Are Teachers the Cause of Educational Inequalities in England?

Recently policy makers have argued that improving the standard of teaching is key to improving children’s educational attainment in those schools which have the lowest levels of performance on national tests. Datasets which contain information on both teacher characteristics and pupil outcomes are rare, however. Whether the quality of teaching is a major factor in the low levels of education in some schools has therefore not been an easy question for researchers to answer, at least directly. Researchers have been able to use routinely collected administrative data, such as the National Pupil Database, to examine the relative importance of schools in explaining differences in children’s educational achievement. The overall finding from this work has been that schools only account for around ten to fifteen percent of the observed variation in children’s achievement. In other words, if all children went to the same school we would expect the variation in children’s achievement to be lower but a significant amount of variation would still exist. The emphasis placed on improving the quality of teaching as a means of increasing educational achievement in the lowest performing schools therefore seems slightly surprising.

A further indication that the main factors causing differences in children’s educational achievement are outside of schools comes from socioeconomic differences in children’s development when they first start school. In England, children’s education is divided into stages with the Early Years Foundation Stage (EYFS) covering the period from birth to age 5 years. The EYFS profile summarises children’s attainment at the end of their reception year in primary school in terms of characteristics which play a central role in learning (e.g. creating and thinking critically). Children who achieved at least the expected level of learning in each area are assessed as having a good level of development. The figure below shows the relationship between the proportion of children who have a good level of development in each local authority in England and the proportion who are eligible for free school meals using data from the Department of Education. The regression line (in blue) shows that as the proportion of children receiving free school meals in each local authority increases there is a fall in the proportion achieving a good level of development. In the local authorities with the lowest proportion of children in receipt of free school meals around 65 percent of children achieved a good level of development while in those areas with the highest proportion of children receiving free school meals only 55 percent of children showed good development at the end of their reception year.

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I am not trying to argue that teaching is unimportant in learning but only question whether differences in teaching are important in causing differences in educational attainment in the population. The idea that the quality of teaching has a key role in causing differences in educational achievement might seem persuasive because most of us would probably have had one or two school teachers who we thought were really great and one or two, perhaps, who we thought were not so good. In considering the effect of teaching on children’s achievement we need to distinguish, however, between the impact of teaching on the level of attainment of the individual child and the impact on the level of attainment in the population. The importance of high quality teaching in explaining the overall pattern of children’s achievement depends both on the degree to which high quality teaching raises educational achievement and the overall proportion of high quality teachers in the teaching workforce. Although having a high quality teacher may have a strong effect on an individual child’s learning, it seems unlikely that the number of high quality teachers are sufficient to explain overall differences in educational achievement in the population. Rather, it seems likely that the impact of teaching is likely to have the most significant impact on the learning of children who have teachers of average quality simply because of the larger number of teachers in this group. This implies that rather than focusing on initiatives, such as Teach First, which aim to increase the numbers of high quality teachers in schools, strategies which try to increase educational achievement by raising teaching quality should aim to improve the quality of teaching across the entire profession. We should acknowledge, however, that few interventions are likely to significantly change the structure of educational inequalities in England on their own. Improvements in the quality of teaching are certainly worthwhile but are unlikely to significantly reduce inequalities in educational achievement without accompanying changes in the socioeconomic situation faced by many families.

Regional Differences in University Admissions in England

In England, the proportion of young people who go to university after completing their A levels varies significantly across regions. In particular, figures from HEFCE show that the proportion of young people from London who enter university is significantly higher than in other regions. At the student level studies have shown that the most important factor influencing admission to university is level of educational achievement. It seems unlikely, however, that variation in the level of educational achievement can entirely explain the higher likelihood of entering university for young people who live in London. The figure below shows the relationship between the proportion of young people going to university and the proportion of school children with 5 GCSEs at grade A to C including English and Maths for parliamentary constituencies in England using data for 2011. The regression line shows that there is a significant positive relationship between participation in higher education and levels of prior achievement. There is significant variation around the regression line, however, suggesting that factors other than prior achievement might be important in influencing the proportion of young people in an area who go to university. In particular, the majority of parliamentary constituencies in London plot above the regression line showing that they have levels of young participation which are higher than that predicted on the basis of prior levels of achievement,

