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Race- and Class-Based Discrimination in the U.S. Labor Market

Race- and Class-Based Discrimination in the U.S. Labor Market
美国劳动力市场中的种族和阶级歧视
批准号:
1729891
负责人:
Kyla Thomas
金额:
$23.5万
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
项目状态:
已结题
起止时间:
2017-07-01 至 2021-06-30

项目摘要

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中文摘要
翻译
种族歧视仍然是美国劳动力市场平等的最大障碍之一。黑人男性获得面试回电的可能性只有白色男性的一半,他们的收入仅为白色男性收入中位数的70%。研究人员和政策制定者面临的挑战是理解为什么雇主歧视黑人求职者。为了解释这种歧视,社会学家通常指出雇主的种族敌意和/或陈规定型观念。经常被忽视的是美国黑人在求职时也会遇到微妙的阶级偏见。在美国,种族和社会阶级的概念深深地交织在一起。定性研究表明,雇主对美国黑人的负面期望往往是基于他们是下层阶级的假设,即,她们获得物质和象征性资源的机会有限,如收入、教育和高雅文化。 然而,种族歧视的模式是否与基于阶级的歧视相互交织,是否可以部分地解释为工作歧视,尚待证明。社会科学文献继续忽视什么可能是种族不平等的一个关键来源在美国本项目旨在解决当前的理论和研究,通过社会阶级偏见,驱动种族歧视在美国的实地实验研究这一差距就业。 首先,它旨在衡量当求职者在简历中表示几乎相同(更高或更低)的阶级起源时,雇主回拨率的种族差异是否会缩小。其次,它旨在衡量回拨率的种族差距是否在女性中比男性更大程度地缩小,这表明女性的阶级出身相似。第三,它旨在衡量回拨率的种族差距是否因申请人在简历中显示的较高或较低的社会出身而有所不同。 为了实现这些研究目标,首席调查员将对美国的就业歧视进行一项基于简历的计算机化审计研究,发送8 000份虚构简历和求职信,申请12个主要城市的4 000个职位空缺。雇主将每隔一天收到两份简历:一份简历表明申请人是黑人,另一份简历表明申请人是白色。两份简历都将表明相同的性别和相同的社会阶级出身:较高阶级出身、较低阶级出身或控制出身(没有明确的阶级标志)。类出身的相似性将通过(1)课外活动和(2)大学奖项申请人在简历中列出的相似性来表示。然后,主要调查员将跟踪雇主对提交的简历的答复,并根据这些答复,衡量雇主对白色和黑人申请人的回拨率如何根据简历中的阶级信号而有所不同。
英文摘要
Racial discrimination remains one of the most formidable obstacles to labor market equality in the United States. Black men are only half as likely as white men with similar qualifications to receive a callback for a job interview and they earn just 70 percent of the median income of their white counterparts. The challenge for researchers and policymakers is to understand why employers discriminate against black job applicants. To account for this discrimination, sociologists typically point to racial animus and/or stereotyping on the part of employers. Often overlooked are the subtle forms of class bias also encountered by black Americans as they apply for jobs. In the U.S., conceptions of race and social class are deeply intertwined. Qualitative studies show employers' negative expectations for black Americans often are predicated on the assumption they are lower class; i.e., they have limited access to material and symbolic resources like income, education and high culture. However, whether patterns of racial discrimination intersect with class-based discrimination and may partially explained job discrimination is yet to be demonstrated. The social science literature continues to ignore what may be a key source of racial inequality in the U.S. This project aims to address this gap in current theory and research through a field-experimental study of the social class biases that drive racial discrimination in U.S. employment.This study has three specific research aims. First, it aims to measure whether the racial gap in rates of employer callback diminishes when job applicants signal near-identical (higher or lower) class origins in their résumés. Second, it aims to measure whether the racial gap in callback rates diminishes to a greater degree among women signaling a similar class origin than among men. Third, it aims to measure whether the racial gap in callback rates varies according to the higher- or lower-class origins signaled by applicants in their résumés. To address these research aims, the Principal Investigator will conduct a computerized, résumé-based audit study of employment discrimination in the United States, sending 8,000 fictitious résumés and cover letters to apply for 4,000 job openings in 12 major cities. Employers will be sent, a day apart, two résumés: one résumé signaling that the applicant is black and one résumé signaling that the applicant is white. Both résumés will signal the same gender and the same social-class origin: a higher-class origin, a lower-class origin, or a control origin (with no explicit class signal). Similarity in class origin will be signaled through likenesses in the (1) extracurricular activities and (2) college awards applicants list in their résumés. The Principal Investigator will then track employers' responses to the submitted résumés and, based on these replies, measure how rates of employer callback for white and black applicants vary according to the class signals included in their résumés.
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