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on Human Capital and Human Resource Management |
| By: | Brian Jabarian; Luca Henkel |
| Abstract: | We study AI agents as information-collection technologies: automated systems that elicit decision-relevant signals from humans through live interactions. We test how such AI automation impacts information collection and organizational outcomes using a natural field experiment with 70, 000 applicants applying for real jobs. Applicants were randomly assigned to be interviewed by either human recruiters or AI voice agents. Afterward, human recruiters evaluate the interviews and make hiring decisions. Applicants interviewed by AI agents are 12% more likely to receive job offers, and these gains translate into higher job starts and worker retention, with no decline in the productivity of hired workers. Analyzing interview transcripts reveals that AI voice agents achieve controlled variance: their interviews are more structured and consistent while remaining responsive to individual applicants, which is associated with more hiring-relevant information collected. Our results suggest that a key advantage of AI automation lies in environments where information collection is delegated across many human workers and repeated such that variance in task execution becomes noise in decision-relevant signals, which AI compresses through adaptive standardization. |
| Keywords: | artificial intelligence, interviews, hiring, organizational design, field experiment |
| JEL: | C93 J24 M15 M51 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12984 |
| By: | David J. Deming; Katrine V. Løken; Alexander Willén; Yaling Xu |
| Abstract: | Why do we have so many meetings? Few workplace features are so scorned, yet seemingly so necessary. This paper provides the first large-scale economic evidence on workplace meetings using an original survey of more than 9, 000 workers linked to matched employer–employee administrative data from Norway. We show that meetings are both common and costly, consuming an average of 12 percent of work hours and 14 percent of firm wage bills. Planning, problem solving, information sharing, and project coordination account for the majority of meeting activity. High-paying and high-revenue firms devote more resources to meetings despite facing a substantially higher opportunity cost of employee time. Meeting frequency and intensity are positively related to worker wage growth. Workers in meeting-intensive firms report greater on-the-job learning, and interactions with more senior colleagues are associated with stronger wage growth, suggesting that knowledge transmission within firms is an important mechanism. Meetings are the broccoli of work – widely disliked, but probably good for us anyway. |
| JEL: | J24 M5 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35706 |
| By: | Shuaizhang Feng; Lars Lefgren; Jieyi Liu; Brennan Platt; Xiaoyu Xia |
| Abstract: | We study age discrimination in a labor market with a job ladder, where worker productivity is revealed over time. Using data from a large Chinese recruitment platform that links job postings, applications, and employer responses, we document sharp declines in applications and callbacks at posted age cutoffs. Discrimination operates in both directions: older applicants are disadvantaged in entry-level jobs, while younger applicants are less likely to be hired into managerial roles. A model of statistical discrimination with imperfect signal screening explains these patterns. Consistent with the model, older applicants to entry-level jobs are negatively selected relative to their peers. |
| JEL: | J71 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35709 |
| By: | Nicholas J. Hallman; Zachary T. Kowaleski; Anu Puvvada; Jaime J. Schmidt |
| Abstract: | We study how sophistication in generative AI (genAI) use varies among the back-office workforce of a large firm. Using proprietary data, we observe 713, 564 employee prompts and their corresponding large language model responses from nearly 4, 000 back-office employees across 15 functional areas over eight months in 2025. We document three main findings. First, senior employees exhibit more sophisticated genAI use, consistent with domain expertise complementing genAI capabilities. Second, sophistication varies considerably across functions and is highest in Strategy, Digital Innovation, and Project Management, three groups that share a focus on firmwide strategic initiatives and organizational change. Third, we observe neither improvements in sophistication over time nor lasting improvements following formal AI training, suggesting that sophisticated use can be difficult to change. Together, our study provides measures of and insights into sophisticated genAI use that managers can use to improve outcomes and that researchers can use in future research. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.27364 |