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There are a range of factors, such as proximity to a large and diverse number of higher education institutions, which might explain why young people from London are more likely to go to university than those in other regions with similar levels of academic achievement. There is a strong geographical pattern, however, to higher education participation within London. The figure below shows the geographical distribution of areas across London which have higher and lower numbers of admissions than expected on the basis of their levels of GCSE achievement. The figure shows that there is a pronounced East/West divide in participation with many of the areas of London which have significantly higher than expected proportions of young people going to university in West London (e.g. Brent, Ealing and Harrow) and many of the areas with lower than expected proportions of young people going to university are in East London (e.g. Havering, Bexley and Bromley). The geographical differences in university participation correspond closely to variations in the proportion of young people in each area from ethnic minority backgrounds. Several recent studies have shown that young people from ethnic minority backgrounds are more likely to aspire to go to university than those from White backgrounds and it seems likely that the higher proportion of young people going to university in London may, in part, be related to ethnicity.

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In order to investigate this further I have recently been using survey data from the Longitudinal Study of Young People in England (LSYPE) to examine regional variations in young people’s attitudes to higher education. The work I have been doing is exploratory and has been using information provided by the cohort member at wave 1 in 2004, when they were in year 9, and at wave 7 in 2010, two years after they would normally have taken their A levels. The figure below shows differences between London and remaining regions in young people’s aspirations to go to university at wave 1, GCSE results at age 16 years and actual admissions to university at wave 7 separately for White and non-White cohort members.

Fig3_PlotLSYPE

The first panel shows the variation in the proportion of respondents who stated that they were either very or fairly likely to go to university at wave 1 by ethnic group and region. The figure shows that respondents from non-White backgrounds were more likely to state that they intended to go to university than those from White backgrounds among both respondents who lived outside of London and those who lived in London. The figure also shows, however, that in both non-White and White groups, there is a regional difference of around 10 percentage points in the proportion of young people who intend to go to university with respondents who live in London more likely to intend to go to university than those in remaining regions.

The second panel shows the corresponding variation in GCSE results by ethnic group and region. The figure shows that there is little variation in GCSE results by ethnicity with non-White and White respondents who lived outside of London and non-White and White respondents who lived inside of London having similar GCSE results. There was also no difference in the GCSE results of White respondents between London and remaining regions, however, non-White respondents who lived in London achieved significantly better GCSE results than those who lived outside of London. Finally, the third panel shows the proportion of respondents who were at university at wave 7 by ethnic group and region. The figure shows that respondents from non-White backgrounds were more likely to be at university than those from White backgrounds both among respondents who lived outside of London and those who lived in London. In each ethnic group, respondents were more likely to be at university if they lived in London with the proportion of respondents at university around 10 percentage points higher in London than in remaining regions.

Overall, therefore, the results suggest that the higher aspirations of non-White and White respondents and the relatively good GCSE scores of non-White respondents in London might be able to explain the higher proportion of young people who go to university in London. This finding can be interpreted in several different ways. The simplest interpretation is that the higher aspirations of young people who live in London directly lead to the higher proportion of young people from London going to university. This type of explanation doesn’t give any reason, however, for why young people in London should have higher aspirations to go to university than in remaining regions. It is also possible to view aspirations as being a consequence of region, however. From this perspective, regional differences including opportunities to go to university and the wider economic situation help shape young people’s aspirations to go to university, while the proportion of young people going to university in turn influences both the economic competitiveness of a region and the educational aspirations of younger generations. Circular processes of this type are a common cause of geographical differences and the cumulative nature of change means that they can lead to durable differences in the characteristics of areas. The important point for policies aiming to increase the number of young people going to university in different regions is to recognise that the wider environment has an influence on young people’s aspirations so that a one-size fits all approach is unlikely to be useful.

Increasing Educational Achievement?

