Progression to Higher Education: in the West Midlands:
Widening Participation
Trends in the participation of disadvantaged learners
2002
2003
2004
2005
2006
An Aimhigher Perspective Chris Brownless & Mike Thompson
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - CONTENTS
Contents section
page
section
page
Foreword
3
4.2 Age - Full-time Students
20
1.
4
4.3 West Midlands Region 22 - Ethnic Group Data within Full-time Students
Introduction: Regional Context
2. Measuring Progress: The Challenge
5
3. Modelling Disadvantage
6
4.3.1 Overall Ethnic Profile of Applicants & Acceptances
22 23
3.2 West Midlands Aimhigher Model 7
4.3.2 Growth in Applicants 2002-06 - Ethnic Groups
3.3 Characteristics of the Model
7
4.3.3 Ethnicity & Disadvantaged Groups 24
3.4 Regional Model & the Regional Profile of Disadvantage
8
4.3.4 Ethnicity & the Proportion of Applicants Accepted
25
3.5 Educational Background of WM
9
4.3.5 Ethnicity & Age Groups
26
4.3.6 West Midlands Region - Area Applicants & Acceptances
27
4.4 Disability - Full-time Students
28
3.1 The Index of Multiple Deprivation 6
4. West Midlands Aimhigher Analysis 10 4.1 Applications & Acceptances - Full-time Students
11
4.1.1 National & Regional Perspective
11
4.5 Gender - Full-time Students
30
4.1.2 Regional Analysis & Disadvantaged Communities
12
4.6 Part-time Students
32 32
4.1.3 Socio-economic Classification
14
4.6.1 Regional Profile of Part-time Students
4.1.4 Non-acceptances & Disadvantaged Communities
16
4.6.2 Regional Profile of Part-time Students & Disadvantage
34
4.1.5 Regional & Area Analysis
18
Next Steps
36
4.1.6 Destination of West Midlands Students
19
Glossary
38
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Foreword I am delighted to introduce this Aimhigher publication and hope that you will find it a significant and useful piece of work. Up to this point the data on widening participation in the West Midlands has been patchy. It has been difficult to understand how Aimhigher partnerships and other widening participation initiatives have been progressing. This publication is both opportune and welcome as it brings together for the first time authoritative data on widening participation in the region and demonstrates that progression to HE amongst disadvantaged learners is improving steadily over time. Aimhigher is tasked with raising participation rates to Higher Education for learners from the poorest communities. This necessitates a robust means of identifying disadvantaged learners and their communities. Aimhigher West Midlands has worked hard for the last two years to establish a regional model which will achieve this and provide a framework within which Aimhigher evaluation can take place with a high degree of consistency. It is, of course early days but we can see from the evidence positive movements in patterns of participation. For example, we can see that applications to HE from learners from the 40% most disadvantaged communities have increased over the last four years and are now in percentage terms outpacing applications from the most advantaged. We can also see that applications from disabled students are increasing. This is excellent news for a region where an under-confidence about progression to Higher Education has been entrenched in community cultures. Inevitably there are areas which need more work – for example the progression of learners with vocational qualifications (who are more likely to come from disadvantaged backgrounds), and the related issue of the lower conversion of applications to acceptances amongst the target cohort. Most importantly, to understand our effectiveness we now need to ascertain how far Aimhigher activity is responsible for changing learner attitudes to HE by drawing on other forms of evidence to inform the statistical data . This publication, however, has an importance beyond Aimhigher. It offers information which will be helpful to widening participation practitioners across the educational sectors, to agencies and organisations working on the Regional Economic Strategy and regeneration initiatives, and to advice and guidance professionals. And it gives all of us cause to be positive about the outcome of our collective work on widening participation. I, and all Aimhigher colleagues, are very grateful to Mike Thompson (area coordinator for Birmingham and Solihull and chair of the regional Aimhigher Evidence Group) and Chris Brownless (regional consultant on data) for undertaking the challenging work presented here.
“It is, of course, early days but we can see from this evidence positive movements in patterns of participation.�
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - INTRODUCTION
1. Introduction: Regional Context The West Midlands faces a challenge in terms of its economic performance and its social inclusivity agenda. The impending demographic decline, the low levels of skills in the current workforce and the increasing number of jobs involving higher level skills combine to present a bleak picture unless some way of unpicking the current patterns can be found. The work done by the Regional Observatory on skills shows with great clarity how, and why, the region lags behind others. The region has a broader base of unqualified people in the workforce than most other regions and a sharp taper in those who have higher level qualifications. At the same time while there are distinct improvements in attainment at levels 2 and 3 these do not substantively reduce the gap between the West Midlands and the national average. Moreover the poor A level performance in the region also raises some concerns. In terms of higher level qualifications the West Midlands performs poorly. In 2005 14.8% of its working population were educated to graduate level while the national average was 17.9%. Of the 190,000 students registered in West Midlands HEIs in 2005/6 (excluding the OU) 47% came from the region, and of those West Midlands domiciled students completing in 2005 58% found employment locally. From the perspective of developing a skilled workforce it is clear that more West Midlands learners need to enter HE and more need to be retained in the region to supply the needs of new high value industries. However, the Aimhigher data shows that the participation rate amongst the most advantaged groups, the traditional entrants to HE, has flattened or fallen over the last few years. This would suggest that we may have reached saturation point with these groups and that the larger capacity for increased participation lies with the disadvantaged groups National Statistics Socio-economic Classification (NS SEC) 4-8 who are now beginning to aspire to HE. It is these groups whom for economic and social reasons we need to engage. These are learners who whether young full time or mature part time participants are more likely to study locally and to bring their graduate skills to the region’s labour market. These are learners from the most disadvantaged communities who can help transform local cultures and create virtuous circles.
“More West Midlands learners need to enter HE.�
The work of the Observatory shows that people with lower or no qualifications are more prevalent in the West Midlands urban areas and amongst disadvantaged groups. However, we should not forget the rurally isolated learner in the Marches who finds few educational and guidance opportunities within a convenient distance of home. A priority for the region, which is central to the regional economic and skills strategies has to be the engagement of young learners from a variety of disadvantaged backgrounds and the re-engagement of more mature learners. This will achieve a more equitable participation in higher education across all socioeconomic groups while at the same time helping the region to meet its future employment profile. The task is considerable because of the correlation of low aspiration and poor attainment with disadvantage. However, while numbers are still slight in terms of participation because the base line is so low, the data suggest that learners from disadvantaged backgrounds are beginning to see Higher Education as an opportunity for them.
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2. Measuring Progress: The Challenge Efforts to widen participation in higher education have many stakeholders, all requiring evidence of changes in the volume and nature of entrants to higher education in order to plan, monitor and evaluate the activities they variously fund, engage with or deliver. The primary objective for Aimhigher is to support progress against the Public Sector Agreement target of 50% participation in higher education amongst 17 to 30 year olds by 2010, by raising the aspirations of people from under-represented communities and supporting their attainment and subsequent progression. The mechanism by which progress against this target is assessed is the Higher Education Initial Participation rate (HEIPR). This measure was introduced in 1999 / 2000, and, following several revisions, now incorporates all the statistically significant forms of higher education that England domiciled people aged 17 – 30 might participate in. However, the HEIPR is a national measure, and does not currently allow any interrogation of participation at regional or sub-regional levels, or by gender, ethnicity, disability or social class. It does not provide local and regional Aimhigher partnerships with the evidence they require to demonstrate progress towards the target they have been established to meet. There are numerous other national datasets that seek to measure participation in higher education. Each provides valuable insights, but each has its limitations as a tool for measuring the impact of Aimhigher on the ground.
Some data explore participation in full-time higher education but do not capture the significant and growing cohort of part-time learners, who comprise over 40% of all higher education students. Some focus on youth participation, and thus do not reflect mature entrants. Some are produced annually, enabling trends over time to be monitored, whilst others have been produced for specific year cohorts, and provide a more detailed snapshot of participation that subsequently becomes historic. Most are national in their scope, with few offering insights into patterns of participation at local or regional levels. Many measure the volumes of learners applying to, being accepted and entering higher education, some the proportionate participation of a defined population. The criteria by which the various data seek to measure changes in the numbers and proportions of disadvantaged learners entering higher education also vary. For example, Universities and Colleges Admission Service (UCAS) seek to capture the socio-economic group of full-time applicants using the NS-SEC classification of occupations. This is perhaps the most widely cited evidence of the extent to which participation is or is not widening, but must be viewed with some caution, as over 25% of all applicants do not provide a response. Moreover, there is evidence to suggest that the socio-economic characteristics of this significant cohort of “unknowns� may skew any conclusions drawn from the 75% who do respond. (see pages 14 to 17 for a full exploration of this aspect of UCAS data). Given this abundance of partial or highly aggregated data Aimhigher and its precursor initiatives have faced the challenge of producing evidence of widened participation at a regional and local level. In response Aimhigher West Midlands have developed a Regional Evidence Model with the active support of the six area partnerships.
