Research
Research
Supported by 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-nationals 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 implications for two-way fixed effects models. Incorporating our insights into 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. Our results imply that the type of employers available upon refugees' first entry is an important determinant of their success in host countries.
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. Using detailed online job postings data, we construct and validate a precise measure of labor market tightness at the commuting-zone level. To address endogeneity, we build a shift-share IV that leverages the heterogeneous impact of Covid-19 on labor demand across industries, conducting several validity exercises to ensure we appropriately address the complexity of the shock. We find that, for low-wage, low-skill jobs that are constrained in how low the skill level listed in the job posting can be, tightness reduced the likelihood that employers listed education and experience requirements, but increased the years of education required in postings that listed them. Employers either removed requirements to overcome this lower bound or increased advertised wages for these positions in response to labor market tightness. This paper shows that employers use multiple complementary levers to attract candidates, offering an additional mechanism that contributed to the post-pandemic decline in US wage inequality.
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.