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Works Discussion Paper No.48
Who Can Work from Home?
The Roles of Job Tasks and HRM Practices ͊Daiji Kawaguchi (The University of Tokyo)
Hiroyuki Motegi (Recruit Works Institute)

August 16th, 2021
Abstract
This paper examines the characteristics of remote work using a unique Japanesesurvey dataset that provides information on engagement in remote work together withthe specific job tasks and human resource management (HRM) characteristics workers
face. We show that the opportunity to work remotely was more likely to be available tothose engaged in non-routine and non-interactive tasks as well as to workers subject
to HRM practices presupposing that worker performance is measurable. The
implications of these findings for income transfer policies and management practices
in light of the COVID-19 pandemic are also discussed.
Keywords Remote work, Human resource management, Pay for performance, Key
performance indicators, Job tasks, Shirking

All contents and opinions in this discussion paper are the personal views of theauthor and do not represent the views of their organizations or Recruit Works
Institute.

†We thank a referee for valuable comments and Akito Kamei, and also thank Philip C.

MacLellan for editorial assistance. The authors are responsible for all remaining
errors and interpretations.

1Introduction
Throughout 2020, the spread of COVID-19 and the implementation of social distancing policies have con-ﬁned millions of workers to their homes. While primarily a public health measure, social distancing has had aprofound economic impact, but its eﬀect on the aggregate labor supply and any distributional consequences
will be determined largely by the extent to which remote work is possible. Against this backdrop, there
has been a heightened interest in better understanding which jobs can and are being performed remotely.
Studies using job task descriptions to estimate the proportion of jobs that can technically be accomplishedfrom home have arrived at numbers ranging from 37% in the US to 56% in Germany.
This study takes a diﬀerent approach by documenting who actually worked from home just prior to the
COVID-19 crisis and identifying their speciﬁc job characteristics. In contrast to the literature, this approachallows us to understand the conditions facilitating a remote work arrangement and to articulate any expectedchallenges as remote work expands. To accomplish this, we utilize a unique panel data set from Japan that
includes job task characteristics and human resource management conditions. As of December 2019, 8%
of Japanese workers worked outside of their oﬃcial workplace, and while the prevalence of remote work
does not vary much across industries, it does vary considerably across occupations. In particular, workers
involved in non-routine and non-manual tasks and those employed under performance-based human resource
management (HRM) practices are signiﬁcantly more likely to engage in remote work. Together, these results
indicate that workers engaging in manual or interactive tasks are less likely to work remotely, as previous
studies have found, and also that the extent to which employers can prevent “shirking from home” by, forexample, quantifying work output is a critical determinant in the adoption of remote work. We show that job
task and HRM characteristics, which vary considerably within a given 2-digit occupation or 1-digit industry
category, largely explain the remote work experience, conditioned on industry and occupation.
In addition, as the determinants of earnings and remote work potential are positively related, the burdenof social distancing policies disproportionately falls on low income earners. For instance, while the 50% ofconsultants who worked remotely earned 6 million yen annually, the 10% of care workers who worked remotely
earned only 2.8 million yen annually. More generally, those who worked remotely earned 23.4% more than
non-remote workers. However, we ﬁnd that remote workers did not earn more than non-remote workers
when conditioned on observed job and demographic characteristics, which suggests that income transfer
policies conditioned on observable characteristics or socioeconomic status would mitigate the disadvantage
experienced by non-remote workers under the hardships caused by the pandemic.
Remote work increased from 8% in December 2019 to 14% in December 2020, and we further examine how
the determinants of remote work evolved during the COVID-19 pandemic by examining the job characteristics

1

associated with the increase in remote work using the latest wave of the panel survey implemented in
December 2020. We examine the impact of job characteristics on December 2019 on the engagement inremote work in December 2020, and ﬁnd that as remote work expanded, workers engaging in interactive
tasks, holding a university degree, and belonging to larger ﬁrms caught the wave, while workers engaging
in manual tasks were left behind. Thus, the increase in remote work during COVID-19 exacerbated thepre-existing inequality.
Since the outbreak of COVID-19, numerous studies have investigated the potential for working remotely
based on occupation. Dingel and Neiman (2020), for example, determine whether a job can be performed
remotely from responses to an O*NET questionnaire on “work context” and “generalized work activities”.
By aggregating feasibility according to the distribution of the 6-digit standard occupational classiﬁcations
published by the U.S. Bureau of Labor Statistics, they conclude that 37% of U.S. jobs can be performed fromhome.1 Using a similar mapping, Boeri et al. (2020) ﬁnd that 24-31 % of jobs can be performed at home inmajor European countries, and Holgersen et al. (2021), who determine the potential for working remotely
through detailed ISCO-08 job descriptions and the marginal distribution of occupations through online job
advertisements, similarly ﬁnd that 36% of jobs in Norway can be performed from home. Meanwhile, Alipour
et al. (2020) ﬁnd from employee surveys that 56% of jobs in Germany can be performed from home. Finally,
Brussevich et al. (2020) and Hatayama et al. (2020) independently calculate the possibility of remote work formore than 50 countries based on task characteristics recorded in the OECD Survey of Adult Skills (PIAAC)
and ﬁnd that per capita GDP and prevalence of remote work are positively associated. They also reportthat women, college graduates, and salaried and regular-contract workers have jobs that are more amenable
to working from home than the average worker.
In addition to these studies of the potential for remote work based on occupation, research appears on the
proportion of the workforce that is actually working from home in the midst of the COVID-19 crisis. Basedon a survey conducted from April 1-5, 2020, Brynjolfsson et al. (2020) ﬁnd that 15% of U.S. workers had
already been working remotely and that 38% of those workers who formerly commuted were now working
from home. Bick et al. (2021) reports that the proportion of newly remote workers rose from 8% in February
to 35% in May, but Adams et al. (2020) notes substantial heterogeneity in the roll-out of remote workwithin industries and occupations. Meanwhile, in Japan, Okubo (2020) indicates that the percentage of
remote workers in 2020 was 6% in January, 10% in March and 17% in June, but Morikawa (2020) ﬁnds the
proportion in June to be higher, at 32%, based on a June 2020 survey.
This paper contributes to the literature by examining the relationship between job characteristics and
1 Mongey et al. (forthcoming) validates this measure using SafeGraph cell phone mobility data and actual labor market

outcomes from the March 2020 Current Population Survey.

2the potential for remote work by exploiting the unique features of a Japanese worker panel data set which
directly surveys whether a worker worked remotely. In addition, it records two measurable variables that areimportant determinants of the potential for remote work: the direct measurement of job task characteristics
and the speciﬁc HRM characteristics of each employee’s work environment. The survey was implemented
just before the outbreak of COVID-19 pandemic, and this makes this data set ideal for examining the natural
determinants of the possibility for remote work. Further, identifying these natural determinants is useful forforeseeing the problems that might arise when employers are forced to adopt a remote work arrangement.
The high explanatory power of job characteristics variables has two important implications. First, on
redistribution policy, the literature demonstrates that the negative impact of COVID-19 is heterogeneous
across occupations and skill levels (Adams et al., 2020; Mongey et al., forthcoming; Brussevich et al., 2020;
Kikuchi et al., 2021) and the likelihood of working remotely is strongly associated with a negative income
shock (Mongey et al., forthcoming). Thus, our ﬁnding of a heterogeneous probability of engaging in remote
work according to job tasks within a standard industry/occupation category implies that a heterogeneous
negative income shock will exist even within the category. This therefore calls for a careful examination of
the current income change and a much more ﬁne-grained transfer policy to compensate for any earnings loss
due to COVID-19.
Second, this study has important implications for the human resource management policies of privatecompanies in terms of maintaining productivity. There is a strong relationship between HRM practices
based on individual performance measures (such as pay for performance, management by key performanceindicator, or management by objectives) and the potential for remote work, implying that remote work canoccur only when output is observable and measurable (Allen et al., 2015; Bloom et al., 2015; Sewell andTaskin, 2015; Groen et al., 2018).2 However, the output of certain jobs is inherently diﬃcult to observeor quantify, and this potential for “shirking from home” is the major reason why some workers are notpermitted to work remotely. Despite this concern, however, the sudden implementation of social distancing
policies during the COVID-19 pandemic has forced ﬁrms to encourage remote work even for those workers injobs deemed unsuitable for it. Thus, unless ﬁrms take measures to improve their observation of worker eﬀort
or output, the remote work phenomenon is likely to lead to reduced productivity in certain jobs, mainly
because worker eﬀort is diﬃcult to sustain in the remote work environment.
2 Another mechanism that enables remote work without productivity loss is to make the job task fun and self rewarding(Dutcher, 2012).

