Cultural and social factors contributing to gender gaps in the labour market Lena Adamus 12.03.2024 Brno Gender equality index - EU https://eige.europa.eu/gender-equality-index/2022 Gender equality index - CR CR vs EU Work Time Education Adobe Systems Important concepts ̶Glass ceiling ̶Sticky floor ̶Glass escalator ̶Matilda effect ̶Motherhood penalty ̶ ̶ Diagnosis – not an explanation Adobe Systems Important concepts ̶Glass ceiling: a metaphorical barrier preventing women from advancing in a work hierarchy. ̶Sticky floor: a metaphor indicating that women tend to occupy lower-paid occupations with lower mobility potential. ̶Glass escalator: in female-dominated occupations, men tend to advance faster that women. A premium for being a man in a female-dominated field. ̶Matilda effect: attributing female scientific achievements to their male peers (e.g., Skłodowska-Curie, Rosalind Franklin). ̶Motherhood penalty: percentage wage decrease after every child born by a woman. The larger the drop, the more incline women are not to come back to work. ̶ ̶ Diagnosis – not an explanation Presentation tips •How does the gender gaps look in your country? What are legal regulations in your country? •How changes in labour force participation affect wellbeing, household work arrangement (and vice versa)? • • • • A not-so-funny game Further read •Williams, A. (2020). Why women are poorer than men and what we can do about it? Presentation tips •Which occupations are female-dominated and which are male-dominated in your country? •How occupational seggregation (both vertical and horizontal) contribute to gender wage gap? •Are the female-dominated jobs less paid? • So what is gender? •Politically correct term for sex? •The term sex refers to “biologically determined aspects of men and women’s behaviour, whereas gender denotes male-female differences that are shaped by sociocultural factors” (Ashmore & Sewell, 1998, p. 378). •When referring to men and women as members of a social group one should use the term gender, while sex is more appropriate in contexts where biological differences predominate. •Sex characteristics are attributes that are directly related to biological features, while gender characteristics are those that are culturally associated with a person because of his or her biological sex. •Two debates: heredity vs. environment (nature-nurture) and essentialism vs. constructivism • • Economics of gender •Definition: Gender Economics is the area in economics that explicitly considers the effects of having two sexes as they interact in families, firms, and markets. •Theoretical models including two sexes; •Empirical work addressing differences between the sexes; •Analysis of policies that affect genders in different ways; •The ultimate question: why men and women differ? • Answers to the why questions on various level: •Individual and biological •Institutional •Social and cultural • • The drawback of focusing on individual level is that when we divide population into two subsamples, we are likely to find a difference. When the sample is sufficiently large, the difference is even likely to be statistically significant. The key point is thus to think about actual meaning of the detected difference. When we find differences between men and women, we often tend to think that their sources are rooted in biology or genetics. But without considering other possible sources, we should not make hasty conclusions. For instance, in economics, we like to think that whenever there is a difference, it can be traced back to individual differences (e.g. personality or cognitive skills), preferences and motivation. Indeed, it sounds plausible and may even be correct. Of course only under one assumption: that the portfolio of feasible options is the same for both men and women. Only if the playing field is equal for all actors. But is it? If we finish our analysis satisfied with results with a dummy variable we may never know. Key conepts •Cultural differences •Stereotypes •Discrimination •Backlash • Focus Why are women underrespresented in science? Culture, gender and math •Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008), Culture, gnder and math, Science, 320, 1164-1165. • Anecdotical evidence • • • • • • • •Shortly after I changed sex, a faculty member was heard to say: Ben Barres gave a great seminar today, but then his work is much better than his sister’s. •Barres, B. (2006). A commentary, Nature, 442, 133-136 • Further read •Maney, D.L. 2016. Perils and pitfalls of reporting sex differences. Phil. Trans. R. Soc. B 371: 20150119. Presentation tips •Does reversing the general educational gender gap contribute to closing wage gap? • • • Cross-culture approach Are you WEIRD? •Gneezy, Leonard, & List (2009) •Used a controlled experiment to explore whether there are gender differences in selecting into competitive environments across distinct societies: the Maasai in Tanzania and the Khasi in India. •The Maasai represent an example of a patriarchal society, whereas the Khasi are matrilineal. •Similar to the extant evidence drawn from experiments executed in Western cultures, Maasai men opt to compete at roughly