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.

摘要美国劳动力市场的失业率和劳动力收入存在显著且持续的异质性，导致整体经济不平等。自20世纪90年代以来的发展情况表明，尽管通过网站发布的招聘信息扩大了工人对原籍地以外就业机会的获取和认识，但移民人数有所下降。该项目通过探索本地和非本地求职者获得工作机会的途径，探索在线招聘的地理方面。该研究调查了雇主对求职者的偏好，考虑了求职者是住得离工作地点近还是远。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招聘员工的雇主。该项目通过考虑候选人与工作地点的距离来检查雇主回调率，以了解雇主对雇员原籍地的不同偏好。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本地工人而不是非本地工人。当地劳动力市场不景气的居民留在这些地区往往会产生与福利成本相关的成本，而有利于预期收入增长的移民可能是跨地区均衡的重要机制。这个研究项目调查了与求职者的地理来源有关的劳动力市场机会。该项目采用简历审计研究，通过反映不同的居住地点、教育水平和申请人的性别，随机分配虚构的申请人到工作岗位，了解雇主与空间因素相关的招聘偏好。该项目建立了一个理论模型，对雇主为什么从事基于地点的歧视并倾向于本地申请人的潜在机制进行了合理化。通过将这些机制嵌入简历审计研究设计中，本项目探讨了申请人所在地对雇主招聘偏好的异质性影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。