Uncovering Gender Bias in Talent Shows: Insights from The Voice

Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Table of Links Abstract Background Data Methodology Results Discussion and Conclusion, and References A Appendix Tables and Figures 5 Discussion and Conclusion In this paper, we examine whether the gender of the coach and artist matters when it comes to selecting the performance in the blind audition stage in the Voice TV show. Results provide strong evidence that gender matters and I show that coaches choose other gender artists 12 percent more when they hear similar performances in the audition. The results are robust with many variations, such as coach and artistspecific covariates. Under the interpretation of opposite-gender bias, the results would be due to male and/or female coaches being either too lenient to opposite-gender artists, being too tough on own-gender artists, or both. Relatedly, a potential mechanism through which opposite-gender interaction cause statistically significant gender bias could be employers hiring for female-dominated occupations showing a preference for male applicants (Weichselbaumer, 2004; Protsch, 2021). However, Weichselbaumer (2004) and Protsch (2021)’s research also found that employers preferred male applicants in male-dominated occupations. Still, because of data limitations, they do not observe the gender of the recruiter. Others who do not observe what I observe in my work found that employers prefer male applicants in male-dominated occupations and female applicants in female-dominated occupations (Campero and Fernandez, 2018; Benard and Correll, 2010; Glick et al., 1988; Ridgeway and Correll, 2004). These works provide robust evidence of gender bias in hiring. Still, we cannot distinguish whether it is an opposite-gender or own-gender bias hiring since the gender of the decision-makers is unobservable or decisions are made collectively. In my setting, I observe the gender of the coach and the coach’s individual choice in selecting the artists. Therefore I incorporate this gender composition feature into my setting to address whether the coach changes their behavior based on the gender composition in the team. For example, If one coach’s team is dominated by one gender, would the coach shift behavior during the audition and select an artist from another gender, a minority gender? I conduct a heterogeneity analysis assessing the effect of different gender compositions in the team on the likelihood of being selected. To do so, I took advantage of the fact that the sample I used includes both selection of male and female coaches in the blind audition stages and if the artist ended up being in the team of the selected coach. Additionally, the other advantage of the sample is substantial variations in gender composition in each team, including both male-dominated and female-dominated settings. Typically, we observe only one type of setting, where the firm is femaledominated or male-dominated. Leveraging this variation in the gender composition, I estimate the heterogeneous effect of gender bias for male and female coaches. I found that male artists have an advantage over female artists in a male-dominated team when the coach is male. Likewise, women have an advantage over men in a female-dominated team. When the coach is female, we can see opposite scenarios. Female coaches tend to choose women over men in male-dominated teams and men over women in the female-dominated team. This difference is partially statistically significant, but the magnitude remains consistent. The literature provides robust support for a hypothesis on group formation dynamics, which is different for male and female leaders. One study of bias in other contexts suggests that men might not respond favorably to the presence of gender diversity, particularly in domains that men have historically dominated. Also, genderrelated issues were most likely to be raised in groups where women are a minority or where the female is a leader (Crocker and McGraw, 1984). In addition, there is substantial evidence that women in team formation care about equality, men care about efficiency, and men consider male or female dominance in the team as more efficient. More directly, Kuhn and Villeval (2014) found that women respond differently to alternative rules for team formation in a manner consistent with advantageous inequity aversion. In contrast, men show greater responsiveness to efficiency gains associated with team production, meaning that when forming a team, men make their selections based on who will be the most efficient rather than on the grounds of gender equality. Overall, our findings indicate that gender composition and the gender of the decisionmaker are important determinants in the hiring process. While it is difficult to know if these findings extend to other industries, these results corroborate the debate over gender discrimination in the hiring process. In conclusion, the results add evidence to a growing literature testing own or opposite gender bias in decision-making. This finding provides insights into the extent to which situational factors work to mitigate or exacerbate gender disparities in the hiring