8227 2020

Original Version: April 2020

This Version: December 2020

Germany’s Capacity to Work from Home Jean-Victor Alipour, Oliver Falck, Simone Schüller

Impressum:

CESifo Working Papers ISSN 2364-1428 (electronic version) Publisher and distributor: Munich Society for the Promotion of Economic Research - CESifo GmbH The international platform of Ludwigs-Maximilians University’s Center for Economic Studies and the ifo Institute Poschingerstr. 5, 81679 Munich, Germany Telephone +49 (0)89 2180-2740, Telefax +49 (0)89 2180-17845, email office@cesifo.de Editor: Clemens Fuest https://www.cesifo.org/en/wp An electronic version of the paper may be downloaded · from the SSRN website: www.SSRN.com · from the RePEc website: www.RePEc.org · from the CESifo website: https://www.cesifo.org/en/wp

CESifo Working Paper No. 8227

Germany’s Capacities to Work from Home

Abstract We propose an index of working from home (WfH) capacities for industries and counties in the German economy. Our analysis draws on individual-level survey information and administrative employment statistics. We find that overall 56 percent of jobs are suitable for part-time or full-time remote work. WfH-compatible jobs are characterized by cognitive and PC-intensive tasks, typically located in more urban areas, and performed by high-skilled workers. We document that especially part-time (rather than full-time) WfH capacity tends to be associated with highly complex non-routine cognitive tasks, which have become increasingly important. We validate our index by showing that it is a strong predictor of industry- and occupation-level variation in WfH during the Covid-19 crisis. Overall, our measure is a useful input for regional or industry-level analyses investigating the impact of the Covid-19 pandemic and labor market adjustments during and beyond the crisis. JEL-Codes: D240, J220, J240, R120. Keywords: Covid-19, working from home, remote work, Germany.

Jean-Victor Alipour

ifo Institute – Leibniz Institute for Economic Research at the University of Munich

Poschingerstr. 5 Germany – 81679 Munich

alipour@ifo.de

Oliver Falck* ifo Institute – Leibniz Institute for Economic

Research at the University of Munich Poschingerstr. 5

Germany – 81679 Munich falck@ifo.de

Simone Schüller

German Youth Institute (DJI) Nockherstr. 2

Germany – 81541 Munich schueller@dji.de

*corresponding author First version: April 14, 2020. This version: December 16, 2020

1. Introduction

In the wake of the Covid-19 pandemic and the associated curfews, working fromhome (WfH) has experienced an unprecedented boom. Survey evidence suggeststhat 42 (Bloom, 2020) to 50 percent (Brynjolfsson et al., 2020) of U.S. workersworked from home during April and May 2020. Similar shifts are recorded in Eu-rope, with nearly 60 percent of workers switching to remote work due to the crisisin Finland and the Netherlands and close to 40 percent in Germany (Eurofound,2020). These adjustments are likely to induce lasting changes to the organizationof work for several reasons: First, companies that switched to WfH incurred fixedcosts from digitizing work processes, upgrading IT infrastructure, implementingdigital communication tools and training employees in their usage. Second, evi-dence shows that remote work policies can represent a competitive advantage inattracting qualified labor (Mas and Pallais, 2017) and generate sizable productivitygains once implemented (Angelici and Profeta, 2020; Bloom et al., 2015; Choud-hury et al., 2020). Thus, once fixed costs are borne and WfH stigmas lifted, apermanent expansion of WfH relative to the pre-crisis level is likely. This raises thecentral question: How many jobs are suitable for remote work?

In this paper, we estimate the German economy’s overall capacity to work fromhome and develop a WfH capacity index for industries and regions. We docu-ment demographic differences in the feasibility to work from home and character-ize WfH-compatible jobs using information on job tasks and work conditions. Ouranalysis is based on information from a large representative employment surveycombined with administrative employment statistics. We contribute to the litera-ture in mainly two ways: First, in contrast to most previous studies, we do notlimit our scope to jobs that can be done at home entirely, but also include partialWfH. Restricting the focus to jobs compatible with full-time WfH tends to underes-timate actual capacities in the economy, as evidence on actual WfH rates during the

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Covid-19 lockdown shows (Brynjolfsson et al., 2020).1 Second, our survey-basedmeasure is independent of plausibility judgements about the WfH-compatibility ofcertain job tasks, which are prone to measurement error. Instead, we rely on em-ployees’ assessments about their own job.

We find that overall 56 percent of employees in Germany can entirely or partlywork from home. WfH-compatible jobs are typically located in more urban areas,characterized by cognitive and PC-intensive tasks and carried out by high-skilledworkers. We further document that the pre-pandemic untapped WfH capacity (thatamounts to roughly half of the estimated capacity) was mainly due to employer-rather than employee-side restrictions. Moreover, highly complex non-routine cog-nitive job tasks, particularly those requiring social or creative intelligence, appear tobe strongly associated with part-time WfH compatibility, but tend to preclude full-time WfH. Finally, leveraging data from employer and employee surveys, showsthat our measure is a strong predictor of WfH patterns during the Covid-19 pan-demic. Our WfH capacity index is therefore a useful input for regional or industry-level analyses investigating the impact of the Covid-19 pandemic and labor marketadjustment during and beyond the crisis. The index at the industry-level (88 2-digitNACE industries) and at the county-level (401 NUTS-3 level regions) is availablefor download for research purposes.2

