“Potential Capital”, Working From Home and Economic Resilience


“Potential Capital”, Working From Home and Economic Resilience



By Janice Eberly, Jonathan Haskel and Paul Mizen

The impact of an economic shock depends both on its severity and the resilience of the economic response. The COVID-19 pandemic caused a widespread decline in recorded GDP. Yet, as catastrophic as the collapse was, it was buffered by an unprecedented and spontaneous deployment of what we call “Potential Capital”, the dwelling / residential capital and connective technologies used alongside working from home. Potential capital and labor working from home together provided additional output margins and capacity which roughly halved the decline in GDP in the US and revises downwards estimated total productivity gains in the business sector during the pandemic from 8 log points to 5 log points in 2020Q2.

Large shocks test an economy’s ability to adapt, adjust, and continue – a capability called “resilience” – in response to the unexpected (Brunnermeier, 2021). Critical infrastructure may be damaged or unavailable, leaving other systems stretched beyond capacity. Whether a natural disaster, terrorism or cyber-attacks, or a global pandemic, the severity of a crisis is determined not only by the size of the shock, but also by the resilience of the response. The COVID-19 economic and health crisis greatly stressed the major economies (see Altig et al 2020, Aneyi et al 2021), led to labor market adjustments (see Barrero et al 2020), but in some cases also elicited unplanned resilience.

In this paper we argue that fungibility of factors of production at different locations contributed substantially to economic resilience during the COVID-19 pandemic. This required not only the much-discussed ability of some (fortunate) workers to work from home (see Bloom et al 2015), but also the capital they needed to deploy from these remote-from-work locations, including the capital to connect them to each other and the workplace. We call this “Potential Capital”. Conceptually, it is the equipment – home offices, laptop computers and internet connections – that can be combined with remote-from-workplace labor to produce output.

Consider the Covid pandemic – one of the largest economic shocks in living memory – the largest in three hundred years for some countries.[1] The dark bars in Chart 1, show the peak to trough (2020Q1 to Q2) actual decline in GDP for the seven countries we study in this paper. The decline is dramatic: it shows quarterly declines of between 9 and 20 log points.[2] Yet, catastrophic as the collapse was, it could have been worse. Many workplaces were closed and people were advised to isolate and to work from home (WFH). As we shall document, since much of the workforce was working from home, there was a fall in hours at the workplace of nearly 30 log points from 2020Q1 to 2020Q2 (an unweighted average in our seven countries). Based on an output elasticity oftwo-thirds, output should have fallen by 20 log points, far more than the actual (average) drop of 14 log points. Thus the first puzzle: why did output fall by so little in response to these large changes in hours at the workplace? Could the decline in output have been buffered by economic resilience arising from remote labor?

Chart 1: actual output and workplace output*

*Actual Output is the decline in GDP from Q1 to Q2 of 2020 in National Accounts data.  Workplace Output is calculated based on factor use at the workplace, from Eurostat, BEA, National Accounts and own calculations (see data appendix in the full paper). Work from home from ONS and Google mobility data is described in our appendix. Average is an unweighted average of all countries

A second puzzle emerges if we consider capital in addition to labor. As Mokyr (2001) documents, the history of industrialisation shows the point of going to the workplace is that workers have capital with which to work. It seems hard to believe that the economy substituted a 30 log point fall of hours with a rise in workplace capital in a matter of weeks. Rather, with fewer workers at the workplace, it seems very likely that workplace capital utilization also fell. Based on industrial electricity consumption, we estimate a fall in capital use at the workplace of just over 20 log points, which with an output elasticity of one-third, should have reduced output by a further 7 log points. Thus in Figure 1, the light bars, which are the implied workplace output changes, lie below actual bars illustrating our puzzle: why did output fall by so little in response to these large changes at the workplace? Expanding our question above, could the decline in output have been buffered by economic resilience arising from capital deployed from home?

We propose that the answer to these puzzles is that capital equipment and structures at home were brought into use for production providing the capacity to respond to a large unanticipated shock. During the pandemic, potential capital alongside labor working from home helped to offset the expected decline in output due to low workplace labor and capital utilization rates, which explains why output did not fall as much as it might have done. Production was buffered by use of capital and labor at home. Productive labor and capital at home gave firms flexibility to respond to a large shock like Covid. Fungibility between capital and labour at home and the workplace is therefore a source of economic resilience because capital at home has both productive capacity (e.g. can run software) and connectivity with other workers making home working both possible and productive.

