WORKING PAPER · NO. 2021-56

Work from Home & Productivity:
Evidence from Personnel & Analytics Data
on IT Professionals
Michael Gibbs, Friederike Mengel, and Christoph Siemroth
JULY 2021

5757 S. University Ave.
Chicago, IL 60637
Main: 773.702.5599
bfi.uchicago.edu

Work from Home & Productivity: Evidence from
Personnel & Analytics Data on IT Professionals∗
Michael Gibbs†

Friederike Mengel‡

Christoph Siemroth§

July 13, 2021

Abstract
We study productivity before and during the working from home [WFH] period of the Covid-19
pandemic, using personnel and analytics data from over 10,000 skilled professionals at a large AsianIT services company. Hours worked increased, including a rise of 18% outside normal business hours.
Average output declined slightly, thus productivity fell 8-19%. We then analyze determinantsof changes in productivity. Employees with children at home increased work hours more andhad a larger decline in productivity than those without children. Women had a larger decline inproductivity, while those with longer company tenure fared better. An important source of changes
in WFH productivity is higher communication and coordination costs. Time spent on coordination
activities and meetings increased, while uninterrupted work hours shrank considerably. Employees
communicated with fewer individuals and business units, both inside and outside the firm. They
also received less coaching and 1:1 meetings with supervisors. The findings suggest key issues forfirms to address in implementing WFH policies.
Keywords: Collaboration, Coordination, Covid-19 Pandemic, Productivity, Remote Working,Telecommuting, Working From Home, Work Hours, Work TimeJEL Classification: D2, M5.

∗

We are grateful to several employees of the company who spent a great deal of time helping us collect the data
and understand the firm and context, during a difficult period of time. We thank the Tata Center for Development
at the University of Chicago for funding that helped cover the cost of WPA licenses. We appreciate helpful commentsfrom Sonia Bhalotra, Ron Burt, Matthew Clancy, Hans Peter Grüner, Emma Harrington, Kathryn Ierulli, Alan Kwan,
Duk Gyoo Kim, Michael Lotus, Adam Ozimek, Tim Perri, Paul Smeets and Wim Van der Stede, and from seminar
participants at the University of Mannheim and the University of Essex.
†
University of Chicago & IZA. E-mail: gibbs@uchicago.edu.
‡
University of Essex, Department of Economics, Wivenhoe Park, Colchester, CO4 3SQ, UK and Department of
Economics, Lund University, Tycho Brahes vaeg 1, Lund, Sweden. E-mail: fr.mengel@gmail.com.
§
University of Essex, Department of Economics, Wivenhoe Park, Colchester, CO4 3SQ, UK. E-mail:christoph.siemroth@essex.ac.uk.

1Introduction

Working from Home [WFH] has been rising for years, as more occupations use computers and telecommunications, more people have reliable home Internet connections, and more families have both parents
working full time. The Covid-19 pandemic accelerated this process by forcing a large fraction of the
global workforce to switch to WFH at least temporarily. Compared to Working from the Office [WFO],WFH has the potential to reduce commute time, provide more flexible working hours, increase job
satisfaction, and improve work-life balance. However, little is yet known about some of the more
fundamental consequences of WFH, including its effects on productivity and which factors play a rolein making WFH more or less productive than WFO (WSJ, 2020; Financial Times, 2021b).
In this paper we provide an analysis of the effects of WFH in a large Asian IT services company.
The company abruptly switched all employees from WFO to WFH in March 2020, in response to thelargely unanticipated pandemic shock. Our study has several novel and interesting features.
The industry and occupations analyzed here are among those predicted to be most amenable to
WFH. The employees are highly-skilled professionals in an information technology company where ahigh degree of work has always been computer driven. At the same time they are some of the mostdifficult to analyze. The jobs involve significant cognitive work, collaboration on teams, working withclients, and innovation. Productivity is hard to measure for such professions. WFH for occupations
with such characteristics has not previously been studied with non-survey data.
For a panel of over 10,000 employees and a period of 17 months including both WFO and WFH,
we obtained unusually rich data data from the company’s personnel records and workforce analytics
systems. These include each employee’s key output and work hours, which provide a natural measureof productivity. For a sub-sample, the firm provided data on how employees allocated time between
work tasks. This includes meetings, collaboration and time focused on performing work without
distractions. It also includes information on networking activities (contacts) with colleagues both
inside and outside the firm. We also have employee experience, tenure, age, commute time (for
WFO), gender, and the number of children at home.
These data provide a unique opportunity to obtain a measure of productivity for this complex
type of work, and to investigate the determinants of productivity during WFH. We analyze how
WFH productivity varied by employee characteristics, whether or not children were at home, andcommute time. We also analyze how it varied with the nature of the work: the extent of collaboration,
networking, supervision and coaching. Our analysis of the productivity differences between WFH and
WHO provide valuable insights about the issues that are likely to be most important when designing
future WFH schemes.
Our findings are presented in two parts. We first analyze how average work time, output, andproductivity changed during WFH. With that foundation, we then analyze what drives those changes
and which employees are more affected. We consider the role of both employee and job characteristics
and study extensively changes in working patterns induced by WFH.
We find that employees significantly increased average hours worked during WFH. Much of thiscame outside of normal office hours. At the same time, there was a slight decline in output as measured
2

by the employee’s primary performance measure. Combining these, we estimate that productivity
declined by 8-19%. These results are consistent with employees becoming less productive during
WFH, and working longer hours to try to compensate.
Employees with children at home had a greater decline in productivity than those without, buteven those without suffered productivity losses. Moreover, women were more negatively affected by
WFH than men, but this gender difference was not due to the presence of children in the home. We
conjecture that it might be due to other demands placed on women in the domestic setting whileworking from home.1
Employees with lower company tenure decreased output slightly during WFH, whereas output
remained about the same for those with longer tenure. This is separate from age or experience effects.
This suggests that employees who are more adapted to firm culture and processes are better able toperform in WFH, where there is no colleague at the next desk for quick help or advice.
WFH also affected working patterns in substantial ways. Employees spent more time participatingin various types of meetings, but less time in personal meetings with their manager or receiving
coaching. They engaged in fewer contacts with colleagues inside and outside of the firm. At thesame time, they had less “focus time,” i.e., uninterrupted time to perform tasks. All of these factors
were significantly correlated with the productivity changes due to WFH. These were not temporary
adjustments to a switch to WFH, but persisted over time. These findings suggest that increased
coordination costs during WFH at least partially explain the drop in productivity.
A potential concern is that the pandemic affected estimates of productivity changes during WFH.
However, several pieces of evidence suggest that this is not a major concern. First, the effects onwork time and productivity begin immediately at the move to WFH, not gradually as the pandemic
developed. Second, the decline in productivity is also observed among employees without children at
home, though to a lesser degree, so the detrimental productivity effects are not solely driven by school
closures. Third, changes in work time and productivity do not correlate with the evolution of thepandemic, such as the rate of infections or easing of lockdown measures. Fourth, there is a decline,
not an increase, in sick days during WFH. Finally, as with many information technology firms, thecompany’s economic performance was quite strong throughout the pandemic, so employees were not
at more risk of job loss.
The evidence presented below provides important insights into how WFH may vary across different
types of occupations and firms employing a blended WFH / WFO approach. Our analyses indicate that
communication, coordination and collaboration are more costly in a virtual work setting. This is likely
to present a significant challenge to WFH in occupations where such aspects are important, especiallyfor less experienced employees. While WFH is likely to remain a feature of modern workplaces,
some aspects of in-person interactions cannot easily be replicated virtually, including the quality of
1

In the Western context it has often been reported that the burden of childcare and home-schooling disproportionately
affected women during the pandemic (Financial Times, 2021a). In the country from which our data are drawn, extended
families often live together, and middle and upper class families often have domestic staff. Having extended family andstaff at home can provide help with childcare, but may place other demands on women at home whether or not children
are present.

3

collaboration and coaching, and “productive accidents” that arise from spontaneously meeting people(including those with whom there is not yet a working relationship).

2

Literature

Our research contributes to a broad agenda in economics trying to understand determinants of individual productivity. A significant amount of work has focused on incentive pay (e.g., Lazear, 2000;
Hamilton et al., 2003; Shearer, 2004; Babcock et al., 2015; Friebel et al., 2017; Aakvik et al., 2017;
Dohmen and Falk, 2011). Some research looks at effects of other human resource practices, particularly
those aimed at eliciting employee participation in continuous improvement, and on complementarities
between these policies (Ichniowski et al., 1995; Ichniowski and Shaw, 2003; Bartel et al., 2007). Thereis limited research in other areas, such as ways to engage employees in innovation (Gibbs et al., 2017).
Some literature studies the effects of supervisors (Lazear et al., 2015) or peers (Bandiera et al., 2005;
Arcidiacono et al., 2017; Song et al., 2018). Presumably peer effects would be weaker during WFH as
there is no face-to-face interaction and probably less overall interaction. Supervisor effects might bestronger, if managers vary significantly in their ability to lead and coordinate virtual teams.
A growing literature analyzes working from home policies. At the start of the pandemic, a few
papers predicted the likelihood that a job would shift from WFO to WFH, typically using descriptionsof occupations in classifications such as O*NET (e.g., Dingel and Neiman, 2020; Adams-Prassl et al.,2020). The industry and occupations analyzed here are among those predicted most likely to effectively
switch to WFH.
Several surveys document incidence of WFH, and perceptions of its effects (Bick et al., 2020;
Brynjolfsson et al., 2020; Von Gaudecker et al., 2020; Gottlieb et al., 2021; Hensvik et al., 2020).
Professionals, managers, knowledge workers, those in clerical support or data processing, and thosewith higher education or income make more use of WFH. In the UK Household Longitudinal Survey,
employees who work from home state that they are about as productive as in the office (Etheridge
et al., 2020). Those who perceive declines also experience lower levels of well-being from WFH.
Bellmann and Hübler (2020) find that working remotely has no long-run effect on work-life balance,
and that a switch to WFH increases job satisfaction only temporarily. Barrero et al. (2020) estimatethat WFH reduced total commuting time among US workers by more than 60 million hours per work
day at the height of the pandemic, and that about 35% of this time saved was reallocated to work.
We do not find that commute time predicts increases in WFH work hours.
Barrero et al. (2021) provide evidence from waves of a large panel of US employees working fromhome. Respondents report benefits from lower commute time, more flexible work hours, and increased
productivity. Employers have made investments in technology, revised practices, and moved up thelearning curve with respect to WFH. They suggest that use of WFH will remain four times moreprevalent than before the pandemic.
DeFilippis et al. (2020) use communication and email meta-data. Their estimate that WFH
employees work 0.8 more hours per day is between estimates from our two time measures. They also4

