metadata: authors: 0: author: Hatayama,Maho
1: author: Viollaz,Mariana
2: author: Winkler-Seales,Hernan Jorge
count: World
docna: 0: docna: Jobs&#39; Amenability to Working from
            Home : Evidence from Skills Surveys for 53 Countries
docty: Policy Research Working Paper
subtopic: Labor Markets,Information Technology,Educational Sciences
teratopic: Social Protections and Labor,Information and Communication Technologies,Education
abstracts: cdata!: The spread of COVID-19 and
            implementation of "social distancing" policies
            around the world have raised the question of how many jobs
            can be done at home. This paper uses skills surveys from 53
            countries at varying levels of economic development to
            estimate jobs' amenability to working from home. The
            paper considers jobs' characteristics and uses internet
            access at home as an important determinant of working from
            home. The findings indicate that the amenability of jobs to
            working from home increases with the level of economic
            development of the country. This is driven by jobs in poor
            countries being more intensive in physical/manual tasks,
            using less information and communications technology, and
            having poorer internet connectivity at home. Women, college
            graduates, and salaried and formal workers have jobs that
            are more amenable to working from home than the average
            worker. The opposite holds for workers in hotels and
            restaurants, construction, agriculture, and commerce. The
            paper finds that the crisis may exacerbate inequities
            between and within countries. It also finds that occupations
            explain less than half of the variability in the
            working-from-home indexes within countries, which highlights
            the importance of using individual-level data to assess
            jobs’ amenability to working from home.
repnme: repnme: Jobs' Amenability to Working from Home
            : Evidence from Skills Surveys for 53 Countries
display_title: Jobs' Amenability to Working
            from Home : Evidence from Skills Surveys for 53 Countries :
            Jobs' Amenability to Working from Home : Evidence from
            Skills Surveys for 53 Countries
listing_relative_url: /research/2020/05/32026109/jobs-amenability-working-home-evidence-skills-surveys-53-countries-jobs-amenability-working-home-evidence-skills-surveys-53-countries
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Information and Communication Technologies
            m1326624 133,Education m1327048 470,Jobs m1503931 2670,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2020-05-11T00:00:00Z
historic_topic: Social Protections and Labor,Information and Communication Technologies,Education
origu: JOBS- CCSA - IBRD (HSPJB)
no_of_pages: 32
author_keys: 0: author_key: 1725187
1: author_key: 1473282
2: author_key: 776733
model_extractions: Policy Research Working Paper 9241 Jobs’ Amenability to Working from Home Evidence from Skills Surveys for 53 Countries _Maho Hatayama Mariana Viollaz Hernan Winkler_ Social Protection and Jobs Global Practice May 2020
Policy Research Working Paper 9241

# **Abstract**

The spread of COVID-19 and implementation of “social distancing” policies around the world have raised the question of how many jobs can be done at home. This paper uses skills surveys from 53 countries at varying levels of economic development to estimate jobs’ amenability to working from home. The paper considers jobs’ characteristics and uses internet access at home as an important determinant of working from home. The findings indicate that the amenability of jobs to working from home increases with the level of economic development of the country. This is driven by jobs in poor countries being more intensive in physical/manual tasks, using less information and communications technology, and having poorer internet connectivity at home. Women, college graduates, and salaried and formal workers have jobs that are more amenable to working from home than the average worker. The opposite holds for workers in hotels and restaurants, construction, agriculture, and commerce. The paper finds that the crisis may exacerbate inequities between and within countries. It also finds that occupations explain less than half of the variability in the working-from-home indexes within countries, which highlights the importance of using individual-level data to assess jobs’ amenability to working from home.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hwinkler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Jobs’ Amenability to Working from Home:**

**Evidence from Skills Surveys for 53 Countries**<sup>∗</sup>

Maho Hatayama<sup>§</sup> Mariana Viollaz<sup>ɬ</sup> Hernan Winkler<sup>ʄ</sup> _Updated August 2020_ JEL codes: J22, J61, O30 Keywords: Home-based-work, telework, internet, ICT, tasks.

> ∗ The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent. The empirical results for the Middle East and North Africa region are part of a background paper for the report “Economic Transformation and Jobs: Making markets work for people in the MENA region” of the World Bank MNA Chief Economist Office.