The remainder of the paper is structured as follows: In Section 2, we describehow we construct our WfH capacity index and discuss alternative approaches tomeasure WfH capacity. Section 3 reports the results, describing sectoral, regionaland demographic differences in WfH capacity and characterizing WfH-compatiblejobs. Section 4 explores differences between part- and full-time WfH-compatiblejobs, employing the task-based approach by Dingel and Neiman (2020) to identifyjobs that can be done entirely from home. In Section 5, we validate our measure by

1While the actual WfH rates during the US lockdown are about 50 percent (Brynjolfsson et al.,2020), the most prominently discussed (task-based) estimate for full-time WfH capacity suggeststhat only about 37 percent of US jobs can be performed entirely at home (Dingel and Neiman,2020).

2The data are available at: https://github.com/jvali1/alipouretal wfh germany/tree/master.

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showing that it performs well in predicting industry-level adjustments in terms ofWfH during the Covid-19 crisis. Section 6 concludes.

2. Measuring WfH Capacity

2.1. Approaches to measure WfH in the literature

Our paper relates to a recent strand of research aimed at quantifying WfH capacities.In their influential paper, Dingel and Neiman (2020) determine job characteristicsthat preclude the possibility of entirely working from home (e.g. working outdoors)based on O*NET task data and accordingly classify occupations as either compat-ible or incompatible with WfH. Combining the classification with the prevalenceof each occupation in the economy, the authors find that a maximum of 37 per-cent of U.S. jobs can entirely be done at home. Variants of this method have beenproposed and extended to several countries.3 Estimates for the German economyvary between 17 (Pestel, 2020), 29 (Boeri et al., 2020), 37 (Dingel and Neiman,2020) and 42 percent (Fadinger and Schymik, 2020). The range of estimates is siz-able and may reflect both different judgements about which job characteristics areincompatible with WfH as well as different data limitations.

A common theme of these studies is the focus on jobs that can potentially be per-formed entirely at home. We argue that excluding jobs which only allow for afraction of work to be carried out at home might miss important adjustments inthe economy for several reasons: First, recent survey evidence indicates that mostworkers can perform some fraction (rather than all or none) of their job tasks athome (Adams-Prassl et al., 2020b). This finding is also in line with the observationthat full-time WfH rates before the pandemic were typically low, e.g. 3.5 percentin Germany (own calculation), 5.1 percent in the U.K. (Watson, 2020), and around4 percent in the U.S. (Mas and Pallais, 2020, Fig. 1). Second, evidence suggeststhat partial WfH has both contributed to maintaining economic activity (measured

3See e.g. Barbieri et al. (2020), Boeri et al. (2020), Del Rio-Chanona et al. (2020), Fadingerand Schymik (2020), Holgersen et al. (2020), Mongey et al. (2020), OECD (2020), Pestel (2020) orYasenov (2020).

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via likelihood of job-loss or short-time work) and to mitigating the spread of Covid-19 (Adams-Prassl et al., 2020a; Alipour et al., 2020). This is corroborated by thefact that measures of full-time WfH capacity have underestimated actual WfH ratesduring the pandemic lockdown (see Brynjolfsson et al., 2020 for this observation inthe U.S. context). Hence, the pandemic-related WfH shock and its ramifications forthe economy are not solely driven by the subset of employees with full-time WfHcapacity, but also include those with partial WfH feasibility. A policy-relevant mea-sure of WfH capacities should therefore account for both types of jobs.

Another set of studies draws on pre-pandemic employment surveys in which indi-viduals directly report on their WfH practices to measure capacities to WfH—seee.g. Alon et al. (2020), Hensvik et al. (2020) and Papanikolaou and Schmidt (2020)using the American Time Use Survey. An advantage of this approach is that assess-ments about jobs’ WfH-compatibility is independent of researchers’ own judge-ments. A drawback is that most pre-pandemic surveys inquire about actual WfHprevalence rather than feasibility.4,5 During the pandemic, the focus of surveys hasshifted toward the latter. For instance, Adams-Prassl et al. (2020b) surveyed about25,000 U.S. and U.K. employees in April and May 2020 about the fraction of jobtasks they could perform at home and document sizable degrees of dispersion withinoccupations.

Our approach leverages information from a representative pre-pandemic survey forGermany, in which employees are inquired about WfH feasibility, explicitly ab-stracting from employer-side restrictions. At the employee-level, we do not captureWfH feasibility on a continuous scale (as in Adams-Prassl et al., 2020b), but in-stead rely on binary information about WfH feasibility. It is worth noting that anemployee reporting that WfH is “not possible” does not necessarily correspond to0 percent WfH-compatible tasks as measured by Adams-Prassl et al. (2020b). It

4In the American Time Use Survey (ATUS) respondents are asked “As part of your (main) job,can you work at home?”, which still conveys employer-side restrictions.

5A notable exception is Mas and Pallais (2020), who employ questions about the fraction ofwork a respondent could feasibly complete from home (module 82 of the 2017 Understanding Amer-ica Survey). However, the sample size (N =625) is relatively low to draw meaningful conclusionsabout occupational, sectoral or regional distributions of WfH feasibility.

