ESRI Discussion Paper Series No.375

Telework, Management, and Technology
Atsushi Ohyama, Ryo Kambayashi
April 2023

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

Telework, Management, and Technology*
Atsushi Ohyama† and Ryo Kambayashi‡
Abstract
We empirically examine what factors facilitate remote work arrangements by using thedata from the 2020 JP MOPS. In this study, we exploit variations in establishment
characteristics and shed light on the relationships between telework, management, andtechnology. Our empirical investigation reveals several relationships between telework,
management, and technology. First, well-management establishments tend to arrange
telework environments. Second, both human resource and non-human resource
management practices have positive impacts on the arrangement of telework. Third, it isa long-run improvement in the quality of management that is positively associated withtelework arrangements. Finally, technology factors are strongly correlated with the
adoption of telework. This study shows that management and technology contribute to
telework adoption and that establishment heterogeneity regarding management andtechnology can explain an unexplained part of the variation in telework arrangement
within a given occupation.

Keywords: Telework, Structured management, Technology*

We appreciate invaluable comments from Mari Tanaka. We are also grateful for the comments fromESRI researchers and participants at ESRI workshops in November of 2021 and April of 2023. This
research is supported by the Japanese Society for Promotion of Science grant No. 18H03633.
†
Hitotsubashi University, Institute of Innovation Research, ohyama@iir.hit-u.ac.jp‡
Hitotsubashi University, Institute of Economic Research, kambayashi@ier.hit-u.ac.jp

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

1. Introduction
Even before the 2010s, working from home (WFH) had become an increasingly importantwork arrangement, and the onset of the COVID-19 pandemic accelerated the number of
WFH individuals. According to the OECD report (OECD, 2021), the rates of teleworking
more than doubled compared to one year before the onset of the COVID-19 pandemic inmany countries such as France, Japan, Italy, and Brazil. In Japan, for example, "teleworkrates increased markedly from 10% in December 2019 to almost 28% in May 2020" (p.4,
OECD, 2021).
However, there are considerable variations in WFH rates across occupations,firms, industries, and countries. The occupation-level WFH variation is associated withjob characteristics. This is mainly because some jobs and tasks are assumed to betechnologically difficult to be performed at home, generating differences in the feasibilityof WFH across occupations. Dingel and Neiman (2020) report the share of jobs that canbe done at home based on the O*NET database. According to their calculations, morethan 95 percent of jobs can be done at home for computer, education, and legal
occupations, whereas the corresponding number is 1 percent or 0 percent for farming,
production, and construction occupations. Adams-Prassl et al. (2022) examine the US and
UK survey data and confirm considerable differences in the feasibility of WFH acrossoccupations.
We examine what factors facilitate teleworking in this paper, but we take adifferent approach from the ones examining job characteristics and occupation-levelvariations. We shed light on establishment-level variations in teleworking by focusing on
establishment heterogeneity regarding management and technology. As discussed in theliterature on telework, management can affect telework arrangements because incentives
and monitoring embedded in the output control and human resource management mustbe aligned with workers’ incentives for taking desirable actions and behaviors when theywork from home. Furthermore, Bloom et al. (2015) argue that telework is also one of the
management practices. Therefore, management can be a potential driver for teleworkadoption. Similarly, advanced technology affects the feasibility of teleworking because
access to advanced technology enables teleworking. The systematic analysis of how thesefactors contribute to telework arrangements is lacking mainly because high-quality data
are not available. We try to fill this gap by overcoming the data limitations as well as
providing findings that complement insights obtained from the individual- andoccupational-level analyses.
Our data come from the Japanese version of the Management and Organizational
Practices Survey for 2020 (manegimento-ya-soshiki-ni-kansuru-chosa, hereafter the 2020

