Working from home and the explosion of enduring divides: income, employment and safety risks.

Why are there so many non-teleworkable occupations? Is teleworking only a matter of ICT usage or does it also reflect the division of labour and the underlying hierarchical layers inside organizations? What does it happen to those workers not able to telework in terms of socio-economic risks, and how does the gender dimension interact with risk stratification? Hereby, we intend to shed light on these questions using a detailed integrated dataset at individual and occupational level (Indagine Campionaria delle Professioni, Indagine delle Forze di Lavoro and Inail archive) which provides information on different nature of risks (income, employment and safety). Our results entail that, first, class attributes, intended as execution of tasks, degrees of autonomy in doing the job, layers of the occupational categories, strongly influence the chance of working from home; second, those individuals who are not able to perform their work remotely are more exposed to transition to unemployment, to earn low wages, and to safety and health risks; third, being woman and employed with a temporary contract significantly amplify risk stratification. © Springer Nature Switzerland AG 2021.

Introduction

With the outburst of the COVID-19 induced crisis, societies are facing a major transformation of the established organization of productive activities, in particular the way in which work is physically performed at workplaces. Related, another deep challenge concerns the exploding socio-economic divides in the new pandemic phase. Indeed, not all segments of the population have been equally hit by the economic damages arising from the impossibility of performing their own job. For some segments direct and indirect pandemic risks have been stratifying and conflating. This is the case of Black, Coloured and Latino communities in the US which have been facing rising health and poverty risks (Selden and Berdahl 2020; Gonzalez et al. 2020; Montenovo et al. 2020). These workers however were suffering profound injustices in terms of access to medical assistance, income insecurity and occupational segregation well before the pandemic (Millett et al. 2020). Similarly, indigenous and suburb communities in Latin America did have far less chance to stay at home during lock-downs forced to choose between income security and health protection (Dueñas et al. 2020).

From the other side of the Atlantic, the Eurozone established for the first time a common plan to finance unemployment subsidies, the SURE, to face enormous job losses. However, European responses to tackle the labour market impacts of the COVID-19 crisis have been heterogeneous, ranging from extensions of sick-leaves, furlough schemes, redundancy pay systems, extraordinary income transfers, suspensions of layoffs. The only common denominator across all countries has been the switch to telework. Clearly, the higher the presence of social protection schemes and of labour market institutions operating in a given country, the lower the possibility that job losses will result into individual socio-economic risks. On the contrary, the higher the level of informality and the weakness of labour market institutions, the higher the associated individual risks.

In this paper we focus on a country presenting a combination of formal and informal labour markets, Italy, the first European economy hit by the pandemic and immediately adopting measures of social distancing since the mid of March 2020. As a consequence of lock-down measures, productive activities have been overwhelmed by the imposition of teleworking. Firms and public bodies have faced the pressure to reshape their organizational set-up introducing for the first time forms of remote-working. In Italy, however, working-from-home appears to be more a privilege for a few occupations rather than a generalized possibility. In fact, we recently documented that only thirty percent of Italian workers may work remotely (Cetrulo et al. 2020b). Those workers tend to belong to the upper echelon of the occupational distribution, are better remunerated and employed with permanent contracts. This figure has been confirmed by survey data reporting between 6.5 and 8 million workers abruptly shifted to remotely-work against approximately 500, 000 workers in 2018 (Fondazione Di Vittorio 2020). It is also in line with the US experience wherein, according to a web-survey carried out between April and June 2020 by Brynjolfsson et al. (2020), only one-third of the US workforce shifted to telework, confirming the previous estimate by Dingel and Neiman (2020). Other studies on advanced economies confirm this ratio, generally ranging from to of the workforce.

Why are there so many non-teleworkable occupations? Is teleworking only a matter of ICT usage or does it also reflect the division of labour and the underlying hierarchical layers inside organizations? What does it happen to those workers not able to telework in terms of socio-economic risks, and how does the gender dimension interact with risk stratification? Hereby, we intend to shed light on these questions using a detailed integrated dataset at individual and occupational level (Indagine Campionaria delle Professioni, Indagine delle Forze di Lavoro and Inail archive) which provides information on different nature of risks (income, employment and safety).

More in detail, to address the first question, after having distinguished among the two populations of working and not-working from home, we dissect which are the attributes of teleworkability. We resort to the anatomy of the Italian occupations developed in Cetrulo et al. (2020a) assigning scores to attributes of power, knowledge and learning, ICT skills, creativity and team-working, per each 4-digit occupation. Then, we investigate what happens to those segments not able to work remotely. In this respect, we study the events of transition to unemployment (occupational risk), of getting low-income (income risk) and of job related injuries and diseases (health risk). We therefore identify those occupations which face stratifying risks, namely characterized by the co-occurrence of these three events. We finally estimate a probit model at both individual and occupational level, accounting for a large set of covariates, and focusing on the role played by teleworkability, contractual, and gender as determinants of risk stratification.

The first result of our study is that class attributes, intended as execution of tasks, degrees of autonomy in doing the job, layers of the occupational categories, strongly affect the chance of working from home. Although the use of ICT devices and related knowledge are dramatically important to remotely-work, the degree of power and autonomy exercised in decision-making processes, and therefore the positioning along internal hierarchies, significantly differs between teleworkable and non teleworkable occupations. Women look to be endowed by a lower degree of power and autonomy compared to men in teleworkable occupations, and in general to be largely concentrated in the bottom part of the ISCO classification in non-teleworkable occupations, with gender and class divides intersecting. Moving to the stratification of socio-economic risks, according to our second result, those individuals who are not able to perform their work remotely are more exposed to the risks of becoming unemployed, earning a lower wage, and face significant safety and health risks. The most exposed occupations to risk stratification include food preparation-cooking-and-distribution personnel, waiters and similar professions, unqualified staff in charge of cleaning services in offices and shops, these latter being all professions with a predominant female share. Indeed, the third result entails that being woman and being employed with a temporary contract significantly amplify risk stratification.

The novelty of our contribution is threefold: first, we enrich the notion of risk stratification, bundling together three types of risk often thought to be unrelated to teleworking, and we detect the micro-occupational determinants of risk transition; second, we dissect the attributes of teleworkability and analyse the readiness of the Italian productive structure toward a potential durable teleworking shift; third, the use of micro-level information allows to exactly identify segments of the population toward which addressing selected policy interventions, beyond now-casting (Adams-Prassl et al. 2020b). Taken at large, our work supports the view that the COVID-19 crisis is more a syndemic rather than a pandemic (Horton 2020), characterised as it is by the interrelation between health and socio-economic risks.1 It is by no coincidence that what before was an unequal system of organizing societies it is now getting a socially unjust one (Dosi et al. 2020) marked by exploding enduring divides.

The paper is organised as follows: in Sect. 2 we discuss the streams of literature relevant to inform the empirical analysis, while in Sect. 3 we detail data, methodology and descriptive evidence. Results are shown in Sect. 4 and further discussed in Sect. 5 which concludes the paper.

Background literature

In this section we first discuss the evidence on diffusion and impact of teleworking as an organizational choice in usual times (Sect. 2.1), we next devote attention to teleworkability as a must in pandemic times (Sect. 2.2), and finally we highlight the relevance of the Italian case (Sect. 2.3).

Teleworking as a choice in usual times

The notion of “telecommuting” has been coined by Nilles (1975) with reference to the remotely execution of working tasks (including communications) at home or in other places different from the office. Early studies focusing on the diffusion of telework and related impacts on firms’ and workers’ performance have been stimulated by the outburst of computers (Nilles 1975) as well as by the effect of the 1970s’ energy crisis on mass transport (Harkness 1977). However, contrary to the expectation of a progressive disappearance of offices and the spreading of nomad workers operating from their “electronic cottages” (Toffler and Alvin 1980; Makimoto and Manners 1997), telework has been only slowly diffusing, with the highest rates recorded in the Northern European countries, Japan and the US (Messenger 2017). Indeed, since 1980 the proportion of employees who primarily work from home has more than tripled and the range of teleworkable activities has also increased including a wide spectrum of service jobs, ranging from sales assistants and realtors to managers and software engineers (Bloom et al. 2015).

Sectoral, occupational and firm characteristics are crucial to understand the extent to which a given task is “teleworkable”. Indeed, “teleworkability” depends on the executed functions, availability of computers and digital infrastructures allowing to perform tasks remotely, firm managerial and organizational capabilities, worker ICT skills (Bailey and Kurland 2002). In terms of hierarchical layers inside organizations (Huws 1991; Huws et al. 1999; Bailey and Kurland 2002; Corso et al. 2006; Neirotti et al. 2011), clerks, managers and professionals are seen as the most apt recipients of telework because of the more frequent use of computer, lower physical requirements and higher level of discretion and autonomy in defining the work pace characterizing those segments (Olson 1983).2 More recent evidence confirms the importance of adopting an occupational-based perspective to understand the patterns of telework diffusion, as the largest share of those working remotely are concentrated in specific occupational categories such as managers, professionals and, to a lower extent, clerical workers (Messenger 2019).

From micro-level occupational differences to country-level ones, telework diffusion ranges from 30% adoption rates in Sweden and Finland, to much lower rates recorded in Italy, namely 3.6% in 2018.3 Those differences are mainly due to heterogeneity in ICT infrastructures and in active policies aimed at promoting the diffusion of ICT skills and internal workplace flexibility (i.e. flexible working hours) (Huws et al. 1999; Messenger 2019). Clearly, the industrial composition matters as well, with countries having larger shares of manufacturing less apt to teleworkability. Additionally, firm size matters being dimensionality a carrier of both technological and organizational capabilities. At the European level, Vazquez and Winkler (2017) report that the share of teleworking labourers has increased more than 15% in ICT intensive industries during the last decade, while according to the 2015 European Working Condition Survey (EWCS), around 13.5% of European workers had some experience of telework, with only 5.2% of them usually working from home (Vargas-Llave et al. 2020).4

Teleworking is supposed to reduce spared time (log-in), eventual unproductive working phases (breaks) and sick leaves. This seems to be confirmed by Bloom et al. (2015) which find that being assigned to telework raises individual productivity. Dutcher (2012), via a quasi-experimental setting, shows that working from home can have positive implications on productivity in the case of creative tasks, while a negative relationship is detected in the case of repetitive and low-skilled tasks.