> § Junior Professional Officer. The World Bank, Jobs Group, Washington DC, e-mail: mhatayama@worldbank.org ɬ Senior Researcher. CEDLAS-FCE-UNLP. E-mail: mviollaz@cedlas.org

> ʄ Senior Economist. The World Bank, Jobs Group, Washington DC, e-mail: hwinkler@worldbank.org
# **1. Introduction**

The spread of COVID-19 and the implementation of “social distancing” policies around the world have raised the question of how many jobs can be done at home. Most of the existing efforts to estimate these figures rely on US-based measures of the type of tasks required by different occupations (Dingel & Neiman, 2020a; Avdiu & Nayyar, 2020; Mongey, Pilossoph, & Weinberg, 2020; Leibovici, Santacreu, & Famiglietti, 2020).<sup>1</sup> However, the task content of jobs exhibits substantial variation across countries (Lo Bello, SanchezPuerta, & Winkler, 2019; Hardy, Lewandowski, Park, & Yang, 2018). Differences in the organization of production or in the level of technology adoption across countries imply that the same occupation may be more intensive in face-to-face interactions or in physical tasks in poorer economies. As a result, using USbased measures to estimate the amenability to _working from home_ (WFH) in developing countries may lead to biased conclusions.

To overcome this challenge, this paper uses skill and household surveys from 53 countries at different levels of economic development with rich information on the type of tasks carried out by people at work. We estimate indexes of the task content of jobs to rank them by their vulnerability to social distancing measures according to their amenability to a remote setup. In addition, given that the task data vary at the individuallevel—and not by occupation, as in the Occupational Information Network (O*NET) classification—we show how the likelihood of being able to work from home correlates with other characteristics of the individual and his or her job.

We build on the literature by estimating jobs’ amenability to WFH, as opposed to estimating the fraction of jobs that can be done at home. Estimating the latter is challenging since choosing the tasks that determine whether a person can work from home is largely arbitrary, specifically if one does not have a model linking such tasks to the probability of WFH during a pandemic. Dingel & Neiman (2020b) and Saltiel (2020) consider that an occupation cannot be performed from home if at least one of several conditions holds. For instance, in the Dingel & Neiman (2020b) study, some of categories that are sufficient to consider that an occupation cannot be done at home include “Performing for or Working Directly with the Public is very important”, “Handling and Moving Objects is very important” or “Repairing and Maintaining Electronic Equipment is very important.” However, occupational requirements can change during exceptional conditions. For example, while for professionals in communications or in law it is very important to have contact with the public, they can still carry out some (but not all) of their tasks using ICT (Information and Communication Technologies); craft workers for whom handling and moving objects is crucial may still be able to sell their products through e-commerce; individuals repairing equipment can still work on portable objects at home, to name a few examples. More generally, occupations comprise a bundle of tasks, and while

> 1 An exception is Saltiel (2020), who uses STEP surveys.

2
it may be optimal to work at a specific location and in _face-to-face_ (F2F) contact with the public or co-workers, suboptimal work arrangements are also feasible for some occupations, particularly during a pandemic.<sup>2</sup> Another caveat of using criteria where at least one sufficient condition has to be satisfied to categorize jobs is that it is not clear how to choose the number of conditions to consider when several alternatives are available. If only one condition needs to be satisfied to classify an occupation as not being able to be done at home, then the more conditions that the researcher adds to the list, the higher are the chances that at least one of them will be satisfied by a given job. For example, one of the data sets used in this paper includes a battery of questions to measure F2F contact. Two of these questions are “How often does your job usually involve sharing work-related information with co-workers?” and “How often does your job usually involve instructing, training or teaching people, individually or in groups?” A priori, both are valid proxy variables for F2F work, but while almost 100 percent of people respond “very often” to the first question in most countries, there is substantially more variation in the responses to the latter. More generally, the more questions we consider to measure F2F, the higher the fraction of workers that would be classified as having an F2F-intensive job. Discarding questions and data based on this empirical observation is somewhat arbitrary.

There are two studies that are exceptions to the one-sufficient-condition criteria. Mongey et al. (2020) construct WFH and physical proximity measures for the United States using O*NET data. For the WFH measure, they use the same set of task variables as Dingel & Neiman (2020a, 2020b), but instead of defining binary indicators, they allow both the WFH and physical proximity measures to vary between 0 and 1. Leibovici, Santacreu, & Famiglietti (2020) construct a contact-intensity measure for the United States using occupation-level information from O*NET and aggregating the possible scores for the question about performing tasks in close physical proximity to other people. The final measure can take a value from 0 to 100.

