For our indicator of attitudes toward migration and asylum, we use the share of votes in each canton that express a preference for a more restrictive regulation of migration and asylum. A first challenge is that each vote concerns a different project. Therefore, it is difficult to carry out comparisons between votes over time. We solve this problem by standardizing each vote outcome: what matters for our indicator is each canton’s _relative_ position at each moment in time. An important implication of this standardization is that our indicator cannot measure the change over time in average attitudes (at the country level). This is, however, not a problem for our empirical analysis, since we use year fixed effects in all our regressions.<sup>20</sup> We use the voting data to create our yearly indicator in the following way. When there is more than one vote within a year, we take the average of these vote outcomes. Then we use a simple method to fill in the missing values: for each missing value, we take the simple average of the two observations that are closest in time.<sup>21</sup> Figure 4 shows the resulting data for all 26 cantons over the period 1998–2018. Standardized vote outcomes are shown as black dots, whereas the filled-in data appears in red. Several patterns are clearly visible. First, the French-speaking cantons (FR, VD, VS, NE, GE, JU) generally exhibit less restrictive attitudes toward migration and asylum than Italian-speaking (TI) or German-speaking cantons. However, the attitudes in most French-speaking cantons have become more restrictive over time, whereas several German-speaking cantons (especially those with big cities, ZH and BS) have evolved toward more open attitudes. There is also a third group of cantons, including some small conservative cantons and Italian-speaking Ticino (TI), where initially restrictive attitudes have become even more restrictive over time.<sup>22</sup>

# **4.2 Empirical Strategy**

Our empirical analysis is carried out in two parts. First, we study the integration of refugees in the Swiss labor market by looking at trajectories of employment rates (and earnings) after the refugees’ arrival in Switzerland. Second, we analyze the impact of initial conditions on the employment of refugees over time.

## **Trajectories of Labor Market Integration**

When analyzing the employment (and earnings) trajectories of refugees, we rely on two comparison groups: natives with similar individual characteristics and migrants with similar backgrounds (in terms of origin countries and individual characteristics). We compare refugees with natives, migrants with natives and refugees with migrants. The reason for doing this is that we can account for different unobserved factors in these comparisons.

We start by estimating the following linear probability model for natives and refugees: where _Yitτc_ denotes employment (or the log of earnings) in year _t_ of refugee _i_ , who arrived in Switzerland in year _τ_ from origin country _c_ . To measure the integration trajectories as precisely as possible, we use a flexible specification of the integration process: we define _Years since migration_ dummies _YSMitj_ for categories _j_ that denote two-year intervals (except for the _j_ = 0 dummy, which stands for the period less than one year after arrival). This allows us to analyze the integration process over a period of 20 years

> ( _j_ = 10 denotes the interval 19–20 years after arrival). As these variables are not observed for natives,

> 20. We choose to standardize the vote outcomes rather than just centering them. Some vote outcomes are less dispersed across cantons because there is a large majority in favor of the project. Standardizing the vote results attenuates these differences between votes.

> 21. In almost all cases of missing data, we simply take the average of the standardized vote outcome in the preceding and the following year. The only exception are the years 2011 and 2012 for which we have no votes. In this case, we use the simple average of the standardized vote outcomes in 2010 and 2013 to fill in these two missing values.

> 22. Figure 5 shows the relation between initial attitudes in 1998 and the change over the period 1998–2018. The size of each dot represents the canton’s population size (of potential voters).

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