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

TL;DR

This study analyzes the career trajectories of over 130,000 planning alumni in the US, revealing how boundaryless career patterns, soft skills, geographic mobility, and networks influence upward mobility, using large language models to extract data from LinkedIn.

Contribution

It applies large language models to LinkedIn profiles to systematically analyze career mobility patterns among planning alumni, integrating theories like boundaryless careers and social capital.

Findings

Boundaryless career patterns correlate with higher upward mobility.

Soft skills and lateral moves become more important at senior levels.

Geographic mobility and diverse metropolitan markets aid career advancement.

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.