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

JP MOPS). The 2020 JP MOPS follows the protocol of the previous JP MOPSs andcollects information on the management practices of establishments in the manufacturingsector. Management scores are calculated from this information by using the procedure
employed in the management survey literature (Bloom et al., 2019). In addition, survey
respondents were instructed to answer what percentage of workers work from home atleast once a week and to what extent their establishment introduces and uses the Internetof Things devices (IoT), Artificial Intelligence (AI), and 3D CAD/CAM, respectively.
These pieces of information are utilized to construct telework and technology measures.
We are thus able to use these empirical measures of telework, management, andtechnology to understand a mechanism that facilitates telework.
The main results of this paper are summarized as follows. First, managementscores (a proxy for management quality) are positively associated with the likelihood of
adopting telework even after technology factors are controlled for. This indicates thatwell-management establishments tend to arrange work-from-home environments. Second,
it is a long-run improvement, not a short-run improvement, in the quality of managementthat is positively associated with the current telework adoption. One possible
interpretation of this result is that the organization’s ability to design, build, andimplement good management practices over a long timeframe helps to adopt telework.
Finally, technology factors are strongly correlated with the adoption of telework. The
overall findings from this study suggest that establishment heterogeneity regarding
management and technology contributes to variation in telework. This heterogeneity can
explain an unexplained part of the variation in telework arrangement within a given
occupation.
The paper is organized as follows. In Section 2, we briefly review the literatureon the determinants of teleworking and its effects on performance. Section 3 describes
the 2020 JP MOPS and explains how telework, management, and technology measures
are constructed. In Section 4, we estimate the baseline specification in which thedependent variable is telework and the main independent variables are management andtechnology and report the results from the logit model estimations. Section 5 concludes.
2. Brief Literature Review
Job characteristics are an important determinant for enabling individuals to work fromhome. Dingel and Nieman (2020) use the O*NET database to compute a share of jobs
that can be done at home, and report that this work-from-home measure varies
considerably across occupations. According to their calculations, more than 95 percent
of jobs can be done at home for computer, education, and legal occupations, and more

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

than 85 percent of jobs can be done at home for business and financial operation, andmanagement occupations. The corresponding number is 1 percent or 0 percent forfarming, production, and construction occupations. There are occupations where amoderate percentage of jobs can be done at home, such as community and social service(37 percent) and sales and related (28 percent) occupations. Adams-Prassl et al. (2022)
use survey data on workers collected in the United States and the United Kingdom andexamine who can work from home by paying attention to the heterogeneity in the ability
to work from home. They find considerable variation in the ability to work from home
not only across but also within occupations and industries. According to their estimation,
occupation and industry fixed effects can only explain about one-quarter of the variationin the share of tasks that can be done at home. They also document that male workers,
workers with a university degree, and workers with permanent contracts can perform
higher shares of their tasks from home than female workers, workers without a university
degree, and workers with temporary contracts.
Our study focuses on examining the heterogeneity in the ability to work fromhome across establishments within the manufacturing sector. In contrast to their study, aunit of analysis is an establishment in our study, not a worker or an occupation. This
allows us not only to investigate this heterogeneity at the establishment level but also toanswer what work environment facilitates telework arrangements. Especially, evidenceis scant regarding whether good management, advanced technology, or both promote
working from home. We try to answer this question in this study.
Kawaguchi and Motegi (2021) try to answer who can work from home by using
the Japanese Panel Study of Employment Dynamics. They examine how job
characteristics and human resource conditions facilitate a remote work arrangement.
They find that those workers who perform routine, manual, interactive tasks or somecombinations of these tasks are less likely to work remotely than those workers who
perform non-routine, non-manual, non-interactive tasks or some combinations of thesetasks. They also document that pay-for-performance, key performance indicators, andmanagement by objectives have positive impacts on the likelihood of remote work
experience. Given that these human resource management practices are adopted typicallywhen outputs are measured and observed accurately, this renders support to the claim thatthe availability of output measures is a critical factor for remote work. However, itremains unclear whether such management practices per se or the ability to implement
such management practices allow firms to arrange work-from-home environments. We
try to offer some insight into this issue based on our empirical results. In addition to theanalysis using the aggregate measure of management practices, we decompose

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

management practices into a set of promotion, bonus, and dismissal practices (humanresources) and a set of monitoring and targeting practices (non-human resource
management) and examine how different management practices impact the adoption of
telework differently.
Bloom et al. (2015) examine the experimental data on the employees of a
Chinese travel agency and document that working from home is a management practicefor raising productivity. This productivity improvement comes from the flexibility andconvenience of working from home, such as flexible working time and styles. According
to the study by Kawaguchi et al. (2021), firms that arranged remote work environments
prior to the onset of the COVID-19 pandemic were able to substantially mitigate thenegative impacts on their sales, and firms that increased workers who worked from homeduring the pandemic were also able to cope with the crisis. Telework can be considered
one of the management practices that have a significant impact on firm performance.
Given that management practices tend to be complementary to each other, our study canbe viewed as examining whether telework requires other complementary management
practices.
3. DataThis study focuses on management and technology factors and examines whether andhow these two factors prompt establishments to arrange and promote teleworkenvironments. To answer this question, we construct empirical measures of telework,
management, and technology from the 2020 JP MOPS. This survey was conducted
between January 25th and March 12th of 2021 by adopting a similar survey protocol
employed in the previous waves of the JP MOPSs (See Kambayashi et al., 2021 for the
results from the previous JP MOPSs). Survey questionnaires were mailed to 40,000
manufacturing establishments with at least 30 workers, and 4,344 establishments
responded to the survey questions by mail or online. The response rate of this survey is
10.9 percent.
Survey respondents were instructed to answer most survey questions as ofJanuary 2020, December 2020, and 2015, respectively. COVID-19 infection was notwidespread in Japan as of January 2020, whereas various preventive measures were
imposed on social and economic behaviors and activities as of December 2020１. Thus,
the January 2020 part of the survey answers provides information on establishments prior
to the onset of the COVID-19 pandemic, and the December 2020 part provides this piece