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - MODELLING DISADVANTAGE
3. Modelling Disadvantage Given the difficulties and limitations of available data it is necessary to consider what other measures of disadvantage or deprivation are available. One that offers new insights is the Index of Multiple Deprivation (IMD), introduced alongside the 2001 census by the Office of the Deputy Prime Minister. It allows the degree of deprivation of very small communities to be identified, which can then be related to UCAS, Higher Education Statistics Agency (HESA) and other data. It is this measure that is used as the basis of the West Midlands Aimhigher Regional Model.
3.1 The Index of Multiple Deprivation
The Index of Multiple Deprivation provides an overall indication of deprivation that is based on seven domains reflecting various dimensions of deprivation. These are weighted, as indicated in the table below, with Income and Employment deprivation being the two largest components. The index provides a measure of deprivation of very small communities called Super Output Areas (SOAs). These can be ranked so that the most deprived communities can be identified. SOAs are smaller than wards and have a typical population of 1,500 with a minimum of 1,000. The index is well recognised and increasingly used to inform public spending and policy development.
The Index of Multiple Deprivation
All IMD domains
West Midlands Aimhigher Model
Domain Weight
Weighted average of selected domains
Domain Weight
Income deprivation
30%
13.5%
Education skills & training deprivation
60%
Barriers to housing & services
9.3%
Barriers to housing & services
10%
Crime
9.3%
Living Environment deprivation
9.3%
Income deprivation
22.5%
Employment deprivation
22.5%
Health deprivation & Disability
13.5%
Education skills & training deprivation
The charts in this section are derived from data from the Office of National Statistics (ONS), [http://neighbourhood.statistics.gov.uk/dissemination/] as well as commissioned data from UCAS. The data is analysed using the West Midlands Aimhigher Model. Information on the Index of Multiple Deprivation can be found on the Communities and Local Government website [http://www.communities.gov.uk/publications/communities/englishindices] from where the data can be downloaded.
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3.2 West Midlands Aimhigher Model
The West Midlands Aimhigher Model uses the IMD as a means of identifying educationally deprived communities at SOA level. A subset of the IMD domains is used and weighted to reflect what are believed to be the most important factors from the IMD to affect opportunities in education and progression to HE. This approach is a better discriminator of Socio-economic Classification (SEC), as well as a whole range of education indicators, than using all domains. The three domains and their weighting are shown on the right of the diagram on page 6. Deprivation is ranked for all West Midlands SOAs and then calibrated in a variety of ways. The main approach in the analysis that follows is based on quintiles where Q1 is the most disadvantaged or deprived and Q5 the least. The Higher Education Funding Council for England (HEFCE) have recently suggested identifying disadvantaged communities as those that are found in the 13,000 most deprived SOAs in England. This corresponds to the 40% most deprived communities or Q1 and Q2 on a quintile scale. Disadvantage
more Quintiles
Q1
Q2
HEFCE
Q3
less Q4
Disadvantaged
Q5
Advantaged
3.3 Characteristics of the Model
It is important to test whether the model reflects other indicators of disadvantage. The charts below show that the qualifications and socio-economic group profiles are highly correlated with the quintiles of deprivation identified by the model. Only 1.5% of 16 to 74 year olds are in the Higher Professional classification in the most deprived quintile in contrast to the least deprived, where the proportion is nearly five time greater. Half of the communities in the most deprived/disadvantaged quintile (Q1) have no qualifications whereas only one in five of the most advantaged quintiles (Q5) lack qualifications and less than one in ten persons in Q1 have level 4/5 qualifications compared with Q5 where more than one quarter have these higher qualifications.
Socio-economic Group % of 16-17 year olds in each classification
20
Qualifications Profile % of 16-17 year olds in each classification
Higher Professional Routine
50
No Qualifications Level 4/5
40
15
30 10 20 5
0
10
Q1
Q2
Q3
Q4
Q5
0
Q1
Q2
Q3
Q4
Q5
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - MODELLING DISADVANTAGE
3.4 Regional Model & the Regional Profile of Disadvantage
The Model’s distribution of SOAs across the West Midland’s Areas is shown in the table below. Birmingham and Solihull is the largest area with 22% of the SOAs and Shropshire, Telford and Wrekin the smallest with 9%. There is a marked difference between the areas in the distribution of disadvantaged SOAs. Only one fifth of Herefordshire and Worcestershire’s SOAs are in the two most disadvantaged quintiles (Q1 and Q2) whereas the Black Country has nearly three times that proportion with 59% in Q1 and Q2.
Percentage of an Area’s SOAs in 2 most Disadvantaged Quintiles (Q1 & Q2)
Area Distribution of SOAs
800
40
700
35
600
30
500
25
400
20
300
15
200
10
100
5
0
B&S
BC
STAFFS COV & W H & W
0
STW
The distribution of SOAs by quintiles of deprivation shown in the chart to the right is the result of using all of the IMD domains and an all-England ranking of deprivation, that is the SOAs in Q1 are the SOAs that are in the fifth most deprived communities across the country. As the West Midlands is relatively deprived compared with England as a whole there are more SOAs in Q1 using this approach, 26%, compared with the 20% of the WM AH Model and less in the least deprived 15% in Q5 instead of 20%.
Q1 Q2
B&S
BC
STAFFS COV & W H & W
STW
Comparison of % Distribution of West Midlands SOAs using England Ranking (All Domains) & Model
30 25 20 15 10 5 0
Q1
Q2
Q3
Q4
Q5
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3.5 Educational Background of West Midlands
GCSE performance is highly correlated with disadvantage. The chart below shows that in 2002/3 the most disadvantaged quintile had less than a third of 15 year olds achieving 5+ passes at grades A*-C whereas 70% of Q5 achieved this level of passes. Also the chart shows that there has been a marked improvement by the most disadvantaged between 2002 and 2005 and the chart on the right shows that the average point score for GCSE and equivalent has increased most in the most disadvantaged quintile.
Average Point Score GCSE & Equivalent % Change 2003/4 to 2004/5
GCSE % of 15 year olds achieving 5+ A*-C 80
8
2002/3
70
2003/4
7
60
2004/5
6
50
5
40
4
30
3
20
2
10
1
0
Q1
Q2
Q3
Q4
0
Q5
Q1
The Model can be used with UCAS data on tariff points to examine the difference between quintiles of disadvantage. The chart to the right shows the distributional difference between quintiles Q1 and Q5. The most advantaged has larger proportions with higher points.
Q3
Q4
Q5
UCAS Applicants % Tariff Distribution Quintiles 1 & 5, 2006
Q2
20
Q1 Q5
15
10
The distribution for both quintiles Q1 and Q5 for 2002/3 and 2006/7 is shown below. In the case of both quintiles there has been a slight shift to the right over these years. This is slightly greater with the most disadvantaged quintile, though its mode is associated with a lower tariff score. West Midlands UCAS Acceptances: % Distribution of A level points of most Disadvantaged Quintile (Q1)
25
2002
5
0
00
9 9 -419 9 9 40 179 -239 0240-299 -79 8080-119 5540 -119 20120-179 11-79 -47 -53 -35 60360-419 420-479 480-539 -29 00300-359 1 - 180180-239 3 420 480 3 24
West Midlands UCAS Acceptances: % Distribution of A level points of least Disadvantaged Quintile (Q5)
20
2002 2006
2006
20
15
15 10 10 5
5 0
00
79 080-119 40 119 -179 0-180-239 239 0-240-299 479 0-480-539 5540 539 359 60360-419 299 0-300-359 1-1-79 -419 20-420-479 8 - 120120-179 18 3 4 30 24 48
0
00
9 -419 9 9 179 -239 0240-299 -79 8080-119 540 539 -119 20120-179 11-79 -47 80-480-539 -35 60360-419 -29 00300-359 540 1 - 180180-239 3 420 420-479 3 24 4
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4. West Midlands Aimhigher Analysis The analysis in the following pages is mainly derived from data commissioned from UCAS and HESA. In combination with the West Midlands Aimhigher Model it produces a clear and largely encouraging picture concerning access to higher education from disadvantaged communities. Whilst the pattern of applications and acceptances has been affected by the new system of student finance (causing an acceleration in applications in 2005 and a consequential decline in 2006) the overriding feature of the analysis is the impact and influence of the disadvantage or deprivation of the community in which the students are domiciled. The more disadvantaged the community the less likely it is that young people will enter higher education. The encouraging feature of this analysis is that this inequality is starting to be addressed. Over the last five years the more disadvantaged communities have produced the largest increases in applications and acceptances. This favourable pattern is also found in applications and acceptances from those with disabilities and most minority ethnic groups (particularly from Black families). The differences between areas within the WM Region would seem to be largely due to the distribution of disadvantaged communities. The degree of disadvantage is a major influence on where a student studies – the more disadvantaged the community the more likely it is the student will study in the West Midlands rather than study in other regions. There is still a long way to go to produce greater equality of access to higher education. Disturbingly there is a correlation between lower acceptance rates (the proportion of applicants who are accepted) and disadvantage. However, the overall conclusion from this analysis is that inequality is being reduced and that the evidence is consistent with the activities of Aimhigher being effective as well as some early indications that the new system of student finance is not discouraging people from lower income backgrounds from applying to higher education.
