their gender. We expect to observe faster integration in the labor market for refugees who arrive at a younger age, and for those with higher levels of educational attainment.

In the analysis related to the role of the initial local conditions, we expect to find a negative role in the case of unemployment. High unemployment rates at arrival predict future lower employment rates, lower earnings, and lower human capital investment (Åslund and Rooth 2007; Aksoy et al. 2020). We expect to find a significant effect related to the size and density of the co-ethnic networks in the host locations, though the sign and magnitude of the estimated coefficient remains an empirical question. Co-ethnic networks can reduce transaction costs and help refugees to gain a faster access to the labor market, but on the other hand may reduce the incentives for refugees to further invest in their human capital which could lead to better labor market outcomes in the long run (Edin et al. 2003; Damm 2009; Battisti et al. 2021; Martén et al. 2019). We also expect to find a significant effect of attitudes towards refugees in affecting their trajectory of economic integration but the expected sign could be either positive or negative depending on the mechanism that prevails (Aksoy et al. 2020; Jaschke et al. 2020). An open empirical question remains on the relative importance of the three initial conditions (i.e., unemployment, co-ethnic enclaves and attitudes) when examined together. Moreover, we expect the effect of the individual factors to vary in the short, medium and long run over the 20 years examined.

In the next section we empirically investigate these issues, and examine whether these assumptions are supported by the empirical evidence.

# **4 Research Design**

## **4.1 Data and Descriptive Statistics**

In our analysis we construct a unique longitudinal dataset which covers the universe of refugees and migrants in Switzerland over 1998-2018. It allows us to follow refugees over the life-cycle for 20 years, and allows us to follow them even after they change residence permit and status.

To construct this longitudinal dataset we are combining three administrative datasets. The first dataset, that covers all asylum seekers is AUPER (Automatisierte Personen Registratursystem) which is provided by the State Secretariat for Migration. The data includes information about the residence permit, year of arrival, country of origin, canton of allocation and socio-demographic characteristics. Once an asylum seeker has obtained a residence permit other than permit N (asylum seekers) or F (provisionally admitted foreigners), he or she is registered in ZAR (Zentrales Ausländerregister). This longitudinal dataset is also coming from the SEM. The ZAR dataset includes all foreigners residing in Switzerland and contains similar information than AUPER. We are combining these two datasets with the yearly population census data that includes ZAR and AUPER starting from 2010. By combining these three datasets we can follow refugees and migrants over 1997-2018 and natives over 2010-2018.

We define as refugees, all foreign-born individuals that went through an asylum process, and as migrants all other foreign-born individuals. Natives are defined as individuals born in Switzerland. In the final sample, we are drawing a random sample of 6% of the native population.

For the main outcome variables used in the descriptive analysis, we are first adding Swiss social security data provided by the Federal Compensation Office. This data collects information about every Swiss resident that contributed to old age provision (i.e., the old-age and survivor’s insurance OASI or AVS in French). We know the size and nature of the contribution made by individuals (from paid work, independent work, voluntary contribution or other kinds). This data is available from 1998-2018. Our main outcome variables measuring economic integration include employment, earnings and selfemployment, and are constructed from the social security data. An individual is defined as employed if he or she contributed to old age provision from salaried or independent work. Earnings are defined as the sum of all positive contributions made from salaried and independent work in a year. Lastly, we 10