１

The first victim of covid-19 in Japan was discovered on 13th Feb. 2020.
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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

of information posterior to the onset of the COVID-19 pandemic. This allows us to follow
changes in establishment choices and behaviors before and after the pandemic as well as
to examine management and organizational issues related to the COVID-19 pandemic.
Our key dependent variable is the establishment’s adoption of telework. The
2020 JP MOPS contains information on the telework arrangements of each establishment.
Survey respondents were asked to answer what percentage of workers work from home
at least once a week for each of the five worker categories (managers, IT and data
specialists, other specialists, full-time workers, and part-time workers). We use this pieceof information to construct two empirical measures that capture the adoption andimplementation of telework at the establishment level. One measure is a dummy variableindicating whether an establishment adopts some form of telework or not at a given point
in time. This variable takes the value of 1 if some workers in at least one worker category
work from home, and the value of 0 if no workers in all the five categories work fromhome. The other measure is a dummy variable indicating whether an establishment
increases the percentage of workers who work from home in at least one category ofworkers between two reference time points. This variable takes the value of 1 if anestablishment increases the percentage of workers who work from home, and the valueof 0 otherwise.
The quality of management is one of our key independent variables in this study.
In the 2020 JP MOPS, survey respondents were instructed to answer a total of 16
management questions regarding monitoring, targeting, bonuses, promotion, anddismissal practices. We follow the procedure employed in the management survey
literature (Bloom et al., 2019, for US MOPSs; Kambayashi et al., 2021, for JP MOPSs)
and use this piece of information to construct a measurement of management quality foreach establishment. More specifically, we evaluate a respondent’s answer to each of thesemanagement questions on a scale of 0 to 1 from the viewpoint of incentives andproduction efficiency and calculate a simple average of scores from the 16 questions. Thisaverage score is called the “management score” and is intended to capture the quality ofmanagement. Past studies have shown that management scores are positively associatedwith establishment/firm performances such as productivity, survival, and profitability(Bloom and Van Reenen, 2007; Bloom et al., 2019; Kambayashi et al., 2021). In thisstudy, we interpret a high management score as indicating that an establishment ismanaged well.
We also use the information on each establishment’s technology in the 2020 JP
MOPS. Survey respondents were instructed to choose to what extent their establishment
introduces and uses the Internet of Things devices (IoT), Artificial Intelligence (AI), and6

ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

3D CAD/CAM, respectively. For example, for the Artificial Intelligence survey question,
they were asked to choose one answer from the following four alternatives: “1. Anestablishment has not yet introduced AI and does not plan to introduce AI,” “2. Anestablishment has not yet introduced AI but plans to introduce AI,” “3. An establishment
has already introduced AI in some process,” and “4. An establishment has alreadyintroduced AI in most processes.” From such information, we construct a dummy variableindicating whether an establishment has already introduced each of these technologies ornot. We also construct a “technology score” by evaluating the usage of each technology
on a scale of 0 to 1 and taking their simple average. A high technology score is interpreted
as indicating that establishments introduce and utilize IoT, AI, and 3D CAD/CAM in theirproduction process to a great extent.
4. Results
4.1 Summary Statistics
We first use the telework dummy variable to see how prevalent telework arrangements
are in our sample of Japanese manufacturing establishments. Table 1A presents thepercentages of the establishments where some workers work from home at least once aweek for January 2020, December 2020, and 2022. Note that the 2020 JP MOPS wasconducted in early 2021 and telework data for 2022 are regarding establishments’ plans
for arranging telework environments in 2022.
Two features are noteworthy in Table 1A. First, most establishments in our
sample did not adopt telework at all in January 2020 and December 2020, and they did
not plan to do so in 2022. As of January 2020, less than 10 percent of the establishments
arranged once-a-week telework in each of the five worker categories. As the teleworkdummy indicates, only about 10 percent of the establishments arranged once-a-weektelework for workers in at least one worker category. Compared with other studies(Cabinet Office Japan, 2022; Kawaguchi and Motegi, 2021), the percentage ofestablishments adopting telework in our sample is low. One possible reason for this isthat our sample comes from the manufacturing sector, where work-from-home is not as
feasible and efficient as other sectors (Dingel and Nieman, 2020). Another possible
reason is that our telework data are linked to establishments, not to individual workers.
The second feature is that the percentage of establishments adopting once-a-weektelework more than doubled in most worker categories from January 2020 to December
2020 and is expected to keep increasing in 2022. For example, this telework percentage
for managers increased to 17.1 in December 2020 (after the onset of the COVID-19