find that employees attend more meetings, with more attendees. Teevan et al. (2020) present similar
evidence for Microsoft employees. Kwan (2021) analyzes reading of Internet content by employees ina very large sample of firms in ten countries. He uses IP addresses to identify when employees WFH,
and creates proxies for employee interactions and need for coordination. These proxies are negatively
associated with a shift to WFH.
Survey data on productivity might be biased if employees enjoy WFH and hope to retain the practice, conflate output with productivity, or refer to individual performance without full considerationof collaboration with others. Only a few papers have studied WFH using employee data. Bloom et al.
(2015) analyzed call-center employees at a Chinese firm. Output rose for those assigned to WFH,
partly because they worked more hours, and partly because productivity increased. Employee satisfaction increased, and attrition declined. Emanuel and Harrington (2021) studied call-center workers
at a large US company, including those who abruptly moved to WFH in response to Covid-19. Productivity rose in the switch to remote work. However, average productivity was lower for remoteworkers than office workers. They conclude that remote work has an adverse selection effect, andmore productive workers prefer to be at the office.
WFH may be relatively effective for call-center workers, who work independently and follow scripts.
Künn et al. (2020) analyze an occupation with extremely high cognitive demands but no collaboration:
professional chess players. They had lower quality performance when playing online during Covid-19,
compared to in-person tournaments. Of course, the highly unusual occupation makes it difficult togeneralize from their findings.
A policy that goes beyond WFH is Working from Anywhere [WFA], in which an employee isallowed to choose where they live and work. This might provide additional benefits in quality oflife, managing a dual-career couple, or real estate prices. Choudhury et al. (2019) study a naturalexperiment in which examiners at the US Patent and Trademark Office were allowed to work fromanywhere. Productivity rose by 4.4%, with no decline in the quality of work. However, they note thatthese jobs do not require significant collaboration and coordination.
None of these studies were able to analyze potential mechanisms underlying differences in productivity between WFH and WFO. This study is, to our knowledge, the first that is able to provide
evidence on this question.

3

Data and Empirical Strategy

The setting for this study is one of the world’s largest IT services companies, with over 150,000
employees who work with clients across the globe. While it employs workers in many countries, thegroup studied here are employed at its corporate campuses in the home country, a rapidly developing
Asian nation. The company provides a wide range of technology consulting and outsourcing services.
That includes outsourced product and process improvement and R&D to develop new products andservices.

5

The workforce is highly skilled and educated. Virtually all have at least a bachelor’s degree, often
in a technology field such as computer engineering or electronics. Most work at the company’s large,
modern corporate campuses in several cities of its home country. These campuses look and feel very
similar to what one sees at Microsoft, Apple or Amazon.
We were provided with anonymized employee data of various kinds, and executives spent considerable time explaining practices and business conditions. All authors have visited company headquarters.
A brief description of how the employees in our sample are evaluated is helpful. The company has
a semi-annual planning process that culminates in goals for each organizational level from the CEO
down. Ultimately the process leads to goals for each team, based on business unit objectives andexpectations about customer requests during the next 6 months. The team supervisor then sets goals
for each employee.2 Analytics systems (described below) are used to track progress against the plan
throughout the organization. Each manager is provided with reports for his or her unit. For example,
the CEO reviews the corporate-wide report at least once per month. Such a “data-driven” approachis common in technology firms (Brynjolfsson and McAfee, 2012).
Company executives told us that this process was not changed during the sample period. Most
customers are also information technology firms. That sector tended to perform well during the
pandemic. This firm and its clients largely continued expected business activities. All of this means
goals were not changed. Company financial statements reveal that total workforce size and revenue
both rose by more than 5% in 2020 compared to 2019, and profit margins rose even more. That isnoteworthy, since it also means that employees had no larger concerns about job loss.
These plans and goals form the basis of supervision. Informally, managers monitor employee work
time and performance across key applications and tasks, in order to better supervise and coach them.
This is supported by the analytics platforms described below. Formally, each employee is measured
by performance against the semi-annual goal on a key performance metric, which we will refer to as“Output.” He or she also receives quarterly supervisor feedback, and bi-annual subjective performance
ratings.
A notable advantage of this study is that the performance measure is relatively rigorous andobjective. The supervisor devises the key metric to reflect the most important aspect of the job, andthis is tracked with the analytics systems. For example, for a software engineer it might be the numberof code segments completed. Importantly, such a measure does not merely reflect quantity (which
would lead to obvious multitask incentive problems). Rather it is Output conditional on adequate
performance on other dimensions of the task, such as quality or customer satisfaction. For example,
code segments will not be counted as complete until they meet company and customer standards
for errors, speed, and functionality. Moreover, since the measure is output-based, it reflects various
employee inputs, including time, effort, skill development, client interactions, drawing upon colleagues
for advice, etc.
The employees in our sample do not receive incentive pay tied directly to this or any other measure. Compensation is comprised of salary, an annual merit bonus based on overall performance, and2

If an employee is reassigned, or a project is completed and the team begins a new one, this process is refreshed for
affected employees before the next cycle begins.

6

Table 1: Summary statistics for outcome variables
Mean

SD

1st Quartile

3rd QuartileN

WFO (pre March 2020)
Input
OutputProductivity

5.08
100.82
1.36

2.03
9.00
2.99

3.78
100.000.75

6.35
100.00
1.27

47387
47387
47387

WFH (post March 2020)
Input
OutputProductivity

7.04
100.30
1.11

2.75
8.80
2.41

5.38
100.000.52

8.90
100.00
0.88

22862
22862
22862

occasional small rewards for activities such as suggestion of valuable new ideas. That the performancemeasure is tied to the company’s business plan suggests employees are motivated to try to meet theirgoals, and our evidence confirms this. However, motivation is broader than this, due to supervisor
monitoring, feedback, subjective evaluation, merit pay, and the potential to earn a promotion (as istrue at most companies).

3.1

Main outcome variables

The company uses two systems, Sapience Analytics and IDMS (“Internal Data Management System”), to track employee activity and performance. Our three key outcome variables, Input, Output,
and Productivity, derive from these. Managers use analytics reports based on these data to support
decisions, goal-setting, monitoring, and performance evaluation. Because of its corporate-wide significance, the company has devoted substantial effort to making sure that the data are meaningful andreliable.
We obtained data for all employees from the R&D part of the firm who are managed via thesesystems. Data cover April 2019 through August 2020, resulting in a panel dataset with 10,384 uniqueemployees observed over 17 months. The company moved abruptly to WFH in March 2020 as Covid-19
became serious in that country.
Sapience Analytics software is installed on the employee’s computer. Sapience records the timethat an employee is working, by tracking applications or websites used, and whether the employee isactive (i.e., using the keyboard or mouse). If an employee procrastinates on a social media platform,
and this is not part of the job, that would not be recorded as work time. If a program from a predefined list of relevant tools (programming, collaboration, document production, communication, etc.)
is active, that is recorded as work time. It includes meetings entered in the Outlook calendar, which
is used throughout the company, even if these are face-to-face (off the computer). Sapience therefore
records effective work hours. If an employee sits at his or her desk for 8 hours a day, Sapience might
only record 5 hours, because breaks or surfing the web are not effective work time. Employees areaware that the software is used in this way.

7

Based on these data, we calculate the outcome variable Input, equal to average working hours per
working day that month. That is, we take the total time worked that month and divide it by the
number of working days (taking into account weekends and local holidays). In section 3.3 below, we
describe additional input measures that generate similar qualitative results; our findings are robust to
details of the definition of hours worked.
IDMS is a proprietary system by which the company tracks employee performance, including
the primary performance measure. That measure is normalized to make different jobs and roles
comparable.3 For example, key measures might be the number of code segments, code reviews, orreports delivered per month. In each case, these are divided by the monthly goal, multiplied by 100.
It is possible to complete more tasks than are assigned, so the outcome variable Output can takevalues in R+
0 , but is typically between 0 and 100. By far the most common value is 100, meaningthat employees continue working longer hours until they meet their target, but we also see employees
falling short of their targets or even exceeding them.
Finally, our outcome variable Productivity is calculated by dividing Output by Input. Differencesin productivity will often be a consequence of differences in Input needed to reach the goal, but they
can also come from employees falling short of or exceeding targets. Table 1 displays summary statistics
before and during WFH. The number of observations under the two regimes differs, because we havemore pre-WFH months.

3.2

Employee/HR variables

We obtained information on employee characteristics, collected as of March 20, 2020 (roughly thedate on which WFH was implemented). Summary statistics are in Table 2. For some employees somevariables are missing. One reason is that HR data are deleted if an employee leaves the company ortransfers to a branch in a different country.
We have Age, company Tenure, and industry Experience (collected at hiring and updated to thecurrent date), all measured in years. For each we generate median splits, HighAge, HighTenure and
HighExperience. Mean age is quite young, which is not unusual in the IT sector. Mean tenure is low
at about 4 years, as is expected since employee turnover is high in the IT sector. Male is a dummy
variable representing male employees. As in tech companies around the world, men are a significant
majority.
The variable NumChildren is the number of children up to age 21 who are covered under thecompany’s employee health insurance plan. The company believes that the vast majority of employees
who have dependent children insure them via the company, because of its relatively generous healthinsurance coverage. However, some might instead be insured through a partner’s employer. Hence, azero means that there are either no children at home, or there are but they have not been declared.
A positive number is the actual number of children at home. The dummy Children equals one if andonly if NumChildren is positive.
3
Our analysis does not rely on this normalization, as our fixed effects regressions compare the same employee before
and during WFH.

