small. Accordingly, most results for gender comparisons include these five variables as controls whenever it is possible for the employers to use these variables in their decision.<sup>18</sup> Our first finding suggests that a small share of employers use gender as a filter. Employers are slightly more likely to select female CVs when making their longlists, although this result is only marginally significant. Employers can create their longlist of applicants by entering criteria manually. The criteria can include many variables, including age, gender, experience, city and neighborhood, sector, occupation as well as a keyword search. Note that, when making the longlist, employers cannot filter using beauty, for two reasons. First, photos are not visible at this stage. Second, it is simply not possible to enter beauty as a criterion for filtering. This is why we do not use objective and subjective beauty measures as controls for this stage. Furthermore, whether the candidate has worked in one firm only is also not possible to use as a filter at this stage.

Table 6 provides the results from OLS regressions, using cluster-robust errors at the vacancy level. Models 1 to 6 show that females are 2 to 3 percent more likely to make it to the longlist. Although small in magnitude, this systematic difference indicates that some employers enter gender as a filter when making their longlist and have a preference for longlisting female over male candidates. Model 1 shows the most basic specification, with only a control for age.<sup>19</sup> In Model 2 we add a control for the duration of work experience, and in Model 3 we also add a control for whether the candidate has ever worked in the same sector where the employer operates. Model 4 shows that the observed preference for women result does not change according to clusters.<sup>20</sup> Models 5 and 6 show that employers with vacancies that receive high and low number of applications behave similarly in terms of their gender preferences for longlisting. On the other hand, the tendency to filter males out seems to be somewhat less pronounced in Ankara compared to the European side of Istanbul (Model 7). Finally, the models show occupational clusters (in particular, accounting) and total application size also affect the probability of passing through the filter and making it to the longlist but do not affect gender filtering.

Not all employers use the longlist as the first stage: 11 percent of applicant CVs that are clicked on are CVs that are not in the longlist. This is why we consider both unconditional regressions and regressions conditional on an applicant CV being in the longlist when looking at the determinants of what makes an employer click on a CV. Unconditional regression results are shown in Table 7. A similar result to the case with longlist emerges in this case, where female applicants are significantly more likely to be clicked on compared to their male counterparts. On the other hand, this tendency seems to be more a feature for sales and accounting occupations: Model 4 shows that the effect disappears for marketing and IT clusters. While there seems to be no difference in behavior according to local labor markets, models 3, 5, 6 and 7 show that applicants that are in the same sector with the firm opening the vacancy are more likely to be clicked on by the employers. Finally, as the total application size increases, employers presumably have more CVs

> 18 Separate balance tables for each sector are available upon request from the authors. Balance tables look very similar across sectors, with the exception that for cluster B there is no unbalance by gender on experience.

> 19 As noted, we include a linear age control in all regressions because by construction male candidates are older than female candidates, due to the mandatory military service for men in Turkey.

> 20 Separate regressions for each cluster are in line with these findings and are available upon request from the authors.

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