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is likely that our measure additionally includes an implicit qualitative assessmentby employees as to what share of WfH-compatible tasks makes WfH meaningfullypossible in their job.

2.2. A new survey-based approach

Our measure of WfH capacity builds on survey information from 17,160 employ-ees (aged 18-65, excluding the marginally employed) from the 2018 wave of theBIBB/BAuA6 Employment Survey (Hall et al., 2020). Specifically, we exploit theanswers to the following question: “If your company would allow you to work at

home temporarily, would you accept this offer?”—Yes; No; Is not possible with

my work. We suppose that a job is not suitable for remote work if the respondentnever works from home and indicates that WfH is “not possible” in her job.7 Con-sequently, our binary measure captures the full range of WfH feasibility, includingjobs suitable for entire, partial or temporal relocation to the home office.

To obtain the overall share as well as the geographical and industry-level distribu-tion of WfH-compatible jobs in the German economy, we first collapse our employee-level WfH feasibility indicator (population weighted) to the occupation level (2-digit German Classification of Occupations, KldB 2010, excluding military ser-vices).8 Next, we combine the resulting shares with 2019 data from the FederalEmployment Agency (BA) on occupational employment counts in the overall econ-omy, in each German county (401 Kreise and kreisfreie Stadte), and in each industry(88 2-digit NACE industries) and aggregate over occupations.

It should be noted that regional employment statistics distinguish between employ-ment at the county of work and at the county of residence. Using the former toconstruct our measure yields a distribution of WfH-compatible jobs independent ofemployees’ place of residence, while using the latter allows to measure local shares

6BIBB: Federal Institute for Vocational Education and Training (Bundesinstitut fur Berufsbil-dung); BAuA: Federal Institute for Occupational Savety and Health (Bundesanstalt fur Arbeitsschutzund Arbeitsmedizin).

7See Mergener (2020a) and Brenke (2016) for similar definitions of WfH feasibility at the em-ployee level.

8See Figure A1 in the Appendix for occupational-level WfH capacity.

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of employees who could work from home independent of the location of their job.In the following, we restrict our scope to the former case as both approaches yieldvery similar results.9

Moreover, the survey contains information on employees’ WfH usage rates, allow-ing us to distinguish between WfH capacities already exploited before the pandemicand previously untapped capacities. We can further characterize exploited capac-ities by WfH intensity (frequent WfH and occasional WfH).10 Finally, we use theinformation whether employees (who never work from home) would accept or de-cline an offer to WfH to characterize untapped capacities. We suppose that em-ployees who would accept the offer experience constraints from their employer inpractice (supply side restrictions), while those who would reject the offer have nodemand for remote work (demand side restrictions).11

3. WfH Capacities in Germany

We find that overall about 56 percent of jobs in Germany can be entirely or partlyperformed from home. Before the Covid-19 pandemic, less than half of this capac-ity was exploited. While about two-thirds of the untapped WfH capacity was dueto employer-side restrictions, one-third can be attributed to a lack of demand byemployees.

3.1. Where are WfH-compatible jobs?

Figure 1 reports WfH capacities overall and by sector (NACE main sections).12

Sectors are displayed in descending order according to their contribution to GDP.There is a large variation in WfH capacity across industries, with values rangingfrom 37 percent in the transportation or agricultural sector to nearly 90 percent inthe financial sector.

9Discrepancies are essentially due to cross-county commuting. The correlation between county-level WfH-capacities calculated from both approaches is .81. For completeness, we publish our WfHcapacity index based on both approaches in our online repository.

10Frequent WfH includes working from home “always” or “often”, while occasional WfH in-cludes working from home “sometimes” and “rarely”.

11See also Mergener (2020b).12Results at the 2-digit NACE level are available in our online repository.

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In most sectors, actual WfH rates before the pandemic fall well below the capacitylimit. Only employees in the education and the ICT sector used distinctly more thanhalf of their WfH capacity. Across all industries, pre-pandemic untapped capacitiesare mainly driven by employer-side rather than demand-side restrictions.

Figure 1 about here

Figure 2 depicts the geographic distribution of WfH capacity across the 401 Germancounties (Kreise and kreisfreie Stadte). The map reveals a clear divide betweenEast and West Germany and among urban and rural counties. While on average59 percent of employees in West Germany (including Berlin) can work from home,in East Germany (excluding Berlin) only 50 percent of employees can do so. Urban-rural inequalities are even more pronounced: WfH capacity amounts to roughly65 percent in counties with 500,000 inhabitants or more, versus 53 percent in therest of the country.

Figure 2 about here

By construction, the industry-level and regional variation in WfH capacity is deter-mined by the occupational composition in each industry and county, respectively.13

Compared to other worker characteristics, occupations do indeed explain most ofthe individual-level variation in WfH feasibility: Table A1 in the Appendix reportsgoodness of fit measures for separate logistic models explaining WfH-feasibilityas a function of 2-digit occupation fixed effects (Column 1), occupation and sectorfixed effects (Column 2), 3-digit occupation fixed effects (Column 3) and occupa-tion and sector fixed effects together with a set of employee and job task character-istics (Columns 4 and 5). Accuracy, i.e., percent of observations correctly classifiedby the model (using .5 as classification threshold) increases moderately from 75 to80 percent when moving from a model only including 2-digit occupations to oneusing the full set of explanatory variables. Similarly, Youden’s J statistic, defined asthe sum of the rate of correctly classified positives (sensitivity) and the rate of cor-rectly classified negatives (specificity) minus 1, increases by less than 8 points. We

13In the aggregate, the discrepancy between WfH capacity calculated from the survey alone andcalculated employing occupational employment counts is only about 1 percentage point.

