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

2026-05-12

AI Summary

This study addresses a critical gap in the literature by providing systematic evidence on the career trajectories of U.S. urban planning graduates and how they are shaped by social, spatial, organizational, and educational factors. Drawing on boundaryless career theory, social capital theory, and spatial opportunity models, this work pioneers the application of large language models (LLMs) to extract and analyze structured data from over 130,000 LinkedIn profiles. Integrating natural language processing, career trajectory modeling, and social network analysis, the research reveals that multi-sector experience, lateral job moves, and geographic mobility significantly accelerate advancement, while soft skills exert a decisive influence at senior career stages. Findings indicate that individuals pursuing boundaryless career paths achieve faster promotions, that employment in major metropolitan areas and broad professional networks enhance upward mobility, and that although AI-related skills are increasingly common, they confer limited promotional advantage.

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.

Problem

• career mobility
• planning professionals
• urban change
• career trajectories
• professional advancement

Innovation

• large language models
• career mobility
• structured data extraction
• professional profile analysis
• boundaryless careers