metadata: authors: 0: author: Nas Ozen,Selin Efsan
1: author: Hut,Stefan
2: author: Levin,Victoria
3: author: Munoz Boudet,Ana Maria
count: Türkiye
docna: 0: docna: A Field Experiment on the Role of
            Socioemotional Skills and Gender for Hiring in Turkey
docty: Policy Research Working Paper
subtopic: Gender and Development,Labor Markets,Rural Labor Markets,Human Rights
teratopic: Gender,Social Protections and Labor,Rural Development,Law and Development
abstracts: cdata!: A vast literature shows the importance
            of socioemotional skills in earnings and employment, but
            whether they matter in getting hired remains unanswered.
            This study seeks to address this question and further
            investigates whether socioemotional skill signals in job
            applicants' resumes have the same value for male and
            female candidates. In a large-scale randomized audit study,
            an online job portal in Turkey is used to send fictitious
            resumes to real job openings, collecting a unique data set
            that enables investigating different stages of candidate
            screening. The study finds that socioemotional skills appear
            to be valued only when an employer specifically asks for
            such skills in the vacancy ad. When not asked for, however,
            candidates can face a penalty in the form of lower callback
            rates. A significant penalty is only observed for women, not
            for men. The study does not find evidence of other gender
            differences in the hiring process.
geo_reg_key: 517208,517191,119229,517198
repnme: repnme: A Field Experiment on the Role of
            Socioemotional Skills and Gender for Hiring in Turkey
display_title: A Field Experiment on the Role of
            Socioemotional Skills and Gender for Hiring in Turkey
listing_relative_url: /research/2020/02/31793343/field-experiment-role-socioemotional-skills-gender-hiring-turkey
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Labor Markets m1326488 855,Gender m1327621 615,Jobs m1503931 2670,Discrimination m1505707 3379
publishtoextweb_dt: 2020-02-18T00:00:00Z
historic_topic: Gender,Social Protections and Labor,Rural Development,Law and Development
origu: EFI-Poverty and Equity-GE (EPVGE)
no_of_pages: 42
author_keys: 0: author_key: 1643465
1: author_key: 1145909
2: author_key: 687981
3: author_key: 950872
model_extractions: Policy Research Working Paper 9154 A Field Experiment on the Role of Socioemotional Skills and Gender for Hiring in Turkey _Efsan Nas Ozen Stefan Hut Victoria Levin Ana Maria Munoz Boudet_ Social Protection and Jobs Global Practice & Poverty and Equity Global Practice February 2020
Policy Research Working Paper 9154

# **Abstract**

A vast literature shows the importance of socioemotional skills in earnings and employment, but whether they matter in getting hired remains unanswered. This study seeks to address this question and further investigates whether socioemotional skill signals in job applicants’ resumes have the same value for male and female candidates. In a large-scale randomized audit study, an online job portal in Turkey is used to send fictitious resumes to real job openings, collecting a unique data set that enables investigating different stages of candidate screening. The study finds that socioemotional skills appear to be valued only when an employer specifically asks for such skills in the vacancy ad. When not asked for, however, candidates can face a penalty in the form of lower callback rates. A significant penalty is only observed for women, not for men. The study does not find evidence of other gender differences in the hiring process.

This paper is a product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at snasozen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# A Field Experiment on the Role of Socioemotional Skills and Gender for Hiring in Turkey<sup>1</sup>

Efsan Nas Ozen, Stefan Hut, Victoria Levin and Ana Maria Munoz Boudet<sup>2</sup> JEL classification: J71, C93, J24, D63 Keywords: gender discrimination, socioemotional skills, labor market signaling, field experiments

> 1 This paper was produced with the support of a Trust Fund of the World Bank Umbrella Facility for Gender Equality. The grant supported a joint program of research on women’s economic participation in Turkey, implemented together between the World Bank and the Ministry of Family and Social Policy (Department for Women’s Economic Status). We would like to thank colleagues from the Ministry of Family and Social Policy and the (former) Ministry of Development of Turkey for their feedback an input at all stages of the research. We would also like to thank Maria Bigoni, Marco Casari, Juan-Camilo Cardenas, Margherita Fort, Alaka Holla, Macartan Humphreys, Bengisu Ozenc, Davide Raggi, Tanya Rosenblat, Eliana Rubiano, Giulio Zanella, and participants at the ESA World Meeting 2018 in Berlin, University of Bologna, IDEE Bologna and WESSI at NYUAD presentations for helpful comments and suggestions on previous versions of this paper. The team is thankful for Yonca Toker help in constructing the socioemotional skill signals, analyzing the reverse audit data, and coding the socioemotional skills for our dataset, and Meryem Dogan, Volkan Erdem, Burak Urun and Merve Dundar for their assistance. Almira Sekerci and Omer Faruk Zararsiz have also helped in collecting data for this project. The implementation of this project was done together with TEPAV (The Economic Policy Research Foundation of Turkey).

This study is registered in the AEA RCT Registry and the unique identifying number is AEARCTR-0002326. The usual disclaimer applies.

2 Authors affiliations: World Bank, Brown University, World Bank, World Bank. Corresponding author Efsan Nas Ozen (snasozen@worldbank.org)
# **1. Introduction**

Do socioemotional skill signals in CVs play a role in employers’ hiring decisions? A vast literature shows the importance of socioemotional skills in labor market outcomes in the form of earnings or employment, but whether signals of these skills matter in getting hired remains unanswered. Suppose that a young graduate is looking for a job and comes across a vacancy ad with a requirement of, say, teamwork skills, listed alongside other requirements, such as certain levels of education and experience. Should she include a signal demonstrating she has strong teamwork skills, along with her academic degrees and the jobs she worked at? Furthermore, would such a signal have the same effect in terms of advancing the candidate in the recruitment process if that candidate is male? This study seeks to provide answers to these questions.

Economics literature has firmly established that socioemotional skills are valuable in the labor market. Numerous studies demonstrate that possession of socioemotional skills has a positive impact on lifetime earnings (e.g., Cameron and Heckman, 1993; Heckman and Rubinstein, 2001; Bowles et al., 2001a,b; Heckman et al., 2006; Cunha et al., 2006; Cunha and Heckman, 2008). It is also quite common to see socioemotional skills requirements in vacancy ads, and online job search websites recommend job seekers to include some aspect of socioemotional skills in their CVs.

However, little is known about whether including socioemotional skill signals in CVs would help in securing a job interview. Evidence from the literature is scarce and indirect (e.g., the effect of volunteering activities as studied in Baert and Vujijic, 2018 and the effect of sport skills as studied in Rooth, 2011). Piopiunik et al. (2018) is among the first studies to provide evidence that socioemotional skills may matter for employers when they evaluate candidates, although the evidence is collected using an unincentivized survey with hypothetical CVs. Even if employers would consider socioemotional skills during hiring, it remains unclear whether employers can get the relevant information on the candidate's socioemotional skills. While they can rely on educational attainment or technical certifications of prospective workers as signals of cognitive and technical skills, socioemotional skills are more difficult for employers to assess and for job seekers to signal. Furthermore, if socioemotional skill signals are not credible, they might hurt the applicant’s chances as they can be perceived by the employer as an attempt to oversell oneself.

The above complications are in part due to the vagueness in the definition of, and the difficulty in measuring, socioemotional skills. There is still no consensus on the definition or even the term: “soft skills", “personality traits", “non-cognitive skills", “non-cognitive abilities", “character", and “socioemotional skills" are all used to identify the personality attributes (Heckman and Kautz, 2012). In practice, the investigation of certain socioemotional skills in the literature depends heavily on data availability (Brunello and Schlotter, 2011). Perhaps the most used measure of socioemotional skills is the Big Five personality traits, and many studies find them important in career success (e.g., Boudreau et al., 2001; Seibert, Scott E. and Kraimer, Maria L., 2001; Gelissen and de Graaf, 2006) and earnings (e.g., Nyhus and Pons, 2005; Mueller and Plug, 2006; Heineck and Anger, 2010). However, the Big Five traits are too general to signal in a CV and some are not clearly applicable to the work context, so employers may not find such signals valuable or credible.