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also regress the shares of WfH-compatible jobs for 139 3-digit occupations on 362-digit occupation fixed effects. The R-squared is relatively high (R2 = .89), sug-gesting that there is little within-variation in terms of WfH feasibility at the 2-digitoccupation level.

3.2. Who can work from home?

Figure 3 breaks down the capacity to WfH by demographic characteristics. Thepossibility to work from home is strongly correlated with education: Employeeswith an academic degree are almost twice as likely to have a WfH-compatible jobcompared to non-academics. Proportionally, lower-skilled groups exploited muchless of their WfH potential before the crisis. These imbalances are reflected in thestrong positive correlation between WfH capacity and monthly wage. Women’sWfH capacity is higher than men’s by about 9 percentage points, while pre-crisisWfH uptake is very similar. Distinguishing by child-care requirements (presence ofchildren below age 11 in the household) shows that family-oriented employees tendto sort in WfH-compatible jobs and work from home more frequently. There areno substantial differences between native Germans and employees with a migrationbackground. Furthermore, WfH capacity tends to decline with age, with the excep-tion of the youngest employees (below age 25), whose WfH capacity is the lowestof all age bins. This discrepancy can likely be explained by the different skill com-position in the lowest age cohort due to the absence of university graduates, whotypically enter the labor market only in their twenties.

Figure 3 about here

Across all demographic dimensions employer-side restrictions account for a greaterproportion of the pre-pandemic untapped WfH potential than a lack of demand byemployees. Table A2 in the Appendix compares the characteristics of employeeswho do not work from home because they are unwilling to (demand-side) withthose who are not granted the option to (supply-side) work from home, conditionalon WfH feasibility. We find that “the unwilling” are statistically significantly older,more likely female, and more likely to have young children, while there are no sig-nificant differences in marital status, migration background or academic education.

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To shed light on the distribution of supply-side restrictions across different employ-ers, we break down the fraction of WfH capacity that is untapped due to supply-siderestrictions by sector and plant size (Figure A2 in the Appendix). Supply-side re-strictions appear strongest in construction and retail/wholesale, where about 46 per-cent of WfH capacity has been unused because employers do not grant the optionto work from home. We find no considerable variation across different plant sizes.Overall, supply-side restrictions are consistently the dominant factor for unusedWfH potential.

3.3. Which jobs can be done from home?

To shed light on the characteristics that determine a job’s suitability for remotework, we draw on information on job tasks and work conditions included in the2018 BIBB/BAuA Employment Survey. Employees are asked, for instance, howoften they carry heavy loads, monitor machines or use a computer on the job. Wecode each job feature as one if the respondent indicates that it occurs frequently,and zero otherwise. Figure 4 reports the average marginal effects from a simple lo-gistic model, regressing the indicator of having a WfH-compatible job on frequenttasks and work conditions. Cognitive and manual tasks are labeled with (c) and(m), respectively. The results suggest that jobs requiring frequent computer usageand “developing, researching, constructing” are most likely to be compatible withWfH. In contrast, jobs featuring “work standing up” and “nursing, caring, healing”are least likely to be compatible with remote work. Overall the findings are con-sistent with previous research suggesting that cognitive, non-manual tasks are mostlikely to be transferable to the home office (Mergener, 2020a). These type of tasksare typically performed by higher-skilled workers, which explains much of the dif-ferences in WfH capacities across wage groups documented above. Unsurprisingly,industries with the highest WfH capacity, such as financial services, also exhibitparticularly high frequent-PC-user rates of over 90 percent.

Figure 4 about here

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4. Part-time versus Full-time WfH Capacities

The rich data contained in the BIBB/BAuA Employment Survey also allows usto construct a task-based measure of full-time WfH feasibility following the task-based approach proposed by Dingel and Neiman (2020) (hereafter DN). ReplicatingDN’s measure using the BIBB/BAuA Employment Survey 2018 instead of O*NETdata enables us to analyze the extent of WfH feasibility directly at the employeelevel. This approach also rules out that specificities of the US economy inherent inthe O*NET data enter the measure for Germany. In particular, we impose that anemployees’ job is incompatible with full-time WfH if at least one of 11 conditionsis met. These include for instance, working the majority of the time outdoors, neverusing the Internet or Email or frequently carrying heavy loads. The full list of con-ditions is reported in Table A4 in the Appendix. Overall, the tasked-based measuresuggests that 34 percent of jobs can be performed entirely at home, a value whichis remarkably close to the 37 percent calculated by DN for the German economybased on O*NET data.