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

pandemic) from 7.7 in January 2020 (before the onset of the COVID-19 pandemic), and
it is expected to increase up to 19.5 in 2022.
Table 1B summarizes changes in each establishment’s telework arrangements
between January 2020 and December 2020. According to Table 1B, 9.7 percent of theestablishments did not adopt telework for managers as of January 2020 but adopted it asof December 2020. This number ranges from 1.9 percent (part-time workers) to 9.7
percent (managers). We can thus see that the COVID-19 pandemic acted as a catalyst for
the adoption of telework at the establishment level. This is consistent with other studies(Cabinet Office Japan, 2022; Kawaguchi and Motegi, 2021). Table 1B also reports thepercentages of establishments that increased telework percentage for each worker
category between January 2020 and December 2020.
[Table 1 here]
Table 1C reports the percentages of workers at each establishment who workfrom home, conditional on some form of telework arranged (i.e., the telework dummytakes the value of 1). As we can see, the average percentage of workers at eachestablishment who work from home increased between January 2020 and December 2020in all the worker categories except the full-time worker category. Notice that this average
percentage is affected by composition effects because the composition of establishments
arranging telework differs between January 2020 and December 2020. Our close
examination reveals that the percentage of workers who work from home is much smaller
for the establishments that did not arrange telework in January 2020 but arranged
telework in December 2020 than the establishments that arranged telework at both points
in time. Even though the latter type of establishment, on average, increased the percentage
of full-time workers who work from home, the composition effect from the former typeof establishment is strong enough to drive down the overall average percentage of fulltime workers who work from home. We see a similar pattern when using the medianpercentage of workers who work from home.
We next look at summary statistics on management scores and technology
variables. Table 2A reports summary statistics on management scores, human resource
management scores, and non-human resource management scores. As explained above,
management scores are the overall average scores from the 16 management questions.
We break management scores up into human resource management scores and nonhuman resource management scores. The former scores are the average scores from bonus,
promotion, and dismissal practices questions, whereas the latter scores are the average

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

scores from monitoring and targeting practices questions. The average management scoreis 0.503, which can be interpreted as indicating that a typical establishment in the sample
adopts about 50 percent of the “best” management practices that are evaluated on thebasis of incentives and efficiency. The maximum management score is 0.901, and theminimum management score is 0. Consistent with the results from past studies, themanagement scores vary considerably across the establishments in the sample. The
average human resource management score is 0.627, and the average non-human resource
management score is 0.523. Both scores also vary considerably across the establishmentsin the sample.
Table 2B presents summary statistics on technology variables. As of 2015, 70.8
percent (= 62.5 + 8.3) of the establishments in the sample did not adopt IoT. This number
falls to 59.3 percent as of January 2020 and 57.1 percent as of December 2020. While themain mode of IoT adoption was a client-server system throughout all the periods, thepercentage of using a cloud system rose over time. We can see a similar pattern regarding
the adoption and use of 3D CAD/CAM. Many of the establishments did not adopt 3D
CAD/CAM throughout all the periods. When it was adopted, it was utilized in some partsof the process. Our data show that only a few establishments use AI. As of December
2020, less than 10 percent of the establishments in the sample adopted AI. To evaluateeach establishment's overall technology adoption, we score each technology variable andconvert the three technology variables into a single index, “technology score”. As of
December 2020, the average technology score is 0.245 and the median technology scoreis 0.220. This also indicates that most establishments in the sample did not adopt
advanced technologies.
[Table 2 here]
4.2 Analysis of the Relationship between Telework, Management, and Technology
We first examine the relationship between telework and management by using logit
model estimations. In the first specification, the dependent variable is the teleworkdummy variable, and the main independent variable is the management score. Rather thantaking the individual unobservable effects of establishments, in the second specification,
we replace the dependent variable with a dummy variable indicating whether an
establishment increases the percentage of workers who work from home between January
2020 and December 2020. The number of employees, a year established, a dummy forheadquarters, and dummies for three-digit industry codes are also included as control
variables in the estimation equation.

