Doctoral Dissertation Research in Economics and Public Policy: Employer Preference for Applicants based on Geographic Location - A Resume Audit

经济学与公共政策博士论文研究：基于地理位置的雇主对申请人的偏好 - 简历审核

• 批准号: 2018750
• 负责人: Brian Kovak
• 金额: $ 3.9万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018750&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Economics; Public

AbstractThere is significant and persistent heterogeneity in unemployment rates and labor earnings across labor markets in the US, contributing to overall economic inequality. Developments since the 1990s indicate that migration has declined even as job postings through websites expanded access to and awareness of employment opportunities beyond a worker’s location of origin. This project explores the geographic aspects of online job postings by exploring access to job opportunities for job seekers who are local and non-local. The research investigates employer preference for job applicants by considering whether applicants reside close by the job site or farther away. The project conducts a correspondence experiment that randomly assigns the resumes of fictitious candidates with different geographical locations to employers who are actively seeking employees. The project examines employer callback rates by considering the distance of candidates from job locations to understand differential employer preference for employees’ location of origin. The project further constructs a descriptive and mathematical theory for why employers may prefer local workers over non-local workers.Residents of depressed local labor markets often incur welfare costs associated with remaining in these areas, and migration in favor of expected income gains may serve as an important mechanism for equalization across regions. This research project investigates labor market opportunities in relation to the geographic origin of job applicants. The project employs a resume audit study to understand employers’ hiring preferences in relation to spatial factors by randomly assigning fictitious applicants to job postings, by reflecting varying locations of residence, level of education, and gender of applicants. The project develops a theoretical model that rationalizes potential mechanisms for why employers engage in place-based discrimination and prefer local applicants. By embedding these mechanisms in the resume audit research design, the project explores heterogeneous effects of applicant location on employer hiring preferences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

摘要：美国各个劳动力市场的失业率和劳动力收入存在显着且持续的异质性，这导致了整体经济不平等。自 20 世纪 90 年代以来的发展表明，尽管通过网站发布的招聘信息扩大了就业机会的范围并提高了人们对就业机会的认识，但移民数量却有所下降。该项目通过探索本地和非本地求职者获得工作机会的方式来探讨在线招聘信息的地理方面。该研究通过考虑求职者是否居住在工作地点附近来调查雇主对求职者的偏好。或者该项目进行了一项通信实验，将不同地理位置的虚构候选人的简历随机分配给正在积极寻找员工的雇主。该项目通过考虑候选人与工作地点的距离来检查雇主的回访率，以了解雇主的不同偏好。该项目进一步构建了一个描述性和数学理论，解释为什么雇主可能更喜欢当地工人而不是非当地工人，因为当地劳动力市场的低迷往往会带来与留在这些地区相关的福利成本，并且有利于移民。预期收入增长可能会有所帮助作为跨地区平等的重要机制，该研究预测了与求职者地理来源相关的劳动力市场机会。该项目采用简历审核研究，通过随机分配虚构的求职者来了解雇主与空间因素相关的招聘偏好。该项目通过反映不同的居住地点、教育水平和申请人的性别，开发了一个理论模型，通过将这些机制嵌入简历审核中，合理化雇主为何进行基于地点的歧视并偏爱当地申请人的潜在机制。研究设计,该项目探讨了申请人所在地对雇主招聘偏好的异质性影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。