3

2

Data
This paper uses the Japanese Panel Study of Employment Dynamics (JPSED), a panel survey with astandard set of demographic and labor market variables that has been conducted by the Recruit Works
Institute every year since 2015. JPSED is a nationwide survey that is representative of all men and womenover the ages of 15 years old and is conducted by an internet monitor registered to Intage Corporation.3
The ﬁrst wave of the survey included 49,131 people, and while JPSED has contracted and expanded throughpanel attrition and sample addition, it collects around 50,000 observations each year. The surveys, which
are conducted each January, ask about the work situation in the previous month and previous year. For
this study, we use the 2020 wave of the survey conducted from January 9-31, 2020, when the outbreak of
COVID-19 in China had been covered by the media but Japan was as yet virtually unaﬀected. In addition,a supplemental 2020 survey about the Human Resource Management conditions workers face was conducted
between January 15th and February 5th for those who responded on the main survey that they had worked
in December 2019 (see Appendix A for the speciﬁc questions asked about remote work, task characteristics
and human resource management practices). The original 2020 wave includes 57,284 observations, consistingof 47,833 continuing observations, 5,025 additional observations, and 4,426 revived observations. We restrict
our analysis sample to only those who are employed, excluding the self-employed because we are interested inassessing the impact of human resource management conditions on the potential for remote work. Dropping
invalid variables further reduces the sample size to 38,292 observations, of which 24,728 observations had
valid responses to the supplemental survey. This basic analysis sample consists of employed men and womenwhose ages are 15 years old or above.
A unique feature of this survey is the set of questions on engagement in remote work. The ﬁrst questionis hours worked remotely per week in December 2019, where remote work includes work from home, satellite
oﬃces, coﬀee shops or restaurants. Respondents provide a continuous number representing the exact hours
worked, which may include zero hours. The second question addresses company rules about remote work,with respondents choosing from 1) the company sets the rule and it applies to the respondent, 2) the company
sets the rule but it does not apply to the respondent, 3) the company does not set a rule, and 4) do not
know. Other questions ask about the range of workers who are allowed to work remotely and the actual
place the respondent worked the previous December.
This array of questions allows us to deﬁne remote workers in two alternative ways. The ﬁrst deﬁnition isthose workers who actually worked remotely in December 2019, while the second deﬁnition is those workers
who were allowed to work remotely according to the company rule. Table 1 shows the proportion of workers
3 We compare the distribution of worker characteristics of the 2019 JPSED with the 2015 Census. Appendix A.5 demonstrates
that the distributions of types of employment and industry are fairly close to the distribution based on the Census.

4who reported positive remote hours worked in each of the above company rule categories. We see that while
only 4% of respondents replied that their companies set the rule for remote work and it applied to them,
58% of these respondents actually worked outside of the workplace. On the other hand, 6 to 8% of those forwhom the rule on remote work did not apply or those who did not know about the rule actually reported
positive hours worked outside of the workplace. Given that a large number of workers actually worked
outside the workplace irrespective of the formal work rule, we deﬁne a remote worker as one who actually
worked outside of the work place.
Thus, in total, 8% of workers engaged in remote work in December 2019, which is higher than the 3-4%
for 2016-2017 reported by Kazekami (2020) and 5.2% for 2017 reported by Morikawa (2018), but close to the
10% for January 2020 reported by Okubo (2020). Our number is higher than those reported by Kazekami(2020) and Morikawa (2018) because we deﬁne the remote worker more broadly by including those who
were not oﬃcially permitted to work remotely but who actually did. According to the descriptive statistics
reported in Table 2, average hours worked per week was 38.7 hours, of which 8.81 hours occurred remotely,
indicating that those who engaged in remote work spent 23% of their working hours outside the workplace.
The other unique feature of the JPSED survey is the question about task characteristics, which asks
respondents to characterize the tasks required by their current job along the following three dimensions: 1)
routine vs. non-routine, 2) manual vs. cognitive, and 3) working alone vs. working interactively with others.
As described in detail in Appendix A, respondents choose the task characteristics in terms of percentages
using a slide bar on the computer screen. For example, if the respondent chooses that routine tasks comprise
60% of the job, then the remaining 40% is automatically counted as the proportion of non-routine tasks. Table
2 shows that non-remote workers describe their job tasks as 62% routine, 43% manual, and 44% interactive,
while remote workers describe their job tasks as 49% routine, 33% manual, and 42% interactive. Thus we seethat remote workers are much less likely to engage in routine and manual tasks. The analysis in Appendix
A demonstrates that although the task measures are highly associated with the two-digit occupation codes,
signiﬁcant variation remains within an occupation code, suggesting that task measurement in addition to
the two-digit occupation code is needed to accurately capture job heterogeneity.
Turning now to the implications of human resource management (HRM) practices for remote work, basic contract theory demonstrates that wages are strongly associated with output when the output depends
heavily on the eﬀort of workers, given that employers are risk neutral and employees are risk averse (Milgrom and Roberts, 1992). On the other hand, if output is diﬃcult to measure or it depends heavily on
factors other than individual eﬀort, wages are less dependent on output and a ﬁxed wage prevails instead,provided that employers can monitor worker eﬀort, for workers will otherwise completely slack oﬀ. Worker
eﬀort is more diﬃcult to observe in a remote work setting, as reﬂected in the phenomenon of “shirking

5from home” (Bloom et al., 2015). Indeed, Sewell and Taskin (2015) and Groen et al. (2018) both ﬁnd apositive association between remote work and the adoption of output control measures. Reviewing the broad
literature, Allen et al. (2015) claim that the availability of output measures lowers the hurdle for engaging
in remote work, so we expect that output that is easily observed will facilitate the adoption of remote work
arrangements. As this mechanism entails a positive association between HRM practices that presuppose
individual performance (output) measures and the availability of remote work, we test this hypothesis using
a battery of questions from the JPSED supplementary survey in which respondents describe the humanresource management conditions applicable to them. To identify HRM style, we pay particular attention to
the following three variables which all involve output measures: pay-for-performance in determining compensation, management based on key performance indicators (KPI), and a system of management by objectives(MBO). Survey responses are recorded on a 5-point Likert scale ranging from not applicable to applicable,
and we characterized those who responded either “applicable” or “if anything, applicable” (1 or 2 on thescale) as workers for whom that speciﬁc HRM variable applies. Table 2 shows that pay-for-performance
applies to 37% of remote workers, KPI to 31% and MBO to 31%. In the Appendix, we examine how the
HRM characteristics are associated with occupation.

3

Job Characteristics of Remote Workers
Before presenting our empirical speciﬁcation for investigating the eﬀects of task and HRM characteristics

on the potential for remote work in Section 4, in this section, we discuss the relationship between remote
work and speciﬁc job characteristics independently.

