From next week, officials in the Department for Education are going to be busy sifting through responses to the consultation exercise around the new School Admissions Code.
Two important issues in the proposed code relate to the priority given to school staff, and to random allocation. We believe that as they currently stand, these provisions will set back the goals that the Government has set for its education policy.
1. Prioritising the children of staff
Paragraph 1.33 of the code says: “If admissions authorities decide to give priority to children of staff, they must set out clearly in their admission arrangements how they will define staff and on what basis children will be prioritised.”
This suggests that admissions authorities are to be allowed to prioritise the children of staff, reversing the policy of recent Admissions Codes.
One group very likely to be included in most definitions of “staff” are teachers. For those teachers with children, this will add a new aspect to their decision on which school to seek a job at. Like many other parents, teachers will be keen for their children to attend high-performing schools.
Following the White Paper “The Importance of Teaching”, one of the leading education policy issues is how to attract the particularly effective teachers into the more challenging schools. Research evidence does not tell us whether teachers who are parents are on average more effective teachers, but there are two points to make:
- This policy change will differentially increase the flow of applicants to high-performing schools. If the Headteachers of those schools are skilled at spotting effective teachers, then simply having access to a much bigger applicant pool will raise the average effectiveness of teachers the hired at those schools.
- They are less likely to be novices, which is one of the few clear findings on teacher effectiveness, so in that sense alone teachers who are parents are likely to be more effective.
Given that, this policy change is very likely to work against any efforts to attract effective teachers to challenging schools, and thus set back the Government’s stated educational policy goals of narrowing the outcome gap between affluent and disadvantaged pupils.
The proposed code change will also complicate disciplinary procedures because firing a teacher from a school would also have implications for her/his children. This is likely to make it even less likely that headteachers will engage in robust performance management.
We know that any work-based privileges that are specific to particular establishments tend to cement people in that job and reduce turnover. Such privileges include health insurance, pension rights, and so on. This reduces labour mobility and typically will make the labour market less efficient. This proposed change will have the same effect in the teacher labour market as teachers will be less willing to move as it will disrupt their children’s education.
The core issue is where the most effective school staff work. It would clearly be in high-performing schools’ interests to define staff quite widely to attract highly motivated and effective governors and other staff. School leadership is key to excellent schools, ever more so as more schools become autonomous. If the children of governors were also prioritised for admission, then we would likely see an improvement in the quality of governors at high-performing schools and a decline at more challenging schools.
These points are about the effectiveness and efficiency of schools. There is also a fairness point about equity of access to high-performing schools. Some schools might define “staff” quite widely to include Teaching Assistants, lunchtime supervisors, and governors as well as teachers. In such cases, becoming one of those staff is an attractive route of access into the school, as an alternative to buying a nearby house. In extreme circumstances, individuals could offer to work for free as lunchtime supervisors, or could offer to provide goods or services to the school to become governors. While many may apply for such positions, schools will naturally choose the more able and articulate applicants, particularly those with a professional background. This will not help to foster more equal access to high-performing schools.
It may be that the provision to allow this prioritisation of staff was a way of finessing the problem of how to guarantee that the founders of free schools can get their children into ‘their’ school. If so, it might solve a problem affecting possibly 5% of pupils at the cost of the problems outlined here for the rest.
2. Random allocation
Paragraph 1.28 says “Local authorities must not use random allocation as the principal oversubscription criterion for allocating places at all the schools in the area for which they are the admission authority.”
This paragraph bans the use of lotteries as the main mechanism for resolving over-subscription across an LA. The problem of who is admitted to the high-performing over-subscribed schools is highly relevant to the Government’s goal of closing the outcome gaps between affluent and disadvantaged pupils.
The Government has stated that the primary goal of its social policy is to raise social mobility. One of the central ways in which where you were born influences your life chances is through the assignment of children to schools, governed by the Admissions Code.