“Over the last five years the more disadvantaged communities have produced the largest increases in applications and acceptances.�
The charts and tables in sections 4.1 to 4.5 refer to full-time students and are derived from data commissioned from UCAS and analysed using the West Midlands Aimhigher Model.
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4.1 Applications & Acceptances - Full-time Students 4.1.1 National & Regional Perspective
There has been an overall increase in applicants of 7% in the West Midlands in the period 2002-2006, a little lower than the national average of 8%. The pattern of growth of applicants has been affected by the changes in student financing with a spike in applications in 2005 followed by a subsequent fall in 2006/07 when the new system of fees was introduced.
UCAS Applicants England & West Midlands
120
UCAS Accepted Applicants England & West Midlands
120
West midlands
West midlands
England
England 115
110
110
105
105
2002=100
2002=100
115
100
2002
2003
2004
2005
2006
100
2002
2003
2004
2005
2006
The acceptances follow a similar pattern though there are lower rates of increase compared with the growth of applicants. As with applicants the West Midlands has grown slightly less than England, growing by 4% over the five year period 2002-05 compared with the national figure of 5%. Region
2002
2003
2004
2005
2006
Applicants
Region
2002
2003
2004
2005
2006
WM
100
101
101
107
104
England
100
102
103
110
106
Acceptances
WM
100
102
103
109
107
England
100
102
102
111
108
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.1.2 Regional Analysis & Disadvantaged Communities
Using the WM Aimhigher Regional Model the pattern of growth across communities of varying disadvantage or deprivation can be examined.
The more advantaged communities still dominate applications. In 2002 over half (53%) the applicants came from the two most advantaged quintiles, and these groups still account for half of the applicants in 2006. The pie charts show the uneven distribution but indicate that the inequality is being reduced. These differences are not the result of different population bases. Indeed the 2001 census data indicates that the most disadvantaged quintile, Q1, has the largest number of young people of all the quintiles hence the picture conveys an impression that underestimates the inequality of access to HE.
Distribution of Applicants by Disadvantaged Communities in 2002
Distribution of Applicants by Disadvantaged Communities in 2006
Q1 13%
Q5 31%
Q1 15%
Q5 28% Q2 16%
Q2 17%
Q3 18%
Q4 22%
Q4 22%
Q3 18%
Looking at the five year period, 2002-06 there is a clear correlation between disadvantage and growth of applicants - the more disadvantaged the community the higher the rate of growth. There was a 23% growth of applicants from the most disadvantaged quintile compared with only a 1% growth in applicants from the least disadvantaged quintile. The year by year pattern shows that there was a continued growth in 2006 by the two most disadvantaged quintiles whereas the less disadvantaged quintiles followed the national pattern of decline. The often expressed fear that the most disadvantaged groups would be particularly adversely affected by the new student financing arrangements is contradicted by these findings. Indeed the findings are consistent with the view that new arrangements, which give additional support to those from low income families, along with Aimhigher initiatives are encouraging applications from disadvantaged communities.
West Midlands UCAS Applications 2002-2006 % change
West Midlands UCAS Applications 2002-06
25
130
20
120
Q1
Q4
Q2
Q5
Q3
Total
15 110 10 100
0
2002=100
5
Q1
Q2
Q3
Q4
Q5
WM Total
90
2002
2003
2004
2005
2006
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West Midlands UCAS Applications 2002-2006 Quintiles
2002
2003
2004
2005
2006
2006/02 Nos.
%
Q1
4,481
4,643
4,836
5,371
5,518
1,037
23%
Q2
5,296
5,344
5,430
5,895
5,991
,695
13%
Q3
6,180
6,255
6,252
6,579
6,458
,278
4%
Q4
7,551
7,802
7,811
8,266
7,797
,246
3%
Q5
10,072
10,217
10,155
10,564
10,166
,094
1%
WM Total
33,999
34,655
34,889
37,062
36,306
2,307
7%
Acceptances follow a similar pattern to that seen with applicants. The overall increase from 2002 to 2006 was 4%, three percentage points lower than the growth of applications. As with applications the largest increase (19%) was by the most disadvantaged quintile and unlike the three least disadvantaged quintiles there was no decline in 2006.
West Midlands UCAS Acceptances 2002-2006
West Midlands UCAS Acceptances 2002-2006
20
2006/02
120
15
110
10 100
5 Q4
Q2
Q5
Q3
Total
2005
2006
2002=100
-5
Q1
90
0
Q1
Q2
Q3
Q4
Q5
WM Total
80
2002
2003
2004
West Midlands UCAS Acceptances Quintiles Q1
2002 3,492
2003 3,649
2004 3,667
2005 4,029
2006
2006/02
2006/02
Nos.
%
4,156
,664
19%
Q2
4,306
4,310
4,297
4,718
4,734
,428
10%
Q3
5,140
5,240
5,149
5,443
5,292
,152
3%
Q4
6,442
6,588
6,564
6,980
6,462
,020
0%
Q5
8,812
8,806
8,786
9,202
8,660
-,152
-2%
28,548
28,923
28,789
30,689
29,614
1,066
4%
WM Total
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.1.3 Socio-economic Classification
A useful insight into the progress of under-represented groups ought to arise from the data on socio-economic classification collected by UCAS. Unfortunately this is not the case because the question on SEC allows a voluntary response and as a consequence the category ‘unknown’ generates the largest single response. The value of the other responses is reduced further because the ‘unknown’ category has grown by nearly 50% over the last 5 years.
West Midlands UCAS Applicants 2002
2003
2004
2005
2006
2006/02 Nos.
5,523
5,398
5,393
5,304
5,208
-,315
-6%
2 Lower managerial and professional occupations
8,138
8,496
8,509
8,675
8,071
,-67
-1%
3 Intermediate occupations
3,766
3,648
3,741
3,784
3,443
-,323
-9%
4 Small employers and own account workers
1,949
1,945
1,891
2,005
1,889
,-60
-3%
5 Lower supervisory and technical occupations
1,401
1,484
1,491
1,383
1,384
,-17
-1%
6 Semi-routine occupations
4,153
4,161
4,312
4,523
4,251
,098
2%
7 Routine occupations
2,313
2,180
2,151
2,274
2,138
-,175
-8%
8 Unknown
6,756
7,343
7,401
9,114
9,922
3,166
47%
33,999
34,655
34,889
37,062
36,306
2,307
7%
2002
2003
2004
2005
2006
West Midlands UCAS Acceptances 2006/02 Nos.
2006/02 %
1 Higher managerial and professional occupations
4,833
4,719
4,678
4,600
4,496
-,337
-7%
2 Lower managerial and professional occupations
6,948
7,177
7,207
7,387
6,770
-,178
-3%
3 Intermediate occupations
3,162
3,062
3,118
3,100
2,821
-,341
-11%
4 Small employers and own account workers
1,661
1,638
1,560
1,669
1,556
-,105
-6%
5 Lower supervisory and technical occupations
1,182
1,245
1,236
1,181
1,139
,-43
-4%
6 Semi-routine occupations
3,451
3,380
3,472
3,566
3,362
,-89
-3%
7 Routine occupations
1,886
1,798
1,748
1,866
1,756
-,130
-7%
8 Unknown
5,425
5,904
5,770
7,320
7,714
2,289
42%
28,548
28,923
28,789
30,689
29,614
1,066
4%
WM Total
%
1 Higher managerial and professional occupations
WM Total
2006/02
The West Midlands Aimhigher Model can be used to examine the distribution of disadvantage across the ‘unknown’ SEC category. The most disadvantaged quintile accounts for nearly a quarter of all in the ‘unknown’ SEC. The chart and the table on page 15 compare the distribution of applicants and ‘unknown’ SEC by quintiles of disadvantage in 2006. Whilst Q1 account for 15% of applicants it is responsible for 24% on those in the ‘unknown’ category, whereas Q5 accounts for 28% of the applicants but only 19% of those in the ‘unknown’ category.
