Higher Education and the Economically-disadvantaged

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Table of Contents

Introduction
Literature Review
Methodology
Findings
Bibliography

Introduction

The egalitarian tradition of the UK school system manifests itself in maximum accessibility, even in higher education. In the absolute, fees are low, to begin with, and can be repaid after graduation. The government subsidizes all costs for students at the lowest family income levels. In addition, loans are available to students for maintenance and living expenses.

The universities themselves offer bursaries dependent on the size of their trust funds and special revenue streams such as donations for research. Before the economic travails triggered by trans-Atlantic speculation in second-class securities, the higher education sector in the UK was generally sound and in fact, flush with funds from the preceding period of economic prosperity (Times Higher Education, 2008). The pace of donations was brisk, loan sources were highly receptive, foreign student enrolment (with higher fees charged) steadily expanded, and university deficits were lower than they had ever been in decades. Hence, the prospects for broadening access to university for even the poorest students were bright.

However, revenue growth was uneven. Income increases were greatest for large, most established, and already-prosperous universities. This fuelled more extensive research, enabled them to attract more talent, and advertise even more for lucrative foreign students. On this basis, the universities that have the most flexibility for assisting UK students are those that have diversified best beyond just state funding, generating positive income from students outside the UK and the European Union, from higher education funding councils, teaching income (tuition fees), research income, endowments, and in-campus commercial operations.

As well, the economic straits brought on by the current recession dampen prospects already dimmed by still-unimplemented increases in top-up fees, by local enrolment expected to fall commencing 2012, and by the volatile nature of foreign student enrolment.

Literature Review

Deprivation and Higher Education

The deprivation that low family incomes bring is universal, have long-lasting effects beginning soon after birth and through to one’s deathbed. Summarizing the evidence, the Schools Analysis and Research Division of the Department for Children, Schools, and Families (2009) revealed that deprivation even trumps being born with naturally higher mental ability. Even when cognitively superior at 22 months, the children of the poor began to lag by the time their cohort entered primary school. More crucially, the disadvantage in academic achievement persists through all later stages of schooling, including in higher education (Feinstein, 2003). And this gap is ameliorated only if both parents have higher education or postgraduate training but not if the mother alone has gone to university.

The adverse effects of deprivation are not limited to the UK. In other industrialized and economic powerhouses, the members of the Organisation for Economic Cooperation and Development, findings from the Programme for International Student Assessment (PISA) study reveal that deprivation and reduced educational attainment also go hand in hand (OECD, 2000). And there is even a small but measurable decline in life expectancy, as demographic analysis from the United States shows. One less year of formal schooling is correlated with about a 1.7-year reduction in life expectancy (Feinstein et al., 2008).

In the domestic context, deprivation early in life not only reduces educational opportunity but corollary shapes career choices towards blue-collar, manual labor that is inherently physically taxing and accident-prone ((Blanden, Hansen & Machin, 2008). Even worse, Feinstein et al. (2008) show, lower educational attainment is itself associated with pronounced social detachment and a greater willingness to engage in a life of crime.

Indices of Deprivation

One index of deprivation is the distribution of the free school meal (FSM) subsidy because entitlements to secondary school students can be viewed as antecedents of the ability to proceed to higher education.

Being supplied based on unemployment in the child’s family, among other criteria, FSM paid out in cities and neighborhoods could well be an easy single indicator of socio-economic disadvantage.

However, Davenport (2004) argues that unemployment can be seasonal or industry-based. The large uptake of shop girls during the holiday season, for instance, is counterbalanced by low demand during the lean winter and spring months. The same contractual employees may, however, derive other gainful employment such as being nannies or temp clerks during the off-season. Similarly, a long-term decline in coal mining and coal power generation can portray coal towns as centers of deprivation. On the other hand, FSM flows to such communities can mask successful efforts to retrain for heavy industry or labor surplus going to the extensive cabling and servicing that convergence of new media around Internet access all over the UK has encouraged.

The Department for Children, Schools, and Families (DCSF) also realized that FSM does not capture the true extent of children enduring deprivation in a community if eligible parents do not apply or if both parents are in employment but combined household incomes still do not suffice to meet all the needs of pupils.

Hence, the Department also takes into account other measures of deprivation such as the Index of Multiple Deprivation (IMD, currently the official indicator when assessing local areas), Income Deprivation Affecting Children Index (IDACI), tax credits, and multiple indicators of family income class.

Communities and Local Government (2010) is responsible for compiling and acting on IMD. This is a complex set of indicators comprising social, housing, and socio-economic measures and serves to rank every small area throughout England – 32,482 so-called “Lower Super Output Areas” – against all others based on which CLG can propose policy initiatives and recommend funding streams. As with every neighborhood-based indicator, however, IMD does not specifically target the academic achievement of children.

Children under 16 qualify for IDACI income-related benefits based not on family means but deprivation in the surrounding community.

This permits children with working parents to qualify if family income is below a localized subsistence level.