twice the rate as Maasai women. •Interestingly, this result is reversed among the Khasi: women choose the competitive environment more often than Khasi men, and even choose to compete weakly more often than Maasai men. • Cross-culture approach Are you WEIRD? •Finucane et al. (2000) •Data collected as part of a national telephone survey designed to test hypotheses about risk perceptions over a range of hazards. The survey contained questions about worldviews, trust, and a range of demographic variables. •All respondents were asked to consider health and safety risks `to you and your family' and to indicate whether there is almost no risk, slight risk, moderate risk, or high risk from each of 13 hazardous activities and technologies (for example, blood transfusions; motor vehicles; nuclear power plants; vaccines) and safety risks from 19 hazards for `the American public’. •Claimed that there are no universal gender differences, there is only a “white male effect”. • • Eurobarometer on stereotypes Further read •Henrich, J. (2020). The WEIRDest people in the world: How the West became psychologically perculiar and particularly prosperous? •Henrich, J., et al. (2010). The weirdest people in the world? Behavioural and brain sciences, vol. 33, pp. 61-83. doi:10.1017/S0140525X0999152X •Cukrowska-Torzewska, E. & Lovasz, A. (2019). The role of parenthood in shaping the gender wage gap – A comparative analysis of 26 European countries • Discrimination •Discrimination = „the valuation in the market place of personal characteristics of the worker that are unrelated to worker productivity” • •Statistical discrimination = imperfect productivity information → use of statistic information/stereotypes to evaluate a person (judging by belonging to a group rather than individual competences and skills) Understanding statistics •d=.2 (small), d=.5 (medium), d=.8 (large) •https://sexdifference.org/ • • • • • • • • Discrimination •Discrimination = „the valuation in the market place of personal characteristics of the worker that are unrelated to worker productivity” • •Statistical discrimination = imperfect productivity information → use of statistic information/stereotypes to evaluate a person (judging by belonging to a group rather than individual competences and skills) Further read •Arrow, K. (1973). The theory of discrimination. In: O. Ashenfelter and A. Rees (eds.), Discrimination in Labor Markets, Princeton, NJ: Princeton University Press. •Phelps, E. (1972). "The Statistical Theory of Racism and Sexism". American Economic Review. 62 (4): 659–661. • Stereotypes •Gender stereotypes = reflections of observed behaviour •May be biased or incorrect •Two types of stereotypes: descriptive and prescriptive • Gender stereotypes and identity •Men and women are socialised to different roles. •Specifically, already at an early stage of development, boys and girls learn gender-appropriate activities and behaviours (Eagly, 1987). •Boys are socialised to be masculine (instrumental or agentic) and to develop traits such as aggression, independence, ambition and rationality. •Girls are praised for being feminine (expressive or communal) and encouraged to be warm, caring, emotional and socially-oriented (Bem, 1974). Gender stereotypes and identity •Consequently, occupations are not gender-neutral either, with some being considered appropriate for men and some reserved almost exclusively for women. •Gender stereotypes can, thus, distort individuals ’ preferences for occupations. •Congruence theory (Eagly and Karau, 2002) further explains that the preferences are likely to be distorted because of the biases against adopting masculine roles by individuals with predominantly feminine characteristics (and vice versa). •Consequently, individuals who perceive themselves as incongruent with the gendered notion of a given job are likely to feel discouraged from pursuing it as a potential career. • Backlash •Backlash = a strong negative reaction •In the gender context = social desirability (expectations) of behaviour increases when it is consistent with prescriptions applicable to one’s gender; •Individuals are likely to be penalized for non-conforming behaviour—i.e., inconsistent with gender-relevant prescriptive norms •Often related to a trade-off faced by women: they may be perceived as either competent or likeable •Trade-off: maintaining identity or pursuing a career Further read •Akerlof, G. & Kranton, R. (2010). Identity Economics. Princeton University Press. •Eagly, A.H. (1987), Sex Differences in Social Behaviour: A Social-Role Interpretation, Lawrence Erlbaum, London. •Eagly, A.H. and Karau, S.J. (2002), Role congruity theory of prejudice toward female leaders, Psychological Review, 109(3), pp. 573-598. •Eagly, A.H. and Steffen, V.J. (1984), Gender stereotypes stem from the distribution of women and men into social roles, Journal of Personality and Social Psychology, 46(4), pp. 735-754. • Presentation tips •How gender stereotypes contribute to gender gaps across countries? •How strongly are gender stereotypes associated with labour market outcomes in your country? • • • Case study - entrepreneurship Entrepreneurs impact