Furthermore, employers may value similar socioemotional skill signals of male and female candidates differently. This may be particularly important considering that women experience higher rates of 2
joblessness or long-term unemployment in most countries. While there is no consensus on the existence of gender discrimination in hiring (see Bertrand and Duflo, 2017 for a review), there is ample evidence on the existence of a gender wage gap (Blau and Kahn 2017, and Ñopo, Daza and Ramos 2012), and that, among other factors, such gender wage gap may, to some degree, be explained by the differences in socioemotional skills between men and women (Palomino and Peyrache, 2010; Cobb-Clark and Tan, 2011). Furthermore, gender differences in preferences and actions are important in labor market outcomes. Research has shown that differences in competitiveness and risk aversion may lead to more unfavorable labor market outcomes for women compared to men, not only because women shy away from asking for more favorable outcomes, but also because employers expect them to have less competitive preferences.<sup>3</sup> Signaling socioemotional skills may thus be a way for women to mitigate employers’ potential biases arising from these socially ascribed qualities based on gender.

On the other hand, if the same socioemotional skills are valued differently in the labor market for women and men, there may be more unfavorable labor market outcomes for women who signal the socioemotional skills that are rewarded for men, as noted in research related to the gender double bind (see e.g. Armstrong, 1988). The bind refers to the trade-offs women face due to being perceived differently in relation to certain skills or attributes than men. For example, features associated with leadership, such as assertiveness, can be perceived as a positive attribute for a man but a negative attribute for a woman (Rudman et al 2012, Gipson et al 2017); similarly, for women, being perceived as personable can be associated with an assumption of lower competence, while the same does not hold for men (Phelan et al 2008).<sup>4</sup> In this paper we conduct a correspondence audit study to assess the effects of an explicit socioemotional skill signal in a CV during the hiring process. We do so by introducing signals of socioemotional skills in fictitious male and female CVs and sending these CVs to real vacancies. We record employer selection behaviors at various stages of the hiring process, including whether the employer selects our candidate for a longlist, whether our candidate’s profile is clicked on, and whether the candidate gets a callback (i.e. invitation for an interview). These outcomes enable us to assess (1) the extent to which employers favor men or women in the hiring process, (2) whether employers value socioemotional skills, and (3) whether there is any differential value of such skills for men and women.

To address these questions, we use a two-by-two experimental design, where the first dimension is the existence of socioemotional skill signals in the CV, and the other is gender. By randomly assigning socioemotional skills and gender, we can compare employer selection across otherwise equivalent candidates, isolating the effect of gender and socioemotional skills on hiring. For the socioemotional skills treatment, we carefully define and match socioemotional skills for four different occupational clusters: accounting, marketing, sales, and IT. We use precise skills requirements from the definitions of tasks in

> 3 For example, individuals’ own perception of “male” traits are linked to entry into male-dominated study fields and occupations (Antecol and Cobb-Clark, 2010), and there is evidence that women negotiate wages less often compared to men (Babcock and Laschever, 2009). Studies also show that women are more risk-averse, less likely to prefer competition, and are less likely to overestimate their qualities, whereas men are more overconfident compared to women (Niederle and Vesterlund, 2007; Croson and Gneezy, 2009; Dohmen and Falk, 2011; Ludwig et al., 2017). 4 This also applies to other characteristics, such as being married or having children, which might be positively evaluated for men but not for women (Riach and Rich 2002, Petersen and Togstad 2006).

3
O*NET, the occupation dictionary widely used in labor market research. By carefully tailoring skills to occupations, we aim to capture the heterogeneity of skills requirements for different jobs in the labor market.

To investigate whether including socioemotional skill signals in CVs might help women’s job prospects, we selected a labor market with a traditionally low representation of women. The experiment is run in the two largest cities of Turkey, a country with a labor market characterized by the lowest female labor force participation and among the highest female unemployment rates among the OECD countries.<sup>5</sup> We consider the high-skilled segment of the Turkish labor market, where women with a university degree form almost a quarter of the total unemployed population in Turkey, while representing less than 7 percent in total population.<sup>6</sup> Altogether we applied for 2,687 real vacancy ads using 10,748 CVs. The vacancy ads were posted on a large online job portal in Turkey. A unique feature of this job portal is that it allows us to track employer behavior at different stages of candidate screening.<sup>7</sup> The first stage is the longlist, formed when the employer filters applicants using hard criteria based on the candidates’ profile (age, gender, experience, city and neighborhood, sector, occupation or other keyword search). The second stage, the shortlist, is when the employer clicks on some of the candidates’ profiles to view their CVs, and, the third and final stage is when the employer contacts the shortlisted candidates to invite them to an interview, which is the stage evaluated in all other correspondence studies.<sup>8</sup> We are thus able to pinpoint at which screening stage, if any, different CV characteristics, including gender and socioemotional skills, come into play.

Examining selection at various stages of the hiring process, we do not find strong evidence of gender discrimination. There is suggestive evidence of a small bias in favor of women during the longlisting process, but this effect is only marginally significant. Furthermore, we find that employers value socioemotional skill signals positively only when they specifically ask for such skills in the vacancy ad. On the other hand, when socioemotional skill signals are not solicited in the ad, employers do not appear to value them; indeed, in these cases, job seekers may even face a penalty (i.e. lower probability of callback) for including such signals on CVs . This particular penalty for signaling unrequested socioemotional skills is only observed for female candidates, however. The gender-specific penalty for signaling unsolicited socioemotional skills suggests that such signals on CVs may be interpreted differently for men and women.

The following section provides details on the design of the experiment and the collected data, Section 3 presents the results, and Section 4 concludes the paper.

> 5  The 2017 national estimate of labor force participation for women aged 15 and above was 34 percent, the lowest female labor force participation rate among the OECD countries. Unemployment levels among working-age women were at 14 percent, almost double the OECD levels (Source: World Bank World Development Indicators).

> 6 Source: Calculated using Turkey Household Labor Force Micro Dataset 2016.

> 7 To our knowledge, the only other study using a similar data set, albeit with a smaller sample, is Balkan and Cilasun (2018), which investigates whether gender discrimination plays a role in the low female labor force participation in Turkey.

> 8 See Neumark (2018) for a survey of audit studies in the labor market.

4
# **2. Experimental Design and Data**

The experimental design follows the classic design of Bertrand and Mullainathan (2004): we use fictitious CVs and apply for real vacancy ads, varying the CVs in the characteristics of interest.

Our treatments are summarized in Table 1. We have a two-by-two design where the first dimension is the gender of the applicant, and the second is related to whether socioemotional skills are signaled or not. In the latter dimension, we signal socioemotional skills in treatment CVs explicitly through extracurricular activities (e.g., participating in debate tournaments to signal persuasion skills), in the job description (e.g., by indicating that the candidate persuaded current customers to try new products, which enabled meeting targeted sales volume and profit), and in the tagline (i.e. a summary of the individual’s profile, reported in the top portion of the CV). For each of the above, the control CVs include neutral text of similar length in the same fields.

Within this design, we first select the aspects of the labor market to focus on, including occupational clusters, location, and the job portal. We then create control and treatment CVs for fictitious male and female candidates. Using these CVs, we apply for a total of 2,687 vacancies between August 2017 and January 2018. The sections below outline each of these procedures.<sup>9</sup> _Table 1: Treatments_

||No Socioemotional Skill Signal|Socioemotional Skill Signal|
|---|---|---|
|Male|(C , M)|(T , M)|
|Female|(C , F)|(T , F)|

# 2.1  Labor market

# _Labor markets_

The experiment is conducted in Turkey, where about 32 million people are currently in the labor force. We focus on the two cities with the largest labor markets that make up about 27 percent of the employed population in Turkey: Istanbul (20 percent) and Ankara (7 percent). Furthermore, we separate Istanbul into two regions, Istanbul-Asia and Istanbul-Europe, since they largely represent two different labor markets in terms of hiring decisions.

# _Occupational clusters_

The list of occupational clusters we selected for this experiment is presented in Table 2. The clusters include financial occupations, retail and sales occupations, as well as technical occupations. This varied set of jobs allows us to draw conclusions about the role of gender and socioemotional skills in the broader high-skilled labor market. In addition, we can compare effects across occupations that vary in terms of tasks and hence may require different skills.

The selection of the specific occupational clusters is based on multiple criteria. The first is the gender composition of occupations: we have selected occupations that do not have extreme values of female shares

> 9 All procedures used in the experiment are approved by the IRBs of the Middle East Technical University in Ankara, Turkey and the University of Bologna in Bologna, Italy.

5
in employment, based on data from the 2015 Turkish Household Labor Force Survey.<sup>10</sup> We also collected vacancy information from newspapers and online job portals for a period of 3 months, and we filter out occupations that tend to recruit explicitly one gender (e.g., administrative assistants).

Second, we select occupations that have a large pool of vacancies in the online job portal: the four occupational clusters that we select represent around 65 percent of the total vacancy ads in the online job portal for the selected locations.