A recent report from the National Audit Office argued that school inspections are ineffective in increasing the achievement of failing schools in England. The assumption that policies to improve educational achievement should target schools might seem natural. Policies aimed at improving educational achievement have, however, been targeted at different levels of intervention. Area-based policies which see the broad contexts in which schools are located as influencing educational achievement have been popular with policy makers at different times (e.g. Sure Start in the early 2000’s), while currently the main emphasis is on interventions at the level of the individual child rather than the context (see, for example, the Toolkit produced by the Education Endowment Foundation).
Unfortunately, the research evidence doesn’t provide a great deal of advice for policy makers on whether to intervene at the level of area, school or individual child. Part of the reason for this is that researchers have used different types of relationships to try to understand the determinants of school achievement. Some studies have used cross-sectional data and examined the variation in school achievement across children while other studies have collected longitudinal data and based their analyses on the variation in school achievement within individual children. It is helpful to illustrate the difference between these types of studies with an example. The figure below shows the relationship between the average points score of children at Key Stage 2 (age 11 years) and deciles of neighbourhood deprivation separately for each English region (using data for 2008). The figure shows that if we compare children living in neighbourhoods characterised by different levels of deprivation we find that there is a marked negative gradient in school achievement with neighbourhood deprivation in each region. In contrast, however, those studies which have based their conclusions on the relationship between neighbourhood deprivation and school achievement on the within child relationship between changes in neighbourhood deprivation and changes in school achievement have concluded that neighbourhood deprivation has little effect on school achievement.
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In most studies the tendency has been to assume that the within-child relationship gives the more reliable estimate of the effect of neighbourhood deprivation on school achievement. The reason for this is that families in different types of neighbourhood differ in terms of both observed and unobserved family characteristics such as income, health, parenting style etc. If these effects are constant over time they can be removed from the analysis when we take differences within a child. In consequence, the within-child relationship is often considered to be a more accurate reflection of the true relationship between neighbourhood deprivation and children’s achievement than the between-child relationship.
There are some drawbacks, however, to this approach. Firstly, the extent of change in neighbourhood deprivation experienced by children over time will typically be much smaller than the variation in neighbourhood deprivation between individual children. For example, the figure below shows the relationship between the proportion of the working age population in receipt of a low-income benefit in 2002 and 2010 for areas with a population of around 1500 people for each region in England. The figure shows that the variation in the proportion of the population in receipt of a low-income benefit across areas is much higher than the change within an area. While the proportion of the population in receipt of a low-income benefit does vary from year-to-year, these changes are small in comparison to the extent of variation across areas. Studies which base their conclusions on the within child relationship discard the between area variation in neighbourhood deprivation and throwing away the majority of information in the data should be more of a concern for researchers.
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From a substantive perspective, it also seems doubtful whether changes in neighbourhood deprivation capture the structural aspect of neighbourhood deprivation in a reasonable way. In the social sciences, structural characteristics refer to factors over which individuals have limited control and which are therefore relatively durable. The concept of neighbourhood deprivation most relevant to children’s achievement should therefore refer to the long-run average conditions in a neighbourhood rather than to year-to-year changes in neighbourhood deprivation. Unless children move home, the ranking of where they live in terms of neighbourhood deprivation would appear likely to be durable throughout their childhood and it is this structural aspect of neighbourhood deprivation which influences the routines that families adopt and which in turn have the most direct influence on children’s achievement. This structural characteristic of neighbourhood deprivation is not captured in studies which measure the effect of neighbourhood deprivation on children’s attainment using changes in the level of neighbourhood deprivation experienced by individual children.
It is possible to estimate how much of the variability in children’s experience of neighbourhood deprivation is due to differences between individual children, differences due to neighbourhood change or due to residential mobility using statistical techniques. I’ve recently been working on this using nine years of data for a cohort of children who started school in 2002 using data from the National Pupil Database. The results show that more than 85 percent of the variation in children’s exposure to neighbourhood deprivation is between individual children while around 10 percent is due to residential mobility with neighbourhood change accounting for the remainder. While approaches that discard the between child component of neighbourhood deprivation and estimate the effect of neighbourhood deprivation on educational achievement from variation within children might be technically sophisticated they don’t seem to be answering a useful question.

Is Money Multiplicative?

When I was looking at data on the wealth of UK universities recently I plotted the value of institutions net assets on the y-axis using a log scale. Using a log scale means that equal differences refer to a constant multiplicative factor, rather than, as is more usually the case, a constant additive factor. For example, the distance on the y-axis from 500000 to 1000000 is the same as the distance from 1000000 to 2000000. It is standard to use a log scale when there are a significant number of observations which are more than three standard deviations above the mean. There are quite a few variables which have this type of distribution with lots of low values but only a few high values, including household wealth, city populations and the number of citations for academic papers.

Distributions which have a long tail are almost always the result of processes which are multiplicative rather than additive. Phenomenon which result from multiplicative processes can be modelled using a power law distribution which has the form:

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where c and α are constants. In the social sciences, variables that have a power law distribution are often produced by what are termed processes of cumulative advantage. These refer to processes in which patterns of cumulative causation lead to small initial differences between people or between institutions becoming magnified over time. Multiplicative phenomena may also be described, however, using the lognormal distribution. The log-normal distribution is the natural way to model phenomena, like investment funds, which grow by a small multiplicative factor each period.