Currently, the main mechanism to resolve over-subscription is proximity. It is clear that the widespread use of the proximity rule widens the socio-economic gap in the available quality of schooling. In fact, the gap in accessible school quality between rich and poor families widens by over 50% once a proximity criterion is imposed.
We can illustrate this straightforwardly, using data from the annual Census of all state school pupils in England. We can approximate the neighbourhood of a pupil as the area within 3km of her home, and calculate the average difference in the academic performance of schools in the neighbourhood of poor and rich pupils. As we would expect, more affluent families tend to live in neighbourhoods with higher quality schools. We can also use the database to estimate an approximate proximity criterion, and to look at the schools that a particular pupil could reasonably expect to be admitted to. The difference in school “quality” is now much starker, over 50% higher in fact. This is the impact of the proximity rule, and operates over and above the simple fact that rich and poor live in different places.
This shows the mean academic quality of primary school attended (measured by the mean Keystage 2 score achieved in that school) available to high and low socio-economic status (SES) families in England. ‘Neighbourhood’ is defined simply as an area within 3km of the student’s house; ‘Catchment’ is defined as schools that the student would have a very high chance of getting into based on residential location and the school ’s catchment area. Standard error bars are displayed on the levels.
Clearly some criterion has to be used to resolve over-subscription. One possibility for use in cities is a lottery. But by its very nature, a lottery ensures that places are allocated in a way that ignores social background. All those families who have applied to an over-subscribed school are entered into a ballot, and as many names are drawn out as there are free places available (that is, once looked-after children, children with special educational needs, and siblings of current pupils have been assigned slots).
It is clear that lotteries are not a panacea and are not without difficulties, as our own research has shown. However, they do offer one potentially important way of raising social mobility.
We wait to see which way the new Admissions Code turns in the end.
The debate around the costs and benefits of attending university is, at present, very narrowly focused on expected earnings over the working lifetime (see my previous blog-post for example). However this debate needs to be broadened out. The returns to education in general and university in particular may be far wider than the private financial returns that are the focus of so much of the economics literature.
For a start, to compare earnings we are conditioning on the individual being in a job – and while graduates earn more when employed, they are also more likely to be in a job, which has never been more important given current labour market conditions. Recent figures from National Statistics show that in the third quarter of 2010, unemployment amongst 21-24 year olds with a degree was lower (11.6%) than for the same age group without a degree (14.6%) and far lower than the unemployment rate of 18-20 year olds (27.0%).
The range of job opportunities available to graduates is also larger (not many job adverts specify that not having a degree is a requirement), moreover as a graduate you are more likely to be in a job that you actually enjoy doing, and that offers opportunities for self-accomplishment and social interaction – which are all important for mental health, happiness and general “well being” outcomes. Oreopoulos and Salvanes (2011) show for US data that amongst individuals with similar family backgrounds, those with more education are more likely to report being happy with life and be in a job that they enjoy and that gives them a sense of achievement. This is the case even after taking into account the fact that more education increases income which itself may increase happiness and related outcomes. The largest increases in each measure are associated with the difference between those who do and do not attend university.
Though this US evidence is only suggestive – it could be that people who go to university have unobserved characteristics that mean that they would always be happier regardless of whether or not they went to university – the fact that the relationships exists even taking into account a wide range of background characteristics and income makes a strong case that higher education positively impacts these health and well-being outcomes. Moreover, other evidence from the US (Stowasser, Heiss, McFadden and Winter, 2011) and the UK (Oreopoulos, 2007) shows that increasing education has a positive causal impact on physical and mental health.
Aside from these effects on the likelihood of being in a job and enjoying that job there are other considerable non-pecuniary benefits of going to university. A university education can impact major life outcomes such as location, marriage/relationships and child-bearing. It can also enhance key personal skills, characteristics and preferences – for example critical thinking, decisiveness, communication, confidence, self esteem, self awareness, risk attitude and future orientation – that are not easily captured by qualifications.