In terms of workers’ satisfaction, Arntz et al. (2019), relying on the German Socio-economic Panel (GSP) between 1997 and 2014, highlight the importance of workers’ socio-demographic characteristics: while childless employees, even working an unpaid extra-hour per week, report higher satisfaction due to telework, the latter penalizes women compared to men in terms of monthly wages, therefore increasing the gender-pay gap, with women accepting wage reduction against available free time to reconcile home caring schedules (Mas and Pallais 2017). Increasing overtime is also reported in Lott and Chung (2016).

Overall, if teleworking remains an attribute characterizing only few countries and occupations, having been generically configured as a complementary rather than a unique organizational choice, it is crucial to understand and detect which are the underlying characteristics making teleworking possible, and to estimate the socio-economic risks for those who cannot telework. This is of paramount importance nowadays since teleworking has shifted from being an organizational option (based on workers’ voluntary choice) for those few innovative firms and countries of adoption, to a must necessary to keep operating productive activities under pandemic times.

Teleworking as a must in pandemic times

Teleworkability significantly depends on technical attributes of occupations and on the internal division of labour and knowledge inside organizations. Jobs requiring in-person interactions, or alternatively, transforming external objects/environment and/or deploying complex and voluminous machines can hardly be performed from home. The opposite holds for jobs characterized by the use of ICT devices and software which do not require social exchanges. Therefore, the actual performed tasks, rather than the sheer sector of activity, represent the appropriate level of information to detect teleworkability.5

Indeed, the explosion of the pandemic has seen the emergence of a growing literature based on occupation-level data to produce some quantitative assessment of the share of teleworkable jobs. The first study has been Dingel and Neiman (2020) which, relying on the US O*NET dataset, provides a figure of 37% of the US workforce having the technical feasibility to work from home. Occupations able to work from home include those in STEM, education, training, and library services, legal and financial activities and managerial ones. At the opposite are manual workers in building and grounds cleaning and maintenance, food preparation and serving, construction and extraction, and installation, maintenance, and repairing. Corroborating evidence is in Hensvik et al. (2020) which rely on the American Time Use Survey. Among the top-5 most teleworkable occupations at 4-digit, the authors report medical transcriptionists, computer scientists, economists, farmers and artists. Relying on the BIBB/BAuA Employment Survey for German jobs, Alipour et al. (2020) document that 56% of the workforce can potentially shift to telework. The estimate for Italy stands at 30% according to Cetrulo et al. (2020b). Remarkably lower estimates are reported for Latin American Countries in Delaporte et al. (2021) and for overall developing countries in Gottlieb et al. (2021). Figures comparing European countries are reported in Palomino et al. (2020). All studies agree in documenting strong heterogeneity across sectors and occupations.

Granted such estimates, the question is what happens to the rest of non-teleworkable occupations. Confirming the evidence in Cetrulo et al. (2020b), Brussevich et al. (2020), covering 35 OECD countries, find that workers less likely to work remotely are largely concentrated in sectors more hit by the pandemic, such as accommodation and food services, transportation, and retail and wholesale sectors. According to their results, about 15% of the workforce employed is at high risk of layoffs mostly involving vulnerable occupations and sectors, and informal labour markets. Montenovo et al. (2020) report heterogeneous economic impacts of the pandemic across US subgroups. They identify the role played by occupational characteristics (degree of teleworkability and social interaction) and industry as pivotal in explaining job losses.

Systematic risk analyses are however scant. Beland et al. (2020), relying on the Current Population Survey (CPS) to study the impact of stay-at-home orders on employment and wages in the US, find higher job security for remote occupations. Consistently, Adams-Prassl et al. (2020a) report that the higher the fraction of tasks executable from home, the lower the risk for workers of being furloughed under the UK Job Retention Scheme. For Italy, Barbieri et al. (2021) and Boeri et al. (2020) have looked at those sectors of activity more exposed to contagion via physical proximity, with the highest exposure registered in the health sector.

In the following, we aim at contributing to the extant literature by focusing on the underlying characteristics of teleworkability, clarifying, first, which attributes of the working activities allow to telework and, second, quantifying, from a multi-level perspective, the socio-economic risks that those who cannot telework are facing.

Reaction of the Italian labour market to telework

Italy has been the first European country to implement social distancing policies. In the second half of March 2020, a decree of the Prime Minister imposed a lock-down to so-called “non-essential” activities, pushing both public and private companies toward telework. The local press welcomed such emergency measure as a chance to diffuse telework on a larger scale given that, in 2018, only 3.6% of the Italian workers regularly worked from home according to Eurostat.6

Such an acceleration towards telework, however, has turned out to be uneven across sectors, occupations and workers, in turns unequally affecting an already divided labour market. Very often, not surprisingly, occupations that are non tele-workable are performed by low-income/precarious workers. As extensively documented (Cirillo et al. 2017, Cetrulo et al. 2021 for a review), during the last twenty years the Italian labour market became increasingly fragmented with a growing share of precarious jobs. The latter are concentrated in social consumption industry, i.e. tourism, restaurants, trade and retail, populated by SMEs with poor technological, organizational and financial capabilities, and largely located in the southern regions. Precarious jobs are indeed overrepresented in sectors most exposed to closures.

Despite the introduction of a Law forbidding dismissals for economic reasons, between March 2020 and April 2021 about 900,000 workers lost their jobs. Pandemic induced job losses are largely concentrated among young, part-time and temporary workers employed in the service sector with an annual contraction of – 2.8%, – 3.7% and – 7.3% respectively.7 As expected, the major toll was paid by those who were structurally more fragile in terms of contractual regulations, sectors of activity and occupations. Among the latter, as early anticipated in Cetrulo et al. (2020b), there is a high incidence of workers performing tasks that cannot be accomplished remotely. This evidence asks for a more refined investigation of the ongoing risk stratification upon the most vulnerable segments induced by task-teleworkability.

Data, methodology and descriptive evidence

In this section we first present the integrated dataset used to conduct the empirical investigation (Sect. 3.1), and we then move to describe our classification to distinguish those occupations which can and cannot perform their activity from home (Sect. 3.2). Health risks deriving from working activity are presented in Sect. 3.3, while gender divides in terms of teleworkable occupations are discussed in Sect. 3.4.

Integrated dataset description

Our empirical analysis draws on the matching of three different databases, namely the RLFC-ISTAT (Rilevazioni Forza Lavoro) which allows to recover information on the Italian labour force at individual level, the Banca dati delle Professioni-INAIL which provides occupation-based information on working conditions, namely accidents at work and job-related diseases, and finally the ICP-INAPP (Indagine Campionaria delle Professioni) providing information on tasks and activities performed at workplaces. From the matching, we exploit a huge informative set, part of the so called Italian Informative System of Occupations (see Table 1 for more details).8

Table 1

Integrated dataset description

Database	Source	Year	Unit of analysis	Observations	Variables
Rilevazioni Forza Lavoro	ISTAT	2011–2017	Individuals	More than 85,000	Monthly wage
					Employment status (employed, not employed, inactive)
					Socio-demographic variables (age, gender, education, occupation, geographical area, sector)
					Type of contract
Indagine Campionaria delle Professioni	INAPP-ISTAT	2012–2016 wave	4-digit occupation	506	Selection from section G
					Selection from section H
Banca dati delle Professioni	INAIL	2017	4-digit occupation	506	Number of accidents at work
					Number of diseases at work (e.g. osteo-muscular, oncological, nervous, mental diseases)

The RLFC collects detailed information on workers employment status, income, socio-demographic characteristics (i.e. education, age, gender, region), type of employment contract, 4-digit occupation, and sector of activity. The survey, an annually repeated cross-section, is conducted by the ISTAT three times per year with a quarterly frequency, interviewing around 250 thousand families resident in Italy, corresponding to a total of about 600 thousand individuals, across 1,400 Italian municipalities. Each individual is interviewed four times in two subsequent quarters, at year t, and in the corresponding quarters at year .9 We focus on the most recent available wave, 2016–2017, while the remaining annual waves up to 2011 are used as robustness checks (available upon request).

The ICP represents the only European source comparable with the US O*NET, the latter being the most comprehensive database reporting qualitative and quantitative information on tasks, skills, work contexts and organisational characteristics at the 5-digit level of observation. The construction of the dataset entails a complex, multi-layer strategy of data collection and information processing allowing for both detailed occupational descriptions and inter-occupational comparability. Currently, two waves of the ICP database are available (2007 and 2012) with a spectrum covering 797 occupational codes, excluding armed forces. We rely on the 2012 wave. The interviews were administered to 16,000 Italian workers to ensure statistical representativeness with respect to sectoral, occupational, firm-size and geographical heterogeneity. Both O*NET and ICP questions are organised in six main sections, expressions of a content model that simultaneously provides information from both a job-oriented and a worker-oriented perspective. The descriptors are: worker characteristics (enduring abilities), worker requirements (skills and education), occupational requirements (organisational and work context), experience requirements (training, cross functional skills), workforce characteristics (labour market information) and occupation-specific information (generalised activities and work context). Therefore, descriptors are formulated by making it possible to distinguish, for instance, inner individual abilities from competences acquired on the job. For each question, two rating scales are generally provided: level and importance. Questionnaire set-up, interviews administration and ex-post validation activities are designed to minimize subjective biases related to personal perspectives/attitudes.

The Banca dati delle Professioni released by the INAIL (National Institute for Occupational Accident Insurance) contains information on work accidents’ and occupational diseases’ incidence at 5-digit occupational level from 2017 to 2018. The public release of this dataset is part of an integrated project aimed at progressively matching different sources of information on occupations. To our knowledge, this is the first time the INAIL dataset is used in combination with other two sources of information on occupations. To get time-consistent estimation, we employ the cross-sectional 2017 wave.

