Unequal success? A comparison of employment outcomes between graduates with and without

disabilities

By Pearl Mok April 2010

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Contents Contents .............................................................................................................. 2 List of tables ........................................................................................................ 3 List of figures ....................................................................................................... 4 List of models ...................................................................................................... 4 List of abbreviations ............................................................................................. 6 Copyright statement ............................................................................................. 7 Summary ............................................................................................................. 8 Acknowledgements ............................................................................................ 13 Chapter 1 - Introduction ..................................................................................... 14 Chapter 2 - Background and Context ................................................................ 17

2.1 Defining disability ..................................................................................... 17 2.2 Disabled people in Britain and in the labour market ................................. 19 2.3 Growth in disabled students in HE ........................................................... 22 2.4 Characteristics of disabled students in HE ............................................... 24 2.5 Disabled graduates in the labour market .................................................. 24 2.6 Limitations of research on disabled people .............................................. 26

Chapter 3 - Methods of Analysis ........................................................................ 28 3.1 Aim and objectives ................................................................................... 28 3.2 Sources of data ........................................................................................ 29 3.3 Destinations of Leavers from Higher Education (DLHE) survey ............... 29 3.4 Labour Force Survey (LFS) ...................................................................... 30 3.5 Analytic strategies .................................................................................... 33 3.6 Technical notes ........................................................................................ 33

Chapter 4 - Comparison of Characteristics and Employment Outcomes Between Graduates With and Without Disabilities ............................................................................. 35

4.1 Six months after graduation - what does the DLHE data tell us? ............. 35 4.2 Destinations six months after graduation by types of disability ................ 56 4.3 Graduate careers later on - what does the LFS tell us? ........................... 58 4.4 Summary .................................................................................................. 70

Chapter 5 - Untangling the Relationship Between Disability and Graduate Employment Outcomes .......................................................................................................... 72

5.1 Multivariate analysis ................................................................................ 72 5.2 Results from multivariate analysis ............................................................ 75 5.3 Summary .................................................................................................. 90

Chapter 6 - Discussion and Conclusion ............................................................. 92 6.1 Discussion ................................................................................................ 92 6.2 Conclusion and suggestions for further research ..................................... 97

Appendix A - Defining graduate jobs - SOC(HE) ............................................... 99 Appendix B – Subject areas by types of disability ........................................... 100 Appendix C - Employment outcomes six months after graduation, by types of disability 101 Appendix D - Demographic and educational background of LFS respondents 107

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Appendix E - Standard errors and 95% C.I. of some of the main types of industries reported in the LFS ........................................................................................................ 109 Appendix F - Binary and multinominal logistic regression ............................... 110 Appendix G – Model parameters ..................................................................... 112 References ...................................................................................................... 136 List of tables Table 4.1 Characteristics of respondents and non-respondents to the 2006/07 DLHE survey

................................................................................................................... 36 Table 4.2 Types of disability reported by students ............................................. 37 Table 4.3 Comparison of demographic and academic background between disabled and

non-disabled graduates .............................................................................. 41 Table 4.4 Activities of disabled and non-disabled graduates ............................ 47 Table 4.5 Employment circumstances of disabled and non-disabled graduates 48 Table 4.6 Duration of employment ..................................................................... 50 Table 4.7 Requirement of qualification to get the job ......................................... 51 Table 4.8 Types of industry of employed graduates .......................................... 52 Table 4.9 Major occupational group six months after graduation ...................... 53 Table 4.10 Comparison of SOC(HE) between disabled and non-disabled graduates 55 Table 4.11 LFS respondents by disability status ............................................... 58 Table 4.12 Main health problems reported by LFS respondents ....................... 59 Table 4.13 Employment outcomes of graduates in LFS ................................... 66 Table 4.14 Types of industry of employed graduates in the LFS ....................... 68 Table 4.15 Gross weekly pay reported in the LFS (£) ........................................ 69 Table 4.16 Gross hourly pay reported in the LFS (£) ......................................... 69 Table A. SOC(HE): a classification of graduate occupations ............................. 99 Table B. Subject areas by types of disability (% of disabled graduates) .......... 100 Table C1. Activities six months after graduation by types of disability ............. 101 Table C2. Employment circumstances by types of disability ........................... 102 Table C3. Duration of employment by types of disability ................................. 103 Table C4. Whether a degree qualification was required in obtaining the job, by types of

disability .................................................................................................... 104 Table C5. Major occupational groups, by types of disability ............................ 105 Table C6. SOC(HE) by types of disability ........................................................ 106 Table D. Demographic and educational background of LFS respondents ....... 107 Table E. Standard errors and 95% C.I. of some of the main types of industries reported in

the LFS ..................................................................................................... 109

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List of figures Figure 5.1 How disability affect the probabilities of being in different employment outcomes

(relative to being in full-time paid work) ...................................................... 75 Figure 5.2 Probability of being unemployed relative to the probability of being in full-time

paid work, by types of disability .................................................................. 76 Figure 5.3 Probability of being in part-time paid work relative to the probability of being in

full-time paid work, by types of disability ..................................................... 77 Figure 5.4 Probability of being in voluntary/unpaid work relatively to the probability of being

in full-time paid work, by types of disability ................................................. 78 Figure 5.5 Probability of being in further study only relative to the probability of being in full-

time paid work, by types of disability .......................................................... 79 List of models Model 1a. Multinominal logistic regression for predicting graduates’ activities six months

after graduation, by disability status ......................................................... 112 Model 1b. Multinominal logistic regression for predicting graduates’ activities six months

after graduation, by types of disability ...................................................... 113 Model 2a. Multinominal logistic regression for predicting graduates’ employment

circumstances six months after graduation, by disability status................ 115 Model 2b. Multinominal logistic regression for predicting graduates’ employment

circumstances six months after graduation, by types of disability ............. 116 Model 3a. Multinominal logistic regression for predicting graduates’ duration of employment

six months after graduation, by disability status ....................................... 117 Model 3b. Multinominal logistic regression for predicting graduates’ duration of employment

six months after graduation, by types of disability .................................... 118 Model 4a. Multinominal logistic regression for predicting graduates’ major occupational

group six months after graduation, by disability status ............................. 119 Model 4b. Multinominal logistic regression for predicting graduates’ major occupational

group six months after graduation, by types of disability .......................... 120 Model 5a. Binary logistic regression for predicting graduates’ employment in a graduate or

non-graduate job as defined by SOC(HE), six months after graduation ... 121 Model 5b. Binary logistic regression for predicting graduates’ employment in a graduate or

non-graduate job as defined by SOC(HE) six months after graduation, by types of disability .................................................................................................... 122

Model 6a Multinominal logistic regression for predicting graduates’ employment in the four SOC(HE) graduate job categories six months after graduation, by disability status 123

Model 6b. Multinominal logistic regression for predicting graduates’ employment in the four SOC(HE) graduate job categories six months after graduation, by types of disability 124

Model 7a. Binary logistic regression for predicting whether a degree qualification was required for entry into a job, by disability status ........................................ 125

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Model 7b. Binary logistic regression for predicting whether a degree qualification was required for entry into a job, by types of disability ..................................... 125

Model 8a. Multinominal logistic regression for predicting the level of agreement with whether a degree qualification was required for entry into a job, by disability status126

Model 8b. Multinominal logistic regression for predicting the level of agreement with whether a degree qualification was required for entry into a job, by types of disability127

Model 9. Multinominal logistic regression for predicting graduates’ economic activity, by disability status ......................................................................................... 128

Model 10. Multinominal logistic regression for predicting graduates’ socio-economic class (NS-SEC), by disability status................................................................... 129

Model 11. Multinominal logistic regression for predicting graduates’ major occupational group, by disability status ......................................................................... 130

Model 12. Binary logistic regression for predicting employment in a job with supervision responsibilities .......................................................................................... 131

Model 13. Binary logistic regression for predicting graduates’ employment in private/public sector ........................................................................................................ 131

Model 14. Binary logistic regression for predicting full- or part-time employment, by disability status ........................................................................................................ 132

Model 15. Binary logistic regression for predicting employment in a graduate or non-graduate occupation as defined by SOC(HE) ........................................... 132

Model 16. Multinominal logistic regression for predicting employment in the different categories of SOC(HE) ............................................................................. 133

Model 17 Linear regression model for predicting log of gross weekly earnings for full-time employed graduate employees................................................................. 134

Model 18 Linear regression model for predicting log of hourly pay for full-time employed graduate employees ................................................................................. 134

Model 19 Linear regression model for predicting log of gross weekly earnings for part-time employed graduate employees................................................................. 135

Model 20 Linear regression model for predicting log of hourly pay for part-time employed graduate employees ................................................................................. 135

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List of abbreviations AGCAS Association of Graduate Careers Advisory Services CI Confidence interval DDA Disability Discrimination Act DIUS Department for Innovation, Universities and Skills DLHE Destination of Leavers from Higher Education DRC Disability Rights Commission DSA Disabled Student Allowance HE Higher education HECSU Higher Education Careers Services Unit HESA Higher Education Statistics Agency ILO International Labour Organization LFS Labour Force Survey -2LL -2 Log likelihood NS-SEC National Statistics Socio-economic Classification ONS Office for National Statistics PGCE Postgraduate Certificate in Education SE Standard errors SENDA Special Educational Needs and Disability Act SMEs Small-medium enterprises SOC Standard Occupational Classifications UCAS Universities and Colleges Admission Services

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Copyright statement Copyright in text of this dissertation rests with the author. Copies (by any process) either in

full, or of extracts, may be made only in accordance with instructions given by the author.

Details may be obtained from the appropriate Graduate Office. This page must form part of

any such copies made. Further copies (by any process) of copies made in accordance with

such instructions may not be made without the permission (in writing) of the author.

The ownership of any intellectual property rights which may be described in this dissertation

is vested in the University of Manchester, subject to any prior agreement to the contrary, and

may not be made available for use by third parties without the written permission of the

University, which will prescribe the terms and conditions of any such agreement.

Further information on the conditions under which disclosures and exploitation may take

place is available from the Head of the School of Social Sciences.

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Summary The number of students in higher education who reported a disability has been increasing in

recent years. Although there has already been much research into the labour market

experiences of graduates and of disabled people in the general population, relatively little

has been reported on the experiences of disabled graduates. Using data from the

Destinations of Leavers from Higher Education (DLHE) Survey and the Labour Force Survey

(LFS), this study compares the characteristics of graduates with and without disabilities, and

their employment outcomes at six months after graduation and further on in their careers.

In 2006/07, there was a total of 225,365 UK-domiciled full-time first degree graduates aged

40 or under, of which 19,355 (8.6%) were registered as disabled. The most commonly cited

disability was dyslexia (59.9%), followed by an unseen disability such as diabetes, epilepsy

and asthma (15.9%). The analyses show that:

• Male graduates were more likely to have reported a disability than their female

counterparts.

• Graduates with disabilities were likely to be older than their non-disabled peers, and

were more likely to be White than coming from a minority ethnic background.

• Disabled graduates were less likely to have entered HE with A-level or equivalent

qualifications, and were more likely to hold other HE and professional qualifications or

come from Access courses. For those who did have A-level qualifications, their tariff

points also tended to be lower than those of their non-disabled counterparts.

• Compared with those without disabilities, disabled graduates were less likely to have

studied in a Russell Group institution and more likely to have come from a post-92

institution. The difference between the two groups in the percentage graduating from

a pre-92 institution, however, is not statistically significant.

• Graduates with disabilities were more likely than non-disabled graduates to study

creative arts and design, which was a particularly popular subject area amongst those

with dyslexia. They were less likely to study: medicine and dentistry, subjects allied to

medicine, mathematical and computer sciences, law, business and administrative

studies, languages and related subjects, or education. There are, however, no

statistically significant differences between the two groups in the study of: biological

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sciences, physical sciences, engineering and technologies, social studies, or mass

communication and documentation.

• Overall, disabled graduates were slightly less likely than their non-disabled peers to

have obtained a 1st or 2.1 degree.

The DLHE data reveals that, six months after graduation:

• All disabled graduates, irrespective of the type of disability, were found to be more

likely than their non-disabled counterparts to be unemployed, although for graduates

with deaf/hearing impairment or an unseen disability, the difference compared with

that of their non-disabled peers is not statistically significant. Graduates with dyslexia

also have relatively low unemployment rates, whilst those with mental health

problems or mobility difficulties were the most likely to be out of work.

• Multivariate analysis reveals that with the exceptions of those with mental health

problems and a disability not elsewhere classified, there are no statistically significant

differences between disabled and non-disabled graduates in their propensity to be

employed in part-time paid work relative to being in full-time paid work six months

after graduation. Graduates with mental health difficulties or a disability not elsewhere

classified were, however, around 1.4 times more likely to be employed part-time

compared with those without disabilities.

• Irrespective of the type of disability, all disabled graduates were more likely than their

non-disabled peers to continue with their study after completing their first degree.

Graduates with mobility difficulties, multiple disabilities and mental health problems

were the most likely to do so, whilst those with dyslexia were the least likely, and the

differences exist even after controlling for a range of personal and academic

characteristics, including degree subject. Coupled with their relatively high

unemployment rates, these findings seem to suggest that graduates with mental

health issues, mobility difficulties or multiple disabilities encounter greater barriers in

entering the labour market and may want to improve their chances by gaining extra

qualifications, or may even enter further study simply to avoid unemployment.

• Overall, graduates with disabilities were just as likely as those without disabilities to

be employed in managerial or senior official types of jobs, although graduates with

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mental health difficulties were 70% less likely to be in these types of jobs compared

with their non-disabled counterparts, even after controlling for a range of

demographic, educational and employment related factors.

• Graduates with mental health difficulties were, however, relatively well-represented in

professional occupations: 26.9% were employed in these types of jobs six months

after graduation compared with an average of 23.9% of all disabled graduates, and

the figure was close to that for non-disabled graduates (27.7%). In fact, although

overall, disabled graduates were found to be less likely than their non-disabled peers

to be employed in professional occupations, analysis by types of disability suggests

that with the exception of a disability not elsewhere classified, there are no

statistically significant differences between disabled and non-disabled graduates in

their likelihood of being employed in these occupations, after controlling for a range of

demographic and academic factors.

• Overall, a higher percentage of disabled graduates than their non-disabled peers

were employed in associate professional and technical occupations. The differences,

however, are not statistically significant after controlling for a range of demographic

and academic factors.

• Analysis by SOC(HE), a typology of job classifications which distinguishes between

four ‘graduate-level’ occupations (‘traditional’, ‘modern’, ‘new’, and ‘niche’) and ‘non-

graduate occupations’, reveals that graduates with disabilities were less likely than

those without disabilities to be employed in traditional graduate occupations, but there

were no differences between the two groups in their employment in modern, new, or

niche graduate jobs. After controlling for a range of factors including degree subject,

however, statistically significant differences are only found for niche graduate

occupations, with graduates with mental health issues being 39% less likely than their

non-disabled peers to be found in these types of jobs.

• Although graduates with mobility difficulties have one of the highest rates of

unemployment, there is some evidence to suggest that despite their difficulties in

entering the labour market, those who found employment appeared to be able to

obtain a ‘better level’ job than graduates with other types of disabilities.

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Looking further beyond six months after graduation and including those who graduated up to

22 years ago, the LFS data reveals that:

• In agreement with the results from the DLHE survey, disabled graduates were more

likely than their non-disabled peers to be unemployed or economically inactive. They

were more likely to be employed part-time and in the public sector. Some of the

differences between disabled and non-disabled graduates’ employment in the latter,

however, can be accounted for by age, and once this has been controlled for

(together with other factors), the difference is no longer statistically significant.

• Disabled graduates were just as likely as their non-disabled peers to be employed in

a job with supervision responsibilities, and as the DLHE survey has suggested, there

are no statistically significant differences between the two groups in their likelihood of

being employed as managers or senior officials. Disabled graduates are, however,

more likely than their non-disabled counterparts to be found in intermediate or lower

managerial occupations rather than being employed in higher managerial

occupations.

• Graduates with disabilities were less likely to be found in professional occupations

and more likely to be employed in associate professional and technical occupations.

The difference was only small for the latter although, unlike for the DLHE survey

results, it is statistically significant even after controlling for factors such as degree

subject and other personal and academic characteristics.

• Also in contrast with the DLHE survey findings, having controlled for a number of

demographic, academic and employment related factors, disabled graduates were

found to be 31% less likely than their non-disabled counterparts to be employed in

traditional graduate jobs. No statistically significant differences, however, can be

found between disabled and non-disabled graduates in their likelihood of being

employed in modern, niche, or new graduate occupations. Differences in conclusions

drawn from the two sets of data could be attributable to a number of reasons,

including the large differences in sample sizes and the types of factors available for

use as explanatory/control variables in the models.

• Analysis of weekly and hourly pay of full-time employees shows that not only is there

a pay gap between disabled and non-disabled graduates, but that the differences in

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pay still exist after controlling for a number of factors which impact on graduates’ pay.

There is thus evidence of a pay penalty for disabled graduates, compared with their

non-disabled peers.

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Acknowledgements

The majority of this report is based on the work that I did for my dissertation which was

submitted to the University of Manchester for the MSc in Social Research Methods and

Statistics programme in 2009. I would like to acknowledge and express my thanks to the

following people and organisations:

• The support of the Higher Education Careers Services Unit (HECSU) for funding my

MSc study and the procurement of the Destination of Leavers from Higher Education

(DLHE) survey dataset used for this research. In particular, I am grateful to the

HECSU Research Director, Jane Artess, for the support she showed me over the

course of my study.

• My supervisor, Dr Mark Tranmer (University of Manchester), for the guidance he gave

me on this project.

• Dr Jo Wathan (University of Manchester), for her advice on technical issues with the

Labour Force Survey (LFS).

• The Higher Education Statistics Agency (HESA) for preparing and providing the

DLHE dataset.

• The Office for National Statistics, Social and Vital Statistics Division, The Northern

Ireland Statistics and Research Agency, Central Survey Unit, and the UK Data

Archive, for the provision of the LFS. These organisations bear no responsibility for

the analysis and interpretation of the LFS data reported here. The data are also

acknowledged as Crown Copyright.

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Chapter 1 - Introduction

It is well-established that disability has a negative impact on an individual’s employment

prospects. Compared with people without disabilities, disabled people have lower

employment rates, higher levels of unemployment and economic inactivity, and lower

earnings (see, for example, Smith and Twomey, 2002; Berthoud, 2006). Disabled people

also tend to have lower educational qualifications than those without disabilities (Smith and

Twomey, 2002), and the strong link between educational attainment and economic activity

suggests that raising the educational credentials of disabled people is key to facilitating their

participation in the labour market and improving their life chances.

The need for action to tackle this issue was recognised back in 1995, by the passing of the

Disability Discrimination Act (DDA), which aimed to protect disabled people from

discrimination in employment and service provision. An amendment to the 1995 Act –

Special Educational Needs and Disability Act 2001 (SENDA) – extended it to educational

providers including universities, and the subsequent DDA 2005, took this further by

introducing new duties for most public bodies to positively promote disability equality. Under

the DDA, universities are required to make ‘reasonable adjustments’, which can include

making changes to practices, procedures, facilities and providing extra learning support, to

ensure that disabled students are not discriminated against (Directgov, 2009a).

The reports of the National Committee of Inquiry into Higher Education (commonly known as

‘The Dearing Report’) identified those with disabilities as one of the under-represented

groups in higher education (HE) and called for widened access for these students

(Robertson and Hillman, 1997). Indeed, those from state schools or colleges, low-income

families, low participation areas, and students with a disability, have been the main targets

of increasing and widening participation in recent years. Providing all those who have the

potential to benefit from HE with the opportunity to do so is not only considered as essential

in meeting rising skills needs, but also seen as fundamental to a more socially just society.

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Coupled with the HE expansion in recent years is an increased interest in graduates’

employment and careers prospects. For example, the research programmes ‘Seven Years

On: Graduates in the Changing Labour Market’ (Purcell and Elias, 2004) and ‘The Class

of ’99 – Graduate Careers Four Years After Graduation’ (Purcell et al, 2005), investigated

the relationship between HE and employment. Much research has also been conducted in

relation to specific groups, including gendered career development, employment of mature

graduates, the disadvantages that graduates from lower socio-economic background face

compared to their ‘middle class’ counterparts, and the employment outcomes of minority

ethnic graduates (Gorard et al, 2006).

Although the graduate labour market has been a widely investigated area, as Gorard (2006,

p.106) pointed out, ‘There is surprisingly little research about disabled graduates and the

labour market’. In fact, a study from the Department for Innovation, Universities and Skills

(DIUS, now the Department for Business, Innovation and Skills) reported that ‘Disabled

students have rarely been considered in the Widening Participation literature’ (DIUS, 2009,

p.1). The employment figures of disabled graduates are, however, published annually by

The Association of Graduate Careers Advisory Services (AGCAS). Their 2009 report, based

on data from the 2006/07 Destinations of Leavers from Higher Education (DLHE) survey, is

a snapshot of employment destinations of those who graduated in the 2006/07 academic

year, six months after graduation (Leacy and Tunnah, 2009). Consistent with previous years’

findings, the study reported that disabled graduates were less likely than their non-disabled

counterparts to be in full-time work and more likely to be employed part-time or unemployed.

There are, however, indications that both groups of graduates were equally likely to access

some of the ‘graduate-level’ occupations. The study concluded that there is ‘a more positive

picture for disabled graduates than is sometimes imagined’, but that ‘we can still make no

conclusions as to whether or not disability affects career choice or whether certain industries

and occupations appear more/less welcoming to disabled graduates’ (ibid, p.26).

Although the AGCAS report has been a useful resource, it is a solely descriptive study and

does not control for any potentially confounding factors which may influence the findings.

Building on previous research and using multivariate analysis techniques, this study will:

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• investigate the employment outcomes of disabled graduates and see how they

compare with their non-disabled counterparts;

• compare graduates’ destinations by types of disability;

• explore how outcomes differ having controlled for a range of variables;

• explore whether there is an employment penalty facing disabled graduates compared

with their non-disabled peers.

The research will draw on two data sources: the 2006/07 DLHE survey, collated by the

Higher Education Statistics Agency (HESA) and linked to the HESA Student Record, and

pooled data from 16 quarters of the Labour Force Survey (LFS). More information about the

aims and objectives of the research and the data sources will be given in Chapter 3.

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Chapter 2 - Background and Context

2.1 Defining disability

Disability is defined and measured in a number of ways, and as a result, comparing disability

indicators and statistics between studies and across times can be difficult (Walby et al, 2008;

White, 2009). Disability status collected in surveys and most administrative records is also

essentially self-assessed. Not all disabled people are officially registered as such and

disability can be interpreted differently across individuals. Self-assessment is thus objective

and may not be a good indication of disability status, although Burchardt (2000) pointed out

that it may be a better way to tease out the complex barriers faced by disabled people than

by using fixed criteria assessed by a third party.

In addition, confusion often arises on the distinctions between impairment, ill health and

disability. The Prime Minister’s Strategy Unit defines impairments as long-term

characteristics of an individual that affect their functioning, whilst ill health is the short- or

long-term consequence of sickness (PMSU, 2005, p.8). Burchardt (2000, p.650) also drew a

distinction between ill health and disability: ‘a person may be disabled and perfectly healthy,

or be suffering from an illness but not be disabled by it.’ Under the DDA 1995, a person has

a disability:

“if he has a physical or mental impairment which has a substantial and long-term

adverse effect on his ability to carry out normal day-to-day activities.”