3.1

Industry and Occupation

As the literature has typically used occupation to deﬁne the feasibility of working from home (see Dingel and Neiman (2020), for example), we ﬁrst document the prevalence of remote work by industry andoccupation. Figure 1 reports the percentage of workers allowed to work outside of workplace by industry,
and compared to the overall average of 8%, the variation across industries is rather small, with the highest being 17% in the telecommunications industry and the lowest being 5% in the transportation and the
postal industry. The high prevalence of remote work in the telecommunications (and information) industry
is understandable given that their work is computerized. On the other hand, workers in transportation,
the postal industry and the public sector, who are considered essential workers, are less likely to engage inremote work.
While we observe little variation in remote work by industry, there is substantial variation across occu-

6pations. Figure 2 shows that remote work is close to zero in hospitality positions such as chef, customer
service and waitperson, as well as in blue collar jobs such as security guard, driver, and manufacturing process worker. Meanwhile, the percentage is 20% or higher for occupations such as planning and sales clerk,
sales personnel, brokerage business, internet professional, writer and reporter, and consultant. Employers
presumably allow this latter group to work from home because they work independently and their output iseasily observable. Also notable is that the large observed variation in remote work across occupations but
small variation across industries is indicative of the heterogeneity of remote work among occupations thatis averaged out when observed by industry.

3.2

Task Characteristics

Digging deeper than the occupation level for a more ﬁne-grained analysis, we next examine the potential
for remote work based on the speciﬁc characteristics of the tasks associated with each worker’s job. Figure 3
shows the distribution of routine, manual and interactive scores by remote work status as of December 2019,
and all three panels show that the task characteristics of non-remote workers and remote workers overlap.
However, by comparing the medians of the two groups, some diﬀerences can be identiﬁed. The ﬁrst twopanels show that remote workers are less likely to engage in routine and manual tasks, with the latter ﬁnding
consistent with our occupation analysis from Figure A2 that blue collar workers are less likely to engage inremote work than white collar workers. The third panel shows that task interactivity has little to say about
the potential for remote work, which is probably related to the diﬀerent types of interaction that might occur
on the job. At one extreme, managerial jobs require interaction with subordinates, superiors and colleagues
to coordinate tasks, while at the other extreme, service sector jobs such as restaurant wait staﬀ or cashierrequire interaction with customers. These varying types of interaction with others are arguably the reason
why we ﬁnd a similar distribution of task interactivity between remote and non-remote workers.

3.3

Human Resource Management Style

In this subsection, we examine the association between remote work and human resource management,
focusing on three variables that capture whether the respondent is subject to pay-for-performance style
HRM practices: whether compensation is determined by Pay-for-Performance (PFP), whether performanceis managed by Key Performance Indicators (KPI) and whether the respondent is subject to Management
by Objective (MBO). Table 2 shows that 37% of remote workers were paid according to their performancecompared to 23% of those without remote work experience. Similarly, remote workers are more likely to
be managed by KPI and MBO. Overall, remote workers were as much as 61% more likely to experience7

some form of pay-for-performance style HRM practice, which is consistent with our hypothesis that workers
whose output is easily observable are more likely to engage in remote work because there is less concern
about “shirking from home.”

3.4

Annual Income

The variation in remote work by occupation suggests that the distribution of earnings may also be related
to the prevalence of remote work, and Figure 4 shows that workers who did not engage in remote work earned
less than remote workers.4 In particular, the majority of those who did not engage in remote work earned
less than 5 million yen in 2019, while a large fraction of those who worked from home earned more than
5 million yen. This ﬁnding supports the claim that low earners suﬀer more from social distancing policies
because they cannot work from home.
To articulate the relationship between remote work and earnings by occupation, Figure 5 plots the
proportion of workers who worked from home and average annual earnings by occupation. We see thatoccupation is an important mediator of the relationship between the prevalence of remote work and earnings,
and that the proportion of workers engaging in remote work and average annual earnings are positively
correlated. Consultants and drivers provide a striking example of this pattern, with the propensity for
consultants to engage in remote work about 50% and income about 6.0 million yen annually, while fewer
than 5% of drivers engage in remote work and annual income is 3.4 million yen. There are a few notable
outliers such as medical doctors, who are less likely to engage in remote work but yet earn a high income.
However, with few exceptions, the plot shows a general pattern that high-income occupations tend to havea higher proportion of remote workers.

4

Determinants of Remote Work
The analysis thus far has discussed the relationship between remote work potential and various occu-

pation and task characteristics independently. In this section, we analyze how task characteristics, basic
demographic characteristics, and HRM practices together aﬀect the status of working from home by estimating the following probit model:
Yi∗ = Xi β + Zi α + θind + ηocc + i
4 The JPSED survey records the respondent’s annual income without an upper limit but for our analysis, we top-coded
income at 20 million yen annually.

8

Yi =

⎧
⎪
⎨ 1

(Yi∗ ≥ 0)⎪⎩ 0(Yi∗ < 0)where Yi is a binary variable indicating whether the respondent engaged in remote work; Xi is a set of
demographic variables including age and its square and indicator variables for whether the respondent was
female, had a child age 6 or younger, and was a university graduate; Zi is the set of job characteristic variables
including routine, manual, and interactive task characteristics, indicator variables for HRM practices (Payfor Performance, Key Performance Indicator, Management by Objective), as well as indicator variables
for working under a non-regular employment contract and ﬁrm size; and θind and ηocc are industry andoccupation dummy variables. We report the speciﬁcations with and without industry and occupation dummy
variables to demonstrate the extent to which these dummy variables capture the eﬀect of demographic andjob characteristics on the remote work experience.

4.1

Basic Results

Column 1 of Table 3 shows the marginal eﬀects of the probit regression of remote work status on job
characteristic and demographic variables. In terms of task characteristics, workers performing routine andinteractive tasks are less likely to engage in remote work, conditional on job and demographic characteristics
and industry×occupation ﬁxed eﬀects. This ﬁnding gives support to the approach of attempting to identify
remote-workable jobs through task characteristics (Dingel and Neiman, 2020; Boeri et al., 2020; Holgersen
et al., 2021; Alipour et al., 2020) and, further, indicates that important variation in job tasks remains within
an industry×occupation cell.
We next focus on the estimated coeﬃcients for HRM style variables. Those workers subject to payfor-performance (PFP), KPI and MBO are 2.3, 3.3 and 1.0 percentage points more likely to engage inremote work, respectively, and these partial correlations are economically signiﬁcant. While the partial
correlations are not causal, they do show that the employer’s ability to accurately observe each worker’s
output, approximated by the HRM style, is associated with the potential for remote work. Further, themagnitudes of these coeﬃcients are large when considering that only 8% of employees work remotely. Thisﬁnding is consistent with the prediction that ﬁrms that are concerned about workers ”shirking from home”
tend to be reluctant to adopt a remote work arrangement. Furthermore, workers classiﬁed as non-regular(on a ﬁxed-term contract, working part time or dispatched from temporary help agencies) are 1.7 percentage
points less likely to engage in remote work, which suggests that employers may be less able to discipline aworker’s shirking behavior without a long-term and continuing relationship.
Workers’ demographic characteristics also play an important role in determining remote work status.

