research is subject to at least two limitations.First, because we employ the IV estimator, our results show effects only for a subgroup of the population, that is, working fathers whose jobs can be readily performed from home.Our results may not be immediately extrapolated to other groups.Second, whether the effect of WFH on withinhousehold gender equality persists is outside the scope of the current study, although we note that Alon et al. (2020) expect that it does persist.Further studies should address whether increased WFH would have longerterm effects.The dependent variable in Columns 10-12 is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.In the first columns for each dependent variable (Columns 1, 4, 7, and 10), we additionally include prefecture fixed effects.In the second columns for each dependent variable (Columns 2, 5, 8, and 11), we additionally include industry and job category fixed effects, and the number of employees of the firm.In the last columns for each dependent variable (Columns 3, 5, 9, and 12), we additionally include a dummy variable that takes a value of one if the spouse worked from home, and zero otherwise.The last row reports the Fstatistics from the firststage regressions.Note that the number of observations varies because of missing values in the outcomes.and 7), we additionally include prefecture fixed effects.In the second columns for each dependent variable (Columns 2, 5, and 8), we additionally include industry and job category fixed effects, and the number of employees of the firm.In the last columns for each dependent variable (Columns 3, 5, and 9), we additionally include a dummy variable that takes a value of one if the spouse worked from home, and zero otherwise.The last row reports the Fstatistics from the firststage regressions.Note that the number of observations varies because of missing values in the outcomes.Notes: Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.The dependent variable in Columns 10-12 is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.In the first columns for each dependent variable (Columns 1, 4, 7, and 10), we restrict the sample to married male workers who graduated from high school or junior University.In the second columns for each dependent variable (Columns 2, 5, 8, and 11), we restrict the sample to married male workers who graduated from university.In the last columns for each dependent variable (Columns 3, 6, 9, and 12), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that take a value of one if the husband graduated from university, and zero otherwise.The coefficient of the interaction of WFH and the University dummy variable indicates whether the effect of WFH is heterogeneous between those who graduated from university and others.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the University dummy variable as another IV in Columns 3, 6, 9, and 12, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.Notes: Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.In the first columns for each dependent variable (Columns 1, 4, and 7), we restrict the sample to married male workers who graduated from high school or junior college.In the second columns for each dependent variable (Columns 2, 5, and 8), we restrict the sample to married male workers who graduated from university.In the last columns for each dependent variable (Columns 3, 6, and 9), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that take a value of one if the husband graduated from university, and zero otherwise.The coefficient of the interaction of WFH and the college dummy variable indicates whether the effect of WFH is heterogeneous between those who graduated from university and others.The last two rows report the Fstatistics from the firststage regressions.