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UCAS Applicants & Unknown SEC 2006 Distribution by Quintiles of Disadvantage 30
Applicants Unknown SEC
25 20 15 10 5 0
Q1
Q2
Q3
Q4
Q5
This variation across quintiles of disadvantage means that there is a large variation in the proportion of applicants that are of unknown SEC across disadvantaged groups. The chart below shows the SEC of 43% of the most disadvantaged quintile (Q1) is unknown in comparison to 19% of the most advantaged quintile (Q5).
Unknown SEC as % of Applicants 2006 Quintiles of Disadvantage 50
40 30 20 10 0
Q1
Q2
Q3
Q4
Q5
West Midlands UCAS Applicants Unknown SEC as % of Applicants Quintiles
2002
2003
2004
2005
2006
Q1
36%
38%
38%
41%
43%
Q2
26%
28%
29%
30%
34%
Q3
20%
21%
20%
23%
26%
Q4
16%
17%
16%
20%
22%
Q5
12%
14%
14%
18%
19%
WM Total
20%
21%
21%
25%
27%
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.1.4 Non-acceptances & Disadvantaged Communities
The proportion of applicants accepted for HE courses has declined slightly over the last five years from 84% to 82%. There is a marked correlation between the acceptance rate and the degree of disadvantage. The most disadvantaged communities (Q1) has an acceptance rate of 75% in 2006 compared with 85% for the most advantaged group (Q5). This warrants further exploration. One of the challenges of widening participation is to raise aspirations and encourage underrepresented groups to apply for HE. Here there is evidence of cases where this has been achieved but is not then converted into acceptances and places in HE.
Acceptances as % of Applicants 2006 (All ages)
West Midlands Acceptance Rate
85
Quintiles
2002 2003 2004 2005 2006
80
Q1
78%
79%
76%
75%
75%
75
Q2
81%
81%
79%
80%
79%
Q3
83%
84%
82%
83%
82%
Q4
85%
84%
84%
84%
83%
Q5
87%
86%
87%
87%
85%
WM Total
84%
83%
83%
83%
82%
70
0
Q1
Q2
Q3
Q4
Q5
A closer examination of 18 year old applicants shows the same pattern- 21% of the most disadvantaged quintile are not accepted compared with 12% of the most advantage quintile. Non-acceptance Rate 2006 (18 year olds)
25 20 15 10 5 0
Q1
Q2
Q3
Q4
Q5
Total
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For A level applicants there is a difference in the tariff point distribution of the two quintiles Q1 and Q5 with the latter having more applicants in the higher point categories. This may well account for much of the lower acceptance rate for applicants from the most disadvantaged communities when places are rationed on the basis of tariff scores. The graph below shows how the non-acceptance rate for quintiles Q1 and Q5 varies with point score. Not surprisingly the rate declines as point score rises. Both quintiles follow a very similar pattern though for all the categories, except one, the most disadvantaged quintile has the higher non-acceptance rate.
Non-acceptances as % of Applicants (18yo) compared with A-level Points 80
Q1
70
The non-acceptance rate for the most disadvantaged is higher (29% for Q1) than for the most advantaged (23% for Q5)
Non-acceptance rate for BTEC Lower Applicants (18yo) 2006
Q5
30
60 50
25
40
20
30 20
15
10
10
0
0
0
79 0-80-119 40 119 0-120-179 179 0-180-239 419 0-420-479 239 0-240-299 479 0-480-539 5540 359 60-360-419 539 299 0-300-359 1-1-79 8 12 18 3 42 30 24 48
There are some disturbing aspects to the data for disadvantaged communities following a BTEC Lower route to HE. The non-acceptance rate for this route is higher at 22% for all applicants compared with 14% for all routes for 18 year olds. It is a route that is more favoured by the most disadvantaged applicants. BTEC applicants account for 14% of all applicants from the most disadvantaged quintile, Q1, compared with only 6% of the most advantaged, Q5. BTEC Lower Applicants as % of Total Applicants (18yo) 2006
15
5 0
Q1
Q2
Q3
Q4
Q5
All
For the most disadvantaged quintile the combination of the popularity of the BTEC route and the higher non-acceptance rates associated with it and this quintile means nearly 1 in 5 of non-acceptances (19%) for the most disadvantaged is accounted for by the BTEC route compared with 12% for the most advantaged.
BTEC Lower Non-acceptances as % of all Non-acceptances (18yo) 2006
20
12
15
9 10 6 5
3 0
Q1
Q2
Q3
Q4
Q5
All
0
Q1
Q2
Q3
Q4
Q5
All
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.1.5 Regional & Area Analysis
West Midlands UCAS Applications % Change 2002-06 20
The analysis of applicants and acceptances over the last five years in the West Midlands Aimhigher areas shows considerable variation. The following chart and tables show the five year change in applicants and the changes vary from a 16 % rise in applicants in the Birmingham and Solihull area to a 2% fall in Shropshire and Telford and Wrekin.
15 10 5
0 -5
West Midlands UCAS Applicants 2002-06 Areas
2002
2003
B&S
BC
2004
COV & W STAFFS & SoT H & W
2005
2006
STW
2006/02 Nos.
2006/02 %
B&S
8,263
8,690
8,884
9,506
9,577
1,314
16%
BC
6,060
6,021
6,216
6,455
6,478
,418
7%
COV & W
5,341
5,471
5,581
5,882
5,597
,256
5%
H&W
4,839
4,890
4,902
5,190
4,935
,096
2%
STAFFS & SoT
6,265
6,299
6,207
6,735
6,574
,309
5%
STW
2,812
2,890
2,694
2,907
2,769
,-43
-2%
33,999
34,655
34,889
37,062
36,306
2,307
7%
WM Total
WM
West Midlands UCAS Acceptances % Change 2002-06 12
A similar pattern for growth rates is found with acceptances, as the chart to the right and the table below illustrate.
10 8 6 4 2 0 -2 -4 -6
West Midlands UCAS Acceptances 2002-06 Areas
2002
2003
B&S
BC
2004
COV & W STAFFS & SoT H & W
2005
2006
STW
WM
2006/02 Nos.
2006/02 %
B&S
6,787
7,080
7,154
7,609
7,531
,744
11%
BC
5,010
4,963
5,094
5,308
5,254
,244
5%
COV & W
4,487
4,560
4,682
4,894
4,655
,168
4%
H&W
4,089
4,191
4,113
4,427
4,073
,0-16
0%
STAFFS
5,419
5,384
5,137
5,678
5,529
,110
2%
STW
2,400
2,415
2,283
2,456
2,262
-,138
-6%
28,548
28,923
28,789
30,689
29,614
1,066
4%
WM Total
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Growth in Acceptances 2002-06 & % of SOAs in most Disadvantaged Quintile
12
Acceptances
10
% of LSOAs in Q1
8 6 4 2 0 -2 -4 -6
B&S
BC
COV & W
STAFFS
H&W
STW
The variations in growth of acceptances of different areas is highly correlated with the distribution of disadvantaged communities - the higher the proportion of the most disadvantaged communities the higher the growth in acceptances. The chart on the left shows the growth in acceptances for each of the areas and the percentage of the most disadvantaged quintile of SOAs that are found in each of the West Midlands areas. Section 1.2 showed that the more disadvantaged the quintile the higher the growth in applicants and acceptances and given the differences in the distribution of disadvantaged communities between Aimhigher areas, this explains much of the variation in growth of applicants and acceptances between areas.
WM
4.1.7 Destination of West Midland students
There has been a growing tendency for HE students to study near their homes. In 2006 half of WM domiciled students began HE courses in the West Midlands region – an increase of 3 percentage points since 2002.
There is a very marked difference between communities depending on the degree of disadvantage. Three quarters of the most disadvantaged quintile (Q1) study in the West Midlands whereas one third of the most advantaged quintile study in their own region.
Quintiles Of Disadvantage, % of West Midlands Domicles Entering HE in the West Midlands 80
Quintiles Of Disadvantage, % of West Midlands Domicles Entering HE in the West Midlands Quintiles
2002
2003
2004
2005
2006
70
Q1
74%
75%
74%
73%
75%
60
Q2
62%
62%
64%
62%
66%
Q3
50%
50%
53%
51%
53%
50
Q4
41%
42%
40%
41%
42%
40
Q5
33%
33%
34%
33%
34%
30
WM Total
47%
47%
48%
48%
50%
20 10 0
Q1
Q2
Q3
Q4
Q5
Total
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.2 Age - Full-time Students
120
People under 30 years old account for well over 90% of applicants and acceptances. Seventy percent of the applicants are under 20 years old. In the last five years the largest growth rate in applicants was from those over 30 years of age (see the following chart and table).