The difference between the type of process that lead to power law and lognormal distributions can be illustrated with an example. If we put a given sum of money in the bank and reinvest the interest, the amount by which the sum accumulates depends on the initial investment. If the interest rate is 10 percent then a sum of £1000 grows by £100 but a £10000 sum grows by £1000. The difference in the value of the two investments grows exponentially over time from £9900 at the start to £14400 after 5 years and £23300 after 10 years. The ratio of the value of the two investments remains constant over time (at 10:1), however. If instead the interest rate depends on the initial sum (for example, 5 percent interest on £1000 but 10 percent interest on £10000) then both the difference and the ratio of the value of the two investments would grow over time.
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One way for determining whether a variable follows a power law or a lognormal distribution is to plot P[X > x] on a log-log scale. Both power law and lognormal distributions should follow a straight line but the power law distribution should have a shallower slope because of its longer tail (see Mitzenmacher 2005). For an empirical example, I decided to look at whether the size of universities assets followed either a power law or lognormal distribution which might tell us something about the kind of processes influencing inequalities in higher education. I used data from 2013 for the value of the endowment funds for US and Canadian higher education institutions (www.nacubo.org). The US institution with the largest endowment fund is Harvard ($32 billion) followed by Yale ($20 billion) and the University of Texas ($20 billion). In contrast, over 90 percent of institutions have an endowment fund with a value of less than $1 billion. The figure above shows a log-log plot of P[X > x] for the endowment funds of US institutions with fitted lines for both a power law distribution and a lognormal distribution. The figure suggests that the lognormal distribution is a better fit than the power law distribution. Although the universities with the largest endowments are wealthier than other institutions, inequalities in endowments between institutions seem to be described by differences in inherited wealth and constant proportional growth, rather than by the ability of the most wealthy institutions to benefit preferentially from their financial assets.

The Wealth of Universities

The Sunday Times published its annual league table of university rankings a few weeks ago. The league table uses a set of indicators to try to rank universities along a single dimension which can broadly be thought of as prestige. The University of Cambridge and Oxford were placed joint first followed by the University of St. Andrews, Imperial College London and the London School of Economics. Institutions do change their position in the league table from year-to-year although long-term movement in position tends to be uncommon. The top ten institutions were the same this year as last year with only minor changes in the ranking of individual institutions. The single most important factor in the stability of a universities position in the league table is the entry qualifications of the students at each institution. In general, students who have achieved good A level grades want to go to the more prestigious institutions, leaving students who didn’t do so well in their exams with little option other than to apply to institutions where competition for places is less intense. In consequence, there is a strong pattern of cumulative causation influencing an institutions league table position. League table position influences the level of achievement of applicants while the level of achievement of entrants influences league table position.

The choices made by students with different levels of academic achievement are not the only factor influencing a universities league table position, however. The figure below shows the relationship between the league table items that measure the inputs of different resources into education: the entry qualifications of students but also student/staff ratio and spending on student services (£). The Russell group institutions which are usually considered to be the elite universities are in blue. The figure shows that there is a positive relationship between entry qualifications and spending on services (r = 0.71) while the staff/student ratio shows a negative relationship to both entry qualifications (r = -0.77) and spending on services (r = -0.60). The league table of the more prestigious institutions are not therefore due to a single factor but are due to cumulative advantages including the level of financial and staff resources which they have available. pairs

The magnitude of the variation in student spending and staff resources across institutions seems to be quite large. For example, the spending on services varies by a factor of around three from over £3000 per student each year to less than £1000 per student. It would seem therefore that universities do not all have the same financial resources per student, despite the majority of institutions charging the same tuition fee of £9000 per year. The financial resources available to universities to fund their teaching activities include tuition fees but also income from endowment funds that the more prestigious institutions, in particular, have accumulated over time. The plot on the left in the figure below shows the relationship between the net assets of universities (on a log scale) and their position in the Sunday Times league table. While the majority of universities have relatively modest assets, the endowment funds of the most prestigious institutions are significant. For example, Cambridge has net assets of over £3 billion and Oxford assets of £2.4 billion while, at the other end of the scale, the University of Bolton has net assets of around £50 million. The figure also shows that there is a relationship between the financial position of an institution and its league table position with those institutions with greater assets achieving better positions in league tables than the less wealthy institutions. The financial advantages that prestigious institutions have over other institutions seem important therefore in attracting the best students and maintaining their position at the top of league tables.