(Disability Discrimination Act 1995, c.50)

The Labour Force Survey (LFS), one of the two data sources used in this study,

distinguishes between ‘DDA disabled’ and ‘work-limiting’ disabled. For a person to be

considered as disabled in the LFS, the health problem(s) must last for more than 12 months,

and if the problem limits normal day-to-day activities but not the kind or amount of paid work

that a person can do, the respondent is considered as ‘DDA disabled’ only. If it affects the

kind and amount of paid work but not activities, the respondent is considered as ‘work-

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limiting disabled’; if it affects both work and normal day-to-day activities, the respondent

would be classified as ‘both DDA and work-limiting disabled’ (ONS, 2008a, p.99).

Berthoud (2006, p.13) reported that the boundary between ‘disabled’ and ‘non-disabled’ in

the LFS is not always clear, however, with more individuals, for example those with minor

impairments, being counted as disabled compared with some other sources. As a result, the

LFS is reported to present a higher estimate of the prevalence of disability, and a higher

estimate of the disability employment rates compared with some other studies, which in turn

underestimates the disadvantages associated with disability. Due to the inconsistencies in

the definitions of disability between survey sources, the Office for National Statistics (ONS)

is currently undertaking a review to harmonise the future collection of disability data in

national household surveys and administrative sources (White, 2009).

The definition and measurement of disability used in many of the government household

surveys, including the LFS, is predominately based on the individual/medical model, as

opposed to the social model (White, 2009). While the individual model relies on medical

criteria to define and measure disability, with the locus of the problem being the individual’s

impairment and the extent it limits functioning, the social model identifies attitudinal and

environmental factors in a society as the cause of barriers (Oliver, 1990, ch.1). In terms of

employment practices, the individual model assumes that disabled people are restricted in

their job opportunities as a result of their impairments, whilst the social model places the

focus of the issue with the employer, for example, in their reluctance in adapting the

workplace or working practices to suit disabled people’s needs (Berthoud, 2006).

Issues with measuring disability also exists in the HE sector, where disability status is

collected on the basis of the students’ own assessment, and is collated by the Higher

Education Statistics Agency (HESA) in the Student Record.1 A more stringent criteria,

however, is used in the compilation of HE performance indicators: only those who are in

receipt of the Disabled Student Allowance (DSA), which ceased to be means-tested in

1 For those who entered through the Universities and Colleges Admission Services (UCAS), information was sought at the application stage and transferred to HESA.

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1998/99, are counted towards the widening participation indicator for the disabled (HESA,

2009). This is less than the number of students reported as disabled, but is thought to be a

more robust measure. Since students do not have to inform their institution that they are in

receipt of DSA, there is potentially an issue with substantial undercounting, although this is

not seen as a major problem. Disabled students in HE will be discussed further in Section

2.3.

For the purpose of this study, using the DLHE dataset, disabled graduates are defined as

those who were self-assessed as disabled in the HESA Student Record, irrespective of

whether the individual was in receipt of DSA while they were a HE student. With the LFS

dataset, those who are DDA disabled, work-limiting disabled, or both DDA and work-limiting

disabled, will all be included in the analysis, although owing to the relatively small sample

sizes, no distinction will be made between the three types of disability.

2.2 Disabled people in Britain and in the labour market Before reviewing what previous research has found about disabled students in HE and how

disabled graduates fare in the labour market, we will first look at disabled people in the

general population. Putting the issues associated with measuring disability aside, analysis

of figures from the LFS by the former Disability Rights Commission (DRC) revealed that the

size of the working age disabled population in Britain grew from 6.4 million in 1999 to 6.9

million in 2006 – an increase of 8% compared with 2% for the non-disabled population (DRC,

2007).2 Possible explanations for the rise include increases in impairment, rates of disability,

and rates of reporting (Rigg, 2005, p.3). Disabled people were found to be only half as likely

as non-disabled people to be qualified to degree level and were twice as likely as non-

disabled people to have no qualification at all (DRC, 2007). Although the overall employment

rate of disabled people rose from 47% to 50% between 1999 and 2006, it remained far

below that of the non-disabled population, which had over four fifths of people in

employment. In addition, 9% of disabled people of working age in Britain were unemployed 2 The working age population is 16-64 for male and 16-59 for female.

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in 2006, compared with 5% of non-disabled people, and nearly half of the working age

disabled population were economically inactive.

Employment levels also vary greatly depending on the types of disability. Figures from the

DRC (2007) revealed that only 21% of disabled people with mental health problems and

23% of those with learning difficulties were in work in 2006 – the lowest amongst all types of

impairments. Conversely, two-thirds of disabled people with diabetes or chest, breathing

problems, skin problems, or allergies were employed. Other research has also found that

disabled people with mental illness have the poorest employment prospects compared with

those with other types of disability (see, for example, Berthoud, 2006, p.36; Pilling, 2002).

Examining the different factors impacting on employment rates, Berthoud (2006, p.3)

reported that among all adults (disabled and non-disabled combined), demographic

characteristics are the most important influence, followed by disability characteristics (which

include conditions, impairments and severity), and lastly, economic characteristics (mainly

education). Within the group of disabled people, however, disability characteristics become

the main determinants of employment rates, followed by demographic and economic factors.

Disability, however, is associated with age, and their influences on employment rates are

confounding: the prevalence of disability rises with age, and for both disabled and non-

disabled people, employment levels fall after the age of 45. It is thus not always clear which

variable has the greater impact.

Despite being the least important influences of the three, economic factors are found to

exert a bigger impact on severely disabled people than on those with no disabilities or with

mild disabilities. The Berthoud research found that a higher level of education is associated

with a higher likelihood of being in employment: the job prospects of severely impaired

people with a degree and living in a prosperous area are much better than those who left

school early and are living in an area with few job opportunities.

Research has also found that disabled people in employment are more likely than their non-

disabled counterparts to work part time (Smith and Twomey, 2002), and are overall under-

21

represented in high paying managerial and professional occupations and over-represented

in manual occupations (Kidd et al, 2000). Although the number of disabled people working in

the public sector grew by four times that of non-disabled people over the period 1998 to

2004 (in the areas of local government and the health service in particular), disabled people

are less likely to work in the public sector than non-disabled people, with those having

mental health problems or learning difficulties being the least likely to do so (Hirst and

Thornton, 2005).

In addition to having a lower employment rate and higher unemployment and inactivity rates,

Rigg (2005, p.16) reported that disabled people are approximately three times more likely to

exit work than their non-disabled counterparts, and the difference increases for more

severely disabled people. Based on data from five quarters of the LFS, the research also

revealed that disabled people who remain in work are more likely to move from full- to part-

time employment. In addition, the earnings growth over the five quarters period was found to

be smaller for those with disabilities than for those without. The study, however, found little

evidence to suggest that the occupational progression of disabled people is less favourable

than their non-disabled peers, once demographic, educational and other employment-

related factors have been taken into account (ibid, p.18).

Making a distinction between employment gaps and employment penalties, with gaps

defined as the ‘crude differences between the social groups being compared’ and penalties

being ‘differences that can not be accounted for by observed characteristics…’, Berthoud

and Blekesaune (2007, p.1-3, 21) reported that not only do disabled people face one of the

largest employment penalties of all social groups (which include women, mothers, older

workers, ethnic minorities and religious minorities), their employment position has

deteriorated over the last 30 years with the size of the penalty growing from 16 percentage

points in the early 1970s to 30 percentage points in the early 2000s. Comparing the two

periods 1996/97 and 2004/05, Li et al (2008) also found that having controlled for a number

of demographic and educational factors, there had been no improvement in the employment

positions of disabled people relative to their non-disabled peers over the decade. In fact, the

Li research revealed that although education protects disabled people in their labour market

22

position, its role is mostly in helping them to gain access to employment. In terms of

earnings, disabled men and women were found to earn less (though not substantially) than

their non-disabled counterparts with the same level of education, ie disabled people do not

get as high a return to their education as non-disabled people.

Examining recent research evidence in pay gaps in the UK, Metcalf (2009) found that the

size of the disability pay gap varies widely between studies, with the unadjusted hourly pay

gap estimated to be between 6% and 26% for men and 6% and 17% for women (ibid, p.58).

Reasons for the differences in estimates are thought to include: different data sources with

differing definitions of disability, differences in other definitions (eg of employment), the age

group examined, and coverage of different time periods. The adjusted (‘unexplained’) pay

gap ranges from 6% to 36% for men and 0% to 18% for women – controlling for

characteristics is found to reduce the disabled hourly gap in most cases, although there are

exceptions. Overall, the Metcalf study concluded that although the actual size of the gap is

uncertain, disability appears to have a greater negative impact on men’s pay than on

women’s pay, and that the pay gap widens for more severely disabled people.

2.3 Growth in disabled students in HE

The above review indicates that there has already been much research into disabled people

in the labour market, and that they are disadvantaged in many ways compared with their

non-disabled counterparts, which is in part due to their lower levels of qualifications. It has

been reported that prior to 1993, HE was largely inaccessible to disabled people with

significant impairments (Riddell et al, 2005), but amid the HE expansion in recent years and

under the widening participation agenda, this situation appears to be improving. As

mentioned in Chapter 1, there has so far been relatively little research focusing on how

disabled graduates fare in the job market, probably because they are still a relatively new

sub-group in HE and sample size is often a constraint. In this section, we will give an

overview of what previous research has said about some of these issues.

23

According to DIUS (2009), the number of HE students who reported a disability increased by

around 19% between 2001 and 2007. In the 2006/07 academic year, there were 31,065 (8%)

UK-domiciled, first year, full-time undergraduates who had declared a disability on the HESA

record. Analysis by types of disability showed that students with dyslexia or an unseen

disability accounted for around 70% of the first year undergraduate disabled population.

Although still small as a proportion of the total, the number of students reporting mental

health problems has increased by more than ten times between 1994/95 and 2006/07, whilst

those with multiple disabilities have tripled, and the number of wheelchair users/students

with mobility difficulties have more than doubled, over this period.

Despite this growth, the DIUS study stated that it is unclear whether the increase is due to

more students declaring a disability, more institutions recording a disability, or an actual

increase in the number of disabled students in HE. In addition, owing to the inconsistencies

of how disability is defined and the different methods of data collection, estimates of the

prevalence of disability in the general population are found to vary widely. As a result, it is

not known how well represented disabled students are in HE, or whether their participation

rates have changed over time. Riddell et al (2005), examining the period between 1995/96

and 1999/2000, reported that the increase in the proportion of disabled students during this

period was likely to be due in part to increased incentives (through the DSA) to disclose an

impairment, particularly for students with dyslexia, which rose from 17.9% of the disabled

student population to 32.7%. Corresponding to this increase was a substantial drop in the

percentage of students with an unseen disability (from 48.6% to 29.7%), and a decrease in

the proportion of students (from 12.6% to 6%) with an unknown disability.

Despite the difficulties associated with calculating a formal participation rate, DIUS

estimated that by age 19, around 30% of disabled people have participated in HE courses,

compared with 45% of those without disabilities. The gap is found to be mainly due to the

differences in prior attainment between the two groups: disabled people are less likely than

their non-disabled peers to have attained five or more GCSEs at grade A*-C or two or more

A-levels. Once these factors have been controlled for, the DIUS research found little

difference in HE participation rates between the two groups of youngsters.

24

2.4 Characteristics of disabled students in HE

Examining the various characteristics of the HE student population, the DIUS research

found that, compared with those without a disability, disabled students who are in receipt of

DSA are more likely to have lower qualifications on entry and less likely to have entered via

‘traditional’ routes (such as A-levels). They are also more likely to be male, to study creative

arts and design, agriculture and related subjects, social studies, or architecture, but less

likely to study medicine, mathematical sciences, languages, and law.

Disabled students were also found to be slightly less positive than non-disabled students

about the quality of their course, even after controlling for a range of factors (ibid). Analysis

of non-continuation rates, however, have shown that students who are in receipt of DSA are

less likely than students with no disabilities to drop out from HE.

In Chapter 4 of this report, we will be looking at the characteristics of disabled graduates

more closely, and see how they compare with their non-disabled peers.

2.5 Disabled graduates in the labour market

This is an under-researched area, but as mentioned in Chapter 1, the Association of

Graduate Careers Advisory Services (AGCAS) publishes annually the employment figures

of disabled graduates, based on latest data from the Destinations of Leavers from Higher

Education (DLHE) survey, conducted on the graduate cohort who completed their HE

qualification six months earlier. In agreement with previous years’ findings, their 2009 report,

on the destinations of the 2006/07 full-time first degree graduate cohort, found that disabled

graduates were less likely than their non-disabled counterparts to be in full-time work and

more likely to be employed part-time or unemployed six months after graduation (Leacy and

Tunnah, 2009). Disabled graduates were also less likely than those without disabilities to be

employed in professional occupations and more likely to enter associate professional and

technical occupations, but there is little difference between the two groups in the

25

percentages employed in management and administration.3 By defining ‘graduate-level’

occupations as a combination of the above three broad occupational groupings, the study

also found little difference between disabled and non-disabled graduates in their

employment in ‘graduate-level’ jobs: 65.8% compared with 67.2% respectively. In addition,

analysis by types of disabilities suggested that those with non-visible disabilities have better

employment outcomes than graduates with more visible/apparent physical and mental

health disabilities.

Parker et al (2007) also uses the DLHE data to explore the employment patterns of disabled

graduates, with a focus on how disability and gender interact. The research found that

‘differences in employment outcomes between women and men, and between disabled and

non-disabled graduates, were comparatively small and no clear picture emerged when

different types of disability were considered’ (ibid, p.81). The study, however, did not include

graduates with dyslexia, as it was thought that their inclusion would understate the

disadvantages associated with disability. In addition, both the AGCAS and Parker et al

studies, have not controlled for other potentially confounding factors in their analysis of the

DLHE survey – a gap which needs to be addressed by further research.

Although disabled graduates were not the main focus of the study, research by Purcell et al

(2005) on the career paths of a sample of graduates from 1999 three years after graduation

found that, compared with those without disabilities, graduates who reported a disability or

long-term illness were more than three times as likely to have accumulated at least six

months of unemployment since graduation (ibid, p.57) and were more than twice as likely to

be employed in a non-graduate occupation (ibid, p.120).4 Graduates with disabilities were

also half as likely to report being in a ‘high quality’ job, defined in the study as one which

include the following features: competitive salary, continual skills development, interesting 3 Associate professional and technical occupations are defined as ‘occupations whose main tasks require experience and knowledge of principles and practices necessary to assume operational responsibility and to give technical support to Professionals in the natural sciences, engineering, life sciences, social sciences, humanities and related fields and to Managers and Senior Officials.’ (Great Britain, 2000, p.101). 4 See Appendix A for the definitions of graduate and non-graduate occupations, SOC(HE), used by Purcell et al.

26

and challenging work, long term security, progressive and dynamic organisations, and

working with people you enjoy socialising with (ibid, p.124). Unlike the AGCAS and Parker

research, the analysis has controlled for a number of personal and academic factors; the

findings therefore suggest that there are ‘unexplained’ differences in the employment

patterns of disabled and non-disabled graduates. An aim of this research is thus to explore

this issue further.

2.6 Limitations of research on disabled people

As indicated in the summary of literature in this chapter, although there are exceptions,

much of the research into disabled people in the labour market focuses on a cross sectional

group of people and provides a ‘snapshot’ of their situations. Burchardt (2000) pointed out

that people move into, and out of, disability and it is misleading to conceive that disabled

people and non-disabled people constitute two entirely distinct and fixed groups in the

population. The labour market trajectories of those with short-term disability (perhaps

following an accident) are likely to be very different from those with long-term, chronic

problems. Using longitudinal data from the British Household Panel Survey, Burchardt found

that only a small proportion of working age people who experience disability are long-term

disabled. Intermittent disability is found to be particularly common amongst those with

mental health problems: just under one in ten who had a spell of mental illness were still ill

after six years, although many more have repeat spells. It is thus important to investigate

how the dynamics of disability impact on employment trajectories, and longitudinal data are

needed to investigate these complex issues.

Another related point is that much of the research does not take into account when people

became disabled. For example, a study of young people has shown that those who became

disabled when they were less than five years old have more positive expectations than

those who became disabled later in life (ages 11-16) (Burchardt, 2005). Given that

aspirations are associated with educational attainment, this is a factor to be considered. For

those of working age, Jenkins and Rigg (2004) found that employment rates fall with

disability onset, and continue to drop the longer a person remained disabled, whereas

27

average income falls sharply with onset and recovers subsequently, although not to the

same level as before disability set in. As the prevalence of disability rises with age, it is also

thought that older workers who became disabled relatively recently may take longer to adapt

to their new circumstances than those who became disabled at a younger age (Rigg, 2005).

Given that the longitudinal aspects of employment outcomes of disabled graduates will not

be investigated in this study, and that it was not possible to take into account the onset of

disability with the data used, these are recognised as some of the limitations of this research.

These will be further discussed in Section 6.2.

28

Chapter 3 - Methods of Analysis

3.1 Aim and objectives The aim of this research is to investigate the employment outcomes of disabled graduates

and how they compare with graduates without a disability. The objectives are:

• To investigate and compare disabled and non-disabled graduates’ employment

outcomes, including their employment and unemployment rates, type of occupation

entered into, employer sector, job quality, progression on to further study, likelihood

of being economically inactive, and earnings.

• To predict the employment outcomes of disabled and non-disabled graduates,

accounting for a range of confounding factors including demographic and academic-

related variables.

• To explore and to predict how outcomes differ by types of disability.

• To explore whether there is an ‘employment penalty’ facing disabled graduates

compared with their non-disabled peers.

The research will focus on those aged 40 or below. Including graduates up to the age of 40

allows for the fact that disabled people in HE tend to be older than those without disabilities

(DIUS, 2009) and provides a larger sample size than if only ‘young’ graduates were included.

In addition, for the LFS data, setting a cut off point of age 40 will help to ensure that only

those who graduated during the HE expansion in the last 15 years or so are included in the

analysis; those who are older and graduated prior to the recent HE expansion are likely to

have more ‘substantial’, and perhaps very different, experiences in the labour market.

29

3.2 Sources of data

Two data sources will be used for this study:

• The Destinations of Leavers from Higher Education (DLHE) survey. This is a

census of all HE leavers who graduated in the previous academic year, and is

conducted annually by HESA. The survey results are linked to the HESA Student

Record, with information on the graduates’ demographics including any disabilities

reported.5

• The Labour Force Survey (LFS). The LFS is the largest regular household survey

in the UK, and is carried out continuously throughout the year. It covers around

53,000 households every quarter and provides detailed information about

individuals’ employment circumstances as well as education history and health

status.6

Both sources will be discussed further in the next two sections.

3.3 Destinations of Leavers from Higher Education (DLHE) survey The DLHE survey is a census of all UK- and other EU-domiciled graduates who completed a

HE qualification. It includes both postgraduates and undergraduates. The 2006/07 dataset,

used in this research, details the destinations of HE leavers from the 2006/07 graduating

cohort, six months after graduation.7 The DLHE data are also matched with the HESA

Student Record to provide demographics and course-related information about the

graduates. 5 DLHE: www.hesa.ac.uk [Accessed 4 August 2009]. 6 LFS: http://www.esds.ac.uk/government/lfs/ [Accessed 4 August 2009]. 7 The 2006/07 cohort graduated just before the economic downturn which began towards the end of 2007. Employment figures for this cohort suggest that the effect of the downturn had not yet been felt at the time of the survey (HECSU and AGCAS, 2008). Since the graduate labour market had been relatively stable for the few years before the recession, it can be said that the 2006/07 cohort is a ‘typical year’, unlike the 2008 cohort who graduated during the recession and for whom a higher level of unemployment has been recorded (HECSU and AGCAS, 2009).

30

For the purpose of this study, only those who:

• have obtained a first degree level qualification;

• are domiciled in the UK;

• are aged 40 or below;

• completed their degree via full-time study,8 and,

• whose disability status is known,

have been included in the analysis. Under these criteria, there were a total of 225,365 first

degree graduates of whom 180,250 (80%) responded to the survey. One reassuring

characteristic about the data is that there was not a great difference in the response rates

between disabled and non-disabled graduates: 80.7% for the former compared with 79.9%

for the latter. In addition, amongst UK-domiciled full-time first degree graduates aged 40 or

below, 99.4% have known disability status.

Due to data protection issues, HESA is not able to supply a dataset containing information

on both ethnicity and types of disability. It is, however, possible to obtain disability

information in the form of a ‘disability marker’ - disabled/no known disability/disability status

not known - together with ethnicity. As a result, two DLHE datasets are used for this study:

one with information on the types of disability but with no ethnicity information, the other

containing a ‘disability marker’ together with ethnicity.

Although the DLHE survey collects salary information, the raw data is not made available for

analysis. Analysis of graduates’ earnings will, therefore, be carried out on the LFS data only.

3.4 Labour Force Survey (LFS)

8 Graduates who completed their study part-time often have different prior labour market experience compared with their full-time counterparts. For example, they are more likely to be working full time during their study (Callender et al, 2006), and so are likely to continue to work with the same employer after graduation. As such, part-time graduates have been excluded from this analysis.

31

The LFS is a quarterly sample survey of households living at private addresses in the UK.

The survey is based on a panel design where each quarter’s sample of 53,000 households

is made up of five waves, each of approximately 11,000 households. Individuals are

interviewed in five consecutive waves/quarters, and a fifth of the sample is replaced each

quarter. Information on earnings are collected in waves 1 and 5 (ONS, 2007).

While the DLHE data will provide us with a snapshot of graduates’ employment outcomes

six months after graduation, at a time when many graduates are still settling down in the

labour market or looking for a job, data from the LFS will not only include new graduates, but

also those who have completed a degree-level qualification at any time in the past. The LFS

data will, therefore, allow for the investigation of employment outcomes further along since

graduation, while controlling for a variety of variables including, where appropriate, the

length of time since graduation. The two datasets also cover different variables. For example,

the DLHE survey does not distinguish between private and public sector employment,

whereas this information is available from the LFS. The two datasets, therefore, complement

each other.

32

Although the LFS has a short (five quarters) longitudinal element incorporated into its design,

for the purpose of this study, we will only be looking at a cross section of respondents

pooled together across 16 quarters between Jan-March 2005 and Sept-Dec 2008.9 This

means that data from the LFS will be ‘averages’ across four years. Merging quarterly data to

increase sample size is essential for this research due to the small number of degree-level

respondents with a disability in each quarter of the LFS. Since respondents are interviewed

five times over five quarters, to avoid having more than one record for each respondent in

the combined dataset, only respondents from wave 1 and who satisfied the criteria below:

• those who reported their highest qualification as degree or equivalent

• those aged 16-40

have been included in the study. The sample thus includes respondents with a first degree

as well as those with a postgraduate-level qualification or other types of degree

qualifications, which gives a larger sample size than would be available if only first degree

respondents are included. It is, however, possible to explore employment outcomes

controlling for the type of degree qualification, given that this information is available in the

dataset.