Working from home and teleworkability

Our first step entails the identification of those occupations which can and cannot be performed from home (FH and NFH respectively thereafter). With this purpose, we start with the analysis of the ICP dataset. To identify those jobs, thirty questions belonging to the “generalised activities” (G) and “work context” (H) sections of the ICP have been selected (see Table 6 in the Appendix for reference).10

Table 6

Variables used to build the Not from home index

Question	Category
H.17 How often does your profession require you to work outdoors exposed to all weather conditions?	Outdoor activities
H.18 How often does your profession require you to work outdoors but sheltered (like in an open shack)?	Outdoor activities
H.19 How often does your profession require you to work in a piece of equipment or an open vehicle (such as a tractor)?	Outdoor activities
H.20 How often does your profession require you to work in closed equipment or vehicle (such as a machine)?	Use of machine or specific equipment
H.27 How often in your work are you exposed to vibrations throughout your body (such as when operating a jackhammer or bulldozer)?	Use of machine or specific equipment
H.32 How often does your work require you to expose yourself to dangerous equipment (such as working with saws, near machines with moving parts or vehicles)?	Use of machine or specific equipment
H.40 In your work, how long do you use your hands to manipulate, control or feel objects, tools or control systems?	Use of machines or specific equipment
H.43 In your work, how long do you wear protective or safety equipment such as shoes, glasses, gloves, earplugs, helmets or jackets?	Use of machines or specific equipment
H.44 In your work, how long do you wear specialist protective or safety equipment such as self-contained breathing apparatus, harnesses, full protective suits or radiation protection clothing?	Use of machines or specific equipment
H.55 How important is it in your work to keep sequences of machinery and equipment under control?	Use of machines or specific equipment
G.18 Managing machines and processes	Use of machines or specific equipment
G.20 Maneuvering vehicles, vehicles and equipment	Use of machines or specific equipment
G.22 Repair and maintain equipment	Use of machines or specific equipment
G.23 Repairing and maintaining electronic equipment	Use of machines or specific equipment
G.4 Inspect equipment, structures or materials	Use of machines or specific equipment
H.25 How often are you exposed to contaminants (such as polluting gases or dust) in your work?	Bio-chemical risk exposure
H.28 How often does your work require you to be exposed to radiation? (This may happen, for example, to people working in chemistry or radiology laboratories)	Bio-chemical risk exposure
H.29 How often does your work require you to expose yourself to disease or infection? (This may happen, for example, to people working in hospitals, or in medical or analytical laboratories, or to those engaged in disinfection activities)	Bio-chemical risk exposure
H.31 How often does your work require you to expose yourself to hazardous situations (such as working with high voltage electricity, flammable materials, explosives or chemicals)?	Bio-chemical risk exposure
H.33 How often does your work require you to expose yourself to small burns, small cuts, bites, stings?	Bio-chemical risk exposure
H.30 How often does your work require you to expose yourself in places or places high above the ground (such as working on poles, scaffolding, stairs, walkways higher than 2.5 m)?	Highly Physical or manual activities
H.35 In your work, how long do you climb ladders, poles, scaffolding, etc.?	Highly Physical or manual activities
H.36 How long do you walk or run in your work? (excluding home-work trips)	Highly Physical or manual activities
H.37 In your work how long do you kneel, crouch, crawl, crawl or bend ?	Highly Physical or manual activities
H.38 How long in your work do you maintain or recover your balance?	Highly Physical or manual activities
G.16 Perform physical activities that require moving the entire body, or considerable use of arms and legs (such as climbing stairs, balancing, walking, bending and handling materials)	Highly Physical or manual activities
G.17 Handling and moving objects	Highly Physical or manual activities
G.29 Assisting and caring for others	Social contact
G.32 Working in direct contact with the audience and performing	Social contact
H.4 How often does your profession require the use of e-mail?	E-mail Use

Our analysis adapts and expands the methodology proposed by Dingel and Neiman (2020). The selected questions provide insights on the relative importance of: The correlation matrix in Fig. 1 shows a relatively low degree of overlapping information among our selected variables, and this supports our choice of retaining all thirty entries.

Fig. 1

Correlation matrix among ICP questions to construct the binary indicator

performing activities involving (i) use, control and repairing of machines, equipment, vehicles, (ii) social contact, taking care of/or assisting others, (iii) email correspondence;

performing activities which (i) are carried out outdoors, (ii) require exposure to diseases and infections, (iii) imply the execution of risky movements or the wearing of protective equipment.

For each 5-digit occupation,11 these variables are ranked according to an importance or frequency scale ranging from 0 to 100. In order for an occupation to be classified as “Not from home”, most of the respondents should spend a large fraction of their working time in external environments or use equipment, machinery, tools. Alternatively, they should have continuous contact with the public.

More in detail, our indicator “Not from home” is a binary variable taking value 1 if at least one out of 29 questions (except the use of e-mail) shows a score equal or higher than 60 (corresponding respectively to “once or several times per week” in the time scale of section H, and to “very important” in the importance scale of section G), or if the question on the use of e-mail takes a value lower than 40; vice versa the indicator is equal to zero if for all 29 questions, intensities are lower than 60, or alternatively if the question on the use of mail is higher or equal to 40. The Appendix presents a series of robustness tests on the threshold level, variable selection and comparison with alternative available indicators.

Therefore, if for a given occupation most respondents report that it is very important to control machinery and use equipment, the latter cannot be carried out from home. Similarly, if most respondents report that they perform outdoor tasks for the majority of working time, this occupation cannot be carried out from home. Conversely, if sending e-mails represents a very infrequent activity, the occupation cannot be performed remotely. The classification is useful in order to identify jobs that can and cannot be executed from home on the basis of the actual performed tasks and work contexts, and starts by excluding all those occupations that require working in a well-defined physical space (e.g. because of the use of working instruments or because of intensive social contact). Of course, in case of compulsory social distancing, an occupation as primary school teacher which could not be carried out from home according to our classification, will eventually done remotely. In fact, there are tasks, largely related to activities as “taking care of others” or “working with the public” that could potentially be digitized, however at the cost of entirely reconfiguring the very nature of the profession.

An interesting example to appreciate and validate our classification is the case of teachers which, according to the education-level, belong to the two different categories. In fact, while school teachers cannot work from home, almost all university professors and researchers can actually perform their job remotely. This result depends precisely on the different degree of importance attributed by workers to social contact variables, being the latter more relevant in primary education. Overall, the index performs quite well in consistently assigning the entire set of 4-digit occupations12 to the two groups From Home and Not From Home, in such a way that only eight occupations are manually moved from one group to another after an ex-post evaluation of the classification.

After identifying occupational categories at 4-digit, these are aggregated at 1-digit according to the ISCO classification, and then are linked to the Labour Force Survey providing information on the number of employees, wages, contractual types and socio-demographic characteristics of workers (age, gender and level of education). Table 2 presents the top-ten occupations at 3-digit for each category. Occupations are ranked in terms of the number of variable co-occurrences, out of thirty selected variables. The higher the number of co-occurrences, the higher the ranking. Occupations like woodcutters, miners, construction workers, fishermen rank among the top-professions which cannot be performed remotely. On the contrary, occupations involving specialised field knowledge, as legal or linguistic experts, managerial and executive professions are among the top ones which can be performed remotely. In terms of organizational hierarchies, occupations that cannot be performed remotely tend to be located at the low-end of the employment structure. On the contrary, those who self-organize their working activity, give orders or are responsible for high-level administrative tasks can operate remotely.

Table 2

Top-ten occupations which can and cannot be performed from home (3-digit, ISCO classification). Source: ICP-RCFL (2016)

Top-ten occupations which cannot be performed from home
644 Specialised forestry workers
711 Plant and machinery operators for the extraction and initial treatment of minerals
724 Machinery workers in plants for the mass production of wooden items
743 Agricultural machinery drivers
841 Unqualified mining and quarrying personnel
842 Unqualified construction personnel and similar professions
716 Plant operators for the production of thermal energy and steam, for waste recovery and for the treatment and distribution of water
645 Fishermen and hunters
712 Metal processing and hot working plant operators
612 Craftsmen and skilled workers in the construction and maintenance of building structures

Overall, only of the workforce has a job that can be done remotely, corresponding to broadly 6.7 million workers (2016 data). For the remaining part, including more than 15 million workers, the activities carried out and the work context to which they are exposed to do not make working from home feasible (Cetrulo et al. 2020b). This figure is in line with Dingel and Neiman (2020) reporting as the share of occupations which can be done from home for the United States.13 Notice that our estimate becomes consistent with survey-based figures reporting between 6.5 and 8 million remotely workers in Spring 2020, once we account for school teachers.

By aggregating at 1-digit according to the ISCO classification and distinguishing for gender, in Fig. 2 a highly polarized occupational structure emerges with a strong concentration of opportunities to work from home for the upper four occupational groups. Working remotely is feasible for the majority of those who are at the top of the organizational hierarchy (managers, entrepreneurs and legislators), for scientific-intellectual professions, for technical professionals. It increases in administrative tasks. For the lower part of the ISCO classification the scenario radically changes. Service-based occupations, such as entertainment operators, sales workers, artisans, plant and machine operators, as well as elementary professions, see the chance for working remotely drastically shrinking, or mostly nil. The first take home message from this battery of analyses is that working from home is more a privilege for a tiny fraction of the workforce rather than a generalized and widespread possibility.

Fig. 2

Distribution at 1-digit (ISCO groups) for employees which can and cannot work from home. Source: ICP-RCFL (2016)

Why teleworkability is so rare? We now turn to analyse which are the underlying determinants of working from home by employing for the two occupational categories the factor analysis developed in Cetrulo et al. (2020a), the latter developed to identify the dominant traits of the Italian occupational structure. According to their results the Italian occupational structure might be explained by five latent factors, with the factor collecting attributes of power, intended as the role in defining the division of labour inside organizations, explaining most of the variability. Other relevant factors are cognitive and manual dexterity, ICT knowledge, creativity, and team-work.

To which extent teleworkability is affected by these determinants? Fig. 3 shows the kernel density distributions at 4-digit level of the five factor scores applied to FH and NFH occupations. The factors read as (i) power, entailed by activities requiring decision-making authority, influence and control over other people, (ii) cognitive and manual dexterity, entailed by activities requiring both physical and cognitive selection of appropriate tools, inspection, control over the process, (iii) ICT knowledge, (iv) team, entailed by those activities requiring coordination with others, (v) creative, involving activities which require creative thinking.