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The most important question about economic inequalities tends to be whether inequalities are increasing over time rather than the absolute size of differences between those at the top and those at the bottom. The plot on the right in the figure above shows the growth in net assets (on a log scale) for Russell group institutions over the period since 2005/06, adjusted for inflations using the CPI. The figure shows that the net assets of most institutions have grown at a rate of somewhere between 6 and 8 percent per year. The net assets of the most well-off institutions, such as Cambridge and Oxford, do not appear to have increased at a significantly faster rate than those of other institutions in the Russell group. The common rate of growth in the value of net assets across institutions would seem to suggest that investment returns have been the main factor in the increased value of assets. The extent to which the variation in wealth across institutions can be explained by donations from alumni is unclear. At least over the period considered, however, the variation in the donative wealth of alumni would not seem to be the prime factor in the variation of wealth across institutions.

Destinations of Students Leaving Higher Education

In the UK, research on higher education has tended to focus on factors associated with admission to university while, only a handful of studies have examined the labour market outcomes of students following university. The majority of studies which have examined the labour market outcomes of students following university have focused on the returns to having a degree and how this varies by factors such as gender, subject of study and institution. The results of these studies have shown that graduates receive a significantly higher wage than non-graduates. The returns to having a degree vary with the subject of study and type of institution attended, however, with students who studied medicine, mathematical and computer sciences or law and those who attended more elite institutions obtaining the highest returns to having a degree.

A further dimension of labour market inequality among graduates relates to the type of job obtained following graduation. In particular, there has been increasing concern about the number of students leaving higher education who do not find jobs for which a degree is necessary. The extent of over-education may be measured using the occupations of the jobs obtained by graduates and using this criterion around 20 percent of graduates do not find jobs for which a degree is usually necessary. Although some studies have sought to explain over-education as a result of individual differences in skills, the extent of over-education among graduates can be related to wider patterns of economic change. Research has shown that over the last several decades there has been a polarisation in the nature of employment with an increase in the number of jobs at both the top and the bottom of the occupational hierarchy and a decline in intermediate occupations. Occupational changes in the labour market have been accompanied by a rise in wage inequality. Although wages vary within occupations to a greater degree than they do between occupations, all of the rise in wage inequality in the UK over the last several decades can be explained by changes taking place between occupations (Williams 2012).

Although the nature of the labour market that students enter after graduation has changed significantly over the last several decades little research has examined changes in the type of jobs obtained by students leaving higher education. The Destination of Leavers from Higher Education (DLHE) survey can be used, however, to describe changes in the types of job obtained by students leaving higher education. The DLHE is carried out with all students leaving higher education approximately six months after they complete their course. The survey asks respondents for their current activity and, if employed, collects information on the type of job.

The figure below shows the variation in the proportion of first degree respondents in employment who were working in each of the nine major occupational groups, separately by gender and age group for the cohorts of students who left university between 2006 and 2012. The figure shows that in each year between 50 and 60 percent of 21 to 24 year old and around 70 percent of 25 to 29 year old graduates were employed in either professional or associate professional occupations. The most notable change in the type of occupations in which graduates were working is the steady decline in the proportion finding jobs in administrative and secretarial occupations. The decline in the number of graduates working in administrative and secretarial occupations over the period has been concentrated mainly among women, with the percentage of 21 to 24 year old women employed in administrative and secretarial occupations falling from 20 percent in 2006 to around 12 percent in 2012.

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Change from 2006 to 2012 in the occupations in which first degree leavers were employed (as a percentage of all those in employment) separately by gender and age group.

The corresponding figure for postgraduate students is shown below. For postgraduates, the figure shows that at least 90 percent of postgraduate leavers who were in employment were employed in jobs in the top three occupational categories (managers, professional, associate professional and technical). The figure also shows that there has been a steady decline, however, in the proportion of postgraduate students working in professional jobs and a rise in the proportion working in associate professional and technical occupations. The rise in postgraduates working in associate professional jobs suggests either that postgraduates are ‘bumping down’ in the labour market because they can’t get professional jobs or that ‘up-skilling’ has increased the skills needed to do associate professional jobs. What is clear, however, is that the chances of getting a professional job are much higher for postgraduate students in comparison to undergraduates. The introduction of higher tuition fees for undergraduate students has been accompanied by a number of steps aiming to ensure that young people from disadvantaged backgrounds are not excluded from going to university. In contrast, there has been no attempt by government to offset the effect on students from different socioeconomic backgrounds of rising fees for many postgraduate courses. Given that postgraduate degrees are now needed for many professional jobs, this is likely to limit the extent to which the expansion of higher education will increase levels of social mobility in the UK.