Finally, we aim to select clusters that use different socioemotional skills in their daily tasks, based on the classifications in O*NET and the organizational psychology literature. More information on this aspect is given in Section 2.2.

# _Job vacancies_

We collect the vacancy ads and make our applications using the largest online job portal in Turkey, which has around 75,000 registered companies and 24 million CVs.

We use two criteria for selecting vacancies. The first criterion is that the minimum required work experience did not exceed 3 years. We focus on early-career candidates because socioemotional skills, especially in the form of extracurricular activities, are arguably more salient in CVs for early-career candidates. On the other hand, for more mature candidates, aspects of job experience (such as tenure, progression, and gaps in work history) may be strong signs of socioemotional skills that make other signals less salient but that are harder to manipulate in an audit study experiment.

The second criterion in vacancy selection is minimum required level of education. Here, we mostly focus on higher-skilled jobs that require a university degree. However, for vacancies in the sales cluster, we also apply for jobs that would consider candidates with high school degrees.

In addition, we took into account whether we have recently applied to a job with the same employer. If this is the case, in order to avoid detection, we wait at least 4 weeks until we apply to another job with this employer. In addition, we do not apply to more than 5 vacancies with the same employer across the entire study period. Finally, we use occupation classifications of the online platform as filters when we select the vacancies to apply for each occupational cluster. Included occupations are provided in the final column of Table 2.

> 10 Extreme values were defined as more than one standard deviation away from the mean female employment share in an occupation.

6
_Table 2: Occupational clusters used in the experiment_

|**Occupational cluster**|**Share of cluster in**<br>**total number of vacancy**<br>**ads**|**Included occupations (according to online**<br>**portal’s classification)**|
|---|---|---|
|Accounting|9%|Accounting, Audit, Finance|
|Marketing|11%|Marketing, Business Development|
|Sales|19%|Sales|
|IT|20%|IT, Engineering, R&D|

Note: Average shares of cluster in total number of vacancy ads are calculated using the ad counts in the online job portal between January and February 2015.

# 2.2.  Treatments and resume construction

# _Socioemotional skill signals_

A particular challenge associated with this study is the difficulty in defining and measuring socioemotional skills, which also includes personality traits like the Big Five (Heckman and Kautz, 2012). For our purposes, however the Big Five are too general to signal in a CV, and it is not clear whether employers may understand or value a statement that signals, for example, “high conscientiousness." Depending on data availability, some studies use more specific measures, such as misbehavior in childhood (Segal, 2013), leadership positions or behavioral reports in high school (Kuhn and Weinberger, 2005; Protsch and Solga, 2015), and skills such as locus of control, aggression, and withdrawal (Groves, 2005), but most of these studies use information on skills and labor market outcomes for real individuals, which presents a challenge for causal inference.<sup>11</sup> One problem is the potential association between different aspects of an individual’s skill set: for example, there is evidence of a significant correlation between an individual’s IQ (i.e. cognitive skills) and openness to experience (i.e. socioemotional skills).<sup>12</sup> The other challenge arises because job experience is not orthogonal to socioemotional skills once the individual has started their career: A person with high teamwork skills might be likely to get a job that requires teamwork, but working in a team would improve teamwork skills as well, making it difficult to disentangle the effect of socioemotional skills from that of job experience. Thus, the ideal but unattainable case study would be random assignment of socioemotional skills to two identical individuals and subsequent observation of their performance on the labor market. A more feasible second-best is the audit study methodology, which creates CVs for fictitious similar candidates that significantly differ only on the variables of interest (here, gender and socioemotional skills) and tracks real employers’ recruitment decisions for these fictitious candidates.

For this study, the selection process for one of our main variables of interest - occupation-specific socioemotional skills - involved two steps. In the first step, we carefully reviewed the organizational psychology literature and the O*NET occupation descriptors to identify the most relevant socioemotional

> 11 Apart from Protsch and Solga (2015), which uses an experimental methodology with fictitious CVs.

> 12 E.g. McRae (1987), Ackerman and Heggestad (1997).

7
skills for each of the selected occupations. O*NET categorizes occupations using one or more of the categories Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (i.e. RIASEC) based on the daily tasks involved in the occupation.<sup>13</sup> In the second step, which we call the reverse audit study, we collaborated with a private company that allowed us to include solicitation of specific socioemotional skills in two of their vacancy ads, and then provided the research team with anonymized CV information of the received applications. This enabled us to understand the ways in which candidates tend to signal their socioemotional skills (more information on both steps is provided in Appendix B).

Using these two steps, we were able to identify and construct realistic socioemotional skill signals that match the socioemotional skill descriptors in the literature as well as the O*NET. Although a rather long list of socioemotional skills is provided in O*NET for each occupation, we selected three socioemotional skills for each occupational cluster based on their usage in real CVs. These skills are given in Table 3.

_Table 3: Selected socioemotional skills for each cluster_

|**Occupational cluster**|**Socioemotional skill**|
|---|---|
|Accounting|Detail orientation, Organization, Communication|
|Marketing|Dynamism, Teamwork, Persuasion|
|Sales|Persuasion, Networking, Teamwork|
|IT|Detail orientation, Perseverance, Teamwork|

We signal all the selected socioemotional skills through activities performed in the context of job-related tasks, through extracurricular activities during undergraduate studies, and in the profile summaries (or “taglines”). We selected to signal socioemotional skills through activities and not as mere collection of adjectives after conducting a discrete choice experiment with senior undergraduate students of psychology and MBA students. This experiment demonstrated that socioemotional skills are salient features in the CV, but that they matter only when signaled through activities and not as mere adjectives (Nas Ozen, 2018).

We include signals of socioemotional skills in three different places in the CV: 1) job descriptions within the listed current job experience, 2) in a section called “Scholarships and projects” including extracurricular activities, and 3) in the CV tagline that is shown at the top of the CV on the online job portal. For the job descriptions, we create sentences of neutral job descriptions for control CVs and alternative sentences for

> 13 Definitions for these categories are as follows (from www.onetonline.org): _Realistic_ occupations frequently involve work activities that include practical, hands-on problems and solutions. They often deal with plants, animals, and realworld materials like wood, tools, and machinery. Many of the occupations require working outside, and do not involve a lot of paperwork or working closely with others. _Investigative_ occupations frequently involve working with ideas and require an extensive amount of thinking. These occupations can involve searching for facts and figuring out problems mentally. _Artistic_ occupations frequently involve working with forms, designs and patterns. They often require selfexpression and the work can be done without following a clear set of rules. _Social_ occupations frequently involve working with, communicating with, and teaching people. These occupations often involve helping or providing service to others. _Enterprising_ occupations frequently involve starting up and carrying out projects. These occupations can involve leading people and making many decisions. Sometimes they require risk taking and often deal with business. _Conventional_ occupations frequently involve following set procedures and routines. These occupations can include working with data and details more than with ideas. Usually there is a clear line of authority to follow.

8
treatment CVs that include socioemotional skill signals, both providing information on the same type of task performed at work. For example, for the IT cluster we identified four types of tasks: tasks related to (1) server, (2) internet, (3) software or website, and (4) hardware and maintenance. For each of these tasks, we create alternative bullets for control and treatment CVs. For example, one neutral sentence for a hardware-related task would state “Provided support for technical failures with equipment such as PC, printer or scanner,” whereas the alternative sentence that signals teamwork would state “Worked as a team in identifying deficiencies and supplying the necessary hardware.” We then randomly allocate four neutral sentences to the control CVs’ job description, and one neutral sentence and three sentences that signal each of the three socioemotional skills separately to the treatment CVs’ job description.

For generation of realistic, credible, and comparable extracurricular activities, we benefitted from the way candidates signaled their socioemotional skills in the reverse audit study as well as interviews with human resources personnel and a focus group discussion with university placement directors of two prominent universities in Ankara, Turkey. To keep the CV length compatible between control and treatment CVs and to signal high cognitive skills for our candidates, in the same section we also added a sentence in both control and treatment CVs that indicates the candidate was an honors student in their undergraduate university.

Finally, for the taglines, we create comparable statements regarding the current job of the candidate. For the control versions, we include information that is available in the CV characteristics listed below the tagline, for example, of the form “IT specialist who has an experience of 3 years in solving software/hardware issues, internet and servers.” For the treatment versions instead, we signal at least one socioemotional skill within the tagline as well: “A determined IT specialist who can coordinate with the team members to provide detailed solutions to server, internet, software or hardware problems.” Examples of alternative job descriptions, extracurricular activities, and taglines created in this way are given in Table 18 in Appendix C.