Fig. 3

Factor scores based on Cetrulo et al. (2020a) - Kernel density distributions for FH and NFH occupations

Regarding the first three factors, the distinctive kernel density distributions highlight structural differences among the two categories. First of all, performing activities which entail the exercise of power attributes within organisations prevalently characterises FH occupations, confirming empirical studies underlying the importance of holding a relevant degree of autonomy, authority in doing the job, and setting deadlines in order to be able of working remotely.14 On the other hand, those workers performing activities which require manual dexterity and cognitive ability in dealing with production processes, or in keeping the sequence of machine tools, are largely employed in non teleworkable occupations. ICT skills, which are notably under-diffused in Italy, mainly characterise FH jobs. A similar but less distinctive pattern is shown by team-working which in general prevails in FH occupations. Being creative is instead an attribute not such unique for neither categories. If teleworkability is not only a matter of executing (or non-executing) activities which require manual ability (Sostero et al. 2020), but it also regards the internal position inside organizations, say the hierarchical layer to which one belongs, it becomes even clearer why working from home is more a privilege for restricted social groups rather than a widespread opportunity.

We now turn to present some descriptive statistics on the employment evolution (2011–2016) of occupations distinguished in the two categories (FH and NFH respectively). Indeed, if teleworking from being an organizational option becomes a compulsory choice, it is important to understand the degree of readiness of the Italian occupational structure in absorbing/generating those teleworkable occupations. During the period under analysis no relevant discontinuity in the growth rate of the two groups can be observed (Fig. 4), with a stable figure of less than 7 million workers employed in teleworkable jobs with respect to about 15 millions in non-teleworkable jobs. Together with a stable trend in NFH occupations, regional disparities clearly emerge, being those relatively few teleworkable occupations concentrated in the North.

Fig. 4

Time-evolution in the number of employees by regional area and teleworkability (2011–2016)

Health risk at work: physical proximity, accident rates and occupational illness

If working from home represents a privilege in terms of employment stability and income security, with the outburst of the pandemic FH occupations appear also to be the most resilient to the risk of contagion. Indeed, face-to-face interactions represent one of the thirty variables included to characterize the two populations: who can telework enjoys also the chance of reducing interpersonal contacts.

Physical proximity and face-to-face interactions have been used to identify sectors of activity and related occupations more exposed to contagion risk (Barbieri et al. 2021), retrieving information from the corresponding ICP variables. We deem this approach too restrictive to estimate the risk of contagion: physical proximity might be the result of the very nature of the working activity (primarily in the health sector), but also of the physical organization of workplaces (take the case of assembly workers using common spaces as canteens or wardrobes, or of open-space offices in administrative services). The use of the ICP variable tends to confine contagion risk to a sector-specific event, leading to a potential underestimation of the risk level in non-health and non-service sectors. For example, in manufacturing or in elementary occupations, workers tend to under-report face-to-face interactions and physical proximity. However many activities are actually performed in quite crowded workplaces, while sharing of workstations with other operators often occurs. Our doubt is confirmed by the distribution of physical proximity across 1-digit occupational groups: it is a prevalent variable, above , only for service and sales workers while it disregards the majority of other occupations (Fig. 5.a).

Fig. 5

Distribution of physical proximity (ICP), accident rate at work (INAIL), occupational disease rate (INAIL), health risk (authors’ elaboration combining accident and disease rates) at 1-digit (ISCO classification)

Relying exactly on disease exposure, physical proximity and gathering, the first release of the INAIL classification on sectoral contagion risks, adopted to regulate workplaces during the post lock-down phase, ranked doctors, nurses, pharmacists, police agents, funeral parlours and hairdressers as the most exposed workers, while a low degree of contagion risk was assigned to manufacturing and logistics workers (INAIL 2020a). However, update figures on contagion at workplaces showed an increasing number of cases in logistics and meat processing plants (INAIL 2020b), wherein working and employment conditions are far from being safe even in normal times (EFFAT 2020). Although at the beginning of the pandemic the highest recorded cases were in hospitals, mainly because of the lack of protective devices and adequate sanitizing procedures, recent data record a significant increase in contagion rate within sectors of activity initially classified at low risk.

To overcome these limitations, we construct a more comprehensive indicator of the actual conditions of safety and health at workplaces, looking at cases of accidents and occupational illnesses collected by the INAIL database. In fact, even if not directly informing about contagion risk, structural, pre-existing information on health and safety conditions at work might be a proxy for the status of existing (or not) employee protection schemes, at each 4-digit occupational level. Figure 5.b and c show the distributions of these events across 1-digit occupations. Although rare, because only certified by legal procedures, accidents and diseases are more concentrated in the bottom part of the ISCO classification.

Looking at the joint distribution of occupational illnesses and accident rates (defined as health risk in Fig. 5d) offers a comprehensive understanding on safety conditions at work, considering a variety of physical and psychological risk factors. Not surprisingly, the explosion of the pandemic has also spurred inequalities in terms of health at work. These disparities do not only depend on the type of job performed, but they are strictly related to both socio-demographic and organisational factors (ETUI 2020). Adopting or not rigid health and safety protocols within firms becomes crucial to prevent contagion.15

Gender divides

Up to the COVID-19 crisis, male and female occupations have never been such differently hit during downturns: recent empirical evidence documents the phenomenon of she-cession to underline the asymmetric labour market effects suffered by women, either for occupational segregation in sectors more exposed to closures (e.g. social consumption services), or for the highly unbalanced distribution of domestic burden, inducing many women to leave their job to taking care of children (Zamarro and Prados 2021; Farré et al. 2020).

Risks, vulnerabilities and socio-economic hardships affecting women intersect in the pandemic phase. With reference to Italy, on the one hand, many women kept working because employed in so-called essential sectors but, on the other hand, those who carried out domestic and care jobs, such as housekeepers and carers, were largely unable to access income and welfare supports due to the still predominantly irregular and informal nature of employment relationships in this sector. In terms of load of housework and work-life balance, mothers of children in the 0–5 age enormously suffered the burden of the lock-down (Del Boca et al. 2020).

A gender dimension enlarges our comprehension of risk stratification. Figure 6 presents the breakdown of FH and NFH occupations by distinguishing between male and female workers. Women working from home are mostly concentrated among clerical support workers doing administrative activities and to a less extent among scientific and technical professions. They hardly materialize among the top professions of the first ISCO group. Teleworking women, although maintaining income and job, enormously suffered the burden of conciliation between working and caring activities, primarily children education. Non-teleworking women, which indeed represent the largest fraction, are mainly concentrated among service and sales, and elementary occupations: together with the care-work burden, they had also to cope with income, employment and safety risks.

Fig. 6

Gender distribution at 1-digit (ISCO classification) for employees which can and cannot work from home

Patterns of occupational segregation, detailed at 3-digit level in Table 7 in the Appendix, map into lower income (Fig. 7a) and lower power in female dominated professions (Fig. 7b), which also look to be endowed by lower ICT skills (Fig. 7c). Indeed, power and ICT skills predominantly characterize teleworkable jobs and therefore appreciable heterogeneities regard FH occupations, in accordance with Fig. 3.

Table 7

Top-ten female-dominated occupations which can and cannot be performed from home (3-digit, ISCO classification). Source: ICP-ILFS (2016)

Top-ten female dominated occupations which can be performed from home	Female workers (share)
112 Directors, executives and equivalent in public administration and in health, education and research services	100
411 Secretarial and general affairs clerks	88
265 Other education and training specialists	83
432 Clerical, accounting and financial management employees	82
412 Office machine employees	69
422 Employees in charge of welcoming and informing clients	68
113 Magistrate Directors	68
331 Technicians in the organisation and administration of production activities	66
346 Public service and security technicians	66
513 Other qualified professions in commercial activities	65
441 Employees in charge of checking documents and sorting and delivering mail	61

Fig. 7

Kernel density distributions of wages, power and ICT skills factor scores by gender and status

Estimates of risk stratification

After having identified (i) occupations which can and cannot be performed from home, (ii) the underlying attributes of teleworkability, (iii) the importance of considering a more comprehensive nature of safety conditions at work, (iv) gender divides in the access to teleworkable occupations, we now move toward the empirical estimation of three forms of risk, namely employment, income and health safety. The goal is to verify whether a different risk profile emerges with respect to the probability of losing the job, earning a low income and facing more frequently accidents at work and occupational illnesses, which will be our outcome variables, once we classify workers according to their teleworkability, also in line with the extant literature (Mongey and Weinberg 2020).

Figure 8 shows the histograms of our three outcome variables distinguishing between FH and NFH occupations. Already at a first glance it emerges a distinctive pattern characterizing the two populations: indeed all three events are extremely concentrated among not working from home occupations, while the frequency of occurrence strongly decays for the other group.

Fig. 8

Histograms of the events: a earning a low income; b transition to unemployment; c having an accident at work and/or occupational illness

Tables 8, 9, 10, 11 in the Appendix present the co-occurrence of the three events for occupations at 4-digit, considering all possible combinations. They are indeed quite revealing, pointing at occupations such as “Retail sales assistants”, “Industrial product packaging machine workers”, “Unqualified cleaning staff in accommodation services and ships” among the most exposed to multi-dimensional risks (Table 8). If we exclude health risk (Table 9), female dominated professions such us “Supervisors of children and similar professions”, “Personal care workers”, “Machinery operators for the treatment and conservation of food” come more prevalently, while occupations in essential and caring activities as “Workers in charge of hygiene and cleaning services” and “Qualified professions in health and social services” emerge when looking at the co-occurrence of low-income and health risks (Table 10). Finally, manual workers and machine operators are more exposed to combined employment and health risks (Table 11). We are therefore able to pinpoint stratifying vulnerabilities.