Unlike the DLHE data, the LFS does not provide information on whether the highest

qualification was obtained via full- or part-time study. This is not considered to be an

important issue as the mode of study effect on employment outcomes is likely to be more

significant soon after graduation (as in the DLHE survey), than after a longer length of time

when graduates have had the time to find a job and settle down in the labour market (as in

the LFS).

9 Office for National Statistics. Social and Vital Statistics Division and Northern Ireland Statistics and Research Agency. Central Survey Unit, Quarterly Labour Force Survey, Jan-March 2005 to Sept-Dec 2008 [computer files]. Colchester, Essex: UK Data Archive [distributor]. SN: 5426, 5427, 5428, 5429, 5369, 5466, 5547, 5609, 5657, 5715, 5763, 5796, 5851, 6013, 6074, 6119.

33

3.5 Analytic strategies The DLHE and LFS datasets were analysed in the following ways:

• In Chapter 4, the characteristics and employment outcomes of disabled and non-

disabled graduates will be compared. The DLHE data will tell us the situation six

months after graduation, whereas the LFS will include those who graduated up to 22

years ago.

• In Chapter 5, graduates’ employment outcomes will be explored by multivariate

analysis. This includes using binary and multinominal logistic regression and multiple

linear regression. These techniques allow us to compare the outcomes of disabled

and non-disabled graduates whilst controlling for a range of demographic,

educational and other potential confounding factors, and help to isolate any

contributory effects due to disability alone.

Due to the relatively small number of graduates with disabilities in the LFS, no analysis by

types of disability/health problems will be carried out on the LFS data. The different

categories used to define types of disabilities or health problems in the DLHE and LFS

datasets would also make comparisons between the two difficult (as will be discussed later

in Chapter 4).

3.6 Technical notes

In accordance with HESA’s data protection guidelines, all raw and total figures from the

DLHE survey published in this report have been rounded to the nearest 5, ie 0, 1, 2 are

rounded to 0 and all other numbers are rounded to the nearest multiple of 5. As a result of

the rounding, the sum of numbers in each row or column may not match the total shown

precisely. All percentages, however, have been calculated using unrounded figures.

HESA also require percentages calculated on populations which contain 52 or fewer

individuals to be suppressed, as with averages based on populations of 7 or fewer. These

guidelines are adhered to where necessary.

34

All the LFS data have been weighted to compensate for differential non-response in

accordance with ONS’s guidelines (ONS, 2007).

For this study, the statistically significant level is set at 5%. It needs to be borne in mind,

however, that even when the p values are statistically significant (ie <0.05), it does not

necessarily imply that the results are significant in a practical sense (Agresti and Finlay,

2008, p.163). In addition, standard errors of estimates derived from both DLHE data and the

LFS will be reported where appropriate.

Although the DLHE survey is a census of all HE leavers, the fact that only 80% of the

graduates eligible to complete the survey actually did so suggests that the study can be

treated as a survey rather than as a census. As such, the standard error, and the

corresponding 95% confidence interval (CI), would allow us to determine the probability that

the results reported here fall within a certain distance of the true population values.

35

Chapter 4 - Comparison of Characteristics and Employment Outcomes Between Graduates With and Without Disabilities

It is well-established that personal characteristics, course choices, types of institution

attended, and educational attainments (both pre-HE and HE), all impact on graduates’

employment outcomes (for example, see Elias, 1999; Brennan and Shah, 2003; Purcell et al,

2005). In this chapter, we will, therefore, look at the demographic and academic background

of graduates in both the DLHE survey and the LFS, as well as their employment outcomes

six months after graduation and further on.

4.1 Six months after graduation - what does the DLHE data tell us? 4.1.1 Demographics and academic background of the 2006/07 graduate cohort

As mentioned in Section 3.2, the DLHE survey data is linked to the HESA Student Record.

The two datasets provided by HESA for this study contain demographic information of ALL

those who graduated in the 2006/07 academic year, in addition to destinations information

provided by respondents to the survey. This allows for both respondents’ and non-

respondents’ demographics and background to be explored, and would give a more

accurate picture than if only respondents are included.

There was a total of 225,365 UK-domiciled full-time graduates aged 40 or under, who

obtained their first degree in the 2006/07 academic year. Of these graduates, 19,355 (8.6%)

were registered as disabled (based on self-assessment). Amongst respondents alone,

15,630 (8.7%) reported having a disability and 164,620 had no known disability.

Table 4.1 shows the gender, age and disability status of the graduates, broken down by

survey respondents and non-respondents. Females constituted over half (56.5%) of the

graduating population and the vast majority of graduates (84.1%) were aged between 21

and 24. Male graduates were more likely to have reported a disability than their female

36

counterparts: 9.2% reported a disability compared with 8.1% of the latter, and the difference

is statistically significant.10

For the 19,355 disabled graduates, just over half (54.7%) were in receipt of Disabled

Student Allowance (DSA), and over a third (35.9%) were not. For the remaining 9.4%,

information on DSA was not known or sought. DSA is not means-tested, but in order to be

qualified, medical proof of the condition must be provided (Directgov, 2009b). DSA can thus

be thought of as a more stringent measure of disability status, although in this study, all

those who were self-assessed as disabled were included as such (as mentioned earlier in

Section 2.1).

Table 4.1 Characteristics of respondents and non-respondents to the 2006/07 DLHE survey Non-

respondents Respondent All

Gender

Female

55.3%

56.8%

56.5%

Male 44.7% 43.2% 43.5% Age group 20 or under 3.1% 3.2% 3.2% 21-24 81.8% 84.6% 84.1% 25-29 9.6% 7.3% 7.7% 30-39 5.5% 4.9% 5.0% Disability status

Disabled 8.3% 8.7% 8.6%

No known disability

91.7% 91.3% 91.4%

Total

45,115

180,250

225,365

Source: 2006/07 DLHE (HESA)

10 χ2 = 88.24 (1), p<0.001

37

Table 4.2 shows the types of disability reported, based on the student’s own self-

assessment. The most commonly cited disability was dyslexia (59.9%), followed by an

unseen disability such as diabetes, epilepsy and asthma (15.9%). Due to the small number

of graduates with personal care support type of disability and with autistic spectrum disorder,

these two categories have been combined with ‘a disability not listed above’ in further

analysis.

Table 4.2 Types of disability reported by students Respondent who

reported a disability

Total (respondent and non-respondent)

Dyslexia

9295 (59.5%)

11590 (59.9%)

Blind/partially sighted

305 (1.9%) 380 (2.0%)

Deaf/have a hearing impairment

480 (3.1%) 590 (3.0%)

Wheelchair user/have mobility difficulties

360 (2.3%) 440 (2.3%)

Personal care support

10 (0.1%) 15 (0.1%)

Mental health difficulties

545 (3.5%) 690 (3.6%)

An unseen disability, eg. diabetes, epilepsy, asthma

2545 (16.3%) 3075 (15.9%)

Multiple disabilities

620 (4.0%) 790 (4.1%)

A disability not listed above

1375 (8.8%) 1685 (8.7%)

Autistic spectrum disorder

90 (0.6%) 105 (0.5%)

Total 15630 (100%) 19355 (100%)

Source: 2006/07 DLHE (HESA)

Table 4.3 compares the personal and academic characteristics of graduates with and

without a disability. All the differences discussed below are statistically significant at the 0.05

level unless otherwise stated.

38

Graduates with disabilities were likely to be older than their non-disabled peers: 17.2% were

aged 25 or above compared with 12.4% of the latter. They were more likely to be White:

9.1% of White graduates were disabled compared with only 5.8% of minority ethnic

graduates (not shown in Table 4.3). Disabled graduates were also less likely to have

entered HE with A-level or equivalent qualifications, which are seen as the ‘traditional’ HE

entry qualifications, and were more likely to hold other HE and professional qualifications or

come from Access courses. For those who did have A-level qualifications, their tariff points

also tended to be lower than those of their non-disabled counterparts: 55.6% obtained 300

or more tariff points compared with just under a third (64%) of non-disabled graduates.

The two groups of graduates also tend to attend different types of HE institutions, with

disabled graduates being less likely to have studied in a Russell Group institution and more

likely to have come from a post-92 institution. The differences in percentages graduating

from a pre-92 institution, however, are not statistically significant. There were also

differences in degree subject choice. Graduates with disabilities were more likely to study

creative arts and design (20.5% compared with 11% of non-disabled graduates) and, to a

lesser extent, historical and philosophical studies. They were less likely to study: medicine

and dentistry, subjects allied to medicine, mathematical and computer sciences, law,

business and administrative studies, languages and related subjects, or education. There

are, however, no statistically significant differences between the two groups in the study of:

biological sciences, physical sciences, engineering and technologies, social studies, or

mass communication and documentation.

39

Further analysis of subject areas by types of disability (Table B in Appendix B) shows that:

• The high representation of disabled students in creative arts and design was largely

due to the subject’s popularity amongst those with dyslexia - a quarter (24.5%) of

dyslexia graduates came from this discipline.

• 3.7% of dyslexia graduates studied languages or related subjects, the lowest

amongst all disabled graduates. On the contrary, those with mental health difficulties

(11.5%) had the highest representation in these subjects.

• Graduates with mental health problems were also well-represented in biological

sciences (15.4% compared with averages of around 10% of all disabled and non-

disabled graduates), and social studies (13.8% compared with 10% of all disabled

and non-disabled graduates). However, only 6.1% of these graduates came from

business and administrative studies, the lowest of all disabilities.

Overall, disabled graduates were slightly less likely than their non-disabled peers to have

obtained a ‘good’ class of degree: 56.5% obtained a 1st or 2.1 in 2006/07, compared with

59.5% of non-disabled graduates.

The above findings suggest that, compared with their non-disabled peers, disabled

graduates may be disadvantaged in a number of ways in terms of the factors which are

commonly associated with less favourable employment outcomes. For example, across all

subjects, arts graduates have been reported to have the highest rate of unemployment and

lowest rate of entering graduate-level occupations (Purcell et al, 2005). In addition, it has

been reported that graduates with higher A-level scores are more likely to enter ‘traditional’

graduate occupations, although they are less likely to enter employment straight after

graduation due to their higher propensity to continue with further study (Elias, 1999, p.30).

Graduates with a poorer class of degree have also been found to be more likely to be

unemployed (Purcell et al, 2005).

The factors affecting graduates’ employment outcomes, however, are complex. The

personal characteristics and background of these graduates reported here would need to be

controlled for if we are to get a better understanding of any difference in outcomes between

disabled and non-disabled graduates.

40

41

Table 4.3 Comparison of demographic and academic background between disabled and non-disabled graduates

Categories Disabled graduates (%) Non-disabled graduates (%)

Gender Male 46.7 43.2

Female 53.3 56.8

Total 100 (n = 19,355) 100 (n = 206,005)

Age 20 or under 2.3 3.2

21-24 80.5 84.4

25-29 10.4 7.5

30-39 6.8 4.9

Total 100 (n = 19,355) 100 (n = 206,005)

Ethnicity White 87.2 81.7

Black or Black British Caribbean 1.2 1.0

Black or Black British African 1.6 2.1

Other Black background 0.2 0.2

Asian or Asian British Indian 2.2 4.6

Asian or Asian British Pakistani 1.4 2.4

Asian or Asian British Bangladeshi 0.4 0.8

Chinese 0.3 1.2

Other Asian background 0.7 1.1

42

Table 4.3 (continued). Comparison of demographic and academic background between disabled and non-disabled graduates

Categories Disabled graduates (%) Non-disabled graduates (%)

Ethnicity (continued) Other 2.9 3.1

Unknown 1.9 1.8

Total 100 (n = 19,355) 100 (n = 206,005)

Highest qualification on entry to HE Postgraduate (exc PGCE) 0.2 0.2

PGCE 0.0 0.0

First degree of UK institution 1.5 1.6

Other graduate and equivalent qualifications 0.2 0.2

HE credits 0.8 0.6

Other HE and professional qualifications 8.4 6.2

GCE A-level/A-level equivalent qualifications,

SQA Highers and equivalent

81.3 85.9

Access courses 3.2 1.9

GCSE/O-level qualifications only; SCE O

grades and SQA Standard grades

0.5 0.4

Other qualifications 3.0 2.1

No formal qualification held 1.0 0.7

Total 100 (n = 19,135) 100 (n = 203,670)

43

Table 4.3 (continued). Comparison of demographic and academic background between disabled and non-disabled graduates Categories Disabled graduates (%) Non-disabled graduates (%)

Tariff points (For A-levels and Highers only)

1-79 2.7 1.4

80-119 2.5 1.8

120-179 7.7 5.4

180-239 13.8 10.8

240-299 17.8 16.5

300-359 20.2 19.9

360-419 16.5 18.6

420-479 10.0 12.9

480-539 5.5 7.5

540 3.4 5.2

Total 100 (n = 11,715) 100 (n = 141,510) Type of first degree awarding institution

Post-92 54.1 48.3

Pre-92 24.6 25.0

Russell Group 21.3 26.7

Total 100 (n = 19,355) 100 (n = 206,005)

44

Table 4.3 (continued). Comparison of demographic and academic background between disabled and non-disabled graduates Categories Disabled graduates (%) Non-disabled graduates (%)

Degree subject Medicine and dentistry 1.6 3.0

Subjects allied to medicine 6.3 7.3

Biological sciences 10.3 10.6

Veterinary sciences, agriculture and related subject 1.2 1.0

Physical sciences 4.9 4.7

Mathematical and computer sciences 6.3 6.9

Engineering and technology 5.4 5.1

Architecture, building and planning 2.2 2.0

Social studies 10.0 9.9

Law 3.3 5.2

Business and administrative studies 8.9 12.0

Mass communications and documentation 3.3 3.4

Languages, linguistics and related subjects 5.3 7.5

Historical and philosophical studies 6.4 5.7

Creative arts and design 20.5 11.0

Education 3.7 4.3

Combined subjects 0.5 0.5

Total 100 (n = 19,355) 100 (n = 206,005)

45

Table 4.3 (continued). Comparison of demographic and academic background between disabled and non-disabled graduates Categories Disabled graduates (%) Non-disabled graduates (%)

Class of degree awarded 1st 10.5 11.7

2.1 46.1 47.8

2.2 31.3 28.7

3rd 6.6 5.2

Unclassified 5.6 6.7

Total 100 (n = 19,355) 100 (n = 206,005)

Source: 2006/07 DLHE (HESA)

46

4.1.2 Employment outcomes six months after graduation

Table 4.4 compares the activities reported by disabled and non-disabled DLHE survey

respondents, six months after graduation. The figures show that disabled graduates were

less likely than those without disabilities to be in full-time paid employment six months after

graduation and more likely to be unemployed. They were also more likely to be in part-time

paid work, voluntary/unpaid work, going into further study only, or were not available for

employment. Disabled graduates, however, were no more likely than their non-disabled

peers to be working and studying.

Further analysis of the employment circumstances of the respondents (Table 4.5) shows

that 7.5% of disabled respondents were unemployed and looking for work, further study, or

training, compared with 5.4% of non-disabled graduates. Disabled graduates were also

more likely than non-disabled graduates to be unemployed but not looking for employment

or training: 4.0% compared with 3.4% of the latter. In addition, 1.2% of disabled graduates

were temporarily sick, unable to work, or looking after the home, compared with 0.5% of

graduates with no known disability.

47

Table 4.4 Activities of disabled and non-disabled graduates 11

Activity Disabled graduates Non-disabled graduates

% of total S.E. (%) 95% C.I. % of total S.E. (%) 95% C.I. Lower (%) Upper (%) Lower (%) Upper (%)

Full-time paid work only (including self-employed)

51.1 0.41 50.3 51.9 56.7 0.12 56.4 56.9

Part-time paid work only

8.1 0.22 7.7 8.6 7.6 0.07 7.5 7.7

Voluntary/unpaid work

1.6 0.10 1.4 1.8 1.0 0.02 0.9 1.0

Work and further study

8.7 0.23 8.3 9.2 8.6 0.07 8.4 8.7

Further study only

16.2 0.30 15.7 16.8 15.2 0.09 15.0 15.4

Assumed to be unemployed

7.7 0.22 7.2 8.1 5.6 0.06 5.5 5.7

Not available for employment

5.1 0.18 4.7 5.4 4.3 0.05 4.2 4.4

Other 1.5 0.10 1.3 1.7 1.1 0.03 1.0 1.1

Total 100% (n = 15,160)

100% (n = 160,515)

Source: 2006/07 DLHE (HESA)

11 4,570 (2.5%) of the survey respondents did not report their activities and were not included in Table 4.4.

48

Table 4.5 Employment circumstances of disabled and non-disabled graduates

Employment circumstances

Disabled graduates Non-disabled graduates

% of total S.E. (%) 95% C.I. % of total S.E. (%) 95% C.I. Lower (%) Upper (%) Lower (%) Upper (%)

In employment

69.5% 0.37 68.8 70.2 73.8% 0.11 73.6 74.0

Unemployed and looking for employment, further study or training

7.5% 0.21 7.1 7.9 5.4% 0.06 5.3 5.6

Not employed but not looking for employment, further study or training

4.0% 0.16 3.7 4.4 3.4% 0.05 3.4 3.5

Permanently or temporarily unable to work due to sickness or having to look after home or family

1.2% 0.09 1.0 1.3 0.5% 0.02 0.5 0.6

Others 17.8% 0.31 17.2 18.4 16.8% 0.09 16.6 17.0

Total 100% (n = 15,160)

100% (n = 160,515)

Source: 2006/07 DLHE (HESA)

49

Disabled graduates were found to be more likely than their non-disabled peers to be self-

employed or working freelance six months after graduation: 3.8% of disabled graduates who

reported their employment circumstances were self-employed or working freelance

compared with 2.1% of non-disabled graduates (not shown in Table 4.5), perhaps partly

reflecting on the fact that disabled graduates were more likely to have studied creative arts

and design courses. Graduates who were in employment were asked about the duration or length of contract of

their employment. Their responses are shown in Table 4.6. Overall, 13.1% of disabled

graduates were employed in temporary work, compared with 11.7% of non-disabled

graduates and the difference is statistically, though not substantially, significant.12 In the DLHE survey, graduates were also asked whether they would have been able to get

their job without the qualification they have recently obtained (the actual qualification, not the

subject of study). Their responses, shown in Table 4.7, suggest that disabled graduates

were less likely than non-disabled graduates to report that their qualification was a formal

requirement for their job, and were more likely to report being in jobs where their degree

qualification was not needed for entry. It needs to be borne in mind, however, that around

one in five respondents eligible to answer this question did not do so. The question was also

likely to be prone to measurement errors as the distinction between whether a degree

qualification was ‘expected’ or was an ‘advantage’ might not be clear and was subjective.

12 Temporary work (through an agency and not through an agency) vs all other types of employment combined, χ2 = 13.92 (1), p<0.001

50

Table 4.6 Duration of employment

Duration of employment Disabled graduates Non-disabled graduates

% of total S.E. (%) 95% C.I. % of total S.E. (%) 95% C.I. Lower (%) Upper (%) Lower (%) Upper (%)

Permanent or open-ended contract

58.6% 0.52 57.6 59.6 62.0% 0.15 61.7 62.3

Fixed-term contract: 12 months or longer

10.0% 0.32 9.4 10.6 11.8% 0.10 11.6 12.0

Fixed-term contract: shorter than 12 months

9.3% 0.31 8.7 9.9 9.1% 0.09 9.0 9.3

Self-employed/freelance

6.1% 0.25 5.6 6.6 3.3% 0.06 3.2 3.4

Temporarily, through an agency

6.9% 0.27 6.3 7.4 6.4% 0.08 6.3 6.6

Temporarily, other than through an agency

6.2% 0.25 5.7 6.7 5.3% 0.07 5.2 5.5

Other 2.9% 0.18 2.5 3.2 2.0% 0.04 1.9 2.1

Total 100% (n = 9,020)

100% (n = 101,730)

Source: 2006/07 DLHE (HESA)

51

Table 4.7 Requirement of qualification to get the job 13

Requirement of qualification

Disabled graduates Non-disabled graduates

% of total S.E. (%) 95% C.I. % of total S.E. (%) 95% C.I. Lower (%) Upper (%) Lower (%) Upper (%)

Formal requirement

32.4% 0.51 31.4 33.4 35.8% 0.16 35.5 36.1

Expected

11.2% 0.35 10.6 11.9 11.6% 0.10 11.4 11.8

Advantage

22.9% 0.46 22.0 23.8 21.6% 0.13 21.3 21.9

No 33.4% 0.52 32.4 34.4 31.0% 0.15 30.7 31.3

Total 100% (n = 8,315)

100% (n = 93,630)

Source: 2006/07 DLHE (HESA)

13 Excluding respondents who reported ‘don’t know’.

52

Looking at the types of industry that graduates were employed in, Table 4.8 shows that

disabled graduates were more likely than non-disabled graduates to be employed in

education and other community, social and personal activities, and less likely to be working

in financial activities, or health and social work. There were, however, no statistically

significant differences between the two groups in the percentages employed in

manufacturing, wholesale and retail trade, property development, renting, business and

research activities (the largest group of all), or in public administration and defence.

Table 4.8 Types of industry of employed graduates Type of industry Disabled

graduates

Non-disabled graduates

Agriculture, forestry and fishing 0.6% 0.4% Mining and quarrying 0.4% 0.5% Manufacturing 7.0% 6.7% Electricity, gas and water supply 0.5% 0.7% Construction 1.8% 1.9% Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods

11.9% 12.4%

Hotels and restaurants 4.3% 4.1% Transport, storage and communication 2.7% 3.0% Financial activities 5.6% 7.3% Property development, renting, business and research activities

20.3% 20.5%

Public administration and defence; social security

5.5% 5.9%

Education 12.1% 11.3% Health and social work 15.5% 17.1% Other community, social and personal activities

11.5% 8.0%

Private households with employed persons 0.1% 0.1% International organisations and bodies 0.1% 0.1% Total 100%

(n = 10,515) 100% (n = 118,240)

Source: 2006/07 DLHE (HESA)

53

Analysis of major occupational group (Table 4.9) suggests that there is no difference

between disabled and non-disabled graduates in the percentages employed as managers

and senior officials. Disabled graduates, however, were less likely to be in professional

occupations but more likely to be in associate professional and technical occupations (for

definition, see footnote 3 in Chapter 2).

Table 4.9 Major occupational group six months after graduation Major occupational group

Disabled graduates Non-disabled graduates

Managers and senior officials

8.0% 7.9%

Professional occupations

23.9% 27.7%

Associate professional and technical occupations

32.9% 30.9%

Administrative and secretarial occupations

12.3% 13.3%

Skilled trades occupations

1.7% 0.9%

Personal service occupations

5.9% 4.9%

Sales and customer service occupations

9.6% 9.8%

Process, plant and machine operatives

0.6% 0.4%

Elementary occupations 5.0% 4.2% Total 100% (n = 10,525) 100% (n = 118,335) Source: 2006/07 DLHE (HESA)

Table 4.10 shows the percentages of disabled and non-disabled graduates employed in

‘graduate level’ occupations. The typology of job classifications used, SOC(HE), was

developed by Elias and Purcell (2004) for their research into graduate careers seven years

after graduation, and distinguishes between four ‘graduate-level’ occupations (‘traditional’,

‘modern’, ‘new’, and ‘niche’ – see Appendix A for more details) and ‘non-graduate

occupations’. The figures show that over a third of all graduates were employed in non-

graduate jobs six months after graduation. Disabled graduates were more likely to be found

in non-graduate occupations than their non-disabled peers, although the differences are not

54

great: 36.6% compared with 34.8% of the latter. Graduates with disabilities were also less

likely than those without disabilities to be employed in traditional graduate occupations.