8

Table 2: Summary statistics for employee variables

Age (in years)
HighAgeTenure (in years)
HighTenure
Experience (in years)
HighExperience
Male
NumChildrenChildrenCommuteTime
RatingMean

SD

1st Quartile

3rd Quartile

N

31.91
0.50
4.21
0.52
8.10
0.50
0.76
0.52
0.39
0.65
2.66

5.95
0.50
3.90
0.50
5.22
0.50
0.43
0.73
0.49
0.33
0.88

27.10
0.00
1.11
0.00
4.04
0.00
1.00
0.00
0.00
0.38
2.00

36.03
1.00
5.11
1.00
11.10
1.00
1.00
1.00
1.00
0.85
3.00

7969
7969
7969
7969
7969
7969
7969
8934
8934
4323
5354

CommuteTime is an estimate of the time in hours needed to get from the home address to theoffice (during WFO), one-way. The company calculated this based on home and office addresses,
using the Google Maps API to incorporate factors such as traffic and not merely distance. Thus,
it is an estimate of the usual time taken, assuming that the employee commutes by car.4 Address
data are often incomplete, so there are more missing values than for other variables. We discarded
extreme values (larger than 2 hours). According to the company these are cases where commutetime is unreliable; for example, an employee actually worked at a client office closer to home, not thecompany office where his or her team is located.
Rating is the supervisor’s subjective evaluation of the employee on an integer scale of 1 to 5, where
1 is the best rating. We have the most recent rating from May/June 2020. The outcome measures
discussed above are predictive of performance ratings: mean input and mean productivity in months
prior to the rating significantly improve that rating (see Table A.1 in the Appendix). Figure B.1 in the
Appendix plots kernel density estimates of subjective ratings for different levels of Output. Ratings
generally rise with Output, but start to decrease once Output substantially exceeds the target. A
possible interpretation is that such an employee gave too much emphasis to meeting the goal, andthe supervisor gives a lower subjective rating to balance multitask incentives. Another is that thegoal was too easy to achieve. Overall, this indicates that the outcome measures introduced above aremeaningful.

3.3

Workplace Analytics Data

Microsoft Workplace Analytics [WPA] is a tool that many companies use to track and analyze various
aspects of their workforces. For example, it can be used to study collaboration or professional networking activity by using data on emails, calendar appointments, amount of time spent in meetings,
etc. WPA data have been used in several organizational studies (Brynjolfsson and McAfee, 2012;
Hoffmann et al., 2012; Levenson, 2018).
4

Some employees use public transport, but we have no data about mode of travel.

9The company has been considering adoption of this tool. For the purposes of this study theypurchased 914 licenses to apply to a subset of employees in our full sample. Appendix Table A.2
compares those in the WPA sample to those not in the WPA sample. The WPA group are slightly
younger, have lower tenure and are less productive, but are overall quite similar on average.
Table 3: Summary statistics for WPA variables
Mean

SD

1st Quartile

3rd QuartileN

WFO (before March 15th 2020)
Working Hours
After Hours
Focus Hours
Collaboration Hours
Meetings Manager
Meetings 1:1
Coaching Meets
MS Teams Calls
Internal NW
External NW
NW EXT
NW ORG
Emails

44.71
9.64
34.49
10.20
3.97
0.18
0.13
0.36
18.91
2.58
0.98
0.05
23.61

5.16
9.55
9.02
9.24
4.35
1.37
1.03
1.63
14.21
3.61
1.04
0.22
23.68

43
2.33
30
3.55
0.5
0
0
0
10
0
0
0
9

46.46
14.04
41.25
13.75
5
0.5
0
0
24
3
1
1
30

6755
6755
6755
6755
6755
6755
6755
6755
6755
6755
6755
6755
6755

WFH (after March 15th 2020)
Working Hours
After Hours
Focus Hours
Collaboration Hours
Meetings Manager
Meetings 1:1
Coaching Meets
MS Teams Calls
Internal NW
External NW
NW EXT
NW ORG
Emails

49.03
12.98
32.73
11.07
5.48
0.11
0.09
21.46
23.44
3.05
0.91
0.05
25.26

7.58
12.70
9.99
9.97
6.57
1.07
0.98
25.22
19.89
4.36
0.89
0.23
29.89

45.14
3.71
28
4.08
1
0
0
3
11
0
0
0
8

52.49
18.44
40
15
7.33
0
0
30
30
4
1
0
30

19220
19220
19220
19220
19220
19220
19220
19220
19220
19220
19220
19220
19220

Note: “Working hours” are weekly hours worked. After hours are weekly hours worked outside regular work time. “Focus
Hours” are hours with two or more hour blocks not spent in meetings, on calls or writing e-mails. “Collaboration Hours”
are hours spent in meetings or MS Teams calls. “Meetings Manager” is the number of meetings involving the employee’s
manager. “Meetings 1:1” is the number of meetings between the employee and their manager. “Coaching Meets” is thenumber of meetings the employee attended with the manager and all of the manager’s direct reports. “MS Teams Calls”
is the number of calls the employee participated in. “Internal NW” is the number of people inside the company withwhom employee had meaningful contact in the last 28 days. “External NW” is the same measure for people outside thecompany. “NW ORG” is the number of distinct company organizational units (outside his or her own) with which theemployee had at least two meaningful interactions in the last four weeks. “NW EXT” is the same measure for domains
outside the company. Emails is the weekly number of emails sent by the employee.

Table 3 summarizes variables obtained from WPA. WPA data were collected at the weekly level(in contract to Sapience data, collected at the monthly level). We have 10 weeks of data before WFH

10

(starting January 1, 2020) and 24 weeks of data during WFH (ending September 6, 2020). The switch
to WFH happened in the week starting March 16.
Variables fall into several categories. Working Hours measures overall time worked by the employee.
It is best viewed as capturing time “at work” (at home or in the office), not every minute of which
is necessarily spent working; e.g., it will count small breaks in between emails as work time. It can
detect longer work hours due to additional email traffic, meetings in the calendar or online, and otheractivity involving Microsoft services. In contrast, Sapience Input captures time “effectively working,”
excluding breaks or non-work activities. Thus, Input is our preferred measure, as it is specificallydesigned to measure work time and has more technical access to employee activities than WPA.
During WFO, employees on average spent 44 Working Hours per week (Table 3), which is close
to the contractual 40 hours per week, so Working Hours is close to the nominal work time. After
Hours measures the number of weekly hours worked outside regular office hours. Despite different
definitions and measurement, the Sapience variable Input and the (monthly mean of) Working Hours
are significantly correlated (ρ = 0.2715∗∗∗ ).5 The correlation between the variable After Hours and the
Sapience variable Input is also positive and statistically significant (ρ = 0.3127∗∗∗ ). Working Hours
and After Hours are mechanically related as the latter is part of the former.
A second group of variables (Focus Hours, Collaboration Hours, Meetings Manager, Meetings 1:1,
Coaching Meets and MS Teams Calls) relate to meetings. Focus Hours is time that is uninterrupted
by meetings, calls or emails. It is thus a measure of the amount of time the employee can concentrateon tasks. Collaboration Hours is the total time spent in various forms of meetings. The latter four
variables measure time in meetings by structure and purpose. Meetings Manager is the number of
meetings the employee attends that involve their manager, and Meetings 1:1 are personal meetings
between the employee and manager. Coaching Meets is the number of meetings involving the employee,
their manager and all of the manager’s direct reports. MS Teams Calls is the number of calls using
MS Teams (which allows for video meetings similar to Zoom or Skype).
Appendix Table A.3 shows (pre WFH) pairwise correlations between these meeting-related variables. As expected, all correlate negatively with Focus Hours, especially Collaboration Hours andMeetings Manager. All pairwise correlations are statistically significant at the 1% level. The different
types of meetings are positively related among each other, but with smaller coefficients. These correlations are positive both across employees – some job roles involve more meetings than others – andacross time – some periods involve more meetings of all types.
The third group of variables (Internal NW, External NW, NW EXT, NW ORG, Emails) relate to
networking with colleagues and clients more explicitly. The first two measure the number of individuals(inside and outside of the company, respectively) with whom the employee had contact. The latter
two measure the number of business units (e.g., teams) involved in those contacts. These measure
the breadth of the employee’s communications and networking contacts. Appendix Table A.4 shows5

The correlation is larger when aligning definitions by focusing on components of “Working Hours” measured by bothtools. For example, WPA’s measure of time spent writing emails correlates to Sapience Input with ρ = 0.4099∗∗∗ . The
correlation is still below 1 because it relates WPA’s measure of only one activity with Sapience’s measure of overall
working time.

11the (pre WFH) pairwise correlation between these networking-related variables. All correlations are
positive and highly statistically significant, across employees as well as across time.

3.4

Empirical strategy

As a first step, in Section 4.1 we estimate the average WFH effect, using the data discussed in Sections
3.1 and 3.2. Our main specification exploits differences in outcomes for each employee, when working
from home compared to working in the office during that month in the previous year, controlling for
employee and customer team fixed effects. The unit of observation is the employee-month. Index theemployee by i and the month by t = 1, 2, . . . , 17. For outcome variable yit , we estimate by OLS:
yit = αi + βWFHt +X

γj CustomerTeamjit +X

δs Monthst + εit ,

(1)

s

j

where αi is the employee fixed effect, WFH is a dummy variable indicating months working from
home, and CustomerTeamjit is a dummy variable equal to one if and only if employee i in month t
was part of team j. Monthst is a month (not month-year) dummy variable, so that Month1t = 1 if andonly if t is January, Month2t = 1 if and only if t is February, etc. In addition, we report an alternative
specification controlling for a linear rather than seasonal time trend:
yit = αi + βWFHt +X

γj CustomerTeamjit + δt + εit .

