2021
DOI: 10.2139/ssrn.3846680
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Work from Home & Productivity: Evidence from Personnel & Analytics Data on it Professionals

Abstract: Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Founda… Show more

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Cited by 99 publications
(79 citation statements)
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References 29 publications
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“…Time fragmentation could possibly lead workers to report increased work hours, even though our study finds both increased and decreased work hours. Additionally, a recent study uses software-recorded work time before and after the pandemic to examines changes in work hours among skilled professionals at a large Asian IT services company (similar to the high-SES portion of our sample) (Gibbs et al, 2021). Similar to our evidence on high-SES workers, they found that total hours worked increased by roughly 30%, and workers with more industry experience (presumably managers, as they speculated) see greater increases in work hours.…”
Section: Discussionsupporting
confidence: 70%
“…Time fragmentation could possibly lead workers to report increased work hours, even though our study finds both increased and decreased work hours. Additionally, a recent study uses software-recorded work time before and after the pandemic to examines changes in work hours among skilled professionals at a large Asian IT services company (similar to the high-SES portion of our sample) (Gibbs et al, 2021). Similar to our evidence on high-SES workers, they found that total hours worked increased by roughly 30%, and workers with more industry experience (presumably managers, as they speculated) see greater increases in work hours.…”
Section: Discussionsupporting
confidence: 70%
“…Working remotely represents a “weak” situation, where employees have high levels of autonomy, the goals (nor the means to achieve them) are not clearly specified, and the attainment of these goals is often unlinked to predefined rewards ( Mischel and Shoda, 1995 ). Moreover, given that this new context of work seems characterized by less frequent interactions between leaders and employees ( Gibbs et al, 2021 ) and that close monitoring during remote work is shown to have adverse effects on employees’ wellbeing ( Wang et al, 2020 ), it is crucial to gain a better understanding of the role of self-leadership for proactivity during remote work after COVID-19.…”
Section: Introductionmentioning
confidence: 99%
“…Unpacking such processes of self-leadership and proactivity, and their link with work-related wellbeing during remote working is timely and relevant. Remote-work arrangements are likely to be used much more in the future, with positive net effects depending on whether they are implemented well ( Gibbs et al, 2021 ).…”
Section: Introductionmentioning
confidence: 99%
“…This cautiously suggests that WFH might be more beneficial for both developers and organizations than working in the office, or at least for some group of professionals [28]. However, while some studies support our conclusion that WFH increases or does not impact productivity [2,3,19,69], some studies also found that WFH has a negative impact on productivity [33,45,59]. As there are too many potential differences between the studies (e.g., cultural factors, working conditions at home, type of work, measurement of productivity), we need to wait for cross-country and cross-profession studies with large sample sizes or meta-analyses that synthesize the findings to get a better idea as of why some studies found that WFH did not impact productivity during the Covid-19 pandemic.…”
Section: Implications For Research and Practicementioning
confidence: 47%
“…Select three tasks." Participants selected three of the tasks we used in Study 1, except breaks, interruptions, and various, which were excluded, leaving 12 tasks: Coding (n = 192), buxfixing (111), testing (96), specification (22), reviewing (91), documenting (40), meetings (87), emails (51), helping (33), networking (11), learning (93), and administration (14). Participants then completed 17 items for each task, 8 measuring our two dependent variables, well-being and productivity, and 9 measuring our three independent variables, need for autonomy, competence, and relatedness.…”
Section: Measurement Of Task-specific Variablesmentioning
confidence: 99%