Language fluency is a strong predictor of the type of jobs in which migrant workers are employed and the associated level of vulnerability that surfaces in the multinomial logit regression (Column 5, tables 1-33-5). Compared to workers for whom the language used in their place of residence is also their mother tongue, those with beginner or intermediate levels of language fluency are about 10 percentage points more likely to be employed in a safe job (Column 3, Table 3). This effect is very large, given that less than half of the employed population has jobs with neither income nor health risks. Moreover, language ability explains a significant gap in the share of “safe jobs” for several migrant groups. For example, the gap between migrants from NMS13 countries and natives’ likelihood of having held a “safe” job during the pandemic falls from 16 percentage points to 9 percentage points when language skills are taken into account; the gap falls from 19 percentage points to 13 percentage points for migrants from other European countries, from 13 percentage points to 7 percentage points for Asians, from 10 percentage points to 6 percentage points for those from Sub-Saharan Africa, and from 9 percentage points to 3 percentage points for those from MENA countries.

Barriers to recognition of foreign credentials are also significantly correlated with the exposure to the labor-market risks of the pandemic. Migrants who report a lack of recognition of their credentials as a main barrier to integrate in the host-country labor market are 16 percentage points less likely to be employed in “safe jobs” compared to similar migrants who did not face difficulties validating their degrees (Column 6, Table 1). Although skill validation is an important factor, it does not explain a large part of the overall higher vulnerability of migrants in the labor market; as captured in the EU-LFS, it represents a main barrier for only 6 percent of working-age migrants. Moreover, when we compare the coefficients for different world regions (tables 1-3, columns 5- 6), we do not see much of a change either. This indicates that barriers for certifying foreign credentials, although relevant for some migrants, are not one of the main factors that would explain the higher economic risk exposure of migrants from lower-income countries to the EU15.

6.3. Immigrant Network Effects Even after we control for personal characteristics such as education level and fluency in the local language, there are still unexplained differences between migrants from different regions of the world, in terms of their occupations and the resulting income and health risks to which they are exposed. Results (tables 1-3, columns 5-6) show that migrants from NMS13 countries are still 9.5 percentage points less likely than native workers to be in safe jobs. The gap is 13.5 percent for migrants from other eastern European countries, 6.0 percent for those from Sub-Saharan Africa, 6.6 percent for those from Asia, and 10.5 percent for those from Latin America (Table 1, Column 5).

Another pattern found in the literature for the clustering of migrants into specific occupations is the role of migrant networks. Existing social and cultural networks provide numerous services, especially to newly arriving migrants. In addition to helping them with housing, schools and deciphering the cultural norms of the destination country, migrant networks help the new migrants 18