IDACI also qualifies pupils with unemployed parents, of course. Hence, IDACI ought to be beneficial in working-class neighborhoods where incomes are low and there is, for some reason, low FSM penetration.

DCSF also routinely employs the level of tax credits that flow to an affected community as a rough measure to trigger, for instance, such subsidies as the Dedicated Schools Grant. As with IMD and IDACI, community-based estimates of tax credits can under- or overestimate deprivation for single families and pupils. As a result, the potential loss of educational opportunity may not be correctly measured.

The Concern with Young People from Low-Income Backgrounds

More directly relevant to the objective of this paper is the assessment of socio-economic status (SES). DCSF acknowledges the existence of multiple designations for “low income,” “lower socio-economic group (SEG),” having “low income,” and belonging to the lower social class (Schools Analysis and Research Division, 2009). After all, low income in the absolute is a major factor in classifying a family as low-SEG or lower social class.

It is official government policy since 1999 to bring vanquish the poverty levels that specifically afflict children within just one generation or by 2020. At the time, the interim goals were a fifty percent reduction in child poverty by 2010.

Nor is this a selfless objective. The Exchequer has gone on record as expecting to boost government revenue by no less than £17 billion annually once child poverty is eliminated. Among the most salient and relevant measures adopted by the government is a single-minded focus on children progressing to finding work as the most sustainable route for ending child poverty (HM Treasury, 2008).

Certainly, there is no question that higher education is the best possible route for being able to move into better-paid work and thus, solidifying financial security for the next generation of children. Midway towards the goal of halving child poverty by 2010, data from the Higher Education Statistics Agency as of 2004/05 and 2005/06 revealed that the poverty gap (as measured by FSM recipients) was proportionately greatest where entry to higher education was concerned. Fig. 1 below shows that a) Non-FSM children are about three times more likely to enter higher education than are FSM pupils graduating KS 4 to 5 levels; and, b) just one in eight FSM children can proceed to a university.

*Figure 1: FSM Attainment Gap From Foundation to Higher Education*

The Low Participation Neighbourhood Measure

Striking directly at the phenomenon of low progression rates toward, and entry into, higher education amongst those who are deprived primarily because of income, the Higher Education Funding Council for England (2008) effectively made youth participation in higher education a performance indicator for all UK higher educational institutions (HEI’s). This fundamentally meant that HEI’s needed to proactively recruit degree students from low-participation neighborhoods in their immediate communities and implement measures, as well, to ensure their retention for desired first degrees or foundation courses, at least.

The designation of low or high participation and ranking of local areas is most recently based on the POLAR2 (Participation of Local Areas) method. POLAR2 is a performance indicator based on the youth cohort that turned 18 years between 2000 and 2004 and enrolled in a higher education course the following academic year, from AY 2000-01 to 2005-06. Low-participation wards are defined as those that rank in the bottom quintile (the lowermost 20%) for entry into HEI. For a variety of purposes, POLAR2 segregates “…young and mature, full-time and part-time entrants” (Anderson, n.d., p. 2).

Methodology

Rationale

For the concededly limited scope of this short paper, the analysis will focus on the performance of HEI’s in respect of the POLAR2 criterion. In particular, the focus shall be on young students enrolling at least for a first degree on a full-time basis. After all, the overall thrust of this paper is on family deprivation limiting opportunities for gaining a university education. Further, a four- or five-year degree serves the aims of financial stability better than a vocationally-oriented foundation course.

Research Objective

Given the adequate teaching staff and larger physical plant implied by a larger student body, the primary research question revolves around whether capacity correlates strongly with provoking entrants from surrounding low-participation neighborhoods.

Research Design, Methodological Justification, and Crucial Variables

This is secondary research based on an analysis of data on UK HEI student populations and percentage participation from the immediate environs available from the Higher Education Statistics Authority.

The base data is broken down by a single HEI for England, Wales, and Northern Ireland (POLAR2 for Scotland being under review for showing significantly lower performance indicators relative to the first, POLAR ranking). The student body criterion employs the absolute numbers of young, full-time, and first-degree entrants at each HEI. In turn, performance against the POLAR2 criterion employs a percentage of the student body with known data that come from low-participation neighborhoods.

Since both variables are ratio-type data, both correlation and linear regression may be employed to test for a positive or negative relationship.

Findings

The base data for the analysis is shown in Table 1, commencing overleaf, for a total of 157 HEI’s. Visual inspection via a scatter plot reveals the possibility of a positive relationship, modulated by a) the fairly low maximum participation rate of around 27%; and, b) the significant clustering (in the upper-left quadrant) of HEI’s with small populations that manage to induce comparatively high participation rates.

The results of the correlation and linear regression runs in SPSS. — *Figure 2 : The results of the correlation and linear regression runs in SPSS are presented in page 13.*

Table 1: The Base Data for Statistical Analysis.