To measure jobs’ amenability to WFH, we exploit all the variables available in the data that describe job tasks related to home-based work. Instead of using a criterion based on satisfying at least one sufficient condition to classify occupations, we argue that the more (less) the conditions that are satisfied, the lower (higher) the amenability of a given job to be carried out at home. For example, according to our criteria, a job that satisfies three conditions would be less amenable to home-based-work than one that satisfies only one or two of those conditions. Accordingly, we also exploit categorical variables describing the intensity of different tasks, instead of transforming them into binary outcomes. While this approach still relies on the assumption that all

> 2 As a robustness check, we construct an index following a methodology more similar to that of Dingel and Neiman (2020a, 2020b) and Saltiel (2020) where only one condition needs to be satisfied in order for a job not to be amenable to WFH and find that it is highly correlated with our WFH measures (see Figure A 3).

3
characteristics related to the probability of working from home have the same weight, it exploits more information than a binary approach.

We use four groups of tasks to assess jobs’ amenability to WFH. First, we use measures of physical intensity and manual work to capture tasks that are more likely to be location-specific—because they require handling large items or use specific equipment, for example—and cannot be done at home. Second, we use measures of F2F-intensive tasks such as those that involve supervision or contact with the public. Third, we create an index of ICT use at work, to reflect the fact that while some jobs may require substantial F2F intensity, some of such tasks can be carried out using ICT and do not necessarily have to be done in-person. Finally, and in contrast to existing studies, we also exploit information on having an internet connection at home as an important factor to determine the likelihood of a remote setup. This is important since workers in developing countries who may use ICT and have internet connectivity at the workplace, do not necessarily have access to the same resources at home. Another reason why we estimate the amenability—and not the fraction—of jobs that can be performed remotely for such a large set of countries is that several of the surveys were collected circa 2012, when internet connectivity was significantly lower than today. However, under the assumption that relative connectivity across countries or types of workers remained stable, our estimates can still be used to compare the WFH measures across these categories.<sup>3</sup> We find that the social distancing measures associated with COVID-19 may exacerbate the jobs divide that preceded the crisis. The jobs intensive in tasks that are amenable to WFH are more prevalent in richer countries, and among workers with high levels of education, in salaried employment and with access to social insurance. Low-income, self-employed, and informal workers are not only less likely to do their jobs when WFH is the only option, but also less likely to have access to income protection schemes if these are channeled through the existing social security infrastructure.

Our findings highlight the importance of social protection programs to safeguard the most vulnerable during the crisis. Efforts to reach informal workers are crucial since they are less likely to have access to existing social insurance benefits, and also to additional programs launched in response to the crisis through the social security or tax administration infrastructures. Our results also show the importance of fostering technology adoption to protect jobs while respecting social distancing measures. To a large extent, the between and within country divide in WFH amenability is driven by unequal access to ICT. These benefits of digital technologies should be considered by governments in developing countries when investing in broadband infrastructure.

> 3 In fact, the coefficient of correlation between the share of internet users by country in 2012 vs 2017 is 0.94 (own estimates based on data from World Development Indicators, WDI).

4
Finally, it is important to mention that this paper does not consider the role of essential sectors or workers (whose jobs are not affected by social distancing measures) since there is substantial heterogeneity in these policies between and within countries that cannot be fully accounted for in this paper.<sup>4</sup> Our individual-level measures of WFH amenability, however, can be used to assess the potential impacts of essential work policies.

The rest of this paper is structured as follows. Section 2 describes the data and the main features of the methodology. Section 3 describes the results and Section 4 concludes. The paper includes an Appendix with more detailed information on the methodology, and the estimated indexes by detailed socioeconomic group and country.

# **2. Data**

We use three data sets covering 53 countries at different levels of development to estimate our WFH measure (see Table 1). First, we use the Surveys of Adult Skills of PIAAC (Programme for the International Assessment of Adult Competencies) for 35 countries. This survey collects information about working-age individuals and covers both rural and urban areas. Second, we use the STEP (Skills Towards Employability and Productivity) surveys for 15 developing countries.<sup>5</sup> The surveys are representative of urban areas (except Sri Lanka and the Lao People’s Democratic Republic, which included both urban and rural areas) and collect information about working-age individuals. Finally, we use the Labor Market Panel Surveys (LMPS) for three countries in the Middle East and North Africa (MENA) region, namely the Arab Republic of Egypt, Jordan and Tunisia. These are standard labor force surveys that, in addition to the typical labor market information, also collect data about specific tasks carried out at work. Our final sample for all three data sets includes employed individuals ages 16 to 64 years.

> 4  Garrote Sanchez, Gomez Parra, Ozden, & Rijkers (2020) consider the role of essential workers in their assessment of WFH measures in the European Union.