West Midlands UCAS Applications 2002-06 Age Groups Under 20 20-29 Over 30
115
110
2002=100
105
100
2004
2005
2006
West Midlands UCAS Acceptances 2002-06 Age Groups Age Under 18 18 19 20 21 22 23 24 25-29 30-39 40 and over Under 20 20-29 over 30 WM Total
2002
2003
2004
2005
2006
2006 % of Total
2006/02 Nos.
2006/02 % -29%
,084
,097
,063
,070
,060
0%
,-24
16,967
17,365
17,100
18,450
18,109
50%
1,142
7%
7,366
7,397
7,757
7,711
7,366
20%
-
0%
2,616
2,656
2,550
2,883
2,731
8%
,115
4%
1,410
1,445
1,490
1,462
1,597
4%
,187
13%
1,019
,934
,966
1,049
1,022
3%
,013
0%
,665
,713
,651
,752
,722
2%
,057
9%
,418
,473
,548
,565
,575
2%
,157
38%
1,293
1,391
1,496
1,648
1,649
5%
,356
28%
1,546
1,524
1,591
1,682
1,621
4%
,075
5%
,615
,660
,677
,790
,854
2%
,239
39%
24,417
24,859
24,920
26,231
25,535
70%
1,118
5%
7,421
7,612
7,701
8,359
8,296
23%
,875
12%
2,161
2,184
2,268
2,472
2,475
7%
,314
15%
33,999
34,655
34,889
37,062
36,306
100%
2,307
7%
2003
2002
The increase in applicants of those over 30 years old has not been converted into acceptances as the following chart and tables show.
West Midlands UCAS Acceptances 2002-06 Age Groups
120
Under 20 20-29 Over 30
115
100
2002=100
105
80
2002
2003
2004
2005
2006
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West Midlands UCAS Acceptances 2002-06 Age Groups Age Under 18 18 19 20 21 22 23 24 25-29 30-39 40 and over Under 20 20-29 over 30 WM Total
2002
2003
2004
2005
2006
2006 % of Total
2006/02 Nos.
2006/02 % -36%
,072
,084
,046
,055
,046
0%
,- 26
14,715
14,976
14,819
16,160
15,521
52%
,806
5%
6,298
6,381
6,572
6,616
6,243
21%
,- 55
-1%
2,169
2,199
2,062
2,332
2,200
7%
,031
1%
1,135
1,111
1,120
1,126
1,222
4%
,087
8%
,799
,705
,,696
,741
,728
2%
,- 71
-9%
,498
,528
,454
,536
,487
2%
,- 11
-2%
,308
,352
,389
,,362
,391
1%
,083
27% 22%
,919
,,991
1,041
1,080
1,118
4%
,199
1,162
1,085
1,112
1,131
1,068
4%
,- 94
-8%
,473
,511
,478
,550
,590
2%
,117
25%
21,085
21,441
21,437
22,831
21,810
74%
,725
3%
5,828
5,886
5,762
6,177
6,146
21%
,318
5%
1,635
1,596
1,590
1,681
1,658
6%
,023
1%
28,548
28,923
28,789
30,689
29,614
100%
1,066
4%
The table below shows the growth rates in applicants for the period 2002-06 for different age groups and quintiles of disadvantage. There is a tendency for the most disadvantaged groups to apply at an older age than the most advantaged group (see the chart below, which is colour-coded to reflect these trends).
Growth in Applicants 2002-2006 by Age & Quintile of Disadvantage Age ALL AGES 18 19 20 21 22 23 24 25-29 30-39 40 and over
Applicants 2006 - Age Distribution
Q1
Q2
Q3
Q4
Q5
19%
10%
3%
0%
-2%
12%
7%
5%
5%
4%
21%
14%
-1%
-7%
-12%
30%
11%
0%
-6%
-13%
35%
17%
4%
-11%
3%
0%
-14%
-8%
-8%
-7%
21%
-3%
44%
-28%
-20%
22%
50%
29%
22%
16%
39%
47%
12%
18%
-2%
12%
-5%
-15%
-8%
-22%
22%
39%
33%
38%
-3%
80
Q1
70
Q5
60 50 40 30 20 10 0
18
19
20
21
22
23
24
25-29 30-39 40+
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.3 West Midlands Region - Ethnic Group Data within Full-time Students 4.3.1 Overall Ethnic Profile of Applicants & Acceptances
The pie charts and tables below show the distribution across the main ethnic groups of UCAS applicants and acceptances. White applicants dominate with 70% of all applicants and acceptances. The next largest group is Asians with 16% followed by Black applicants with 5% of the applicants (and 4% of the acceptances) in 2006. Black applicants have grown more rapidly than other groups over the last five years (see next section) and now account for 5% of all applications in 2006 compared with 3% in 2002. It is not easy to calculate the progression rates for different groups because there are no up-to-date figures for population size of the relevant age groups, but comparison with 2004 KS2 figures for the distribution of young people across ethnic groups suggest that Whites and to a lesser extent Mixed Race are ‘underrepresented’ in the UCAS figures whereas Asians and to a lesser extent Blacks are ‘over-represented’.
West MIdlands UCAS Applicants 2006
West Midlands UCAS Acceptances 2006
Asian 16%
No Data 5%
Asian 15%
No Data 5%
Black 5%
Black 5% Mixed Race 5%
Mixed Race 5%
Other 1%
White 70%
Other 1%
White 70%
West Midlands UCAS Applicants % by Ethnic Group
West Midlands UCAS Acceptances % by Ethnic Group
Ethnic group
2002
2003
2004
2005
2006
Ethnic group
2002
2003
2004
2005
2006
Asian
16%
16%
16%
16%
16%
Asian
16%
16%
16%
16%
16%
Black
3%
3%
4%
5%
5%
Black
3%
3%
4%
4%
4%
Mixed Race
2%
2%
2%
3%
3%
Mixed Race
2%
2%
2%
2%
3%
Other
0%
0%
0%
1%
1%
Other
0%
0%
0%
1%
1%
White
71%
70%
70%
70%
70%
White
71%
71%
71%
72%
70%
No Data
8%
8%
7%
5%
5%
No Data
8%
7%
6%
5%
6%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
WM Total
WM Total
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4.3.2 Growth in Applicants 2002-06 - Ethnic Groups
The largest increases in applicants in the period 2002-06 were from the Black (79%) and Mixed Race (71%) ethnic groups. White and Asian applicants grew by 6% over the period, close to the regional average of 7%. The year-by-year pattern for Asian applicants was almost identical to that of White applicants with both groups showing a sharp increase in 2005 followed by a decline in 2006. In contrast, both Black and Mixed Race applicants increased in each year with double-digit percentage increases in all years except for Black applicants in 2006 when the increase was just over 1%.
The pattern of acceptances is similar to that of applicants. The Black and Mixed Race groups display the fastest growth, far in excess of that of the Asian and White groups. As with applicants the last two groups follow a similar path on a year-by-year basis. There is a differential relationship between acceptances and applicants which is considered in section 4.3.4.
West Midlands UCAS Applicants 2002-06
200
200
Asian
Black
Mixed Race
Mixed Race
White
White
2002=100
2002
2003
2004
2005
2006
100
2002
2003
2004
2004
2005
2006
2005
2006
2006/02
2006/02
Nos.
%
West Midlands UCAS Applicants Ethnic group
2002
2003
Asian
5,551
5,681
5,736
6,065
5,891
,340
6%
Black
1,029
1,162
1,419
1,822
1,838
,809
79%
,598
,705
,755
,939
1,020
,422
71%
Mixed Race
WM Total
150
2002=100
100
Asian
Black
WM Total
150
West Midlands UCAS Acceptances 2002-06
Other
,130
,134
,251
, 249
,,119
92%
White
23,979
24,355
24,502
,174
26,056
25,339
1,360
6%
WM Total
33,999
34,655
34,889
37,062
36,306
2,307
7%
2002
2003
2004
2005
2006
West Midlands UCAS Acceptances Ethnic group
2006/02
2006/02
Nos.