Table 8

Occupations recording the co-occurrence of low income risk, unemployment risk (based on individual level data) and health risk (based on occupation data)

4 Digit code	Status	Occupation	Female %
3413	NFH	Tourist entertainers and similar professions	56.8
3427	NFH	Athletes	5.66
5122	NFH	Retail sales assistants	67.84
5221	NFH	Cooks in hotels and restaurants	28.9
5222	NFH	Food preparation, cooking and distribution personnel	72.1
5223	NFH	Waiters and similar professions	60.9
5472	NFH	Funeral parlour attendants	8.3
5486	NFH	Private security guards	11.2
5487	NFH	Lifeguards and similar professions	14.9
6112	NFH	Stone cutters, stonemasons and stonemasons	1.2
6123	NFH	Carpenters and carpenters in the building industry (excluding parking lots)	0.50
6133	NFH	Plasterers	0
6216	NFH	Divers and diving workers	0
6221	NFH	Blacksmiths, ingotters and press operators for forging	1.02
6332	NFH	Artisans of handmade textiles, leather and the like	52.3
6413	NFH	Farmers and farm workers specialising in gardens and nurseries	15
6441	NFH	Specialised forestry workers	0
6452	NFH	Inshore and inland fisheries fishermen	1.15
6531	NFH	Fibre preparers	28.1
6532	NFH	Weavers and knitters by hand and on manual looms	52.4
7275	NFH	Assemblers in series of articles in wood and similar materials	28.3
7281	NFH	Industrial product packaging machine workers	54.7
7328	NFH	Industrial winemakers and brewers	6.3
7421	NFH	Taxi drivers, drivers of cars, vans and other vehicles	4.6
7431	NFH	Agricultural tractor drivers	0.54
8131	NFH	Porters, goods handlers and similar	7.3
8132	NFH	Unqualified packaging and warehouse staff	21
8133	NFH	Delivery staff	7.6
8141	NFH	Unqualified cleaning staff in accommodation services and ships	66.6
8142	NFH	Personnel not qualified in catering services	60.8
8143	NFH	Unqualified staff in charge of cleaning services in offices and shops	73.9
8145	NFH	Green operators and other waste collectors and separators	7.1
8311	NFH	Farm labourers	32
8312	NFH	Unqualified green maintenance personnel	5.7
8321	NFH	Unqualified forestry personnel	11.3
8411	NFH	Maneuvers and other unskilled personnel from mines and quarries	0
8421	NFH	Skilled workers and unskilled civil construction workers and similar professions	0.53
8422	NFH	Construction and maintenance of roads, dams and other public works	2.05
8431	NFH	Unqualified personnel from industrial activities and similar professions	35.7

Table 9

Occupations recording the co-occurrence of low income risk and unemployment risk (based on micro data)

4-Digit code	Status	Occupation	Female %
2655	NFH	Teachers of artistic and literary disciplines	80.5
3216	NFH	Other technical health professions	11.4
3333	NFH	Commissioners, evaluators and commercial auctioneers	71.7
3414	NFH	Travel agents	71
3423	NFH	Instructors of techniques in the artistic field	85.2
3424	NFH	Non-competitive sports instructors	47.7
3452	NFH	Reintegration and social integration technicians	74
4216	NFH	Travel agency counter clerks	90.3
4222	NFH	Receptionists in accommodation and catering services	51.8
4224	NFH	Information officers in Call Centres (without sales functions)	78.3
5124	NFH	Cashiers of commercial establishments	85.4
5224	NFH	Barmen and similar professions	59.9
5231	NFH	Hostesses, stewards and similar professions	71.2
5232	NFH	Tourist guides	65.6
5422	NFH	Bookmakers, croupiers and similar professions	35
5431	NFH	Hairdressers	66
5432	NFH	Beauticians and make-up artists	94
5442	NFH	Supervisors of children and similar professions	90.4
5443	NFH	Personal care workers	90.5
5452	NFH	Keepers and breeders of pets and show animals	47.3
6215	NFH	Equipment and assemblers of metal cables for industrial and transport use	0
6453	NFH	Deep sea fishermen	0
6512	NFH	Artisan bakers and pasta makers	22.5
6513	NFH	Confectioners, ice-cream makers and artisan canners	41.3
6533	NFH	Artisan tailors and cutters, modellers and hatters	82.3
6535	NFH	Whiteworkers, hand embroiderers and similar professions	86.2
6536	NFH	Upholsterers	35.4
6542	NFH	Craftsmen and skilled workers of footwear and similar products	40.8
6543	NFH	Suitcases, handbags and similar professions	63.2
7151	NFH	Conductors of oil product refining plants	0
7267	NFH	Shoe series production machinery operators	34.2
7324	NFH	Machinery operators for the treatment and conservation of food	75.2
8161	NFH	Unqualified personnel in charge of building and goods custody services	25.4
8211	NFH	Unqualified personnel in recreational and cultural services	36.5
8221	NFH	Domestic workers and similar professions	88.8
8322	NFH	Unqualified animal care staff	21.7
3442	FH	Museum technicians, libraries and similar professions	78.8
4111	FH	Secretarial staff	87.9
4121	FH	Video-writers, typists, stenographers and similar professions	69
4122	FH	Data entry officers	57.9
4215	FH	Ticket sales staff	53.6
4321	FH	Accountants	81.7
4324	FH	Statistical services employees	65.2
4422	FH	Employees in libraries and similar professions	64.8
5125	FH	Home and distance sellers and similar professions	64.1

Table 10

Occupations recording the co-occurrence of low income risk (individual data) and health risk (occupation data)

4-Digit code	Status	Occupation	Female %
3215	NFH	Technical professions of prevention	60
4312	NFH	Warehouse management and similar professions	21.2
4412	NFH	Travel documentation checkers	25
5311	NFH	Qualified professions in health and social services	82
5481	NFH	Territorial guardianship staff	1.47
6111	NFH	Brillators (blastingers)	0
6151	NFH	Workers in charge of hygiene and cleaning services	46.7
6234	NFH	Refrigerators	5.1
6324	NFH	Painters and decorators on glass and ceramics	48
6331	NFH	Artisans of artistic woodworking and assimilated materials	12.4
6414	NFH	Farmers and specialized agricultural workers of mixed crops	32
6511	NFH	Butchers, fishmongers and similar professions	15
6515	NFH	Craftsmen and workers specialized in dairy craftsmanship	19.5
6521	NFH	Craftsmen and workers specialized in wood treatment	23.9
6523	NFH	Strippers, basket makers, sweepers, cork-blowers and similar professions	19.5
7131	NFH	Plant operators for the production of glass, ceramics and bricks	18.2
7241	NFH	Machinery workers in plants for the mass production of furniture and wooden articles	12.7
7312	NFH	Olive processing plant workers	7.6
7313	NFH	Workers in charge of refrigeration, hygienic treatment and first processing of milk	3.3
7322	NFH	Conductors of equipment for the industrial processing of dairy products	31.3
7325	NFH	Sugar production and refining machine operators	11.4
7413	NFH	Ropeway operators	0
8121	NFH	Ushers and similar professions	24.8
8122	NFH	Meter readers, coin collectors and similar professions	5.2
8144	NFH	Vehicle washers	3.2
8151	NFH	Bidels and assimilated professions	67.8
8152	NFH	Porters and similar professions	66.8
5121	FH	Wholesale shop assistants	20

Table 11

Occupations recording the co-occurrence of unemployment risk (individual data) and health risk (occupation data)

4-Digit code	Status	Occupation	Female %
4413	NFH	Mail sorting and delivery staff	38.2
6121	NFH	Stone, brick, refractory bricklayers	0.09
6122	NFH	Reinforced concrete masonry workers	0.59
6124	NFH	Scaffolders	0
6125	NFH	Tunnel owners, railway equipment operators and similar professions	0
6126	NFH	Road pavers and similar professions	0
6127	NFH	Prefabricated and pre-formed products assemblers	0.98
6132	NFH	Floor and wall tile installers	0.64
6135	NFH	Glassmakers	4.1
6136	NFH	Hydraulics and gas and hydraulic piping installers	0.58
6137	NFH	Electricians in civil construction and similar professions	0.16
6138	NFH	Window and door and window installers	0.27
6141	NFH	Painters, plasterers, lacquers and decorators	2.4
6152	NFH	Sewerage maintenance workers and similar professions	0
6213	NFH	Sheet metal workers and boilermakers, including tracers	1.1
6214	NFH	Metal carpentry fitters	1.2
6218	NFH	Ironworkers	2.7
6235	NFH	Mechanics and assemblers of industrial thermal, plumbing, air conditioning equipment	1.4
6244	NFH	Installers and repairers of telecommunications equipment	0
6342	NFH	Offset and press printers	19.4
6522	NFH	Woodworking machine carpenters and toolmakers	1.5
6541	NFH	Leather and fur tanners	11
6551	NFH	Stage machinists and toolmakers	0
7123	NFH	Metal heat treatment plant operators	4.4
7134	NFH	Kiln drivers and other plants for the production of bricks, tiles and similar products	5.5
7153	NFH	Operators of machinery for the manufacture of chemical derived products	17.4
7212	NFH	Machinery workers for the production of cement and similar products	0
7233	NFH	Machinery operators for the manufacture of plastic and similar products	21.9
7279	NFH	Other workers involved in the assembly and mass production of industrial items	22.1
7423	NFH	HGV and truck drivers	0.64
7432	NFH	Harvesting, harvesting, harvesting, chopping and pressing machine operators	12.7
7441	NFH	Earthmoving machinery drivers	0
6514	FH	Food and beverage tasters and classifiers	68.5

Empirical strategy and variables description

The estimation strategy applies the binary response methodology on two different databases:We assume that the response probability takes the following form:16with being a standard normal density function:We perform four univariate probit models, with dependent variables expressed as binary dummies: Where individual with , and j=occupation at 4-digit with .

a micro dataset built merging ISTAT RLFC-ICP, on which we estimate for each individual i those factors affecting the probability of (i) transition to unemployment, and (ii) earning a low income;

an aggregated data-set merging ICP-INAIL-ISTAT, where for each occupation j at 4-digit we look at those characteristics having an impact on the probability of (iii) low income, and (iv) high accident risk and illness at work.

Transition to unemployment (i): , where if individual i is employed at time t but he/she becomes unemployed or inactive at time ; if otherwise;

Low income (i): , where if the income of individual i belongs to the lowest income quartile of the entire workforce wage distribution; if otherwise;

Low median income (j): , where if the median income of occupation j belongs to the lowest income tercile of occupations’ median wage distribution; if otherwise;

High health risk (j): , where if the rate of accidents at work and occupational illnesses j belong to the highest tercile of the distribution; if otherwise.

We estimate four univariate probit models, at individual and occupation-level, against the indicator “Working from home” built on the ICP dataset (2012) and a set of covariates expressed in terms of dummies or categorical variables, as described in Table 3. The choice of a parametric model implies the loss of information on potential heterogeneous effects for each population of interest. For example, it might be that the employment risk increases for some particular 4-digit occupations, because of processes of restructuring of the sector of activity. However, being our covariates dummy or categorical variables it is not possible to proceed with non-parametric probit estimations allowing for local effects of the regression coefficients, changing with the intensity of explanatory variables. The diagnostic ability of the four models has been assessed through sensitivity (detection of true positives) and specificity (detection of true negatives) analysis. ROC curves (Fig. 13 in the Appendix) show a positive concave relationship, with areas under the curve always above which indicate a satisfying diagnostic ability of the model with respect to a random classification.

