1 The Hiring Prospects of Foreign-Educated Immigrants: A Factorial Survey among German Employers Andreas Damelang, Martin Abraham, Sabine Ebensperger, Felix Stumpf Supplementary material part I: More detailed information on vignettes, case selection and data collection Construction of the vignettes The vignette universe, which comprises all of the distinct combinations of vignette levels, is 576 (2×2×3×2×3×2×2×2). For the logistics manager vignettes, we utilized the whole universe and randomly compiled 96 decks á six vignettes. For the precision machinist and hotel specialist vignettes, we drew a random sample of 120 vignettes and randomly assigned these to 20 decks á six vignettes. The vignette order within each deck was randomized and each respondent was randomly assigned one deck of vignettes. Case selection and data collection The selection of occupations allows for a viable application of our factorial survey design in three respects. First, all three occupations are skilled but unlicensed, where in principle, foreigners are directly eligible for work regardless of formal degrees but where employers usually demand vocational education and training certificates. Second, because the occupations are currently affected by shortages of skilled workers or apprentices, our selection ensures that the foreign-educated immigrants presented in our vignettes generally form a relevant pool of candidates for the employers concerned. Third, at the same time, the share of foreign workers in these occupations is comparable to the average share of foreign workers across all of the occupations in Germany. We applied these criteria to avoid letting a particularly positive selection or inherent discrimination against foreigners bias our survey results. Our data are unique in that they use a comprehensive sample of employers for each of the selected occupations in Germany. To make sure that our samples cover the occupations extensively, we applied adequate sampling strategies. Accordingly, sampling and data collection differ between the occupations. Employers of logistics managers and precision machinists are very difficult to sample because these occupations can be concerned with a wide variety of products and services across different sectors. Thus, one cannot cover them by simply sampling employers from a particular sector. Instead, we drew representative samples of employers for the occupations across all industries based on official