%
Asian
4,546
4,684
4,634
4,902
4,693
,147
3%
Black
,740
,860
1,024
1,231
1,263
,523
71%
Mixed Race
,497
,580
,599
,747
,800
,303
61%
Other
,103
,101
,122
,185
,182
,079
77%
White
20,332
20,531
20,573
22,051
20,855
,523
3%
WM Total
28,548
28,923
28,789
30,689
29,614
1,066
4%
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.3.3 Ethnicity & Disadvantaged Groups
Applicants from the minority ethnic groups are far more likely to come from disadvantaged backgrounds than their White counterparts. The distribution of each ethnic group across the quintiles of disadvantaged communities is shown in the bar charts and tables below. For the Asian, Black and Mixed Race groups the largest percentage of applicants in the West Midlands is found in the most disadvantaged quintile. The sharpest contrast is between the White and Black ethnic groups - nearly half (48%) of Black applicants are from the most disadvantaged quintile whereas only 8% of White applicants are from this quintile. This picture is reversed when the least disadvantaged quintile is considered - only 4% Black applicants are from this quintile but a third of White applicants are from this most advantaged quintile.
The relationship between acceptances and disadvantaged groups is essentially the same as the distribution observed with applicants.
UCAS Applicants Ethnic Groups & Disadvantage 2006 50
50
White Black
40
UCAS Acceptances Ethnic Groups & Disadvantage 2006
30
20
20
10
10
Q1
Q2
Q3
Q4
50
0
Q5
UCAS Applicants Ethnic Groups 2006
Q1
Q2
50
Black
Asian
Mixed Race 30
20
20
10
10
Q1
Q2
Q3
Q4
0
Q5
UCAS Applicants - 2006, Ethnic Group Distribution: Disadvantage Quintiles Quintile
Q5
Black
Mixed Race 30
0
Q4
White
40
Asian
Q3
UCAS Acceptances Ethnic Groups 2006
White
40
Black
40
30
0
White
White
Black
Asian
Mixed
Q1
8%
48%
32%
25%
Q2
13%
28%
26%
Q3
19%
13%
Q4
26%
Q5
34%
Q1
Q2
Q3
Q4
Q5
UCAS Acceptances - 2006, Ethnic Group Distribution: Disadvantage Quintiles Quintile
White
Black
Asian
Mixed
Q1
7%
47%
31%
23%
21%
Q2
12%
28%
27%
21%
16%
18%
Q3
19%
13%
16%
18%
6%
12%
16%
Q4
26%
7%
12%
17%
4%
13%
18%
Q5
36%
4%
13%
20%
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4.3.4 Ethnicity & the Proportion of Applicants Accepted
There are differences in acceptance rates (the proportion of applicants that are accepted) between ethnic groups. The rate is highest with White applicants (82% in 2006) and lowest with Black applicants where the rate was below 70% (69%). Much of these differences is likely to be related to the varied distribution of ethnic groups.
West Midlands UCAS Acceptances as % of Applicants 2006
West Midlands UCAS Acceptances as % of Applicants 2002-06
85 80 75 70 65 0
White
Asian
Mixed Race
Ethnic group
2002
2003
2004
2005
2006
Asian
82%
82%
81%
81%
80%
Black
72%
74%
72%
68%
69%
Mixed Race
83%
82%
79%
80%
78%
Other
79%
75%
70%
74%
73%
White
85%
84%
84%
85%
82%
WM Total
84%
83%
83%
83%
82%
Black
Generally acceptances are just under 80% of applicants for all ages and around 87% for 18 year olds but there is a significant difference according to the degree of disadvantage of the applicant. There is a clear correlation between disadvantage and acceptance rates with the lowest quintile experiencing about a 75% acceptance rate in 2006 compared with a rate ten percentage points more for the most advantaged quintile (see page 16).
“There is a clear correlation between disadvantage and acceptance rates.�
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.3.5 Ethnicity & Age Groups
The age profile is similar for most ethnic groups except for Black applicants. The age distribution of applicants is shown in the charts and tables below.
West Midlands UCAS % Age Distribution of Applicants, White & Black Applicants
60
West Midlands UCAS Applicants (2002 = 100)
40 30
10
2005
2006
Under 30
100
102
102
109
105
Over 30
100
98
104
108
109
Total
100
102
102
109
106
Under 30
100
102
103
109
106
Over 30
100
104
112
136
120
Total
100
102
103
109
106
Under 30
100
110
133
163
168
Over 30
100
127
160
241
227
Total
100
113
138
177
179
Under 30
100
120
131
164
177
Over 30
100
91
70
66
93
Total
100
118
126
157
171
Black
0
2004
Asian
20
2003
White
Black
50
2002
White
18
19
20
21
22
23
24 25-29 30-39 40+
The largest single age category for all ethnic groups is the 18 year olds accounting for over half of all White applicants (56%) and around 45% for Asians (44%) and Mixed Races (46%). However only 21% of Black applicants are 18 years old. The Black applicant age profile is also distinctive for the high proportion of over 30 year olds accounting for 20% of all applicants , whereas only 6% of White applicants and 2% of Asians are over 30. These differences do not change the overall picture presented in section 4.3.2. The chart below shows the changes in applicants under 30 years old.
Mixed
Under 30s
2002
2003
2004
2005
2006
White
100
102
102
109
105
Asian
100
102
103
109
106
Black
100
110
133
163
168
Mixed
100
120
131
164
177
West Midlands UCAS Applicants - Under 30s 200
Asian
2002=100
Black Mixed Race White
150
100
2002
2003
2004
2005
2006
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4.3.6 West Midlands Region- Area Applicants & Acceptances
West Midlands UCAS Applicants 2006*
The charts and tables show the growth of applicants and acceptances in each of Aimhigher areas for different ethnic groups. In broad terms the patterns seen for the region as a whole are found in the areas – Asian and White groups have similar patterns and Black and Mixed Race groups grow significantly more rapidly than other groups. Where there are differences from these general patterns it is normally associated with very small numbers. There are differences in growth of applicants and acceptances between areas and these differences are often associated with different distributions of disadvantaged communities and as section 1 showed growth rates are higher the more disadvantaged the community. There are large differences in the distribution of ethnic groups between areas. This is shown in the tables below.
Asian 17% Black 5% Mixed Race 3%
Other 1%
White 74%
West Midlands UCAS Applicants (All ages) 2006 Ethnic Group Asian Black Mixed Race Other White Total* Ethnic Group Asian Black Mixed Race Other White Total*
Birm'ham & Solihull
Black Country
Coventry & Warwick
Hereford & Worcester
Staffs & S-o-T
Shrop & Telford & W
Total WM
2,858
1,737
,748
,123
,280
,100
5,846
1,031
,478
,186
,025
,072
,025
1,817
,416
,205
,143
,067
,126
,049
1,006
,148
,043
,020
,009
,021
,008
, 249
4,580
3,677
4,199
4,576
5,627
2,487
25,146
9,033
6,140
5,296
4,800
6,126
2,669
34,064
Birm'ham & Solihull
Black Country
Coventry & Warwick
Hereford & Worcester
Staffs & S-o-T
Shrop & Telford & W
Total WM
32%
28%
14%
3%
5%
4%
17%
11%
8%
4%
1%
1%
1%
5%
5%
3%
3%
1%
2%
2%
3%
2%
1%
0%
0%
0%
0%
1%
51%
60%
79%
95%
92%
93%
74%
100%
100%
100%
100%
100%
100%
100%
Hereford & Worcester
Staffs & S-o-T
Shrop & Telford & W
Total WM
West Midlands UCAS Acceptances (All ages) 2006 Ethnic Group Asian Black Mixed Race Other White Total* Ethnic Group Asian Black Mixed Race Other White Total*
Birm'ham & Solihull
Black Country
2,216
1,415
, 616
,104
, 224
,0 80
4,655
,702
,344
,120
,016
, 050
,0 19
1,251
,323
,158
,113
,055
, 102
,0 39
,790
,107
,0 29
,0 14
,006
,020
,006
,182
3,693
3,007
3,503
3,767
4,705
2,025
20,700
7,041
4,953
4,366
3,948
5,101
2,169
27,578
Birm'ham & Solihull
Black Country
Coventry & Warwick
Hereford & Worcester
Staffs & S-o-T
Shrop & Telford & W
Total WM
31%
29%
14%
3%
4%
4%
17%
10%
7%
3%
0%
1%
1%
5% 3%
5%
3%
3%
1%
2%
2%
2%
1%
0%
0%
0%
0%
1%
52%
61%
80%
95%
92%
93%
75%
100%
100%
100%
100%
100%
100%
100%
* excludes cases of unknown/no response data
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.4 Disability - Full-time Students
150
More people with disabilities are gaining access to higher education. There was an increase of 31% in acceptances over the last five years from those with a disability. This contrasts with an overall increase of 4% for all students and a rise of 1% for those with no disability. The tables and chart below show the changes. There was no decline in acceptances for students with a disability in 2006 despite the general fall as a consequence of reacting to the new fee regime.
