1. Methods and Approaches to investigate the UK Education System
Sandra McNally,
University of Surrey and
Centre for Economic Performance, London School of Economics

2. Overview Some questions addressed in the CEP Education Programme Data
Core methodological issues

3. Some current themes in the CEP Education Research Programme
What works in schools…
Teachers
Peers, neighbours and compositional effects
Parental preferences and admissions
Higher education in the UK

4. What works (or not) in schools: CEP research

5. Most important data sets
The National Pupil Database
(potentially linkable with UCAS and HE data)
Longitudinal data sets which can be linked with administrative data, e.g. Longitudinal Survey of Young People in England; Millennium Cohort Study

6. Core methodological issues
What is an appropriate counter-factual? – and therefore can we establish causality?
For whom is the causal effect identified?
3. Can we look at longer-term effects?
4. Can we do a cost-benefit analysis?
5. Can we extrapolate outside the study context?

7. Approaches Randomised Control Trials (Education Endowment Fund)
Difference-in-difference approaches
Instrumental Variable Approaches
Regression Discontinuity Approaches
OLS/Propensity Score Matching

8. Randomised Control Trials
‘Treatment’ is randomly assigned amongst target population
An example:
Design of an ‘information campaign’ about the costs and benefits of staying-on in education targeted to Year 10 students in London schools (McGuigan, McNally, Wyness, 2012)

9. Difference-in-Difference Approaches
Compare the outcomes of a treatment and control group before and after the policy.
An example:
Structured way to teach reading (‘literacy hour’) was piloted in schools in some Local Authorities before it was rolled out to the rest of the UK (Machin and McNally, 2008)

10. Instrumental Variable Approaches
Trying to find a variable that predicts the outcome variable only via the impact it has on the ‘treatment’ variable.
An example
Using changes in the minimum school-leaving age to estimate the impact of additional education on future earnings (Harmon and Walker, 1995).

11. Regression Discontinuity Designs
Making use of a ‘cut-off’ for eligibility to a programme/policy. Compare the outcomes of people either side of this ‘cut-off’.
An example
Using administrative cut-offs in school admissions policies to identify the relationship between when children start school and their later outcomes. Crawford, Dearden and Greaves (2013).

12. OLS/Propensity Score matching
Trying to measure the effect of a ‘treatment’ while controlling for all observable characteristics.
Example
Looking at whether the increase in ‘non-native’ English speakers has an influence on the educational attainment of native English speakers (Geay, McNally and Telhaj, 2012)