# Endogenous labour flow networks

## TL;DR

This paper introduces a new model for understanding how job transitions change over time, adapting to shifts in the labor market.

## Contribution

The novel model generates labor flow networks from agent-level behavior without assuming static network structures.

## Key findings

- The model accurately generates empirical labor flow networks using UK microdata.
- It explores how shocks to job and wage distributions alter network topology.
- The framework provides a foundation for modeling future labor 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 in 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 economic theory and 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.

The online version contains supplementary material available at 10.1140/epjds/s13688-025-00539-9.