This paper proposes a new classification of occupations based on the extent to which they put workers at risk of being infected by aerial-transmitted viruses. We expand on previous work that mainly focused on the identification of jobs that can be done from home by providing a more nuanced view of infection risks: in particular, we identify jobs that, although impossible to be done from home, expose workers to a low risk of infection. Jobs that cannot be done from home and that present a high risk of infection are labelled 'unsafe jobs'. We then combine our classification of infection risk with a list of 'essential occupations' that have been carried out even during the most severe lockdown measures: this provides a taxonomy ranking jobs along two dimensions, one related to workers' health and the other related to economic conditions. Using both survey and administrative data, we show that this taxonomy successfully predicts outcomes along these two dimensions, such as sick leaves, COVID-19-related work injuries, recourse to short-time work (STW) schemes and work from home. We also find that unsafe jobs are very unequally distributed across different types of workers, firms and sectors. Workers who are more vulnerable economically (women, youngsters, low educated, immigrants and workers on fixed-term contracts) are more likely to hold unsafe jobs and therefore more at risk of suffering from the economic consequences of a prolonged pandemic. We finally discuss possible paths to reform social protection systems, so that they can better support workers during the labour market adjustments that are likely to be spurred by the COVID-19 pandemic.
{"title":"Unsafe jobs, labour market risk and social protection","authors":"G. Basso, T. Boeri, A. Caiumi, M. Paccagnella","doi":"10.1093/epolic/eiac004","DOIUrl":"https://doi.org/10.1093/epolic/eiac004","url":null,"abstract":"This paper proposes a new classification of occupations based on the extent to which they put workers at risk of being infected by aerial-transmitted viruses. We expand on previous work that mainly focused on the identification of jobs that can be done from home by providing a more nuanced view of infection risks: in particular, we identify jobs that, although impossible to be done from home, expose workers to a low risk of infection. Jobs that cannot be done from home and that present a high risk of infection are labelled 'unsafe jobs'. We then combine our classification of infection risk with a list of 'essential occupations' that have been carried out even during the most severe lockdown measures: this provides a taxonomy ranking jobs along two dimensions, one related to workers' health and the other related to economic conditions. Using both survey and administrative data, we show that this taxonomy successfully predicts outcomes along these two dimensions, such as sick leaves, COVID-19-related work injuries, recourse to short-time work (STW) schemes and work from home. We also find that unsafe jobs are very unequally distributed across different types of workers, firms and sectors. Workers who are more vulnerable economically (women, youngsters, low educated, immigrants and workers on fixed-term contracts) are more likely to hold unsafe jobs and therefore more at risk of suffering from the economic consequences of a prolonged pandemic. We finally discuss possible paths to reform social protection systems, so that they can better support workers during the labour market adjustments that are likely to be spurred by the COVID-19 pandemic.","PeriodicalId":43996,"journal":{"name":"Ekonomicheskaya politika","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60827593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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