Endogenous Labour Flow Networks

TL;DR

This paper introduces a dynamic model of labour flow networks that accounts for agent-level behaviour and shifting job landscapes, improving understanding of how shocks affect labour market structures.

Contribution

It develops a novel agent-based model that generates empirical labour flow networks without assuming static flow paths, capturing real-world shifts in the job market.

Findings

Model accurately reproduces UK labour flow networks

Shocks to job and wage distributions alter network topology

Framework advances understanding of future labour market dynamics

Abstract

In the last decade, the study of labour dynamics has led to the introduction of labour flow networks (LFNs) as a way to conceptualise job-to-job transitions, and to the development of mathematical models to explore the dynamics of these networked flows. To date, LFN models have relied upon an assumption of static network structure. However, as recent events (increasing automation in the workplace, the COVID-19 pandemic, a surge in the demand for programming skills, etc.) have shown, we are experiencing drastic shifts to the job landscape that are altering the ways individuals navigate the labour market. Here we develop a novel model that emerges LFNs from agent-level behaviour, removing the necessity of assuming that future job-to-job flows will be along the same paths where they have been historically observed. This model, informed by microdata for the United Kingdom, generates empirical LFNs with a high level of accuracy. We use the model to explore how shocks impacting the underlying distributions of jobs and wages alter the topology of the LFN. This framework represents a crucial step towards the development of models that can answer questions about the future of work in an ever-changing world.