(2)

jOnce the average WFH effect is established, we analyze variation in this effect. Section 4.2 studies how
effects vary with employee characteristics. We interact the WFH dummy in the previous specifications
with additional explanatory variables X1i , X2i , . . .. Because the Xji variables are employee specific but
time invariant, we do not separately control for them, as that is already achieved by the employee
fixed effects.6
We exclude March 2020 (t = 12) from regressions because our main outcome variables are collected
at the monthly level, and working from home started in mid-March 2020.7 Thus, this month is neither
purely WFO nor WFH. Moreover, it is likely that WFH increased in the days prior to the official WFH
start, so the switch date was not clear-cut. An implication of excluding March 2020 is that teethingproblems and short-term adaption effects are not reflected in our estimate.8
Section 4.3 analyzes the effects of mechanisms such as time spent in communication, and focus
time. For these we rely on the WPA data described above. Here our empirical strategy is identical tothe one described in equation (2), except that we control for weekly instead of monthly time trends,
as these data are available weekly. Hence, in these regressions t = 1, ..., 34 represents weeks. For all
analyses we cluster standard errors at the employee level.
6

While age, tenure, and experience are not time invariant, our sample window is only 17 months, so there is no
meaningful variation during that window. Hence, to avoid collinearity issues, we use only employee fixed effects.
7This month is nevertheless plotted in the graphs.
8
For example, not every employee had suitable hardware at home to work with in the days after the switch. This was
fixed quickly, but can explain some of the noticeable drops in work time and output in March 2020, see Figure 1a.

12

103

8

99

100

Output by month
101
102

Time worked per working day
56
74−11 −10 −9 −8 −7 −6 −5 −4 −3 −2 −1
Month

0

1

2

3

4

5

−11 −10 −9 −8 −7 −6 −5 −4 −3 −2 −1
Month

1

2

3

4

5

(b) Output: Tasks completed relative to target

1

Productivity
1.2
1.4

1.6

(a) Input: Time worked per working day

0−11 −10 −9 −8 −7 −6 −5 −4 −3 −2 −1
Month

0

1

2

3

4

5

(c) Productivity

Figure 1: Average outcomes by month. The vertical line (month 0) indicates the switch to working from home.

4

Results

4.1

Average WFH effect

Before proceeding to the regression analysis, Figure 1 plots the three main outcomes by month to get
an intuitive idea about the WFH effect. This will also help us understand which of the econometricmodels (1) or (2) seems most appropriate for controlling for time trends.
According to Figure 1a, Input, employees provide about 5-5.5 hours of daily input; i.e., time inwhich they are actively using their software or programming tools, or in meetings or communications.
There is relatively little variation in average input pre-WFH, with a slight upward trend. Hence, a13

Table 4: Average Working-From-Home effect
(1)

(2)

(3)

(4)

(5)

(6)

Dependent variable

Input

Input

OutputOutputProductivity

Productivity

WFH

2.117***(0.034)

1.592***(0.038)0.040***(0.003)-0.530***(0.125)-0.098(0.155)-0.035**(0.015)-0.256***(0.031)-0.138*(0.074)-0.010(0.007)

Yes
Yes
Yes
0.24
70249
10312

Yes
Yes
No
0.22
70249
10312

Yes
Yes
Yes
0.02
70249
10312

Yes
Yes
No
0.02
70249
10312

Yes
Yes
Yes
0.01
70249
10312

Yes
Yes
No
0.01
70249
10312

Linear month trend

Employee FE
Team FE
Month FE
R2
Observations
Clusters

Note: Input is the time in hours per working day the employee worked in a month. Output is the employee’s normalized
output relative to target in a month. Productivity is output divided by time worked. The unit of observation is theemployee-month. Standard errors are shown in brackets below the point estimates, and are clustered on employee level.
***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

linear time trend as in model (2) might be more appropriate for this outcome measure. From the firstmonth of WFH, there is a large and persistent jump in input by more than 1.5 hours per day.9
For Output, Figure 1b, there is a noticeable spike in May 2019, but no visible monotonic or linear
trend otherwise, so a seasonal time correction might be more appropriate here. Moreover, average
output appears to be roughly comparable to pre-WFH months, with the exception of a dip in March
2020 (this month is neither fully WFO nor WFH). This dip might be due to disruption caused by thetransition from WFO to WFH.
Finally, for Productivity, Figure 1c, the graph is more volatile, which is not surprising for a ratio.
There is no clear linear time trend pre-WFH, but some variation from month to month, so a seasonalcorrection might be more appropriate. Productivity drops visibly during WFH.
To quantify the WFH effect, and to control for employee and team time-invariant variables (viaemployee and team fixed effects), we now turn to the regression analyses. Informally, the estimatesgive us average differences in outcomes pre- and during WFH for the same employee, controlling forteam effects (since employees sometimes switch teams) and time trends.
Table 4 reports WFH effect estimates based on OLS regressions for all three outcome variables,in each case with linear and seasonal time trend corrections. All estimates are in line with the visible
effects in the raw data in Figure 1.10
According to the estimates, WFH increased the time worked per day by roughly 2.1 hours (basedon a seasonal time trend) or by 1.6 hours (with a linear time trend). Both estimates are economically9Note that a national statistical office, which does not have access to these effective work hours, would not pick up
these changes in input at the intensive margin when using nominal work hours (which did not change during WFH) to
compute productivity.
10
Table A.5 in the appendix estimates the same regressions, but truncates the most extreme observations to accountfor outliers. The qualitative results of our preferred specifications remain the same.

14

very meaningful, and statistically significant at all conventional levels.11 Since both Figure 1a andthe regression indicate a linear time trend, we prefer column (2). The estimate of the WFH effect incolumn (1) is larger, because it does not take the pre-WFH time trend into account in the same way.
Columns (3) and (4) estimate that Output changed by -0.5 or -0.1 percentage points, depending ontime controls (recall that fulfilling all monthly tasks implies an output of 100%). Both point estimates
are slightly negative, but only the estimate with the seasonal time trend is significantly different fromzero at all conventional levels. Hence, we conclude that WFH had no or only a small negative effecton Output. While the regression indicates a significantly negative linear time trend, due to the outlier
in May 2019, we prefer specification (3) since a linear trend does not reflect the raw data very well.
Columns (5) and (6) estimate that Productivity decreased by 0.26 or 0.14 output percentage points
per hour worked, depending on the time controls. Given an average WFO productivity of 1.36, theseeffects correspond to a 19% and a 10% drop in productivity. Both are economically significant: if
employees worked a fixed 40 hours per week, this fall in productivity would imply a drop in output of
10.2 or 5.5 output percentage points in a week. In other words, if employees had not increased time
worked during WFH, on average they would have completed only 90-95 of 100 assigned tasks. The
WFH effect in specification (6) is significantly different from zero only at the 10% confidence level,
whereas the effect in specification (5) is statistically significant at all conventional levels. We prefer
specification (5), since both the plot and the linear time trend coefficient indicate that a linear trend
is not as appropriate.12
In summary, this evidence indicates that employees worked longer but less productively, with
output remaining about the same or dropping slightly. Thus, there appear to be two countervailing
effects on output that roughly offset each other. Our interpretation of these patterns is that employees
were less productive during WFH, but still aimed to reach the same output or goals, and hence work
longer until the same output was reached. In the next sections, additional results will suggest that
productivity decreased due to increased distractions and coordination costs.
A potential alternative explanation for the jump in work time during WFH might be that employees“gamed” the Input numbers. This is unlikely for several reasons. First, Sapience time measurement
is sophisticated and designed to be resilient to manipulation attempts. Merely keeping the computeron for longer or watching videos instead of working does not increase Input. Rather, it would require
having the relevant work software as the active window, and giving continuous mouse or keyboard
input. Second, gaming time measurement in Sapience would not translate into increases in WPA’stime measurement.13 WPA time recording is from activity in MS Outlook, MS Teams etc., rather thanprogramming tools or similar, and is not dependent on mouse / keyboard activity. Yet the WFH effectwe see with this alternative time measurement is also positive, see section 4.3. Third, employees are
not paid by the hour. To impress superiors, time is better spent generating output. Fourth, Sapience11
For comparison, the contractual working day (pre-WFH) is 9 hours at the company we study, which includes a 1hour lunch break.
12
We also estimated the productivity regression without time controls. The WFH estimate is -0.2 output percentage
points per hour worked with a t-statistic of -6.8; i.e., a highly significant effect consistent with the other specifications.
13
Unlike Sapience, employees were not aware of the use of WPA analytics. WPA licenses were purchased for the firsttime and for this study specifically. Very few people at the company knew about it and had access to these data.

15

was in use long before the switch to WFH, so this potential concern cannot explain the WFH effectwell. Fifth, the additional WFH effects we find from WPA activity (section 4.3), such as more timespent on conference calls and fewer focus hours, cannot be explained by “gaming.”
Another potential concern might be that the increase in Input is due to measurement error, as
spontaneous (unscheduled) face-to-face meetings during WFO might not be counted as working time,
while spontaneous online meetings during WFH are counted. This is not backed up by the data. We
are able to reproduce the findings in this section using other measures of working time, available fora sub-sample of employees (Section 4.3). We also find adverse effects of WFH on direct measuresof networking and training; in particular the number of some types of meetings decreases during
WFH. Moreover, employees with children, and those working in roles where networking with people
outside the company is more important, are more adversely affected by the switch to WFH. This isdifficult to explain with unscheduled offline meetings, but is consistent with increased disruptions andcoordination costs. Last, we observe when an employee starts and ends their working day. The lengthof the workday measured in this way increases from 7.64 hours to 9.17 hours during the WFH period.
Taken together, these results suggest that spontaneous offline meetings explain at most a small partof our overall results.
If employees used their own devices to work from home, we would not track that activity (in either
period). However, this company did not allow employees to do so, to insure integrity of confidential
client information. Any employees who worked at home in both periods had to use a company-owned
laptop computer and phone, both of which included the tracking software used to collect our variables,with one small exception. During the transition to WFH, a small percentage of employees who did not
yet have a company-owned laptop computer were briefly allowed to use personal devices to perform
some work. This lasted only until the company could provide them with laptops. In such cases
Sapience would not track their work, so our Input variable has missing values. This affects a verysmall percentage of our data.
Another possible explanation for the increase in time worked is that pandemic lockdown measuresclosed restaurants, cinemas, etc., thereby reducing the value of leisure time. Under this explanation,
however, we would expect Output to increase and Productivity to remain approximately constant,which is not what we observe. Appendix Figure B.5 shows that while we see a slight dip in working
hours after every stage of lockdown easing, the effect is small and, more importantly, only temporary.
We also do not find evidence that productivity or other outcomes co-vary with national or regional
indicators of the severity of Covid, such as deaths or case rates.
Finally, we are able to measure the number of days the employee worked relative to the numberof work days in a month. According to Table A.6 in the appendix, the number of sick days decreased
significantly. This suggests that absences or sickness were not a driver of the decrease in productivity.