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Change from 2006 to 2012 in the occupations in which postgraduate degree leavers were employed (as a percentage of all those in employment) separately by age group and gender

Higher Education League Tables and Performance Indicators

The Guardian university league tables for 2015 were published earlier this month.

http://www.theguardian.com/news/datablog/2014/jun/03/undergraduate-university-guide-2015-download-the-guardian-tables

Cambridge was in first place followed by Oxford and, in third place, St. Andrews. The league tables are constructed using information on a range of university characteristics including ratings of student satisfaction from the National Student Survey, the resources available to students at an institution (the average entry tariff of students, expenditure per student and student-staff ratio) and student’s career destinations following university. The indicators are first standardised and then added-up using a series of weights which reflect the importance of the different indicators. The use of the entry tariff as an indicator of university quality is often criticised because the educational achievement of students prior to starting university would seem not to have a clear link to anything universities actually do themselves. It is clear, however, that students with higher achievement tend to choose to go to the more prestigious Russell group institutions. The entry tariff measures the number of points students have been awarded based on their A level grades. The figure below shows the average entry tariff for the top twenty universities in the league table ordered by their ranking. The average entry tariff of students at Oxbridge is equivalent to more than four A* grades while further down the hierarchy three A* grades are the average entry requirement for students at universities such as Lancaster, York and Southampton.

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League tables are not the approach used by the government bodies responsible for regulating universities, however. For this purpose, the Higher Education Statistics Agency (HESA) produce an annual set of performance indicators. The performance indicators are chosen to reflect important aspects of how higher education functions. For example, the proportion of students from low social class backgrounds is used as an indicator of widening participation. The performance indicators are published individually rather than aggregated into an overall index and are also distinguished from league tables in using indirect standardisation to calculate a benchmark value of each indicator for each institution. The standardisation used in the calculation of the performance indicators aims to adjust for factors related to performance but which institutions can’t do much about, including the entry tariff of students. The benchmarks for institutions indicate what the national value of the relevant indicator would be if all universities in the country had a particular institutions students. The benchmarks therefore indicate roughly what an effective level of performance would be on an indicator for each institution.

The table below gives a simplified example of how the benchmark is calculated using the proportion of students from low social class backgrounds as an example. In the table, students are grouped into three tariff score categories (lower tariff, middle tariff and higher tariff). In the country as a whole, it is assumed that the proportion of students from low social class backgrounds varies from 70 percent for lower tariff students to 50 percent for middle tariff and 30 percent for higher tariff students. The benchmark at our hypothetical University X is calculated by applying the national rates to the number of students in each tariff score category. For example, if the proportion of students from low social class backgrounds was the same at our hypothetical institution as in England as a whole, we would expect that among students with lower tariff scores we would have 1750 students (0.7 x 2500) from low social class backgrounds. Repeating the calculation for the other tariff score groups shows that while 44.2 percent of students at this institution come from low social class backgrounds, we would expect a benchmark of 58.8 percent of students to have been from low social class backgrounds given their tariff scores. Students from low social backgrounds seem therefore to be underrepresented at our hypothetical institution.

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Table 1 Illustration of indirect standardisation

Because there are large differences in the tariff scores of students at different institutions standardisation can make a big difference to the value of an indicator. The figure below shows the benchmark (or standardised) and actual figures for 2012 for the institutions with the highest and lowest proportion of students from low social class backgrounds. The figure shows that the proportion of students from low social class backgrounds at the most selective universities are well below their benchmarks. For example, while around ten percent of students admitted to Oxford come from low social class backgrounds we would expect the figure to have been around 18 percent based on student’s tariff scores. The difference between the benchmark and the actual proportion of students can be used to estimate the number of students with specific characteristics who are missing at the top universities. The Sutton Trust have estimated that there are around 3000 fewer students from state schools than expected at the top dozen universities suggesting that factors other than tariff score play a part in influencing admissions at these institutions.

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