> 5 We exclude China since the data are only representative of Yunnan province. There is no STEP survey for El Salvador, thereby we use instead a skills survey that includes a similar questionnaire.

5
**Table 1. List of skills surveys**

|**Dataset**|**Countries**|**Year**|
|---|---|---|
|PIAAC|Austria, Belgium (Flanders), Canada, Czech Republic, Denmark,<br>Estonia, Finland, France, Germany, Ireland, Italy, Japan, Korea,<br>Netherlands, Norway, Poland, Russian Federation, Slovak Republic,<br>Spain, Sweden, United Kingdom (England and Northern Ireland),<br>United States|2011/2012|
||Chile, Greece, Israel, Lithuania, New Zealand,<br>Singapore, Slovenia, Turkey|2014/2015|
||Ecuador, Hungary, Kazakhstan, Mexico, Peru|2017|
|STEP|Bolivia, Colombia, Lao PDR, Sri Lanka, Vietnam|2012|
||Armenia, El Salvador, Georgia, Ghana, Kenya, North Macedonia,<br>Ukraine|2013|
||Serbia|2015/2016|
||Kosovo, Philippines|2015|
|LMPS|Tunisia|2014|
||Jordan|2016|
||Egypt|2018|

Table 2 shows the types of tasks used to estimate the WFH index, Appendix 1 describes the rationale for choosing these tasks, and Table A 1 in Appendix 2 shows the complete list of variables. Such variables are slightly different across the three data sets. For example, while STEP has information on whether the job requires contact with customers, such information is not collected in the LMPS for Jordan and Tunisia. Thereby, while the indexes can be compared across countries within the STEP, PIAAC and LMPS data sets, comparisons are not possible across them.

6
**Table 2. Description of the task indexes**

|**Task index**|**Tasks**|
|---|---|
|**a)Physical and manual**<br>(a higher value indicates more<br>physical/manual intensity)|Job is physically intensive<br>Repairing equipment<br>Operatingheavymachinery|
|**b)Face-to-Face (F2F)**<br>(a higher value indicates more<br>F2F intensity)|Supervising others<br>Contact with customers, public, students|
|**c)Low ICT use at work**<br>(a higher value indicates lower<br>ICT use at work)|Low or no computer use at work<br>Low or no cell phone use at work|
|**d)Low ICT at home**<br>(based on a dummy variable equal<br>to one if the home has no<br>internet connection)|No internet connection at home|
|**e)WFH**<br>(a higher value indicated higher<br>WFH amenability)|Combination of Physical/Manual, F2F, Low ICT use<br>at work, multiplied by -1|
|**f)WFH adjusted**<br>(a higher value indicated higher<br>WFH amenability)|Combination of Physical/Manual, F2F, Low ICT use<br>at work, Low ICT at home, multiplied by -1|

# **3. Results**

# **3.1 Cross-country results**

Figure 1 shows the correlation between the physical/manual and F2F task indexes and GDP per capita. The magnitude of the indexes is equivalent to the number of standard deviations above/below the average worker among all the countries in the sample. For example, a physical/manual index equal to 0.45 in the case of Turkey (Figure 1, panel (a)) means that jobs in Turkey are on average 0.45 standard deviations above that of the average worker among PIAAC countries in terms of physical/manual intensity. Richer countries have jobs less intensive in physical/manual skills (Figure 1, panels (a) and (c)). This factor would tend to reduce the amenability of jobs to be done at home disproportionately among poorer countries, given that their jobs would tend to be more location or equipment-specific according to this measure. In contrast, the intensity of jobs in F2F tasks tends to increase with economic development (Figure 1, panels (b) and (d)). This is driven by jobs intensive in non-routine interpersonal tasks, whose duties require more supervision or contact with the public.

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**Figure 1. Physical/Manual and F2F intensity, by GDP per capita**

