Life Sequence Transformer: Generative Modelling of Socio-Economic Trajectories from Administrative Data

TL;DR

This paper introduces a Transformer-based generative model for simulating socio-economic life trajectories from administrative data, enabling realistic and plausible alternative life histories for policy research.

Contribution

It presents a novel encoding method for socio-economic sequences and adapts generative modeling to produce plausible life trajectories conditioned on past histories.

Findings

Model trained on large-scale Italian social security data

Reproduces realistic labour market patterns

Generates coherent hypothetical life paths

Abstract

Generative modelling with Transformer architectures can simulate complex sequential structures across various applications. We extend this line of work to the social sciences by introducing a Transformer-based generative model tailored to longitudinal socio-economic data. Our contributions are: (i) we design a novel encoding method that represents socio-economic life histories as sequences, including overlapping events across life domains; and (ii) we adapt generative modelling techniques to simulate plausible alternative life trajectories conditioned on past histories. Using large-scale data from the Italian social security administration (INPS), we show that the model can be trained at scale, reproduces realistic labour market patterns consistent with known causal relationships, and generates coherent hypothetical life paths. This work demonstrates the feasibility of generative modelling for socio-economic trajectories and opens new opportunities for policy-oriented research, with counterfactual generation as a particularly promising application.