Table 3

Probit’s variables (individual and occupational level data)

Variable	Type	Values
Individual level data
Y1_i: Transition wide	Dummy	1 (if employed at time t and unemployed or inactive at time t+1), 0 (if otherwise)
Y2_i: Low income	Dummy	1 (if income belongs to the first quartile of income distribution), 0 (if otherwise)
Occupational level data
Y3_j: Low income	Dummy	1 (if the median wage belongs to the lowest tercile of income distribution), 0 (if otherwise)
Y4_j: High health risk at work	Dummy	1 (if the health risk belongs to the highest tercile of the health risk distribution, that equals to the sum of job accidents and occupational illnesses), 0 (if otherwise)
Not From Home (4-digit occupational level)	Dummy	1,0
Individual level controls
Female	Dummy	1 (if sex = female), 0 (if sex=male)
Age Group	Categorical	1 (if age = 16–35), 2 (if age=36–50), 3 (if age = 51–75)
Education level	Categorical	1 (if level =lower secondary), 2 (if level = secondary), 3 (if level = bachelor), 4 (if level = master)
Job Contract	Categorical	1 (if contract = permanent), 2 (if contract = temporary), 3 (if contract = autonomous)
Area	Categorical	1 (if area = Northern Italy), 2 (if area = Central Italy), 3 (if area = Southern Italy)
Agriculture	Dummy	1 (if nace = 1), 0 (if otherwise)
Mining and quarrying	Dummy	1 (if nace = 2), 0 (if otherwise)
Manufacturing	Dummy	1 (if nace = 3–9), 0 (if otherwise)
Electricity Gas Water & Waste	Dummy	1 (if nace = 10), 0 (if otherwise)
Construction	Dummy	1 (if nace = 11), 0 (if otherwise)
Wholesale Transport & Accommodation	Dummy	1 (if nace = 12), 0 (if otherwise)
Information & Communication	Dummy	1 (if nace = 13), 0 (if f otherwise)
Financial & Insurance Act	Dummy	1 (if nace = 14), 0 (if otherwise)
Real estate activities	Dummy	1 (if nace = 15), 0 (if otherwise)
Professional scientific support activities	Dummy	1 (if nace = 16), 0 (if otherwise)
Public administration, education & human health	Dummy	1 (if nace = 17), 0 (if otherwise)
Art & Other services	Dummy	1 (if nace = 18), 0 (if otherwise)

Fig. 13

Model diagnostic (ROC Curves)

Employment and income risks

Our first variable of interest is the risk of losing the job for an individual employed in a FH occupation, as a baseline, compared with an individual in a NFH occupation. In order to define the employment risk we look at individual transition events from employment to unemployment or inactivity, from time t (2016) to (2017).17 Given the lack of longitudinal panel data at individual level, we are able to capture only yearly based transitions to unemployment, therefore discarding information from longer transition spells. Likely, the baseline transition year, 2016, is not characterised by strong cyclical macroeconomic factors which could have alternatively impacted upon estimation results. Indeed, it was a period of anaemic recovery since the 2008 crisis. Additionally, we are not able to capture persistent unemployment and duration effects.

Table 4 (column 1) presents the probit regression coefficients. Confirming the information from Fig. 8, but now controlling for a comprehensive set of covariates, the variable “Not working from home”, shows a positive and significant effect on the probability of transiting to unemployment status for a worker being employed in a NFH occupation as compared to a FH occupation. This positive sign confirms the presence of an inherent higher risk of losing the job, independently from external shocks such as the pandemic, which characterizes those occupations classified as NFH, after controlling for factors such as age, gender, education level and contractual framework. We also observe that being employed in sectors such as Construction, Art and other Services significantly increases the risk of losing the job (with respect to the manufacturing sector), whereas the opposite holds for those working in Public Administration, Education, Health and also Agriculture. Positive and statistically significant coefficients of the two geographical controls confirm the presence of regional disparities in terms of employment security, with workers located in Southern and Central Italy being more exposed to risks of unemployment with respect to their colleagues in the North.18

Table 4

Probit models (individual level data 2016–2017)

	(1)	(2)
	Unemployment Risk	Low Income
Not From Home	0.187^∗∗∗	0.374^∗∗∗
	(5.31)	(18.41)
Female	0.197^∗∗∗	0.749^∗∗∗
	(7.41)	(44.76)
36–50 years old	–0.222^∗∗∗	–0.257^∗∗∗
	(− 7.90)	(–13.64)
50–75 years old	–0.358^∗∗∗	–0.448^∗∗∗
	(–10.84)	(–21.05)
Lower secondary education level	0.230^∗∗∗	0.717^∗∗∗
	(4.67)	(24.74)
Secondary education level	0.0815	0.498^∗∗∗
	(1.80)	(18.94)
Bachelor education level	0.185^∗	0.141^∗∗
	(2.52)	(3.19)
Temporary Contract	0.780^∗∗∗	0.271^∗∗∗
	(25.80)	(12.11)
Autonomous Contract	0.0628^∗	–1.458^∗∗∗
	(1.97)	(–44.12)
Central Italy	0.119^∗∗∗	0.145^∗∗∗
	(3.71)	(7.61)
Southern Italy	0.369^∗∗∗	0.348^∗∗∗
	(13.97)	(20.08)
Agriculture	–0.236^∗∗∗	0.671^∗∗∗
	(–3.72)	(16.84)
Mining & Quarrying	− 0.223	0.341^∗
	(–0.89)	(1.97)
Electricity Gas Water & Waste	–0.153	–0.0982
	(–1.13)	(–1.47)
Construction	0.280^∗∗∗	0.182^∗∗∗
	(5.95)	(4.50)
Wholesale Transport & Accommodation	0.0602	0.451^∗∗∗
	(1.60)	(19.07)
Information & Communication	0.0124	0.177^∗∗
	(0.12)	(2.72)
Financial & Insurance Activities	–0.301^∗	–0.206^∗∗
	(–2.16)	(–3.22)
Real Estate Activities	0.298^∗	0.573^∗∗∗
	(2.16)	(5.25)
Professional Scientific Support Activities	0.130^∗∗	0.790^∗∗∗
	(2.66)	(26.86)
Public Administration, Education & Human Health	–0.396^∗∗∗	0.0517
	(–7.56)	(1.85)
Art & Other Services	0.292^∗∗∗	1.067^∗∗∗
	(6.33)	(35.60)
_cons	–2.339^∗∗∗	–2.251^∗∗∗
	(–38.09)	(–60.73)
N	82,177	85,763
PseudoR^2	0.124	0.256

t statistics in parentheses

, ,

, ,

Figure 9, left panel, presents the average marginal effects for NFH occupations. This effect, as expected, turns out to be relatively small because of the “rare” event we are measuring (one year based transition to unemployment). Other relevant worker attributes which increase the probability of transition to unemployment, or inactive status, are being woman, young and holding a low education title. Indeed, temporary workers experience an employment risk higher with respect to workers with a permanent contract. In the post lock-down phase, reports on the labour market released by the ISTAT record a huge rise in job losses for temporary workers (ISTAT 2020).

Fig. 9

Average marginal effects on employment and low income risks - Regression in Table 4

Our second measure of risk concerns the probability of earning a low income. Income risks are particularly important to be analysed because of the reduced access to work for those individuals who cannot operate from home. Therefore, it is pivotal to understand the pre-existing probabilities of getting a low income whenever a worker employed in a NFH job stops doing its own activity for social distancing measures and related policy regulation. To study the probability of earning a low income, we distinguish among four wage quantiles, namely low, medium-low, medium-high and high. We focus on the low wage quantile.

Table 4, column 2, shows the probit regression coefficients for income risk. The coefficient of the NFH variable is positive and statistically different from zero, implying that belonging to an occupation which cannot be performed remotely inherently increases the probability of earning a low wage. Figure 9, right panel, presents the average marginal effects. The effect of NFH is now sizeable and much bigger than the corresponding one on employment risk (around ). This occurs also because of the higher persistence characterizing the wage distribution, which from year to year tends to show a relatively stable support. With respect to the role played by other covariates, being woman now increases the probability of earning a low income of . Indeed, holding a temporary contract increases the probability of earning a low income of . Also in this case regional disparities are at stage, with workers from Southern and Central Italy recording higher risks of earning a low income. With respect to sectoral heterogeneity, only workers in Finance and Insurance Activities exhibit a lower income risk (compared to the base manufacturing group), as shown by its negative and statistically significant coefficient.

Figure 10 presents differentiated marginal effects by gender and contractual categories highlighting gender divides and the role played by precariousness.

Fig. 10

Differentiated marginal effects by gender and contractual categories from probit estimates in Table 4

Safety risks

After having identified employment and income risks, we now move toward the estimation of safety risks. To accomplish the latter task, we employ the occupational level dataset ICP-INAIL-ISTAT whose unit of observation is not the individual (as in previous analyses) but the occupation at 4-digit level. More precisely, we investigate whether occupations that cannot be performed from home are more likely to be characterized by a higher health risk (built as the sum of accidents at work and occupational illnesses) and, as robustness check, also by a lower level of income. In order to control for several factors and to be consistent with the previous estimations, we exploit information from the labour force survey to build gender, regional, sectoral, education and contractual dummies. The routine adopted is as such that if of workers of a given occupation are e.g. female, that occupation is defined as “female dominated”, and so on.

According to Table 5, the coefficient of NFH is positive and statistically different from zero in both probit models. This outcome confirms the result obtained in the previous analysis concerning income risk, but it also adds an important information related to the dimension of health and safety at work. Indeed, as shown in Fig. 11, moving from teleworkable to non-teleworkable jobs increases the probability of facing a higher safety risk at work by more than . Clearly, the computed probabilities are much higher than the ones presented in the previous section because in this case the analysis is based on occupational rather than individual level data, increasing by construction the average marginal effects. Regarding the role played by other covariates, while belonging to a female/temporary contracts dominated profession strongly increases the probability of getting a low income, safety risks are higher in male dominated professions with permanent contracts.

