On Analyzing Job Hop Behavior and Talent Flow Networks

TL;DR

This paper presents a data-driven analysis of job hopping behavior and talent flow networks using publicly shared professional profiles of nearly 490,000 individuals, revealing insights into career progression and organizational competitiveness.

Contribution

It introduces scalable metrics and network analysis methods to study job hops and talent flow, overcoming limitations of traditional surveys.

Findings

Metrics correlate with job hopping propensity

Job hop behavior relates to promotions and demotions

Network analysis reveals talent flow patterns

Abstract

Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user input to specially designed questions, they are often not scalable and timely enough to cope with fast-changing job landscape. In this paper, we present a data science approach to analyze job hops performed by about 490,000 working professionals located in a city using their publicly shared profiles. We develop several metrics to measure how much work experience is needed to take up a job and how recent/established the job is, and then examine how these metrics correlate with the propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Finally, we perform network analyses at the job and organization levels in order to derive insights on talent flow as well as job and organizational competitiveness.