Table 1 compares the survey-based and the task-based measures of WfH capac-ity across the 17,093 employees included in our sample: In 2 out of 3 cases bothmeasures are consistent, by either both indicating WfH feasibility (28.9 percent) orboth indicating WfH infeasibility (37.6 percent). 28.2 percent of jobs classify ascompatible with WfH according to the survey-based indicator but not according tothe task-based measure. As the latter is meant to capture full-time WfH capacity,this discrepancy can be interpreted as the portion of jobs that is only suitable forpart-time WfH, but not for full-time WfH. Finally, 5 percent of survey respondentsrule out the possibility to WfH while the task-based measure instead implies thatWfH is feasible full-time. While this inconsistency point to the possibility that thetask-based measure may be too lax at identifying job characteristics that precludeWfH, we also cannot reject the possibility of measurement error in the survey ques-tion. We therefore exclude the small portion of these “misclassifed” jobs from thesubsequent analysis.

Table 1 about here

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The assumption that the task-based approach measures full-time WfH capacitywhereas the survey-based measure includes both part-time and full-time capacityfor remote work, allows us to further explore potential differences between part-time and full-time WfH compatible jobs. Part-time compatible jobs are then definedas those classified as compatible with WfH according to the survey-based indicatorbut not according to the task-based measure.

In particular, we explore whether part-time and full-time WfH compatible jobs sys-tematically differ with respect to job tasks and work conditions. To this end, weemploy a generalized ordered logit model at the individual level and regress the ex-tent of a job’s WfH compatibility (none, part-time, full-time) on the full set of jobtasks and work conditions used to characterize overall WfH capacity in Section 3.3(see Figure 4). While the full set of tasks and work conditions is included in themodel, we only discuss those tasks and work conditions that appear to be positivelyassociated with overall WfH capacity (see Figure 4). Figure 5 reports the marginaleffects of those covariates on the probability of observing each of the three possibleWfH compatibility types (no, part-time or full-time compatibility). The depictedjob characteristics include mainly cognitive tasks that unsurprisingly have a nega-tive (or statistically insignificant) effect on the likelihood of not being able to workfrom home.

Figure 5 about here

Comparing effect sizes with respect to part- and full-time WfH compatibility re-veals that in ten out of sixteen tasks and work conditions the marginal effect isdistinctly larger for part-time WfH than for full-time WfH feasibility. Some jobcharacteristics even significantly increase the chance of having a part-time WfHcompatible job while not affecting or actually reducing the likelihood with regardto full-time WfH feasibility. For instance, this is the case if a job involves frequentorganizing and planning, solving problems, researching and developing, improvingand innovating existing procedures, or closing knowledge gaps. Interestingly, theseparticular job characteristics do stand out as highly complex non-routine tasks, i.e.,activities that do not follow well-defined rules, in the taxonomy of Autor et al.(2003) or as “bottlenecks to computerization”, i.e., tasks that are unlikely to be au-

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tomatable using computer-controlled equipment in the coming decades accordingto Frey and Osborne (2017).14

Our results indicate that although cognitive tasks are generally easier to be per-formed at home, complex, non-routine cognitive activities, particularly those thatrequire social or creative intelligence, involve elements that cannot be entirely relo-cated to the home office. These are likely the kinds of sophisticated interaction withpeers or subordinates that rely on the face-to-face exchange of (formal or informal)knowledge, information or argument.

5. Validating our WfH Capacity Index

We evaluate the performance of our WfH capacity index by assessing its power topredict firms’ and employees’ adjustments to the Covid-19 shock. With respect tofirms, we leverage data from the ifo Business Survey (IBS), a monthly represen-tative survey of roughly 9,000 German companies from all relevant industries (seeBuchheim et al., 2020 and Sauer and Wohlrabe, 2020 for details). We use datafrom the April 2020 wave, in which companies were questioned about managerialresponses to the Covid-19 crisis.15 Nearly two-thirds of the firms indicated to “relymore heavily on working from home” as part of their strategy to cope with thepandemic. The measure thus captures efforts to increase WfH uptake both at the in-tensive and the extensive margin. We compute the industry-specific shares of firmsrelying on WfH and plot these against our measure of WfH capacity at the 2-digitindustry level (Figure 6). Observations are weighted with total employment in June2019, giving more importance to larger industries. The plot shows that our mea-sure of WfH capacity performs remarkably well in predicting WfH patterns acrossindustries. The index explains about 58 percent of the variation in crisis-induced

14Frey and Osborne classify such bottlenecks into three distinct categories, namely Perceptionand Manipulation tasks, including working in cramped work spaces or awkward positions, CreativeIntelligence tasks, e.g., coming up with new ideas or creative ways to solve problems, and SocialIntelligence tasks including negotiating, persuading and providing emotional attention or care toothers.

15Nationwide containment measures were in place between late March and early May 2020 inGermany with closures of restaurants and bars, as well as daycare facilities, schools, universitiesand non-essential shops, followed by a gradual easing of these measures.

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WfH. The correlation between industry-specific WfH capacity and WfH uptake is.76 and highly statistically significant.