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

Table 3 reports the results from logit model estimations. The estimation result
for the first specification shows that the coefficient on the management score is positiveand statistically significant at the conventional significance level for all the reference time
points. Thus, the telework dummy is positively associated with the management score.
We have qualitatively the same result for the second specification. The coefficient on the
management score is 2.16, and statistically significant at the one percent significancelevel. In this estimation, the telework dummy includes establishments that had alreadyadopted telework as of January 2020 as well as establishments that had not yet adopted
telework as of January 2020. When the sample is limited to establishments that did not
adopt telework at all as of January 2020 (before the onset of the COVID-19 pandemic),
the coefficient on management score shrinks to 2.05, and is statistically significant at theone percent significance level.
The estimation results above indicate that well-managed establishments are more
likely to have telework arrangements in place at a given point in time and that wellmanaged establishments are more likely to introduce telework and expand the percentageof workers who work from home between January 2020 and December 2020. The needfor telework had changed dramatically as the COVID-19 infection rapidly spread. It isthus a possible case that well-managed establishments responded to this need and thattelework and good management are complementary relations (See also Table 5 andfurther discussion below).
[Table 3 here]
The management score may serve as a proxy variable for other establishment
characteristics in our estimations. We are especially concerned that advanced
technologies enable establishments to arrange telework relatively easily, and wellmanagement establishments are more likely to adopt advanced technology than poorly
managed establishments.
Table 4 presents estimation results for the relationship between management andadvanced technologies. In this estimation, the dependent variables are dummies for the
adoption of each technology and the technology score for December 2020. In all the
specifications, the coefficient on the management score is positive and statisticallysignificant at the one percent significance level. Indeed, well-managed establishments are
more likely to adopt IoT, AI, and 3D CAD/CAM and have higher technology scores thanpoorly managed counterparts.

10

ESRI Discussion Paper Series No.375"Telework, Management, and Technology"[Table 4 here]
Given that the management score is positively correlated with the technology
variables, we estimate the relationship between telework and management by adding thetechnology score to the baseline estimation model. Table 5 presents estimation resultsfrom logit model estimations using the January 2020 and December 2020 data. The
coefficient on management score is positive in all the specifications and statisticallysignificant at the one percent significance level in all the specifications except January
2020. The positive correlation between management and telework still holds even after
the technology factor is controlled for. The estimation results show that both themagnitude and statistical significance of the management score are larger for December
2020 than for January 2020. As we discussed above, this also renders support to theinterpretation that well-managed establishments responded to the need for teleworkduring the COVID-19 pandemic. The technology score is also positively correlated withthe telework dummy, indicating that establishments with advanced technologies are more
likely to adopt telework than establishments without them.
[Table 5 here]
What management practices matter for the positive relationship with thearrangement of telework? To answer this question, we decompose the management score
into human resource management score and non-human resource management score anduse them as the main independent variable in the estimation specification used in Table
5.
Table 6A presents estimation results for the human resource management score.
While the coefficient on the management score is statistically insignificant for January
2020, it is positive and statistically significant at the conventional significance level for
December 2022 and pooled data. Table 6B reports estimation results for the non-humanresource management score and shows qualitatively the same results as the ones in Table
6B. Thus, both human resource and non-human resource management practices havepositive impacts on the arrangement of telework. In both cases, the impact of the
management score on the arrangement of telework is smaller, compared with the overallmanagement score.
[Table 6 here]