# _Background characteristics_

The goal in the design of the CVs is to generate CVs that are equivalent except for the treatment variables. We therefore assign the other background characteristics either randomly, or we make the characteristic the same for all candidates of the same cluster and/or location.

Age (date of birth): We varied date of birth from September 1989 to December 1991, which implies that our candidates were 25 to 28 at the time of application. We imposed about a 6-month difference in age by gender, with female candidates being, on average, 6 months younger than male candidates. The imposed difference was necessitated by most vacancies requiring men to have completed their compulsory military service at the time of application. Military service in Turkey is compulsory only for men and lasts for about 6 months. Since men and women graduate at the same time of the year (June), our male and female CVs could be equated either on the length of work experience (in months) or on age. We chose the former option. As we know that employers can have age-related preferences, we always control for age in the analysis.

Job experience: Jobs are assigned randomly from a set of available jobs and positions collected from online sources for the selected occupational clusters. We assigned the number of positions held so that 75 9
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.

10
through the survey. In selecting the final set of photos, we eliminated those that have extreme scores on either the objective or the subjective beauty measure, and obtained two sets of photos that have no significant difference in mean objective or subjective beauty scores by gender. More details on how we do this are given in Appendix A.

We create two dummy variables to be used in the regressions, one on objective and the other on subjective beauty. The one on objective beauty takes on the value 1 if the rating is above 0.6, the score threshold that the PrettyScale website considers the face to be “pretty”. For the subjective scores, the variable takes on the value 1 if the beauty score is above the median score obtained in the survey.

Other signals: All candidates have an advanced level of English proficiency, but we introduced random variation in the exact levels of listening, speaking, writing, and reading between levels 4 and 5, the two highest levels in the online job portal.

Computer skills are included only for the IT cluster, where we provide the same list of software for all candidates, but in randomized order.

As it is customary in Turkey to include marital status in CVs, we indicate that all our candidates are single.

# _Final CV construction and application_

To ensure comparability across genders, we create duplicate CVs for men and women that have exactly the same information in terms of all background characteristics, except name, photo, contact information, and the date of birth (see the above discussion on the imposed 6-month difference in age by gender due to military service).

For each selected vacancy, we randomly chose four profiles without replacement from our set of 16 profiles in the cluster/location. These four profiles correspond to the four cells in the experiment: male control, female control, male treatment, and female treatment.

# 2.3. Data

The online job portal used in this experiment allows us to collect information on selection at three different stages of the hiring process. Figure 1 provides the details on these three screening stages. The first stage consists of making the longlist. In screening the applicants, employers can use filtering, whereby they enter search criteria to create a filtered longlist of all applicants. For job seekers, the online portal provides information on whether their application made it to this filtered longlist. In other words, for each application of our fictitious candidates, we observe whether the fictitious candidate made it to the longlist. This indicator is used as a dependent variable for this first recruitment stage.

After creating the longlist, the employer can observe the name, photo, current position, and city of residence for all longlisted applicants. The employer can then click on a candidate from the longlist to obtain more information (i.e. view the candidate’s full CV). Note that clicking on a candidate is costly for the employer as there is a fee associated to clicking on a CV. For jobseekers, the portal provides information on whether the employer from a specific vacancy viewed their full profile. In other words, for each 11
application of all our fictitious candidates, we observe whether the fictitious candidate’s CV has been viewed. This indicator is used as a dependent variable for the second recruitment stage.

After clicking on a longlisted candidate and obtaining more information from their full CV, the employer can invite the applicant for an interview. Like previous correspondence audit studies, we collect information on whether our fictitious candidate has received a callback for an interview (either via phone or via email). Upon receiving a callback, we immediately reject the interview offer. We use an indicator for receiving a callback as a dependent variable for the third recruitment stage.

Note that, among our two treatments, gender is visible to the employer at all stages - the employer can filter by gender, they can decide to click only on candidates of a certain gender (using photo and name to identify gender), and they can see the same variables in the full CV. On the other hand, employers can only view socioemotional skills treatment after they click on the longlisted candidate and see their full CV – in other words, employers cannot filter by socioemotional skills (unless they put those skills in a keyword search) in order to create a longlist, and they cannot see those skills in a filtered longlist, as only name, photo, current position, and city of residence are visible at that stage.

_Figure 1: Employers’ screening process after application_ <!-- Start of picture text --> Stage 1: Employer uses filters to make the long list<br>No specific information on candidates yet, but employer can filter using various criteria<br>including keywords<br>Stage 2: Employer screens the long list and decides whether to click on the candidate’s<br>CV<br>Employer observes each applicant's name,<br>Among treatments, only gender is visible<br>photo, current position, city<br>Stage 3: Employer screens the full CV and decides whether to invite the candidate for an<br>interview<br>Both gender and socioemotional skill<br>Employer is able to view the entire CV<br>signals are visible<br><!-- End of picture text -->

# **3. Results**

The results are organized around two main blocks. First, we consider the aggregate effect of the gender treatment and differentiate the treatment effect for the three recruitment stages. We then move to the socioemotional skills treatment and show the aggregate results, as well as differentiating between genders. Table 4 provides the descriptive statistics for our sample. About 71 percent of our applicants made it to the longlist, 32 percent had their CVs clicked on (i.e. their full CVs viewed) and 6 percent received a callback for an interview.<sup>16</sup> Figure 2 provides the distribution of CVs on the three outcome variables.

> 16 Callback rates in the studies using a similar methodology range from around 3 percent (e.g., in Kroft et al., 2013) to 30 percent (e.g., in Booth et al., 2012).

12
<!-- Start of picture text --> CV sent<br>10,748 applications<br>CV in the long CV not in the long<br>St list list<br>age 2% 28.8%<br>Stage 2<br>Stage 3<br><!-- End of picture text -->
_Table 4: Descriptive statistics_

||_N_|Mean|Std. dev.|Min|Max|
|---|---|---|---|---|---|
|_Dependent variables_||||||
|Applicant in the longlist|10748|0.71|0.45|0|1|
|Applicant profile clicked on|10748|0.32|0.47|0|1|
|Applicant invited for an|10748|0.06|0.24|0|1|
|interview||||||
|_Experimental variables_||||||
|Female|10748|0.50|0.50|0|1|
|SES treatment|10748|0.50|0.50|0|1|
|_Resume attributes_||||||
|Experience (months)|10748|49.17|5.62|37|61|
|Age at application|10748|26.41|0.65|25|28|
|Worked in one job only|10748|0.26|0.44|0|1|
|Objective beauty|10748|0.94|0.23|0|1|
|<br>Subjective beauty|10748|0.50|0.50|0|1|
|English proficiency: Speaking|10748|4.52|0.50|4|5|
|English proficiency: Reading|10748|4.58|0.49|4|5|
|<br>English proficiency: Writing|10748|4.51|0.50|4|5|
|_Vacancy attributes_||||||
|Occupational cluster: Accounting|10748|0.29|0.45|0|1|
|Occupational cluster: Marketing|10748|0.28|0.45|0|1|
|Occupational cluster: Sales|10748|0.30|0.46|0|1|
|<br>Occupational cluster: IT|10748|0.13|0.33|0|1|
|Total application size (in|10748|5.49|9.11|0|302|
|<br>hundreds)||||||
|Signaled SES required in vacancy|10748|0.66|0.47|0|1|
|Locality: Ankara|10748|0.18|0.38|0|1|
|Locality: Istanbul Asia|10748|0.31|0.46|0|1|
|Locality: Istanbul EU|10748|0.51|0.50|0|1|

# 3.1 Gender

Table 5 provides the balance table for the gender treatment of the CVs that were sent out to vacancies. Although for most observed variables, there is no significant difference between the two genders, the male and female CVs are unbalanced on several observables. First, as discussed in Section 2, female applicants are, by design, significantly younger (by about 8 months) due to military service completion for men. To account for any differences in age by construction, we control for this variable in all specifications.

In addition, female applications have a slightly higher level of experience, although the difference is less than two weeks relative to the average job duration of around 50 months. Also, 27 percent of male compared to 24 percent of female candidates spent all their job experience in one job only. Furthermore, female applicants have higher objective and subjective beauty scores, although these differences are quite 14
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.

15
to go through and the probability of a particular CV being clicked on gets smaller. In these cases, female CVs are slightly less likely to be clicked on (Models 5 and 6).

