Ability to telework: This indicator measures the compatibility of occupations with teleworking. The estimate considers the tasks and context that typically characterize each occupation to determine the feasibility of moving to the digital sphere. For example, working remotely becomes practically impossible for those jobs that frequently require physical interaction with others, the use of machinery in a specific place, or working outside or aboard a vehicle. On the other hand, activities such as telephone assistance, data processing, or computer programming could easily be carried out by teleworking. The compatibility of jobs with working from home arrangements became extremely relevant with the COVID-19 pandemic and the ensuing social distancing measures implemented around the world, thus understanding the limits and potential of teleworking is fundamental to analyze the impact of the pandemic in labor markets and technological change. The classification of occupation is developed by Dingel and Neiman (2020) based on several variables from the US O*NET surveys.
Probability of automation This indicator measures the level of repetition that the tasks of each occupation imply and, therefore, their susceptibility to automation. It allows a complementary analysis of the previous indicators to assess the impact of the pandemic on labor markets. The classification of occupations is taken from Frey and Osborne (2017), who estimate the probability of computerization for 702 US occupations.
Physical proximity: This indicator aims at quantifying the risk of physical proximity. Jobs requiring higher physical proximity with other people have become riskier in times of a pandemic and, hence, more vulnerable to job loss and automation. The classification of occupations comes from the US O*NET surveys, for more details visit the following link: https://www.onetonline.org/find/descriptor/result/4.C.2.a.3?a=1
Sanitary risk: This variable measures the sanitary risk of each occupation based on the exposure to a disease of infections. It is clear that, in addition to physical proximity, safety and hygiene measures represent a key infection prevention mechanism. Moreover, individuals employed in occupations with higher sanitary risks are at greater risk of losing their jobs during the pandemic. The classification of occupations comes from the US O*NET surveys, for more details visit the following link: https://www.onetonline.org/find/descriptor/result/4.C.2.c.1.b?a=1
Following Dingel and Neiman (2020), we merge the classification for these four indicators of each 6-digit SOC (US Standard Occupational Classification) with occupational employment data at 2-digit ISCOs (International Standard Classification of Occupations) level from the International Labour Organization (ILO)1. As Dingel and Neiman (2020), we use a crosswalk between the SOC and ISCO classifications from the US BLS (Bureau of Labor Statistics), hence the mapping of 6-digit SOCs to 2-digit ISCOs is common to all countries, and the weighted average for each 2-digit ISCO is country-specific.
The dataset also includes information on the rate of informality by occupation and gender, which is obtained from the ILOSTAT database. For some countries, the latest information available on the informal employment rate may not coincide with the latest information on employment level. In that cases, we impute the latest information available on the informal employment rate: Brazil (2015), Barbados (2016), Brunei Darussalam (2014), Ghana (2015), Honduras (2017), Cambodia (2012), Thailand (2018), Timor-Leste (2013).
The classification of countries by income group and geographic region is taken from the World Development Indicators (World Bank), while skill grouping of 2-digit ISCO 08 occupations is based on Goos, Manning, and Salomons (2014) and Acemoglu and Autor (2011).
Acemoglu, D. and D. Autor (2011), Skills, tasks and technologies: Implications for employment and earnings, http://dx.doi.org/10.1016/S0169-7218(11)02410-5.
Dingel, J. I. and Neiman, B., (2020). “How many jobs can be done at home?” Journal of Public Economics, Elsevier, vol. 189(C).
Frey C. B. and M. A. Osborne (2017), The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, (C), 254-280
Goos, M., A. Manning and A. Salomons (2014), “Explaining Job Polarization: Routine-Biased Technological Change and Offshoring”, American Economic Review, Vol. 104/8, pp. 2509– 2526.
1 We estimate the indicators using the most recent employment data available.