9University graduates are about 1.6 percentage points more likely to engage in remote work, and while
women are slightly less likely to engage in remote work, workers with younger children are 2.1 percentage
points more likely to do so. This result is not surprising, as the majority of working mothers with pre-school
children use child care facilities that typically operate from 7:30 AM to 5:30 PM, and ﬁrms often allow
working mothers with young children to engage in remote work to facilitate this. Further, the relationshipbetween age and working from home forms a U shape, bottoming at 42 years old. This is perhaps because
those in middle management positions are likely to work at the oﬃce while those who are earlier or later intheir careers may be more free to work remotely.
In addition to age, the relationship between ﬁrm size and remote work is also U-shaped. Compared withworkers in small ﬁrms with less than 100 employees, workers in medium-sized ﬁrms with 100-299 or 300-999
employees are about 1.2 or 1.8 percentage points, respectively, less likely to engage in remote work. In
contrast, workers in large ﬁrms with 1000-4999 and 5000+ employees are equally likely to engage in remote
work as those in small ﬁrms. The reason why remote work is prevalent in both small and mega-sized ﬁrms
is probably diﬀerent, for small ﬁrms tend to have more casual and informal employee control that might
permit ﬂexibility while mega-sized ﬁrms have a formalized network and HRM practices that can facilitate
remote work while maintaining appropriate monitoring.
As previous studies used diﬀerent analysis samples to analyze the relationship between ﬁrm size and theprevalence of remote work, we here attempt to replicate ﬁndings of previous studies by using a consistentanalysis sample. Speciﬁcally, Okubo (2020) included self-employed workers while our main analysis sample
has excluded them, so Column (7) of Table 3 reports the regression result using a sample that includes
self-employed workers. We see that when we also include self-employed workers, our results are consistent
with Okubo (2020) in that self-employed workers are 7.2 percentage points more likely to engage in remotework. These secondary results support both our main ﬁndings and those of Okubo (2020) of a U-shaped
relationship between ﬁrm size and the prevalence of remote work in the pre- COVID-19 period.
The basic results heretofore indicate that there are substantial diﬀerences in the engagement in remote
work according to job and demographic characteristics within an industry or occupation. These ﬁndings
suggest that a ﬁrm’s ability to suppress its workers’ incentive to shirk is an important determinant of remote
work adoption. In addition, task characteristics, educational background, presence of pre-school age children,
and age are also important determinants of the remote work engagement.

10

4.2

Alternative Deﬁnitions of Remote Work

In order to ensure that our analysis is not aﬀected by the deﬁnition we have chosen for remote work,in this section we implement several robustness checks by varying the deﬁnition.5 While our main analysis
deﬁned a remote worker as anyone who reported any positive hours worked outside of the work place, thisdeﬁnition could include those who worked only a very few hours at home or at a cafe on the commuting
route. Therefore, our ﬁrst robustness check is to include only those who worked more substantial hours
outside of the workplace, which we consider here to be 3 (alternatively, 6) hours or more. The regression
results based on these alternative deﬁnitions of the remote worker are reported in Columns 2 and 3 of Table
3, and show that the results do not change qualitatively, except for the estimated impact of the manual task
intensity, which we discuss below.
Another consideration is whether there might be a diﬀerence in the total hours worked between remoteand non-remote workers, and how this might aﬀect the results. To adjust for any potential diﬀerence in the
total hours worked per week, in this next robustness check we deﬁne the dependent variable as the hours
worked remotely divided by the total hours worked, and estimate using the Tobit model, because the variable
Remote/Work is left-censored at 0 and right-censored at 1. The results reported in Column 4 of Table 3
using the intensive margin of remote work hours does not change the probit estimation results substantially,
which suggests that these characteristics are determinants not only of the extensive margins but also of theintensive margins of remote work.
We next deﬁne a remote worker not by actual hours worked but as a worker who is formally permitted towork outside of the workplace by the employer. As discussed above, while 8% of workers reported positive
hours worked outside of the workplace, only 4% of workers were formally allowed to do so (Table 1). Column
5 shows the results based on this alternative deﬁnition, and we see that the results do not change substantially
except for two things. Firstly, workers engaging in manual tasks are much less likely to work under a formal
remote work arrangement. Combined with the previous result that manual workers are less likely to workoutside of the workplace for more than 6 hours, we can infer that while manual workers are less likely to be
in a formal remote work arrangement, they do perform some of their work tasks outside of the workplace.
Secondly, while those who work for large ﬁrms with 1,000 employees or more are signiﬁcantly more likely to
be in a formal remote work arrangement, this is in contrast to our ﬁnding about their actual remote work
engagement. This suggests that while workers in large ﬁrms may be given the right to work remotely, in
reality they do not exercise it often.
5 We report the regression results without using a sampling weight because our JPSED analysis sample is fairly similar to the2015 Census in terms of worker characteristics. To conﬁrm this, we conducted a probit estimation applying sampling weights.
The results in Column (6) of Table 3 show that, as expected, the estimated marginal eﬀects are suﬃciently close to the estimatesobtained without using a sampling weight.

11

4.3

Adoption of Remote Work in December 2020

Our main analysis has focused on the determinants of the adoption of remote work as of December 2019,
before the onset of COVID-19, in order to identify the nature of jobs that ﬁt well with remote work innormal times. However, the rapid spread of COVID-19 forced many ﬁrms to quickly adopt remote work
arrangements on an ad hoc basis, with remote work increasing from 8% in 2019 to 14% in 2020. Because
the nature of jobs adopting remote work in an emergency may well be diﬀerent from that during normal
times, it is natural to ask how the determinants of remote work adoption diﬀer before and after the onset
of COVID-19. To answer this question, we draw on the 2020 wave of the JPSED that was implemented in
December 2020. For this analysis, we use engagement in remote work in December 2020 as the dependent
variable, but for comparability, we continue to use the job characteristics as of December 2019. As a result,
this analysis sample includes only those who worked for the same ﬁrm in both 2019 and 2020.
This comparison of remote work determinants in 2019 and 2020 reveals that the further penetration of
remote work in 2020 exacerbated pre-existing inequalities. Table 4 compares the determinants of remote
work engagement across diﬀerent timelines and, for ease of comparison, Column (1) reproduces Column(1) of Table 3 to show the baseline results in December 2019. Restricting the analysis sample to those who
worked for the same ﬁrm in both December 2019 and 2020 reduces the sample size from 22,769 to 16,341, andColumn (2) reports the probit regression results with the same dependent and independent variables based on
this restricted sample. We see that the estimated coeﬃcients are broadly similar to the baseline coeﬃcientsin Column (1), which indicates that replacing the analysis sample does not change the estimation results
in any meaningful way. Column (3) reports the probit regression results in December 2020, and Column(4) presents the diﬀerence in the estimated coeﬃcients of Columns (2) and (3) to show the diﬀerence inthe determinants of remote work engagement in 2019 and 2020. Column (4) reveals that workers engaged
in manual tasks became substantially less likely to engage in remote work in 2020, indicating that manual
workers did not beneﬁt from the increased penetration of remote work during COVID-19, becoming relatively
less likely to engage in remote work.
On the other hand, the relative disadvantage of interactive workers in terms of remote work engagement
disappeared in 2020. Furthermore, the advantage of holding a university degree or working for a large ﬁrm
was enhanced in 2020. In particular, the impact of ﬁrm size on remote work engagement grew substantially.
For instance, while in December 2019, workers at ﬁrms with 5000+ employees were equally likely to engage
in remote work as those at ﬁrms with less than 100 employees, workers at larger ﬁrms became as much as
8.6% more likely to work remotely in December 2020. Also, the probability of engaging in remote work
monotonically increases as the ﬁrm size increases in December 2020. This ﬁnding is consistent with the12ﬁnding by Morikawa (2020) based on the sample after the onset of COVID-19. In sum, as workers withadvantageous characteristics in the labor market such as higher education or employment at a large ﬁrm
beneﬁted more from the expansion of remote work in 2020, it increased existing inequalities.
The results reported in Table 4 suggest the presence of adjustment costs to adopt a remote work setting.
Using the same analysis sample and the same set of explanatory variables, the probit model predicts the
adoption of remote work in 2020 much better than the adoption of remote work in 2019, as indicated by
the substantial increase in Psuedo R2 . The improvement of the goodness of ﬁt implies that the relationshipbetween job characteristics and the adoption of remote work settings becomes more distinct during COVID19. Arguably, the pandemic nudged the ﬁrms to adopt remote work settings if it is technically possible.