There are, however, no statistically significant differences between the two groups in the

employment in modern, new, or niche graduate jobs.

Combining the four SOC(HE) graduate job categories reveals that 63.4% of disabled

graduates overall were employed in graduate occupations and 36.6% were in non-graduate

occupations, compared with 65.2% and 34.8% of non-disabled graduates. The differences

are statistically significant although not substantially so.14

14 Graduate occupations vs non-graduate occupations, χ2 = 13.79 (1), p<0.001

55

Table 4.10 Comparison of SOC(HE) between disabled and non-disabled graduates

SOC(HE) Disabled graduates Non-disabled graduates

% of total S.E. (%) 95% C.I. % of total S.E. (%) 95% C.I. Lower (%) Upper (%) Lower (%) Upper (%)

Traditional graduate occupations

9.9% 0.29 9.4 10.5 12.2% 0.10 12.0 12.4

Modern graduate occupations

14.1% 0.34 13.5 14.8 13.6% 0.10 13.4 13.8

New graduate occupations

17.1% 0.37 16.4 17.8 17.1% 0.11 16.9 17.3

Niche graduate occupations

22.2% 0.40 21.4 22.9 22.2% 0.12 21.9 22.4

Non-graduate occupations 36.6% 0.47 35.7 37.6 34.8% 0.14 34.6 35.1

Total 100% (n = 10,525)

100% (n = 118,335)

Source: 2006/07 DLHE (HESA)

56

4.2 Destinations six months after graduation by types of disability Table C1 in Appendix C shows employment activities by types of disability. Compared with

their non-disabled counterparts (Table 4.4), disabled graduates of all types showed lower

participation rates in full-time paid work and higher rates of unemployment. Unemployment

rates for wheelchair users or for those with mobility difficulties, as well as for graduates with

mental health difficulties, were the highest at 10%. On the contrary, those with an unseen

disability, a hearing impairment, or who have dyslexia have relatively low unemployment.

Further analysis of employment circumstances (Table C2 in Appendix C) shows that 9.8% of

graduates with mental health difficulties were unemployed and looking for work or training,

and another 6.1% were not employed but not looking for work/training. On the contrary,

while there were similarly high percentages of blind/partially sighted graduates and

graduates with mobility difficulties who were unemployed and looking for work, only 2.7%

and 4.9% respectively reported not looking for employment/training.

Table C1 also shows that graduates with mobility difficulties (22.3%), mental health

problems (21.2%), or multiple disabilities (20.5%) were the most likely to have gone on to

further study whilst those with dyslexia (14.1%) were the least likely. Coupled with their

relatively high unemployment rates, these findings seem to suggest that graduates with

mental health issues, mobility difficulties or multiple disabilities encounter greater barriers in

entering the labour market and may want to improve their chances by gaining extra

qualifications, or may even enter further study simply to avoid unemployment. The

differences in subject choices between these graduates and those with dyslexia are also

likely to influence their propensity in going into further study.

Not only did graduates with mental health difficulties have one of the lowest employment

and highest unemployment rates, they were also the most likely to be in temporary

employment (Table C3). Just under one in five (18.4%) employed graduates with mental

health problems were in temporary work, compared with around one in eight (12-13%)

graduates with other types of disabilities (except multiple disabilities). In addition, graduates

57

with mental health problems, as well as graduates who were blind or had sight problems,

were the most likely to report being in a job for which their degree qualification was not

needed for entry (Table C4). On the contrary, graduates with mobility difficulties or who have

a hearing impairment were the least likely to report that their degree qualification was not

needed in getting their job.

It was reported earlier (Section 4.1.2) that disabled graduates were more likely than their

non-disabled peers to be self-employed or working freelance six months after graduation.

Table C3 shows that those with dyslexia were the most likely to be in this type of

employment, which is perhaps unsurprising considering their high representation in creative

arts and design subjects, graduates of which are known to commonly take up freelance

employment (Harvey and Blackwell, 1999).

Tables C5 and C6 show major occupational groups and SOC(HE) by types of disability. The

‘others’ category in Table C5 is an amalgamation of the following categories shown in Table

4.9: administrative and secretarial occupations, skilled trades occupations, personal service

occupations, sales and customer service occupations, process, plant and machine

operatives and elementary occupations, most of which can be thought of as ‘non-graduate’

level occupations. This recoding was carried out to compensate for some of the small

sample sizes for these categories. Both Tables C5 and C6 indicate that graduates with

mental health difficulties were the most likely to be employed in these types of work, further

illustrating the barriers faced by these graduates in the job market. These graduates were

also the least likely to be in managerial occupations: only 2.5% were employed in these

occupations compared with an average of 8% for all disabled and non-disabled graduates.

Graduates with mental health difficulties, however, were relatively well-represented in

professional occupations: 26.9% were employed in these types of jobs compared with an

average of 23.9% of all disabled graduates, and the figure was close to that for non-disabled

graduates (27.7%, Table 4.9).

Overall, the analyses reported in this section suggest that amongst all disabled graduates,

those with mental health problems might have encountered the biggest employment

58

disadvantages in the labour market. On the contrary, those with dyslexia, a hearing disability,

or an unseen disability, appeared to face fewer obstacles. In addition, although graduates

with mobility difficulties have one of the highest rates of unemployment, amongst all disabled

graduates, they were the least likely to report being in a job for which their degree

qualification was not needed and the least likely to be found in the ‘non-graduate’ job

category. This seems to suggest that despite their difficulties in entering the labour market,

those who found employment appeared to be able to obtain a ‘better level’ job than

graduates with other types of disabilities.

4.3 Graduate careers later on - what does the LFS tell us? 4.3.1 Demographic and academic background

Under the criteria used for this study, there were a total of 24,315 people aged 40 or below

with a degree-level highest qualification in the LFS, of whom 1,605 (6.6%) were reported as

disabled – either DDA disabled, work-limiting disabled, or disabled under both definitions

(Table 4.11). By definition, all those who were reported as disabled must have a health

problem which lasted for at least a year. Amongst these disabled people, just under half

(46.6%) reported having a health problem which affected the amount of work that they could

do whilst two-thirds (64.7%) had one which affected the kind of work that they could take.

As mentioned In Section 2.1, no distinction will be made between the three types of disability

in the analysis reported here, and they will all come under the ‘disabled’ category.

Table 4.11 LFS respondents by disability status Types of disability Number Percentage (%)

DDA disabled

491

2.0

Work-limiting disabled 470 1.9

DDA disabled and work-

limiting disabled

644 2.6

59

Total disabled 1,605 6.6

Not disabled 22,710 93.4

Total 24,315 100

Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

Respondents in the LFS are allowed to report the number(s) and type(s) of their health

problems up to a maximum of 17. Amongst those who were disabled, the majority (72.1%)

reported having one health problem only, whilst 15.6% reported having two, and 12.1%

reporting having three or more. Amongst non-disabled respondents, the vast majority

(92.6%) have no health problems, with the remaining 7.4% reporting one or more problems,

although these were not considered as disabling. As with the Rigg study (2005), the number

of health problems will be used as a proxy for disability severity later in this study, as a

control variable where appropriate for multivariate analysis (Section 5.3.2). Although there

are issues with this approach: it assumes that one health problem is as serious as the other

and the effects are additive, and that disabled people have worse health than non-disabled

people, it has been found to work well at least in terms of rates of economic activity (ibid).

The categories of health problems used in the LFS are different from those used in the

DLHE survey. For example, there is no ‘dyslexia’ category in the LFS, which is the most

common type of disability reported in the DLHE survey. Table 4.12 shows the main health

problems reported by LFS respondents. Due to the small number of people in some of the

categories, the 17 categories have been aggregated into nine categories, with the ‘other’

category including speech impediment, skin conditions, epilepsy, learning difficulties,

progressive illness not elsewhere classified, and other kinds of disability not listed elsewhere.

Those with dyslexia are counted towards having learning difficulties in the LFS, and the

latter accounted for only 1.2% of the reported main health problem amongst disabled

graduates, thus illustrating the difficulties present in making comparisons between the LFS

and the DLHE survey by types of disability/health problems.

Table 4.12 Main health problems reported by LFS respondents

60

Disabled Non-disabled

Arms, hands, legs, or feet

11.3%

8.4%

Back or neck

14.2% 7.0%

Chest, breathing problems 16.6% 34.4%

Diabetes

5.6% 3.8%

Heart, blood, pressure, circulation 4.2% 7.9%

Mental illness, phobias, panics, depression, bad nerves

8.8% 2.8%

Difficulty in seeing or hearing 4.8% 4.8%

Stomach, kidney, liver, digestion 6.8% 7.3%

Other*

27.7% 23.6%

Total 100% (n=1,598) 100% (n=1,697)

*Including speech impediment, skin conditions, epilepsy, learning difficulties, progressive illness not elsewhere classified, and other kinds of disability not listed elsewhere. Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

For both disabled and non-disabled respondents, the most commonly reported health

problems were chest and breathing problems (16.6% for the disabled and 34.4% for the

non-disabled). Amongst disabled respondents alone, the second most commonly cited

health problem was back or neck (14.2%), followed by limbs (11.3%) and mental health

issues (8.8%). Mental health problems thus appears to be a more common problem

amongst LFS respondents than amongst DLHE survey graduates, of which only 3.5%

reported mental health difficulties (Table 4.2). In addition to the differences in health

problem/disability categories used in the two surveys, another possible reason for the

differences found could be the reported surge in students with dyslexia in recent years

(DIUS, 2009): the DLHE survey only covers those who graduated in 2006/07 whilst the LFS

includes those who graduated at any time in the past 22 years. This further illustrates the

difficulties in making comparisons between the two studies.

Table D in Appendix D gives further demographic and academic background information

about the LFS respondents. Under the sample selection criteria used for this part of the

61

research, respondents with a degree-level highest qualification have been included, and this

includes higher degree, first degree, foundation degree, graduate membership of a

professional institute and other types of degree. Table D shows that the majority, two-thirds

of respondents, had a first degree as their highest qualification, and another 27% had a

higher degree. There were no statistically significant differences between the percentages of

disabled and non-disabled graduates with a higher degree15, or more specifically, with a

Masters degree16 which was the most common type of higher degree reported. Although

including other degree qualifications other than a first degree makes it more difficult to

compare the results with the DLHE survey, this was done so that a larger sample size could

be achieved.

15 Higher degree vs all other degree qualifications combined, χ2 = 0.069 (1), p=0.793 16 Masters degree vs all other higher degree qualifications combined, χ2 = 1.565 (1), p=0.211

62

4.3.2 Employment outcomes from the LFS

Table 4.13 compares the employment outcomes between disabled and non-disabled

graduates from the LFS.17 The biggest difference in labour market indicators between

disabled and non-disabled graduates was in the percentages of economically inactive:

15.6% and 9.1% respectively. Disabled graduates were also more likely to be employed

part-time and in the public sector. The latter finding is in contrast with that found amongst

the general population (ie graduates and non-graduates) where the proportion of disabled

people employed in the public sector was found to be less than that of non-disabled people

(Hirst and Thornton, 2005).

Unlike what the DLHE survey data has suggested, the LFS data shows no statistically

significant differences between disabled and non-disabled graduates in the percentages in

temporary employment18 or self-employment. There are several possible explanations for

the differences in findings between the two surveys: the sample size of disabled graduates

in the LFS was much smaller than that in the DLHE survey, which makes it more difficult to

detect a statistically significant result even if there are ‘true’ differences between the two

groups of graduates. Another possible reason is the coverage of the two surveys: the DLHE

data only includes graduates who obtained their first degree six months earlier, whilst the

LFS includes graduates from all years, and with other degree-level qualifications as well as

a first degree. It is possible that any differences in job tenure and self-employment rate

between the two groups at an early stage after graduation no longer exist as the graduates

gained more experience in the labour market. In the case of self-employment, a third

possible reason relates to the differences in the distribution of degree subjects amongst the

respondents of the two surveys: in the DLHE survey, the proportion of disabled graduates

from creative arts and design subjects was almost twice that of non-disabled graduates

17 Unemployment in the LFS is based on the ILO (International Labour Organization) definition. It is a count of jobless people who want to work, are available to work, and are actively seeking employment. On the other hand, people who are economically inactive are those who are out of work but who do not satisfy all of the ILO criteria for unemployment, because they are either not seeking work or are unavailable to start work (ONS, 2009). 18 In the LFS, this is categorised as ‘Not permanent in some way’.

63

(20.5% compared with 11%, Table 4.3), whist in the LFS, the difference was much smaller

(9.2% vs. 6.7%, Table D in Appendix D). As self-employment is a common employment

option for these graduates, it is possible that the much smaller difference found between

disabled and non-disabled creative arts graduates in the LFS could contribute to the

differences in findings on self-employment between the two studies. Judging from the size of

the standard errors and the percentage estimates in Table 4.13, however, the first

explanation is perhaps the most likely reason for the statistically non-significant results.

Figures in Table 4.13 indicate that disabled graduates were just as likely as those who were

non-disabled to have supervision responsibilities in their job. Analysis by major occupational

group also reveals no difference in the percentages of disabled and non-disabled graduates

being employed as managers or senior officials. There were, however, differences in their

employment in professional and in associate professional and technical occupations, with

non-disabled graduates being more likely to be found in professional occupations and

disabled graduates more likely to be employed in the latter, although the difference was only

small for the latter.19 This is consistent with the DLHE survey findings reported in Section

4.1.2. Similarly, analysis by socio-economic class (NS-SEC) found that just under a third

(31.4%) of non-disabled graduates were found in higher managerial occupations, compared

with only a quarter (24.4%) of their disabled peers. Disabled graduates were more likely to

never have worked or be unemployed (12.7% compared with 9.4%), or be employed in

lower managerial occupations, although the difference between the two groups of graduates

for the latter was not substantially different.20

Unlike the DLHE survey results for SOC(HE), which suggest that there were differences in

the employment of the two groups of graduates in traditional graduate occupations and non-

graduate occupations, the LFS data reveals no statistically significant differences between

the two groups in their employment in any of the four SOC(HE) graduate job categories, or

in the non-graduate occupation category. In addition, if we combine the four graduate job

categories into one graduate job category, 76.2% of disabled graduates were found to be 19 Associate professional and technical occupations vs all other occupational categories combined, χ2 = 5.67 (1), p=0.017 20 Lower managerial occupations vs all other NS-SEC combined, χ2 = 5.02 (1), p=0.025

64

employed in graduate occupations, compared with 78% of non-disabled graduates, and the

difference is not statistically significant.21 This latter result suggests that although sample

size is again a potential issue here, it is unlikely that any statistically significant differences

found with a larger sample would be substantially significant.

Table 4.14 shows the types of industry reported in the LFS, whilst Table E in Appendix E

presents the standard errors and C.I. of the percentage estimates of some of the major

sectors. As the DLHE survey data suggested (Table 4.8), the LFS data show no differences

between disabled and non-disabled graduates in the percentages employed in real estate,

renting & business activities, or public administration and defence. Unlike the DLHE survey,

disabled graduates in the LFS were more likely than those without disabilities to be

employed in the health and social work sectors. Also contrary to what the DLHE data has

suggested, there were no statistically significant differences in the employment of the two

groups in: financial intermediation, education, or other community, social and personal

activities. The lack of statistically significant difference between disabled and non-disabled

graduates in the LFS in financial intermediation, however, is most likely to be due to the

small number of disabled graduates in the sample (79) and the corresponding large

standard error. Using another industry classification in the LFS where financial

intermediation and real estate, renting & business activities are combined to form ‘banking,

finance and insurance’, the difference between disabled and non-disabled graduates in this

new category then became statistically significant (Table E).

Earnings information is also available from the LFS. The mean gross weekly pay of disabled

graduate employees working full time was £566.7, 7.8% lower than that reported by their

non-disabled counterparts (Table 4.15), and the difference is statistically significant.22, 23 On

21 Graduate occupations combined vs non-graduate occupations, χ2 = 2.14 (1), p=0.144 22 t = -3.79 (13570), p<0.001 23 In accordance with ONS guidelines, only those with gross weekly income and hourly pay above 0, and hourly pay less than £100, have been included in the analysis. The top limit was set to exclude those who have reported ‘abnormally high’ level of hourly pay (ONS, 2008b, p.376).

65

the contrary, there is no statistically significant difference in the mean gross weekly pay

between disabled and non-disabled part-time employees.24, 25

Similarly, in terms of gross hourly pay (Table 4.16), disabled graduates in full-time

employment earned 5.8% less than their non-disabled peers26, but the difference in part-

time employees’ hourly pay between disabled and non-disabled graduates was not

statistically significant.27, 28

24 t = -1.74 (1910), p=0.083 25 The ONS has recommended measuring the gender pay gap using median, rather than mean, hourly earnings. The Equality and Human Rights Commission, however, prefers to use the mean earnings as this measure does not exclude those on very high earnings which tend to be the more privileged group (Metcalf, 2009). In line with the Equality and Human Rights Commission preference, the disability pay gaps in this study were calculated from the mean earnings rather than the median. 26 t = -2.83 (13570), p=0.005 27 t = -0.65 (1910), p=0.52 28 As expected, the weekly pay gap was found to be larger than the hourly pay gap (Longhi and Platt, 2008). This is because disabled people have reported fewer hours of work and this was taken into account when calculating the hourly pay, which is the gross weekly pay divided by the number of hours worked (ONS, 2008a, p.174).

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Table 4.13 Employment outcomes of graduates in LFS

% of total S.E. (%) Lower (%) Upper (%) % of total S.E. (%) Lower (%) Upper (%)Economic activityIn employment 79.8 1.00 77.8 81.8 87.9 0.22 87.4 88.3ILO unemployed 4.6 0.52 3.6 5.6 3.0 0.11 2.8 3.2Inactive 15.6 0.91 13.8 17.4 9.1 0.19 8.8 9.5Total 100 (n = 1,605) 100 (n = 22,710)

Employment statusEmployee 90.4 0.83 88.8 92.0 91.8 0.19 91.4 92.2Self employed 9.6 0.83 8.0 11.2 8.2 0.19 7.8 8.6Total 100 (n = 1,272) 100 (n = 19,916)

Full- or part-time employmentFull-time 81.3 1.09 79.2 83.4 87.0 0.24 86.5 87.5Part-time 18.7 1.09 16.6 20.8 13.0 0.24 12.5 13.5Total 100 (n = 1,278) 100 (n = 19,947)

Job tenurePermanent 91.2 0.83 89.6 92.9 92.6 0.19 92.2 93.0Not permanent 8.8 0.83 7.1 10.4 7.4 0.19 7.0 7.8Total 100 (n = 1,151) 100 (n = 18,276)

Private or public sector employmentPrivate 63.5 1.35 60.8 66.1 67.5 0.33 66.9 68.2Public 36.5 1.35 33.9 39.2 32.5 0.33 31.8 33.1Total 100 (n = 1,151) 100 (n = 18,276)

Disabled graduates Non-disabled graduates

95% C.I. 95% C.I.

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Table 4.13 (continued). Employment outcomes of graduates in LFS.

% of total S.E. (%) Lower (%) Upper (%) % of total S.E. (%) Lower (%) Upper (%)NS-SEC class (main job)Higher managerial 24.4 1.07 22.3 26.5 31.4 0.31 30.8 32.0Lower managerial 41.4 1.23 39.0 43.8 38.6 0.32 38.0 39.2Intermediate 9.1 0.72 7.7 10.5 9.6 0.20 9.2 9.9Other occupations 12.4 0.82 10.8 14.0 11.0 0.21 10.6 11.5Never worked/unemployed, nec 12.7 0.83 11.1 14.3 9.4 0.19 9.0 9.7Total 100 (n = 1,605) 100 (n = 22,710)

Major occupation groupManagers 17.7 1.07 15.6 19.8 19.2 0.28 18.6 19.7Professional 32.5 1.31 29.9 35.0 36.1 0.34 35.4 36.8Associate prof 27.5 1.25 25.0 29.9 24.5 0.30 23.9 25.1Other occupations 22.4 1.17 20.1 24.6 20.3 0.29 19.7 20.8Total 100 (n = 1,275) 100 (n = 19,885)

SOC(HE)Traditional 16.5 1.04 14.5 18.6 18.7 0.28 18.2 19.3Modern 19.5 1.11 17.4 21.7 19.2 0.28 18.7 19.8New 16.9 1.05 14.8 18.9 18.4 0.27 17.8 18.9Niche 23.3 1.18 21.0 25.6 21.7 0.29 21.1 22.3Non-graduate occupation 23.8 1.19 21.4 26.1 22.0 0.29 21.4 22.6Total 100 (n = 1,275) 100 (n = 19,883)

Supervision ResponsibilitiesYes 46.3 1.37 43.6 49.0 45.6 0.35 44.9 46.3No 53.7 1.37 51.0 56.4 54.4 0.35 53.7 55.1Total 100 (n = 1,329) 100 (n = 19,737)

Disabled graduates Non-disabled graduates

95% C.I. 95% C.I.

Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

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Table 4.14 Types of industry of employed graduates in the LFS Type of industry Disabled

graduates (%)

Non-disabled graduates (%)

Agriculture, forestry and fishing 0.2 0.4 Mining and quarrying 0.4 0.4 Manufacturing 7.8 10.3 Electricity, gas and water supply 1.1 0.8 Construction 2.4 3.1 Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods

7.5 7.5

Hotels and restaurants 1.9 2.3 Transport, storage and communication 4.2 3.8 Financial intermediation 6.2 7.8 Real estate, renting & business activities 18.7 21.1 Public administration and defence 9.3 8.5 Education 17.2 15.1 Health and social work 15.4 12.8 Other community, social and personal activities

7.3 5.9

Private households with employed persons Extra-territorial organisations, bodies

0.2 0.0

0.1 0.1

Workplace outside UK 0.2 0.0 Total 100%

(n = 1,275) 100% (n = 19,885)

Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

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Table 4.15 Gross weekly pay reported in the LFS (£) Disabled graduates Full-time employment

Mean 566.7 (n = 813) S.E. of mean 10.94 95% C.I. 545.3 - 588.2 Median 510.0

Part-time employment

Mean 243.7 (n = 165) S.E. of mean 13.76 95% C.I. 216.5 – 270.8 Median 205.5

Non-disabled graduates

Full-time employment

Mean 614.6 (n = 12758) S.E. of mean 3.11 95% C.I. 608.5 – 620.7 Median 531.0 Part-time employment

Mean 273.9 (n = 1747) S.E. of mean 5.20 95% C.I. 263.7 – 284.1 Median 227.3

Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

Table 4.16 Gross hourly pay reported in the LFS (£) Disabled graduates Full-time employment

Mean 14.6 (n = 813) S.E. of mean 0.27 95% C.I. 14.1 – 15.2 Median 13.2 Part-time employment

Mean 12.7 (n = 165) S.E. of mean 0.76 95% C.I. 11.2 – 14.2 Median 10.5

Non-disabled graduates

Full-time employment

Mean 15.5 (n = 12758) S.E. of mean 0.076 95% C.I. 15.4 – 15.7 Median 13.6 Part-time employment

Mean 13.2 (n = 1747) S.E. of mean 0.21 95% C.I. 12.8 – 13.6 Median 11.3

Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

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4.4 Summary

The findings reported in this chapter suggest that six months after graduation, disabled

graduates were less likely than their non-disabled counterparts to be in full-time paid work.

