Research
Research
Supported by the Humane Studies Fellowship from the Institute for Humane Studies (IHS)
Drawing on matched employer–employee data from Denmark, we study the role of early employers in the labor market integration of refugees. First, using the two-way fixed effects model of Abowd, Kramarz, and Margolis (1999), we estimate firm-specific wage premia that we use as a proxy for workplace quality. Second, we leverage the role of social connections and a dispersal policy implemented between 1986 and 1998, which quasi-randomly allocated refugees across municipalities, to obtain exogenous variation in their exposure to the quality of first potential employers. We find that placement in a municipality where, at arrival, co-ethnics are employed by high-quality employers has positive and statistically significant effects on refugees' employment and earnings for up to ten years. We also present a set of novel stylized facts on refugees and the firm ladder, highlighting the lasting influence of first employers for this group of workers and discussing potential issues for two-way fixed effects models. Our results suggest that policymakers should consider the type of employers offering jobs to refugees as an additional determinant of their success in host countries. Incorporating our insights in a data-driven algorithm to optimally match refugees with Danish municipalities leads to a 46% increase in short-run employment probability relative to the status quo dispersal policy.
This article revives and enhances the factor-supply approach to estimate the effects of changes in the supply of immigrants on national wages and employment of US natives. We introduce three novelties: (i) the use, and test of validity, of a skill-based shift-share for immigrants and a demographic predictor for natives, as instruments for labor supply changes; (ii) the implementation of a simple model and estimation of the impact of immigration on natives' labor supply, measured by the employment-population ratio; and (iii) an analysis of native occupational specialization and upgrading that can explain those effects. We find a significant elasticity of complementarity between US natives and immigrants, which boosts natives' relative wages, as well as a positive employment response of natives to immigrants. Both are consistent with significant occupational task-complementarity and upgrading, which we also find in the data. We calculate that immigration over the period 2000-2023 increased the wages of non-college-educated natives by 2.6 to 3.4%, the average wage of natives by 0.6 to 0.7%, and had no significant effect on native college-educated wages.
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.
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.