4.4

Implications

Policy Implication
Our basic ﬁndings have implications for both income transfer policies and corporate management. First,
regarding income transfer policy, we ﬁnd signiﬁcant variation in the potential for remote work both acrossand within occupations. Even within an industry, there are workers who can work from home and those
who cannot. Thus, policies that target speciﬁc industries or occupations are not suﬃciently ﬁne-grained toaccurately target those workers who are most severely impacted by a lack of opportunity to work remotely.
We must be cognizant that some workers might fall through the cracks, including those who may not beable to work remotely in an industry in which many can indeed work from home. Additionally, there ispresumably substantial variation in the earnings shock or possibility of job loss within an industry beyond
merely the potential for remote work. Second, we argue that policies that target small and medium-sizedﬁrms (SMEs) to promote the adoption of remote work settings are justiﬁed. Our ﬁndings show that smallerﬁrms have a lower probability of adopting remote work by December 2020, arguably due to limited ﬁnancial
and human resource capacities. Removing these barriers through policy intervention is justiﬁed because
remote work adoption is an eﬀective non-pharmaceutical intervention to reduce mobility and generate apositive externality. Of course, we do not intend to justify the general company-based rescue policies suchas special loan programs or employment subsidies targeting speciﬁc industries or ﬁrm sizes. Instead, ourﬁndings speak to speciﬁc policies to promote the adoption of remote work settings of SMEs.
Managerial Implication
Regarding advice for corporate managers, we’ve found a strong correlation between HRM style andengagement in remote work that has important implications for managers seeking to expand the opportunities13

for remote work. When a manager cannot observe the output or eﬀort of each worker, moral hazard cancause a worker to slack oﬀ, and our results suggest that “shirking from home” was indeed a practical concernof employers in adopting remote work arrangements. However, the current social distancing policy forces
ﬁrms to allow certain workers to work from home regardless of their ability to eﬀectively observe worker
output or eﬀort. This could have a detrimental impact on the ﬁrms’ productivity unless managers canre-design the job or compensation scheme so that the eﬀort or output of individual workers is accurately
measured and appropriately compensated. However, even if this output measurement issue is resolved, there
remains the usual problem of multitasking that arises from pay-for-performance incentive schemes, whereby aworker with a multiple-task assignment will focus excessively on the task whose output is measured relatively
easily. For example, middle-level managers of a sales team who are expected to both expand sales and coachsubordinates will put more eﬀort on expanding sales if the eﬀect of coaching subordinates is not properly
measured and rewarded. While professionals who were permitted to work remotely before COVID-19 arepresumably not subject to a serious multitasking problem, these output measurement and multitasking
issues pose a serious challenge for managers who want to introduce or expand remote work arrangements in
response to the spread of COVID-19.

5Eﬀect of Remote Work on Earnings
To this point we have demonstrated that workers in high-earning occupations or with high human cap-

ital are more likely to engage in remote work. Now we ask if engagement in remote work aﬀects earnings
after conditioning on observable job and demographic characteristics. This exercise is important for deriving implications for government transfer policies, for if remote work and earnings are positively correlated
through observed job and demographic characteristics, an income transfer policy based on these observed
characteristics would eﬀectively mitigate inequality-exacerbating negative shocks. On the other hand, if engagement in remote work and earnings are positively correlated conditional on observed characteristics, theneven after receiving income transfers based on these characteristics, workers who cannot engage in remote
work remain low earners. Thus, if the government aims at mitigating a negative income shock, an income
transfer policy based on the possibility of remote work engagement should be considered. With this policy
implication in mind, we next examine the impact of remote work engagement on earnings, conditional on
job and demographic characteristics.
Panel A in Table 5 lays out the estimation results of log annual earnings in 2019 on engagement in remotework. While Column (1) shows that remote workers earn 0.234 log points more than non-remote workers,
Column (2) shows that remote workers do not earn more than non-remote workers when conditioned on the14

job characteristics and demographic variables used in the regression analysis reported in Table 3. This result
does not change by adding Industry × Occupation dummy variables to the conditioning set. These results
demonstrate that the positive association between remote work engagement and earnings is induced by job
and earnings characteristics.
We next address any concerns that job and demographic characteristics of remote and non-remote workers do not overlap by conducting a propensity score matching (PSM) estimation. While we conﬁrm thatengagement in remote work varies substantially within an industry and an occupation (Figures 1 and 2),
the high dimension of the job and demographic characteristics vector may nonetheless entail no overlap in
the characteristic variables between remote and non-remote workers. If there is no overlap in characteristics, OLS is estimated by extrapolation, assuming that treatment and outcome out of common support isparametric linear. We then relax this assumption in our PSM estimation.
Figure A7 shows the distribution of the propensity score, and we see that there is a substantial degree ofoverlap between the treatment and control groups, indicating that PSM estimation is appropriate. Column(4) of Table 5 shows the results for nearest-neighbor matching, and while we see that a remote worker can earn
0.3% less than non-remote workers, this is a small eﬀect and not signiﬁcant. Next, we conduct kernel matching
and local linear regression (LLR) matching, and Columns (5)-(7) show similar results that remote workers do
not earn more than non-remote workers. Column (8), which shows the results of inverse probability weighting(IPW), obtains similar results. Consistent with the overlap of the propensity scores between remote andnon-remote workers, the observations do not drop in Table 5 except for kernel matching. In sum, the PSM
estimation conﬁrms that engagement in remote work and earnings are not partially correlated, conditional
on job and demographic characteristics.
The policy implication derived from the analysis in this subsection is straightforward: while non-remoteworkers are low earners in general, after any earnings loss is compensated based on job and demographic
characteristics, remote work status does not need to be included in the policy targeting variable. Whiletargeting an income-supplementary policy based on the vast range of job and demographic characteristics is
not realistic, taxable income in the previous year may be considered a plausible proxy, or suﬃcient statistics,
for the set of job and demographic characteristics.
A ﬁnal consideration is that the labor economics literature typically uses hourly wage instead of annual
earnings as a measurement of income. The main analysis did not use hourly wage because the data set does
not contain information on annual hours worked corresponding to the annual earnings. However, to obtain
results comparable with standard results in the literature, we calculate annual hours worked by multiplying
monthly hours worked in December 2019 by twelve by restricting the sample to the workers who worked
for 12 full months. Panel B in Table 5 reports the regression results using the natural logarithm of hourly

15rate of pay as the dependent variable, and Column (1) in Panel B shows that remote workers earn 0.170 log
points more than non-remote workers per hour. This wage diﬀerential remains even after conditioning on thejob characteristics and demographic variables. Column (2) in Table 3 indicates that remote workers receive
higher wages than non-remote workers by 0.033 log points, but this diﬀerence disappears when Industry ×
Occupation ﬁxed eﬀects are added to the conditioning set, indicating that the positive association between
remote work engagement and wages is induced by industry and occupation characteristics. These results do
not change by using propensity score matching to allow for ﬂexible functional form on the impact of workers’
characteristics on wages. Overall, the hourly wage analysis conﬁrms our main analysis that engagement inremote work does not aﬀect wages in a causal sense.