<!-- Start of picture text --> (a) Physical/Manual Intensity vs. GDP (PIAAC) (b) F2F Intensity vs. GDP (PIAAC)<br>0.5 0.5<br>TUR NZL<br>0.4 0.4 FIN<br>USA<br>0.3 0.3 GBR SWE NOR<br>ECU MEX 0.2 CAN DNK SGP<br>0.2 PER IRL<br>USA 0.1<br>0.10 9 9.5 10KAZGRCPOLCHL RUS SVKLTUHUNSVNKOR10.5CZENZLGBR ITA IRL DEU SWENLDAUTDNK11 NOR 11.5 -0.10 9 ECU 9.5PER MEX10 CHLPOLGRCESTRUS SVNCZEKOR10.5ESPISR JPNFRABELDEUAUT NLD 11 11.5<br>-0.1 EST ESP CAN -0.2 TUR ITA<br>FRA BEL<br>-0.2 SGP -0.3 KAZ SVK<br>ISR FIN HUN<br>-0.3 -0.4 LTU<br>JPN<br>-0.5 GDP per capita PPP (log)<br>-0.4 GDP per capita PPP (log)<br>(c) Physical/Manual Intensity vs. GDP (STEP) (d) F2F Intensity vs. GDP (STEP)<br>0.5 0.3<br>LKA<br>COL<br>0.4 0.2<br>SLV<br>0.3 LAO ARM MKD<br>SLV 0.1<br>0.2 BOL UKR KOS<br>0 KEN GEO SRB<br>7.5 8 8.5 9 9.5 10<br>0.1 COL VNM<br>GHA<br>-0.1<br>GHA<br>0 BOL ARM<br>7.5 8 8.5 9 UKR 9.5 10 LKA<br>KEN PHL -0.2<br>-0.1 PHL<br>-0.2 VNM GEO KOS MKD SRB -0.3<br>LAO<br>-0.3 GDP per capita PPP (log) -0.4 GDP per capita PPP (log)<br>F2F index<br>Physical/Manual index<br>F2F index<br>Physical/Manual index<br><!-- End of picture text --> Note: The vertical axis measures the corresponding task index, in standard deviations from the mean for all PIAAC/STEP countries. GDP per capita PPP comes from the World Development Indicators (WDI) and corresponds to the same year of the respective PIAAC and STEP surveys.

The fact that the intensity of jobs on physical/manual tasks tends to decline with economic development, and that the intensity on F2F tasks shows the opposite pattern suggests that two opposing forces are at play when shaping the relationship between WFH measures and GDP per capita. However, F2F occupations also tend to be more intensive in ICT use. As seen in Figure 2, countries such as Singapore or the United States that have jobs more intensive in F2F tasks also use more ICT at work than countries such as Lithuania or Kazakhstan. That is, several of the tasks embedded in such occupations are more prone to be performed remotely. In other words, ICT use at work would tend to weaken the effect of F2F intensity on WFH measures. This correlation between F2F tasks and ICT use at work can also be observed within countries (see Figure A 1 in Appendix 2).

8
**Figure 2. ICT use and F2F intensity across countries**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 1<br>PER<br>0.8<br>MEX LKA<br>TUR 0.4<br>RUS ECU 0.6<br>GRC LAO<br>LTU KAZ<br>ITA 0.2 CHL GHA<br>POL 0.4<br>SVK ESP JPN<br>HUN FRA 0 IRL 0.2 SLV<br>-0.5 -0.4 -0.3 -0.2 CZE-0.1 DEU0 ISR0.1 0.2 0.3 0.4 0.5 BOL COL<br>AUT KEN<br>EST 0<br>KOR-0.2 BELSVN -0.4 -0.3 -0.2 -0.1 VNM 0 UKR 0.1 0.2 0.3<br>CAN USA<br>NLD SWE DNKNORGBR FIN NZL -0.2 KOSGEO ARM<br>-0.4<br>SGP PHL -0.4<br>SRB<br>MKD<br>-0.6 -0.6<br>F2F index<br>F2F index<br>Low ICT use index Low ICT use index<br><!-- End of picture text --> Note: The vertical axis measures the Low ICT use index (a higher value means lower ICT use at work), while the horizontal axis measures F2F contact index (a higher value means more intense F2F contact). Both indexes in standard deviations from the mean for all PIAAC/STEP countries.

9
Figure 3 illustrates the importance of distinguishing between ICT use at work and the availability of an internet connection at home. While both variables are highly correlated—i.e. countries where people use more ICT at work also have higher internet connectivity at home—there are some differences, particularly among less developed countries. For instance, Peru, Mexico and Ecuador are closer to the average with respect to ICT use at work, but are lagging more with respect to internet access at home. Accordingly, while the Philippines ranks relatively high in terms of ICT use at work, it has relatively low levels of internet connectivity at home (Figure 3 (b)). Thereby, while their jobs could be amenable to telecommuting based on a tasks approach, poor internet connectivity implies that many workers may not be able to do their jobs at home.