Table 8 shows the determinants of applicant CV being clicked on, this time conditional on the applicant making it to the longlist first.<sup>21</sup> Results show that, once they make it to the longlist, females and males are virtually equally likely to be clicked on, and factors other than gender, such as the type of occupation, total application size for the vacancy, and whether the sector of applicant and firm matches affect the probability of employer clicking on the candidate.

_Table 5: Balance table for gender treatment_

|Variable|(1)<br>Males|(2)<br>Females|(3)<br>Difference|
|---|---|---|---|
|~~Ankara~~|0.179|0.179|0.000|
||(0.384)|(0.384)|(0.007)|
|Istanbul Asia|0.311<br>|0.311<br>|-0.000<br>|
||(0.463)|(0.463)|(0.009)|
|Istanbul EU|0.509|0.509|0.000|
||(0.500)|(0.500)|(0.010)|
|Experience (months)|48.954|49.388|0.434|
||(5.578)|(5.651)|(0.108)***|
|Age|26.767|26.043|-0.725|
||(0.561)|(0.509)|(0.010)***|
|Accounting|0.292|0.292|0.000|
||(0.455)|(0.455)|(0.009)|
|Marketing|0.276<br>(0.447)|0.276<br>(0.447)|0.000<br>(0.009)|
|Sales|0.304|0.304|0.000|
||(0.460)|(0.460)|(0.009)|
|IT|0.128<br>|0.128<br>|0.000<br>|
||(0.334)|(0.334)|(0.006)|
|Worked in one job|0.270|0.247|-0.023|
|<br>only||||
||(0.444)|(0.431)|(0.008)***|
|Objective beauty|0.926|0.963|0.037|
||(0.262)|(0.189)|(0.004)***|
|Subjective beauty|0.450<br>(0.498)|0.549<br>(0.498)|0.100<br>(0.010)***|
|Reading|4.581<br>(0.493)|4.574<br>(0.495)|-0.007<br>(0.010)|
|Speaking|4.520|4.522|0.002|
||(0.500)|(0.500)|(0.010)|
|Writing|4.518|4.508|-0.009|
||(0.500)|(0.500)|(0.010)|
|Observations|5,374|5,374|10,748|

> 21 Conditional balance tables are provided in Appendix D.

16
_Table 6: Determinants of applicant making it to the longlist_

|Model 1<br>Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|
|b/se<br>b/se|b/se|b/se|b/se|b/se|b/se|
|Female<br>0.0188**<br>0.0193*|0.0193*|0.0222**|0.0179*|0.0208**|0.0278**|
|(0.0082)<br>(0.0106)|(0.0106)|(0.0105)|(0.0105)|(0.0105)|(0.0115)|
|Accounting||-0.1028***<br>(0.0223)||-0.0979***<br>(0.0221)|-0.0974***<br>(0.0222)|
|Marketing||<br>0.0083||<br>0.0046|<br>0.0051|
|||(0.0215)||(0.0214)|(0.0214)|
|IT||<br>0.0450*||<br>0.0137|<br>0.0127|
|||(0.0266)||(0.0312)|(0.0314)|
|Istanbul Asia|||||<br>-0.0015<br>(0.0193)|
|Ankara|||||<br>0.0147<br>(0.0231)|
|Total application size (100)|||-0.0026**|-0.0021**|<br>-0.0020**|
||||(0.0011)|(0.0009)|(0.0009)|
|Female * Istanbul Asia|||||<br>-0.0101<br>(0.0070)|
|Female * Ankara|||||<br>-0.0156*<br>(0.0091)|
|Female * Acct||0.0049<br>(0.0057)||||
|Female * Mrkt||<br>-0.0059<br>(0.0052)||||
|Female * IT||<br>-0.0015<br>(0.0039)||||
|Female * Total application size|||0.0002<br>(0.0003)|0.0002<br>(0.0003)||
|Vacancy and CV sectors match|0.0647***||<br>0.0606***|<br>0.0324|0.0327|
||(0.0192)||(0.0192)|(0.0212)|(0.0212)|
|Experience (in months)<br>-0.0001|<br>-0.0001|-0.0005|<br>-0.0001|<br>-0.0005|<br>-0.0005|
|<br>(0.0010)|(0.0010)|(0.0010)|(0.0010)|(0.0010)|(0.0010)|
|<br>Age<br>0.0351***<br>0.0359***|<br>0.0357***|<br>0.0396***|<br>0.0350***|<br>0.0389***|<br>0.0390***|
|<br>(0.0106)<br>(0.0135)|(0.0135)|(0.0134)|(0.0135)|(0.0134)|(0.0135)|
|<br> <br>Constant<br>-0.2250<br>-0.2395|<br>-0.2454|<br>-0.2979|<br>-0.2106|<br>-0.2666|<br>-0.2717|
|(0.2837)<br>(0.3321)|(0.3317)|(0.3307)|(0.3318)|(0.3307)|(0.3323)|
|N.obs.<br>10748<br>10748|10748|10748|10748|10748|10748|
|R-squared<br>0.002<br>0.002|0.004|0.015|0.007|0.017|0.017|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses.  Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant makes it through the first screening and into the longlist.  Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise.  Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗ ∗ ∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.

17
_Table 7: Determinants of applicant’s CV being clicked on_

|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|
|Female<br>0.0286***|0.0230*|0.0234**|0.0259**|0.0285**|0.0309**|0.0313**|
|(0.0100)|(0.0118)|(0.0117)|(0.0116)|(0.0122)|(0.0121)|(0.0136)|
|Accounting|||-0.1244***||-0.1216***|-0.1212***|
||||(0.0189)||(0.0184)|(0.0185)|
|Marketing|||<br>-0.0636***||<br>-0.0742***|<br>-0.0739***|
||||(0.0202)||(0.0194)|(0.0195)|
|IT|||<br>0.2084***||<br>0.1515***|<br>0.1504***|
||||(0.0279)||(0.0324)|(0.0325)|
|Istanbul Asia||||||<br>0.0096<br>(0.0183)|
|Ankara||||||0.0108|
|||||||(0.0220)|
|Total application size<br>(100)|||||||
|Female * Istanbul Asia||||||-0.0152|
|||||||(0.0155)|
|Female * Ankara||||||<br>-0.0078<br>(0.0191)|
|Female * Acct|||-0.0015||||
||||(0.0085)||||
|Female * Mrkt|||<br>-0.0189*<br>(0.0100)||||
|Female * IT|||-0.0250*||||
||||(0.0149)||||
|Female * Total applicatio|n size|||-0.0011*<br>(0.0006)|-0.0011*<br>(0.0007)||
|Vacancy and CV sectors|match|0.2004***||<br>0.1945***|<br>0.0470**|0.0474**|
|||(0.0200)||(0.0201)|(0.0225)|(0.0225)|
|Experience (in months)|0.0008|<br>0.0009|0.0005|<br>0.0008|<br>0.0005|<br>0.0005|
||(0.0011)|(0.0011)|(0.0010)|(0.0010)|(0.0010)|(0.0010)|
|Age<br>-0.0008|<br>-0.0067|<br>-0.0071|<br>-0.0037|<br>-0.0083|<br>-0.0050|<br>-0.0046|
|(0.0101)|(0.0130)|(0.0128)|(0.0127)|(0.0128)|(0.0126)|(0.0127)|
|<br>Objective beauty|<br>0.0310|<br>0.0309|<br>0.0194|<br>0.0324*|<br>0.0218|<br>0.0222|
||(0.0193)|(0.0191)|(0.0189)|(0.0191)|(0.0189)|(0.0190)|
|Subjective beauty|-0.0007|-0.0057|-0.0033|-0.0052|-0.0026|-0.0022|
||(0.0087)|(0.0086)|(0.0084)|(0.0085)|(0.0084)|(0.0084)|
|Total application size (100|<br>)|||<br>-0.0030***|<br>-0.0024***|<br>-0.0029***|
|||||(0.0010)|(0.0008)|(0.0009)|
|Constant<br>0.3290|0.4207|0.3996|0.3985|<br>0.4489|<br>0.4424|<br>0.4302|
|(0.2714)|(0.3173)|(0.3127)|(0.3093)|(0.3112)|(0.3080)|(0.3100)|
|N.obs.<br>10748|10748|10748|10748|10748|10748|10748|
|R-<br>0.001|0.001|0.025|0.047|0.030|0.051|0.051|
|squared|||||||

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses.  Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant’s CV is clicked on. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗∗∗, ∗∗, and ∗indicate significance at the 1%, 5% and 10% level, respectively.