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

We further investigate whether good management facilitates establishments toadopt telework by exploiting panel aspects of our data. For this purpose, the sample isrestricted to establishments that had not yet adopted telework as of January 2020. The
establishments in this subsample were likely to face similar needs for adopting telework,
arising from the rapid spread of COVID-19 infection, and were markedly different fromestablishments that had already adopted telework before the COVID-19 pandemic. In thisestimation, the dependent variables are telework dummies for December 2020 and 2022
(plan), and the main independent variable is a change in management scores over time.
Table 7 reports estimation results for the relationship between the telework
dummies and management score. First, as consistent with the results from the full sample,
the telework dummies are positively associated with the management score for thissubsample. Second, the telework dummy variables are not associated with a change inmanagement score between January 2020 and December 2020. This also applies to achange in technology score between January 2020 and December 2020. Finally, thecoefficients on a change in management score between 2015 and January 2020 are
positive and statistically significant at the conventional significance level.
The results above indicate that short-run improvement in management practices
is not positively associated with the arrangement of telework, but long-run improvement
in management practices is positively associated with the adoption of telework. Past
studies about management reveal that a management structure is stable over time and is
hard to improve continuously. One interpretation of this result is that an establishment’s
capability to build a structured management system and operate it smoothly has positive
impacts on the adoption of telework (not management practices per se), although further
investigation is required to test this hypothesis.
[Table 7 here]
5. Conclusion
We empirically examined what factors facilitate remote work arrangements by using thedata from the 2020 JP MOPS. Our empirical investigation revealed several relationships
between telework, management, and technology. First, well-management establishments
tend to adopt and use telework. This relationship holds true even after technology factors
are controlled for. Second, both human resource and non-human resource management
practices have positive impacts on the arrangement of telework. Third, a long-run
improvement in the quality of management is positively associated with teleworkadoption, but we do not observe such a relationship for a long-run improvement in the12

ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

quality of management. Finally, technology factors are strongly correlated with the
adoption of telework.
These results provide several fresh insights into potential drivers for remote work
arrangements. While past studies have examined the roles played by work environments
in remote work arrangements, many of such studies have lacked systematic analyses
mainly because a unit of analysis is not an establishment or a firm, but rather an individual
or an occupation. This study exploited establishment-level variation in management andtechnology and showed that these factors had positive impacts on the arrangement oftelework. This study thus points to the importance of establishment-level characteristics
such as work environments in determining telework arrangements.
By the nature of our measure of management, a high management score indicatesthat an establishment employs objective goals and evaluations and it manages production
processes and human resources efficiently. As discussed by Groen et al. (2018) and
Kawaguchi and Motegi (2021), our result can be intercepted along with the hypothesisthat objective management and reliable monitoring make output controls easily
implementable, and this, in turn, facilitates telework arrangements. However, ourmeasure of management is not linked directly to incentives and work environments
designed for telework arrangements. In addition, only a long-run improvement in thequality of management has an impact on the adoption of telework. These findings lead us
to infer that the organization’s ability to design, build and implement good management
practices is a critical factor for the adoption of telework. In other words, as Bloom et al.
(2015) pointed out, telework is a good management practice and is complementary withother management practices.
It is also worthwhile to emphasize the strong positive association of thetechnology variables with the adoption of telework. Our result carries two interpretations,though they are not mutually exclusive. One interpretation is that our technology
variables capture how easily workers can work from home. According to thisinterpretation, advanced technology is one of the establishment characteristics thatenables workers to work from home easily. The other interpretation is that establishmentswith advanced technology possess a tendency and capability to arrange teleworkenvironments. This trait might allow establishments to respond timely to the need fortelework caused by the spread of COVID-19 infection.
This study is descriptive in nature and is unable to disentangle one mechanismof telework arrangements from another one. Therefore, the findings from this study
should be interpreted as suggestive, and further investigation is required to draw
definitive conclusions. Nonetheless, this study exploits variation in establishment

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characteristics and sheds light on the relationships between telework, management, andtechnology that past studies have not examined sufficiently. This study also shows that
establishment heterogeneity regarding management and technology can explain an
unexplained part of the variation in telework arrangement within a given occupation.