4.2

Who copes better with WFH? Heterogeneous WFH effects

We now explore what drives the WFH effect in more depth, and which subgroups are most affectedby the shift to WFH. Table 5 displays estimates for all outcome variables, separately by whether

16

Table 5: Working-From-Home: Children at home and gender differences
(1)

(2)

(3)

(4)

(5)

(6)

Dependent variable

Input

Input

OutputOutputProductivity

Productivity

WFH

1.416***(0.046)0.307***(0.059)1.599***(0.086)0.091(0.128)-0.211**(0.094)0.252*(0.145)-0.528***(0.150)0.061(0.205)-1.176***(0.267)0.673(0.431)0.895***(0.300)-0.822*(0.495)-0.206***(0.042)-0.169***(0.058)-0.361***(0.075)-0.027(0.111)
0.149(0.093)-0.157(0.133)

Yes
Yes
No
Yes
0.23
64392
8865

Yes
Yes
No
Yes
0.25
58644
7911

Yes
Yes
Yes
No
0.02
64392
8865

Yes
Yes
Yes
No
0.02
58644
7911

Yes
Yes
Yes
No
0.01
64392
8865

Yes
Yes
Yes
No
0.01
58644
7911

WFH × Children
WFH × MaleWFH × Male × Children

Employee FE
Team FE
Month FE
Linear Month Trend
R2
Observations
Clusters

Note: Input is the time in hours per working day that the employee worked in a month. Output is the employee’s
normalized output relative to target in a month. Productivity is output divided by time worked. The unit of observationis the employee-month. Standard errors are shown in brackets below the point estimates, and are clustered on employee
level. ***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

employees have children at home and by gender, using our preferred time control from the previous
section. The number of observations is slightly reduced since the additional explanatory variables
are missing for some employees. We converted the explanatory variables (except commute time) into
dummy variables for easier interpretation.
In the country in which the company is located, all schools closed in March 2020 during the Covid19 pandemic, so working from home was presumably an even greater challenge for some parents, as
children needed to be supervised and perhaps taught. Hence, we investigate whether having children athome changed an employee’s WFH effect. An important qualification is that under normal conditions,children would attend school and any adverse effects of WFH on productivity of parents might belower.
Column (1) shows that employees who have at least one child at home (as measured by company
health insurance coverage) increased work time more during WFH than did their counterparts withoutchildren. Possibly, this is due to the fact that employees with children get distracted more often
during WFH and compensate by working longer hours. Employees with children at home work almosta third of an hour more per working day during WFH than those without children, who themselves
still work 1.4 hours more during WFH. These effects are highly significant. Column (3) reveals nosignificant change in the WFH effect on output with children at home. However, column (5) showsthat the increased working time implies a larger drop in productivity when there are children at home.
Consequently, the patterns seen for the average employee are exacerbated for employees with children.

17

The even columns in Table 5 investigate whether there was a gender difference in how outcomeschanged during WFH, conditional on whether there were children at home.
The WFH × Male interaction represents the difference in the WFH effect between male and female
employees without children. Male employees without children increased working time by about 0.2
hours less per day than did female employees without children, a significant effect. They also suffereda smaller decline in output.
The WFH × Children interaction represents the difference in the WFH effect between female
employees with and without children. Female employees with children did not significantly increase
working time during WFH compared to female employees without children, nor did their output or
productivity significantly differ.
Finally, the sum of WFH × Male and WFH × Male × Children is the difference in the WFH effectbetween male and female employees with children. This difference is roughly zero for all outcome
measures, so there is no gender difference among employees with children at home.
Thus female employees are more adversely affected by WFH, but this is not due to child care
responsibilities. The latter finding contrasts with much of the narrative in western countries, wherechildcare responsibilities are given as a main reason why women are more adversely affected by WFH(Financial Times, 2021a). We conjecture that this is due to greater expectations placed on women byparents and parents-in-law in the domestic setting.
Next, we investigate whether employees with more industry experience or company tenure wereaffected differently by WFH. One reason this could be the case is that they have greater institutional
knowledge and social capital, and are less reliant on help from colleagues or find it relatively easier toobtain during WFH. To investigate the effects of age, company tenure, and industry experience (atthe company or elsewhere), we generate dummy variables with a median split. Since these variables
are highly correlated, we estimate their effect on the WFH estimate jointly in Table 6.14
Column (1) shows that work experience has the largest and only significant impact on the WFH
effect for time worked. During WFH, experienced employees worked roughly a quarter hour more
per day compared to less experienced employees, holding age and company tenure constant.15 Ourinterpretation is that more experienced employees have more managerial duties. The increased costs ofcoordination (also see next section) during WFH are therefore borne by these experienced employees,
who have to put in more time to coordinate their team. Additionally, it is likely that the lion’s shareof managing the WFH transition falls on experienced employees with more responsibility.16
14

Appendix Figure B.3 illustrates individual employee productivity during WFH as a function of productivity before
WFH. Two patterns emerge. First, most employees are more productive in WFO than WFH. Second, those who are
more productive in WFO tend to also be more productive WFH. However, there is considerable heterogeneity.
15
When estimating the regression with one interaction for age, tenure, and experience at a time (not displayed),
all interactions show significantly positive point estimates due to their positive correlation. That is, older employees
increased WFH work hours more compared to younger employees, but this is no longer true when holding tenure andexperience constant, see Table 6.
16
While more experienced employees might be more likely to have children at home, this experience effect is unrelated
to having children. When adding the interaction with Children to regression (1) in Table 6 (not displayed), the interaction
with HighExperience remains significantly positive.

18

Table 6: Working-From-Home: Age, experience, tenure, commute times
(1)

(2)

(3)

(4)

(5)

(6)

Dependent variable

Input

Input

OutputOutputProductivity

Productivity

WFH

1.361***(0.058)0.036(0.067)0.057(0.092)0.270***(0.094)

1.516***(0.095)-0.743***(0.194)0.519**(0.229)0.097(0.366)-0.138(0.390)-0.603*(0.334)-0.245***(0.048)0.003(0.090)-0.084(0.073)-0.038(0.084)-0.286***(0.083)

WFH × HighTenure
WFH × HighAge
WFH × HighExperience
WFH × CommuteTime

Employee FE
Team FE
Month FE
Linear Month Trend
R2
Observations
Clusters

0.107(0.122)
Yes
Yes
No
Yes
0.25
58644
7911

0.316
(0.405)

Yes
Yes
No
Yes
0.26
31848
4295

Yes
Yes
Yes
No
0.02
58644
7911

Yes
Yes
Yes
No
0.03
31848
4295

-0.030(0.093)
Yes
Yes
Yes
No
0.01
58644
7911

Yes
Yes
Yes
No
0.01
31848
4295

Note: Input is the time in hours per working day that the employee worked in a month. Output is the employee’s
normalized output relative to target in a month. Productivity is output divided by time worked. The unit of observationis the employee-month. Standard errors are shown in brackets below the point estimates, and are clustered on employee
level. ***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

The change in Output during WFH is roughly 0.5 percentage points larger per hour for employeeswith longer company tenure, holding age and experience constant. Thus, while low tenure employees’
Output falls, high tenure employees keep meeting their goals. The other characteristics do not show
a significant effect. It appears that employees who had worked at the company for longer were able
to adapt more effectively to the WFH-shock, and that this was more important than general industry
experience. This finding suggests that greater firm-specific human capital in the form of familiarity
with company procedures, or more fully-developed networks and working relationships with colleagues
and clients, were helpful during WFH. Alternatively, those with greater experience or tenure might
be in positions with more responsibility, and so responded more to the shift to WFH. For the lastoutcome measure, productivity, there is no significant difference in the WFH effect among these
employee groups.
The even columns in Table 6 estimate the WFH effect by employee commute time (when working
from the office). The WFH effect does not significantly differ by commute time for any outcome
measure. Hence, the finding that WFH increased hours worked is not merely due to more available
time. Rather, it supports the interpretation that productivity fell during WFH, and employees worked
more to compensate.
Figure 2 shows the mean and 95% confidence intervals of the estimated effect of WFH on productivity separately for different customer teams. Those are teams of employees who work for the same19

Figure 2: Heterogeneity of WFH Effect on Productivity Across Teams.

customer, but not necessarily on the same project. In the figure teams are sorted by size (number ofteam members) in increasing order from left to right. The effect is negative for almost all teams. Thisillustrates that the overall negative effect on productivity (illustrated by the dashed line in Figure 2)
is not driven by a few customers or teams. Rather, WFH productivity declined broadly across thegroup studied.
For 43 teams the negative effect is statistically significant. Five teams, by contrast, show a statistically significant positive effect on productivity. With the exception of one team, those are all teamsin the bottom half of the size distribution. In smaller teams it might be easier to solve coordination
problems during WFH. Still, even among the smaller teams, the effect on productivity is negative forthe vast majority.

4.3

Mechanisms: What contributes to lower productivity?