They were more likely to continue with their study, or to be unemployed (either looking or not

looking for work). For those disabled graduates who were in employment, they were more

likely than their non-disabled peers to work part-time, be in voluntary/unpaid work, be self-

employed/freelancing, or have a temporary job. They were also more likely to consider their

degree qualification was not needed for entry to their job. On the positive side, disabled

graduates were just as likely as non-disabled graduates to enter managerial and senior

official occupations, and although they were less likely to be found in professional

occupations, they were better represented than non-disabled graduates in associate

professional and technical occupations. Analysis by graduate job types SOC(HE) also

suggests that although disabled graduates were slightly more likely than their non-disabled

peers to be found in non-graduate occupations and less likely to be in traditional graduate

occupations, there were no differences between the two groups in their employment in

modern, new or niche graduate occupations.

In terms of types of disabilities, the results suggest that graduates with mental health

problems were the most likely to encounter difficulties in the labour market, although they

were relatively well-represented in professional occupations. On the contrary, those with

dyslexia, a hearing disability, or an unseen disability, appeared to face fewer obstacles.

Some of the differences in employment outcomes between the two groups of graduates

persist beyond the initial stage after graduation. Years after obtaining their degree-level

highest qualifications, disabled graduates continued to have a higher unemployment and

inactivity rates than those without disabilities, and have lower gross weekly and hourly pay.

Disabled graduates were also less likely to be in the higher managerial socio-economic

group. Both groups of graduates were, however, equally likely to report being in a job with

supervision responsibilities.

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Unlike the situation six months after graduation, the LFS data shows no statistically

significant differences between the two groups of graduates in the percentages employed in

temporary work and in self-employment, although this is likely to be due to the small number

of disabled graduates in the survey. No statistically significant differences were also found

between the two groups of graduates in their employment in the various graduate job

categories (SOC(HE)).

In the analyses conducted thus far, we have not been able to tell whether any differences in

employment outcomes between disabled and non-disabled graduates are due to the

disability itself (or the lack of it), or differences in other characteristics between the two

groups of graduates. In the next section, multivariate techniques will be used to disentangle

some of these factors.

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Chapter 5 - Untangling the Relationship Between Disability and Graduate Employment Outcomes 5.1 Multivariate analysis In this chapter, we will use multivariate analysis techniques to explore the relationship

between disability and graduate employment outcomes. Unlike the descriptive analyses

reported in Chapter 4, these methods allow us to compare the outcomes of disabled and

non-disabled graduates whilst controlling for a range of demographic, educational and other

potential confounding factors, and help to isolate any contributory effects due to disability

alone.

Three types of multivariate analysis will be used here: binary logistic regression,

multinominal logistic regression, and multiple linear regression. The results from binary and

multinominal logistic regression will give us estimates of the probability of an outcome

happening. Binary logistic regression is used when the outcome (ie the dependent variable)

we are investigating can only take on one of two values, eg graduate vs non-graduate job,

full vs part-time employment, whilst multinominal logistic regression is used when the

outcome can take on one of several values, eg one of the five SOC(HE) classifications.

Using one of the two outcome categories as a reference category and after controlling for a

range of factors (ie the explanatory/control variables, eg disability status or the types of

disability) affecting the outcome, the results of binary logistic regression are expressed as

‘odds’, defined as the probability of an outcome happening compared to the probability of it

not happening. For example, using non-graduate occupation as a reference, binary logistic

regression will be used to predict the probability of being in a graduate job29 relative to that

of being in a non-graduate job, whilst controlling for all other factors. Another example of its

use is to predict the probability of being employed in the public sector relative to that of

being in the private sector.

29 Defined as a combination of the four SOC(HE) categories: traditional, modern, new and niche graduate occupations.

73

Similarly, the results for multinominal logistic regression can also be expressed as odds, and

this is defined as the probability of one of several outcomes happening relative to that of a

reference outcome. For example, using non-graduate occupation as a reference,

multinominal logistic regression can tell us the probability of being in a traditional graduate

job relative to that of being in a non-graduate job and the probability of being in a modern

graduate occupation relative to that of being in a non-graduate occupation. More

explanations are given in the next section and in Appendix F.

As mentioned in Section 3.3, due to data protection, two DLHE datasets have been supplied

by HESA for this study: one with information on the types of disability but with no ethnicity

information, the other containing a ‘disability marker’ (disabled/no known disability) together

with ethnicity. As such, the first dataset is used to predict the employment outcomes of

graduates by types of disability, using non-disabled graduates as the reference group, but

without controlling for the ethnicity variable. The analyses are then repeated using the

second dataset, with ethnicity as a control variable (where appropriate), and the outcomes

distinguished between disabled and non-disabled graduates only, with no distinction being

made between the types of disability.

74

Unlike for the categorical graduates’ outcomes data mentioned above, multiple linear

regression will be used to estimate graduates’ earnings using data from the LFS, controlling

for a range of factors including disability status.30

For these multivariate analyses, selection of potentially useful explanatory/control variables

is primarily informed by the literature, and the range of factors impacting on graduate

employment outcomes have been well-researched over the years (for example, see Elias,

1999; Brennan and Shah, 2003; Purcell and Elias, 2004; Purcell et al, 2005).

In addition to the results reported in the next section (5.2), further results from these

multivariate analyses, including the list of control factors used, can be found in Appendix G.

Due to the number of models constructed for this study, the number of explanatory variables

used as controls for each model, and the resulting large number of regression parameters,

only parameters for the disability variable will be reported. This would indicate the

independent effect of the disability variable on the outcome variable for each model, whilst

the additional effects of a range of other factors used as controls would not be shown.31

30 Instead of modelling the gross weekly and hourly pay directly, the transformed log figures will be modelled. In linear regression, one of the assumptions is that the outcome variable needs to be normally distributed (Hutcheson and Sofroniou, 1999, p26). Plots of the gross weekly and hourly pay variables, however, reveal that their distributions are positively skewed – a common occurrence with earnings data as there are often only a very small number of individuals who report extremely high figures. As such, a log transformation was carried out on the two earnings variables to reduce the positive skewness of the data. 31 The primary interest of this study is to investigate the impact of disability on employment outcome. As such, in cases where the disability variable was not significant, it was not removed from the analysis, unlike for other statistically non-significant control variables.

75

5.2 Results from multivariate analysis

5.2.1 Six months after graduation – what does the DLHE survey tell us?

Figures 5.1 – 5.5 present the results of the analyses for predicting graduates’ activities six

months after graduation. Coloured bars are used to indicate that the results are statistically

significant at the 5% significance level, whilst white bars are used to indicate that the results

are not statistically significant. Further results can be found in Appendix G.

Figure 5.1 and the parameters of Model 1a in Appendix G show that six months after

graduation, the odds of being unemployed relative to that of being in full-time paid work are

1.43 times higher for disabled than for non-disabled graduates, ie the ‘odds ratio’ is 1.43, so

disabled graduates are 43% more likely than non-disabled graduates to be out of work

rather than being in full-time paid work. Similarly, the odds of being in further study only or

not available for employment or training are around 1.3 times higher for those with

disabilities. Figures 5.2-5.5 and Model 1b in Appendix G show results of further analysis of the

relationship between employment outcomes and the types of disability (as opposed to

disabled vs non-disabled only). Using full-time paid work as the outcome reference and

controlling for a range of factors, all disabled graduates, irrespective of the type of disability,

are found to be more likely than their non-disabled counterparts to be unemployed, although

the results for deaf/hearing impairment and for unseen disability are not statistically

significant (Figure 5.2). Amongst all types of disabilities, graduates with mental health

problems or those with mobility difficulties are the most likely to be out of work. The odds of

being unemployed relative to being in full-time paid work are 2.35 times higher for those with

mental health problems compared with their non-disabled peers. Similarly, the equivalent

odds ratio for those with mobility difficulties is 2.02. Conversely, those with dyslexia, hearing

difficulties, or an unseen disability fare relatively well in this respect. Figure 5.1 How disability affect the probabilities of being in different employment outcomes (relative to being in full-time paid work)

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1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

Part-tim

e paid

work

Volunta

ry/un

paid

work

Work

and f

urthe

r stud

y

Furthe

r stud

y only

Assum

ed to

be un

emplo

yed

Not av

ailab

le for

emplo

ymen

tOthe

r

Odds (disabled graduates) / Odds (non-disabled

graduates)

Figure 5.2 Probability of being unemployed relative to the probability of being in full-time paid work, by types of disability

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Dyslex

ia

Blind/p

artial

ly sig

hted

Deaf/h

earin

g impa

irmen

t

Whe

elcha

ir use

rs/mob

ility d

ifficu

lties

Mental

healt

h diffi

cultie

s

An uns

een d

isabil

ity

Multipl

e disa

bilitie

s

A disa

bility

not li

sted a

bove

Odds (disabled graduates by types of disability) / Odds (non-

disabled graduates)

77

It was reported earlier (Table 4.4, Section 4.1.2) that disabled graduates were more likely

than those without disabilities to be found in part-time paid work six months after graduation.

Figure 5.1 shows that the predicted odds ratio is actually small at only 1.09, ie disabled

graduates were only 9% more likely than their non-disabled peers to be in part-time work. In

fact, analysis by types of disability (Figure 5.3) shows that with the exceptions of those with

mental health problems and a disability not elsewhere classified, there are no statistically

significant differences between disabled and non-disabled graduates in their propensity to

be employed in part-time paid work relative to being in full-time paid work six months after

graduation. Graduates with mental health difficulties or a disability not elsewhere classified

are, however, around 1.4 times more likely to be employed part-time compared with those

without disabilities. Figure 5.3 Probability of being in part-time paid work relative to the probability of being in full-time paid work, by types of disability

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

Dyslexia Blind/partiallysighted

Deaf/hearingimpairment

Wheelchairusers/mobility

difficulties

Mental healthdifficulties

An unseendisability

Multipledisabilities

A disability notlisted above

Odds (disabled graduates by types of disability) / Odds (non-disabled graduates)

Disabled graduates are also more likely than their non-disabled counterparts to be in

voluntary/unpaid work, with the predicted odds ratio being 1.72, again controlling for other

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variables (Figure 5.1). Amongst those with disabilities, graduates who are blind/partially

sighted are the most likely to be in this type of work, with the odds being 4.98 times higher

than for non-disabled graduates (Figure 5.4). Similarly, those with multiple disabilities,

mobility difficulties, and mental health difficulties are between two and three times more

likely than their non-disabled peers to be in voluntary/unpaid work. Figure 5.4 Probability of being in voluntary/unpaid work relatively to the probability of being in full-time paid work, by types of disability

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Dyslex

ia

Blind/p

artial

ly sig

hted

Deaf/h

earin

g impa

irmen

t

Whe

elcha

ir use

rs/mob

ility d

ifficu

lties

Mental

healt

h diffi

cultie

s

An uns

een d

isabil

ity

Multipl

e disa

bilitie

s

A disa

bility

not li

sted a

bove

Odds (disabled graduates by types of disability) / Odds (non-

disabled graduates)

Irrespective of the type of disability, all disabled graduates are more likely than their non-

disabled peers to continue with their study (as a sole activity) after completing their first

degree (Figure 5.5). Graduates with mobility difficulties, multiple disabilities and mental

health problems are the most likely to do so, whilst those with dyslexia are the least likely,

and despite having now controlled for a range of variables including degree subject, this still

ties in with the earlier findings from the descriptive analysis (Section 4.2). In addition,

amongst all graduates, those with multiple disabilities or mental health difficulties are the

most likely to be unavailable for employment or further study. The odds of being

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‘economically inactive’ (relative to being in full-time paid work) are almost 2 times higher for

those with multiple disabilities compared with graduates with no disabilities (Model 1b in

Appendix G). Figure 5.5 Probability of being in further study only relative to the probability of being in full-time paid work, by types of disability

1.0

1.2

1.4

1.6

1.8

2.0

Dyslex

ia

Blind/p

artial

ly sig

hted

Deaf/h

earin

g impa

irmen

t

Whe

elcha

ir use

rs/mob

ility d

ifficu

lties

Mental

healt

h diffi

cultie

s

An uns

een d

isabil

ity

Multipl

e disa

bilitie

s

A disa

bility

not li

sted a

bove

Odds (disabled graduates by types of disability) / Odds (non-

disabled graduates)

Looking further into graduates’ employment circumstances, the odds of being unemployed

and looking for work, study or training, relative to that of being in employment, are 1.39

times higher for disabled than for non-disabled graduates (Models 2a in Appendix G). Those

with mobility or mental health difficulties are more than twice as likely as their non-disabled

peers to be unemployed and looking for work (Model 2b). These graduates are also the

most likely to be not employed and not looking for work or training, with the odds ratios

being 1.75 and 1.91 respectively.

Similarly, graduates with multiple disabilities, mobility difficulties, and mental health

problems are the most likely to be permanently or temporarily unable to work, with the odds

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being 6.4, 5.68, and 4.56 times higher than for graduates with no disabilities. Conversely,

the equivalent odds for graduates with dyslexia are only 1.39.

Amongst graduates who are in employment, disabled graduates are overall 17% more likely

than non-disabled graduates to be in temporary employment rather than being on a

permanent or fixed term contract (Model 3a). For those with mental health difficulties and

multiple disabilities, the risks are, however, 45% and 40% higher respectively (Model 3b).

The results of the descriptive analysis in Section 4.1.2 suggested that disabled graduates

were more likely than those without disabilities to be found in self-employment or freelance

work six months after graduation. Results of multivariate analysis here (Model 3a) show that

the odds of being self-employed/working freelance are 1.35 times higher for disabled than

for non-disabled graduates. However, analysis by types of disability (Model 3b) shows that

only the results for dyslexia is statistically significant. The odds of these graduates being

found in self-employment, relative to being on a permanent/fixed term contract, are 1.45

times higher than for their non-disabled peers, even after controlling for degree subject and

other variables.

The results of the descriptive analysis reported earlier also suggested that six months after

graduation, disabled graduates were less likely than their non-disabled peers to be in

professional occupations but more likely to be in associate professional and technical

occupations. Using ‘other occupations’ as the reference category (which include

administrative and secretarial, skilled trades, personal service, sales and customer service,

process, plant and machine operatives, and elementary occupations) and having controlled

for a range of demographic and academic factors including ethnicity, disabled graduates

overall are found to be around 8% less likely than those without disabilities to be in

professional occupations (Model 4a). Analysis by types of disability, however, suggests that

with the exception of ‘a disability not listed above’, there are no statistically significant

differences between disabled and non-disabled graduates in their likelihood of being

employed in professional occupations.

81

In agreement with the descriptive analysis results, multivariate analysis (Model 4a) shows no

statistically significant differences between disabled and non-disabled graduates in their

overall likelihood of being employed as managers or senior officials. There are, however,

exceptions depending on the types of disability: the odds of being employed as managers

and senior officials are 1.12 times higher for graduates with dyslexia than for their non-

disabled peers (Model 4b). At the other extreme, with an odds ratio of 0.31, graduates with

mental health difficulties are 70% less likely to be in these types of jobs compared with their

non-disabled counterparts.

Unlike what the descriptive analysis results suggested, no statistically significant differences

are found between the two groups in their employment in associate professional and

technical occupations.

In Section 4.1.2, results of descriptive analysis suggested that disabled graduates were

overall less likely than their non-disabled peers to be found in graduate occupations (as

defined by SOC(HE)). We now look at whether this difference still exists after controlling for

a range of demographic and academic factors.

By grouping the four different graduate job classifications of SOC(HE) (ie. traditional,

modern, new, and niche graduate occupations) into one ‘graduate job’ category, and using

the non-graduate job category as the reference, results from binary logistic regression

analysis (Model 5a) suggest that there are no statistically significant differences between

disabled and non-disabled graduates in their likelihood of being employed in a graduate job,

after controlling for a range of demographic, academic and employment-related factors

including degree subjects, degree classifications and gender. Analysis by types of disability

(as opposed to disabled vs non-disabled only) suggests that although no differences are

found in most cases, one exception is graduates with mental health difficulties (Model 5b).

Compared with those without disabilities, graduates with mental health problems are 27%

less likely to be employed in a graduate occupation.

82

Again using the non-graduate job category as the reference, if we next look at the four

different graduate job classifications separately, results of multinominal logistic regression

(Model 6a) suggests that there are no differences between disabled and non-disabled

graduates in their chances of being employed in traditional, modern, or new graduate

occupations. Small differences, however, are found between the two groups in the

percentage employed in niche graduate occupations, with disabled graduates being 5.7%

less likely to be employed in these types of jobs than their non-disabled counterparts.

Further analysis by types of disability (Model 6b), however, suggests that the result is only

statistically significant for graduates with mental health difficulties, who are 39% less likely to

be employed in niche graduate jobs than their non-disabled peers. The analysis also shows

that there are no statistically significant differences by types of disability in employment in

traditional or modern graduate occupations. The same conclusion can also be drawn for

most cases for new graduate occupations, except, again, for graduates with mental health

issues. Compared with those with no disabilities, these graduates are 47% less likely to be

found in new graduate occupations. Graduates with a disability not elsewhere classified are

also less likely than non-disabled graduates to be employed in new graduate occupations,

with the predicted odds ratio being 0.73.

We now look at a more subjective measure of job level: whether graduates consider their

degree qualification is needed in getting their job. In Section 4.1.2, results of descriptive

analysis suggested that disabled graduates were less likely than their non-disabled

counterparts to report that their qualification was a formal requirement for their job, and were

more likely to report being in jobs where their degree qualification was not needed for entry.

Results of binary logistic regression (Model 7a) show that after controlling for a range of

demographic and academic factors including ethnicity, there is no statistically significant

difference between disabled and non-disabled graduates in their likelihood of reporting

whether their degree is needed.32 Analysis by types of disability (Model 7b) shows that with

the exception of a disability not elsewhere classified, none of the results from other types of

32 For this binary logistic regression analysis, the ‘degree qualification is needed’ category is a combination of the following three categories: a degree qualification is a formal requirement, is expected, or is an advantage.

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disability are statistically significant, although overall, disability is found to be a significant

predictor for whether a degree qualification was required for entry into a job.33 Unlike for

the binary logistic regression analysis, ethnicity has not been controlled for in the analysis by

types of disability as HESA was not able to supply both the ethnicity and types of disability

variables in the same dataset. As such, some of the variability in outcomes between

disabled and non-disabled graduates found in the latter analysis could perhaps be explained

by ethnicity, and if this was to be controlled for, disability may no longer be a significant

factor (as in Model 7a).

Similarly, multinominal logistic regression analysis (Model 8a) reveals that there are no

statistically significant differences between disabled and non-disabled graduates in their

reporting of whether their degree qualification is a formal requirement in entry to their job, or

whether their qualification is expected or is an advantage. There are, however, again some

differences by types of disability (Model 8b): the odds of reporting that a degree qualification

is a formal requirement, relative to a degree is not required, are 0.67 for graduates with

mental health difficulties compared with their non-disabled peers, ie graduates with mental

health difficulties are 33% less likely than their non-disabled peers to report being in a job for

which their degree qualification was a formal requirement. The analysis also shows that

graduates with mobility difficulties or a hearing impairment are also more likely than their

non-disabled counterparts to report being be in a job where their degree qualification is an

advantage for entry (relative to being not required), with the odds ratios being 1.84 and 1.47

respectively. Overall, the type of disability is found to be a significant predictor in this

analysis, although again, this is likely to be a result of the exclusion of the ethnicity variable

as a control (as suggested for the binary logistic regression Model 7b).

It is important to emphasise that (as has been mentioned in Section 4.1.2), whether or not a

degree qualification was required and the level of its requirement is a highly subjective

measure. In addition, around one in five respondents eligible to answer this question did not

do so. One interesting point to note is that there are no differences between males and

females in their views of whether their degree qualification is required in getting their job,

33 p = 0.044

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although males are found to be more likely than females to be employed in all types of

graduate occupations, with the odds ratios ranging from 1.23 for new graduate occupations

to 1.61 for traditional occupations, having accounted for a range of demographic and

educational factors.

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5.2.2 Graduates’ careers further on – what does the LFS tell us?

The discussion thus far refers only to employment outcomes six months after graduation.

We will now look at graduate careers further on, up to a maximum of 22 years since

completion of the highest degree-level qualification.

In agreement with the literature and with results from the descriptive analysis reported

earlier, multivariate analysis (Model 9) shows that disabled graduates were more likely than

their non-disabled peers to be unemployed or economically inactive. With employment as

the reference category, the odds of being unemployed for graduates with disabilities are

1.85 times higher than for their non-disabled peers, whilst the odds ratio of being

economically inactive is even greater at 2.41.

Disability status is also a significant predictor of graduates’ socio-economic class (NS-SEC)

(Model 10), with the odds of having never worked or being unemployed, relative to being in

a higher managerial occupation, being 2.24 times higher for disabled than for non-disabled

graduates. Disabled graduates are also 37% and 35% respectively more likely than their

non-disabled counterparts to be found in intermediate or lower managerial occupations

rather than being employed in higher managerial occupations.

Similarly, multivariate analysis of major occupational groups reveals that the odds of being

employed in associate professional and technical occupations, relative to being employed in

professional occupations, are 1.31 times higher for disabled than for non-disabled graduates

(Model 11), and the odds of being employed in the ‘other occupations’ category, which

include administrative and secretarial or customer service types of roles, are 1.38 times

higher for the disabled. There are, however, no statistically significant differences between

the two groups in their likelihood of being employed as managers or senior officials. In

addition, disabled graduates are just as likely as their non-disabled peers to be employed in

a job with supervision responsibilities (Model 12).

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It was reported in Section 4.3.2 that disabled graduates were more likely than their non-

disabled peers to be employed in the public sector. Results from multivariate analysis

suggest that some of the differences between disabled and non-disabled graduates’

employment by private/public sectors can be accounted for by age, and once this has been

controlled for (together with other factors), the difference is no longer statistically significant

(Model 13). In agreement with the results from descriptive analysis, however, multivariate

analysis shows that graduates with disabilities are more likely than those with no disability to

be in part-time employment, with the odds (relative to being in full-time employment) being

1.6 times higher (Model 14).

Results from the descriptive analysis using the LFS data reported in Section 4.3.2

suggested that there were no differences between disabled and non-disabled graduates in

their employment in any of the four SOC(HE) graduate job categories or in the non-graduate

occupation category. However, having controlled for a number of demographic, academic

and employment related variables, binary logistic regression analysis suggests that disabled

graduates are 17% less likely than their non-disabled peers to be employed in a graduate

occupation, with an odds ratio of 0.83 (Model 15).34 Further investigation using multinominal

logistic regression shows that differences between the two groups of graduates exist in

relation to their employment in traditional graduate occupations relative to being in a non-

graduate job (Model 16). With an odds ratio of 0.69, disabled graduates are 31% less likely

than their non-disabled counterparts to be employed in traditional graduate jobs. Conversely,

no differences can be found between disabled and non-disabled graduates in their likelihood

of being employed in modern, niche, or new graduate occupations.35 This is different from

34 Graduate occupations here are defined as a combination of the four SOC(HE) categories: traditional, modern, new and niche graduate occupations. 35 The design factor of the LFS due to sampling was not taken into account in the analysis, a consequence of which is that the standard errors may have been underestimated (ONS, 2007). In Model 16, although the result for new graduate occupation is statistically significant with a p value (0.046) just within the 0.05 significance level, it is unlikely that this would still be significant if the design factor of the LFS had been taken into account. For example, if the standard error was 0.110 instead of 0.107, an inflation of a mere 3%, the 95% C.I. for the odds ratio would then range from below 1 to just above 1 and the coefficient would no longer be statistically significant. As such it can be concluded that there are no differences in the likelihood between disabled and non-disabled graduates in their employment in modern, niche and new graduate occupations.