10
**Figure 3. ICT use at work and at home**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.8 1<br>0.6 0.8<br>LKA<br>PER<br>TUR MEX 0.6<br>0.4 LAO<br>RUS ECU<br>GRC 0.4 GHA<br>LTU ITA KAZ<br>0.2 CHL<br>POL<br>HUN ESP 0.2 SLV<br>CZE FRA SVK<br>DEU 0 COL BOL<br>KEN<br>-0.6 -0.4 BELEST-0.2 AUT0 IRLJPN 0.2 0.4 0.6 0.8 1 -1 -0.8 -0.6 -0.4 -0.2 0 0 0.2 0.4 0.6 0.8 1 1.2<br>SWE CANSVN KOR-0.2 ISR GEO VNM<br>NLDFIN KOS ARM UKR -0.2<br>NOR GBR USA<br>DNK NZL -0.4 -0.4 PHL<br>SGP SRB<br>MKD<br>-0.6 -0.6 Low ICT at home<br>Low ICT at home<br>Low ICT at work Low ICT at work<br><!-- End of picture text --> Note: The vertical axis measures the Low ICT use index (a higher value means lower ICT use at work), while the horizontal axis measures the Low ICT at home index (a higher value means poorer internet access at home). The variable to measure internet connectivity at home is not available for El Salvador, thereby we use a different approach for this country. We consider that households have internet access at home if they have a computer and fixed telephone access.

When combining the four indexes, we find substantial cross-country variation in the amenability of jobs to working from home. As seen in Figure 4, the most vulnerable countries in the PIAAC sample are Turkey and those from the LAC region. In the STEP sample, countries from the ECA region have jobs more amenable to working from home, while the opposite is true for Sri Lanka, Lao PDR, El Salvador and Ghana. In contrast to Dingel & Neiman (2020a), we find that the United States ranks lower than most OECD countries in terms of its jobs’ amenability to working from home. Our findings are consistent with Hardy et al. (2018), who use the PIAAC surveys and find that the United States has more jobs that are more manually intensive than most other countries.

11
**Figure 4. WFH amenability index across countries.**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 0.8<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0 0<br>-0.2<br>-0.2<br>-0.4<br>-0.4<br>-0.6<br>-0.6 -0.8<br>-1<br>-0.8<br>-1.2<br>MEX PER ECU TUR CHL RUS GRC KAZ ITA IRL POL USA ESP NZL SVK SVN LTU KOR CZE GBR AUT SGP JPN FRA DEU EST ISR CAN HUN SWE DNK NOR FIN BEL NLD LKA LAO SLV GHA BOL COL KEN UKR ARM VNM PHL GEO KOS MKD SRB<br><!-- End of picture text --> Note: Each bar shows the number of standard deviations below/above the mean. A higher value indicates a greater amenability of jobs to working from home. The magnitude of the estimates is not comparable between the PIAAC and STEP datasets.

The difference between our results and those of Dingel & Neiman (2020a) seems to be driven by the fact that while the United States has a higher share of jobs in occupations that are more amenable to working from home than other countries, the tasks associated with these occupations are different across countries and tend to be less favorable to working from home in the United States. Figure A 2 in Appendix 2 illustrates this issue using Norway, the United States and Spain as examples. The United States has 61 percent of its jobs in the four occupational categories that are more amenable to WFH, a figure higher than for Norway (55 percent) and Spain (59 percent). If we imputed the US WFH measures to each occupation of these other two countries (as in Dingel & Neiman, 2020a), we would conclude that jobs are more amenable to WFH in the United States. However, when comparing the same occupations across countries, we find that most occupations in the United States are less amenable to WFH than in Norway and Spain. For example, the US WFH index for technicians is far lower than that for Norway and Spain. In other words, these findings illustrate the importance of using measures of tasks that vary across occupations and countries, since occupations are not associated with the same tasks in different economies.

12
Our findings also shed light on the importance of using task measures that vary at the individual level instead of at the occupation level. A simple decomposition shows that less than half of the variation in the WFH index is explained by variation between 4-digit ISCO occupations (see Table A 2 in the appendix). Most of the variation in the tasks related to WFH takes place within narrowly defined occupations.