To better understand the mechanisms behind the decrease in productivity, we study the sub-sampleof 914 employees for which WPA data were obtained (see Table A.2). Using these data we document
three patterns: an overall increase in working time; a shift away from performing work tasks andtowards spending time on meetings, calls, or answering e-mails; and reduced time networking withothers or meeting 1:1 with one’s manager.

20(a) Working Hours(b) After Hours(c) Focus Hours(d) Collaboration Hours

Figure 3: Working patterns pre- and post WFH. Panel (a): mean weekly working hours. Panel (b): hours
worked outside regular working hours. Panel (c): number of hours with two or more hour blocks
not spent in meetings, on calls or writing e-mails. Panel (d): hours spent in meetings or in calls.
Week= 0 is 9th-15th March 2020.

Working Hours
Figure 3 illustrates the shift in working patterns after the start of WFH. In line with the evidence
in Section 4.1, the WPA data show that overall hours worked increased, including those after regular
office hours. Panels (c) and (d) show an interesting pattern. Employees spend more time in meetings orcalls and have less “focus time” (i.e., time uninterrupted by meetings or calls to focus on completingtasks). The increased time spent in meetings, and its persistence after the initial WFH transition
phase, suggest substantial and ongoing coordination costs with WFH, which negatively impact time
available to work in a productive manner. The fact that employees have less time to work in a focusedor uninterrupted manner could also explain why they have been found to multitask more during
meetings (Cao et al., 2021).17
17

Appendix Figure B.2 illustrates the technological shift post WFH, with a drastically increased number of hours
spent on virtual meetings using MS Teams. Interestingly, the number of such meetings continues to increase almost

21

Table 7: Shift in Working Patterns due to WFH(1)(2)
Working Hours
WFH

(5)
(6)
Focus Hours(7)(8)
Collaboration Hours

4.431***(0.161)2.743***(0.218)0.096***(0.009)4.212***(0.288)1.822***(0.320)0.137***(0.0141)-2.665***(0.245)-1.417***(0.297)-0.071***(0.012)

1.251***(0.122)0.704***(0.137)0.031***(0.005)YY
0.703
25,893
914

Y
Y
0.709
25,893
914

Y
Y
0.743
25,893
914

Y
Y
0.747
25,893
914

Y
Y
0.717
25,893
914

Y
Y
0.719
25,893
914

Y
Y
0.745
25,893
914

Y
Y
0.746
25,893914

Linear weekly trend

Employee FE
Team FE
R-squared
Observations
Clusters(3)(4)
After Hours

Note: Working hours are weekly hours worked. After hours are weekly hours worked outside regular work time. Focus
Hours are hours with two or more hour blocks not spent in meetings, on calls or writing e-mails. Collaboration Hours
are hours spent in meetings or in calls. The unit of observation is the employee-week. Standard errors are shown inbrackets below the point estimates, and are clustered at the employee level. ***Significant at the 1% level; **significant
at the 5% level; *significant at the 10% level.

Table 7 shows regressions estimating the WFH effect on these outcomes. Both overall working
hours and working hours outside of regular office time increase during WFH. In fact, comparing thesize of coefficients in columns (1) and (3) we see that the increase in overall working hours takes placealmost entirely outside of normal working hours. The table also confirms the increase in time spent inmeetings and on calls, with a corresponding decrease in uninterrupted work time (focus hours). In allcases, the WFH effect persists and remains highly statistically significant when we include a linear timetrend. The estimated effect size is smaller in these cases but remains substantial. After controlling
for time trends, employees work 2.7 hours more per week, out of which 1.8 are spent working outside
regular office hours. However, they also spend 1.4 hours less working in a focused or uninterrupted
manner. These shifts in working patterns could explain why productivity decreases under the WFH
regime.18
We conjectured that some of the increase in working hours and decrease in productivity is due toincreased costs of communication and coordination within teams. If this is the case then we should seethat roles which are characterized by more interaction and networking prior to WFH are more affectedby the switch to WFH. If we do a median split by how many internal networking contacts (variable
Internal NW, see Table 3) an employee had prior to WFH, we see that working hours in specification(1) of Table 7 increase by 6.01∗∗∗ hours for those with above median contacts and by 3.515∗∗∗ hours for
those with below median contacts. The difference is highly statistically significant (t-test p < 0.0001)
suggesting that coordination costs are indeed an important factor behind the decline in productivity
during WFH.
six months after the switch to WFH. Barrero et al. (2021) show evidence of the surge in technological innovations that
support WFH during the pandemic.
18
Additional analysis in Appendix A shows that for overall working hours the time trend is even stronger during WFH.
In this case the pre-WFH trend is only about 60% of the overall trend. For the other outcomes the trend is mitigatedafter the initial shift in levels, in line with ceiling effects (Appendix Table A.7).

22

Table 8: Shift in Networking Patterns and types of meetings due to WFH

WFH
Linear weekly trend

Employee FE
Team FE
R-squared
Observations
Clusters(1)
Internal
NW(2)
External
NW

(3)
NW
ORG

(4)
NW
EXT

(5)
Meetings
Manager(6)
Meetings
1:1(7)
Coaching
Meets(8)E-mails-7.621***(0.369)0.787***(0.026)-0.532***(0.103)0.078***(0.006)-0.009(0.005)0.001***(0.000)-0.150***(0.027)0.008***(0.001)1.723***(0.205)0.016**(0.008)-0.089*(0.048)0.002(0.002)-0.060*(0.033)0.002(0.002)4.282***(0.680)-0.020(0.029)YY
0.801
25,893
914

Y
Y
0.758
25,893
914

Y
Y
0.665
25,893
914

Y
Y
0.624
25,893
914

Y
Y
0.757
25,893
914

Y
Y
0.320
25,893
914

Y
Y
0.386
25,893
914

Y
Y
0.766
25,893914

Note: “Internal NW” is the number of people inside the company with whom the employee had meaningful contact in
the last 28 days. “External NW” the same measure for contacts outside the company. “NW ORG” is the number ofdistinct organizational units within the company with whom the employee had at least two meaningful interactions in
the last four weeks. “NW EXT” is the same measure for domains outside the company. “Meetings Manager” is thenumber of meetings involving the manager. “Meetings 1:1” is the number of meetings between the employee and their
manager. “Coaching Meets” is the number of meetings by the manager with all direct reports including the employee.
“E-mails” is the number of emails sent. The unit of observation is the employee-week. Standard errors are shown inbrackets below the point estimates, and are clustered at the employee level. ***Significant at the 1% level; **significant
at the 5% level; *significant at the 10% level.

Networking and Collaboration
We now focus on networking and collaboration patterns in more detail. Understanding changes in
networking and collaboration can tell us something about the value of additional time spent in meetings. Shifts in networking patterns can also impact productivity in different ways, for example, byaffecting the exchange of ideas and knowledge.
Table 8 shows how networking and collaboration patterns change with WFH. Columns (1) and (2)focus on the number of people inside and outside the company, respectively, with whom an employee
had a meaningful contact (defined as an email, meeting, call, or at least 3 instant messages) during
the last 28 days. There is a generally positive time trend, possibly reflecting the fact that networking
is becoming more important at this company. However, there is a clear negative impact of WFH on
the number of individuals with whom employees share meaningful interactions.19
Columns (3) and (4) contain results for similar measures, focused on the number of organizational
units inside and outside the company at which an employee interacted with someone (column (4)).
Here we also see a decline in contacts caused by WFH, despite a general upward trend, though in thecase of internal organizational units the decline is not statistically significant.
Columns (5)-(7) focus on collaboration patterns. In line with our earlier analysis, we see that the
number of meetings involving the manager increases. By contrast, the number of both 1:1 supervisor
19

Appendix Table A.8 shows specifications where we allow the time trend to interact with WFH. By and large, there
are no significant differences in trend pre- and post WFH. For internal networking the positive time trend is somewhat
bigger during WFH suggesting some potential catch up, but for all other outcomes there is no significant (or even anegative) difference before and after the introduction of WFH.

23

Table 9: Productivity in the WPA sample

WFH
Mean pre WFH

Employee FE
Team FE
Linear Trend
R-squared
Observations
Clusters(1)
Sapience(2)(3)-0.149**(0.072)1.080(1.702)-0.056***(0.008)0.599(0.531)-0.049**(0.020)0.599(0.531)

Y
Y
N
0.356
4,359
888

Y
Y
N
0.783
4,359
888

Y
Y
Y
0.784
4,359
888

WPA

Note: Productivity is as in previous sections, Output divided by Sapience monthly work hours. Productivity WPA
divides Output by the WPA Input measure “working hours” (aggregated to monthly level). The unit of observation isthe employee-month. For 888 employees we have all three sources of information: Sapience, IDMS and WPA. Standard
errors are shown in brackets below the point estimates and are clustered at the employee level. ***Significant at the 1%
level; **significant at the 5% level; *significant at the 10% level.

meetings and coaching meetings decrease during WFH. Employees seem to receive less mentoring andcoaching, even though these effects are only statistically significant at the 10% level. Moreover, in
WFH managers are spending more time managing groups rather than individuals.
Last, column (8) shows that the number of emails sent increased substantially during WFH with
about 4 more emails being sent on average. This corresponds to an about 17% increase over thebaseline (see Table 3).
Overall, these patterns highlight a detrimental impact of WFH on networking. Employees havefewer contacts with different individuals and organizational units both inside and outside the company.
They also have fewer 1:1 meetings with superiors, and receive less coaching. These lost opportunities
to network may help explain why WFH lowers productivity. It is also likely that they slowed employee
development, though that is beyond estimation with our data.

