EAGER: Leveling the Digital Playing Field for the Job Seeker

该项目旨在评估在线数据如何影响招聘流程。在理想的情况下，人们可能会想象雇主会雇佣最有技能的应聘者，但社会学研究表明，情况可能并非如此。对于雇主来说，应聘者与面试官在班级背景和班级休闲活动方面的相似性往往与应聘者的技能或工作经验同样重要，甚至更重要。招聘人员或雇主从看似无关的数据中获取此类信息的能力，导致研究人员表达了对隐私、工作相关性和潜在歧视的担忧。这是一个更大的项目的第一阶段，该项目旨在阐明在线信息如何影响经济衰退后的美国求职者找工作的能力。该项目建立了一个框架，以确定特定区域就业市场的系统性歧视模式，并提供了一个更全面的图景，准确地说明在招聘过程中，某些形式的歧视可能在什么时候发生(在提交简历、面试阶段等)。此外，收集的数据将使求职者能够发现在线信息如何影响他们的就业能力，并帮助制定战略，使在线数据与专业概况保持一致。研究人员将开发一种新的混合方法框架，通过结合对雇主和公司的人种学研究(汇总求职者个人资料)、对应聘者背景、技能和求职历史的调查、在线个人资料汇总和传统数据挖掘技术，更好地了解招聘过程和研究招聘歧视。该项目将有助于了解美国就业是如何运作的，以及可能限制就业能力的在线信息类型。这个问题将在人口结构和技能集各不相同、主要行业差异很大的人群中解决。拟议的分析将捕捉特定地区的招聘做法，并揭示不同地区对求职者有利或不利的人口统计指标的类型。

This project aims to assess how online data impacts the hiring process. In an ideal situation, one might imagine that employers hire the most skilled applicant, but sociological research indicates that this may not be the case. A job applicant's similarity to the interviewer in class background and class-based leisure activities often matters as much or more to employers than a job applicant's skills or work experience. The ability of a recruiter or employer to learn such information from seemingly unrelated data has led researchers to express concerns about privacy, job relevance, and the potential for discrimination. This is the first stage in a larger project that aims to illuminate how online information impacts the ability of job seekers to find employment in post-recession United States. The project creates a framework for identifying systematic patterns of discrimination in regionally specific job markets, and also provide a fuller picture of precisely when in the hiring process are certain forms of discrimination likely to take place (upon submission of resume, at interview stage, and so on). In addition, the collected data will enable job seekers to discover how online information affects their employability, and aid the development of strategies to align online data and professional profiles. The researchers will develop a novel mixed-methods framework to better understand the hiring process and study hiring discrimination by combining ethnographic studies of employers and companies that aggregate applicant profiles; surveys of applicants' background, skillset, and job-seeking history; online profile aggregation; and traditional data mining techniques. This project will contribute to the understanding of how employment works in the United States, and the types of online information that may limit employability. The problem will be addressed across populations that have varying demographic profiles and skill sets, and whose primary industries vary greatly. The proposed analysis will capture regionally specific hiring practices and reveal insights into the kinds of demographic indicators that work for or against job seekers in different regions.