West Midlands UCAS Acceptances 2002-06
No Disability WM Total
2002=100
120
90
Disability
2002
2003
2004
2005
2004
2005
2006
2006/02
2006/02
West Midlands UCAS Applicants Disability
2002
2003
2006
Nos. 1,595
1,590
1,877
1,957
,434
28%
No disability
32,474
33,060
33,299
35,185
33,994
1,520
5%
Total
33,999
34,655
34,889
37,062
36,306
2,307
7%
2002
2003
2004
2005
2006
Disability
West Midlands UCAS Acceptances Disability
2006/02
2006/02
Nos.
%
1,236
1,323
1,297
1,540
1,614
,378
31%
No disability
27,311
27,600
27,492
29,149
27,653
,342
1%
Total
28,547
28,923
28,789
30,689
29,267
,720
3%
Disability
%
1,523
West Midlands UCAS Applicants (2002 = 100)
West Midlands UCAS Acceptances (2002 = 100)
Disability
2002
2003
2004
2005
2006
Disability
2002
2003
2004
2005
2006
Disability
100
105
104
123
128
Disability
100
107
105
125
131
No disability
100
102
103
108
105
No disability
100
101
101
107
101
WM Total
100
102
103
109
107
WM Total
100
101
101
108
103
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A detailed breakdown of acceptances by different category of disability is shown in the table below.
West Midlands UCAS Acceptances Disability
2002
2003
2004
2005
2006
2006/02
2006/02
No disability
27,311
27,600
27,492
29,149
27,653
,342
1%
Learning difficulty
,639
,665
,683
,841
,884
,245
38%
Other disability
,225
,184
,194
,224
,230
,005
2%
Unseen disability
,172
,199
,178
,206
,147
,- 25
-15%
Deaf/partial hearing
,047
,054
,059
,076
,093
,046
98%
Blind/partial sight
,038
,061
,046
,045
,063
,025
66%
Mental health
,030
,040
,038
,033
,060
,030
100%
Wheelchair/mobility
,045
,057
,042
,052
,056
,011
24%
Multiple disabilities
,040
,046
,041
,043
,054
,014
35%
,017
,016
,020
,027
,010
59%
1,066
4%
Nos.
Autistic disorder Unknown/Not Known WM Total
,001 28,548
,347 28,923
The distribution of disability by quintile of disadvantage is shown in the table on the right. There is a correlation between quintile of disadvantage and the proportion of acceptances that have a disability – the less disadvantaged the quintile the higher the proportion of disability. This maybe partly a matter of declaration of a disability as the more disadvantaged the quintile the higher the proportion of ‘not known’. The large difference between the disadvantaged communities is the identification of ‘learning difficulty’, 1.5% of the most disadvantaged quintile (Q1) have declared learning difficulties whilst 3.8% of the most advantaged quintile (Q5) have learning difficulties.
%
28,789
30,689
29,614
West Midlands UCAS Acceptances 2006 - Disability & Disadvantage Disability
Q1
Q2
Q3
Q4
Q5
No Disability
93.8% 94.4% 93.5% 93.2% 93.0%
Disability
4.2%
4.4%
5.5%
5.9%
6.2%
Autistic disorder
0.1%
0.1%
0.1%
0.0%
0.1%
Blind/partial sight
0.3%
0.2%
0.1%
0.3%
0.2%
Deaf/partial hearing
0.3%
0.2%
0.5%
0.3%
0.3%
Learning difficulty
1.5%
1.9%
2.9%
3.7%
3.8%
Mental health
0.2%
0.2%
0.3%
0.2%
0.2%
Multiple disabilities
0.1%
0.1%
0.2%
0.2%
0.2%
Other disability
0.8%
0.7%
0.9%
0.7%
0.7%
Unseen disability
0.6%
0.6%
0.5%
0.4%
0.5%
Wheelchair/ mobility
0.3%
0.2%
0.2%
0.2%
0.2%
Not Known
2.0%
1.3%
0.9%
0.9%
0.8%
WM Total
100%
100%
100%
100%
100%
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.5 Gender - Full-time Students
There has been an 11% increase in female applicants over the last five years. This is significantly greater than the 2% increase in male applicants and has increased further the proportion of female applicants from 54% in 2002 to 56% in 2006.
West Midlands UCAS Applicants Gender
2002
2003
2004
2005
2006
2006/02
2006/02
Nos.
%
Female
18,322
18,858
19,244
20,570
20,361
2,039
11%
Male
15,677
15,797
15,645
16,492
15,945
,268
2%
Grand Total
33,999
34,655
34,889
37,062
36,306
2,307
7%
West Midlands UCAS Applicants
West Midlands UCAS Acceptances
Gender
2002
2003
2004
2005
2006
Gender
2002
Female
54%
54%
55%
56%
56%
Female
53%
54%
55%
55%
55%
46%
45%
45%
45%
100%
100%
100%
100%
Male
46%
46%
45%
44%
44%
Male
47%
WM Total
100%
100%
100%
100%
100%
WM Total
100%
2003
2004
2005
2006
A similar relative picture is seen with acceptances - female acceptances have increased by 7% since 2002 and the proportion of females accepted is 55% in 2006.
The following tables show the relationship between gender and disadvantage for applicants and acceptances. The higher growth rate for females applies across all of the quintiles.
Acceptances 2002-06
Applicants 2002-06 - Gender & Disadvantage
120
Female
2002
2003
2004
2005
2006
Male
55%
53%
54%
56%
56%
45%
47%
46%
44%
44%
56%
56%
57%
57%
57%
Q1 Female Male Q2 Female Male Q3 Female Male Q4 Female Male Q5 Female Male
110
100
2002=100
105
80
2002
2003
2004
2005
44%
44%
43%
43%
43%
56%
56%
56%
57%
58%
44%
44%
44%
43%
42%
54%
54%
56%
56%
57%
46%
46%
44%
44%
43%
51%
54%
54%
53%
54%
49%
46%
46%
47%
46%
2006
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West Midlands UCAS Applications 2002-2006 - % change Gender & Disadvantage
30
25
Female Male
25
West Midlands UCAS Acceptances 2002-2006 - % change Gender & Disadvantage
15
15
10
10
5
5
0
0
-5
Q1
Q2
Male
20
20
-5
Female
Q3
Q4
-10
Q5
Q1
Q2
Q3
Q4
Q5
West Midlands UCAS Acceptances 2002-2006 - Gender & Disadvantage
Female Q1 Q2 Q3 Q4 Q5
2002
2003
2004
2005
2006
100
103
103
110
107
100
101
102
118
121
100
99
101
110
111
100
103
102
108
107
100
103
107
112
105
100
106
104
108
104
Male Q1 Q2 Q3 Q4 Q5
2002
2003
2004
2005
2006
100
100
98
105
100
100
109
108
113
117
100
102
98
109
109
100
101
98
104
98
100
101
96
104
95
100
94
95
101
93
The non-acceptance rate for female students is consistently higher than for males. There is a strong correlation between measures of disadvantage and non-acceptances rates as observed earlier. This analysis suggests that more than one in four (27%) of female applicants from the most disadvantaged communities (Q1) are not accepted for higher education.
West Midlands UCAS Non-Acceptance Rate 2006 - Gender & Disadvantage 30
West Midlands UCAS Non-Acceptance Rate 2006
Female
Quintile
Male
25
Female
Male
20
Q1
27%
22%
15
Q2
23%
19%
10
Q3
19%
17%
5
Q4
18%
15%
Q5
16%
14%
0
Q1
Q2
Q3
Q4
Q5
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.6 Part-time Students 4.6.1 Regional Profile of Part-time Students
There has been an overall growth of part-time students since 2002. From 2002 to 2005 (the latest year for which HESA figures are available) the number of part-time students entering the first year of courses grew faster than full-time students over the same period.
West Midlands Full and Part-time Students 2002-2006
120
FT Acceptances [UCAS] PT 1st Year All Courses All ages [HESA]
115
110
2002=100
105
100
2002
2003
2004
2005
2006
HESA data allows those entering higher education for the first time to be identified. The pie chart on page 33 shows the distribution of part-time students (17-30 year old) on undergraduate programmes in 2005. Those entering the first year of courses account for nearly half of all part-time students and nearly half (46%) of these have not been on courses in HE previously. First year entrants to undergraduate courses generally grew by a large amount (albeit from declines in the previous two years) and within this category those entering HE for the first time in the age group 17-30 years grew most rapidly of all.