Figure 6 about here

Figure 7 about here

To validate our measure of WfH capacity with respect to employees’ adjustmentsto the Covid-19 shock, we employ data from the IAB High-Frequency Online Per-sonal Panel (HOPP), a monthly online panel survey developed by the Institute forEmployment Research (IAB) to investigate how the Covid-19 pandemic affects in-dividuals in the German labor market (Sakshaug et al., 2020).16 We use the May2020 wave, in which about 7,500 employees subject to social insurance contribu-tions report whether they have been working from home during the previous week.Overall, about 32 percent report to have done so. We compute occupation-specificWfH usage rates and plot these against our measure of WfH capacity at the occu-pational (2-digit KldB 2010) level (Figure 7). Observations are weighted with totalemployment in June 2019, assigning more importance to larger occupations. Theplot demonstrates that our measure of occupational WfH capacity is strongly asso-ciated with employees’ WfH utilization during the Covid-19 pandemic. Our WfHindex explains about 86 percent of the variation in crisis-induced WfH. The corre-lation between occupation-specific WfH capacity and WfH use is .92 and highlystatistically significant.

6. Concluding Remarks

The Covid-19 crisis has prompted a massive shift toward WfH. Evidence from Ger-many suggests that firms invested heavily to maintain business alive with employeesunable (or not allowed) to work from company premises. Efforts included primar-ily investments in digitizing work processes: While job advertisements on LinkedIn

16A short survey description can be found at https://www.iab-forum.de/glossar/hopp-befragung/?pdf=17949 and several data tables on special content are available at http://doku.iab.de/arbeitsmarktdaten/ADuI hopp aktuell.xlsx (only in German). The data anddata documentation will be provided at the Research Data Centre (FDZ) of the German FederalEmployment Agency (BA) at the Institute for Employment Research (IAB) in the future.

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plummeted in literally all sectors during the crisis (except in health), the demandfor software and IT services rose by 21 percent compared to the same period in theprevious year (ifo Institute, 2020). These expenses for IT services and digital com-munication solutions as well as investments in digital competences and practical ex-perience are likely to induce permanent organizational adjustments. Evidence fromthe May 2020 wave of the IBS suggests that indeed much of employer-side concernsagainst WfH have waned: 54 percent of German firms anticipate that WfH will playa more important role in their company after the crisis. Also on the employee side,surveys suggest that reservations about WfH have dwindled despite the challengingcircumstances during the crisis.17 There is hence reason to expect a persistent shiftof WfH beyond the pandemic. Nonetheless, full-time WfH will likely remain theexception, even when technically feasible. Besides straining workers’ well-beingdue to social isolation (Bloom et al., 2015), WfH is found less productive for tasksthat require creative problem solving or team production (Choudhury et al., 2020;Mas and Pallais, 2020). In line with these findings, our results indicate that complexnon-routine cognitive tasks requiring creative and social intelligence are associatedwith part-time rather than full-time WfH capacity. It is more likely, therefore, thatthe future of work will be marked by hybrid work models which reconcile the ben-efits of remote work with personal exchange in the office. This underscores theimportance of taking into account jobs which allow for part-time WfH in additionto jobs that can entirely be done from home. Assessing the optimal division of la-bor into remote and on-site jobs while balancing employee preferences and marketimperfections is still a nascent literature (Harrington and Emanuel, 2020). But theexperiences during the Covid-19 crisis have already revealed that several questionsawait answering from policy makers. For instance, who is to cover expenses re-lated to remote work—employees, firms, or possibly the state via tax deductionsand subsidies? How can worker protection regulation concerning working hoursand workplace safety be reconciled with remote work?

17For instance, over 70 percent of employees who only started working from home regularly duethe Covid-19 pandemic stated that they would like to work from home permanently for (at least) partof their working hours, according to a survey conducted by the German health insurer DAK in Apriland May 2020 (DAK, 2020). Sturz et al. (2020) report similar evidence from an unrelated survey.

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Acknowledgements

We thank Anna Kerkhof for helpful comments and Juliane Neumeier for editorialadvice. We also thank the BIBB Research Data Center (BIBB-FDZ) for providingaccess to the BIBB/BAuA Employment Survey. We thank the Institute for Employ-ment Research (IAB), especially Dana Muller, Georg Haas and Stefan Zins for pro-viding us with information on WfH use at the 2-digit occupational level from theIAB High-Frequency Online Personal Panel (HOPP). We further thank SebastianLink for sharing useful code to compile the ifo Business Survey and the LMU-ifoEconomics & Business Data Center (EBDC) for providing data access. Jean-VictorAlipour gratefully acknowledges funding from The Society for the Promotion ofEconomic Research (Freunde des ifo Instituts e.V.). Declarations of interest: none.

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Figure 1: Capacity to Work from Home (WfH) by Industry

Notes: The figure displays WfH capacities and pre-pandemic WfH usage by industry (NACE main sections). Sectorsare displayed in descending order according to their contribution to GDP.Sources: BIBB/BAuA Employment Survey 2018, Employment Statistics of the Federal Employment Agency (BA)2019, own calculations.

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Figure 2: Distribution of WfH-compatible Jobs in Germany

Notes: The map depicts the percentage share of WfH-compatible jobs across German counties (Kreise and kreisfreieStadte). Black dots represent cities with more than 250,000 inhabitants.Sources: BIBB/BAuA Employment Survey 2018, Employment Statistics of the Federal Employment Agency (BA)2019, own calculations.