6

Concluding Remarks
This paper analyses remote work in December 2019, the period just before the breakout of COVID-

19. The opportunity to work remotely was more likely to be available to those in professional occupations
characterized by non-routine, analytical and non-interactive tasks, and less likely to be available to service
sector workers requiring face-to-face interactive tasks or manual laborers performing routine and manualtasks. Furthermore, workers subject to HRM practices that presume the measurability of individual output
were more likely to engage in remote work. Moreover, reﬂecting the heterogeneity of job characteristics
within a given occupation or industry, high income earners were more likely to engage in remote work, which
implies that the cost of social distancing policies does indeed disproportionately fall on low income earners.
On top of the disproportionate probability of job loss among the poor as pointed out by Kikuchi et al. (2021),
this ﬁnding calls for a transfer policy based on household income and targeted at the poor to compensate
for their more limited opportunity to work from home.
Additionally, the strong association between pay-for-performance type HRM practices and the prevalenceof remote work, conditional on industry, occupation, and task characteristics suggests that the phenomenonof “shirking from home” is a legitimate concern that discourages employers from adopting remote work arrangements. Thus, any adoption of remote work as a countermeasure to COVID-19 will require management
to adopt a style of human resource management that ensures that individual worker eﬀort and output can be
accurately monitored in the remote work setting. This poses a signiﬁcant challenge to company management.

16

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18

Table 1: Firm’s introduction of remote work as of December 2019 (JPSED2019, Employee)
Introduced and applied
Introduced but not applied
Not introduced
Do not know
Total
Observations

Percentage
4%
5%
76%
15%
100%
24728

Actual remote work engagement
58%
8%
6%
8%
8%

Note: 1. Data source is Japanese Panel Study of Employment Dynamics.
2. Percentage is the proportion of workers who chose that item.
3. Actual remote work is the conditional probability of working remotely within each category.

Table 2: Summary statistics by remote work status (JPSED2019, Employee)

RoutineManualInteractive
PFP (pay for performance)
KPI (key performance indicators)
MBO (management by objective)
Remote hours per week
Work hours per week
Annual earnings (10,000 yen)
Observations

Non-remote worker
61.58(28.73)42.61(31.26)43.91(30.63)0.23(0.42)
0.17(0.37)
0.20(0.40)
0.00(0.00)
36.36(13.80)338.44(247.64)
22679

Remote worker
49.22(27.47)
33.26(27.60)41.59(28.14)0.37(0.48)
0.31(0.46)
0.31(0.46)
8.81(12.58)38.73(15.60)438.79(317.35)
2049

Diﬀerence-12.36(0.66)-9.35(0.71)-2.33(0.70)
0.13(0.01)
0.14(0.01)
0.12(0.01)
8.81(0.08)
2.36(0.32)
100.35(5.89)

Note: 1. Data source is Japanese Panel Study of Employment Dynamics.
2. Mean values and standard deviations are reported. Standard deviations are in parentheses.
3. t-test is conducted in diﬀerence column. Standard errors are in parentheses.

19

Table 3: Determinants of remote work engagement as of December 2019(1)
ProbitRemote>0-0.071∗∗∗(0.007)(2)
ProbitRemote≥3-0.051∗∗∗(0.005)(3)
ProbitRemote≥6-0.034∗∗∗(0.005)(4)
Tobit
Remote/Work-0.023∗∗∗(0.002)(5)
ProbitRemote Institute-0.024∗∗∗(0.005)(6)
Probit (Weight)
Remote>0-0.068∗∗∗(0.007)(7)
Probit (Self-employed)
Remote>0-0.073∗∗∗(0.006)Manual/100-0.005(0.008)-0.004(0.007)-0.013∗∗(0.006)-0.003(0.003)-0.034∗∗∗(0.007)-0.001(0.008)-0.015∗(0.008)Interactive/100-0.029∗∗∗(0.007)-0.021∗∗∗(0.005)-0.017∗∗∗(0.005)-0.011∗∗∗(0.002)-0.010∗∗(0.005)-0.027∗∗∗(0.007)-0.023∗∗∗(0.006)PFP

0.023∗∗∗(0.004)0.014∗∗∗(0.003)0.007∗∗(0.003)0.007∗∗∗(0.001)0.012∗∗∗(0.003)0.021∗∗∗(0.004)0.028∗∗∗(0.004)KPI

0.033∗∗∗(0.005)0.020∗∗∗(0.004)0.014∗∗∗(0.003)0.010∗∗∗(0.002)0.028∗∗∗(0.003)0.030∗∗∗(0.005)0.034∗∗∗(0.005)MBO

0.010∗∗(0.005)0.007∗(0.004)0.003(0.003)0.003(0.002)0.013∗∗∗(0.003)0.010∗∗(0.005)0.013∗∗∗(0.005)

Nonregular-0.017∗∗∗(0.005)-0.006(0.004)-0.003(0.003)-0.004∗∗(0.002)-0.020∗∗∗(0.004)-0.017∗∗∗(0.005)-0.015∗∗∗(0.005)

Female-0.009∗(0.004)-0.002(0.004)-0.001(0.003)-0.002(0.002)-0.001(0.004)-0.008∗(0.005)-0.009∗(0.004)

With child age 6 or under

0.021∗∗∗(0.005)0.016∗∗∗(0.004)0.012∗∗∗(0.004)0.008∗∗∗(0.002)0.014∗∗∗(0.004)0.019∗∗∗(0.005)0.020∗∗∗(0.006)Age-0.004∗∗∗(0.001)-0.003∗∗∗(0.001)-0.002∗∗∗(0.001)-0.002∗∗∗(0.000)-0.002∗∗(0.001)-0.004∗∗∗(0.001)-0.003∗∗∗(0.001)Age squared/100

0.005∗∗∗(0.001)0.004∗∗∗(0.001)0.003∗∗∗(0.001)0.002∗∗∗(0.000)0.002∗∗∗(0.001)0.005∗∗∗(0.001)0.003∗∗∗(0.001)

University

0.016∗∗∗(0.004)0.015∗∗∗(0.003)0.010∗∗∗(0.003)0.006∗∗∗(0.001)0.015∗∗∗(0.003)0.015∗∗∗(0.004)0.018∗∗∗(0.004)100-299-0.012∗∗(0.006)-0.005(0.005)-0.003(0.004)-0.005∗∗(0.002)-0.013∗∗(0.005)-0.013∗∗(0.006)-0.014∗∗(0.006)300-999-0.018∗∗∗(0.006)-0.015∗∗∗(0.005)-0.017∗∗∗(0.005)-0.007∗∗∗(0.002)-0.002(0.005)-0.015∗∗(0.006)-0.020∗∗∗(0.006)

1000-4999-0.000(0.006)0.005(0.005)0.000(0.004)-0.001(0.002)0.014∗∗∗(0.005)-0.003(0.006)-0.002(0.007)

5000+

0.003
(0.006)0.008∗(0.005)0.006(0.004)-0.000(0.002)0.040∗∗∗(0.004)-0.002(0.006)0.000(0.006)

Routine/100

0.072∗∗∗(0.006)

Self-employed
Industry × Occupation
Pseudo R2
Observation

Yes
0.11
22769

Yes
0.11
22328

Yes
0.10
21174

Yes
0.11
23686

Yes
0.20
21546

Yes
0.11
22741

Yes
0.12
25755

Note: Data source is Japanese Panel Study of Employment Dynamics. ∗ p < .10,∗∗ p < .05,∗∗∗ p < .01. Coeﬃcients are average marginal eﬀects
for all estimation methods. Heteroskedasticity robust standard errors are in parentheses. Remote>0 means whether remote working more than 0hour or not. Remote>3 (6) means whether remote working 3 (6) hours or more or not. Remote/Work is ratio of remote work hours per week ofworking hours in main work place per week. Remote institute is the indicator if the remote working is allowed by the employer and the institution
applies to the worker. Three HRM variables are recorded on a 5-point Likert scale ranging from not applicable to applicable, and we characterized
those who responded either “applicable” or “if anything, applicable” (1 or 2 on the scale) as workers for whom that speciﬁc HRM variable applies.
PFP indicates if the worker’s compensation is determined by pay for performance, KPI indicates if the worker is subject to the management basedon the Key Performance Indicator, MBO indicates if the worker is subject to the management by objectives. The baseline of nonregular is regular
workers, and nonregular workers include part time jobs, dispatched workers, contract workers and part time employees. The base category of ﬁrm
size category variables is less than 100 people. The diﬀerence of sample size between each column are due to a perfect prediction for the case ofprobit estimation.