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References
Adams-Prassl et al., "Work that can be done from home: evidence on variation within
and across occupations and industries," Labour Economics, 2022, Vol. 74, 102083.
Bloom, N., E. Brynjolfsson, L. Foster, R. Jarmin, M. Patnaik, I. Saporta-Eksten and J.
Van Reenen, "What drives differences in management practices? " American Economic
Review, 2019, 109(5), pp.1648-1683.
Bloom, N., J. Liang, J. Roberts, and Z. J. Ying, "Does working from home work?
Evidence from a Chinese experiment," Quarterly Journal of Economics, 2015, 122(4),pp.1351-1408.
Bloom, N. and J. Van Reenen, "Measuring and explaining management practices acrossfirms and countries," Quarterly Journal of Economics, 2015, pp.165-218.
Dingel and Nieman, "How many jobs can be done at home," Journal of Public
Economics, 2020, Vol.189, 104235.
Groen, B.A.C., S.P. van Triest, M. Coers, and N. Wtenweerde, "Managing flexible
work arrangements: Teleworking and output controls," European Management Journal,
2018, 36, pp.727-735.
Kambayashi, R., Ohyama, A., and N. Hori, "Management Practices and Productivity inJapan: Survey Evidence from Six Industries in JP MOPS," Journal of the Japanese andInternational Economies, 2021, Vol.61(Sep).
Kawaguchi, D., S. Kitao and M. Nose, "The impact of COVID-19 on Japanese firms:
Mobility and resilience via remote work," International Tax and Public Finance,
forthcoming.
Kawaguchi, D., and H. Motegi, "Who can work from home? The roles of job tasks andHRM practices," Journal of the Japanese and International Economies, 2021, Vol.62(Dec).
OECD, "Teleworking in the COVID-19 pandemic: Trends and prospects," OECD
Policy Responses to Coronavirus (COVID-19), 2021, OECD Publishing, Paris.

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

TablesTable 1A: Percentages of establishments arranging telework by worker category
January 2020

December 2020

Some

No telework

No telework

telework

2022 (plan)

Some

Some

No telework

telework

telework

Managers

92.3

7.7

82.9

17.1

80.5

19.5

IT&Data specialists

96.3

3.7

91.2

8.8

88.5

11.5

Other specialists

96.4

3.6

91.1

8.9

89.2

10.8

Full-time workers

94.8

5.2

86.4

13.6

85.4

14.6

Part-time workers

97.2

2.8

95.3

4.7

94.6

5.4

Telework dummy

89.4

10.6

76.4

23.6

74.2

25.8

Note) Telework dummy takes “Some telework” (the value of 1) if some workers in at least one worker category
work from home, and takes “No telework” (the value of 0) if no workers in all the five categories work from home.

Table 1B: Percentages of establishments that increase telework by worker category
A change between January and December 2020
From no telework to

Increase in teleworksome telework

percentage

Managers

9.7

10.9

IT&Data specialists

5.2

5.8

Other specialists

5.3

5.9

Full-time workers

8.2

8.6

Part-time workers

1.9

2.1

Table 1C: Average and median percentages of telework by worker category
January 2020

December 2020

Mean

Mean

Median2022(plan)MedianMeanMedianManagers

12.7

10.0

16.3

10.0

18.9

10.0

IT&Data specialists

11.1

4.0

22.8

7.0

23.5

10.0

Other specialists

18.2

8.5

20.3

10.0

21.6

10.0

Full-time workers

37.2

25.0

23.6

10.0

25.8

10.0

Part-time workers

18.3

10.0

18.1

10.0

19.2

10.0

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

Table 2A: Summary statistics on management score
January 2020
MinManagement score

25thMedian

Mean

75th

Max

Observations

0.000

0.401

0.510

0.503

0.613

0.901

3,935

Human resource management score

0.000

0.417

0.627

0.578

0.750

1.000

3,935

Non-human resource management score

0.000

0.380

0.523

0.523

0.665

1.000

3,935

0.000

0.000

0.220

0.227

0.387

1.000

4,190

Technology score

December 2020
MinManagement score

25thMedian

Mean

75th

Max

Observations

0.000

0.3969

0.505

0.500

0.611

0.901

3,939

Human resource management score

0.000

0.415

0.604

0.567

0.750

1.000

3,939

Non-human resource management score

0.000

0.380

0.529

0.524

0.666

1.000

3,939

0.000

0.000

0.220

0.245

0.413

1.000

4,190

Technology score

Table 2B: Summary statistics on technology
IoT

2015

January 2020

December 2020

Do not adopt, and do not plan to do so

62.5

47.8

43.0

Do not adopt, but plan to do so

8.3

11.5

14.1

Adopt stand alone peer-to-peer system

7.0

7.7

7.9

Adopt client server system

17.5

21.3

21.3

Adopt cloud system

4.8

11.7

13.7

AI

2015

January 2020

December 2020

Do not adopt, and do not plan to do so

90.7

80.1

75.3

Do not adopt, but plan to do so

5.6

11.9

15.7

Adopt in some processes

3.3

7.7

8.7

Adopt in most processes

0.3

0.3

0.3

3D CAD/CAM

2015

January 2020

December 2020

Do not adopt, and do not plan to do so

63.7

57.6

55.3

Do not adopt, but plan to do so

6.1

4.5

5.8

Adopt in some processes

25.8

32.4

33.2

Adopt in most processes

4.4

5.5

5.7

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

Table 3: Relationship between telework and management(2) Telework increase dummy(1) Telework dummy