Figures and Tables
Because we use the interaction of the feasibility of WFH and the college dummy variable as another IV in Columns 3, 6, and 9, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.Notes: Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.The dependent variable in Columns 10-12 is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.In the first columns for each dependent variable (Columns 1, 4, 7, and 10), we restrict the sample to married male workers who are 44 years old or younger.In the second columns for each dependent variable (Columns 2,5,8,and 11), we restrict the sample to married male workers who are 45 years old or above.In the last columns for each dependent variable (Columns 3, 6, 9, and 12), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if a respondent is 44 years old or younger, and zero otherwise.The coefficient of the interaction of WFH and the age dummy variable indicates whether the effect of WFH is heterogeneous depending on age.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the age dummy variable as another IV in Columns 3, 6, 9, and 12, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.and 7), we restrict the sample to married male workers who are 44 years old or younger.In the second columns for each dependent variable (Columns 2, 5, and 8), we restrict the sample to married male workers who are 45 years old or above.In the last columns for each dependent variable (Columns 3, 6, and 9), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if a respondent is 44 years old or younger, and zero otherwise.The coefficient of the interaction of WFH and the age dummy variable indicates whether the effect of WFH is heterogeneous depending on age.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the age dummy variable as another IV in Columns 3, 6, and 9, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.In the first columns for each dependent variable (Columns 1, 4, 7, and 10), we restrict the sample to married male workers who have a preschool child.In the second columns for each dependent variable (Columns 2,5,8,and 11), we restrict the sample to married male workers whose youngest child is an elementary, junior high, or high school student.In the last columns for each dependent variable (Columns 3, 6, 9, and 12), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if the husband has a preschool child, and zero otherwise.The coefficient of the interaction of WFH and the preschool dummy variable indicates whether the effect of WFH is heterogeneous depending on the school stage of the youngest child.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the preschool dummy variable as another IV in Columns 3, 6, 9, and 12, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.and 7), we restrict the sample to married male workers who have a preschool child.In the second columns for each dependent variable (Columns 2, 5, and 8), we restrict the sample to married male workers whose youngest child is an elementary, junior high, or high school student.In the last columns for each dependent variable (Columns 3, 6, and 9), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if the husband has a preschool child, and zero otherwise.The coefficient of the interaction of WFH and the preschool dummy variable indicates whether the effect of WFH is heterogeneous depending on the school stage of the youngest child.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the preschool dummy variable as another IV in Columns 3, 6, and 9, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.and 10), we restrict the sample to married male workers whose household size is 3 persons.In the second columns for each dependent variable (Columns 2, 5, 8, and 11), we restrict the sample to married male workers whose household size is 4 persons or more.In the last columns for each dependent variable (Columns 3, 6, 9, and 12), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if the household size is three persons, and zero otherwise.The coefficient of the interaction of WFH and the household size dummy variable indicates whether the effect of WFH is heterogeneous depending on household size.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the household size dummy variable as another IV in Columns 3, 6, 9, and 12, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.and 7), we restrict the sample to married male workers whose household size is 3 persons.In the second columns for each dependent variable (Columns 2, 5, and 8), we restrict the sample to married male workers whose household size is 4 persons or more.In the last columns for each dependent variable (Columns 3, 6, and 9), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if the household size is three persons, and zero otherwise.The coefficient of the interaction of WFH and the household size dummy variable indicates whether the effect of WFH is heterogeneous depending on household size.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the household size dummy variable as another IV in Columns 3, 6, and 9, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.Notes: Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.The dependent variable in Columns 10-12 is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.In the first columns for each dependent variable (Columns 1, 4, 7, and 10), we restrict the sample to married male workers whose wives were at home, i.e., they worked from home or did not undertake paid work.In the second columns for each dependent variable (Columns 2, 5, 8, and 11), we restrict the sample to married male workers with wives worked but did not work from home.In the last columns for each dependent variable (Columns 3, 6, 9, and 12), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if the husband's wife was at home, that is, worked from home or did not undertake paid work, and zero otherwise.The coefficient of the interaction of WFH and the spouseathome dummy variable indicates whether the effect of WFH is heterogeneous depending on whether the husband's wife works away from home or is at home.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the spouseathome dummy variable as another IV in Columns 3, 6, 9, and 12, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.Notes: Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.In the first columns for each dependent variable (Columns 1, 4, and 7), we restrict the sample to married male workers whose wives were at home, i.e., they worked from home or did not undertake paid work.In the second columns for each dependent variable (Columns 2, 5, and 8), we restrict the sample to married male workers with wives worked but did not work from home.In the last columns for each dependent variable (Columns 3, 6, and 9), using the whole sample, we additionally include treatment and control variables that interact with a dummy variable that takes a value of one if the husband's wife was at home, that is, worked from home or did not undertake paid work, and zero otherwise.The coefficient of the interaction of WFH and the spouseathome dummy variable indicates whether the effect of WFH is heterogeneous depending on whether the husband's wife works away from home or is at home.The last two rows report the Fstatistics from the firststage regressions.Because we use the interaction of the feasibility of WFH and the spouseathome dummy variable as another IV in Columns 3, 6, and 9, we report the Fstatistics for the interaction IV in the last row.Note that the number of observations varies because of missing values in the outcomes.

Figure 1 :
Figure 1: The Ratio of Unpaid Work Done by Women to That Done by Men

Figure 2 :
Figure 2: The Predicted and Observed Employment Rates for Married Men with Children

Notes:
Standard errors are shown in parentheses.All columns control for education, age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.The first columns for each dependent variable (Columns 1, 4, and 7) report firstdifference estimates obtained by regressing the change in the outcome on the change in WFH and control variables.The second columns for each dependent variable (Columns 2, 5, and 8) present reducedform coefficients obtained by regressing the change in the outcome on the IV, feasibility of WFH.The last columns for each dependent variable (Columns 3, 6, and 9) show the IV estimates.The last row of the IV columns reports the Fstatistics from the firststage regressions.Note that the number of observations varies because of missing values in the outcomes.
is an outcome variable; D is the number of days of WFH per week; X is a vector of

Table 8 -
15 examine the heterogeneous effects of WFH by education, age, child's educational stage, and household size.

