Career Mobility of Planning Alumni in the United States: Evidence from Professional Profile Data using Large Language Models

• URL: http://arxiv.org/abs/2605.12618v1
• Date: Tue, 12 May 2026 18:06:32 GMT
• Title: Career Mobility of Planning Alumni in the United States: Evidence from Professional Profile Data using Large Language Models
• Abstract summary: This study draws on boundaryless career theory, social capital theory, and spatial opportunity models to analyze career mobility among planning alumni.<n>Results reveal that planning alumni who adopt boundaryless career patterns, specifically multisector experience or lateral and industry-switching trajectories, achieve significantly higher upward mobility.
• Score: 4.2500636748409715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Problem, Research Strategy, and Findings: Planning professions in the United States navigate complex and dynamic career landscapes under rapid urban changes, yet comprehensive evidence regarding their career trajectories, advancement patterns, and the influence of social, spatial, organizational, and educational factors remains limited. This study draws on boundaryless career theory, social capital theory, and spatial opportunity models to analyze career mobility among more than 130,000 planning alumni. Using large language models to extract structured information from LinkedIn profiles, our results reveal that planning alumni who adopt boundaryless career patterns, specifically multisector experience or lateral and industry-switching trajectories, achieve significantly higher upward mobility. While technical competencies provide a foundational entry-level signal, soft skills leveraged through strategic lateral moves become increasingly decisive as planners reach senior stages. Geographic mobility and employment in larger, diverse metropolitan labor markets are both associated with advancement, though the latter provides modest benefits. Larger professional networks and greater organizational engagement are consistently associated with upward career transitions, while AI-related skills, now commonplace, present limited additional advantage. Limitations include reliance on LinkedIn data, which may underrepresent alumni without online profiles, and an individual-level focus that omits organizational factors.

Related papers

• STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling [44.09276843323847]
  We present STEP, a novel career-path recommendation system.<n>It uses temporal and educational signals to predict the next job in a career trajectory.<n>We show it outperforms state-of-the-art baselines in next job prediction.
  arXiv  Detail & Related papers  (2026-07-13T15:48:01Z)
• Can Online GenAI Discussion Serve as Bellwether for Labor Market Shifts? [62.386835769570006]
  This paper examines whether online discussions about Large Language Models can function as early indicators of labor market shifts.<n>We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes.<n>Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration.
  arXiv  Detail & Related papers  (2025-11-20T04:18:25Z)
• Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles [15.31824043202434]
  We present a large-scale analysis of career mobility of college-educated U.S. workers.<n>We investigate how gender, race, and job change options are associated with upward mobility.
  arXiv  Detail & Related papers  (2025-11-15T03:26:57Z)
• Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities [117.49715661395294]
  Data structurization can play a promising role by transforming intricate and disorganized data into well-structured forms.<n>This survey presents a first systematic review of how graphs can empower AI agents.
  arXiv  Detail & Related papers  (2025-06-22T12:59:12Z)
• JobHop: A Large-Scale Dataset of Career Trajectories [43.84577100931558]
  JobHop is a large-scale public dataset derived from anonymized resumes provided by VDAB, the public employment service in Flanders, Belgium.<n>We process unstructured resume data to extract structured career information, which is normalized to standardized ESCO occupation codes.<n>This results in a rich dataset of over 1.67 million work experiences, extracted from and grouped into more than 361,000 user resumes.
  arXiv  Detail & Related papers  (2025-05-12T15:22:29Z)
• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• The Career Interests of Large Language Models [35.18062121410434]
  This study focuses on the aspect of career interests by applying the Occupation Network's Interest Profiler short form to Large Language Models (LLMs) We found distinct career interest inclinations among LLMs, particularly towards the social and artistic domains. This novel approach of using psychometric instruments and sophisticated statistical tools on LLMs unveils fresh perspectives on their integration into professional environments.
  arXiv  Detail & Related papers  (2024-07-11T14:54:46Z)
• Occupation Life Cycle [16.618743552104192]
  This paper introduces the Occupation Life Cycle (OLC) model to explore the trajectory of occupations. Using job posting data from one of China's largest recruitment platforms, we track the fluctuations and emerging trends in the labor market from 2018 to 2023. Our findings offer a unique perspective on the interplay between occupational evolution and economic factors, with a particular focus on the Chinese labor market.
  arXiv  Detail & Related papers  (2024-04-15T03:13:51Z)
• Career Path Prediction using Resume Representation Learning and Skill-based Matching [14.635764829230398]
  We present a novel representation learning approach, CareerBERT, specifically designed for work history data. We develop a skill-based model and a text-based model for career path prediction, which achieve 35.24% and 39.61% recall@10 respectively.
  arXiv  Detail & Related papers  (2023-10-24T08:56:06Z)
• A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics [46.025337523478825]
  Talent analytics has emerged as a promising field in applied data science for human resource management.<n>Recent development of Big Data and Artificial Intelligence techniques have revolutionized human resource management.
  arXiv  Detail & Related papers  (2023-07-03T07:53:20Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
  We develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
  arXiv  Detail & Related papers  (2021-11-16T18:42:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.