process. I show strong evidence of the heterogeneity in gender bias from gender composition in the team. This evidence suggests that the selection process is primarily rooted in the female and male recruiters’ behavioral preferences of inequality or efficiency. In addition, the findings offer a new perspective to enrich past research on gender discrimination, shedding light on the instances of gender bias variation by the gender of the decision maker and team gender composition. References Athey, Susan and Guido Imbens, “Recursive partitioning for heterogeneous causal effects,” Proceedings of the National Academy of Sciences, July 2016, 113 (27), 7353–7360. and Stefan Wager, “Estimating Treatment Effects with Causal Forests: An Application,” Observational Studies, 2019, 5 (2), 37–51. Baert, Stijn, “Field Experimental Evidence on Gender Discrimination in Hiring: Biased as Heckman and Siegelman Predicted?,” Economics, August 2015, 9 (1). “Hiring Discrimination: An Overview of (Almost) All Correspondence Experiments Since 2005,” in “Audit Studies: Behind the Scenes with Theory, Method, and Nuance,” Springer International Publishing, 2018, pp. 63–77. Bagues, Manuel, Mauro Sylos-Labini, and Natalia Zinovyeva, “Does the Gender Composition of Scientific Committees Matter?,” American Economic Review, April 2017, 107 (4), 1207–1238. Bagues, ManuelL F. and Berta Esteve-Volart, “Can Gender Parity Break the Glass Ceiling? Evidence from a Repeated Randomized Experiment,” Review of Economic Studies, August 2010, 77 (4), 1301–1328. Benard, Stephen and Shelley J. Correll, “Normative Discrimination and the Motherhood Penalty,” Gender & Society, September 2010, 24 (5), 616–646. Bertrand, M. and E. Duflo, “Field Experiments on Discrimination,” in “Handbook of Field Experiments,” Elsevier, 2017, pp. 309–393. Blau, Francine D. and Lawrence M. Kahn, “The Gender Pay Gap: Have Women Gone as Far as They Can?,” Academy of Management Perspectives, 2007, 21 (1), 7–23. Bonander, Carl and Mikael Svensson, “Using causal forests to assess heterogeneity in cost-effectiveness analysis,” Health Economics, May 2021, 30 (8), 1818–1832. Booth, Alison and Andrew Leigh, “Do employers discriminate by gender? A field experiment in female-dominated occupations,” Economics Letters, May 2010, 107 (2), 236–238. Breiman, Leo, Jerome H Friedman, Richard A Olshen, and Charles J Stone, Classification and regression trees, Routledge, 1984. Campero, Santiago and Roberto M Fernandez, “Gender Composition of Labor Queues and Gender Disparities in Hiring,” Social Forces, October 2018, 97 (4), 1487–1516. Carlsson, Magnus and Stefan Eriksson, “Age discrimination in hiring decisions: Evidence from a field experiment in the labor market,” Labour Economics, August 2019, 59, 173–183. Crocker, Jennifer and Kathleen M. McGraw, “What's Good for the Goose Is Not Good for the Gander,” American Behavioral Scientist, January 1984, 27 (3), 357–369. Glick, Peter, Cari Zion, and Cynthia Nelson, “What mediates sex discrimination in hiring decisions?,” Journal of Personality and Social Psychology, August 1988, 55 (2), 178–186. Goldin, Claudia and Cecilia Rouse, “Orchestrating Impartiality: The Impact of “Blind” Auditions on Female Musicians,” American Economic Review, September 2000, 90 (4), 715–741. Kuhn, Peter and Marie Claire Villeval, “Are Women More Attracted to Cooperation Than Men?,” The Economic Journal, April 2014, 125 (582), 115–140. Neumark, David, “Detecting Discrimination in Audit and Correspondence Studies,” Journal of Human Resources, 2012, 47 (4), 1128–1157. “Experimental Research on Labor Market Discrimination,” Journal of Economic Literature, September 2018, 56 (3), 799–866. Price, J. and J. Wolfers, “Racial Discrimination Among NBA Referees,” The Quarterly Journal of Economics, November 2010, 125 (4), 1859–1887. Protsch, Paula, “Employers’ recruitment contexts and hiring preferences in the German youth labor market,” Research in Social Stratification and Mobility, June 2021, 73, 100608. Ridgeway, Cecilia L. and Shelley J. Correll, “Unpacking the Gender System,” Gender & Society, August 2004, 18 (4), 510–531. Rudman, Laurie A. and Stephanie A. Goodwin, “Gender Differences in Automatic In-Group Bias: Why Do Women Like Women More Than Men Like Men?,” Journal of Personality and Social Psychology, 2004, 87 (4), 494–509. Weichselbaumer, Doris, “Is It Sex or Personality? The Impact of Sex Stereotypes on Discrimination in Applicant Selection,” Eastern Economic Journal, Spring 2004, 30 (2), 159–186. White, Jessica, “A Full Breakdown of How The Voice Works,” https://www.nbc.com/nbc-insider/how-does-the-voice-work-everything-to-knowabout-the-format 2022. Accessed: 2022-09-19. This paper is available on arxiv under CC 4.0 license. Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Author: Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Table of Links Abstract Abstract Background Background Data Data Methodology Methodology Results Results Discussion and Conclusion, and References Discussion and Conclusion, and References A Appendix Tables and Figures A Appendix Tables and Figures 5 Discussion and Conclusion In this paper, we examine whether the gender of the coach and artist matters when it comes to selecting the performance in the blind audition stage in the Voice TV show. Results provide strong evidence that gender matters and I show that coaches choose other gender artists 12 percent more when they hear similar performances in the audition. The results are robust with many variations, such as coach and artistspecific covariates. Under the interpretation of opposite-gender bias, the results would be due to male and/or female coaches being either too lenient to opposite-gender artists, being too tough on own-gender artists, or both. Relatedly, a potential mechanism through which opposite-gender interaction cause statistically significant gender bias could be employers hiring for female-dominated occupations showing a preference for male applicants (Weichselbaumer, 2004; Protsch, 2021). However, Weichselbaumer (2004) and Protsch (2021)’s research also found that employers preferred male applicants in male-dominated occupations. Still, because of data limitations, they do not observe the gender of the recruiter. Others who do not observe what I observe in my work found that employers prefer male applicants in male-dominated occupations and female applicants in female-dominated occupations (Campero and Fernandez, 2018; Benard and Correll, 2010; Glick et al., 1988; Ridgeway and Correll, 2004). These works provide robust evidence of gender bias in hiring. Still, we cannot distinguish whether it is an opposite-gender or own-gender bias hiring since the gender of the decision-makers is unobservable or decisions are made collectively. In my setting, I observe the gender of the coach and the coach’s individual choice in selecting the artists. Therefore I incorporate this gender composition feature into my setting to address whether the coach changes their behavior based on the gender composition in the team. For example, If one coach’s team is dominated by one gender, would the coach shift behavior during the audition and select an artist from another gender, a minority gender? I conduct a heterogeneity analysis assessing the effect of different gender compositions in the team on the likelihood of being selected. To do so, I took advantage of the fact that the sample I used includes both selection of male and female coaches in the blind audition stages and if the artist ended up being in the team of the selected coach. Additionally, the other advantage of the sample is substantial variations in gender composition in each team, including both male-dominated and female-dominated settings. Typically, we observe only one type of setting, where the firm is femaledominated or male-dominated. Leveraging this variation in the gender composition, I estimate the heterogeneous effect of gender bias for male and female coaches. I found that male artists have an advantage over female artists in a male-dominated team when the coach is male. Likewise, women have an advantage over men in a female-dominated team. When the coach is female, we can see opposite scenarios. Female coaches tend to choose women over men in male-dominated teams and men over women in the female-dominated team. This difference is partially statistically significant, but the magnitude remains consistent. The literature provides robust support for a hypothesis on group formation dynamics, which is different for male and female leaders. One study of bias in other contexts suggests that men might not respond favorably to the presence of gender diversity, particularly in domains that men have historically dominated. Also, genderrelated issues were most likely to be raised in groups where women are a minority or where the female is a leader (Crocker and McGraw, 1984). In addition, there is substantial evidence that women in team formation care about equality, men care about efficiency, and men consider male or female dominance in the team as more efficient. More directly, Kuhn and Villeval (2014) found that women respond differently to alternative rules for team formation in a manner consistent with advantageous inequity aversion. In contrast, men show greater responsiveness to efficiency gains associated with team production, meaning that when forming a team, men make their selections based on who will be the most efficient rather than on the grounds of gender equality. Overall, our findings indicate that gender composition and the gender of the decisionmaker are important determinants in the hiring process. While it is difficult to know if these findings extend to other industries, these results corroborate the debate over gender discrimination in the hiring process. In conclusion, the results add evidence to a growing literature testing own or opposite gender bias in decision-making. This finding provides insights into the extent to which situational factors work to mitigate or exacerbate gender disparities in the hiring process. I show strong evidence of the heterogeneity in gender bias from gender composition in the team. This evidence suggests that the selection process is primarily rooted in the female and male recruiters’ behavioral preferences of inequality or efficiency. In addition, the findings offer a new perspective to enrich past research on gender discrimination, shedding