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Figure 3: Capacity to Work from Home (WfH) by Demographic Characteristics

Notes: The figure displays WfH capacities by demographic characteristics and by gross monthly wage quintiles. “Withchildren” defined as employees with at least one child below the age of 11 living in the household.Sources: BIBB/BAuA Employment Survey 2018, own calculations.

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Figure 4: Job Characteristics and WfH Feasibility

Notes: Figure reports average marginal effects from a logistic regression at the individual level. The dependent variableequals zero if the respondent never works from home and indicates that working from home is “not possible” in herjob, and one otherwise (see section 2.2 for details). Explanatory variables are coded as one if the respondent indicatesthat a task or working condition is performed or occurs frequently, and zero otherwise. The labels (m) and (c) specifymanual and cognitive tasks, respectively (most work conditions cannot be unambiguously classified as either m or c).N = 15,737. Estimation uses robust standard errors and population weights. Confidence intervals reported at the 95percent level. Pseudo R-squared = 0.19.Sources: BIBB/BAuA Employment Survey 2018, own calculations.

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Figure 5: Generalized Ordered Logit estimation results

Notes: The figure reports average marginal effects from a generalized ordered logit model at the individual level. Thedependent variable is an ordinal variable identifying whether a respondent’s job is incompatible, part-time compatibleor full-time compatible with WfH (see section 4 for details). Explanatory variables are coded as one if the respondentindicates that a job task or working condition is performed or occurs frequently, and zero otherwise. Only estimates forjob features with positive marginal effect in Figure 4 are displayed. N = 15,737. Estimation uses robust standard errorsand population weights. Confidence intervals are reported at the 95-percent level.Source: BIBB/BAuA Employment Survey 2018, own calculations.

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Figure 6: Industry-level WfH Capacity and intensified WfH during Covid-19 Pandemic

Notes: The figure reports the linear fit between the industry-specific share of firms reporting intensified WfH in April2020 and industry-level WfH capacity. 56 industry-level observations (2-digit NACE level) computed from 7,227 firm-level responses (only industries with 10 or more respondents). Industry-level observations are weighted with totalemployment in June 2019.Sources: ifo Business Survey (IBS) April 2020, BIBB/BAuA Employment Survey 2018, Employment Statistics of theFederal Employment Agency (BA) 2019, own calculations.

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Figure 7: Occupation-level WfH Capacity and WfH Utilization during Covid-19 Pandemic

Notes: The figure reports the linear fit between the occupation-specific share of employees reporting WfH in May2020 and occupational WfH capacity. 36 occupation-level observations (2-digit KldB 2010 level) computed from 7,460individual-level responses (only occupations with 10 or more respondents). Occupation-level observations weightedwith total employment in June 2019.Sources: IAB High-Frequency Online Personal Panel (HOPP) May 2020, BIBB/BAuA Employment Survey 2018,Employment Statistics of the Federal Employment Agency (BA) 2019, own calculations.

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Table 1: Comparison of Survey-based and Task-based WfH Feasibility Measure

Task-based measure = 1 Task-based measure = 0

Survey-based measure = 1 28.9 28.2Survey-based measure = 0 5.2 37.6

Notes: The table compares WfH feasibility implied by our survey-based measure with WfH feasibility implied by atask-based measure based on 17,093 employee-level observations. Values of 1 indicate that WfH is feasible accordingto the corresponding measure.

Sources: BIBB/BAuA Employment Survey 2018, own calculations.

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Appendix

Figure A1: Capacity to Work from Home (WfH) by Occupation

Notes: The figure displays WfH capacities and pre-pandemic WfH usage by occupation (2-digit KldB).Sources: BIBB/BAuA Employment Survey 2018, own calculations.

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Figure A2: Share of WfH Capacity that is Untapped due to Supply- and Demand-side Restrictions

Notes: The figure displays the shares of WfH capacities that have been untapped as of 2018 due to supply- and demand-side restrictions, respectively, by sectors and by plant size.Sources: BIBB/BAuA Employment Survey 2018, own calculations.

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Table A1: Goodness of Fit from Logistic Regressions of WfH Feasibility on Employee Characteris-tics

(1) (2) (3) (4) (5)

Occupation 2-digit FE X XSector FE X X XOccupation 3-digit FE X X XIndividual-level Controls X XJob Tasks XObservations 17,045 16,818 17,003 15,848 15,756Goodness of fitAccuracy 74.94 75.70 76.34 77.61 79.68Sensitivity 79.71 81.03 83.17 83.57 85.82Specificity 65.51 65.11 62.83 65.56 67.26Youden’s J 45.22 46.14 46.00 49.13 53.08

Notes: The table reports goodness of fit measures (rescaled by 100) from logistic regressions of WfH feasibility ondifferent sets of explanatory variables at the employee level. Occupation fixed effects include 36 categories at the 2-digit and 139 at the 3-digit level. Sector fixed effects control for 21 industries. Individual-level controls include age,contractual working hours, plant size (5 categories) as well as indicators for gender, migration background, childrenbelow the age of 11 living in the household, marital status and academic education. Job tasks include 17 indicatorsequal to one if the respondent performs a given task frequently and zero otherwise. Tasks include “Manufacturing,producing goods and commodities”, “Measuring, testing, quality control”, “Monitoring, control of machines, plants,technical processes”, “Repairing, renovating”, “Purchasing, procuring, selling”, “Transporting, storing, shipping”, “Ad-vertising, Marketing, PR”, “Organizing, planning, preparing work processes”, “Developing, researching, constructing”,“Training, instructing, teaching, education”, “Gathering information, researching, documenting”, “Providing adviceand information”, “Entertaining, accommodating, preparing food”, “Nursing, caring, healing”, “Protecting, guarding,monitoring, regulating traffic”, “Working with computers”, “Cleaning, waste disposal, recycling”.Sources: BIBB/BAuA Employment Survey 2018, own calculations.