The correlation between economic development and the amenability of jobs to working from home is positive within the PIAAC and STEP datasets. When we also consider the availability of internet access at home, the relative ranking of countries does not change significantly. That is, poorer countries have a lower share of jobs that are amenable to be done at home (Figure 5). For example, the Netherlands is 0.38 standard deviations above the average PIAAC country in terms of its jobs’ amenability to working from home, while Ecuador and Turkey are 0.65 and 0.5 standard deviations below the average, respectively. In the PIAAC sample, other countries whose jobs are also more amenable to WFH are Belgium and the Nordic countries. In contrast, Peru, Mexico, and Chile have jobs that are more vulnerable in this regard. The correlation between GDP per capita and the WFH measure is negative for the LMPS countries, as Jordan ranks higher in terms of WFH amenability despite having a lower level of GDP per capita. However, this may be explained by Jordan having higher internet penetration than Egypt and Tunisia.<sup>6</sup>

> 6 According to data from the World Development Indicators, the share of internet users in the corresponding survey year was 62.3 percent in Jordan, and 46.9 and 46.1 in Egypt and Tunisia, respectively.

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# **Figure 5. WFH amenability and GDP per capita**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 0.8<br>SRB<br>0.6<br>0.4 NLD KOS MKD<br>BEL 0.4 GEO<br>FIN DNK PHL<br>0.2 ESTHUN CZEISRJPNFRACAN SWE NOR 0.2 VNM ARM UKR<br>AUT SGP<br>LTU KOR DEU<br>0 SVK SVN NZL GBR 0<br>9 9.5 10KAZ POL ESP10.5 ITA IRL USA11 11.5 -0.2 7.5 8 KEN 8.5 9 9.5COL 10<br>GRC<br>-0.2<br>RUS -0.4 BOL<br>CHL GHA<br>SLV<br>-0.4 -0.6<br>TUR LAO<br>-0.8<br>-0.6<br>ECU LKA<br>PER -1<br>MEX<br>-0.8 GDP per capita PPP (log) -1.2 GDP per capita PPP (log)<br>(c) LMPS<br>0.4<br>JOR<br>0.3<br>0.2<br>0.1<br>0<br>9 9.05 9.1 9.15 9.2 9.25 9.3 9.35<br>EGY<br>-0.1<br>-0.2<br>TUN<br>-0.3 GDP per capita PPP (log)<br>WFH index - adjusted by home internet access WFH index - adjusted by home internet access<br>WFH index - adjusted by home internet access<br><!-- End of picture text --> Note: The vertical axis measures the corresponding task index, in standard deviations from the mean of the (A) PIAAC, (B) STEP and (C) LMPS samples. A higher value indicates that jobs are more amenable to WFH.

# **3.2 Within-country findings**

There are large disparities in terms of jobs’ amenability to working from home within countries. Figure 6 shows differences with respect to the average worker for the whole PIAAC, STEP and LMPS data sets.<sup>7</sup> Across most countries, women are more likely to have jobs more amenable to WFH. This is because they are less likely to have jobs intensive in physical/manual work than men. Educational attainment is strongly linked

> 7 Tables showing country-level findings are available in the Online Appendix

> (http://www.hernanwinkler.com/uploads/5/5/1/1/5511764/appendix_jobs_amenability_to_wfh_v13.xlsx).

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to WFH amenability, since college graduates in all 53 countries have jobs more amenable to WFH than their less educated peers.

Older workers are less likely to have jobs’ amenable to WFH in most countries, and this is due to a combination of counteracting forces. On the one hand, the F2F intensity increases and ICT use declines with age, which tends to reduce older workers’ jobs amenability to WFH. On the other hand, the physical/manual intensity declines with age, making jobs of older workers more amenable to WFH. However, the latter is not strong enough to counteract the role of F2F and ICT tasks for older workers.

Self-employment is associated with lower amenability to WFH in most countries. Their jobs require more physical/manual intensity and require more F2F interaction. On the other hand, they are more likely to use ICT at work than salaried workers, but this factor does not affect their WFH measure to a large extent.

Workers with a formal job—either because they have a contract (PIAAC) or social security contributions (STEP and LMPS)—are more likely to have jobs amenable to WFH than their informal counterparts. This is because informal workers have more physical/manual intensive jobs and lower ICT use at work. This is important because informal workers are less likely to be protected against important risks. For example, subsidies and other forms of assistance during the COVID-19 crisis are easier to implement when using the social insurance infrastructure, which often only includes formal workers.

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**Figure 6. WFH (adjusted for home internet access), by individual characteristics.**

<!-- Start of picture text --> (A) PIAAC<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>Gender Education Age Job type Formal status<br>-0.50<br>(B) STEP<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>Gender Education Age Job type Formal status<br>(C) LMPS<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>-0.20<br>Gender Education Age Job type Formal status<br>Men Women Non-college education College education 16-24 25-34 35-44 45-54 55-65 Self-employed Wage employee Informal Formal<br>Men Women Non-college education College education 16-24 25-34 35-44 45-54 55-65 Self-employed Wage employee Informal Formal<br>Men Women Non-college education College education 16-24 25-34 35-44 45-54 55-65 Self-employed Wage employee Informal Formal<br><!-- End of picture text --> 16
Note: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the (A) PIAAC, (B) STEP and (C) LMPS samples. A higher value indicates that jobs are more amenable to WFH.

