The BBC has a student finance calculator (http://www.bbc.co.uk/news/education-14785676) that can be used to guestimate the quantity of student loan repayments and total value repayed by graduates in various careers. My understanding of the data set used (superficial understanding) is that it is based on past salary data for types of employment.
I was fairly curious when looking at it last night to see how much fee related debt I would be in, were I starting out next September on a 3 year degree course followed by a ‘science professional’ job. I would pay back something in the region of £37k over 29 years. As things in my house are fairly equal, I noticed that I had to tick the ‘female’ box, so I wondered what the difference would be for my equally qualified, male partner. He would repay around £36.5k over 23 years.
And yet, how obvious. While the point of the predictor tool is to estimate graduate debt and is perhaps off-putting for prospective students, the data set used highlights the stunning inequality in salaries when gender is considered.
Figure 1: science professional predicted salary for each gender from the BBC site. http://www.bbc.co.uk/news/education-14785676 (accessed 16/09/11, 9.15 am).
As can be seen from figure 1, at the age of 21 (age at graduation), the starting salaries are approximately level and continue that way until the age of 30. Then the funny stuff starts happening. Obviously the social factors influencing salary are whether someone works full time or part time, takes time out for child rearing, and works in the public or private sectors. I’m assuming that ‘science professional’ represents a decent number of private sector workers. The data stop at different times because that was the point at which the student fee debt was calculated as repaid.
So what? So everything. Firstly, the data used is based on past averages from a time when women were expectecd to be the primary carers for children and careers were something that men-folk did. I don’t think we live in that society any more, at least I hope we don’t. Secondly, I think these projections are irresponsibly off-putting to female students considering degree level study. It might be nice at first to consider that females will pay off less (use the tool for some other professions), but consider that this would be because they will earn substantially lower than men over the 30 year period. You can’t really see that from figure 1 so let’s look at the national average data.
Figure 2: National average predicted salary from BBC site (reference as above) split by gender.
This is horrific. I understand that women are more likely to be public sector workers, or work part time, or take career breaks for child rearing and so be on lower salaries later in life. But it doesn’t have to be that way in the future.
These data send out a horrible message to those considering a degree. Not only will they have more university related debt than any other generation in the UK, but the salary prospects for 50% of the graduate population are predicted to be dispiriting at best. These predictions simply must not be allowed to come true. We cannot seriously be in the second decade of the 21st century and predict that a female graduate’s average earnings will be substantially less than a male.
So what do we do about it? For a start we could ensure that the data used for predictions lists all salaries as full time equivalent rather than part time salary. This would eliminate any lowering of the average due to the choice to work part time. I have no idea whether this applies to these data but it could be a source of error. We could follow it up with decent family friendly workplace policies that grant equal and unbiased rights to both parents irrespective of gender, and sensible, financially viable, solutions for employers when dealing with family leave. It should be possible to construct sensible family-friendly policies that do not leave the other employees with vastly increased workloads to compensate. Perhaps we just need to rethink the issues, but we need to do more to ensure that these predictions don’t come true.