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Table A2: Demographic Differences by Reason for not Working from Home

Supply Demand Diff

Age 41.84 44.18 2.33***(11.59) (11.81) (0.35)

Female 0.52 0.56 0.05***(0.50) (0.50) (0.02)

Migrant 0.18 0.19 0.01(0.38) (0.40) (0.01)

Married 0.52 0.53 0.01(0.50) (0.50) (0.02)

Children 0.31 0.27 -0.05***(0.46) (0.44) (0.01)

Academic degree 0.24 0.24 0(0.43) (0.43) (0.01)

Observations 3,412 1,630

Notes: The table reports mean differences in demographic traits between employees who would accept an offer to WfH(Supply) and who would not accept an offer to WfH (Demand). Only employees who do not WfH and do not excludethe possibility to WfH are included. Means are computed using population weights. Standard deviations (Columns 1and 2) and standard errors (Column 3) reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1Sources: BIBB/BAuA Employment Survey 2018, weighted, own calculations.

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Table A3: Tasks and Work Conditions in the BIBB/BAuA Survey 2018

(1) (2) (3)Survey Label Task or Work condition Mean

F303 Manufacturing, producing goods & commodities 0.17F304 Measuring, testing, quality control 0.47F305 Monitoring, control of machines, plants, techn. processes 0.26F306 Repairing, renovating 0.17F307 Purchasing, procuring, selling 0.19F308 Transporting, storing, shipping 0.26F309 Advertising, Marketing, Public Relations 0.09F310 Organizing, planning, preparing work processes 0.46F311 Developing, researching, constructing 0.13F312 Training, instructing, teaching, education 0.22F313 Gathering information, researching, documenting 0.57F314 Providing advice and information 0.58F315 Entertaining, accommodating, preparing food 0.10F316 Nursing, caring, healing 0.16F317 Protecting, guarding, monitoring, regulating traffic 0.22F318 Working with computers 0.70F320 Cleaning, waste disposal, recycling 0.28F600 01 Work standing up 0.54F600 03 Lift or carry loads of >20 kg (men) >10 kg (women) 0.23F600 04 Exposed to smoke, dust, gases, vapours 0.13F600 05 Work under cold, heat, moisture, humidity, draughts 0.20F600 06 Work with oil, grease, dirt 0.18F600 07a Manual work that requires high degree of skill 0.39F600 07b Work in a bent, squatting, kneeling position or overhead 0.17F600 12 Work under noise 0.27F600 13 Handle microorganisms (pathogens, bacteria, moulds, viruses) 0.13F605 Working majority of the time outdoors 0.11F327 Reacting to and solving new problems 0.72F327 02 Making difficult decisions 0.40F327 03 Recognizing and closing knowledge gaps 0.36F327 04 Taking responsibility for others 0.41F327 05 Convincing and negotiating with others 0.43F327 06 Communicating with others 0.91F411 02 Execution of work prescribed in every detail 0.26F411 03 Repeating same operation in every detail 0.46F411 04 Confronted with new tasks 0.40F411 05 Improving existing procedures or trying new things 0.28F411 13 Working very fast 0.34F301 Supervising others 0.28

Notes: The table lists survey labels and population averages of the tasks and work conditions considered in the analysisoutlined in Section 3.3 and reported in Figure 4. Every job characteristic is coded as one if the respondent indicatesthat it applies frequently in her job and zero otherwise. “Supervising others” and “working majority of the workingtime outdoors” are recorded as binary variables (yes/no) in the survey and coded accordingly in the analysis. Means arecomputed using population weights. N = 16,689.Source: BIBB/BAuA Employment Survey 2018, weighted, own calculations.

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Table A4: Replicating DN’s task-based WfH measure using BIBB/BAuA Survey 2018 data

If respondents report any of the following as true, we code their job as incompat-ible with full-time WfH:

• Never using the Internet or E-Mail processing• Frequently lifting or carrying loads of more than 10 kg (women) or 20 kg

(men)• Frequent exposure to smoke, dust, gases or vapour• Frequent exposure to cold, heat, moisture, humidity or draughts• Frequently handling microorganisms such as pathogens, bacteria, moulds

or viruses• Frequently working with oil, grease or dirt• Works the majority of time outdoors• Frequently repairing or renovating• Frequently protecting, guarding, monitoring, or regulating traffic• Frequently cleaning, disposing waste or recycling• Frequently monitoring or controlling machines, plants or technical pro-

cesses

Notes: The table describes the replication of Dingel and Neiman’s (2020) WfH feasibility index using employee-leveltask information from the BIBB/BAuA Employment Survey 2018.

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