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

这个跨学科的项目将使用尖端技术来研究计算机科学、技术、工程和数学(STEM)领域的毕业生的劳动力市场。尽管女性S在非计算机科学类职业中的STEM就业比例一直在上升，但自20世纪90年代以来，她们在计算机科学类职业中的比例一直在下降。由于计算机科学职业占STEM工人的50%，这一下降正在减缓女性在STEM领域的增长--S在STEM领域的总体份额，并表明了巨大的未开发潜力，可能会提高美国的生产率和竞争力。女性可能不会寻求或留在计算机科学领域的一个原因是，在招聘过程中，她们受到了与男性不同的对待。该项目使用现场一线招聘经理的实验室实验，首先确定在聘用应届计算机科学毕业生时，是否存在对女性的差别待遇。如果存在这样的差异，它将决定更有可能受到负面对待的女性的特征，以及更有可能表现出差别待遇的公司的一般特征。简历将随机生成，包括不同的特征，如果这些特征对女性比男性更有帮助，将表明这种差别待遇的潜在原因。这项实验还将使用眼球跟踪来确定招聘人员如何在视觉上处理计算机专业的简历，以及他们处理男性和女性简历的方式是否存在差异。这些综合结果将有助于区分歧视的经济学理论，并将通过增加我们对差别待遇何时发生以及如何发生的理论理解，促进社会科学的发展。这项研究的结果可以用来向申请这些职位的个人和机构提出建议，向希望聘用最佳候选人的雇主，以及希望在STEM增加精英招聘的政策制定者提供建议。因此，结果将带来更多样化和更具竞争力的劳动力，增加美国的经济竞争力。该项目结合了眼球跟踪和简历随机化两种尖端方法，研究STEM招聘过程第一阶段的性别歧视。它将确定在一线招聘经理处理简历的方式上是否存在性别差异处理，处理沿着申请者质量分布是相似还是不同，以及是否有行业特征(例如。公司规模、行业代码)，这将导致更高或更低水平的差别待遇。最后，这项研究将区分统计学和品味歧视的不同理论。大学招聘会和行业博览会上将邀请负责一线面试决策的技术招聘人员查看和处理计算机专业的假设简历。他们将被要求遵循他们的标准招聘做法，并选择简历进入下一阶段。然后，简历将被重新显示，参与者将对每份简历进行评分，并给出预期的起薪和职位。当参与者查看简历时，他们的眼球运动将通过眼球跟踪设备进行跟踪。在简历评分练习之后，他们将回答一个简短的人口统计调查。根据实际简历随机输入的结果将通过随机化程序创建。感兴趣的结果包括有关评级、将简历移至下一阶段、职位安置、薪资范围、个人简历花费的时间、花费在简历特定部分的时间和查看次数的信息。性别系数的系数和重要性决定了是否存在性别差别待遇，如果有，哪些妇女和什么类型的公司受到差别待遇。按性别分列的简历所花费的时间与差别处理结果相互作用提供了在决策过程中使用启发式方法的信息。花在查看简历特定部分(感兴趣的领域或AOI)上的时间，以及跟踪招聘人员查看简历部分的顺序，可以让招聘人员深入了解他们的决策过程。支持或反对刻板印象的随机输入的性别互动将被用来测试基于员工品味的歧视和基于水平的统计歧视。按性别安排职位将测试客户基于品味的歧视。该项目将直接影响妇女在STEM中的充分参与，并将(1)改善社会个人的福祉，(2)发展多样化和有竞争力的劳动力队伍，(3)提高经济竞争力。这项研究的结果可以用来向申请这些职位的个人和为他们提供建议的机构、希望聘用最佳候选人的雇主以及希望更多妇女和少数族裔进入STEM的政策制定者提出建议。该方法将(4)促进未来对其他招聘和歧视问题的研究。此外，该项目将(5)吸收研究生和本科生，让他们参与前沿研究，并为他们提供承担自己独立工作的平台。研究生和本科生将获得指导和研究技能，增加他们对雇主和高级学位课程的吸引力。

AbstractThis 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.