Machine learning methods for studying the trajectories of young offenders in administrative data

Lead Research Organisation: Institute for Fiscal Studies
Department Name: IFS Research Team

Abstract

Administrative data has the potential to open new and invaluable research opportunities to better understand societal phenomena and support evidence-based policy-making. One research area administrative data can significantly enhance is the analysis of life-course trajectories across key domains of interest to social scientists, including education, economic activity, health, and crime. Administrative data is a rich source of longitudinal information on key socio-economic outcomes and public services use that can be particularly useful for policy-relevant analysis. Yet, in the UK, it has been relatively untapped so far. As administrative data and administrative linked data becomes increasingly available to researchers, it is essential that research teams, both in academia or in government, are equipped with the appropriate methodological tool kit to take full advantage of the research possibilities these data offer.

In this bid, the Institute for Fiscal Studies (IFS) proposes to collaborate with the Ministry of Justice (MoJ) to address this challenge: we propose a package of activities to advance the use of machine learning techniques to exploit the richness of MoJ's administrative and linked administrative datasets as sources of information on individuals' offending and educational trajectories and their journeys through the justice system. These activities include:

a) Innovative research investigating the nature and drivers of offenders' trajectories and how these are shaped by the justice system, by applying, for the first time, sequence analysis methods in UK administrative and administrative linked data.

b) A secondment of an IFS researcher to the MoJ to ensure the co-production and policy relevance of the research, enhance MoJ's analytical capability, and strengthen exchange of ideas and knowledge between the two institutions.

c) A training workshop led by an international expert in sequence analysis methods to build research capability, within research institutions and government, in the use of machine learning techniques for administrative data analysis.

d) A research workshop at the end of the grant for researchers from the IFS and other institutions, as well as analysts and policy-makers from MoJ and other relevant departments, to present and discuss current research using machine learning methods in administrative data.

The project team, led by Professor Imran Rasul, Professor of Economics at University College London and IFS' Research Director, has a strong track record of academic and policy work based on UK and international administrative data. By establishing the viability of these methods in UK administrative linked data and demonstrating their usefulness to better understand individuals' journeys through public service systems, the research will make significant contributions to life-course studies conducted across the social sciences. The research will produce at least two academic outputs, accompanied by non-technical, policy-oriented briefs. The team's collaboration with MoJ, as well as the IFS' well-established links with policy-makers and the media, will ensure that these outputs are well disseminated beyond academia and used to inform the policy-making process.

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