18
_Table 8: Determinants of applicant’s CV being clicked on,_ _conditional on applicant making it to the longlist_

||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|---|
|Female|0.0245*|0.0156|0.0174|0.0180|0.0218|0.0224|0.0253|
||(0.0127)|(0.0149)|(0.0146)|(0.0145)|(0.0152)|(0.0151)|(0.0173)|
|Accounting||||<br>-0.0955***||<br>-0.0923***|<br>-0.0915***|
|||||(0.0238)||(0.0229)|(0.0230)|
|Marketing||||-0.0628***||-0.0714***|-0.0709***|
|||||(0.0238)||(0.0228)|(0.0229)|
|IT||||<br>0.2382***||<br>0.1706***|<br>0.1691***|
|||||(0.0301)||(0.0360)|(0.0361)|
|Istanbul Asia|||||||<br>0.0115<br>(0.0225)|
|Ankara|||||||<br>0.0116<br>(0.0263)|
|Total application size (100)||||||||
|Female * Istanbul Asia|||||||-0.0284<br>(0.0206)|
|Female * Ankara|||||||0.0005<br>(0.0252)|
|Female * Acct||||-0.0044||||
|||||(0.0131)||||
|Female * Mrkt||||<br>-0.0173<br>(0.0129)||||
|Female * IT||||<br>-0.0178<br>(0.0175)||||
|Female * Total application|size||||-0.0011|-0.0011||
||||||(0.0008)|(0.0008)||
|Vacancy and CV sectors m|atch||0.2272***||<br>0.2211***|<br>0.0664**|0.0664**|
||||(0.0221)||(0.0221)|(0.0263)|(0.0264)|
|Experience (in months)||0.0013|0.0015|0.0013|0.0015|0.0013|0.0013|
|||(0.0013)|(0.0013)|(0.0013)|(0.0013)|(0.0013)|(0.0013)|
|Age|-0.0160|<br>-0.0260*|<br>-0.0257*|<br>-0.0243|<br>-0.0277*|<br>-0.0262*|<br>-0.0263*|
||(0.0124)|(0.0157)|(0.0154)|(0.0153)|(0.0154)|(0.0152)|(0.0153)|
|Objective beauty||<br>0.0325|<br>0.0303|<br>0.0228|<br>0.0316|<br>0.0247|<br>0.0250|
|||(0.0246)|(0.0244)|(0.0242)|(0.0243)|(0.0242)|(0.0242)|
|Subjective beauty||<br>-0.0032|<br>-0.0085|<br>-0.0088|<br>-0.0080|<br>-0.0078|<br>-0.0077|
|||(0.0108)|(0.0106)|(0.0105)|(0.0106)|(0.0104)|(0.0105)|
|Total application size (100)|||||<br>-0.0034***<br>(0.0012)|<br>-0.0028***<br>(0.0010)|<br>-0.0034***<br>(0.0010)|
|Constant|0.8164**|0.9899**|0.9415**|0.9724***|<br>1.0102***|<br>1.0287***|<br>1.0280***|
||(0.3330)|(0.3845)|(0.3778)|(0.3741)|(0.3760)|(0.3724)|(0.3759)|
|N.obs.|10748|10748|10748|10748|10748|10748|10748|
|R-squared|0.001|0.001|0.025|0.047|0.030|0.051|0.051|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses.  Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant’s CV is clicked on. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.

19
We now move to the final component of our analysis for the gender treatment, callbacks for an interview. Note that among our candidates invited for an interview, 8 percent had CVs that were not clicked on by the employer. For this reason, we present our results in this part both unconditionally and conditional on the applicant’s CV being clicked on.

Table 9 provides the correspondence table. Overall, around 7.4 percent of females compared to 4.2 percent of males are invited for an interview, indicating a preference for female over male candidates. However, this result may be affected by the remaining imbalances, which is why we look at the regression results. Tables 10 and 11 provide the regression results, both unconditionally and conditional on applicant’s CV being clicked on, respectively.<sup>22</sup> Note that at this stage, applicant’s photo is visible to the employer, and therefore we control for subjective and objective beauty measures in Models 2-7. Both tables show that there is no significant gender effect in the probability of being invited for an interview. Model 4 in both tables shows that the insignificance of gender holds for different occupational clusters, apart from a small positive effect of being female for the sales occupations, significant at 10 percent, when the regressions are unconditional (Model 4 in Table 10). This effect disappears when conditioning on whether the CV is clicked on. On the other hand, although the interaction of female with the accounting cluster has a slightly significant coefficient in both tables, joint significance tests show that the effect for the accounting cluster is not significant either.

_Table 9: Correspondence table for gender treatment on callbacks for an interview_

|Equ|al treatm<br>|ent<br>|Fem|ales fav<br>|ored<br>|M|ales favo<br>|red<br>|
|---|---|---|---|---|---|---|---|---|
||Freq.|Percen||Freq.|Percent||Freq.|Percent|
|||t|||||||
|0M 0F|2313|86.08|0M 1F|124|4.61|1M 0F|76|2.83|
|1M 1F|28|1.04|0M 2F|45|1.67|2M 0F|21|0.78|
|2M 2F|34|1.27|1M 2F|31|1.15|2M 1F|15|0.56|
|Total|2375|88.39|Total|200|7.44|Total|112|4.17|

Local labor markets respond differently to our gender treatment: In Istanbul Asia, females are significantly more likely to be invited for an interview, both unconditionally and conditional on their CV being clicked on. Overall, both of Tables 10 and 11 show that factors other than gender have an effect on the probability of being invited for an interview. Applicants for occupations in accounting and marketing are significantly less likely to be invited for an interview, and the same holds in Istanbul Asia compared to Istanbul Europe.

The findings for the three stages above lead us to the main result on gender:

**Result 1.** There is no clear indication of gender discrimination in this context. If anything, employers show a minor preference for female applicants when they make their initial longlist for screening, but this is only marginally significant. Once applicants pass through this first stage, employers do not differentiate between the two genders at least until the interview phase.

> 22 Conditional balance tables are provided in Appendix D.
_Table 10: Determinants of applicant being invited for an interview_

|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|
|Female<br>0.0129**<br>(0.0055)<br>Accounting|0.0104<br>(0.0065)|0.0105<br>(0.0065)|0.0108*<br>(0.0065)<br>-0.0447***<br>(0.0093)|0.0104<br>(0.0065)|0.0106<br>(0.0065)<br>-0.0491***<br>(0.0087)|0.0069<br>(0.0077)<br>-0.0493***<br>(0.0087)|
|Marketing|||-0.0418***<br>(0.0098)||-0.0414***<br>(0.0094)|-0.0415***<br>(0.0094)|
|IT|||0.0020||0.0108|0.0115|
||||(0.0147)||(0.0174)|(0.0174)|
|Istanbul Asia||||||-0.0149*<br>(0.0079)|
|Ankara||||||<br>-0.0016<br>(0.0106)|
|_Total application size (100)_|||||||
|Female * Istanbul Asia||||||0.0160*<br>(0.0094)|
|Female * Ankara||||||-0.0067<br>(0.0117)|
|Female * Acct|||-0.0097*<br>(0.0051)||||
|Female * Mrkt|||0.0013<br>(0.0056)||||
|Female * IT|||0.0087<br>(0.0113)||||
|_Female * Total application size_|||||||
|Vacancy and CV sectors match||0.0220**||0.0213**|-0.0064|-0.0066|
|||(0.0097)||(0.0097)|(0.0119)|(0.0119)|
|Experience (in months)|0.0004|0.0005|0.0003|0.0004|0.0003|0.0003|
||(0.0006)|(0.0006)|(0.0005)|(0.0006)|(0.0005)|(0.0006)|
|Age<br>-0.0109**|-0.0139**|-0.0139**|-0.0133**|-0.0140**|-0.0134**|-0.0135**|
|(0.0050)|(0.0064)|(0.0064)|(0.0064)|(0.0064)|(0.0064)|(0.0064)|
|<br>Objective beauty|<br>0.0101|<br>0.0101|<br>0.0080|<br>0.0102|<br>0.0081|<br>0.0064|
||(0.0092)|(0.0092)|(0.0091)|(0.0092)|(0.0091)|(0.0091)|
|Subjective beauty|-0.0007|-0.0013|0.0004|-0.0012|0.0005|0.0003|
||(0.0046)|(0.0046)|(0.0045)|(0.0046)|(0.0045)|(0.0045)|
|Worked in one firm only|<br>0.0050|<br>0.0049|<br>-0.0005|<br>0.0048|<br>-0.0006|<br>-0.0001|
||(0.0052)|(0.0052)|(0.0052)|(0.0052)|(0.0052)|(0.0052)|
|Total application size (100)||||<br>-0.0004|<br>-0.0003|<br>-0.0003|
|||||(0.0003)|(0.0003)|(0.0003)|
|Constant<br>0.3428**|0.3906**|0.3883**|0.4089***|<br>0.3944**|<br>0.4139***|<br>0.4240***|
|(0.1346)|(0.1561)|(0.1562)|(0.1552)|(0.1562)|(0.1553)|(0.1554)|
|N.obs.<br>10748|10748|10748|10748|10748|10748|10748|
|R-squared<br>0.002|0.004|0.004|0.012|0.004|0.012|0.013|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.
_Table 11: Determinants of applicant being invited for an interview, conditional on applicant’s CV clicked_