Attending university broadens your experience and may make you better at running your own life and managing your time and resources – making you more attractive in the marriage market and benefitting others, such as your children. This broadening of experience and skills and encountering of new people, new environments, new perspectives and opportunities – the “consumption value” of university if you like – cannot easily be monetarised but is all a part of the “return”.
The likelihood is that if you go to university you will earn more over your lifetime than if you elect not to go, and even paying £9,000 per year in tuition fees you will still see a good return on your investment (see previous blog post). However, I would argue that much more important than this narrow earnings focus is the fact that you will also be less likely to be unemployed, more likely to be in a job that you enjoy, have better physical and mental health and gain in many personal skills and characteristics that will improve your outcomes both inside and outside the labour market over the rest of your life.
National Statistics: http://www.statistics.gov.uk/cci/nugget.asp?id=1162
Oreopoulos, P. (2007). ‘Do dropouts drop out too soon? Wealth, health and happiness from compulsory schooling’, Journal of Public Economics,Vol. 91, pp. 2213-2229.
Oreopoulos, P. and Salvanes, K. (2011). ‘Priceless: The Nonpecuniary Benefits of Schooling’, Journal of Economic Perspectives, Vol. 25, pp. 159-184.
Stowasser, T., Heiss, F., McFadden, D. and Winter, J. (2011). ‘“Healthy, Wealthy and Wise?” Revisited: An Analysis of the Causal Pathways from Socio-economic Status to Health’, NBER Working Paper, No. 17273.
So, you’ve got your A-level results and are now having to decide whether or not to go to university. On the one hand a degree should lead to higher wages throughout your lifetime but on the other there is the issue of having £27,000+ of student debts to pay off. So the big question: is it actually worth it?
Economics studies that have addressed this directly suggest that, on average, a university degree is a worthwhile investment with a positive expected rate of return – indeed in many cases a rate of return that compares favourably with anything the stock market might offer. Surprisingly, this remains the case even with the fee level increased to £9,000 per year: recent work by Ian Walker and Yu Zhu (2011) shows that even with such an increase in tuition fees the returns hold up.
What does make a difference to the expected return is the subject you study and how hard you study it. Variation in lifetime returns by broad faculty is particularly clear for men: with a £9,000 fee the estimated rate of return to a degree in Law, Economics or Management is around 29% if you get a 2:1, whereas a 2:1 degree in other social sciences, arts and humanities has a return of around 6% over your lifetime, compared with not going to university (but having the grades to do so). Only managing a 2:2 drops the return by around 4 percentage points. For women the picture is bright whatever the area of study – the average returns are in the high teens for all faculties, with even the lowest estimated return (for a 2.2 in an arts/humanities subject) more than 14%.
So the numbers suggest that on average investing in a degree is a sound investment and will pay a decent return over and above what you would otherwise have earned during the course of your working lifetime.
However these numbers come from aggregating across universities and across courses within broadly defined faculties. Another recent study (Chevalier, 2011) looks at variation in earnings returns within specific degree subjects, for the cohort of graduates who left university in 2003. The finding here is that variation in wages three years after graduating is large across subjects (as Walker and Zhu suggest) but that the variation within a subject is considerably larger. Moreover, the quality of institution attended matters for the estimated return too.
What students really need to know is whether going to University X to read subject Y is expected to give them a positive lifetime return in wages. At present this information is just not available which is a problem – I’m not sure that we would expect people to invest £27,000+ on anything else without a more accurate idea about the likely return on the investment and the variance of that return. All of the requisite information is in theory available and if it could be released (to researchers) and linked together it would provide students with a much more precise picture allowing them to make informed choices about where to go and what to do.