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the conclusion drawn from the multivariate analysis of the DLHE data, where no differences

are found between disabled and non-disabled graduates in their likelihood of being in a

traditional graduate occupation six months after graduation, and that disabled graduates are

less likely to be in niche graduate occupations. Differences in conclusions drawn from the

two sets of data could be attributable to a number of reasons, including the large differences

in sample sizes and the types of factors available for use as explanatory/control variables in

the models.

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5.2.3 Comparing disabled and non-disabled earnings

In Section 4.3.2, it was reported that compared with their non-disabled counterparts, full-time

employed disabled graduate employees earned on average 7.8% less in gross weekly pay

and 5.8% less in hourly pay, although no statistically significant differences in earnings were

found between part-time employed disabled and non-disabled graduates. Drawing on the

distinctions made by Berthoud and Blekesaune (2007) between earnings gaps and penalties,

the differences in pay reported earlier can be interpreted as the earnings gap between those

with and without disabilities. Using multiple linear regression and controlling for key

demographic, academic and employment-related variables, we will now look at whether

there still exists an earnings differential between the two groups of graduates, ie whether

there is an earnings penalty, and if so, how big it is. As mentioned in Section 5.1, earnings

will be modeled in terms of their log transformed figures.

Multiple linear regression (Model 17) shows that full-time employed disabled graduates have

gross weekly earnings of 93% of their full-time employed non-disabled counterparts.36

Similarly, the hourly pay of full-time working disabled graduates is estimated at 94.6% of that

of their non-disabled peers (Model 18). This suggests that not only do the pay gaps still exist

after having controlled for a range of factors impacting on graduates’ earnings, the

differentials are almost the same as when no controls were used.

Unlike the descriptive analysis results reported in Section 4.3.2 earlier, linear regression

models on part-time employees’ earnings show that not only do statistically significant pay

gaps exist between disabled and non-disabled graduates, the differentials are much more

notable than those found for full-time employees. Part-time employed disabled graduates

are found to have gross weekly earnings averaging at only 80%, and hourly pay of 89%, of

those of their non-disabled part-time employed counterparts, after controlling for a range of

demographic, academic and employment-related factors (Models 19 and 20).37

36 Calculated from the unstandardised coefficient in Model 17: Exp(-0.073) = 0.93 37 One interesting point to note from this analysis is that gender is a significant predictor of the earnings of full-time employed graduates, but not for part-timers.

89

Overall, the results reported in this section show that not only is there a pay gap between

disabled and non-disabled graduates, but that the differences in pay still exist, and in some

cases become bigger, after controlling for a number of factors which impact on graduates’

pay. There is thus evidence of a pay penalty for disabled graduates, compared with their

non-disabled peers.

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5.3 Summary

The findings reported in this chapter have further illustrated the differences in employment

outcomes between disabled and non-disabled graduates. After controlling for a number of

personal and academic factors influencing on graduates’ destinations, the study has found

that perhaps with the exceptions of those with a hearing impairment or an unseen disability,

all disabled graduates are more likely than their non-disabled peers to be unemployed six

months after graduation. Disabled graduates are also more likely to go on to further study, or

be employed in temporary or voluntary/unpaid work. With the exceptions of those with

mental health difficulties and a disability not elsewhere classified, however, there are no

statistically significant differences between disabled and non-disabled graduates in their

propensity to be in part-time paid work six months after graduation.

In agreement with the descriptive analysis results, the DLHE data shows that six months

after graduation, graduates with disabilities are overall less likely than those without

disabilities to be found in professional occupations. There are, however, no differences

between the two groups in their likelihood of being employed as managers or senior officials,

in associate professional and technical occupations, and in the various SOC(HE) graduate

job categories except niche graduate occupations. Disabled graduates are also just as likely

as their non-disabled counterparts to report being in a job where their degree qualification is

required for entry.

Analysis by types of disabilities has again confirmed that graduates with mental health

problems, mobility difficulties, or multiple disabilities, face greater difficulties than others in

the labour market, whilst those with dyslexia or an unseen disability fare relatively well.

Graduates with dyslexia are also well-represented in self-employment.

Looking beyond six months after graduation, and again controlling for a range of factors,

analysis of the LFS data suggests that disabled graduates are more likely than their non-

disabled peers to be in lower managerial or intermediate occupations, as opposed to higher

managerial occupations. They are also more likely to be in part-time employment. Both

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groups of graduates are, however, just as likely to be employed in a job with supervision

responsibilities, or to be employed in the public sector. Analysis by SOC(HE) also suggests

that although disabled graduates are less likely to be found in traditional graduate

occupations, there are no differences between the two groups of graduates in the likelihood

of being employed in modern, new or niche graduate jobs.

Results of multivariate analyses have also shown that full-time employed disabled graduates

earn between 5% and 7% less their non-disabled peers, whilst those in part-time

employment earn between 11% and 20% less. Having controlled for a range of factors

influencing on graduates’ employment destinations, any unfavorable outcomes found for

disabled graduates compared with their non-disabled peers can be interpreted as

employment penalties, rather than employment gaps (as those reported in Chapter 4). There

are, however, other variables which have not been accounted for in the multivariate

analyses, including, for example, graduates’ parental HE background, socio-economic class,

pre-HE attainment, degree classification (for the LFS), work experience, access to social

network, confidence, motivation and ambition, which have all been reported to impact on

employment outcomes (Purcell et al, 2005; Gorard et al, 2006). It is likely that these factors

can account for at least some of the ‘penalties’ found in the analyses reported in this chapter.

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Chapter 6 - Discussion and Conclusion

6.1 Discussion In agreement with other research on disabled people, this research has found that

irrespective of the type of disabilities, disabled graduates have poorer employment

outcomes overall than their non-disabled peers. After controlling for a range of personal,

academic and job related factors, there are still ‘unexplained’ differences between the two

groups of graduates in their early destinations and labour market experience. Although

employment in part-time or voluntary work could be a lifestyle choice, it may also be a

reflection of the greater barriers that disabled graduates face compared with their non-

disabled counterparts in finding full-time/paid employment. Similarly, the higher propensity

for disabled graduates to go on to further study, especially for those with mobility difficulties,

mental health problems or multiple disabilities, may indicate the difficulties these graduates

encounter in accessing suitable job opportunities.

Purcell et al (2005, p.xiii; 22) reported that there is a correlation between the ‘maturity’ of

SOC(HE) categories and job quality, with those in the ‘longer-established’ categories

(traditional and modern graduate occupations) being more likely to report using their degree

qualifications in their job. Having controlled for the degree subject and other factors, the

research reported here has found evidence of disabled graduates being under-represented

in traditional graduate jobs. This, together with their higher unemployment and inactivity

rates, their higher likelihood of being found in lower socio-economic occupational groups,

and their lower earnings, suggests that disabled graduates may be under-utilising their

knowledge and skills and not reaping as high a return to their HE study compared with their

non-disabled peers. In short, the ‘empowering potential’ of HE may not always be achieved

for disabled people (Fuller et al, 2004, p.304).

Although this study has not explored the causes of these disadvantages, Berthoud (2006,

p.12) gave several suggestions for the low employment rates for disabled people. For

example, those with disabilities may have a low level of skills even before the onset of

disability. Although prior educational attainment had not been controlled for in this research

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(except in the form of highest qualification on entry to HE for the DLHE data analysis), low

skill level is unlikely to be the major cause of poorer employment outcomes for disabled

graduates, given that we are focusing on individuals who have progressed on to HE and

obtained a degree qualification. Another possible reason suggested by Berthoud is that

disability, or any prolonged period out of work as a result of it, may have a negative impact

on productivity (or potential productivity), making it more difficult for disabled people to

compete in the labour market. That said, Kidd et al (2000) reported that productivity related

characteristics including education, experience, job tenure, occupation, industry, region and

marital status, are only able to explain 50% of the differentials in wage and labour market

participation rates between disabled and non-disabled people.

Many of the vacancies targeted specially at graduates with high profile companies are based

in London or the South East, where the highest average graduate salaries are also found

(AGR, 2009). According to a report from the Equality Challenge Unit (Lucas, 2008), career

advisors have expressed concerns that disabled students and graduates who are reluctant

to relocate miss out on opportunities offered by companies in these regions, many of which

actively seek disabled people as part of their drive to make their workforce more diverse.

From the DLHE data, there is no evidence to suggest that six months after graduation,

disabled people are less likely than those without disabilities to be employed in London or

the South East. In fact, the opposite has been found: 36.1% of disabled people were found

to be employed in these regions compared with 31.7% of those without disabilities, and the

difference is statistically significant.38 Although the LFS data does suggest that disabled

people are less likely to work in these two regions (40.1% compared with 43.1% for those

without disabilities), multivariate analyses have shown that even after controlling for the

regions of domicile or work, differences in employment outcomes between the two groups

continue to exist.

Two factors influencing graduates’ employment outcomes which have not been investigated

in this study are work experience and term-time working. Brennan and Shah (2003) reported 38 Comparing London and the South East with all other regions combined gave a chi-square value of 86.33 (1), p<0.001. This analysis, however, has not controlled for any factors influencing graduates’ region of employment, eg the types of industry or occupation.

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that ‘substantial’ work experience related to study (including placements and vacation work)

are associated with more successful employment outcomes, whilst large amounts of

unrelated work experience appeared to have no impact on subsequent employment (ibid,

p.14). In a study of the experiences of disabled Foundation degree students, it was reported

that there appeared to be greater concerns amongst small-medium enterprises (SMEs) than

amongst larger organisations, over the perceived cost of making ‘reasonable adjustments’ in

providing work-based learning opportunities to disabled students (FDF, 2008, p.33).

In contrast to work placements, paid work during term-time, which is often undertaken for

financial reasons, was found to have a negative impact on graduates’ employment (Brennan

and Shah, 2003). Purcell et al (2005, p.171) also found that those who undertook paid work

during term time were around a third less likely to gain a ‘good’ degree compared with those

who did not work. These are issues which cannot be properly investigated using the DLHE

survey or the LFS. However, tracking a group of students through their HE study from

application in 2006 to one year after graduation in 2011, Purcell et al (2009, p.128) has

found that those with a disability or long-term illness were less likely than their non-disabled

counterparts to have done paid work during their first year of HE study and were more likely

to have done some kind of voluntary work, with those having a hearing or sight impairment

being the most likely to have participated in the latter activities. As this is a longitudinal study

running until 2011, it remains to be seen whether these differences found between the two

groups of students will have an impact on their early employment profiles.

Another two factors which have been found to impact on employment outcomes and have

not been taken into account in this study are the participation of extra curricular activities

and access to social networks. Brennan and Shah (2003, p.15) reported that students

spending more than 10 hours a week on extra curricular activities were particularly likely to

have more successful employment outcomes, whilst research by Purcell et al (2005, p.89)

has reported that graduates considered networks as being the most useful source of

information about opportunities in the labour market. The DLHE survey did ask graduates

whether they had previously been employed with their ‘current’ employer, and the results

show no differences between the two groups: 25.3% of those with disabilities reported they

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had been previously employed with the same employer compared with 26.2% of their non-

disabled peers.39 In addition, in the longitudinal study of the 2006 cohort of students, Purcell

et al (2009, p.129) found that not only were those with disabilities more likely to have

undertaken voluntary activities in their first year of HE study, these students were also more

likely than those who are non-disabled to be office holders or student representatives at their

HE institution, although it is unclear whether these roles were related to the students’

disability, which would inflate their representation in these areas. Despite the above

evidence, disabled students overall appear to face greater barriers in accessing social

networks. According to Tinkin et al (2005, p.15), many disabled students felt isolated and

were uninvolved in extra-curricular activities, and lack the social networks which offer

opportunities for ‘informal learning’.

One way to tackle the greater barriers faced by those with disabilities is to provide targeted

careers support for these students. Parker et al (2007, p.ii) reported that generic careers

advice was rarely helpful for disabled students, and many seek advice from specialist

organizations outside HE instead. Although it was recognised that a targeted approach may

be beneficial to some students, some career advisers are reluctant to ‘pigeonhole’ students

on the basis of their impairments (Lucas, 2008, p.13). Similarly, some academic staff are

also concerned that adjusting to the needs of disabled students could be seen as providing

them with an unfair advantage (Tinkin et al, 2005, p.15).

Although any unexplained gaps in employment outcomes between disabled and non-

disabled graduates can be seen as employment penalties, as discussed in this chapter,

there are other factors impacting on graduate employment outcomes which have not been

controlled for in this study. Discrimination may be a contributing factor to the differences

found, but its existence would be difficult to prove and its sole impact would be difficult to

verify. However, as Metcalf (2009, p.4) explained in relation to pay gap, any unexplained

employment gap found between those with and without disabilities can be seen as an

indication of the maximum size of the gap attributable to direct discrimination.

39 χ2 = 3.54 (1), p=0.060

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In agreement with results from other research, the DLHE survey data have shown that

disabled graduates are not a homogeneous group. Graduates with dyslexia or an unseen

disability have been found to face fewer difficulties than those with mental health issues,

mobility difficulties or multiple disabilities, in the labour market. As such, if those with

dyslexia, which accounted for the largest group of graduates with disabilities, had been

excluded from the analysis, the employment outcomes of disabled graduates would

undoubtedly be worse than what was reported here.

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6.2 Conclusion and suggestions for further research

As reported in Section 2.3, although it is unclear how well represented disabled students are

in HE, more students have undoubtedly been reporting a disability in recent years and

disabled students are becoming a more ‘prominent’ group in the HE community. The

research here has shown that these students, irrespective of their type of disability, face

greater difficulties than those who are non-disabled in the labour market, both in their early

careers and further on since graduation. Further research is thus needed to explore the

causes of their poorer employment outcomes, perhaps combining both quantitative and

qualitative approaches. In particular, with rising student debts and the prospect of a further

increase in tuition fees, the reasons for why these graduates appear to be not getting as

high a return to their HE study compared with their non-disabled peers need to be explored

and the findings actioned upon if necessary, to ensure that any issues with access to job

opportunities and ‘quality’ employment do not act as disincentives to their entry to HE.

In this study, comparisons were made between disabled and non-disabled graduates only.

Under the widening participation initiative and amid a changing graduate labour market, it

would be interesting to compare the employment outcomes of disabled graduates with other

disabled people who have the same pre-HE educational attainments but have chosen not to

enter HE. In addition, in examining a succession of cross-sectional groups of people over

different years, this would not only allow us to better assess the benefits of a degree

qualification to disabled people, but would also allow us to investigate whether these

benefits change over time with the expansion of HE.

Given that those with a disability show a higher propensity to go on to further study, further

analysis of the DLHE survey data can also be carried out to compare the employment

outcomes of disabled and non-disabled graduates with a higher degree qualification, to

access whether there are any differences in employment gaps or penalties compared with

those found for first degree graduates, who were the focus of this study.

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As mentioned in Section 2.6, one of the limitations of this study is that it has not taken a

longitudinal perspective in its investigation. Such an approach could allow for more in depth

investigation into the barriers faced by disabled people in HE, and the intermittent nature of

disability and its impact on careers. However, research into disabled HE students and their

subsequent labour market experiences are often constrained by their small sample sizes.

This issue also applies with large scale longitudinal studies such as the British Household

Panel Survey or the British Cohort Study, which additionally do not provide as wide a scope

as the DLHE survey or the LFS in their coverage of questions about employment

experiences. With an increasing number of disabled students entering HE, it is hoped that

sample sizes will become less of an issue in years to come.

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Appendix A - Defining graduate jobs - SOC(HE) The SOC(HE) graduate job classification system was designed by Elias and Purcell (2004) for the

research programme ‘Researching Graduate Careers Seven Years On’. The classification was

constructed to reflect the demand for graduate skills and qualifications and the extent these are used

within the jobs (Elias and Purcell, 2003). Table A summarises the definitions of the five categories.

Table A. SOC(HE): a classification of graduate occupations SOC(HE) category Description Examples Traditional graduate occupations

The established professionals, for which, historically, the normal route has been via an undergraduate degree programme.

Solicitors, medical practitioners, HE and secondary education teachers, biological scientists

Modern graduate occupations

The newer professions, particularly in management, IT and creative vocational areas, which graduates have been entering since educational expansion in the 1960s.

Directors (major organisations), software professionals, computer programmers, primary school and nursery teachers, authors/writers/journalists.

New graduate occupations

Areas of employment, many in new or expanding occupations, where the route into the professional area has recently changed such that it is now via an undergraduate degree programme.

Marketing and sales managers, physiotherapists, occupational therapists, management accountants, welfare/housing/probation officers.

Niche graduate occupations

Occupations where the majority of incumbents are not graduates, but within which there are stable or growing specialist niches which require HE skills and knowledge.

Leisure and sports managers, hotel/accommodation managers, nurses, midwives, retail managers.

Non-graduate occupations

Occupations which do not belong to the four groups above. These are jobs that are likely to constitute under-utilisation of graduates’ skills and knowledge acquired through HE study.

Sales assistants, filing and record clerks, routine laboratory testers.

Source: Elias and Purcell (2003, p.7; 2004, p.6). Reproduced with permission from the authors

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Appendix B – Subject areas by types of disability Table B. Subject areas by types of disability (% of disabled graduates)

DyslexiaBlind/partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

Medicine and dentistry 1.3 1.3 2.5 2.1 2.2 2.7 1.0 1.4 1.6Subjects allied to medicine 6.1 7.3 9.8 7.1 4.1 7.0 6.1 6.1 6.3Biological sciences 9.8 9.9 11.2 11.2 15.4 10.7 10.2 10.4 10.3Veterinary sciences, agriculture and related subject 1.4 1.0 0.7 1.4 0.3 1.0 0.8 1.2 1.2Physical sciences 4.9 4.2 4.9 5.7 4.1 4.3 6.5 4.9 4.9Mathematical and computer sciences 5.4 8.1 6.9 10.3 5.8 6.9 7.9 8.8 6.3Engineering and technology 5.9 6.5 5.6 3.0 3.2 4.6 5.6 4.8 5.4Architecture, building and planning 2.7 1.3 3.1 0.7 0.6 1.5 1.5 1.6 2.2Social studies 9.7 8.4 9.5 10.5 13.8 9.6 11.0 10.5 10.0Law 2.3 6.8 3.2 5.5 4.4 4.9 3.6 5.5 3.3Business and administrative studies 8.8 13.6 8.0 9.8 6.1 8.8 8.1 9.7 8.8Mass communications and documentation 3.1 4.2 3.1 4.3 4.1 3.6 3.3 3.3 3.3Languages, linguistics and related subjects 3.7 8.6 7.3 6.6 11.5 7.0 7.1 8.0 5.3Historical and philosophical studies 6.1 4.5 6.6 6.6 8.3 6.4 7.9 7.0 6.4Creative arts and design 24.5 11.8 13.6 10.5 13.9 15.2 16.5 14.0 20.4Education 3.6 1.8 3.7 4.3 2.0 5.4 2.7 2.4 3.7Combined subjects 0.5 0.5 0.3 0.7 0.4 0.3 0.4 0.4 0.5

Total (%) 100 100 100 100 100 100 100 100 100.0

n 11590 380 590 440 690 3075 790 1805 19360 Source: 2006/07 DLHE survey (HESA)

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Appendix C - Employment outcomes six months after graduation, by types of disability Table C1. Activities six months after graduation by types of disability

Dyslexia Blind/ partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

Full-time paid work only (including self-employed)

53.5%

47.1%

52.2%

45.8%

40.2%

51.6%

42.4%

45.0%

51.1%

Part-time paid work only

7.9% 8.4% 8.3% 6.9% 8.7% 8.4% 7.9% 9.3% 8.1%

Voluntary/unpaid work

1.5% 4.0% 1.3% 2.6% 2.1% 0.8% 2.5% 1.6% 1.6%

Work and further study

8.7% 9.1% 7.0% 5.7% 10.2% 9.1% 10.0% 8.5% 8.7%

Further study only

14.1% 18.9% 18.6% 22.3% 21.2% 18.7% 20.5% 18.7% 16.2%

Assumed to be unemployed

7.5% 8.8% 6.4% 10.0% 10.0% 5.9% 8.4% 9.8% 7.7%

Not available for employment

5.3% 3.4% 5.1% 4.3% 6.1% 4.4% 6.4% 4.9% 5.1%

Other 1.5% 0.3% 1.1% 2.3% 1.5% 1.0% 2.0% 2.2% 1.5% Total

100% 100% 100% 100% 100% 100% 100% 100% 100%

n

8995 295 470 350 530 2470 610 1440 15160

Source: 2006/07 DLHE (HESA)

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Table C2. Employment circumstances by types of disability (% of graduates)

Dyslexia Blind/ partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

In employment

71.5

68.7

68.9

61.0

61.2

69.9

62.7

64.4

69.5

Unemployed and looking for employment, further study or training

7.1 9.1 6.0 10.9 9.8 6.1 8.0 10.4 7.5

Not employed but not looking for employment, further study or training

3.5 2.7 3.6 4.9 6.1 4.9 4.4 5.0 4.0

Permanently or temporarily unable to work due to sickness or having to look after home or family

0.8 1.0 1.5 3.2 3.2 1.1 3.6 1.1 1.2

Others

17.0 18.5 20.0 20.1 19.7 18.0 21.2 19.1 17.8

Total

100% 100% 100% 100% 100% 100% 100% 100% 100%

n

8995 295 470 350 530 2470 610 1440 15160

Source: 2006/07 DLHE (HESA)

103

Table C3. Duration of employment by types of disability (% of graduates)

Dyslexia Blind/ partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

Permanent or open-ended contract

57.8

64.5

57.0

58.9

52.3

60.9

58.8

61.3

58.6

Fixed-term contract

19.0 16.6 24.3 18.9 22.3 20.2 18.1 18.5 19.3

Self-employed/freelance

7.5 3.0 3.7 4.3 5.3 3.6 3.6 4.5 6.1

Temporary

12.8 12.4 12.1 13.0 18.4 13.0 15.1 12.6 13.1

Other 2.9 3.6 2.9 4.9 1.8 2.4 4.5 3.1 2.9 Total

100% 100% 100% 100% 100% 100% 100% 100% 100%

n

5450 170 270 185 285 1525 335 800 9020

Source: 2006/07 DLHE (HESA)

104

Table C4. Whether a degree qualification was required in obtaining the job, by types of disability (% of graduates)