||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|---|
|Female|0.0269*|0.0241|0.0234|0.0225|0.0247|0.0234|0.0085|
||(0.0151)|(0.0181)|(0.0181)|(0.0180)|(0.0181)|(0.0180)|(0.0224)|
|Accounting||||<br>-0.0552**||<br>-0.0780***|<br>-0.0763***|
|||||(0.0280)||(0.0252)|(0.0250)|
|Marketing||||<br>-0.0845***||<br>-0.0770***|<br>-0.0763***|
|||||(0.0260)||(0.0246)|(0.0245)|
|IT||||-0.0773***||-0.0259|-0.0238|
|||||(0.0274)||(0.0353)|(0.0352)|
|Istanbul Asia|||||||<br>-0.0508**<br>(0.0225)|
|Ankara|||||||<br>-0.0082<br>(0.0286)|
|Female * Istanbu|l Asia||||||0.0639**<br>(0.0267)|
|Female * Ankara|||||||<br>-0.0268<br>(0.0311)|
|Female * Acct||||-0.0384*<br>(0.0203)||||
|Female * Mrkt||||0.0139<br>(0.0196)||||
|Female * IT||||<br>0.0321<br>(0.0204)||||
|Female * Total a|pplication size|||||||
|<br>Vacancy and CV|<br>sectors match||-0.0292||-0.0274|-0.0413|-0.0410|
||||(0.0195)||(0.0195)|(0.0275)|(0.0274)|
|Experience (in m|onths)|0.0003|<br>0.0003|0.0001|<br>0.0002|<br>0.0001|<br>0.0001|
|||(0.0016)|(0.0016)|(0.0015)|(0.0016)|(0.0015)|(0.0015)|
|Age|-0.0278**|<br>-0.0306*|<br>-0.0303*|<br>-0.0299*|<br>-0.0290*|<br>-0.0288|<br>-0.0292*|
||(0.0138)|(0.0176)|(0.0176)|(0.0175)|(0.0176)|(0.0175)|(0.0176)|
|Objective beauty||0.0176|0.0174|0.0163|0.0187|0.0167|0.0125|
|||(0.0276)|(0.0275)|(0.0275)|(0.0276)|(0.0275)|(0.0275)|
|Subjective beauty||<br>0.0024|<br>0.0035|<br>0.0077|<br>0.0027|<br>0.0069|<br>0.0073|
|||(0.0128)|(0.0127)|(0.0126)|(0.0127)|(0.0126)|(0.0126)|
|Worked in one fi|rm only|<br>0.0000|<br>-0.0001|<br>-0.0041|<br>0.0007|<br>-0.0043|<br>-0.0028|
|||(0.0140)|(0.0140)|(0.0139)|(0.0140)|(0.0140)|(0.0139)|
|Total application|size (100)||||0.0019|0.0022|0.0022|
||||||(0.0016)|(0.0016)|(0.0016)|
|Constant|0.8957**|0.9354**|0.9392**|0.9757**|<br>0.8963**|<br>0.9407**|<br>0.9716**|
||(0.3693)|(0.4280)|(0.4281)|(0.4237)|(0.4292)|(0.4262)|(0.4277)|
|N.obs.|3469|3469|3469|3469|3469|3469|3469|
|R-squared|0.005|0.005|0.006|0.014|0.007|0.015|0.018|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ***, **, and * indicate significance at the 1%, 5% and 10% level, respectively.
# 3.2. Socioemotional skills

Table 12 provides the balance table for our socioemotional skills treatment. Results indicate that randomization has done a fairly good job in generating subsamples that are similar to each other apart from the treatment. In addition, a joint significance test provides an F-statistic of 0.47, implying that variables do not jointly affect the treatment variable.<sup>23</sup> Our socioemotional skills treatment is visible only when the applicant’s CV is clicked. This is why we only consider the treatment effect on callbacks for an interview and not the earlier stages of screening, and why our preferred specification is that conditional on the CV being clicked.<sup>24</sup> Table 13 shows the results from OLS regressions using cluster-robust standard errors at the vacancy level. Model 1 shows an overall insignificant treatment effect for signaling socioemotional skills. On the other hand, in Model 2 we get a differential result by interacting the treatment variable with a dummy variable that takes on the value 1 if the vacancy asks for the skill we signal in treatment CVs. Models 2 to 4 show that employers evaluate a socioemotional skill signal negatively when not asked for in the vacancy, although this effect becomes insignificant when vacancy characteristics are added (Model 4). Including the signal when it is asked for in the vacancy increases the probability of receiving a callback: the coefficients of socioemotional skills treatment and its interaction with whether the signaled socioemotional skill is required in the vacancy are jointly significant at the 10 percent level.<sup>25</sup> Finally, Model 5 shows that, while the effect sizes may change according to cluster, results are qualitatively similar across all clusters.<sup>26</sup>

**Result 2.** Signaling a socioemotional skill decreases the probability of being invited for an interview if the skill is not solicited in the vacancy, and it increases the probability of being invited for an interview if the skill is asked for in the vacancy text.

We finally investigate whether the probabilities of callback for our treatment CVs are different according to the applicant’s gender. Table 14 provides the results from OLS regressions conditional on the applicant’s CV clicked, and with cluster-robust standard errors at the vacancy level. Models 1 to 3 show that female candidates with socioemotional skill signals in their CVs are around 5 percentage points less likely to be invited for an interview when the vacancy text does not solicit the signaled socioemotional skill. On the other hand, Models 4 to 6 show that the same effect is close to zero for the male candidates.

**Result 3.** Firms that do not ask for the signaled socioemotional skills in the vacancy text evaluate these skill signals negatively only for female applicants.

> 23 Separate balance tables for each sector are available upon request. Balance tables look very similar across sectors. 24 Unconditional versions of these regressions are available upon request. Results do not vary.

> 25 Using Model 4, a joint significance test for SE skills * Signaled SE skill required in vacancy and SE skills gives an F- statistic of 3.60, with a p-value of 0.0579.

> 26 Separate regressions for each cluster are in line with these findings and are available upon request from the authors.
_Table 12: Balance table for socioemotional skills treatment_

|Variable|(1)<br>Contro|(2)<br>Treatmen|(3)<br>Differenc|
|---|---|---|---|
||l|t|e|
|Ankara|0.179|0.179|0.000|
||(0.384)<br>|(0.384)<br>|(0.007)<br>|
|Istanbul Asia|0.311|0.311|0.000|
||(0.463)|(0.463)|(0.009)|
|Istanbul EU|0.509|0.509|0.000|
||(0.500)|(0.500)|(0.010)|
|Experience (months)|49.234|49.109|-0.125|
||(5.563)|(5.673)|(0.108)|
|Age|26.411|26.399|-0.012|
||(0.643)|(0.650)|(0.012)|
|Accounting|0.292|0.292|-0.000|
||(0.455)|(0.455)|(0.009)|
|Marketing|0.276|0.276|0.000|
||(0.447)|(0.447)|(0.009)|
|Sales|0.304|0.304|-0.000|
||(0.460)|(0.460)|(0.009)|
|IT|0.128|0.128|-0.000|
||(0.334)|(0.334)|(0.006)|
|Worked in one firm only|0.253|0.264|0.011|
||(0.435)|(0.441)|(0.008)|
|Objective beauty|0.943|0.945|0.002|
||(0.231)|(0.227)|(0.004)|
|Subjective beauty|0.506|0.493|-0.012|
||(0.500)|(0.500)|(0.010)|
|Reading|4.582|4.573|-0.009|
||(0.493)|(0.495)|(0.010)|
|Speaking|4.517|4.526|0.009|
||(0.500)|(0.499)|(0.010)|
|Writing|4.514|4.512|-0.001|
||(0.500)|(0.500)|(0.010)|
|Total application size (100)|5.491|5.491|0.000|
||(9.110)|(9.110)|(0.176)|
|Observations|5,374|5,374|10,748|

