Graduate Income Analysis for University College London

This Analysis is based on a Longitudinal Education Outcomes (LEO) dataset from the UK Department for Education

UCL salary per subject type: Median, lower quantile & upper quantile

The bar heights show the median salary 1, 3 and 5 years after graduation for each subject type. The lower and upper quantiles are illustrated by the vertical black lines on each bar. Assuming an exponential growth of the salary, we can fit an exponential function to each of the three salary points (1, 3 and 5 years) per subject type, in order to estimate the annual salary growth rate. The table below shows the results.

Annual Salary Growth Rates – Year 1 to 5 years after graduation

9.6 %

Biological Sciences (ex. Psycho.)

7.4 %

Architecture, Building & Planning

4.4 %

Business & Admin. Studies

2.4 %

Computer Science

9.2 %

Education

10.1 %

Engineering & Technology

12.2 %

Economics

11.3 %

English Studies

9.1 %

Historical & Philosophic. Studies

6.5 %

Languages (ex. English Studies)

13.8 %

Law

13.3 %

Mathematical Science

5.9 %

Medical & Dentistry

5.9 %

Physical Science

9.6 %

Psychology

9.1 %

Social Studies

12.2 %

Subjects Allied to Medicine

UCL salary per department: Median, mean, lower quantile & upper quantile

The bar heights show the median salary of the four last cohorts 6 months after graduation for each department. The lower and upper quantiles are illustrated by the vertical black lines on each bar and the mean is shown by the dots for each department and cohort.

This Analysis is based on UCL survey data conducted 6 months after graduation

Quality of the data

The statistical population consists of 11,317 participants in the UCL survey and 8,370 in the LEO dataset. The overall participation rate was roughly 70% on average. If not everyone participates in a survey, the results can become non-representative because the sample is systematically different from the target population. This effect is known as the Participation Bias. One of our next steps is to investigate, whether our data has a similar bias. There is a lot of ongoing research to identify such a bias and to deal with it (if such a bias exists). A list of some scientific articles addressing the Participation Bias can be found on the following link:
Link to Academic Papers
2018-09-16T17:58:51+01:00

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