positively economics, poverty and development Gender and EI Entrepreneurs impact positively economics, poverty and development Attractive alternative for formal employment Gender and EI Entrepreneurs impact positively economics, poverty and development Attractive alternative for formal employment For women? Gender and EI Entrepreneurs impact positively economics, poverty and development Attractive alternative for formal employment For women? Why there are so few female entrerepreneurs? Gender and EI Entrepreneurs impact positively economics, poverty and development Attractive alternative for formal employment For women? Why there are so few female entrerepreneurs? The key role of entrepreneurial intentions Sample & procedure Sample & procedure The survey respondents were 552 Slovaks (49.5% women) aged 19 to 65, who were not entrepreneurs. Sample & procedure The survey respondents were 552 Slovaks (49.5% women) aged 19 to 65, who were not entrepreneurs. Recruited through an external participant recruitment agency. Sample & procedure The survey respondents were 552 Slovaks (49.5% women) aged 19 to 65, who were not entrepreneurs. Representative of the general population in terms of gender and age. Recruited through an external participant recruitment agency. Results in brief Results in brief Results in brief Results in brief Results in brief Results in brief Further read •Rudman, L. A. & Glick, P. (2001). Prescriptive Gender Stereotypes and Backlash Toward Agentic Women. Journal of Social Issues, 57(4), 743–762. •Rudman, L. A. & Mescher, K. (2013). Penalizing Men Who Request a Family Leave: Is Flexibility Stigma a Femininity Stigma? Journal of Social Issues, 69(2), 322–340. •Rudman, L. A., Moss-Racusin, C. A., Phelan, J. E. & Nauts, S. (2012). Status incongruity and backlash effects: Defending the gender hierarchy motivates prejudice against female leaders. Journal of Experimental Social Psychology, 48(1), 165–179. • • Gender biases in starting a business Business plans evaluation Do evaluators assess men’s and women’s business plans differently? Business plans evaluation Do evaluators assess men’s and women’s business plans differently? N=498 entrepreneurs Business plans evaluation Do evaluators assess men’s and women’s business plans differently? N=498 entrepreneurs > Entrepreneurs often become evaluators in BP pitch contests Business plans evaluation Do evaluators assess men’s and women’s business plans differently? N=498 entrepreneurs > Entrepreneurs often become evaluators in BP pitch contests 3 BP: (i) cosmetics production, (ii) services provision and (iii) software development Business plans evaluation Do evaluators assess men’s and women’s business plans differently? N=498 entrepreneurs > Entrepreneurs often become evaluators in BP pitch contests 3 BP: (i) cosmetics production, (ii) services provision and (iii) software development All 3 BP presented as either male or female Business plans evaluation Do evaluators assess men’s and women’s business plans differently? N=498 entrepreneurs > Entrepreneurs often become evaluators in BP pitch contests 3 BP: (i) cosmetics production, (ii) services provision and (iii) software development All 3 BP presented as either male or female Applicants’ competence, likeability, and the ability to succeed in business. Business plans evaluation Do evaluators assess men’s and women’s business plans differently? N=498 entrepreneurs > Entrepreneurs often become evaluators in BP pitch contests 3 BP: (i) cosmetics production, (ii) services provision and (iii) software development All 3 BP presented as either male or female Applicants’ competence, likeability, and the ability to succeed in business. Evaluators indicated also success chances of each plan, the amount they would be willing to invest in each of the start-ups, and selected the most prospective applicant Results Results More positive assessment of women Results More positive assessment of women More positive assessment of men Results More positive assessment of women More positive assessment of men Results Results Male evaluators give lower evaluations on average than women evaluators, but no differences are confirmed when men assess female applicants or when evaluators assess applicants of same sex (i.e., the interaction effects are not significant). Results Our results indicate that masculine evaluators give higher assessments on average but negative interaction terms imply that masculine evaluators are harsher to women applicants in terms of start-up success, invested amount, competence, and likeability. Results Results The woman applicant in IT sector has 2.6 times lower probability (odds ratio of 0.38) to be selected as the most prospective applicant in the situation when the start-up is submitted by a woman applicant and evaluated by a man. This result points to the stereotype thinking of men about the potential success of women in different fields Results and conclusions Results and conclusions 1: Business plans written by women are assessed more negatively in sectors stereotypically associated with men. Results and conclusions 1: Business plans written by women are assessed more negatively