Dyslexia Blind/ partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

Formal requirement

32.3 32.3 35.8 35.4 24.7 34.5 32.2 30.0 32.4

Expected

11.7 7.6 10.2 10.7 9.9 11.3 9.2 10.5 11.2

Advantage

22.9 23.4 26.0 28.7 26.6 20.8 24.3 22.3 22.9

No 33.0 36.7 28.0 25.3 38.8 33.4 34.2 37.2 33.4 Total

100% 100% 100% 100% 100% 100% 100% 100% 100%

n

5025 160 255 180 265 1390 305 740 8315

Source: 2006/07 DLHE (HESA)

105

Table C5. Major occupational groups, by types of disability (% of graduates)

Dyslexia Blind/ partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

Managers and senior officials

8.4 6.9 4.3 8.0 2.5 8.1 8.1 8.7 8.0

Professional occupations

22.4 28.2 29.8 25.8 26.9 26.9 23.3 24.5 23.9

Associate professional and technical occupations

35.1 25.2 33.5 36.6 27.6 29.3 31.7 28.0 32.9

Others 34.1 39.6 32.3 29.6 43.0 35.6 36.9 38.9 35.1 Total

100% 100% 100% 100% 100% 100% 100% 100% 100%

n

6430 200 320 215 325 1730 380 925 10525

Source: 2006/07 DLHE (HESA)

106

Table C6. SOC(HE) by types of disability (% of graduates)

Dyslexia Blind/ partially sighted

Deaf/have a hearing impairment

Wheelchair user/have mobility difficulties

Mental health difficulties

An unseen disability, eg. diabetes, epilepsy, asthma

Multiple disabilities

A disability not listed above

All disabled graduates

Traditional graduate occupations

9.2 8.9 14.6 9.9 12.1 11.4 9.7 10.2 9.9

Modern graduate occupations

13.3 17.8 14.6 17.4 18.6 15.0 15.2 14.8 14.1

New graduate occupations

18.4 13.4 16.1 16.0 10.2 16.1 16.5 13.9 17.1

Niche graduate occupations

23.3 20.3 19.6 27.7 15.5 20.3 18.8 21.5 22.2

Non-graduate occupations

35.8 39.6 35.1 29.1 43.7 37.2 39.8 39.6 36.6

Total

100% 100% 100% 100% 100% 100% 100% 100% 100%

n

6430 200 320 215 325 1730 380 925 10525

Source: 2006/07 DLHE (HESA)

107

Appendix D - Demographic and educational background of LFS respondents Table D. Demographic and educational background of LFS respondents

Disabled Non-disabled Gender Male 44.7% 49.4% Female 55.3% 50.6% Total

100% (n=1,605) 100% (n=22,710) Age 20 or under 0.6% 0.3% 21-24 12.1% 16.9% 25-29 22.5% 28.2% 30-34 28.2% 26.5% 35-40 36.6% 28.1% Total 100% (n=1607) 100% (n=22,711) Mean (and median) age 31.8 (32) 30.5 (30) Ethnicity White 87.3% 82.6% Mixed 1.9% 1.1% Asian or Asian British 5.6% 9.2% Black or Black British 2.7% 3.2% Chinese 0.6% 1.6% Other 1.9% 2.4% Total 100% (n=1,602) 100% (n = 22,698) Number of children in family under 16 0 66.4% 66.2% 1 14.9% 15.5% 2 or more 18.7% 18.3% Total 100% (n=1,600) 100% (n=22,671) Type of degree held Higher degree 26.9% 26.7% First degree 65.2% 66.7% Other (including Foundation Degree and graduate membership of professional organisations)

5.6% 4.8%

Don’t know 2.3% 1.8% Total 100% (n = 1,605) 100% (n = 22,710) Type of higher degree held Doctorate 10.6% 11.2% Masters 54.0% 57.0% Post grad cert in education 21.5% 18.5% Other post grad degree or prof qual 13.2% 12.2% Don’t know 0.7% 1.1% Total

100% (n=433) 100% (n=6,086)

108

Table D (continued). Demographic and educational background of LFS respondents

Disabled Non-disabled Degree subjects40 Medicine and dentistry 1.2% 2.1% Social studies 7.5% 6.9% Law 3.5% 4.1% Business and administrative studies 10.7% 14.1% Mass communications and documentation 2.7% 2.3% Languages, linguistics and related subjects 4.8% 4.2% Historical and philosophical studies 4.4% 3.8% Creative arts and design 9.2% 6.7% Education 5.9% 5.3% Subjects allied to medicine 4.9% 5.2% Biological sciences 6.3% 6.4% Veterinary sciences, agriculture and related subject 1.6% 1.2% Physical sciences 4.6% 4.6% Mathematical and computer sciences 6.2% 6.9% Engineering and technology 8.0% 7.8% Architecture, building and planning 1.6% 2.4% Combined 16.9% 16.1% Total 100% (n=1,580) 100% (n=22,433) Number of years since graduation41 0 5.4% 6.1% 1 to 3 22.5% 26.0% 4 to 6 19.0% 19.3% 7 to 10 21.9% 21.8% 11 to 15 22.7% 19.1% 16 to 22 8.5% 7.7% Total 100% (n=1,594) 100% (n=22,546) Mean (and median) number of years 7.59 (7.0) 7.07 (6.0) Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

40 There were some key differences in the distribution of degree subjects between the LFS and the HESA DLHE survey. For example, only 0.5% of the respondents in the DLHE survey were counted in the ‘combined’ category (Table 4.3), compared with around one in six in the LFS. This is likely to be at least partly due to the differences in the two methods of data collection and coding. The HESA subject information was provided by the higher education institutions, with each constituting subject in a combined degree being counted towards their subject area proportionally. On the contrary, the LFS information was gathered from the respondents, who were asked whether their degree was in single or combined subjects. Respondents with a combined subjects degree were then asked about the main subject area. When recoding the subjects in the LFS for this study, this information has not been taken into account and respondents with a combined degree have all been included in the ‘combined’ category. 41 There were a few respondents whose numbers of years since graduation were 23 or more. These were excluded from the analysis.

109

Appendix E - Standard errors and 95% C.I. of some of the main types of industries reported in the LFS Table E. Standard errors and 95% C.I. of some of the main types of industries reported in the LFS

Type of industry Disabled graduates Non-disabled graduates

% of total S.E. (%) 95% C.I. % of total S.E. (%) 95% C.I. Lower (%) Upper (%) Lower (%) Upper (%)

Financial intermediation 6.2 0.68 4.9 7.5 7.8 0.19 7.4 8.2

Real estate, renting & business activities

18.7 1.09 16.5 20.8 21.1 0.29 20.5 21.6

Public administration and defence

9.3 0.81 7.7 10.9 8.5 0.20 8.1 8.9

Education 17.2 1.06 15.1 19.2 15.1 0.25 14.6 15.6

Health and social work 15.4 1.01 13.4 17.4 12.8 0.24 12.3 13.2

Other community, social and personal activities

7.3 0.73 5.9 8.7 5.9 0.17 5.5 6.2

Total 100% (n = 1,275)

100% (n = 19,885)

Banking, finance and insurance (combination of financial intermediation and real estate, renting & business activities)

24.8 1.21 22.5 27.2 28.9 0.32 28.2 29.5

Source: LFS (aggregated Wave 1 data from Jan 05 – Dec 08). Crown copyright.

110

Appendix F - Binary and multinominal logistic regression

Binary logistic regression

This is used to model the probability of ‘success’ of a binary outcome variable, and can be

described by the formula (Agresti and Finlay, 2008, p.484):

Logit [P(y=1)] = α + βx

where Logit [P(y=1)] = log [P(y=1)/1-P(y=1)]

α = constant

β = coefficient of the explanatory variable x

The ratio, P(y=1)/1-P(y=1), is the odds of an event happening. An alternative expression, in

terms of the probability of success, is:

P (y=1) = (e α + βx)/(1 + eα + βx)

For example, in order to assess the probability of a graduate being in a graduate job,

disability status can be used as an explanatory variable (x) with non-disabled as the

reference (x=0). The log odds (logit) of a disabled graduate (x=1) being in a graduate job

would then be α + β, and the log odds for a non-disabled graduate (x=0) would be α.

Therefore, the odds ratio of a disabled graduate being in a graduate job compared with a

non-disabled graduate is eβ. This, however, is only a very simple illustration. In practice, as

in this study, the above logistic regression equations would be extended to include several

explanatory variables. In the above example, the odds ratio would still be eβ, when all other

explanatory variables are held constant.

111

Multinominal logistic regression

This is used when there are more than two categories in the outcome variable and the

categories are nominal, as opposed to ordinal. The model pairs each outcome category with

a baseline (reference) category (Agresti and Finlay, 2008, p.501). For example, using the

non-graduate job category as a reference, the probability of being employed in each of the

four graduate job categories of SOC(HE) is modelled as:

log [P(traditional graduate job)/P(non-graduate job)]

log [P(modern graduate job)/P(non-graduate job)]

log [P(new graduate job)/P(non-graduate job)]l

log [P(niche graduate job)/P(non-graduate job)]

with each logit having its own parameters.

112

Appendix G – Model parameters Model 1a. Multinominal logistic regression for predicting graduates’ activities six months after graduation, by disability status (Model parameters shown for disability status variable only. Ref: non-disabled)

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Part-time paid work only 0.082 0.033 0.012 1.086 1.018 1.158Voluntary/unpaid work 0.544 0.072 0.000 1.723 1.497 1.984Work and further study 0.185 0.032 0.000 1.203 1.131 1.280Further study only 0.300 0.026 0.000 1.349 1.284 1.419Assumed to be unemployed 0.357 0.034 0.000 1.429 1.337 1.528Not available for employment 0.289 0.040 0.000 1.336 1.234 1.445Other 0.390 0.072 0.000 1.477 1.282 1.702

By including the disability variable, the chi-square statistic for the likelihood ratio test is 281.340, with 7 df and p<0.001

-2LL of intercept only model = 131561.043; -2LL of final model = 104120.688, chi-square = 27440.355; 329 df, p<0.00Cox & Snell R Square = 0.145; Nagelkerke R Square = 0.154

95% Confidence Interval for Exp(B)Reference: Full-time paid work only

In additon to disability, other control variables include: degree subject, degree classification, gender, age group, ethnicity, type of first degree awarding institution, highest qualification on entry to HE.

113

Model 1b. Multinominal logistic regression for predicting graduates’ activities six months after graduation, by types of disability (Model parameters shown for disability variable only. Ref: non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Reference: Full-time paid work only Lower Bound Upper Bound

Part-time paid work only Dyslexia -0.045 0.042 0.284 0.956 0.879 1.038Blind/partially sighted 0.224 0.223 0.315 1.252 0.808 1.939Deaf/hearing impairment 0.065 0.179 0.717 1.067 0.752 1.514Wheelchair users/mobility difficiulties 0.020 0.221 0.927 1.020 0.662 1.573Mental health difficulties 0.351 0.167 0.035 1.421 1.025 1.969An unseen disability 0.127 0.077 0.098 1.135 0.977 1.320Multiple disabilities 0.211 0.159 0.185 1.235 0.904 1.686A disability not listed above 0.363 0.097 0.000 1.437 1.189 1.737

Voluntary/unpaid workDyslexia 0.466 0.091 0.000 1.594 1.334 1.905Blind/partially sighted 1.605 0.305 0.000 4.980 2.738 9.058Deaf/hearing impairment 0.352 0.416 0.397 1.422 0.630 3.211Wheelchair users/mobility difficiulties 1.035 0.366 0.005 2.816 1.375 5.768Mental health difficulties 0.855 0.312 0.006 2.351 1.275 4.337An unseen disability -0.094 0.222 0.672 0.910 0.589 1.407Multiple disabilities 1.061 0.278 0.000 2.888 1.676 4.976A disability not listed above 0.646 0.215 0.003 1.908 1.252 2.906

Work and further studyDyslexia 0.146 0.040 0.000 1.158 1.070 1.253Blind/partially sighted 0.266 0.212 0.211 1.304 0.860 1.977Deaf/hearing impairment -0.051 0.187 0.784 0.950 0.659 1.370Wheelchair users/mobility difficiulties -0.204 0.244 0.403 0.815 0.506 1.315Mental health difficulties 0.462 0.154 0.003 1.588 1.174 2.147An unseen disability 0.169 0.074 0.023 1.184 1.023 1.369Multiple disabilities 0.443 0.144 0.002 1.557 1.175 2.064A disability not listed above 0.203 0.100 0.043 1.225 1.007 1.491

Further study onlyDyslexia 0.145 0.034 0.000 1.157 1.082 1.236Blind/partially sighted 0.435 0.168 0.010 1.545 1.112 2.147Deaf/hearing impairment 0.387 0.132 0.003 1.473 1.136 1.909Wheelchair users/mobility difficiulties 0.673 0.147 0.000 1.961 1.470 2.615Mental health difficulties 0.580 0.123 0.000 1.786 1.405 2.271An unseen disability 0.336 0.058 0.000 1.399 1.249 1.567Multiple disabilities 0.595 0.115 0.000 1.813 1.446 2.272A disability not listed above 0.418 0.077 0.000 1.518 1.306 1.765

Assumed to be unemployedDyslexia 0.214 0.043 0.000 1.239 1.138 1.349Blind/partially sighted 0.505 0.216 0.019 1.658 1.085 2.533Deaf/hearing impairment 0.198 0.196 0.312 1.219 0.830 1.789Wheelchair users/mobility difficiulties 0.703 0.189 0.000 2.020 1.394 2.928Mental health difficulties 0.855 0.157 0.000 2.352 1.730 3.197An unseen disability 0.119 0.089 0.182 1.126 0.946 1.342Multiple disabilities 0.492 0.158 0.002 1.635 1.200 2.229A disability not listed above 0.684 0.095 0.000 1.982 1.645 2.388 (continued on next page)

114

Model 1b (continued) - Multinominal logistic regression for predicting graduates’ activities six months after graduation, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled).

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Reference: Full-time paid work only

Not available for employment, further study or trainingDyslexia 0.286 0.051 0.000 1.331 1.206 1.470Blind/partially sighted -0.061 0.329 0.853 0.941 0.494 1.793Deaf/hearing impairment 0.273 0.220 0.215 1.313 0.854 2.021Wheelchair users/mobility difficiulties 0.177 0.280 0.528 1.194 0.689 2.068Mental health difficulties 0.561 0.197 0.004 1.752 1.191 2.577An unseen disability 0.114 0.102 0.264 1.121 0.918 1.369Multiple disabilities 0.679 0.173 0.000 1.972 1.404 2.770A disability not listed above 0.342 0.127 0.007 1.408 1.097 1.806

OtherDyslexia 0.311 0.091 0.001 1.365 1.143 1.631Blind/partially sighted -20.110 0.000 - 0.000 0.000 0.000Deaf/hearing impairment 0.054 0.453 0.905 1.055 0.434 2.566Wheelchair users/mobility difficiulties 0.835 0.365 0.022 2.304 1.127 4.709Mental health difficulties 0.556 0.363 0.125 1.744 0.857 3.551An unseen disability -0.012 0.204 0.953 0.988 0.663 1.473Multiple disabilities 0.713 0.297 0.017 2.039 1.138 3.653A disability not listed above 0.814 0.186 0.000 2.258 1.567 3.253

By including the disability variable, the chi-square statistic for the likelihood ratio test is 372.551, with 56 df and p<0.001

-2LL of intercept only model = 99249.403; -2LL of final model = 73925.788, chi-square = 25323.615; 301 df, p<0.001Cox & Snell R Square = 0.136; Nagelkerke R Square = 0.144

In additon to disability, other control variables include: degree subject, degree classification, gender, age group, type of first degree awarding institution, highest qualification on entry to HE.

95% Confidence Interval for Exp(B)

115

Model 2a. Multinominal logistic regression for predicting graduates’ employment circumstances six months after graduation, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled). Reference: Non-disabled, in employment

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Reference: In employment0.330 0.034 0.000 1.391 1.302 1.487

0.285 0.045 0.000 1.329 1.218 1.451

Permanently or temporarily unable to work 0.717 0.086 0.000 2.048 1.731 2.424Other 0.222 0.024 0.000 1.249 1.192 1.307

By including the disability variable, the chi-square statistic for the likelihood ratio test is 228.835, with 4 df and p<0.001

-2LL of intercept only model = 79066.049; -2LL of final model = 60710.210, chi-square = 18355.839; 188 df, p<0.001Cox & Snell R Square = 0.099; Nagelkerke R Square = 0.122

Unemployed and looking for work, further study or trainingNot employed and not looking for work, further study or training

In additon to disability, other control variables include: degree subject, degree classification, gender, age group, ethnicity, type of first degree awarding institution, highest qualification on entry to HE.

116

Model 2b. Multinominal logistic regression for predicting graduates’ employment circumstances six months after graduation, by types of disability

(Model parameters shown for disability variable only. Ref: Non-disabled).

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)

Reference: In employment Lower Bound Upper Bound

Unemployed and looking for work, further study or trainingDyslexia 0.177 0.044 0.000 1.194 1.096 1.301Blind/partially sighted 0.461 0.207 0.026 1.585 1.056 2.379Deaf/hearing impairment 0.155 0.199 0.436 1.168 0.790 1.726Wheelchair users/mobility difficiulties 0.809 0.179 0.000 2.247 1.582 3.190Mental health difficulties 0.732 0.152 0.000 2.079 1.543 2.802An unseen disability 0.126 0.087 0.151 1.134 0.955 1.346Multiple disabilities 0.388 0.155 0.012 1.474 1.088 1.998A disability not listed above 0.680 0.090 0.000 1.974 1.654 2.356

Not employed and not looking for work, further study or trainingDyslexia 0.151 0.060 0.013 1.163 1.033 1.309Blind/partially sighted -0.340 0.387 0.380 0.712 0.333 1.521Deaf/hearing impairment 0.146 0.259 0.572 1.158 0.697 1.923Wheelchair users/mobility difficiulties 0.560 0.257 0.029 1.751 1.059 2.895Mental health difficulties 0.646 0.189 0.001 1.908 1.318 2.762An unseen disability 0.444 0.097 0.000 1.558 1.290 1.883Multiple disabilities 0.373 0.202 0.065 1.453 0.978 2.159A disability not listed above 0.449 0.125 0.000 1.567 1.226 2.002

Permanently or temporarily unable to workDyslexia 0.330 0.127 0.009 1.391 1.085 1.783Blind/partially sighted 0.685 0.587 0.244 1.983 0.627 6.271Deaf/hearing impairment 0.978 0.389 0.012 2.660 1.242 5.696Wheelchair users/mobility difficiulties 1.737 0.319 0.000 5.682 3.040 10.622Mental health difficulties 1.518 0.259 0.000 4.561 2.748 7.573An unseen disability 0.645 0.203 0.001 1.907 1.281 2.837Multiple disabilities 1.857 0.228 0.000 6.403 4.092 10.018A disability not listed above 0.706 0.258 0.006 2.026 1.222 3.358

OtherDyslexia 0.174 0.030 0.000 1.190 1.121 1.263Blind/partially sighted 0.179 0.158 0.256 1.197 0.878 1.631Deaf/hearing impairment 0.340 0.123 0.006 1.405 1.104 1.787Wheelchair users/mobility difficiulties 0.421 0.145 0.004 1.524 1.148 2.023Mental health difficulties 0.267 0.118 0.024 1.305 1.037 1.644An unseen disability 0.149 0.056 0.007 1.161 1.041 1.295Multiple disabilities 0.392 0.107 0.000 1.480 1.201 1.825A disability not listed above 0.259 0.072 0.000 1.296 1.126 1.492

By including the disability variable, the chi-square statistic for the likelihood ratio test is 297.137, with 32 df and p<0.001

-2LL of intercept only model = 60550.812; -2LL of final model = 43487.306, chi-square = 17063.506; 172 df, p<0.001Cox & Snell R Square = 0.093; Nagelkerke R Square = 0.115

In additon to disability, other control variables include: degree subject, degree classification, gender, age group, type of first degree awarding institution, highest qualification on entry to HE.

117

Model 3a. Multinominal logistic regression for predicting graduates’ duration of employment six months after graduation, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Reference: Permanent or fixed term contract)Temporary employment 0.156 0.035 0.000 1.169 1.092 1.252Self-employed or working freelance 0.303 0.053 0.000 1.354 1.220 1.504Other 0.301 0.068 0.000 1.351 1.182 1.544

By including the disability variable, the chi-square statistic for the likelihood ratio test is 61.023, with 3 df and p<0.001

-2LL of intercept only model = 84806.835; -2LL of final model = 66632.299, chi-square = 18174.536; 186 df, p<0.001Cox & Snell R Square = 0.151; Nagelkerke R Square = 0.215

In additon to disability, other control variables include: degree subject, degree classification, type of industry, major occupational group, gender, age group, ethnicity, type of first degree awarding institution.

118

Model 3b. Multinominal logistic regression for predicting graduates’ duration of employment six months after graduation, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Temporary employmentDyslexia 0.154 0.045 0.001 1.167 1.069 1.274Blind/partially sighted -0.133 0.262 0.611 0.875 0.524 1.462Deaf/hearing impairment 0.090 0.200 0.653 1.094 0.739 1.620Wheelchair users/mobility difficiulties 0.241 0.232 0.300 1.272 0.807 2.004Mental health difficulties 0.373 0.166 0.024 1.452 1.050 2.010An unseen disability 0.119 0.082 0.144 1.127 0.960 1.322Multiple disabilities 0.335 0.163 0.039 1.398 1.016 1.923A disability not listed above -0.018 0.114 0.871 0.982 0.785 1.227

Self-employed or working freelanceDyslexia 0.371 0.063 0.000 1.450 1.282 1.639Blind/partially sighted -0.340 0.482 0.480 0.712 0.277 1.829Deaf/hearing impairment -0.079 0.348 0.820 0.924 0.468 1.826Wheelchair users/mobility difficiulties 0.442 0.396 0.265 1.556 0.716 3.383Mental health difficulties 0.429 0.303 0.157 1.536 0.848 2.781An unseen disability -0.013 0.151 0.930 0.987 0.735 1.326Multiple disabilities -0.079 0.314 0.801 0.924 0.499 1.711A disability not listed above 0.253 0.189 0.182 1.287 0.888 1.866

OtherDyslexia 0.260 0.086 0.003 1.296 1.095 1.535Blind/partially sighted 0.494 0.423 0.242 1.639 0.716 3.753Deaf/hearing impairment 0.309 0.364 0.397 1.362 0.667 2.781Wheelchair users/mobility difficiulties 0.810 0.368 0.028 2.247 1.092 4.624Mental health difficulties -0.245 0.456 0.590 0.782 0.320 1.911An unseen disability 0.121 0.174 0.487 1.129 0.802 1.588Multiple disabilities 0.723 0.270 0.007 2.061 1.214 3.501A disability not listed above 0.359 0.207 0.083 1.432 0.954 2.150

By including the disability variable, the chi-square statistic for the likelihood ratio test is 77.341, with 24 df and p<0.001

-2LL of intercept only model = 80849.263; -2LL of final model = 63041.565, chi-square = 17807.698; 198 df, p<0.001Cox & Snell R Square = 0.150; Nagelkerke R Square = 0.213

In additon to disability, other control variables include: degree subject, degree classification, type of industry, major occupational group, gender, age group, type of first degree awarding institution, highest qualification on entry to HE.

