Pandemic Datasets

Future of work in a post-pandemic world
A FOWIGS Dataset


The COVID-19 pandemic constituted one of the biggest shocks to the labor market in history by its size and persistency. The challenges imposed by the virus were unprecedented: physical proximity and agglomeration became a risk behavior for the first time in centuries. Societies were forced to unlearn centuries of habits and practices regarding the world of work, from face-to-face interactions in factories to take a bus to the office. Some workers managed to redefine their daily tasks to match the low-proximity requirement of COVID19, while others weren’t able to do that, and thus COVID19 became itself a source of inequality.

Who are the workers hardest hit by the pandemics? In which sectors they work? In which countries? This open dataset aims to answer these questions by analyzing a set of variables related to health and economic risks. It combines indicators on the ability to telework (the higher, the less likelyto suffer income losses due to COVID19), the probability of automation (the higher, the more likely to suffer income losses), the physical proximity (the higher, the more likely to suffer income losses) and the sanitary risk at work (the higher, the more likely to suffer income losses). The dataset offers information for different occupations, countries, gender, and levels of skills.

Lying at the intersection of technological change and health data provides insights into the new challenges faced by labor markets across the globe in the current pandemic scenario.





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 here

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 here

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,

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.


Work is changing rapidly. Learning to navigate the changes will benefit us all.

IDRC has launched FOWIGS—a research program that will help understand how these changes are affecting the lives of the most vulnerable and suggest pathways for an inclusive digital future. The challenges are large and the questions are complex. But we need to face them now more than ever. Stay connected. Learn how.


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