in sectors stereotypically associated with men. The study corroborates the view that from early phase of the business creation women may be disadvantaged when aspiring to become an entrepreneur Results and conclusions 1: Business plans written by women are assessed more negatively in sectors stereotypically associated with men. The study corroborates the view that from early phase of the business creation women may be disadvantaged when aspiring to become an entrepreneur 2: The evaluators’ masculinity may have an adverse effect on the evaluation. Results and conclusions 1: Business plans written by women are assessed more negatively in sectors stereotypically associated with men. The study corroborates the view that from early phase of the business creation women may be disadvantaged when aspiring to become an entrepreneur 2: The evaluators’ masculinity may have an adverse effect on the evaluation. Culture-specific barriers that could slow down women’s progress in entrepreneurship. Results and conclusions 1: Business plans written by women are assessed more negatively in sectors stereotypically associated with men. The study corroborates the view that from early phase of the business creation women may be disadvantaged when aspiring to become an entrepreneur 2: The evaluators’ masculinity may have an adverse effect on the evaluation. Culture-specific barriers that could slow down women’s progress in entrepreneurship. 3: Female evaluators described themselves as more masculine than men Results and conclusions 1: Business plans written by women are assessed more negatively in sectors stereotypically associated with men. The study corroborates the view that from early phase of the business creation women may be disadvantaged when aspiring to become an entrepreneur 2: The evaluators’ masculinity may have an adverse effect on the evaluation. Culture-specific barriers that could slow down women’s progress in entrepreneurship. 3: Female evaluators described themselves as more masculine than men Hyper-masculine stereotypes about successful entrepreneurs may lead to self-selection of potential female entrepreneurs. Take-home message Caution is advised when recommending to increase the number of female evaluators of business plans in pitch competitions. If women who get involved in entrepreneurship are excessively masculine and masculinity is associated with less favourable evaluation of potential female entrepreneurs, such policies could backfire against women putting them in more disadvantaged position. Case study – discrimination & backlash • Adobe Systems 82 Case study – discrimination & backlash ̶ Is there differential treatment? Adobe Systems 83 Case study – discrimination & backlash ̶ Is there differential treatment? Sample 155 HR managers Apart from studying discrimination, previous research has attempted to identify sources of the disparity indicating that distinct preferences, motivations, and life choices could contribute to the unequal distribution of household chores, occupational segregation and the gender wage gap. Although there is empirical evidence supporting the hypothesis that women have different risk, loss and competiveness preferences, make different educational choices, are less mobile in the labour market, value more flexibility even at a cost of lower wages and take jobs in sectors offering lower compensations and prefer fixed over volatile compensation schemes (Petrongolo and Ronchi, 2020; Dohmen et al. 2012; Francesconi and Parey, 2018; Bonin et al. 2007; Bertrand, 2018; Jaeger et al., 2010; Chapman and Benis, 2017; Pearlman, 2018; Pearlman, 2019; Croson and Gneezy, 2009; Gneezy, Niederle, and Rustichini, 2003; Dohmen and Falk, 2011; Kalinowski, 2019), it is still possible that some of the persistent labour market disparities are attributable to discrimination and gender biases. As Blau and Kahn (2017) observed, even after controlling for human capital (including educational attainment and professional experience), cognitive and non-cognitive skills, number of working hours, gender division of non-market work, parenthood, vertical and horizontal occupational segregation, a substantial portion of the gender wage gap remains unexplained. Although some of the persisting gap may be due to factors not included in the analyses, it seems likely that at least some portion of the disparity is caused by various forms of discrimination and gender norms regulating men and women choices and preferences. Usually it is assumed that the gap that remains after controlling for other significant factors is attributable to discrimination. In this study, we have decided to investigate discrimination and differential treatment directly Adobe Systems 84 Case study – discrimination & backlash ̶ Is there differential treatment? Sample 155 HR managers Two critical stages Apart from studying discrimination, previous research has attempted to identify sources of the disparity indicating that distinct preferences, motivations, and life choices could contribute to the unequal distribution of household chores, occupational segregation and the gender wage gap. Although there is empirical evidence