Productivity
Last, we study productivity in this sub-sample. We first ask whether the drop in productivity fromour main sample can also be found in this smaller WPA sample of employees.
As we have additional variables for this smaller sample, we consider two measures of productivity:(i) the Sapience based measure used above, and (ii) where we replace the Sapience variable Input by
WPA working hours to compute productivity. Because we only have two months of pre-WFH data,
we do not include time trends for the monthly Sapience data here. Using the Sapience time measure,
we estimate an approximately 15% drop in productivity after the introduction of WFH. With the
WPA time measure, we find an approximately 10% drop in productivity, or 8% when controlling fora linear time trend. Both effects are highly statistically significant (p < 0.040, see Table 9). The drop24

in productivity from the full sample can thus be reproduced in this smaller sample, using different
measures of hours worked. Appendix Figure B.4 plots the change in productivity after the introductionof WFH in this sample.
We next ask how these changes in work patterns are linked to productivity. This will help us
understand whether the documented changes can explain the decrease in productivity. We would
further like to know which variables are the most important predictors of productivity. For theremainder of the section we use the Sapience measure of productivity from Section 4.1.
To address these questions, we first estimate adaptive Lasso regressions (Zou, 2006) in which
the dependent variable is productivity and prediction variables are dummies that identify weeks inwhich a variable is above average for a given employee. Lasso regressions select a set of variables
that best explain variation in productivity by minimizing an estimate of the out-of-sample prediction
error.20 We use dummies identifying the weeks where a variable is above average for a given employee
to focus on variation in productivity within employees.21 We conduct this regression separately for
WFO and WFH periods in order to see whether productivity determinants changed between the twoenvironments. Table 10 presents these results. An X in the table indicates variables that the Lasso
regression includes in the prediction model.
The variables selected include working hours, focus hours as well as most networking variables.
Working After Hours and attending many meetings does not seem to contribute substantially to
productivity, nor does spending time on MS Teams calls. The set of selected variables is quite consistent before and during WFH, with focus hours and the networking measures crucial indicators of
productivity. Interestingly, overall working hours is selected before WFH but not afterwards.
To assess the economic significance of these associations, we compute the elasticity of productivity
with respect to a 1 percentage point increase in the variables selected by Lasso. Table 10 shows
the mean elasticities. Before WFH, the most important variables associated with productivity are
working hours, focus hours and NW EXT. A 1 percentage point increase in overall working hours
is associated with a 0.014 percentage point average increase in productivity. A 1 percentage point
increase in focus hours is associated with a 0.033 percentage point average increase in productivity,
and a 1 percentage point increase in network contacts outside the company is associated with a 0.01
percentage point average increase in productivity. These elasticities show that these variables areimportant correlates of productivity. During WFH, the most important variables are focus hours,
and internal and external networking. As before, both internal and external networking are positively
associated with productivity. Focus Hours are now more than twice as important in terms of theiraverage marginal effect on productivity, with a 1 percentage point increase in focus hours associatedwith a 0.072 percentage point average increase in productivity. There seems to be a broadly stable
20
Lasso models have a free parameter λ which is the weight on the penalty term. Adaptive Lasso performs multiple
Lassos, where in each the λ is selected that minimizes an estimate of the out-of-sample prediction error. After each
Lasso, variables with zero coefficients are removed and remaining variables are given a penalty weight designed to drive
small coefficients to zero. Zou (2006) has shown that adaptive Lasso enjoys oracle properties; they perform as well as if
the true underlying model were known ex ante.
21
An alternative would be to force Lasso to select employee fixed effects. This is not possible for us as in the merged
dataset containing both productivity and WPA variables we do not have enough observations pre-WFH for this approach.

25

Table 10: Variables selected by Lasso and Elasticity of Productivity with respect to selected variable.
pre WFH
Lasso
Elasticity
Hours
Working Hours
After Hours
Meetings
Focus Hours
Collaboration Hours
Meetings Manager
Meetings 1:1
Coaching 1:1
MS Teams Calls
Networking
Internal NW
External NW
NW EXT
NW ORG

X

0.014∗∗∗

X

0.033∗∗∗

X

0.002∗∗∗

X
X
X−0.0000.010∗∗
0.000

EmailsEmailsduring WFH
Lasso
Elasticity

X

0.072∗∗∗

X

0.003

X
X
X
X

0.040∗∗∗
0.029∗∗∗
0.011∗∗∗−0.001X

0.053∗∗∗

Note: Adaptive Lasso linear regression results (X indicates a variable was selected) and mean elasticity of productivity
with respect to an increase of 1 percentage point in the variables selected by Lasso. The left two columns show theresults restricted to the period before WFH and the right columns the results restricted to the period during WFH. For
all variables in the table Lasso regressions include a dummy identifying weeks in which the variable is above average fora given employee.

relationship between working patterns and productivity. The increased importance of focus hours
during WFH might be explained by the fact that employees have less of it during WFH.
In summary, in this section we showed that WFH induced a significant shift in working patterns.
Employees worked more, including outside regular office hours, but had less uninterrupted time tofocus on task completion as they spent more time in meetings. They networked less and spent less time
being evaluated, trained and coached. We further showed evidence that these reductions, especiallyin focus hours and networking, were detrimental to productivity.

5

Conclusion

In this paper we have presented the most detailed analysis of WFH productivity changes for knowledge
workers. The paper makes a number of significant contributions. We study an occupation that is
expected to be amenable to WFH, but involves significant cognitive, collaborative and innovation
tasks. The data provide an unusually high quality measure of employee productivity for knowledge
workers. The breadth of the data allow for the first thorough analysis of determinants of WFH
productivity. We provide evidence on how WFH productivity varies with employee characteristics,
presence of children at home, and WFO commute time. We also use detailed data on how employees
spend their work time to study the effects of job characteristics on WFH productivity. These latter

26

results are important, since they provide insights into how the effectiveness of WFH may vary, andkey issues for firms to consider in deploying WFH.
In our sample, employees were able to maintain similar or just slightly lower levels of output
during WFH. In order to do so, they worked longer hours. Despite this, employees had less focus
time to perform tasks, and the net effect was a drop in productivity. It would be interesting to see if
this change is sustainable, especially in light of evidence of the adverse effect of long work hours on
employee well-being, and mental and physical health (Sparks et al., 1997; Sokejima and Kagamimori,
1998; Sparks et al., 2001).
It is likely that WFH also resulted in a decline in intangibles that are valuable to the employee
and company. Working relationships, professional networks and corporate culture may have suffered.
More subtly, when people work in the same location, they experience unplanned interactions. That canlead to new working relationships, and “productive accidents” that spur innovation. It is not easy togenerate similar unplanned interactions on teleconferences. Finally, employees had fewer opportunities
for coaching, and meeting directly with supervisors. This undoubtedly slowed their development of
human capital.
We explored several possible explanations for the decline in productivity. Our main explanationis that some aspects of work are more difficult to perform in a virtual environment. We provide
clear evidence that this is the case. More time was spent in meetings, and those tended to involve
larger groups. Less time was spent in direct interactions with the supervisor or colleagues. Employees
also narrowed their spheres of communication, interacting with fewer people and business units, both
inside and outside the firm. Collectively these indicate that costs of communication, collaborationand coordination are higher when done virtually. Moreover, these factors are likely causes of changes
in focus time, and in the decline in productivity.
Alternative explanations could be related to the stresses of the pandemic. However, the data show
an immediate and persistent jump in work patterns and productivity at the shift to WFH, which isinconsistent with the gradual evolution of the pandemic. We found no evidence that employee work
behavior varied with changes in lockdown restrictions. Employees actually had a decline in sick days.
Finally, company economic performance was quite good during this period of time, stronger than theprior year, so fear of job loss was not an issue.
Another possible explanation is the presence of children at home, which was exacerbated by theclosing of schools. Indeed, WFH productivity was lower for employees who had children at home,so this is a partial explanation. However, those without children at home also suffered a decline inproductivity, with similar patterns for reduced focus time, increased time spent in large meetings, anddecreased 1:1 communications and meetings.
Company executives were not surprised by the findings in this paper, which accord with theirperceptions of the experience with WFH. As of early July 2021, they estimate that perhaps half of
their employees had moved from their original locations, primarily to live with extended family (there
were still significant pandemic-related restrictions). According to one senior executive, “bringing themback to our base locations will not be easy.” The company expects to make significant use of WFH

27

in the future, with perhaps 30-40% of employees in WFO on any given day. No doubt productivity
will improve as the firm refines implementation of WFH and moves to a blended model with WFO.
Moreover, employees enjoy greater flexibility and lower commute times.
The findings in this paper will be helpful well beyond this firm. We have presented evidence onsome of the challenges of implementing WFH. In particular, WFH may be more difficult for employees
who are less experienced, have lower tenure, and for jobs that involve significant collaboration andcoordination. Firms will have to develop tools, training and policies to give greater emphasis tointerpersonal interactions during WFO, improve effectiveness of virtual communication, and trainsupervisors and employees to schedule work time at home more efficiently.

