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arxiv: 2605.12618 · v1 · submitted 2026-05-12 · 💻 cs.CY

Recognition: no theorem link

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

Yan Wang , Su Jeong Jo
Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:21 UTC · model grok-4.3

classification 💻 cs.CY
keywords career mobilityplanning alumniboundaryless careersLinkedIn datalarge language modelsupward mobilityurban planningprofessional networks
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The pith

Planning alumni who pursue multisector experience and lateral industry switches achieve significantly higher upward mobility.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines the career paths of more than 130,000 planning alumni in the United States by pulling structured data from LinkedIn profiles with large language models. It shows that those who follow boundaryless patterns, such as working across multiple sectors or making lateral and industry-switching moves, reach higher career levels than those on more traditional tracks. Technical skills help secure initial positions, but soft skills deployed through strategic lateral moves matter more for reaching senior roles. Geographic mobility and employment in larger metropolitan areas also correlate with advancement, as do larger professional networks. The analysis draws on boundaryless career theory to explain these patterns amid changing urban environments.

Core claim

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 associated with advancement, larger professional networks and greater organizational engagement aid upward transitions, and AI-related skills present limited additional advantage.

What carries the argument

Boundaryless career patterns, specifically multisector experience and lateral industry-switching trajectories, extracted via large language models from LinkedIn profiles and analyzed against upward mobility outcomes.

If this is right

  • Technical skills secure entry positions while soft skills through lateral moves drive later advancement.
  • Larger professional networks and organizational engagement consistently support upward career transitions.
  • Geographic mobility to diverse metropolitan labor markets provides modest but positive advancement benefits.
  • AI-related skills, now common, deliver limited extra advantage beyond foundational competencies.
  • Strategic multisector experience and industry switches lead to higher mobility than linear career paths.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Planning education could incorporate training in networking and adaptability to prepare students for higher-mobility trajectories.
  • The mobility patterns may extend to other professions with fluid labor markets, suggesting value in cross-field comparisons.
  • Organizations hiring planners might design roles that encourage lateral moves to improve retention and advancement rates.
  • Tracking alumni outcomes through official records rather than self-reported profiles could confirm whether the gains hold across the full population.

Load-bearing premise

LinkedIn profiles provide a representative and unbiased sample of planning alumni careers, and large language model extraction accurately captures detailed career trajectories without significant errors or selection bias.

What would settle it

A direct survey or administrative records of planning alumni absent from LinkedIn showing equal or lower mobility for those with multisector and lateral patterns, or manual checks revealing frequent errors in the model-extracted trajectories.

Figures

Figures reproduced from arXiv: 2605.12618 by Su Jeong Jo, Yan Wang.

Figure 1
Figure 1. Figure 1: Career Length Distribution (left) and Stability Index Distribution (right) by quartile. Dashed lines indicate Q1 [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Median number of LinkedIn connections by Career Stability Index across career stages (Q1 [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript analyzes career mobility among more than 130,000 planning alumni in the US using large language models to extract structured information from LinkedIn profiles. Drawing on boundaryless career theory, social capital theory, and spatial opportunity models, it claims that alumni adopting boundaryless patterns—specifically multisector experience or lateral and industry-switching trajectories—achieve significantly higher upward mobility. Additional results indicate that technical competencies serve as an entry-level signal while soft skills become decisive at senior stages via lateral moves, that geographic mobility and employment in larger metropolitan areas aid advancement, and that larger networks and organizational engagement are positively associated with upward transitions, whereas AI-related skills offer limited additional advantage.

Significance. If the reported associations survive proper statistical controls, LLM validation, and bias corrections, the work supplies large-scale empirical evidence on professional trajectories in urban planning and demonstrates a scalable LLM-based method for mining career data from public profiles. This could inform both theory testing in boundaryless career research and practical guidance on mobility strategies, provided the data limitations are transparently addressed.

major comments (2)
  1. [Abstract] Abstract: the central claim that multisector and lateral trajectories produce significantly higher upward mobility rests on associations whose statistical methods, controls for confounders, LLM output validation, and missing-data handling are not described, leaving the result unsupported by visible evidence.
  2. [Data and Methods] Data and Methods (implied by abstract description): LinkedIn profiles constitute a self-selected sample in which individuals with more dynamic or successful careers are plausibly over-represented; this selection mechanism correlates with both the predictor (boundaryless patterns) and the outcome (upward mobility). No quantitative correction, sensitivity analysis, or external benchmark is reported to assess or mitigate the resulting bias.
minor comments (1)
  1. [Abstract] Abstract: the limitations paragraph notes under-representation of alumni without profiles but does not quantify the potential impact on coefficient estimates or discuss how the LLM extraction pipeline was validated against human-coded subsets.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important issues of transparency and data limitations. We will revise the manuscript to expand methodological details in the abstract and main text, strengthen the discussion of selection bias with additional sensitivity analyses where feasible, and improve overall clarity. Our responses to the major comments follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that multisector and lateral trajectories produce significantly higher upward mobility rests on associations whose statistical methods, controls for confounders, LLM output validation, and missing-data handling are not described, leaving the result unsupported by visible evidence.

    Authors: The abstract is intentionally concise per journal guidelines, but the full manuscript details the LLM-based extraction pipeline (including prompt design and few-shot examples), manual validation on a 500-profile subsample yielding >85% agreement, and regression models with controls for experience, education, gender, and metro size. We will revise the abstract to briefly reference these elements and add an explicit subsection on validation metrics and missing-data imputation (listwise deletion with robustness checks). This will make the evidentiary basis visible without altering the core claims. revision: yes

  2. Referee: [Data and Methods] Data and Methods (implied by abstract description): LinkedIn profiles constitute a self-selected sample in which individuals with more dynamic or successful careers are plausibly over-represented; this selection mechanism correlates with both the predictor (boundaryless patterns) and the outcome (upward mobility). No quantitative correction, sensitivity analysis, or external benchmark is reported to assess or mitigate the resulting bias.

    Authors: We agree this is a substantive limitation of LinkedIn-sourced data. The manuscript already notes under-representation of alumni without profiles, but we did not apply formal selection corrections (e.g., Heckman models) due to the absence of credible exclusion restrictions. We will add a dedicated sensitivity section comparing results across profile-completeness strata, benchmarking aggregate mobility rates against publicly available planning labor statistics where possible, and discussing the likely direction of bias. These additions will better characterize rather than eliminate the issue. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical associations derived from LLM-extracted LinkedIn data

full rationale

The paper conducts an empirical study extracting career trajectories via LLMs from LinkedIn profiles of over 130,000 planning alumni and reports statistical associations between boundaryless patterns (multisector experience, lateral moves) and upward mobility. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the derivation chain. The central claims rest on observed data patterns rather than reducing to the inputs by construction. The analysis draws on external theories (boundaryless career theory, social capital theory) without smuggling ansatzes or renaming known results as novel derivations. Sample bias concerns exist but are separate from circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on standard social science theories without introducing new free parameters, axioms beyond domain assumptions, or invented entities.

axioms (1)
  • domain assumption Boundaryless career theory, social capital theory, and spatial opportunity models provide appropriate frameworks for analyzing career mobility in planning.
    The study explicitly draws on these theories to interpret the extracted data patterns.

pith-pipeline@v0.9.0 · 5506 in / 1134 out tokens · 48526 ms · 2026-05-14T20:21:03.867573+00:00 · methodology

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