supporting the hypothesis that women have different risk, loss and competiveness preferences, make different educational choices, are less mobile in the labour market, value more flexibility even at a cost of lower wages and take jobs in sectors offering lower compensations and prefer fixed over volatile compensation schemes (Petrongolo and Ronchi, 2020; Dohmen et al. 2012; Francesconi and Parey, 2018; Bonin et al. 2007; Bertrand, 2018; Jaeger et al., 2010; Chapman and Benis, 2017; Pearlman, 2018; Pearlman, 2019; Croson and Gneezy, 2009; Gneezy, Niederle, and Rustichini, 2003; Dohmen and Falk, 2011; Kalinowski, 2019), it is still possible that some of the persistent labour market disparities are attributable to discrimination and gender biases. As Blau and Kahn (2017) observed, even after controlling for human capital (including educational attainment and professional experience), cognitive and non-cognitive skills, number of working hours, gender division of non-market work, parenthood, vertical and horizontal occupational segregation, a substantial portion of the gender wage gap remains unexplained. Although some of the persisting gap may be due to factors not included in the analyses, it seems likely that at least some portion of the disparity is caused by various forms of discrimination and gender norms regulating men and women choices and preferences. Usually it is assumed that the gap that remains after controlling for other significant factors is attributable to discrimination. In this study, we have decided to investigate discrimination and differential treatment directly Adobe Systems 85 Case study – discrimination & backlash ̶ Is there differential treatment? Sample 155 HR managers Two critical stages recruitment Apart from studying discrimination, previous research has attempted to identify sources of the disparity indicating that distinct preferences, motivations, and life choices could contribute to the unequal distribution of household chores, occupational segregation and the gender wage gap. Although there is empirical evidence supporting the hypothesis that women have different risk, loss and competiveness preferences, make different educational choices, are less mobile in the labour market, value more flexibility even at a cost of lower wages and take jobs in sectors offering lower compensations and prefer fixed over volatile compensation schemes (Petrongolo and Ronchi, 2020; Dohmen et al. 2012; Francesconi and Parey, 2018; Bonin et al. 2007; Bertrand, 2018; Jaeger et al., 2010; Chapman and Benis, 2017; Pearlman, 2018; Pearlman, 2019; Croson and Gneezy, 2009; Gneezy, Niederle, and Rustichini, 2003; Dohmen and Falk, 2011; Kalinowski, 2019), it is still possible that some of the persistent labour market disparities are attributable to discrimination and gender biases. As Blau and Kahn (2017) observed, even after controlling for human capital (including educational attainment and professional experience), cognitive and non-cognitive skills, number of working hours, gender division of non-market work, parenthood, vertical and horizontal occupational segregation, a substantial portion of the gender wage gap remains unexplained. Although some of the persisting gap may be due to factors not included in the analyses, it seems likely that at least some portion of the disparity is caused by various forms of discrimination and gender norms regulating men and women choices and preferences. Usually it is assumed that the gap that remains after controlling for other significant factors is attributable to discrimination. In this study, we have decided to investigate discrimination and differential treatment directly Adobe Systems 86 Case study – discrimination & backlash ̶ Is there differential treatment? Sample 155 HR managers Two critical stages recruitment dismissal Apart from studying discrimination, previous research has attempted to identify sources of the disparity indicating that distinct preferences, motivations, and life choices could contribute to the unequal distribution of household chores, occupational segregation and the gender wage gap. Although there is empirical evidence supporting the hypothesis that women have different risk, loss and competiveness preferences, make different educational choices, are less mobile in the labour market, value more flexibility even at a cost of lower wages and take jobs in sectors offering lower compensations and prefer fixed over volatile compensation schemes (Petrongolo and Ronchi, 2020; Dohmen et al. 2012; Francesconi and Parey, 2018; Bonin et al. 2007; Bertrand, 2018; Jaeger et al., 2010; Chapman and Benis, 2017; Pearlman, 2018; Pearlman, 2019; Croson and Gneezy, 2009; Gneezy, Niederle, and Rustichini, 2003; Dohmen and Falk, 2011; Kalinowski, 2019), it is still possible that some of the persistent labour market disparities are attributable to discrimination and gender biases. As Blau and Kahn (2017) observed, even after controlling for human capital (including educational attainment and professional experience), cognitive and non-cognitive skills, number of working hours, gender division of non-market work, parenthood, vertical and