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Online Appendix “Productivity, Collaboration & Networking During
Work From Home: Evidence from IT Professionals”
M. Gibbs, F. Mengel, C. Siemroth
For Online Publication

Contents
A Additional Tables

2

B Additional Figures

7

1

A

Additional Tables

Table A.1 presents OLS and ordered Logit regressions, explaining the performance rating with that
employee’s mean time worked, output, and productivity over the most recent 5 or 10 months. All
regressions show that both time worked and productivity significantly improve an employee’s performance rating (a lower score is better). The coefficient on mean output goes in that direction, but is
not statistically significant, because the effect is not monotonic (see also Figure B.1 below). The time
horizon of 5 or 10 months does not noticeably change coefficients. Hence, Sapience input and outcome
measures are meaningful in explaining performance ratings, which the company uses for promotion
decisions, among other things.
Table A.1: Do Sapience outcome measures predict performance evaluations?
(1) OLS(2) Ordered Logit(3) OLS(4) Ordered Logit

Dependent variable

RatingRatingRatingRatingMeanInput5-0.097***(0.007)-0.003(0.002)-0.020***(0.006)-0.219***(0.015)-0.006(0.004)-0.041***(0.015)-0.093***(0.007)-0.002(0.002)-0.023***(0.007)3.365***(0.183)-0.205***(0.016)-0.004(0.004)-0.047***(0.015)MeanOutput5
MeanProductivity5
MeanInput10
MeanOutput10
MeanProductivity10
Constant
R2
Observations

3.498***(0.173)
0.06
4220

4220

0.04
4930

4930

Note: MeanInputX is the average of Input (hours worked) over the most recent X months prior to the performance
rating. Similarly, MeanOutputX and MeanProductivityX are the averages of Output and Productivity, respectively,
over the most recent X months prior to the performance rating. Rating takes integer values 1 to 5, with 1 being thebest. Each observation is one employee. Heteroskedasticity-robust standard errors are shown in brackets below the point
estimates. ***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

Table A.2 provides summary statistics for employees in the WPA sub-sample, compared with other
employees for whom we do not have WPA data. Employees covered by WPA are somewhat younger
and more junior in the company. They are also somewhat less productive. However, these differences
are generally small.
Tables A.3 and A.4 show pairwise raw correlations among meeting variables and networking variables, respectively. As expected focus hours is negatively related to all meeting variables, while themeeting variables have positive correlation among themselves. Networking variables are positively
related among each other, as expected.
2

Table A.2: Summary Characteristics of Non WPA and WPA sample.
Non WPAWPA

p-valueAge (yrs)
MaleChildrenTenure (yrs)
Productivity

31.98
0.76
0.52
4.49
1.66

31.29
0.78
0.50
4.17
1.49

0.001
0.191
0.366
0.023
0.034

N

9398

914

Table A.3: Pairwise Correlation of Meeting-Related Variables.

Focus Hours
Collab. Hours
Meetings Mgr
Meetings 1:1
Coaching Meets
MS Teams Calls

Focus Hours

Collab. Hours

Meetings Mgr

Meetings 1:1

Coaching Meets

Calls

1
−0.4710∗∗∗−0.4223∗∗∗−0.0857∗∗∗−0.1115∗∗∗−0.0883∗∗∗10.4365∗∗∗0.0327∗∗∗0.0814∗∗∗0.1633∗∗∗

1
0.1871∗∗∗0.0261∗∗∗0.0180∗∗∗1
0.0059∗−0.01211
0.0317∗∗∗1

Note: ***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

Table A.4: Pairwise Correlation of Networking-Related Variables.

Internal NW
External NW
NW ORGNW EXT
EmailsInternal NW

External NW

NW ORGNW EXT

Emails10.5553∗∗∗0.3060∗∗∗0.4314∗∗∗
0.6385∗∗∗1
0.1770∗∗∗0.7186∗∗∗0.5342∗∗∗

10.1183∗∗∗0.2316∗∗∗1
0.3854∗1

Note: ***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

Table A.5 estimates the same average WFH effect as Table 4, except with the most extreme 1%of observations truncated to assess the impact of outliers. The comparison between the two tables
shows that qualitative results are all the same, except that the WFH estimate in specification 4 turns
statistically significant. However, as explained in the text, this is not our preferred specification, as
the linear time trend does not represent the raw data well, and the effect in specification 3 remains
not significantly different from zero.
Table A.6 estimates the average effect of WFH on the share of non-sick days. Based on both time
controls, the effect is significantly positive, i.e., there were fewer sick days during WFH.
Table A.7 analyzes the same outcomes as Table 7 in the main text, but now also includes aninteraction between the time trend and the WFH dummy. The table shows that in the case ofworking hours the increasing trend is even steeper after WFH. In all other cases the trend is mitigatedduring WFH.

3

Table A.5: Average Working-From-Home effect (top and bottom 1% of outcomes truncated)
(1)

(2)

(3)

(4)

(5)

(6)

Dependent variable

Input

Input

OutputOutputProductivity

Productivity

WFH

2.045***(0.031)

1.547***(0.035)0.038***(0.003)-0.384***(0.084)-0.388***(0.097)0.000(0.010)-0.300***(0.013)-0.226***(0.015)-0.006***(0.002)

Yes
Yes
Yes
0.24
68845
10217

Yes
Yes
No
0.23
68845
10217

Yes
Yes
Yes
0.01
68911
10258

Yes
Yes
No
0.01
68911
10258

Yes
Yes
Yes
0.06
68846
10217

Yes
Yes
No
0.06
68846
10217

Linear month trend

Employee FE
Team FE
Month FE
R2
Observations
Clusters

Note: Input is the time in hours per working day the employee worked in a month. Output is the employee’s normalized
output relative to target in a month. Productivity is output divided by time worked. The unit of observation is theemployee-month. Standard errors are shown in brackets below the point estimates, and are clustered on employee level.
The top 1% and bottom 1% of outcomes are discarded before running the regression to deal with potential outliers.
***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

Table A.8 analyzes the same outcomes as Table 8 in the main text, but now also includes aninteraction between the time trend and the WFH dummy. The table shows that in the case ofInternal NW the increasing trend is even steeper after WFH. In all other cases the trend is mitigatedduring WFH or not statistically different from the WFO period.

4Table A.6: WFH effect on (non-)sick days

Dependent variable

(1)

(2)

NonSickDayShare

NonSickDayShare

0.069***(0.003)0.007**(0.004)0.005***(0.000)

Yes
Yes
Yes
0.08
70168
10247

Yes
Yes
No
0.05
70168
10247

WFH
Linear month trend

Employee FE
Team FE
Month FE
R2
Observations
Clusters

Note: NonSickDayShare is the share of days in a month where the employee worked at least two hours, based on the
Sapience measurement, relative to the number of work days in that month. The unit of observation is the employeemonth. Standard errors are shown in brackets below the point estimates, and are clustered on employee level. The top
1% and bottom 1% of outcomes are discarded before running the regression to deal with potential outliers. ***Significant
at the 1% level; **significant at the 5% level; *significant at the 10% level.

Table A.7: Shift in Working Patterns due to WFH with Change in Trend

WFH
Linear weekly trend
WFH× Time

Employee FE
Team FE
R-squared
Observations
Clusters(1)
Working Hours(2)
After Hours(3)
Focus Hours(4)
Collaboration Hours

2.919***(0.194)0.058***(0.012)0.041**(0.016)

1.219***(0.305)0.270***(0.029)-0.142***(0.033)-0.819***(0.287)-0.203***(0.028)0.140***(0.031)0.560***(0.130)0.063***(0.011)-0.033***(0.013)YY
0.709
25,893
914

Y
Y
0.748
25,893
914

Y
Y
0.719
25,893
914

Y
Y
0.747
25,893914

Note: Working hours are weekly hours worked. After hours are weekly hours worked after regular work time. Focus
Hours are hours with two or more hour blocks not spent in meetings, on calls or writing e-mails. Collaboration Hours
are hours spent in meetings or in calls. The unit of observation is the employee-week. Standard errors are shown inbrackets below the point estimates, and are clustered at the employee level. ***Significant at the 1% level; **significant
at the 5% level; *significant at the 10% level.

5Table A.8: Networking with Change in Trend

WFH
Linear weekly trend
WFH × Time

Employee FE
Team FE
R-squared
Observations
Clusters(1)
Internal
NW(2)
External
NW

(3)
NW
ORG

(4)
NW
EXT

(5)
Meetings
Manager(6)
Meetings
1:1(7)
Coaching
Meets(8)E-mails-6.537***(0.347)0.549***(0.040)0.254***(0.043)-0.585***(0.104)0.089***(0.011)-0.012(0.013)-0.011**(0.005)0.001**(0.000)-0.000(0.000)-0.164***(0.034)0.011**(0.004)-0.003(0.004)

1.165***(0.201)0.139***(0.020)-0.131***(0.023)-0.094(0.059)0.003(0.005)-0.001(0.005)-0.082*(0.042)0.007*(0.004)-0.005(0.004)1.285**(0.580)0.639***(0.073)-0.703***(0.081)YY
0.801
25,893
914

Y
Y
0.758
25,893
914

Y
Y
0.665
25,893
914

Y
Y
0.624
25,893
914

Y
Y
0.758
25,893
914

Y
Y
0.320
25,893
914

Y
Y
0.386
25,893
914

Y
Y
0.768
25, 893914

Note: “Internal NW” is the number of people inside the company with who employee had meaningful contact in last
28 days. “External NW” the same measure for people outside the company. “NW ORG” is the number of distinct
organizational units within the company that the employee had at least two meaningful interactions in the last fourweeks. “NW EXT” the same measure for external domains outside the company. “Meetings Manager” is the numberof meetings involving the manager. “Meetings 1:1” is the number of meetings between the employee and their manager.
“Coaching Meets” is the number between the employee, the manager and all their direct reports. “E-mails” is thenumber of emails sent. The unit of observation is the employee-week. Standard errors are shown in brackets below thepoint estimates, and are clustered at the employee level. ***Significant at the 1% level; **significant at the 5% level;*significant at the 10% level.

6

B

Additional Figures

Figure B.1: Kernel density estimates of subjective Ratings for different levels of Output.

(a) Number of Emails(b) Number of MS Teams Calls

Figure B.2: Technological shift pre- and post WFH. Panel (a): number of emails sent per week. Panel (b):weakly number of calls a person joined through MS Teams. Time= 0 is the week 9th-15th March
2020.

7

Figure B.3: Productivity during WFH depending on pre-WFH productivity. The solid line is the 45-degreeline.

(a) Productivity Sapience(b) Productivity WPA

Figure B.4: Productivity pre- and post WFH in the WPA sample. To make both measures comparable, we
standardize them to mean 0 and standard deviation 1. Panel (a): standardized productivity using
Sapience measure of Input. Panel(b): standardized productivity using the WPA variable working
hours as input. Week= 0 is the week 9th-15th March 2020.

8

Figure B.5: Working Hours over time with different stages of lockdown and removal of lockdown restrictions.
The leftmost dashed line indicates the time at which a national lockdown was imposed. The
first stage of unlocking (“Unlock 1”) allowed e.g. restaurants and shopping malls to reopen, thesecond stage (“Unlock 2”) allowed limited travel and at the third stage (“Unlock 3”) gyms forexample reopened.

9