“There has been an overall growth of part-time students since 2002.� The charts and tables in this section are derived from data commissioned from HESA, except where otherwise stated and analysed using the West Midlands Aimhigher Model.
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West Midlands PT Students First Year Entrants 2002-05
West Midlands PT UG Students 2005 (17-30 years) 200
PT 1st Year UG 17-30y [HESA]
1st year No prev HE 21%
1st year Not know prev HE 3%
1st year prev HE 23%
PT 1st Year UG New to HE 17-30y [HESA] 150 2002=100
Not 1st year 53%
100
PT 1st Year UG New to HE All ages [HESA]
2002
2003
2004
2005
2006
West Midlands Part-time Students 2002-05 All courses, All ages 1st year No Prev HE 1st year Prev HE
2002
2003
2004
2005
8,741
8,939
9,664
8,706
18,157
20,242
20,247
20,886
2,210
3,102
2,303
2,600
Not 1st year
31,072
29,215
29,392
29,314
Total WM
60,180
61,498
61,606
61,506
UG courses, All ages
2002
2003
2004
2005
1st year Not Known Prev HE
1st year No Prev HE
3,499
5,441
5,973
5,740
1st year Prev HE
5,117
6,217
6,677
7,518
,446
1,174
,726
,805
9,942
15,075
15,648
15,782
Total WM
19,004
27,907
29,024
29,845
UG courses, 30 and under
2002
2003
2004
2005
1st year Not Known Prev HE Not 1st year
1st year No Prev HE
1,464
2,254
2,590
2,548
1st year Prev HE
1,913
2,294
2,386
2,734
,153
,552
,263
,314
Not 1st year
4,395
5,497
5,972
6,307
Total WM
7,925
10,597
11,211
11,903
1st year Not Known Prev HE
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - WEST MIDLANDS AIMHIGHER ANALYSIS
4.6.2 Regional Profile of Part-time Students & Disadvantage
The analysis by quintiles of disadvantage of part-time students joining the first year of courses and entering HE for the first time is shown in the charts below. Both the two lowest quintiles for all ages grew by more than the West Midlands average. The pattern for the under 30’s is less clear, though the highest growth was in the most disadvantaged quintile (Q1).
Growth of West Midlands Part-time Students 2002-05 UG New Entrants to HE
100
UG All Ages
West Midlands Part-time UG New Entrants to HE
200
UG 17-30 yo
80
Q1 Q2
175
Q5 WM Average
60 150 40 125
0
2002=100
20
Q1
Q2
Q3
Q4
Q5
Total
100
2002
2003
2004
2005
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The distribution of disadvantage amongst those entering full-time and part-time courses is almost a mirror image of each other. There is a clear correlation between disadvantage and entrants to HE but the association is reversed between part-time and full-time students. Nearly a quarter of part-tme students come from the most disadvantaged quintile (Q1) compared with only 14% for full-time students whereas the most advantaged quintile (Q5) accounts for 16% of part-time entrants yet 30% of full-time acceptances.
Distribution of New Entrants PT & FT FT Acceptances 17-29 years (UCAS) PT 1st Year New to HE 17-30 years (HESA)
30
The relationship between disadvantage and parttime new entrants with no previous experience is even stronger when Foundation Degrees are considered as the chart below shows
Distribution of New Entrants to Part-time UG courses 30 years or less
30
UG FD
25 20 15
PT
10
FT
25
5
20
0
15
Q1
Q2
Q3
Q4
Q5
10 5 0
Q1
Q2
Q3
Q4
Q5
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - NEXT STEP
Next Steps Progression Rates An important development will be a thorough examination of progression rates. Whilst there is a clear picture of the numbers of students applying for and being accepted into higher education by disadvantaged communities, the population size, which is needed to estimate progression rates, is less straight forward as there will have been population movements since the 2001 census. Nevertheless preliminary work using this data suggests that the progression rates for the least disadvantaged quintile (Q5) is more than five times higher than the most disadvantaged (Q1). Further work will be undertaken using other data sources which are expected to provide more reliable estimates.
Developments to the Model There is a growing recognition and use of SOAs as a measure of disadvantage. Whilst the WM AH have a great confidence in the model used for this publication, which has been subject to considerable research and consideration, it is recognised that there will be an increasing use by other Aimhigher Regions and other bodies of an analysis based on all domains of the IMD (as opposed to the weighted average of selected domains in the present model) and using a national (rather than regional) ranking of deprivation. It is proposed to develop the model so that a comparable analysis is available.
Aimhigher Initiatives & Projects To date the main focus of the model has been on analysing progression to HE using UCAS and HESA data. An important development will be to apply the model (along with some of the existing findings) to data from a range of Aimhigher initiatives and projects. Many of these have been in operation for some years and it is now possible to start to assess the impact and effectiveness of different initiative and project outcomes using the model.
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Use of Data to Prove & Improve Targeting The work outlined in the publication has demonstrated the richness of the West Midlands Aimhigher Model’s analysis of data on progression to HE, and the insights it provides on progress towards widened participation. In providing a framework for identifying disadvantaged communities the model also has applications for the area level targeting of those communities, and the individual learners within them. Recent guidance from HEFCE introduces a requirement for Aimhigher partnerships and institutional higher education widening participation activities to demonstrate the extent to which they engage with learners from deprived IMD areas. It also highlights the importance of familial experience of HE and the occupation of the main wage earner on a learners propensity to progress. West Midlands Aimhigher’s experience of using IMD data provides a firm foundation for development. This data is readily available, easily interpretable, consistent and reliable. However, partnerships and HEIs have expressed concerns about the challenges involved in gathering and interpreting of data on occupational backgrounds, which in turn affect it potential reliability. Young people and parents may not be willing or able to provide such information, and what is captured may not be easily interpreted. The model’s ability to test the correlation of income-contingent indicators such as eligibility for free school meals and the Education Maintenance Allowance enables their use as proxy indicators of socio-economic class. Initial work in this area suggests there is a strong correlation worthy of further exploration.
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PROGRESSION TO HIGHER EDUCATION IN THE WEST MIDLANDS - GLOSSARY
Glossary AH
Aimhigher
B & S
Birmingham and Solihull Area
BC
Black Country Area
BTEC
Business and Technology Council
COV & W
Coventry and Warwickshire Area
GCSE
General Certificate of Secondary Education
H & W
Herefordshire and Worcestershire Area
HE
Higher Education
HEFCE
Higher Education Funding Council for England
HEI
Higher Education Institution
HEIPR
Higher Education Initial Participation Rate
HESA
Higher Education Statistics Agency
IMD
Index of Multiple Deprivation
KS
Key Skills
NS-SEC
National Statistics Socio-economic Classification
ONS
Office of National Statistics
SEC
Socio-economic Classification
SOA
Super Output Area. The SOA used by the WM AH model is the Lower Layer Super Output Area that has a mean population of 1,500
STAFFS
Staffordshire and Stoke-on-Trent Area
STW
Shropshire and Telford and Wrekin Area
UCAS
Universities and Colleges Admission Service
UG
Undergraduate
WM
West Midlands
WM AH Model West Midlands Aimhigher Model (or Regional Evidence Model) The model identifies disadvantaged communities by taking a weighted average of three IMD domains where the weights are:
• Education Domain 60% • Income Domain 30% • Barriers to Housing and Services Domain 10% The model is used to divide the WM region into quintiles where Q1 represents the fifth most deprived SOAs and Q5 the fifth least disadvantaged SOAs Disadvantage
more Quintiles HEFCE
Q1
Q2
Disadvantaged
Q3
less Q4
Q5
Advantaged
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“The work outlined in the publication has demonstrated the richness of the West Midlands Aimhigher Model’s analysis of data on progression to HE.”
Many thanks to John Clawley for pioneering the West Midlands Aimhigher Model and initiating this work. Thanks also to UCAS, HESA, ONS and the Census 2001 for their data that, in conjunction with West Midlands Aimhigher Model, made this document possible. Every effort has been made to ensure the accuracy of data at the date of print (11/07).
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Contact your local Aimhigher office for further information
Birmingham and Solihull Tel: 0121 414 2670 Email: aimhigher@contacts.bham.ac.uk
Black Country Tel: 01902 323592 Email: r.hart@wlv.ac.uk
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Further copies of this publication can be obtained by contacting the Aimhigher West Midlands Regional Office Wolverhampton Science Park, Wolverhampton WV10 9RU Tel: 01902 824437 Email aimhigherwm@wlv.ac.uk
www.aimhigherwm.org
Copies can also be downloaded from www.aimhigherwm.org Alternative formats can be made available on request.
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