Table 5

Probit models (occupational level data 2016)

	(1)	(2)
	Low Income	High Safety Risk
Not From Home	0.860^∗∗∗	1.169^∗∗∗
	(4.54)	(4.74)
Female	1.160^∗∗∗	− 0.445
	(6.13)	(− 1.95)
Permanent	− 0.565^∗∗∗	0.459^∗∗
	(− 3.59)	(2.61)
Degree	− 1.488^∗∗∗	− 1.378^∗∗
	(− 4.74)	(− 2.99)
North	− 0.451^∗∗	− 0.0580
	(− 2.84)	(− 0.34)
Agriculture	1.175^∗∗∗	0.462
	(3.48)	(1.38)
Manufacturing	0.275	0.625^∗∗
	(1.37)	(2.92)
Electricity Gas Water & Waste	0.0409	1.335^∗∗
	(0.06)	(2.85)
Construction	− 0.134	0.667^∗
	(− 0.42)	(2.12)
Wholesale Transport & Accommodation	0.341	0.602^∗
	(1.38)	(2.30)
Real Estate Activities	1.856^∗∗	0
	(3.04)	(.)
Professional Scientific Support Activities	0.894^∗	0.270
	(2.41)	(0.67)
Public Administration, Education & Human Health	− 0.408	0.376
	(− 1.46)	(1.26)
Art & Other Services	0.665^∗	0.290
	(2.16)	(0.84)
_cons	− 0.887^∗∗∗	− 2.140^∗∗∗
	(− 3.94)	(− 6.95)
N	487	485
PseudoR^2	0.307	0.237

t statistics in parentheses

, ,

Fig. 11

Average marginal effects on low income and health risks from probit estimates in Table 5

, ,

Discussion and conclusions

With the outbreak of the COVID-19 pandemic, although heterogeneously in terms of timing and intensity, governments opted for social distancing measures directed at reducing interpersonal contacts, the latter being identified as the main source of contagion. In this context, advising or requiring workers to work from home represented one of the key measures included in the “anti-COVID 19” social distancing policy packages (OECD 2020).

Such a pandemic-induced spreading of telework is showing heterogeneous effects on labour market segments: indeed, maintaining full-time working hours and switching to telework represent a suitable option only for a fraction of the working population, belonging to the upper echelon of hierarchies, being employed in occupations not requiring manual and cognitive dexterity, endowed by ICT-knowledge. Therefore, although telework represents an important safety net in terms of health, employment, and income security, it can also turn out into an inequality-enhancing mechanism between those who can and those who cannot work from home.

In this paper, we aimed exactly at assessing the presence of enduring divides between Italian workers that can work from home and those who cannot. This distinction, grounded on the study of occupational characteristics and their telework feasibility, turns out to be revealing of stratifying vulnerabilities in terms of income remuneration, employment stability and safety at work. Our results show that NFH workers record higher probabilities of earning a low income, losing job, experiencing accidents at work and occupational illnesses with respect to FH workers. Women and temporary workers face stratifying and conflating risks.

Indeed, first available statistics confirm the higher incidence of job losses among NFH and precarious workers (see, for instance, Guven et al. (2020) for Australia; Montenovo et al. (2020) for USA; Adams-Prassl et al. (2020a) for the UK). All this couples with a stagnant labour demand in teleworkable occupations, almost concentrated in the North of Italy. As a consequence, labour and social protection policies should aim at reducing rather than exacerbating those divides, starting with flexible shifts, extension of sick leaves, full-paid paternal and maternal leaves, secure income stability. At the same time, fostering social dialogue and promoting the adoption of effective health and safety protocols through the direct involvement of workers and trade unions is crucial (ILO 2020).

One limitation of our results is the lack of information about firms strategic orientation on telework. Indeed, between-firm variability might count as much as between-occupational variability. According to the annual report by the ISTAT,19 conducting a survey on firms strategic behaviour under the pandemic (repeated sample, two waves May-October 2020), Italian firms switching to telework have done so in a temporary manner, although remarkably rising the overall share of teleworking activities when compared to the pre-pandemic phase. However, the fraction of workers performing their activity from home never exceeded 35% and only with reference to large firms. Considering all firms, this figure drops to 15% on average over the year (2020). Clearly, size and sectoral dimensions are crucial in affecting such variability. What is remarkable is that, independently from sectoral heterogeneity, firms tended to reduce the fraction of teleworking positions after emergency phases. Additionally, the few second-level collective agreements regulating smart-working20 clearly distinguish those job titles and those productive units that can perform activities remotely from those that cannot at all.

At this stage, firms attitude and satisfaction toward teleworking as a durable preferable option, whenever viable, is still unclear. When discussing about telework, we need to distinguish between telework as an organizational option and telework as the only choice. In the first case, it should be conceived as part of a policy strategy pushing for shorter and more flexible working time, preventing and limiting all the documented side effects, such as increasing work intensification and unpaid overtime, difficulties in balancing working and private life and risk of burnout, being only some of the drawbacks reported by workers (Messenger 2019), by means of contractual regulations. Second, given the lack of conclusive evidence on firm performances, on the processes of knowledge diffusion, on creativity, on collaborative practices among workers, a complete switch to telework is not advisable as well.

A second limitation of our study regards the use of cross-sectional information of the ICP dating back to 2012. However, longitudinal analyses on contents of occupations are currently not feasible for Italy, due to the lack of data availability, and just starting for the US (Freeman et al. 2020), albeit in this case confirming a quite stable dynamics of within-occupational variation over ten years (2005–2015) of many of the variables analysed.

Being the aim of the paper to look at (i) the structural determinants of occupations to access to telework, (ii) the distribution of teleworking along hierarchical layers, sectors, geographical areas and gender, (iii) the risks connected to the impossibility of working from home whenever required by eventual external conditions, our results might be of support to design policy interventions, beyond emergency conditions, able to promote a sustainable, durable, coexistence with teleworking. Our analysis should help in understanding that a massive transition toward teleworking is nowadays unfeasible given the extant productive structure largely employing occupations requiring in person presence. In fact, the Italian labour market includes a non-negligible share of young, non-standard and precarious workers facing higher employment, income and safety risks compared to the rest of the workforce. These workers are now further penalized by the inaccessibility to telework as a mean to safeguard their occupation and income. Additionally, lay-offs in the pandemic phase have shown the dramatic lack of an adequate social protection legislation, in terms of both unemployment subsidies and job retention schemes (Guarascio 2021). A stronger and stable welfare support for the most fragile segments should be designed taking into account the additional risk stratification represented by the uneven teleworkability of occupations.

Future lines of research entail the study of heterogeneity across teleworkers, in terms of occupational categories, sectors of activity and employer characteristics. What is more, if telework will essentially turn into working from home, availability of adequate private spaces, responsibility of looking after kids and doing houseworks will strongly influence the overall consequences of telework across hierarchical positions and gender.

Table 12

Comparison between Dingel and Neiman (2020) and Cetrulo et al. (2020)

Dingel and Neiman (2020)	Cetrulo et al. (2020)
H.17 How often does your current job require you to work outdoors, exposed to all weather conditions	Yes
H.18 How often does your current job require you to work outdoors, under cover (like in an open shed)?	Yes
No	H.19 How often does your profession require you to work in a piece of equipment or an open vehicle (such as a tractor)?
No	H.20 How often does your profession require you to work in closed equipment or vehicle (such as a machine)?
No	H.27 How often in your work are you exposed to vibrations throughout your body (such as when operating a jack hammer or bulldozer)?
No	H.32 How often does your work require you to expose yourself to dangerous equipment (such as working with saws, near machines with moving parts or vehicles)?
No	H.40 In your work, how long do you use your hands to manipulate, control or feel objects, tools or control systems?
H.43 In your current job, how often do you wear common protective or safety equipment such as safety shoes, glasses, gloves, hearing protection, hard hats, or life jackets?	Yes
Q.44 In your current job, how often do you wear specialized protective or safety equipment, such as breathing apparatus, safety harness, full protection suits, or radiation protection?	Yes
No	H.55 How important is it in your work to keep sequences of machinery and equipment under control?
G.18 Controlling machines and processes	Yes
G.20 Operating vehicles, mechanized devices or equipment	Yes
G.22 Repairing and maintaining mechanical equipment	Yes
G.23 Repairing and maintaining electronic equipment	Yes
G.4 Inspecting equipment, structures or materials	Yes
No	H.25 How often are you exposed to contaminants (such as polluting gases or dust) in your work?
No	H.28 How often does your work require you to be exposed to radiation?
H.29 How often does your current job require that you be exposed to diseases or infection? This can happen with workers in patient care, some laboratory work,sanitation control, etc	Yes
No	H.31 How often does your work require you to expose yourself to hazardous situations?
H.33 How often does your current job require that you be exposed to minor burns, cuts,bites, or stings?	Yes
No	H.30 How often does your work require you to expose yourself in places or places high above the ground?
No	H.35 In your work, how long do you climb ladders, poles, scaffolding, etc.?
No	H.36 How long do you walk or run in your work? (excluding home-work trips)
H.37 How much time in your current job do you spend walking or running?	Yes
No	H.38 How long in your work do you maintain or recover your balance?
G.16 Performing General Physical Activities	Yes
G.17 Handling and moving objects	Yes
No	G.29 Assisting and caring for others
G.32 Performing or working directly with the public	Yes
H.4 How frequently does your current job require electronic mail?	Yes
H.14.How often is dealing with violent or physically aggressive people a part of your current job?	No

Table 13

Comparison among Barbieri et al. (2021), Montenovo et al. (2020) and Cetrulo et al. (2020)

Barbieri et al. (2021)	Cetrulo et al. (2020)	Dingel and Neiman (2020)
H.21 Physical proximity (it enters reversely)	No	No
G.16 Performing general physical activities (it enters reversely)	Yes	Yes
G.19 Working with computers	No	No
G.20 Manoeuvring vehicles, mechanical vehicles or equipment (it enters reversely)	Yes	Yes
H.1 Face to face discussions (it enters reversely)	No	No
H.8 Deal with external customers (it enters reversely)	No	No
H.39 Time spend standing (it enters reversely)	No	No

Table 14

Occupations and Indicators

	Not from Home (% 4-digit jobs)	From Home (% 4-digit jobs)
Cetrulo et al. (2020)	67	33
Dingel and Neiman (2020)	61	39
Bonacini et al. (2021)	51	49