20

Table 4: The determinants of remote work engagement as of December 2019 and December 2020
(1)
ProbitRemote>02019-0.071∗∗∗(0.007)(2)
ProbitRemote> 0
2019
Restricted-0.070∗∗∗(0.008)(3)
ProbitRemote>02020
Restricted-0.068∗∗∗(0.009)(3)-(2)0.002(0.012)

Manual/100-0.005(0.008)-0.006(0.010)-0.141∗∗∗(0.012)-0.135∗∗∗(0.016)

Interactive/100-0.029∗∗∗(0.007)-0.030∗∗∗(0.008)0.002(0.009)0.032∗∗∗(0.012)PFP

0.023∗∗∗(0.004)0.020∗∗∗(0.005)0.007(0.006)-0.013∗(0.007)KPI

0.033∗∗∗(0.005)0.036∗∗∗(0.006)

0.036∗∗∗(0.006)0.000(0.009)MBO

0.010∗∗(0.005)0.010∗(0.006)0.025∗∗∗(0.006)0.015∗(0.008)

Nonregular-0.017∗∗∗(0.005)-0.023∗∗∗(0.006)-0.031∗∗∗(0.007)-0.008(0.009)Female-0.009∗(0.004)-0.008(0.005)-0.002(0.006)0.006(0.008)With child age 6 or under

0.021∗∗∗(0.005)0.022∗∗∗(0.006)0.013∗(0.008)-0.009(0.010)Age-0.004∗∗∗(0.001)-0.005∗∗∗(0.001)-0.004∗∗∗(0.001)0.001(0.002)Age squared/100

0.005∗∗∗(0.001)0.006∗∗∗(0.001)0.004∗∗∗(0.001)-0.002(0.002)

University

0.016∗∗∗(0.004)0.014∗∗∗(0.005)0.047∗∗∗(0.005)0.032∗∗∗(0.007)100-299-0.012∗∗∗(0.006)-0.029∗∗∗(0.007)-0.007(0.008)0.021∗∗(0.011)300-999-0.018∗∗∗(0.006)-0.027∗∗∗(0.007)0.020∗∗(0.008)0.047∗∗∗(0.011)

1000-4999-0.000(0.006)-0.004(0.007)0.059∗∗∗(0.008)0.063∗∗∗(0.011)

5000+

0.003
(0.006)-0.004(0.007)0.086∗∗∗(0.008)0.089∗∗∗(0.011)

Industry × Occupation
Pseudo R2
Observation
Mean of dependent variables

Yes
0.11
22769
0.08

Yes
0.11
16341
0.08

Yes
0.24
16332
0.14

Routine/100(4)Note: Data source is Japanese Panel Study of Employment Dynamics. ∗ p <.10,∗∗ p < .05,∗∗∗ p < .01. Coeﬃcients are average marginal eﬀects for all estimation methods. Heteroskedasticity robust standard errors are in parentheses.
Remote>0 means whether remote working more than 0 hour or not. Remote>3(6) means whether remote working 3 (6) hours or more or not. Remote/Work
is ratio of remote work hours per week of working hours in main work place per
week. Remote institute is the indicator if the remote working is allowed by theemployer and the institution applies to the worker. Three HRM variables arerecorded on a 5-point Likert scale ranging from not applicable to applicable, andwe characterized those who responded either “applicable” or “if anything, applicable” (1 or 2 on the scale) as workers for whom that speciﬁc HRM variableapplies. PFP indicates if the worker’s compensation is determined by pay for
performance, KPI indicates if the worker is subject to the management based on
the Key Performance Indicator, MBO indicates if the worker is subject to the
management by objectives. The baseline of nonregular is regular workers, andnonregular workers include part time jobs, dispatched workers, contract workers
and part time employees. The base category of ﬁrm size category variables isless than 100 people. We restricted our sample in column (2) and (3) to those
who had not quit their job in 2019 and 2020. The diﬀerence of sample size between column (2) and (3) are due to a perfect prediction for the case of probit
estimation.

21

Table 5: Eﬀect of remote work on ln annual earnings (Average Treatment Eﬀect on Treated)
Panel A(1)
Log(income)OLS
0.234∗∗∗(0.024)(2)
Log(income)OLS-0.007(0.018)Control

No

Yes

Yes

Industry × Occupation
Observations

No
24432

No
23495

(1)
Log(wage)
OLS
0.171∗∗∗(0.020)Control
Industry × Occupation
Observations

Remote work

(3)
Log(income)OLS-0.017(0.018)(4)
Log(income)PSM(nn)-0.003(0.027)(5)
Log(income)PSM(LLR;0.01)-0.010(0.033)(6)
Log(income)PSM(LLR;0.02)-0.007(0.033)(7)
Log(income)PSM(Kernel)-0.009(0.025)(8)
Log(income)
IPW-0.005(0.018)

Yes
23495

23495

23495

23495

23490

23495

(2)
Log(wage)
OLS
0.033∗(0.018)(3)
Log(wage)OLS
0.011(0.018)(4)
Log(wage)
PSM(nn)0.033(0.023)(5)
Log(wage)
PSM(LLR;0.01)
0.021(0.026)(6)
Log(wage)
PSM(LLR;0.02)
0.022(0.026)(7)
Log(wage)
PSM(Kernel)
0.021(0.021)(8)
Log(wage)
IPW
0.023(0.018)

No

Yes

Yes

No
21935

No
21297

Yes
21297

21297

21297

21297

21293

21297

Panel B

Remote work

Note: Data source is Japanese Panel Study of Employment Dynamics. ∗ p < .10,∗∗ p < .05,∗∗∗ p < .01. Standard errors are parentheses and areheteroskedastic consistent for OLS, and are robust Abadie-Imbens type for PSM and IPW. Coeﬃcients are marginal eﬀects for all estimation methods.
nn means nearest neighbor. LLR means local linear regression and subsequent number is bandwidth. Epanechnikov kernel is used for LLR matching
and kernel matching. Wage is calculated by dividing annual income by the number of hours worked per week × 52. We cannot calculate the annual
hours worked because we do not know the weeks worked, so that we restrict the sample to those who worked in all months. This restriction resulted
in the diﬀerence in sample size between Panel A and B. Control variables include three task variables, three HRM variables, employment types, female,whether having child age 6 or under, age, age squared, university or more, ﬁrm size, industry and occupation. The actual analyzed sample sizes are 3916
and 3662 for the case of PSM(nn) in panel A and B respectively.

22

Figure 1: Prevalence of remote work by industry

Note: The industry classiﬁcation of JPSED is according to Japan Standard Industry Classiﬁcation system.
Figure 2: Prevalence of remote work by occupation

23

Figure 3: Distribution of task characteristics by remote work status

Note: The box plot displays a box bordered at the 25th and 75th percentiles of each task variable with a
median line at the 50th percentile. From the box, we further extend a line vertically to the 90th and 10th
percentile values, which are capped by short horizontal lines.

