Research
Research
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, expanding on previous work identifying jobs that can be done from home. Jobs that cannot be done remotely and that present a high risk of infection are labelled ‘unsafe jobs’. We combine our classification with a list of ‘essential occupations’ carried out even during the most severe lockdown measures, creating a taxonomy that ranks 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 related outcomes, 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. Economically vulnerable workers (women, youngsters, low educated, immigrants and workers on fixed-term contracts) are more likely to hold unsafe jobs, placing them at higher risk of suffering from the consequences of a prolonged pandemic. Finally, we discuss potential reforms to social protection systems to better support workers amid labour market adjustments spurred by the COVID-19 pandemic.
How many jobs can be carried out without putting workers at risk of contracting Covid-19? And how many of these jobs can be activated as soon as the most severe restrictions to mobility will be lifted? To which extent do these jobs belong to the chain involved in the war against Covid-19? In this paper, we aim to provide preliminary answers to these questions drawing on the case of Italy, the first Western country to be hit by the pandemic.
In this article we revive, extend and improve the approach used in a series of influential papers written in the 2000s to estimate how changes in the supply of immigrant workers affected natives' wages in the US. We begin by extending the analysis to include the more recent years 2000-2022. Additionally, we introduce three important improvements. First, we introduce an IV that uses a new skill-based shift-share for immigrants and the demographic evolution for natives, which we show passes validity tests and has reasonably strong power. Second, we provide estimates of the impact of immigration on the employment-population ratio of natives to test for crowding out at the national level. Third, we analyze occupational upgrading of natives in response to immigrants. Using these estimates, we calculate that immigration, thanks to native-immigrant complementarity and college skill content of immigrants, had a positive and significant effect between +1.7 to +2.6% on wages of less educated native workers, over the period 2000-2019 and no significant wage effect on college educated natives. We also calculate a positive employment rate effect for most native workers. Even simulations for the most recent 2019-2022 period suggest small positive effects on wages of non-college natives and no significant crowding out effects on employment.
How do employers’ recruitment strategies adapt to labor shortages? This paper estimates the response of employers’ posted wages and skill demand to labor market tightness. Leveraging Covid-19’s heterogeneous impact on labor demand across industries and detailed online job postings data, we propose a precise measure of local tightness whose variation relies on a shift-share IV. We find that tightness decreased the likelihood of employers listing education and experience requirements, but increased required years, when listed. Controlling for composition, these findings are statistically significant for low-wage, low-skill positions, where a lower bound constrains years required. We document complementarity in recruitment levers, as tightness also significantly raised advertised salaries for these positions, contributing to the reduction in wage inequality in the post-pandemic US.
Drawing on matched employer–employee data from Denmark, we study the role of employers in the labor market integration of refugees. First, we estimate firm-specific wage premia that we use as a proxy for workplace quality. Second, we leverage a dispersal policy implemented between 1986 and 1998 that quasi-randomly allocated refugees across municipalities to obtain exogenous variation in their exposure to the quality of first accessible employers. We find that being placed in a municipality where, at arrival, members of the co-ethnic network are employed by high-quality employers has positive and statistically significant effects on refugees' employment and earnings for up to 10 years after arrival. We also present a set of novel facts on refugees and the firm ladder, highlighting the lasting role that early employers play for this group of workers and discussing possible issues for two-way fixed effects models. Our results suggest that policymakers should consider access to high-quality employers as an additional factor contributing to refugees' success when designing policies in host countries