Using administrative data to understand the impact of policies and practices on youth employment at King’s College London on FindAPhD.com

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Prof Michael Sanders, Dr Vanessa Picker No more applications being accepted Funded PhD Project (Students Worldwide)

London United Kingdom Sociology Social Work

About the Project

Young people have born the brunt of the effects of the last two periods of economic decline - first the 2008 financial crisis and then the Coronavirus pandemic. Unemployment at a young age has been shown by research to have lasting impacts on the wellbeing, and the future employment prospects of young people. As a result, identifying interventions, at a policy or practice level that have a positive impact on youth employment, is a key priority for any government, and likely to become all the more important this year as the UK enters recession once again. Policymakers require high quality evidence about the impacts of different policy options in order to make the best decisions for young people given constrained resources.

This studentship will focus on the use of administrative datasets to produce causal estimates of the impact of interventions. Administrative data refers to typically large scale datasets that are create as a part of the administration of a service. Here we have particular interest in the Longitudinal Educational Outcomes dataset (LEO), which includes information on schooling; higher education; employment; wages; and children’s social care involvement in a young person’s life, and the GRADE dataset of higher education access and participation.

The studentship will focus on quasi-experimental approaches - these are a group of statistical approaches that attempt to identify the causal impact of a policy or practice on an outcome, without making use of random assignment. These approaches have been used in the past to identify the impacts of minimum wages on levels of employment. As such, we are particularly interested in research that would focus on;

Using the LEO dataset to (quasi-experimentally) identify the impacts of Active Labour Market Interventions on youth employment
Using the LEO Dataset to identify the impacts of youth interventions, including participation in sports, cultural activities, and mentoring/coaching on youth employment
Using the GRADE dataset to identify the impacts of ALMI interventions on university applications and attendance
Using the GRADE dataset to identify the impacts of university widening participation and outreach activity on university applications and attendance
Developing a framework for quasi-experimental evaluations using LEO and GRADE administrative datasets.

For any or each of these projects, the student will work to identify interventions that have currently or previously been delivered for a specific cohort of young people, and match participants to the relevant administrative data.

Participants could be identified either via the eligibility criteria for the programme (i.e. young people in area x with characteristics y, z and q are eligible for a particular labour market intervention - for example the government’s kickstart employment scheme), or by working with delivery organisations to gather consent from participants for data merging.

Analytical approaches including difference in differences; matching; synthetic controls; and regression discontinuity approaches could then be used to identify the impact of the changes in policy or practice on unemployment for young people overall, and for particular groups of interest.

The student will also have the opportunity to collaborate with the Youth Futures Foundation, which is the UK government’s recognised What Works Centre for Youth Employment.