24

Figure 4: Relationship between annual earnings and remote work

Figure 5: The prevalence of remote work and average annual earnings by occupation

25

A

Appendix

A.1

JPSED QuestionnaireThis appendix provides the complete JPSED questionnaire used to construct the variables for this study.
• Questions about remote work
As of last December, how long did you engage in remote work per week? Remote work is deﬁned asworking from your home, a satellite oﬃce, a cafe/family restaurant, or a workplace (This refers toworking at a location other than the company and its customers).
Total hours per week: ( ) hours
As of last December, you did remote work for a total of (Previous Answer) hours in an average week.
The answer is ”yes”. If there are no mistakes, please click the ”Next Page” button. If you need tomake a correction, please click the ”Back” button and try again.
• Questions about the remote work system
As of last December, did your workplace have a remote work system in place? Were you eligible forthe system and did you apply for it? Please choose the response below that applies to you. A remote
work system refers to a system that allows employees to work from locations other than the workplace(your company and your customers), such as your home, a satellite oﬃce, or a cafe/family restaurant.
1. It was introduced as a system and applied to me.
2. It was introduced as a system, but did not apply to me.
3. It had not been introduced as a system.
4. I don’t know.
• Questions about task characteristics
What percentage of each of the following tasks did you engage in your job in last December? Drag thetranslucent button and slide it to the position that you think ﬁts your job.
routine/ non-routine
manual/ cognitive
working alone/ working interactively
Percentage of work ( ) / Percentage of work ( )If you enter a number on one side, it will automatically calculate the total to be 100%.
• Questions about human resource management
We would like to ask you about your work at your workplace over the last year (January - December2019).
Do any of the following apply at your workplace?
– Results are more important than age or seniority in your personnel evaluations.
– Results are managed through KPI and other measures.
KPI are metrics to manage your performance and actions that you look back on regularly toachieve your goals.
– There is a system for setting clear goals for work, such as the management by objectives (MBO)
system.
1. applicable
2. if anything, applicable
3. I can’t say either.
4. if anything, not applicable
5. not applicable

26

A.2

Task characteristics and occupation

The reported task characteristics depend heavily on occupation. Figure A1 shows the percentage ofroutine work for 45 occupation categories, and while as much as 80% of the job tasks are routine for workers
in transportation and communication occupations other than drivers, only 35% are routine for consultants.
Similarly, Figure A2 shows that the proportion of manual tasks varies signiﬁcantly across occupations, from
10% for OA operator to 80% for manual laborers other than production workers in the manufacturing sector.
Likewise, Figure A3 shows that the percentage of interactive tasks also varies signiﬁcantly across occupations,from 20% for drivers to above 60% for chefs, customer service representatives and waiters, and social welfare
professionals (care workers).
While occupation explains a portion of the job task characteristics a worker faces, non-negligible heterogeneity remains. To examine how much variation in task characteristics is explained by the 45 occupation
codes and 15 industry codes, we regress the task content score on an array of occupation dummy variables,
industry dummy variables, occupation and industry dummy variables, and the interaction of these industry
and occupation dummy variables. Table A1 reports the R2 of the ANOVA regressions, and Column 1 showsthat 3.6% of the total variation in reported routine scores is explained by industry, 10.4% by occupation,
11.2% by industry and occupation, and 13.2% by industry × occupation. Column 2 shows that 14.4% of themanual task score is explained by industry, 41.5% by occupation, 43.3% by industry and occupation, and45.3% by industry × occupation, while Column 3 shows that 3.9% of interactive task score is explained byindustry, 8.8% by occupation, 10% by industry and occupation, and 12.4% by industry × occupation. The
low R2 for routine and interactive tasks imply that these task characteristics vary signiﬁcantly within anindustry and an occupation, so crude industry or occupation codes are not suitable for capturing the routine
and interactive characteristics of jobs. In contrast, the high R2 for the manual task with occupation dummy
variables implies that occupation codes capture about the half of the manual task characteristics of each job
within that occupation. The only moderate correlation between task content and industry or occupation
indicates that signiﬁcant variation in the task characteristics remains within an industry-occupation cell,
casting doubt on the common use of industry or occupation codes to determine the potential for remote
work unless very detailed occupational categories are available, as in some previous studies (Dingel andNeiman, 2020; Holgersen et al., 2021; Alipour et al., 2020).
Table A1: The regression R2 of task variables on industry and occupation dummy variables
Dependent variables
Independent variables
Industry
Occupation
Industry and Occupation
Industry × Occupation
Observations(1)
Routine(2)
Manual(3)
Interactive

0.034
0.113
0.120
0.145
24728

0.139
0.436
0.449
0.471
24728

0.036
0.088
0.098
0.127
24728

Note: 1. Data source is Japanese Panel Study of Employment Dynamics.
2. R squared when regressing each task variable.

27

Figure A1: Percentage of routine tasks by occupation

Figure A2: Percentage of manual tasks by occupation

28

Figure A3: Percentage of interactive tasks by occupation

A.3

HRM characteristics and occupation

The HRM characteristics depend heavily on occupation. Figure A4 shows the percentage of Pay for
Performance (PFP) for 45 occupation categories, and while PFP applies to as many as 56% of ﬁnancial
professionals, only 18% apply for agriculture and ﬁshing. Similarly, Figure A5 shows that Key Performance
Indicator (KPI) is more likely to apply to workers who are ﬁnancial professionals, internet professionals andplanning and sales clerks (43, 32 and 36%, respectively). In contrast, KPI applies to only 3% workers inagriculture and ﬁshing. Likewise, Figure A6 shows that the applications of MBO also varies signiﬁcantly
across occupations, from 3% for agriculture and ﬁshing to 40% for legal aﬀairs professionals.

A.4

Distribution of propensity scores by remote and non-remote workers

29Figure A4: Rate of PFP (pay for performance) by occupation

Figure A5: Rate of KPI (key performance indicator) by occupation

30

Figure A6: Rate of MBO (management by objective) by occupation

Figure A7: Distribution of propensity score

31

A.5

Comparison between JPSED and Census

JPSED is designed to be a nationally representative survey covering those who are 15 years of age orolder. To conﬁrm this sampling design, we compared our data to the 2015 Census, restricting our sample
to those who were working because the analysis sample includes only working people. We pay particularattention to the gaps in types of employment and the distribution of industries of workers. The distributionsof these workers’ characteristics of the 2019 JPSED are broadly similar to the distribution based on the 2015
population census.
Table A2: The distribution of employment types and industry of workers in the 2019 JPSED and the 2015
CensusPanel A: Employment types
Regular workers
Dispatched workers
Part time jobs
OﬃcersSelf-employed workers
Family employee
Internal employment

Census (2015)
53.6
2.7
26.0
5.1
9.0
3.4
0.2

JPSED (2019)
53.7
3.0
29.1
4.0
7.4
1.7
1.2

Census (2015)
3.8
7.4
16.2
0.5
2.9
5.2
15.3
2.4
2.0
5.5
11.9
4.5
13.6
3.4
5.4

JPSED (2019)
1.2
5.1
16.0
1.3
5.9
6.6
11.7
3.6
2.0
5.1
10.6
4.9
10.7
6.2
9.3

Panel B: Industry
Agriculture, Fish and mining
Construction
Manufacturing
Infrastructure(electric, gas, heat and water)
Telecommunications
Transportation and post
Wholesale and retail
Finance and insurance
Real estate
Restaurant and accommodation
Medical and welfare
Education and study support
ServicePublic sector
OthersNote: Data sources are Japanese Panel Study of Employment Dynamics and Census.

32