UK Labour Flow Network (LFN) Model [UNDER CONSTRUCTION]

This public repository contains code and data associated with the UK LFN model. This model emerges the LFN describing the pattern of labour mobility (i.e. job-to-job transitions) within the UK labour market. The associated pre-print is posted here.

This project was developed by K.R. Fair and O.A. Guerrero, within the Public Policy Programme at the Alan Turing Institute. Please direct all correspondence to K.R. Fair (kfair@turing.ac.uk).

You will find all instructions for running simulations here within the code folder.

NOTE: In order to run simulations, the user will need to generate input datasets from the UK Labour Force Survey (LFS) longitudinal dataset, as well as the O-NET database as these data cannot be posted publicly in this repository. All data that can be posted publicly is described here within the data folder.

Repository Structure

├── LICENSE
├── README.md                           <- README for project users.
│
├── data                                <- All publicly available data required to run the model.
│
├── code               
│   ├── preprocessing    
│   │   ├── LFS data collection         <- Python scripts used to collect data from LFS dataset.
│   │   ├── non-LFS data collection     <- Python scripts used to collect data from other sources.
│   │   └── pre-simulation processing   <- Python scripts used to further process data collected from LFS.
│   │
│   └── simulation                      <- Python scripts and notebooks used to run model simulations.
└──

Tutorial Notebooks

We provide several annotated Jupyter notebooks in the code/simulation folder with examples of different uses of the model. These tutorials use some synthetic data, as not all data used in our simulations can be made publicly available.

1. BasicSimulation.ipynb performs a single simulation of the model and presents a visualisation of the LFNs generated, along with associated statistics comparing the simulated and observed LFNs.
2. Calibration.ipynb demonstrates the algorithm used to calibrate the model's free parameters, and includes visualisations of calibration process.
3. ShockSimulation.ipynb performs a suite of Monte Carlo simulations of the model, incorporating a shock.

System & Hardware Requirements

• macOS Monterey version 12.6 (or similar)

• Sufficient RAM to support storage of data structures during simulations

Installation Guides

All software should install within a few minutes on a standard computer, the versions listed here are those the scripts have been tested on.

• Python Version 3.9.12 https://www.python.org/downloads/
• Spyder Version 5.3.3 (IDE for python) https://www.spyder-ide.org/

Acknowledgements

This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the "Shocks and Resilience" theme within that grant & The Alan Turing Institute. We would like to thank Áron Pap for conducting preliminary explorations of this topic, and Andy Jones and his team at BEIS for their interactions with us throughout this project. We also thank Dr. Alden Conner for her contributions to the development of this repository.