95% Confidence Interval for Exp(B)Reference: Permanent or fixed term contract

119

Model 4a. Multinominal logistic regression for predicting graduates’ major occupational group six months after graduation, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Managers and senior officials 0.032 0.041 0.443 1.032 0.952 1.120Professional occupations -0.082 0.034 0.015 0.922 0.863 0.984Associate professional and technical occupations -0.023 0.028 0.422 0.978 0.925 1.033

By including the disability variable, the chi-square statistic for the likelihood ratio test is 8.037, with 3 df and p = 0.045

-2LL of intercept only model = 214175.604; -2LL of final model = 132359.433, chi-square = 81816.171; 201 df, p<0.001Cox & Snell R Square = 0.470; Nagelkerke R Square = 0.509

Reference: Other occupations (which include administrative and secretarial, skilled trades, personal service, sales and customer service, process, plant and machine operatives, and elementary occupations)

In additon to disability, other control variables include: degree subject, degree classification, type of industry, duration of employment, ethnicity, gender, age group, type of first degree awarding institution, highest qualification on entry to HE.

95% Confidence Interval for Exp(B)

120

Model 4b. Multinominal logistic regression for predicting graduates’ major occupational group six months after graduation, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Managers and senior officials Dyslexia 0.117 0.056 0.039 1.124 1.006 1.255Blind/partially sighted 0.072 0.309 0.815 1.075 0.587 1.969Deaf/hearing impairment -0.295 0.306 0.335 0.744 0.408 1.357Wheelchair users/mobility difficiulties 0.027 0.320 0.933 1.027 0.549 1.925Mental health difficulties -1.185 0.394 0.003 0.306 0.141 0.662An unseen disability 0.153 0.103 0.138 1.166 0.952 1.428Multiple disabilities 0.129 0.214 0.546 1.138 0.748 1.732A disability not listed above 0.029 0.136 0.828 1.030 0.789 1.344

Professional occupationsDyslexia -0.090 0.047 0.053 0.914 0.834 1.001Blind/partially sighted -0.008 0.241 0.974 0.992 0.618 1.591Deaf/hearing impairment 0.241 0.190 0.204 1.273 0.877 1.847Wheelchair users/mobility difficiulties -0.016 0.239 0.946 0.984 0.616 1.572Mental health difficulties -0.030 0.180 0.867 0.970 0.682 1.380An unseen disability -0.023 0.084 0.781 0.977 0.829 1.152Multiple disabilities -0.130 0.176 0.460 0.878 0.621 1.240A disability not listed above -0.228 0.114 0.046 0.796 0.637 0.996

Associate professional and technical occupationsDyslexia 0.061 0.039 0.120 1.062 0.984 1.147Blind/partially sighted -0.280 0.221 0.204 0.755 0.490 1.164Deaf/hearing impairment -0.021 0.174 0.903 0.979 0.696 1.377Wheelchair users/mobility difficiulties 0.208 0.207 0.315 1.232 0.820 1.848Mental health difficulties -0.310 0.163 0.056 0.733 0.533 1.008An unseen disability -0.059 0.073 0.420 0.942 0.816 1.088Multiple disabilities -0.033 0.149 0.823 0.967 0.722 1.296A disability not listed above -0.283 0.099 0.004 0.753 0.620 0.915

By including the disability variable, the chi-square statistic for the likelihood ratio test is 54.776, with 24 df and p<0.001

-2LL of intercept only model = 161066.216; -2LL of final model = 92720.113, chi-square = 68346.103; 183 df, p<0.001Cox & Snell R Square = 0.465; Nagelkerke R Square = 0.503

95% Confidence Interval for Exp(B)

In additon to disability, other control variables include: degree subject, degree classification, type of industry, duration of employment, gender, age group, type of first degree awarding institution, highest qualification on entry to HE.

Reference: Other occupations (which include administrative and secretarial, skilled trades, personal service, sales and customer service, process, plant and machine operatives, and elementary occupations)

121

Model 5a. Binary logistic regression for predicting graduates’ employment in a graduate or non-graduate job as defined by SOC(HE), six months after graduation (Model parameters shown for disability status variable only. Ref: Non-disabled and Non-graduate occupation)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability status -0.037 0.025 0.131 0.963 0.918 1.011

Model summary: -2LL = 130830.965 ; model chi-square = 36014.212 , 67 df, p<0.001Cox & Snell R Square = 0.244Nagelkerke R Square = 0.336

In additon to disability, other control variables include: degree subject, degree classification, type of industry, gender, ethnicity, age group, duration of employment, type of first degree awarding institution, highest qualification on entry to HE.

122

Model 5b. Binary logistic regression for predicting graduates’ employment in a graduate or non-graduate job as defined by SOC(HE) six months after graduation, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled and Non-graduate occupation)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability (ref: non-disabled) 0.094Dyslexia 0.023 0.034 0.507 1.023 0.957 1.094Blind/partially sighted -0.031 0.188 0.870 0.970 0.671 1.401Deaf/hearing impairment -0.090 0.148 0.542 0.914 0.683 1.222Wheelchair users/mobility difficiulties 0.249 0.184 0.176 1.283 0.894 1.842Mental health difficulties -0.316 0.139 0.023 0.729 0.555 0.958An unseen disability -0.033 0.063 0.604 0.968 0.855 1.095Multiple disabilities -0.140 0.129 0.280 0.870 0.675 1.120A disability not listed above -0.175 0.085 0.039 0.839 0.711 0.991

Model summary: -2LL = 110822.508; model chi-square = 30249.728, 61 df, p<0.001Cox & Snell R Square = 0.242Nagelkerke R Square = 0.333

In additon to disability, other control variables include: degree subject, degree classification, type of industry, gender, age group, duration of employment, type of first degree awarding institution, highest qualification on entry to HE.

123

Model 6a Multinominal logistic regression for predicting graduates’ employment in the four SOC(HE) graduate job categories six months after graduation, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Ref: Non-graduate occupation

Traditional graduate occupation -0.022 0.045 0.624 0.978 0.896 1.068Modern graduate occupation -0.034 0.037 0.355 0.966 0.898 1.039New graduate occupation -0.016 0.033 0.624 0.984 0.921 1.050Niche graduate occupation -0.059 0.030 0.046 0.943 0.890 0.999

-2LL of intercept only model = 251483.432; -2LL of final model = 157729.512, chi-square = 93753.919; 268 df, p<0.001Cox & Snell R Square = 0.517; Nagelkerke R Square = 0.542

In additon to disability, other control variables include: degree subject, degree classification, type of industry, duration of employment, age group, gender, ethnicity, type of first degree awarding institution, highest qualification on entry to HE.

By including the disability variable, the chi-square statistic for the likelihood ratio test is 4.248, with 4 df and p = 0.373 (ie not significant)

124

Model 6b. Multinominal logistic regression for predicting graduates’ employment in the four SOC(HE) graduate job categories six months after graduation, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Ref: Non-graduate occupation

Traditional graduate occupationDyslexia -0.029 0.062 0.643 0.971 0.860 1.098Blind/partially sighted -0.275 0.361 0.446 0.759 0.374 1.541Deaf/hearing impairment 0.306 0.232 0.187 1.358 0.862 2.138Wheelchair users/mobility difficiulties -0.237 0.348 0.496 0.789 0.399 1.561Mental health difficulties -0.048 0.236 0.839 0.953 0.600 1.514An unseen disability -0.049 0.112 0.662 0.952 0.764 1.186Multiple disabilities -0.074 0.227 0.745 0.929 0.595 1.450A disability not listed above -0.110 0.150 0.467 0.896 0.667 1.204

Modern graduate occupationDyslexia -0.043 0.052 0.408 0.958 0.865 1.061Blind/partially sighted 0.239 0.260 0.359 1.270 0.762 2.116Deaf/hearing impairment -0.010 0.214 0.964 0.990 0.651 1.507Wheelchair users/mobility difficiulties 0.313 0.261 0.231 1.367 0.820 2.281Mental health difficulties 0.110 0.194 0.570 1.117 0.763 1.633An unseen disability 0.026 0.095 0.785 1.026 0.852 1.236Multiple disabilities -0.067 0.194 0.730 0.935 0.640 1.367A disability not listed above -0.009 0.126 0.942 0.991 0.774 1.269

New graduate occupationDyslexia 0.087 0.045 0.056 1.091 0.998 1.192Blind/partially sighted -0.186 0.264 0.480 0.830 0.495 1.392Deaf/hearing impairment -0.135 0.204 0.507 0.873 0.585 1.303Wheelchair users/mobility difficiulties 0.214 0.251 0.393 1.239 0.758 2.025Mental health difficulties -0.645 0.222 0.004 0.525 0.340 0.811An unseen disability -0.028 0.086 0.743 0.972 0.821 1.151Multiple disabilities -0.083 0.180 0.644 0.920 0.646 1.310A disability not listed above -0.318 0.121 0.009 0.727 0.573 0.923

Niche graduate occupationDyslexia 0.024 0.041 0.558 1.024 0.946 1.109Blind/partially sighted -0.047 0.224 0.834 0.954 0.614 1.481Deaf/hearing impairment -0.235 0.187 0.210 0.791 0.548 1.141Wheelchair users/mobility difficiulties 0.390 0.213 0.067 1.477 0.973 2.241Mental health difficulties -0.495 0.185 0.008 0.610 0.424 0.877An unseen disability -0.069 0.077 0.366 0.933 0.802 1.085Multiple disabilities -0.220 0.161 0.173 0.803 0.585 1.101A disability not listed above -0.191 0.103 0.063 0.826 0.676 1.010

By including the disability variable, the chi-square statistic for the likelihood ratio test is 51.954, with 32 df and p = 0.014

-2LL of intercept only model = 189407.157; -2LL of final model = 111354.842, chi-square = 78052.316; 244 df, p<0.001Cox & Snell R Square = 0.510; Nagelkerke R Square = 0.535

In additon to disability, other control variables include: degree subject, degree classification, type of industry, duration of employment, age group, gender, type of first degree awarding institution, highest qualification on entry to HE.

125

Model 7a. Binary logistic regression for predicting whether a degree qualification was required for entry into a job, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled and a degree qualification is not required)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability status -0.009 0.028 0.750 0.991 0.938 1.047

Model summary: -2LL = 100705.873; model chi-square = 25784.582 , 66 df, p<0.001Cox & Snell R Square = 0.223Nagelkerke R Square = 0.314

In additon to disability, other control variables include: degree subject, degree classification, type of industry, ethnicity, age group, duration of employment, type of first degree awarding institution, highest qualification on entry to HE.

Model 7b. Binary logistic regression for predicting whether a degree qualification was required for entry into a job, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled and a degree qualification is not required)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability (ref: non-disabled) 0.044Dyslexia 0.041 0.037 0.261 1.042 0.970 1.119Blind/partially sighted -0.293 0.194 0.131 0.746 0.510 1.091Deaf/hearing impairment 0.193 0.166 0.244 1.213 0.877 1.680Wheelchair users/mobility difficiulties 0.383 0.202 0.058 1.467 0.987 2.179Mental health difficulties -0.183 0.148 0.217 0.833 0.624 1.113An unseen disability -0.050 0.068 0.465 0.951 0.833 1.087Multiple disabilities 0.031 0.144 0.828 1.032 0.779 1.367A disability not listed above -0.202 0.089 0.024 0.817 0.686 0.973

Model summary: -2LL = 95706.688 ; model chi-square = 24635.598 , 60 df, p<0.001Cox & Snell R Square = 0.224Nagelkerke R Square = 0.315

In additon to disability, other control variables include: degree subject, degree classification, type of industry, age group, duration of employment, type of first degree awarding institution, highest qualification on entry to HE.

126

Model 8a. Multinominal logistic regression for predicting the level of agreement with whether a degree qualification was required for entry into a job, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Formal requirement -0.023 0.034 0.506 0.978 0.914 1.045Expected -0.022 0.042 0.595 0.978 0.900 1.062Advantage 0.009 0.033 0.780 1.009 0.946 1.077

-2LL of intercept only model = 140973.192; -2LL of final model = 97029.107, chi-square = 43944.085; 198 df, p<0.001Cox & Snell R Square = 0.350; Nagelkerke R Square = 0.378

Ref: Degree qualification not a requirement

By including the disability variable, the chi-square statistic for the likelihood ratio test is 1.083, with 3 df and p = 0.781 (ie not significant).

In additon to disability, other control variables include: degree subject, degree classification, type of industry, duration of employment, age group, ethnicity, type of first degree awarding institution, highest qualification on entry to HE.

127

Model 8b. Multinominal logistic regression for predicting the level of agreement with whether a degree qualification was required for entry into a job, by types of disability (Model parameters shown for disability variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Formal requirementDyslexia 0.055 0.044 0.219 1.056 0.968 1.152Blind/partially sighted -0.390 0.238 0.102 0.677 0.424 1.080Deaf/hearing impairment 0.048 0.198 0.807 1.050 0.712 1.547Wheelchair users/mobility difficiulties 0.159 0.241 0.509 1.173 0.731 1.880Mental health difficulties -0.405 0.190 0.033 0.667 0.460 0.968An unseen disability -0.044 0.082 0.589 0.957 0.815 1.123Multiple disabilities 0.040 0.175 0.819 1.041 0.738 1.467A disability not listed above -0.261 0.110 0.017 0.770 0.621 0.955

ExpectedDyslexia 0.040 0.054 0.465 1.040 0.935 1.157Blind/partially sighted -0.649 0.334 0.052 0.522 0.272 1.005Deaf/hearing impairment 0.020 0.252 0.936 1.020 0.622 1.674Wheelchair users/mobility difficiulties 0.222 0.299 0.457 1.249 0.695 2.242Mental health difficulties -0.337 0.240 0.161 0.714 0.446 1.143An unseen disability -0.008 0.101 0.940 0.993 0.815 1.209Multiple disabilities -0.225 0.234 0.335 0.799 0.505 1.262A disability not listed above -0.219 0.138 0.112 0.803 0.613 1.053

AdvantageDyslexia 0.029 0.043 0.498 1.030 0.946 1.120Blind/partially sighted -0.114 0.224 0.610 0.892 0.575 1.383Deaf/hearing impairment 0.386 0.186 0.038 1.470 1.022 2.115Wheelchair users/mobility difficiulties 0.610 0.221 0.006 1.840 1.192 2.838Mental health difficulties 0.041 0.167 0.804 1.042 0.752 1.445An unseen disability -0.074 0.082 0.365 0.929 0.792 1.090Multiple disabilities 0.108 0.165 0.513 1.114 0.806 1.540A disability not listed above -0.135 0.106 0.203 0.873 0.709 1.076

By including the disability variable, the chi-square statistic for the likelihood ratio test is 37.705, with 24 df and p = 0.037

-2LL of intercept only model = 115922.002; -2LL of final model = 73929.005, chi-square = 41992.997; 180 df, p<0.001Cox & Snell R Square = 0.351; Nagelkerke R Square = 0.378

Ref: Degree qualification not a requirement

In additon to disability, other control variables include: degree subject, degree classification, type of industry, duration of employment, age group, type of first degree awarding institution, highest qualification on entry to HE.

128

Model 9. Multinominal logistic regression for predicting graduates’ economic activity, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Reference: In employment

ILO unemployed 0.617 0.197 0.002 1.853 1.258 2.728Inactive 0.878 0.127 0.000 2.407 1.876 3.087

By including the disability variable, the chi-square statistic for the likelihood ratio test is 56.610, with 2 df and p<0.001

-2LL of intercept only model = 20994.259; -2LL of final model = 19150.726, chi-square = 1843.534; 100 df, p<0.001Cox & Snell R Square = 0.074; Nagelkerke R Square = 0.125

Government office region was found not to be a significant predictor.

95% Confidence Interval for Exp(B)

In additon to disability, other control variables include: number of health problems reported, gender, ethnicity, age, number of children in family under 16, degree subject, type of degree, number of years since obtaining highest qualification, year and quarter of LFS.

129

Model 10. Multinominal logistic regression for predicting graduates’ socio-economic class (NS-SEC), by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B)Lower Bound Upper Bound

Lower managerial occupations 0.297 0.071 0.000 1.345 1.170 1.547

Intermediate occupations 0.315 0.107 0.003 1.371 1.112 1.690

Other occupations 0.392 0.097 0.000 1.480 1.223 1.791

Never worked, unemployed, nec 0.806 0.100 0.000 2.239 1.842 2.722

By including the disability variable, the chi-square statistic for the likelihood ratio test is 64.211, with 4 df and p<0.001

-2LL of intercept only model = 63638.420; -2LL of final model = 55762.985, chi-square = 7875.435; 156 df, p<0.001Cox & Snell R Square = 0.281; Nagelkerke R Square = 0.299

Reference: Higher managerial occupations

95% Confidence Interval for Exp(B)

Other occupations include administrative and secretarial, skilled trades, personal service, sales and customer service, process, plant and machine operatives, and elementary occupations.

In additon to disability, other control variables include: gender, ethnicity, age, government office region, degree subject, type of degree, number of years since obtaining highest qualification.

130

Model 11. Multinominal logistic regression for predicting graduates’ major occupational group, by disability status (Model parameters shown for disability status variable only. Ref: Non-disabled)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Reference: Professional occupations

Managers and senior officials 0.106 0.096 0.268 1.112 0.922 1.341Associate professional and technical occupations 0.266 0.084 0.002 1.305 1.106 1.538Other occupations 0.321 0.093 0.001 1.378 1.148 1.654

By including the disability variable, the chi-square statistic for the likelihood ratio test is 15.632, with 3 df and p = 0.001

-2LL of intercept only model = 55216.954; -2LL of final model = 44982.133, chi-square = 10234.82; 162 df, p<0.001Cox & Snell R Square = 0.391; Nagelkerke R Square = 0.419

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, full/part-time employment, public/private sector employment, type of industry, degree subject, type of degree, number of years since obtaining highest qualification.

131

Model 12. Binary logistic regression for predicting employment in a job with supervision responsibilities (Model parameters shown for disability status variable only. Ref: Non-disabled and no supervision responsibilities)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability status 0.030 0.070 0.672 1.030 0.898 1.181

Model summary: -2LL = 21584.769; model chi-square = 4584.803, 57 df, p<0.001Cox & Snell R Square = 0.215Nagelkerke R Square = 0.287

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, type of industry, major occupational group, full/part-time employment, job tenure, degree subject, type of degree, number of years since obtaining highest qualification. Model 13. Binary logistic regression for predicting graduates’ employment in private/public sector (Model parameters shown for disability status variable only. Ref: Non-disabled and private sector employment)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability status -0.036 0.098 0.711 0.964 0.796 1.169

Model summary: -2LL = 19645.222; model chi-square = 5278.466, 51 df, p<0.001Cox & Snell R Square = 0.241Nagelkerke R Square = 0.331

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, major occupational group, job tenure, degree subject, type of degree and number of health problems reported.

132

Model 14. Binary logistic regression for predicting full- or part-time employment, by disability status (Model parameters shown for disability status variable only. Ref: non-disabled and full-time employment.)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability status 0.468 0.128 0.000 1.597 1.242 2.052

Model summary: -2LL = 12503.549; model chi-square = 3860.08, 56 df, p<0.001

Cox & Snell R Square = 0.169Nagelkerke R Square = 0.311

In additon to the above variables, other control variables include: gender, ethnicity, public/private sector employment, region of place of work, type of industry, major occupational group, degree subject. Model 15. Binary logistic regression for predicting employment in a graduate or non-graduate occupation as defined by SOC(HE) (Model parameters shown for disability status variable only. Ref: Non-disabled and non-graduate occupation)

B S.E. Sig. Exp(B) 95.0% C.I.for EXP(B)Lower Upper

Disability status -0.191 0.082 0.020 0.826 0.703 0.971

Model summary: -2LL = 16188.492; model chi-square = 3756.629, 54 df, p<0.001Cox & Snell R Square = 0.180Nagelkerke R Square = 0.276

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, type of industry, public/private sector employment, full/part-time employment, job tenure, degree subject, type of degree, number of years since obtaining highest qualification.

133

Model 16. Multinominal logistic regression for predicting employment in the different categories of SOC(HE) (Model parameters shown for disability status variable only. Ref: Non-disabled and non-graduate occupation)

B Std. Error Sig. Exp(B) 95% Confidence Interval for Exp(B)Lower Bound Upper Bound

Reference: Non-graduate occupation

Traditional graduate occupation -0.371 0.114 0.001 0.690 0.551 0.864Modern graduate occupation -0.207 0.107 0.052 0.813 0.660 1.002New graduate occupation -0.213 0.107 0.046 0.808 0.656 0.996Niche graduate occupation -0.059 0.097 0.548 0.943 0.779 1.141

By including the disability variable, the chi-square statistic for the likelihood ratio test is 13.448, with 4 df and p = 0.009

-2LL of intercept only model = 60206.571; -2LL of final model = 49909.056, chi-square = 10297.514; 216 df, p<0.001Cox & Snell R Square = 0.420; Nagelkerke R Square = 0.437

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, full/part-time employment, public/private sector employment, type of industry, job tenure, degree subject, type of degree, number of years since obtaining highest qualification.

134

Model 17 Linear regression model for predicting log of gross weekly earnings for full-time employed graduate employees (Model parameters shown for disability status variable only. Ref: Non-disabled)

Unstandardized Coefficients Sig.B Std. Error Lower Bound Upper Bound

Disability status -0.073 0.014 0.000 -0.100 -0.045

Model summary:Adjusted R Square = 0.478F = 182.020 (67, 13163), p < 0.001

95% Confidence Interval for B

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, major occupational group, type of industry, job tenure, degree subject, type of degree, number of years since obtaining highest qualification, year and quarter of LFS. Model 18 Linear regression model for predicting log of hourly pay for full-time employed graduate employees (Model parameters shown for disability status variable only. Ref: Non-disabled)

Unstandardized Coefficients Sig.B Std. Error Lower Bound Upper Bound

Disability status -0.056 0.014 0.000 -0.084 -0.029

Model summary:Adjusted R Square = 0.446F = 159.974 (67, 13163), p < 0.001

95% Confidence Interval for B

In additon to disability, other control variables include: gender, ethnicity, age, number of children in family under 16, region of place of work, major occupational group, type of industry, job tenure, degree subject, type of degree, number of years since obtaining highest qualification, year and quarter of LFS.

135

Model 19 Linear regression model for predicting log of gross weekly earnings for part-time employed graduate employees (Model parameters shown for disability status variable only. Ref: Non-disabled)

Unstandardized Coefficients Sig. 95% Confidence Interval for BB Std. Error Lower Bound Upper Bound

Disability status -0.219 0.052 0.000 -0.322 -0.116

Model summary:Adjusted R Square = 0.406F = 26.105 (48, 1835), p < 0.001

In additon to disability, other control variables include: ethnicity, age, number of children in family under 16, region of place of work, major occupational group, type of industry, job tenure, degree subject. Model 20 Linear regression model for predicting log of hourly pay for part-time employed graduate employees (Model parameters shown for disability status variable only. Ref: Non-disabled)

Unstandardized Coefficients Sig. 95% Confidence Interval for BB Std. Error Lower Bound Upper Bound

Disability status -0.117 0.038 0.002 -0.193 -0.042

Model summary:Adjusted R Square = 0.470F = 34.946 (49, 1830), p < 0.001

In additon to disability, other control variables include: ethnicity, age, number of children in family under 16, region of place of work, major occupational group, type of industry, job tenure, degree subject, number of years since obtaining highest qualification.

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