light on the instances of gender bias variation by the gender of the decision maker and team gender composition. References Athey, Susan and Guido Imbens , “Recursive partitioning for heterogeneous causal effects,” Proceedings of the National Academy of Sciences, July 2016, 113 (27), 7353–7360. Athey, Susan and Guido Imbens and Stefan Wager , “Estimating Treatment Effects with Causal Forests: An Application,” Observational Studies, 2019, 5 (2), 37–51. Stefan Wager Baert, Stijn, “Field Experimental Evidence on Gender Discrimination in Hiring: Biased as Heckman and Siegelman Predicted?,” Economics, August 2015, 9 (1). Baert, Stijn, “Hiring Discrimination: An Overview of (Almost) All Correspondence Experiments Since 2005,” in “Audit Studies: Behind the Scenes with Theory, Method, and Nuance,” Springer International Publishing, 2018, pp. 63–77. Bagues, Manuel, Mauro Sylos-Labini, and Natalia Zinovyeva , “Does the Gender Composition of Scientific Committees Matter?,” American Economic Review, April 2017, 107 (4), 1207–1238. Bagues, Manuel, Mauro Sylos-Labini, and Natalia Zinovyeva Bagues, ManuelL F. and Berta Esteve-Volart, “Can Gender Parity Break the Glass Ceiling? Evidence from a Repeated Randomized Experiment,” Review of Economic Studies, August 2010, 77 (4), 1301–1328. Bagues, ManuelL F. and Berta Esteve-Volart, Benard, Stephen and Shelley J. Correll, “Normative Discrimination and the Motherhood Penalty,” Gender & Society, September 2010, 24 (5), 616–646. Benard, Stephen and Shelley J. Correll, Bertrand, M. and E. Duflo, “Field Experiments on Discrimination,” in “Handbook of Field Experiments,” Elsevier, 2017, pp. 309–393. Bertrand, M. and E. Duflo, Blau, Francine D. and Lawrence M. Kahn, “The Gender Pay Gap: Have Women Gone as Far as They Can?,” Academy of Management Perspectives, 2007, 21 (1), 7–23. Blau, Francine D. and Lawrence M. Kahn, Bonander, Carl and Mikael Svensson, “Using causal forests to assess heterogeneity in cost-effectiveness analysis,” Health Economics, May 2021, 30 (8), 1818–1832. Bonander, Carl and Mikael Svensson, Booth, Alison and Andrew Leigh, “Do employers discriminate by gender? A field experiment in female-dominated occupations,” Economics Letters, May 2010, 107 (2), 236–238. Booth, Alison and Andrew Leigh, Breiman, Leo, Jerome H Friedman, Richard A Olshen, and Charles J Stone, Classification and regression trees, Routledge, 1984. Breiman, Leo, Jerome H Friedman, Richard A Olshen, and Charles J Stone, Campero, Santiago and Roberto M Fernandez, “Gender Composition of Labor Queues and Gender Disparities in Hiring,” Social Forces, October 2018, 97 (4), 1487–1516. Campero, Santiago and Roberto M Fernandez, Carlsson, Magnus and Stefan Eriksson, “Age discrimination in hiring decisions: Evidence from a field experiment in the labor market,” Labour Economics, August 2019, 59, 173–183. Carlsson, Magnus and Stefan Eriksson, Crocker, Jennifer and Kathleen M. McGraw, “What's Good for the Goose Is Not Good for the Gander,” American Behavioral Scientist, January 1984, 27 (3), 357–369. Crocker, Jennifer and Kathleen M. McGraw, Glick, Peter, Cari Zion, and Cynthia Nelson, “What mediates sex discrimination in hiring decisions?,” Journal of Personality and Social Psychology, August 1988, 55 (2), 178–186. Glick, Peter, Cari Zion, and Cynthia Nelson, Goldin, Claudia and Cecilia Rouse, “Orchestrating Impartiality: The Impact of “Blind” Auditions on Female Musicians,” American Economic Review, September 2000, 90 (4), 715–741. Goldin, Claudia and Cecilia Rouse, Kuhn, Peter and Marie Claire Villeval, “Are Women More Attracted to Cooperation Than Men?,” The Economic Journal, April 2014, 125 (582), 115–140. Kuhn, Peter and Marie Claire Villeval, Neumark, David , “Detecting Discrimination in Audit and Correspondence Studies,” Journal of Human Resources, 2012, 47 (4), 1128–1157. Neumark, David “Experimental Research on Labor Market Discrimination,” Journal of Economic Literature, September 2018, 56 (3), 799–866. Price, J. and J. Wolfers, “Racial Discrimination Among NBA Referees,” The Quarterly Journal of Economics, November 2010, 125 (4), 1859–1887. Price, J. and J. Wolfers, Protsch, Paula, “Employers’ recruitment contexts and hiring preferences in the German youth labor market,” Research in Social Stratification and Mobility, June 2021, 73, 100608. Protsch, Paula, Ridgeway, Cecilia L. and Shelley J. Correll, “Unpacking the Gender System,” Gender & Society, August 2004, 18 (4), 510–531. Ridgeway, Cecilia L. and Shelley J. Correll, Rudman, Laurie A. and Stephanie A. Goodwin, “Gender Differences in Automatic In-Group Bias: Why Do Women Like Women More Than Men Like Men?,” Journal of Personality and Social Psychology, 2004, 87 (4), 494–509. Rudman, Laurie A. and Stephanie A. Goodwin, Weichselbaumer, Doris, “Is It Sex or Personality? The Impact of Sex Stereotypes on Discrimination in Applicant Selection,” Eastern Economic Journal, Spring 2004, 30 (2), 159–186. Weichselbaumer, Doris, White, Jessica , “A Full Breakdown of How The Voice Works,” https://www.nbc.com/nbc-insider/how-does-the-voice-work-everything-to-knowabout-the-format 2022. Accessed: 2022-09-19. White, Jessica This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv