percent of the profiles include two jobs (one current and one previous) in the experience section of the CV, and 25 percent have only one (current) job. We assign job duration independently from the number of jobs. In order to equate the average work experience level of CVs created for males and females, while allowing men to have completed their 6-month military service and having men and women graduate at the same time of the year (June), we use different assignment probabilities of work experience for each gender; this produces average work experience of created CVs for both genders of 3.25 years<sup>.14</sup> Neighborhood: Candidates’ residence neighborhoods within cities are selected so that they have similarly large populations (over 100,000, close to 1 million in the case of Çankaya in Ankara), and similar ratios of votes for the conservative-religious or the secular political party. This was done to avoid confounders based on employers’ perceptions of candidates’ political inclinations.

Education: High schools are assigned based on neighborhood: We selected high schools that have comparable entry scores on the centralized national high school entrance exam. Similarly, for universities, we selected large established public universities that have at least 25,000 students. Our candidates are graduates of Computer Engineering for the IT Cluster, and graduates of Business Administration for the remaining clusters. We make sure that, within occupational clusters, departments have similar minimum entry scores on the centralized national university entrance exam, so that the departments are comparable in terms of quality signals.

Photos and beauty: We also include photos for each candidate, as this is standard practice in Turkey (over 80 percent of all candidates in the online job portal include photos). The photos used in the experiment are artificially generated from publicly available photos or volunteer face shots of Italian and Turkish youth aged 22 to 30. The photos collected in this manner were handed over to a graphic designer, who created sets of fictitious photos in Photoshop using combinations of different facial features from real photos. None of the photos was exactly identical to the rest, and none of them was the original (real) image.

For the resulting photos, two different measures of beauty were collected: objective and subjective beauty scores. The first measure is the attractiveness score based on the face shape, distance between the eyes and lips, mouth size and face symmetry, using the so-called “golden ratio” where appropriate. This type of measurement, which we call the objective beauty score, is on a scale from 0 to 100. The software at www.prettyscale.com was used to obtain these scores.

The objective beauty score depends solely on the placement of facial features without any reference to details such as hair color, color of the eyes, or other features that may affect how beautiful the person in the photo is perceived. Moreover, whereas objective beauty scores do not change according to country context, individuals from different countries may have different conceptions of beauty. This is why we also collected data on a second measure that we call the subjective beauty score. This score is the average of beauty scores obtained from ratings collected through an online survey.<sup>15</sup> We then generated average subjective beauty scores for all photos using the total of 32,676 ratings by 383 participants that we collected

> 14 Note that average years of experience increased during the time between CV creation and job applications. See Table 3 for details.

> 15 The link to the survey was distributed through Twitter accounts of World Bank Turkey and the Economic Policy Research Foundation of Turkey, so anyone could access the survey. As the tweet and the survey itself were in Turkish language, we expect respondents to be Turkish as well.

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