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

SBP：合作研究：STEM 毕业生招聘中的性别歧视

• 批准号: 1658758
• 负责人: Gerianne Alexander
• 金额: $ 34.14万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1658758&HistoricalAwards=false
• 关键词: SBP; Collaborative; Research; Gender; Discrimination

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.

AbstractThis跨学科项目将使用尖端技术来研究科学，技术，工程和数学（STEM）领域的计算机科学毕业生的劳动力市场。 虽然女人？尽管美国在STEM就业中的份额在非计算机科学职业中一直在增长，但自20世纪90年代以来，他们在计算机科学职业中的份额一直在下降。 由于计算机科学职业占STEM工作者的50%，这种下降正在减缓女性的增长？美国在STEM领域的总体份额，并表明巨大的未开发潜力，可以提高美国的生产力和竞争力。 妇女可能不寻求或留在计算机科学领域的一个原因是，在招聘过程中，她们受到的待遇与男子不同，本项目利用该领域一线招聘经理的实验室实验，首先确定在招聘最近的计算机科学毕业生时，是否存在对妇女的差别待遇。如果存在这种差别，它将决定更可能受到负面待遇的妇女的特点以及更可能表现出差别待遇的公司的一般特点。 简历将随机生成，以包括不同的特征，如果这些特征对女性的帮助大于男性，将表明这种差别待遇的潜在原因。该实验还将使用眼动追踪来确定招聘人员如何在视觉上处理计算机科学简历，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理论，并通过增加我们对何时以及如何发生差别待遇的理论理解来推进社会科学。这项研究的结果可用于向申请这些职位的个人和为他们提供建议的机构，希望雇用最佳候选人的雇主以及希望增加STEM精英招聘的政策制定者提出建议。 该项目结合了两种尖端方法，眼动追踪和简历随机化，以研究STEM招聘过程的第一阶段的性别歧视。它将确定一线招聘经理如何对待简历，是否存在性别差异待遇，待遇是否与申请人质量分布沿着，以及是否存在行业特征（例如，公司规模、行业守则），这将导致更高或更低程度的差别待遇。 最后，本研究将区分不同的统计和品味歧视理论。负责一线面试决策的技术招聘人员将在大学招聘会和行业展览会上被要求查看和处理计算机科学专业的假设简历。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。当参与者正在查看简历时，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。基于实际简历的随机输入简历将通过随机程序创建。感兴趣的结果包括有关评级的信息，将简历移动到下一阶段，职位安排，工资范围，在个人简历上花费的时间，在简历特定部分上花费的时间和观看次数。性别系数和性别系数的重要性决定了是否存在性别差别待遇，如果存在，则决定了哪些妇女和哪些类型的公司受到差别待遇。按性别分列的简历所用时间与差别待遇相互作用的调查结果提供了关于决策过程中使用技能的信息。查看简历的特定部分（感兴趣的领域或AOI）和跟踪招聘人员查看简历部分的顺序所花费的时间可以深入了解他们的决策过程。性别互动与随机输入，支持或反对刻板印象将被用来测试员工的品味为基础的歧视和水平为基础的统计歧视。按性别进行的职位安排将测试基于客户品味的歧视。将预测结果与简历性别的实际结果进行比较，以检验基于方差的统计歧视。该项目直接影响妇女在STEM中的全面参与，并将（1）改善个人在社会中的福祉，（2）发展多元化和有竞争力的劳动力，（3）提高经济竞争力。这项研究的结果可用于向申请这些职位的个人和为他们提供建议的机构，希望雇用最佳候选人的雇主以及希望更多女性和少数民族参与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，并为他们提供一个平台，让他们独立开展工作。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。