find jobs. These jobs tend to be in sectors in which previous cohorts of migrants are employed. This process leads migrants from the same communities to cluster in specific occupations and sectors. Beaman (2012) shows immigrant enclaves and networks in different localities of residence influence the occupational choices of different generations of immigrants. Similarly, Patel and Vella (2013) find that immigrants from specific countries cluster in particular occupations across different regions in the United States. This fact is not due to immigrants’ skills or other characteristics but rather to referrals from previous cohorts of immigrants and the informational advantages they offer. While such networks help immigrants integrate in the host labor market and provide invaluable help in finding a job, they can at the same time restrict the occupational and spatial mobility of their members (Munshi and Rosenzweig, 2006). Therefore, the historical clustering of past waves of migrants in specific occupations and regions can have long-lasting repercussions through path dependence.

Although patterns of occupational clustering have been examined in different regional contexts, the role of immigrant networks in Europe has received less attention. We use two different migration waves to assess the path dependence in occupational choices of migrants in the EU. Figure 6 presents the relationship between the percentage of migrants in an occupation in 2005 (xaxis) and the percentage of recently arrived migrants in those occupations in 2018. To show the main trend line, we plot the fitted line of a kernel-weighted local polynomial regression as well as the confidence interval.<sup>15</sup> There is a strong and positive correlation between the relative share of migrant employment in an occupation and destination region in 2005 (representing an older wave of migration) and the share of recently arrived migrants in 2018 in each occupation and region.<sup>16</sup> These results remain robust when differentiating migrants by region of origin and controlling for occupation, region of origin, and region-of-destination (NUTS2 level) fixed effects, or when excluding occupations in which there were no migrants in 2005 (see Appendix 2).

Network effects as drivers of occupational choices can, in turn, have an impact on the vulnerability of migrants to pandemic-related shocks. Past studies analyze the role of networks using different measures, but in general, either using the share of co-ethnic migrants in the same region of residence (as in Beaman, 2012), or the share of co-ethnic migrants in the region of residence working in the same occupation as the migrant (Patel and Vella, 2012). To assess the role of networks, we cannot use these measures in the multinomial logit from the previous section given that the dependent variable is based on occupations; thus, any explanatory factor that is also based on ISCO occupations would create perfect multicollinearity, while region-level measures would be captured by the fixed effects.

We proceed by using a measure of networks at the individual level. The EU-LFS ad hoc migration module of 2014 includes a question on the method the individual used to find a current job. Options include advertisements; public or private employment agencies; training institutions; direct contact with employers; and contacts through relatives, friends, or acquaintances. As equation (1) shows,

> 15 The confidence interval expands as the share of migrants increases (x-axis) since there are fewer number of such occupations.

> 16 We restrict the sample of migrants in the 2018 EU-LFS to those that had been in the country for at most 10 years, so the newer cohort of migrants do not overlap with the older one in 2005.

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