Table 15

Pairwise correlation across indicators

	NFH	Dingel and Neiman	Barbieri et al	Bonacini et al	Montenovo
NFH (discrete)	1
Dingel and Neiman (2020) (discrete)	0.8680*	1
Barbieri et al. (2021) (continuous)	– 0.6640*	– 0.6685*	1
Bonacini et al. (2021) (discrete)	– 0.6014*	– 0.6119*	0.8209*	1
Montenovo et al. (2020) (continuous)	– 0.6040*	– 0.6009*	0.5716*	0.5872*	1

Table 16

Occupations and Indicator’s alternatives

	Not Working from Home (% jobs)	Working From Home (% jobs)
NFH (Cetrulo et al., 2020)	67	33
NFH (threshold 80/50)	48	51
NFH (threshold 50/20)	78	22
NFH with Pc (no mail)	68	32
NFH with Pc and telephone (no mail)	68	32

Table 17

Pairwise correlation across indicators with different thresholds and new variables

	NFH	NFH 50	NFH 80	NFH with PC	NFH with Pc and Telephone
NFH	1.0000
NFH 50/20	0.7487*	1.0000
NFH 80/50	0.6663*	0.5120*	1.0000
NFH with Pc	0.9551*	0.7522*	0.6626*	1.0000
NFH with Pc and Telephone	0.9551*	0.7522*	0.6626*	1.0000*	1.0000

Table 18

Unemployment risk - micro-level model with all alternative indicators

	(1)	(2)	(3)	(4)	(5)	(6)
	Unemployment	Unemployment	Unemployment	Unemployment	Unemployment	Unemployment
Dingel and Neiman (2020)	0.162^∗∗∗
	(5.13)
Bonacini et al. (2021)		− 0.167^∗∗∗
		(− 5.23)
Not From Home (50)			0.171^∗∗∗
			(4.22)
Not From Home (80)				0.201^∗∗∗
				(6.75)
Not from Home With Computer Only					0.213^∗∗∗
					(5.91)
Not From Home With Computer and Telephone						0.213^∗∗∗
						(5.91)
_cons	− 2.311^∗∗∗	− 2.135^∗∗∗	− 2.354^∗∗∗	− 2.316^∗∗∗	− 2.352^∗∗∗	− 2.352^∗∗∗
	(− 38.87)	(− 34.33)	(− 35.69)	(− 39.05)	(− 37.97)	(− 37.97)
N	82177	82177	82177	82177	82940	82940

t statistics in parentheses

, ,

Table 19

Income risk - micro-level model with all alternative indicators

	(1)	(2)	(3)	(4)	(5)	(6)
	Low Income	Low Income	Low Income	Low Income	Low Income	Low Income
Dingel and Neiman (2020)	0.297^∗∗∗
	(16.11)
Bonacini et al. (2021)		− 0.245^∗∗∗
		(− 13.54)
Not From Home (threshold 50)			0.367^∗∗∗
			(16.66)
Not From Home (threshold 80)				0.409^∗∗∗
				(22.56)
Not From Home With Computer					0.376^∗∗∗
					(18.05)
Not From Home With Computer and Telephone						0.376^∗∗∗
						(18.05)
_cons	− 2.172^∗∗∗	− 1.885^∗∗∗	− 2.293^∗∗∗	− 2.195^∗∗∗	− 2.259^∗∗∗	− 2.259^∗∗∗
	(− 60.67)	(− 51.04)	(− 59.50)	(− 61.18)	(− 60.45)	(− 60.45)
N	85763	85763	85763	85763	86545	86545

t statistics in parentheses

, ,

Table 20

Income risk- occupation-level model with all indicators

	(1)	(2)	(3)	(4)	(5)	(6)
	Low Income	Low Income	Low Income	Low Income	Low Income	Low Income
Dingel and Neiman (2020)	0.512^∗∗
	(3.04)
Bonacini et al. (2021)		− 0.703^∗∗∗
		(− 4.37)
Not From Home (threshold 50)			0.900^∗∗∗
			(3.70)
Not From Home (threshold 80)				0.935^∗∗∗
				(5.43)
Not From Home With Computer Only					1.099^∗∗∗
					(5.29)
Not From Home With Computer Telephone						1.099^∗∗∗
						(5.29)
_cons	− 0.665^∗∗∗	0.0403	− 1.036^∗∗∗	− 0.689^∗∗∗	− 1.054^∗∗∗	− 1.054^∗∗∗
	(− 3.32)	(0.20)	(− 3.81)	(− 3.63)	(− 4.34)	(− 4.34)
N	487	487	487	487	487	487

t statistics in parentheses

, ,

Table 21

Health risk - occupation-level model with all indicators

	(1)	(2)	(3)	(4)	(5)	(6)
	Health Risk	Health Risk	Health Risk	Health Risk	Health Risk	Health Risk
Dingel and Neiman (2020)	1.206^∗∗∗
	(5.81)
Bonacini et al. (2021)		− 0.833^∗∗∗
		(− 5.00)
Not From Home (threshold 50)			1.224^∗∗∗
			(4.15)
Not From Home (threshold 80)				1.110^∗∗∗
				(5.77)
Not From Home With Computer					1.376^∗∗∗
					(4.73)
Not From Home With Computer and Telephone						1.376^∗∗∗
						(4.73)
_cons	− 2.140^∗∗∗	− 0.879^∗∗∗	− 2.318^∗∗∗	− 1.827^∗∗∗	− 2.316^∗∗∗	− 2.316^∗∗∗
	(− 7.69)	(− 3.84)	(− 7.27)	(− 7.62)	(− 6.47)	(− 6.47)
N	485	485	485	485	485	485

t statistics in parentheses

, ,

Table 22

Factor analysis on NFH’s variables. Highest loading scores in bold characters

	PA1	PA4	PA2	PA5	PA3
	Manufacturing jobs	Logistics/Distribution	Agriculture	Construction	Health and Care
G.16 Perform physical activities that require moving the entire body	– 0.08	0.78	0.10	0.21	0.06
G.20 Maneuvering vehicles, vehicles and equipment	0.38	0.11	0.72	– 0.19	– 0.07
H.4 How often does your profession require the use of e-mail?	– 0.21	− 0.79	0.00	0.21	0.12
H.17 How often does your profession require you to work outdoors exposed to all weather conditions?	– 0.24	0.12	0.83	0.21	– 0.03
H.18 How often does your profession require you to work outdoors but sheltered (like in an open shack)?	– 0.12	0.09	0.64	0.29	– 0.07
H.19 How often does your profession require you to work in a piece of equipment or an open vehicle (such as a tractor)?	0.11	0.14	0.75	– 0.14	– 0.05
H.20 How often does your profession require you to work in closed equipment or vehicle (such as a machine)?	– 0.01	– 0.24	0.86	– 0.05	0.05
H.25 How often are you exposed to contaminants (such as polluting gases or dust) in your work?	0.51	– 0.07	0.29	0.01	0.22
H.27 How often in your work are you exposed to vibrations throughout your body (such as when operating a jackhammer or bulldozer)?	0.22	0.03	0.55	0.14	– 0.03
H.28 How often does your work require you to be exposed to radiation? This may happen, for example, to people working in chemistry or radiology laboratories)	0.27	– 0.18	– 0.09	0.01	0.67
H.29 How often does your work require you to expose yourself to disease or infection? This may happen, for example, to people working in hospitals, or in medical or analytical laboratories, or to those engaged in disinfection activities.	– 0.04	0.10	– 0.01	– 0.13	0.85
H.30 How often does your work require you to expose yourself in places or places high above the ground (such as working on poles, scaffolding, stairs, walkways higher than 2.5 m)?	0.10	– 0.03	0.05	0.89	– 0.14
H.31 How often does your work require you to expose yourself to hazardous situations (such as working with high voltage electricity, flammable materials, explosives or chemi- cals)?	0.70	– 0.09	– 0.02	0.27	0.19
H.32 How often does your work require you to expose yourself to dangerous equipment (such as working with saws, near machines with moving parts or vehicles)?	0.57	0.19	0.13	0.17	– 0.08
H.33 How often does your work require you to expose yourself to small burns, small cuts, bites, stings?	0.43	0.51	– 0.05	0.08	0.11
H.35 In your work, how long do you climb ladders, poles, scaffolding, etc.?	– 0.01	0.13	– 0.01	0.90	– 0.15
H.36 How long do you walk or run in your work? (excluding home-work trips)	– 0.05	0.71	0.09	– 0.01	0.08
H.37 In your work how long do you kneel, crouch, crawl, crawl or bend ?	– 0.04	0.63	– 0.05	0.48	– 0.02
H.38 How long in your work do you maintain or recover your balance?	– 0.18	0.54	0.09	0.50	– 0.01
H.40 In your work, how long do you use your hands to manipulate, control or feel objects, tools or control systems?	0.59	0.60	– 0.27	– 0.08	0.00
H.43 In your work, how long do you wear protective or safety equipment such as shoes, glasses, gloves, earplugs, helmets or jackets?	0.67	0.27	0.02	0.09	0.03
H.44 In your work, how long do you wear specialist protective or safety equipment such as self-contained breathing apparatus, harnesses, full protective suits or radiation protection clothing?	0.33	– 0.09	0.06	0.56	0.22
H.55 How important is it in your work to keep sequences of machinery and equipment under control?	0.92	0.17	– 0.01	– 0.25	0.00
G.4 Inspect equipment, structures or materials	0.73	0.13	0.04	0.04	0.05
G.17 Handling and moving objects	0.43	0.71	– 0.22	0.07	0.04
G.18 Managing machines and processes	0.87	0.24	0.00	– 0.16	– 0.02
G.22 Repair and maintain equipment	0.68	0.15	0.25	– 0.08	– 0.07
G.23 Repairing and maintaining electronic equipment	0.55	– 0.15	– 0.06	0.13	– 0.02
G.29 Assisting and caring for others	– 0.39	0.18	0.04	– 0.07	0.66
G.32 Working in direct contact with the audience and performing	− 0.60	0.11	0.05	– 0.05	0.27

Table 23

Variance explained by the factor model

	PA1	PA4	PA2	PA5	PA3
	Manufacturing	Logistic/Distribution	Agriculture	Construction	Health and Care
SS loadings	7.01	5.02	3.81	3.36	1.88
Proportion variance	0.23	0.17	0.13	0.11	0.06
Cumulative variance	0.23	0.40	0.53	0.64	0.70
Proportion explained	0.33	0.24	0.18	0.16	0.09
Cumulative proportion	0.33	0.57	0.75	0.91	1.00