SBP: Collaborative Research: Gender Discrimination in Hiring for STEM Graduates

Abstract

This interdisciplinary project will use cutting-edge technology to study the labor market for computer science graduates in Science, Technology, Engineering, and Mathematics (STEM) fields. Although women?s share in STEM employment has been growing in non-computer science occupations, their share in computer science occupations has been declining since the 1990s. Because computer science occupations account for 50% of STEM workers, this decline is slowing the growth of women?s share in STEM fields overall, and suggests significant untapped potential that could improve US productivity and competitiveness. One reason that women may not seek out or remain in computer science fields is that they are treated differently than men during the hiring process.

This project uses a laboratory experiment in the field on first-line hiring managers to determine first if there is differential treatment of women in hiring recent computer science graduates. If there is such a difference, it will determine the characteristics of women who are more likely to be treated negatively as well as general characteristics of firms that are more likely to exhibit differential treatment. Resumes will be randomly generated to include different characteristics that, if their inclusion helps women more than men, will indicate potential reasons for this differential treatment. The experiment will also use eye-tracking to determine how recruiters visually process computer science resumes and whether or not there are differences between how they process male vs. female resumes. These combined results will help to differentiate between economic theories of discrimination, and will advance social science by increasing our theoretical understanding of when and how differential treatment occurs. Results from this study can be used to make recommendations to individuals applying for these positions and institutions which advise them, to employers who desire to hire the best candidates, and to policy makers who want to increase meritocratic hiring in STEM. The results will thus lead to a more diverse and competitive workforce, increasing the economic competitiveness of the U.S.

This project combines two cutting-edge methodologies, eye-tracking and resume-randomization, to study gender discrimination at the first stage of the STEM hiring process. It will determine if there is differential treatment by gender in how first-line hiring managers treat resumes, whether the treatment is similar or different along the applicant quality distribution, and if there are industry characteristics (ex. firm size, industry code) that would lead to higher or lower levels of differential treatment. Finally, this study will differentiate between different theories of statistical and taste-based discrimination.

Technical recruiters in charge of first-line interview decisions will be solicited at university recruitment fairs and industry fairs to view and process hypothetical resumes for Computer Science majors. They will be asked to follow their standard hiring practice and to choose resumes to ?move to the next stage.? The resumes will then be redisplayed and participants will rate each resume and give the expected starting salary and position. While participants are viewing the resumes, their eye-movements will be tracked via an eye-tracking device. Following the resume rating exercise, they will answer a short demographic survey.

Resumes with randomized inputs based on actual resumes will be created via a randomization program. Outcomes of interest include information on ratings, moving the resume to the next stage, position placement, salary ranges, time spent on individual resumes, time spent on and number of looks at specific parts of resumes. The coefficients and significance on the coefficient of gender determine whether or not there is differential treatment by gender, and if so, for which women and by what kinds of firms. Time spent on resumes by gender interacted with differential treatment findings provide information on use of heuristics in the decision-making process. Time spent viewing specific parts of the resume (areas of interest or AOI) and tracking the order that recruiters view parts of the resume provide insight into their decision-making processes. Gender interactions with randomized inputs that support or contradict stereotypes will be used to test employee taste-based discrimination and levels-based statistical discrimination. Position placement by gender will test customer taste-based discrimination. Comparing predicted outcomes with actual outcomes by gender of resume be used to test variance-based statistical discrimination.

This project directly impacts the full participation of women in STEM and will (1) improve the well-being of individuals in society, (2) develop a diverse and competitive workforce and (3) increase economic competitiveness. Results from this study can be used to make recommendations to individuals applying for these positions and the institutions who advise them, to employers who desire to hire the best candidates, and to policy makers who want more women and minorities in STEM. The methodology will (4) promote future research on other hiring and discrimination questions. In addition, this project will (5) incorporate graduate and undergraduate students, involving them in cutting-edge research and providing them with a platform to undertake their own independent work. Graduate and undergraduate students will receive mentoring and research skills, increasing their attractiveness to employers and advanced degree programs.