horizontal occupational segregation, a substantial portion of the gender wage gap remains unexplained. Although some of the persisting gap may be due to factors not included in the analyses, it seems likely that at least some portion of the disparity is caused by various forms of discrimination and gender norms regulating men and women choices and preferences. Usually it is assumed that the gap that remains after controlling for other significant factors is attributable to discrimination. In this study, we have decided to investigate discrimination and differential treatment directly Adobe Systems 87 Case study – discrimination & backlash ̶ Is there differential treatment? Sample 155 HR managers Two critical stages recruitment dismissal Apart from studying discrimination, previous research has attempted to identify sources of the disparity indicating that distinct preferences, motivations, and life choices could contribute to the unequal distribution of household chores, occupational segregation and the gender wage gap. Although there is empirical evidence supporting the hypothesis that women have different risk, loss and competiveness preferences, make different educational choices, are less mobile in the labour market, value more flexibility even at a cost of lower wages and take jobs in sectors offering lower compensations and prefer fixed over volatile compensation schemes (Petrongolo and Ronchi, 2020; Dohmen et al. 2012; Francesconi and Parey, 2018; Bonin et al. 2007; Bertrand, 2018; Jaeger et al., 2010; Chapman and Benis, 2017; Pearlman, 2018; Pearlman, 2019; Croson and Gneezy, 2009; Gneezy, Niederle, and Rustichini, 2003; Dohmen and Falk, 2011; Kalinowski, 2019), it is still possible that some of the persistent labour market disparities are attributable to discrimination and gender biases. As Blau and Kahn (2017) observed, even after controlling for human capital (including educational attainment and professional experience), cognitive and non-cognitive skills, number of working hours, gender division of non-market work, parenthood, vertical and horizontal occupational segregation, a substantial portion of the gender wage gap remains unexplained. Although some of the persisting gap may be due to factors not included in the analyses, it seems likely that at least some portion of the disparity is caused by various forms of discrimination and gender norms regulating men and women choices and preferences. Usually it is assumed that the gap that remains after controlling for other significant factors is attributable to discrimination. In this study, we have decided to investigate discrimination and differential treatment directly Adobe Systems 88 Study design – vignette study ̶ Two critical stages Adobe Systems 89 Study design – vignette study ̶ Two critical stages recruitment Task 1 Adobe Systems 90 Study design – vignette study ̶ Two critical stages recruitment dismissal Task 1 Task 2 Adobe Systems 91 Sample ̶ Sample 155 HR managers Adobe Systems 92 Sample ̶ Sample 155 HR managers Selection criterion: Experience in HR processes Adobe Systems 93 Sample ̶ Sample 155 HR managers 97 female Selection criterion: Experience in HR processes Adobe Systems 94 Sample ̶ Sample 155 HR managers 97 female Mage = 41.48, SDage = 9.57 Selection criterion: Experience in HR processes Adobe Systems 95 Task 1: recruitment ̶ 155 HR managers Adobe Systems 96 Task 1: recruitment ̶ 155 HR managers Cover story Adobe Systems 97 Task 1: recruitment ̶ 155 HR managers Regional sales manager in a winery Cover story Adobe Systems 98 Task 1: recruitment ̶ 155 HR managers Split sample Regional sales manager in a winery Cover story Adobe Systems 99 Task 1: recruitment ̶ 155 HR managers Split sample 3 female CVs 3 male CVs Regional sales manager in a winery Cover story Adobe Systems 100 Task 1: recruitment ̶ 155 HR managers Split sample 3 female CVs 3 male CVs Competence Likeability Hireability Wage proposal Competence Likeability Hireability Wage proposal Regional sales manager in a winery Cover story Adobe Systems 101 Task 1: instrument Adobe Systems 102 Task 1: instrument Adobe Systems 103 Task 1: instrument Adobe Systems 104 Task 1: instrument Table 1 Vignette factors and factor levels Factor Factor levels gender 2 levels male/female age 3 levels 35, 36, 37 years educational attainment 3 levels 3 universities with different quality professional experience 3 levels 8 years and small team, 10 years and medium team, 11 years and big team vocational training 3 levels considerable and job-related, average, none Adobe Systems 105 Task 1: questionnaire ̶Competence (3 items): 1. Did the applicant strike as competent? 2. How likely is that the applicant has the necessary skills for this job? 3. How qualified you think the applicant is? Scale: 1 (not at all) to 7 (very much); ̶Hireability (3 items): 1. How likealy would you be to invite the applicant to interview for the job? 2. How likely would you be to hire the applicant for the job? 3. How likely do you think it is thay the applicant was actually hired? Scale: 1 (not at all) to 7 (very much); ̶Likeability (3 items): 1. How much did you like the applicant? 2. Would you characterize the applicant as someone you want to get to know better? 3. Would the applicant fit well with other team members? Scale: 1 (not at all likely) to 7 (very likely); ̶Wage proposal: starting and after probation ̶ ̶ Adobe Systems 106 Task 1: results ̶ ̶ Table 2 Descriptive statistics of measured variables and differences between assessment of men and women applicants men applicants women applicants CV N ω M SD N ω M SD t df p d best competence 77 .910 5.74 0.93 78 .992 5.73 1.08 0.059 153 .953 hire-ability 77 .893 5.51 1.08 78 .987 5.67 1.02 -0.949 153 .344 likeability 77 .879 5.41 0.88 78 .990 5.61 0.94 -1.403 153 .163 starting wage 77 - 1230.39 451.86 78 - 1055.77 295.64 2.845 152 .005 .459 average wage 77 - 1498.55 531.91 78 - 1314.03 361.49 2.513 151 .013 .406 Note: N – number, ω – reliability (omega), M – mean, SD – standard deviation, t – t-test value, df – degree of freedom, p – significance, d – Cohen’s d Adobe Systems 107 Task 1: results ̶ ̶ Table 2 Descriptive statistics of measured variables and differences between assessment of men and women applicants men applicants women applicants CV N ω M SD N ω M SD t df p d best competence 77 .910 5.74 0.93 78 .992 5.73 1.08 0.059 153 .953 hire-ability 77 .893 5.51 1.08 78 .987 5.67 1.02 -0.949 153 .344 likeability 77 .879 5.41 0.88 78 .990 5.61 0.94 -1.403 153 .163 starting wage 77 - 1230.39 451.86 78 - 1055.77 295.64 2.845 152 .005 .459 average wage 77 - 1498.55 531.91 78 - 1314.03 361.49 2.513 151 .013 .406 Note: N – number, ω – reliability (omega), M – mean, SD – standard deviation, t – t-test value, df – degree of freedom, p – significance, d – Cohen’s d Adobe Systems 108 Task 1: results ̶ ̶ Table 2 Descriptive statistics of measured variables and differences between assessment of men and women applicants men applicants women applicants CV N ω M SD N ω M SD t df p d best competence 77 .910 5.74 0.93 78 .992 5.73 1.08 0.059 153 .953 hire-ability 77 .893 5.51 1.08 78 .987 5.67 1.02 -0.949 153 .344 likeability 77 .879 5.41 0.88 78 .990 5.61 0.94 -1.403 153 .163 starting wage 77 - 1230.39 451.86 78 - 1055.77 295.64 2.845 152 .005 .459 average wage 77 - 1498.55 531.91 78 - 1314.03 361.49 2.513 151 .013 .406 Note: N – number, ω – reliability (omega), M – mean, SD – standard deviation, t – t-test value, df – degree of freedom, p – significance, d – Cohen’s d Adobe Systems 109 Task 1: results ̶ ̶ Table 2 Descriptive statistics of measured variables and differences between assessment of men and women applicants men applicants women applicants CV N ω M SD N ω M SD t df p d best competence 77 .910 5.74 0.93 78 .992 5.73 1.08 0.059 153 .953 hire-ability 77 .893 5.51 1.08 78 .987 5.67 1.02 -0.949 153 .344 likeability 77 .879 5.41 0.88 78 .990 5.61 0.94 -1.403 153 .163 starting wage 77 - 1230.39 451.86 78 - 1055.77 295.64 2.845 152 .005 .459 average wage 77 - 1498.55 531.91 78 - 1314.03 361.49 2.513 151 .013 .406 Note: N – number, ω – reliability (omega), M – mean, SD – standard deviation, t – t-test value, df – degree of freedom, p – significance, d – Cohen’s d Medium and least competent men significantly less likeable than identical women Adobe Systems 110 Task 1: results ̶ ̶ Table 3 Correlations between hire-ability, competence and likeability women applicants men applicants hire-ability likeability hire-ability likeability competence .875** .817** .842** .729** hire-ability .870** .756** Note: **correlation is significant at the .01 level Adobe Systems 111 Task 1: results ̶ ̶ Adobe Systems 112 Task 1: results ̶ ̶ ̶ Gap between 7 and 15% ~ 20% in Slovakia (GEI) Two patterns Better qualified = greater gap Men achieve an additional premium for being perceived as highly skilled? Lower gap after probation Greater uncertainty concerning female candidates even though they were perceived as equally competent? The scope of the wage proposals gap in our study was lower (between 7 and 15 percent) than the raw gender wage gap for Slovakia (about 20% according to GEI). We have observed two interesting patterns related to the gap. First, the greatest difference occurred for the best and the smallest for the worst pair of candidates. The wider gap for better qualified candidates indicates that men achieve an additional premium for being perceived as highly skilled. Second, we found that the gap tends to decrease for the wage proposed following the probation period, suggesting that there is greater uncertainty concerning female candidates even though they were perceived as equally competent. Nevertheless, after they prove they are valuable employees, they can expect their wages to increase more compared to men. They would still receive significantly less than their male counterparts. Adobe Systems 113 Task 2: dismissal ̶ 155 HR managers Adobe Systems 114 Task 2: dismissal ̶ 155 HR managers Split sample Adobe Systems 115 Task 2: dismissal ̶ 155 HR managers Split sample 6 preselected employees Adobe Systems 116 Task 2: dismissal ̶ 155 HR managers Split sample 6 preselected employees Identical except of gender Adobe Systems 117 Task 2: dismissal ̶ 155 HR managers Split sample 6 preselected employees Identical except of gender Which one to dismiss? Adobe Systems 118 Task 2: dismissal ̶ 155 HR managers Split sample 6 preselected employees Identical except of gender Which one to dismiss? Why? Adobe Systems 119 Task 2: dismissal Table 4 Choice of applicant to fire form A, n = 77 form B, n = 78 applicant sex, % reasoning sex, % reasoning 36, 6 years in Co., almost no absences w, 0.0% m, 1.3% 37, 7 years in Co., almost no absences m, 0.0% w, 0.0% 35, 5 years in Co., some absences w, 1.3% m, 12.8% absences, fewer years in Co., a man, young 36, 6 years in Co., some absences m, 2.6% w, 1.3% 37, 7 years in Co., frequent absences w, 9.1% absences m, 41.0% absences, fewer years in Co., a man (too much absences), young 38, 5 years in Co., frequent absences m, 87.0% absences, fewer years in Co., a man w, 43.6% absences, fewer years in Co., young Note: w – woman, m – man Adobe Systems 120 Task 2: dismissal Table 4 Choice of applicant to fire form A, n = 77 form B, n = 78 applicant sex, % reasoning sex, % reasoning 36, 6 years in Co., almost no absences w, 0.0% m, 1.3% 37, 7 years in Co., almost no absences m, 0.0% w, 0.0% 35, 5 years in Co., some absences w, 1.3% m, 12.8% absences, fewer years in Co., a man, young 36, 6 years in Co., some absences m, 2.6% w, 1.3% 37, 7 years in Co., frequent absences w, 9.1% absences m, 41.0% absences, fewer years in Co., a man (too much absences), young 38, 5 years in Co., frequent absences m, 87.0% absences, fewer years in Co., a man w, 43.6% absences, fewer years in Co., young Note: w – woman, m – man Adobe Systems 121 Task 2: dismissal Table 4 Choice of applicant to fire form A, n = 77 form B, n = 78 applicant sex, % reasoning sex, % reasoning 36, 6 years in Co., almost no absences w, 0.0% m, 1.3% 37, 7 years in Co., almost no absences m, 0.0% w, 0.0% 35, 5 years in Co., some absences w, 1.3% m, 12.8% absences, fewer years in Co., a man, young 36, 6 years in Co., some absences m, 2.6% w, 1.3% 37, 7 years in Co., frequent absences w, 9.1% absences m, 41.0% absences, fewer years in Co., a man (too much absences), young 38, 5 years in Co., frequent absences m, 87.0% absences, fewer years in Co., a man w, 43.6% absences, fewer years in Co., young Note: w – woman, m – man Adobe Systems 122 Task 2: dismissal Table 4 Choice of applicant to fire form A, n = 77 form B, n = 78 applicant sex, % reasoning sex, % reasoning 36, 6 years in Co., almost no absences w, 0.0% m, 1.3% 37, 7 years in Co., almost no absences m, 0.0% w, 0.0% 35, 5 years in Co., some absences w, 1.3% m, 12.8% absences, fewer years in Co., a man, young 36, 6 years in Co., some absences m, 2.6% w, 1.3% 37, 7 years in Co., frequent absences w, 9.1% absences m, 41.0% absences, fewer years in Co., a man (too much absences), young 38, 5 years in Co., frequent absences m, 87.0% absences, fewer years in Co., a man w, 43.6% absences, fewer years in Co., young Note: w – woman, m – man Adobe Systems 123 Task 2: dismissal Table 4 Choice of applicant to fire form A, n = 77 form B, n = 78 applicant sex, % reasoning sex, % reasoning 36, 6 years in Co., almost no absences w, 0.0% m, 1.3% 37, 7 years in Co., almost no absences m, 0.0% w, 0.0% 35, 5 years in Co., some absences w, 1.3% m, 12.8% absences, fewer years in Co., a man, young 36, 6 years in Co., some absences m, 2.6% w, 1.3% 37, 7 years in Co., frequent absences w, 9.1% absences m, 41.0% absences, fewer years in Co., a man (too much absences), young 38, 5 years in Co., frequent absences m, 87.0% absences, fewer years in Co., a man w, 43.6% absences, fewer years in Co., young Note: w – woman, m – man Adobe Systems 124 Conclusions ̶ Differential treatment? Adobe Systems 125 Conclusions ̶ Differential treatment? Women Adobe Systems 126 Conclusions ̶ Differential treatment? Women Direct discrimination Adobe Systems 127 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Adobe Systems 128 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Adobe Systems 129 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Adobe Systems 130 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Adobe Systems 131 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Adobe Systems 132 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Greater „dismissability” Adobe Systems 133 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Greater „dismissability” Women’s greater preference for flexibility and part-time work Adobe Systems 134 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Greater „dismissability” Women’s greater preference for flexibility and part-time work Adobe Systems 135 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Greater „dismissability” Women’s greater preference for flexibility and part-time work Less work-life balance Adobe Systems 136 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Greater „dismissability” Women’s greater preference for flexibility and part-time work Less work-life balance Less wellbeing Adobe Systems 137 Conclusions ̶ Differential treatment? Women Direct discrimination Lower wages Men Implicit biases Backlash effect Lower likeability Greater „dismissability” Women’s greater preference for flexibility and part-time work Less work-life balance Less wellbeing Developmental issues