of refugees are male and young. Even after 1 or 2 years in Switzerland, refugees’ employment rates are almost 80 percentage points lower than natives’ and it takes them more than 10 years to reach a level which is 18 to 20 percentage points lower than the employment rate of natives. Controlling for the economic conditions in the host country (using fixed effects for the year of observation) does not change these results (column (2)). In columns (3) and (4) we also control for heterogeneity among arrival cohorts and the age structure of refugees upon arrival in Switzerland (using fixed effects for year of arrival and age at arrival). Adding these controls has a small impact on coefficients, by reducing slightly the employment gap for the first five years after arrival.

In section 4.2 above, we discussed some problems of collinearity that arise in the context of the identification of the profiles of refugees’ labor market integration. The first collinearity problem occurs in columns (3) to (6) of Table 3, where we control for year of arrival, the number of years since migration and the current year, which are perfectly collinear for refugees. We identify the effects of years since migration and the year of arrival by assuming that the impact of the current year dummies on employment rates is identical for refugees and natives. Second, in columns (4) to (6) we introduce fixed effects for the refugees’ age at arrival. As there is perfect collinearity between the refugees’ age at arrival, their age and the number of years since migration, this is equivalent to assuming that the impact of age on employment rates might differ for refugees and natives.<sup>28</sup> These estimates do not take the lower education levels of refugees into account, since this variable is missing in our main sample. This information is available in the Structural Survey, which is carried out yearly since 2010 and covers each year approximately 2.5% of the Swiss population. We match the education data from the Structural Surveys 2010–2017 with the register data in our main sample. This restricted sample contains less than one fifth of the refugees in the main sample. To see if the individuals included in this restricted sample are representative of the entire population of natives and refugees, we first run the same regression as in column (4) for the main sample. Results, which are given in column (5), are close to those of the main sample, which makes us confident that the effect of education can be reliably estimated. When education dummies are added in column (6), results give a more optimistic picture of the integration trajectory: in the long run, employment rates of refugees are 15 percentage points lower than those of natives but it takes more than ten years after arrival in Switzerland to reach this level.

To gain more insight into the role of education in the integration process, we estimate integration profiles separately by level of education (see Table A.2 in the appendix). The employment rates of refugees with an elementary level of education (Secondary I) converge more rapidly toward the long run level, which is reached within 9 to 10 years. By contrast, the minority of refugees who have higher education levels (Secondary II, Tertiary) see their employment rates start at a lower level and converge more slowly toward the long run level (it takes more than 12 years to get there). This might be explained by the difficulty of having foreign diplomas recognized in Switzerland or, especially in the case of younger refugees, the time it takes to complete further education in Switzerland. Our data does not allow us to discriminate between these two explanations.

Some of the fixed effects used in the regressions in Table 3 are interesting in their own right. To analyze the role of the refugees’ arrival cohorts on the employment trajectories, we run separate regressions for different arrival cohorts, which we define by three-year periods (in terms of “year of arrival”) between 1998 and 2018. To obtain a sufficient sample size for each arrival cohort, we use the main sample (without education) and estimate specifications similar to column (4) in Table 3 for each three-year cohort. When looking at the estimation results summarized in Figure 6, three observations stand out. First, the overall shape traced out by all arrival cohorts is very close to the aggregate estimates in column (4) in Table 3. Second, Figure 6 reminds us that the lower part of the integration

> 28. At first glance, it might seem more straightforward to include an interaction between “age” and the refugee dummy in the regression. However, this would make the integration profile difficult to interpret since, for refugees, increasing their number of “years since migration” goes hand in hand with increasing their age. Therefore, we choose to include variables that do not vary with “years since migration”, such as “year of arrival” and “age at arrival”.

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