Photo by Andrew Sorensen, Creative Commons licensed CC BY-NC 2.0

This has implications for the amount of output produced and the measured productivity during the pandemic, as our paper explores in greater detail. But to the extent that WFH was motivated by elevated costs of working at the workplace, these costs may well reverse, at least partially, as the pandemic recedes. A large elasticity – ranging from 1 to 3 even in the more proscribed estimation – suggests that WFH may recede along with the costs of working at the workplace. The elasticity is estimated using both positive and negative changes during the pandemic, though a more permanent reversal of costs may have a more durable and potentially larger effect on work location choices, especially if there are frictions associated with changing. Of course, the costs of working from work may not reverse entirely, as customers and workers may demand better ventilation, hand washing, and other health measures going forward.

The positive level effect of pre-pandemic ICT on WFH suggests that existing capacity to work remotely was crucial to enable substitution, especially early in the pandemic (2020Q2) but also later in the year as the pandemic persisted. This capacity predated the pandemic (the ICT share is measured in 2019) and showed trended growth over time (Oulton 2012), and hence is unlikely to revert to the pre-pandemic mean. Our interpretation is that the pandemic revealed the capability to WFH that already existed with dwellings capital and connective technologies. The pandemic acted as a large shock and coordination device, overcoming the collective action problem needed to demonstrate the possibility of working remotely. Moreover, the sustained period of the pandemic enabled learning. Both the capacity and the learning will surely continue beyond the pandemic, and, coupled with the suggestion that the elasticity of substitution is greater than unity, this should enabling more persistent WFH, as argued by (Barrero, Bloom, and Davis 2020b).


Home capital and home working proved over the duration of the pandemic to be a source of economic resilience that we estimate accounted for 8 to 14 percent of GDP in the trough of the COVID-19 recession. Following one of the largest economic shocks in living memory, our results emphasize the quantitative effects were not as large as they might have been due to the large-scale restructuring of production at pace. If WFH had been ignored along with potential capital, output would have fallen and productivity would have been exceptionally strong in 2020Q1 and 2020Q2, due to mismeasurement of labor and capital inputs to production of goods and services. The pandemic has revealed under-utilized capital across the economy and across the globe. The gig economy previously uncovered and deployed some of this capacity, such as the part-time driver who uses their domestic vehicle for commercial rides, yet, none of these explorations envisioned the deployment of home capital at the scale and speed, with the potential consequences, observed in the context of the COVID-19 crisis. The pandemic revealed unused capacity as a macroeconomic phenomenon.

Our paper also shows that greater WFH will occur when home labor becomes more productive if the elasticity of substitution between tasks at home and work exceeds unity. This elasticity is pivotal, and our estimates suggest it does indeed exceed unity, providing more evidence for the likelihood of further remote work. Networks have enabled companies, marketplaces and facilitators of production and sale from home by unlocking potential capital. The existence of this technology allowed economic resilience in the pandemic that we estimate accounted for 8 to 14 percent of GDP in the trough of the COVID-19 recession. We have found the shift to WFH is largest in industries which had higher existing stocks of ICT capital, suggesting the important role of investment and technology in facilitating resilience. While the on-going economic impact of new digital technologies has been controversial and difficult to measure, the pandemic may have been the moment that demonstrated the value of this resilience. Future work on more data might take up these important issues.

Read the full ESCoE Discussion Paper here.

[1] Wall Street Journal Feb 12, 2021, U.K. Economy Suffers Biggest Slump in 300 Years Amid Covid-19 Lockdowns.

[2] We use natural log changes (times 100) throughout to be consistent with implementation of our growth accounting framework in section 2.  For a change to y from x, the log point change = 100*ln(y/x).

Janice Eberly is Senior Associate Dean for Strategy and Academics at the Kellogg School of Management of Northwestern University
Jonathan Haskel is Professor of Economics at Imperial College Business School, Imperial College London
Paul Mizen is Professor of Monetary Economics at the University of Nottingham and an ESCoE Research Associate

ESCoE blogs are published to further debate. Any views expressed are solely those of the author(s) and so cannot be taken to represent those of the ESCoE, its partner institutions or the Office for National Statistics.

About the authors

Janice Eberly

Jonathan Haskel

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