At the same time, what is also needed is much better communication – particularly to young people from less advantaged backgrounds – of how university is paid for. It is not the case that students pay the £27,000+ cost of a university degree. Graduates do. And only if they are earning £21,000 or more per year. If they don’t earn at least this amount, they don’t pay back a thing. And if they are earning above this threshold, a little is deducted directly from their salary each month. Moreover, these student loans are the cheapest form of borrowing – the interest rate is inflation plus a maximum of 3% per annum. Compare this with a typical credit card: 18% APR – that’s about inflation plus14%. So student loan debt is very different to having £27,000 on your credit card which is racking up 18+% APR and will destroy your credit rating, chances of getting a mortgage and risk a visit from the bailiffs if you can’t pay it back.
It is clear that the variation in expected returns by subject and also within subject means that there is risk associated with the investment in a degree, but this risk is insured against by these loan repayment arrangements and having student loans should not damage your credit rating or undermine your chances of getting a mortgage.
So while we can be pretty sure of the costs of a degree and how it is paid back over the lifetime, it is less clear exactly what the expected return to any specific course at a particular university will be. Nevertheless, the statistical evidence that we do have suggests that if you work hard enough then whatever course you choose to do, there will be a positive lifetime wage return on your investment.
Walker, I. and Zhu, Y. (2011). `Differences by degree: Evidence of the net financial rates of return to undergraduate study for England and Wales’, Economics of Education Review, doi: 10.1016/j.econedurev.2011.01.002
Chevalier, A. (2011). `Subject choice and earnings of UK graduates’, Economics of Education Review, doi: 10.1016/j.econedurev.2011.04.007
Peer effects – the impact of friends, colleagues and family members – are clearly central to social life. But quantifying the causal effect of peers is difficult. Peers operate in the same social environment so are affected by the same common outside influences and the impact of behaviour between two (or more) individuals is generally two way – if two people are friends it is likely that each affects the others behaviour. This makes isolating the effect of one person on another very difficult.
Social scientists have spent their time devising ingenious ways of trying to measure peer effects. A favorite amongst American social scientists is to exploit the fact that individuals at University in the USA generally share rooms and that allocation of roommates is often random within gender. This has allowed them to examine peer effects in smoking, underage drinking, religious beliefs and mental health.
But these studies are limited by the fact that American college students are a special group. In addition, the college roommate design does not allow examination of the effect of a very important group of peers: siblings. The time siblings spend with each other is far larger than the time even close friends spend together.
In recent research undertaken between CMPO and the University of Bergen, we have tried to estimate the effect of siblings on each other’s behaviour. Specifically, we were interested in whether having an older sister who has a teen birth increases the chance that her younger sister has a teen birth too. Teen birth is an important issue and it is clear that these run in families. So isolating the effect of peers from other shared influences, such as family income, attitudes and the more general social environment is also important.
To quantify the effect of an older sister, we exploit an educational reform. In Norway in the 1960s the minimum school leaving age was raised from 14 to 16.But in contrast to many other European countries this did not happen all at once. Instead it was rolled out, pretty randomly, across local areas (municipalities) over a 13 year period. This meant that at any one point during this period, there were some children who could leave school at 14 while others, who had very similar backgrounds and lived in similar areas, had to stay on till 16.
Exploiting this ‘natural experiment’, earlier research found that the extra years of education reduced the chance of a girl having a teen birth. So essentially this gave us a ‘natural experiment’ in teen births. Using this we looked at the effect of the reforms on the probability that an older sister would have a teen birth and then of this teen birth on the chances that her younger sister would also have a teen birth. We found large effects: having an older sister who had a teen birth raised the chance of her younger sister having one too from around one in five to two in five. This effect was larger when the sisters were close in age and where families had less resources. This all makes sense: closer age sisters are more likely to spend time together and girls in families with lower resources have more to gain from sharing the costs of having a child (for example, child care).
Finally, the positive peer effect dwarfed the negative effect of an extra two years at school on teen births. This suggests that if policy makers want to reduce teen births, they need to be able to influence what happens within the family.
The research can be found at http://www.bristol.ac.uk/cmpo/publications/papers/2011/abstract262.html