**Notes** : Standard errors are given in parentheses. Symbols ***, **, and * indicate significance at the 1%, 5% and 10% level, respectively.
_Table 13: The effect of socioemotional skills, conditional on applicant CV clicked_

||Model 1|Model 2|Model 3|Model 4|Model 5|
|---|---|---|---|---|---|
|SE skills|0.007|-0.027*|-0.027*|-0.026|-0.051*|
||(0.010)|(0.016)|(0.016)|(0.016)|(0.029)|
|Signaled SE skill required in vacancy||0.004|0.004|0.001|0.001|
|||(0.022)|(0.022)|(0.022)|(0.043)|
|SE skills * Signaled SE skill required in vacancy||0.049**|0.050**|0.049**|0.092**|
|||(0.020)|(0.020)|(0.020)|(0.036)|
|SE skills * Acct|||||-0.010|
||||||(0.042)|
|SE skills * Mrkt|||||0.036|
||||||(0.044)|
|SE skills * IT|||||0.076*|
||||||(0.045)|
|Signaled SE skill required in vacancy * Acct|||||0.035|
||||||(0.061)|
|Signaled SE skill required in vacancy * Mrkt|||||-0.002|
||||||(0.061)|
|Signaled SE skill required in vacancy * IT|||||-0.030|
||||||(0.062)|
|SE skills * Signaled SE skill required in vacancy * Acct|||||-0.055<br>(0.054)|
|SE skills * Signaled SE skill required in vacancy * Mrkt|||||-0.052<br>(0.054)|
|SE skills * Signaled SE skill required in vacancy * IT|||||-0.080|
||||||(0.056)|
|Age|||-0.0460***|-0.0439***|-0.0433***|
||||(0.0118)|(0.0116)|(0.0117)|
|Experience (in months)|||0.0015|0.0012|0.0012|
||||(0.0013)|(0.0013)|(0.0013)|
|Objective beauty|||0.0190|0.0181|0.0180|
||||(0.0274)|(0.0273)|(0.0275)|
|Subjective beauty|||0.0043|0.0081|0.0072|
||||(0.0128)|(0.0126)|(0.0126)|
|Worked in one firm only|||-0.0004|-0.0025|-0.0022|
||||(0.0140)|(0.0139)|(0.0139)|
|Constant|0.172***|0.169***|<br>1.291***|<br>1.241***|<br>1.280***|
||(0.010)|(0.018)|(0.294)|(0.291)|(0.296)|
|Vacancychar.|No|No|No|Yes|Yes|
|N.obs.|3469|3469|3469|3469|3469|
|R-squared|0.000|0.002|0.007|0.017|0.019|

**Notes:** Models 1 to 6 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable _SE skills_ takes on the value 1 for treatment CVs, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Vacancy characteristics include total number of applications for the vacancy, occupational clusters and location. Symbols ∗ ∗ ∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.
_Table 14: The effect of socioemotional skills for women and men, conditional on applicant CV clicked_

|||Women only|||Men only||
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
|SE skills|-0.050**|-0.049**|-0.046**|-0.002|-0.001|-0.003|
||(0.023)|(0.023)|(0.023)|(0.022)|(0.022)|(0.022)|
|Signaled SE skill required in vacancy|-0.004|-0.004|-0.010|0.014|0.013|0.010|
||(0.028)|(0.028)|(0.028)|(0.025)|(0.025)|(0.025)|
|SE skills * Signaled SE skill required in|||||||
|vacancy|0.071**|0.069**|0.066**|0.027|0.028|0.029|
||(0.029)|(0.029)|(0.029)|(0.028)|(0.028)|(0.028)|
|Experience (in months)||-0.0014|-0.0020||0.0034|0.0033|
|||(0.0022)|(0.0022)||(0.0022)|(0.0022)|
|Age||-0.0147|-0.0110||-0.0524**|-0.0534**|
|||(0.0250)|(0.0247)||(0.0233)|(0.0233)|
|Objective beauty||-0.0075|-0.0029||0.0373|0.0256|
|||(0.0517)|(0.0519)||(0.0315)|(0.0311)|
|Subjective beauty||-0.0231|-0.0092||0.0316*|0.0292|
|||(0.0188)|(0.0186)||(0.0189)|(0.0191)|
|Worked in one firm only||-0.0019|-0.0062||-0.0040|-0.0021|
|||(0.0210)|(0.0212)||(0.0193)|(0.0192)|
|Constant|0.201***|0.670|0.558|0.134***|1.323**|1.384**|
||(0.023)|(0.588)|(0.584)|(0.020)|(0.558)|(0.560)|
|Vacancy char.|No|No|Yes|No|No|Yes|
|N.obs.|1813|1813|1813|1656|1656|1656|
|R-squared|0.003|0.005|0.022|0.002|0.008|0.016|

**Notes** : Models 1 to 6 report the results from OLS regressions. Models 1 to 3 include women only, and models 4 to 6 include men only. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable SE skills takes on the value 1 for treatment CVs, 0 otherwise. Vacancy characteristics include total number of applications for the vacancy, occupational clusters and location. Symbols ***, **, and * indicate significance at the 1%, 5% and 10% level, respectively.

# **4 Discussion and Conclusions**

Economics literature has clearly demonstrated that socioemotional skills are important in determining earnings, but it is not obvious whether socioemotional skills matter during hiring. Focusing on the hiring stage, this study answers whether and how these skills should be signaled for male and female candidates. Our results suggest that signaling a socioemotional skill increases the probability of receiving a callback only if the skill is specifically solicited in the vacancy text. There is also a penalty for female applicants: if they signal a skill not asked for, their probability of receiving a callback decreases; the same penalty does not apply to men.

Our unique data set allows us to open the black box of candidate screening, and we investigate the importance of gender in all three stages of employer screening: making the longlist, clicking on a candidate’s CV, and inviting a candidate for an interview. We find that some employers indicate a preference for women when making the longlist, and that gender discrimination does not exist conditional on candidates making it to the longlist. Interestingly, this result suggests that candidate’s beauty does not play a role in gender
preference of employers, since employers are simply not able to filter using beauty, and the photos of applicants are not visible at this stage.<sup>27</sup> Our results imply that for socioemotional skill signals to help in securing job interviews and advance through the hiring process, one must be careful on when and which skills to include in the CVs. A CV tailored only to the candidate’s qualifications, at least in terms of socioemotional skill signals, may backfire in the job hunt even though the candidate may in fact have quite strong socioemotional skills that would be useful for the jobs he or she is after. This is especially true for female applicants, whom employers seem to punish for adding unsolicited socioemotional skills. Previous literature suggests that women and men have different traits on risk aversion, competition, negotiation, and aversion to have overestimated themselves (Babcock and Laschever, 2009; Niederle and Vesterlund, 2007; Croson and Gneezy, 2009; Dohmen and Falk, 2011; Ludwig et al., 2017). Women are also less likely to be overconfident compared to men (Lundeberg et al., 1994; Barber and Odean, 2001). Employers may then expect female candidates to be less overconfident compared to the male candidates with similar characteristics. Arguably, a candidate who includes a socioemotional skill signal not specifically asked for in the vacancy may be evaluated as overconfident by the employer. If the candidate is female, employers may evaluate the signals negatively because they evaluate her overconfidence negatively, whereas male candidates are expected to show overconfidence in their CVs, pointing to another expression of the double bind women face in the labor market, where women need to tailor their CVs much more than men do.

While our results provide a detailed assessment of whether and how socioemotional skill signals may be useful (or detrimental) in the hiring stage, we can observe what happens only before the interview stage. It is plausible that employers evaluate socioemotional skills of applicants during the interview through specific tests or questions. In this sense, the signaled socioemotional skill signals may be valuable conditional on making it to the interview stage; this would imply that the effects we find are underestimates of the total effects of socioemotional skills in recruitment. It may also be the case that employers value all socioemotional skill signals, but again, conditional on making it to the interview. Unfortunately, these two aspects cannot be investigated using our design and methodology, but they present valuable opportunities for future research.

> 27 Hamermesh and Biddle (1994); Barry (2000); Mobius and Rosenblat (2006); Scholz and Sicinski (2015); Doorley and Sierminska (2015) find beauty affects earnings, Deryugina and Shurchkov (2015) find that it does so only when beauty is expected to matter for performance, López Bóo et al. (2013) find that attractiveness increases invitations for interview.