Table 1 :
Descriptive Statistics This table presents descriptive statistics for the main treatment variable (change in days of WFH per week), IV (the feasibility of WFH), outcome variables, and control variables.The first and second columns report the mean and the standard deviation of each variable.The last column reports the number of observations.Some variables have fewer observations because respondents can choose "I do not wish to answer" for these questions, with such responses treated as missing values in our data.

Table 3 :
First Stage Regression

Table 4 :
The Effect of Working From Home on Involvement with Family Standard errors are shown in parentheses.All columns control for education, age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.The dependent variable in Columns 10-12 is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.The first columns for each dependent variable (Columns 1, 4, 7, and 10) report firstdifference estimates obtained by regressing the change in the outcome on the change in WFH and control variables.The second columns for each dependent variable (Columns 2, 5, 8, and 11) present reducedform coefficients obtained by regressing the change in the outcome on the IV, feasibility of WFH.The last columns for each dependent variable(Columns 3, 6, 9, and 12)show the IV estimates.The last row of the IV columns reports the Fstatistics from the firststage regressions.Note that the number of observations varies because of missing values in the outcomes.

Table 5 :
The Effect of Working From Home on WorkRelated Outcomes

Table 6 :
The Effect of Working From Home on Involvement with Family with Additional Controls Standard errors are shown in parentheses.This table presents the coefficients of the change in WFH from the IV regression using feasibility of WFH as the IV.All columns control for education, age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.

Table 7 :
The Effect of Working From Home on WorkRelated Outcomes with Additional Controls Standard errors are shown in parentheses.This table presents the coefficients of the change in WFH from the IV regression using feasibility of WFH as the IV.All columns control for education, age, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.In the first columns for each dependent variable (Columns 1, 4,

Table 8 :
The Effect of Working From Home on Involvement with Family by Education Level

Table 9 :
The Effect of Working From Home on WorkRelated Outcomes by Education Level

Table 10 :
The Effect of Working From Home on Involvement with Family by Age

Table 11 :
The Effect of Working From Home on WorkRelated Outcomes by Age Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, the number of household members, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.In the first columns for each dependent variable (Columns 1, 4,

Table 12 :
The Effect of Working From Home on Involvement with Family by Child's Educational Stage Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, age, and the number of household members.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.The dependent variable in Columns 10-12

Table 13 :
The Effect of Working From Home on WorkRelated Outcomes by Child's Educational Stage Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, age, and the number of household members.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.In the first columns for each dependent variable (Columns 1, 4,

Table 14 :
The Effect of Working From Home on Involvement with Family by Size Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, age, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in time spent on housework.The dependent variable in Columns 4-6 is a dummy variable that takes a value of one if the husband increased his share of housework, and zero otherwise.The dependent variable in Columns 7-9 is the percentage change in time spent with family.The dependent variable in Columns 10-12 is a dummy variable that takes a value of one if the husband became more life rather than work oriented, and zero otherwise.In the first columns for each dependent variable (Columns 1, 4, 7,

Table 15 :
The Effect of Working From Home on WorkRelated Outcomes by Size Standard errors are in parentheses.This table presents the coefficients of the change in WFH from the IV regression that uses the feasibility of WFH as the IV.All columns control for education, age, and the school stage of the youngest child.The dependent variable in Columns 1-3 is the percentage change in commuting time.The dependent variable in Columns 4-6 is the percentage change in working hours.The dependent variable in Columns 7-9 is the percentage change in selfreported productivity.In the first columns for each dependent variable (Columns 1, 4,

Table 16 :
Contribution of Working From HomeNotes: This table shows how much WFH contributes to the overall changes in each outcome.The first column reports the actual mean of the outcomes, which are exactly the same as the values in Table1.The second column presents the counterfactual mean of each outcome if no workers in our sample worked from home.The third column shows the percentage of the WFH contribution, which is given by ActualMean−MeanwithoutW FH ActualMean × 100.The last column provides the 95% confidence intervals of the WFH contribution calculated by the delta method.

Table 17 :
The Effect of Working From Home on Involvement with Family by Spouse's WFH Status

Table 18 :
The Effect of Working From Home on WorkRelated Outcomes by Spouse's WFH Status