The sectors that emerge as more amenable to WFH tend to be the same across most countries in the PIAAC and LMPS data sets. These sectors include ICT, professional services, the public sector, and finance (Figure 7). In contrast, jobs in hotels and restaurants, agriculture, construction, and commerce are the least amenable to WFH.

# **Figure 7. WFH index by sector of economic activity, PIAAC sample**

<!-- Start of picture text -->
1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>A=Agriculture, forestry and fishing I=Hotels and Restaurants T=Activities of households as employers F=Construction G=Commerce S=Other service activities H=Transportation and storage E=Water N=Administrative activities C=Manufacturing B=Mining and quarrying Q=Human health and social work activities R=Arts, entertainment and recreation P=Education D=Utilities O=Public administration L=Real estate activities K=Financial and insurance activities M=Professional activities J=Information and communication<br><!-- End of picture text -->

Note: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the PIAAC sample. A higher value indicates that jobs are more amenable to WFH.

Finally, we regress the WFH index for each data set on individual and job characteristics and confirm that even after controlling for observable characteristics, women, college graduates and salaried workers are more likely to have jobs amenable to WFH than men, lower educated, and self-employed workers (Table 3).<sup>8</sup>

> 8 Tables showing country-level findings are available in the Online Appendix

> (http://www.hernanwinkler.com/uploads/5/5/1/1/5511764/appendix_jobs_amenability_to_wfh_v13.xlsx).

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Differences in educational attainment predict large gaps in WFH measures: the jobs of college graduates are 0.70 standard deviation more amenable to WFH than those of their less educated counterparts in the PIAAC sample. That figure for the MNA region is 0.61. In all three samples, workers aged 25 and older have jobs less amenable to WFH than those 24 years or younger. In PIAAC countries, the relationship between amenability to WFH and age has an inverted U-shaped pattern, where those aged 25 to 34 years have the jobs most amenable to WFH, while those younger than 25 and older than 55 are at the opposite end. Among countries in the STEP and LMPS data sets, workers in the youngest age bracket have the jobs most amenable to WFH, but there are little differences by age among those aged 25 to 65.

**Table 3. OLS regression of the WFH index (adjusted for home internet access)**

||PIAAC|STEP|LMPS|
|---|---|---|---|
|Women|0.0611|0.267|0.38|
||[0.00613]***|[0.0108]***|[0.0167]***|
|College education|0.702|0.487|0.615|
||[0.00642]***|[0.0130]***|[0.0173]***|
|25-34|0.305|-0.138|-0.0788|
||[0.0141]***|[0.0181]***|[0.0213]***|
|35-44|0.302|-0.177|-0.144|
||[0.0121]***|[0.0186]***|[0.0215]***|
|45-54|0.224|-0.200|-0.162|
||[0.0119]***|[0.0191]***|[0.0229]***|
|55-65|0.0747|-0.193|-0.139|
||[0.0122]***|[0.0213]***|[0.0292]***|
|Wage employee|0.204|0.109|0.0471|
||[0.00870]***|[0.0127]***|[0.0155]***|
|Constant|-1.045|-0.205|-0.297|
||[0.0264]***|[0.0186]***|[0.0240]***|
|Observations|138,954|24,085|22,088|
|R-squared|0.21|0.319|0.148|

Notes: All models include country fixed effects. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

# **4. Concluding Remarks**

This paper provides new evidence on which countries and types of workers have jobs that are less amenable to working from home. Using data from 53 countries on the types of tasks that each person does at work—as 18
opposed to occupation-level measures from the United States—it finds that poorer countries and workers who are male, with lower levels of education, self-employed, and with informal jobs are more vulnerable to social distancing policies, since the nature of their jobs makes them less amenable to working from home. These findings highlight the importance of income protection policies for workers who are not in the formal sector and thereby are less likely to be reached by social protection programs channeled through formal mechanisms. At the same time, it highlights the importance of accelerating ICT adoption to facilitate homebased work when working on-location is not an option. Finally, it shows that using individual information on the tasks that people do at work is important, since occupations capture only half or less of the types of tasks that workers do on-the-job.

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