Management Score

Controls
Observations

from Jan. 2020 to Dec. 2020
Increase in

From notelework

telework topercentage

some telework

2.562***

2.160***

2.046***(0.300)(0.294)(0.364)(0.404)

Yes

Yes

Yes

Yes

January

December

2020

2020

1.431***

1.938***(0.404)
Yes
3,216

2022

3,237

3,178

3,143

2,831

Notes: (i) The 2020 JP MOPS data are used. (ii) Coefficients are estimated by logit model. (iii) Numbers in parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in a t-test for coefficients; *<10% , **<5%, and***<1%.

Table 4: Relationship between management and technology
Dependent variables：Technology variables (December 2020)
Technology

IoT

AI

3D CAD/CAM

3.176***

3.707***

1.790***(0.249)(0.426)(0.268)(0.023)

Model

Logit

Logit

Logit

OLS

Controls

Yes

Yes

Yes

Yes

Management score

Observations

3,937

3,801

3,808

score
0.404***

3,822

Notes: (i) The 2020 JP MOPS data are used. (ii) Coefficients are estimated by logit model or OLS. (iii) Numbers in parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in a t-test for coefficients; *<10% , **<5%, and***<1%.

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ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

Table 5: Relationship between telework, management, and technology
Dependent variable：Telework dummy(1) January 2020

(2) December 2020

0.787*

1.307***

1.032***

1.591***(0.420)(0.312)(0.269)(0.588)1.552***

1.622***

1.418***

3.103***(0.269)(0.201)(0.170)(0.414)

Model

Logit

Logit

Pooled logit

Random logit

Controls

Yes

Yes

Yes

Yes

Observations

3,209

3,226

5,900

5,900

Management score

Technology score(3) January and December 2020

Notes: (i) The 2020 JP MOPS data are used. (ii) Coefficients are estimated by logit model. (iii) Numbers in parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in a t-test for coefficients; *<10% , **<5%, and***<1%.

Table 6A: Relationship between telework and human resource management
Dependent variable：Telework dummy(1) January 2020
Human resource(2) December 2020

(3) January and December 2020

0.312

0.675***

0.511***

0.680*(0.281)(0.204)(0.176)(0.380)1.644***

1.747***

1.512***

3.268***(0.264)(0.197)(0.165)(0.409)

Model

Logit

Logit

Pooled logit

Random logit

Controls

Yes

Yes

Yes

Yes

Observations

3,209

3,226

5,900

5,900

management score

Technology score

Notes: (i) The 2020 JP MOPS data are used. (ii) Coefficients are estimated by logit model. (iii) Numbers in parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in a t-test for coefficients; *<10% , **<5%, and***<1%.

19

ESRI Discussion Paper Series No.375"Telework, Management, and Technology"

Table 6B: Relationship between telework and non-human resource management
Dependent variable：Telework dummy(1) January 2020
Non-human resource(2) December 2020

(3) January and December 2020

0.412

0.775***

0.562***

0.967**(0.333)(0.251)(0.212)(0.475)1.603***

1.678***

1.465***

3.182***(0.267)(0.199)(0.167)(0.416)

Model

Logit

Logit

Pooled logit

Random logit

Controls

Yes

Yes

Yes

Yes

Observations

3,209

3,226

5,900

5,900

management score

Technology score

Notes: (i) The 2020 JP MOPS data are used. (ii) Coefficients are estimated by logit model. (iii) Numbers in parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in a t-test for coefficients; *<10% , **<5%, and***<1%.

Table 7: Effects of short-run and long-run management improvement
Dependent variable：telework dummy (December 2020)
Management score (January 2020)

1.399***(0.425)

Management score difference

0.245

between January 2020 and December 2020

(1.185)

Management score difference

1.500**

between 2015 and January 2020
Technology score (January 2020)(0.624)
1.668***(0.266)

Technology score difference
between January 2020 and December 2020
Observations

2,826

1.219

1.064

(0.966)(0.977)

2,820

2,778

Notes: (i) The 2020 JP MOPS data are used. (ii) Coefficients are estimated by logit model. (iii) Numbers in parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in a t-test for coefficients; *<10% , **<5%, and***<1%.

20