Label	HEI	FTFDY	PLPN
1	Anglia Ruskin University	1520	17.0
2	Aston University	1375	12.6
3	Bath Spa University	1115	8.3
4	The University of Bath	1740	4.4
5	University of Bedfordshire	1285	12.1
6	Birkbeck College(#3)	0	..
7	Birmingham City University	2815	14.9
8	The University of Birmingham	4495	6.3
9	University College Birmingham	475	12.7
10	Bishop Grosseteste University College Lincoln	290	19.8
11	The University of Bolton	565	24.3
12	The Arts University College at Bournemouth(#2)	605	6.1
13	Bournemouth University	2460	7.4
14	The University of Bradford	1450	17.2
15	The University of Brighton	2530	8.2
16	The University of Bristol	2985	3.4
17	Brunel University	2675	5.0
18	Buckinghamshire New University	730	8.7
19	The University of Buckingham	50	9.5
20	The University of Cambridge	2815	3.7
21	Canterbury Christ Church University	1600	15.9
22	The University of Central Lancashire	3130	15.5
23	Central School of Speech and Drama(#3)	140	6.4
24	University of Chester	1660	13.5
25	The University of Chichester	860	13.0
26	The City University	1265	5.7
27	Conservatoire for Dance and Drama	120	5.0
28	Courtauld Institute of Art(#3)	45	7.0
29	Coventry University	2590	9.6
30	University for the Creative Arts	1245	11.3
31	University of Cumbria	1145	15.5
32	De Montfort University	2860	11.5
33	University of Derby	1965	20.6
34	University of Durham	3005	4.6
35	The University of East Anglia	2245	9.1
36	The University of East London	1815	10.0
37	Edge Hill University	1685	21.3
38	The University of Essex	1665	16.0
39	The University of Exeter	3110	4.0
40	University College Falmouth(#1)	725	9.9
41	University of Gloucestershire	1480	8.6
42	Goldsmiths College(#3)	1145	5.5
43	The University of Greenwich	1995	10.8
44	Guildhall School of Music and Drama	95	8.3
45	Harper Adams University College	300	4.7
46	University of Hertfordshire	3635	8.2
47	Heythrop College(#3)	115	6.1
48	The University of Huddersfield	2375	16.7
49	The University of Hull	2700	17.0
50	Imperial College of Science, Technology and Medicine	1380	4.4
51	Institute of Education(#3)	0	..
52	The University of Keele	1425	14.2
53	The University of Kent	3035	9.9
54	King’s College London(#3)	2275	3.7
55	Kingston University	3730	5.6
56	The University of Lancaster	2255	8.5
57	Leeds College of Music	190	8.4
58	Leeds Metropolitan University	4825	13.4
59	The University of Leeds	5910	6.2
60	Leeds Trinity University College(#2)	670	20.9
61	The University of Leicester	2435	6.7
62	The University of Lincoln	2105	16.3
63	Liverpool Hope University	1035	19.6
64	Liverpool John Moores University	4220	17.2
65	The Liverpool Institute for Performing Arts	160	9.0
66	The University of Liverpool	3310	8.7
67	University of the Arts, London	1645	4.6
68	London Metropolitan University	1770	7.6
69	London South Bank University	1180	9.7
70	London School of Economics and Political Science(#3)	700	4.5
71	Loughborough University	3295	5.2
72	The Manchester Metropolitan University	5935	15.7
73	The University of Manchester	6235	7.4
74	Middlesex University	2125	7.7
75	The University of Newcastle-upon-Tyne	3420	6.3
76	Newman University College	465	23.9
77	The University of Northampton	1365	14.4
78	The University of Northumbria at Newcastle	3995	14.2
79	Norwich University College of the Arts	285	13.7
80	The University of Nottingham	4885	5.6
81	The Nottingham Trent University	5395	12.1
82	Oxford Brookes University	1940	4.2
83	The University of Oxford	2725	2.7
84	University College Plymouth St Mark and St John	415	12.8
85	The University of Plymouth	3540	10.0
86	The University of Portsmouth	4045	8.7
87	Queen Mary and Westfield College(#3)	2495	5.3
88	Ravensbourne College of Design and Communication	170	10.1
89	The University of Reading	2245	5.8
90	Roehampton University	1570	6.6
91	Rose Bruford College	155	6.4
92	Royal Academy of Music(#3)	40	5.4
93	Royal Agricultural College	195	3.1
94	Royal College of Music	65	1.6
95	Royal Holloway and Bedford New College(#3)	1635	4.0
96	Royal Northern College of Music	100	3.0
97	The Royal Veterinary College(#3)	225	4.9
98	St George’s Hospital Medical School(#3)	410	6.4
99	St Mary’s University College, Twickenham	820	8.7
100	The University of Salford	2220	21.0
101	The School of Oriental and African Studies(#3)	465	2.6
102	The School of Pharmacy(#3)	140	4.3
103	Sheffield Hallam University	5160	17.7
104	The University of Sheffield	3880	8.7
105	Southampton Solent University	2515	11.0
106	The University of Southampton	3300	5.9
107	Staffordshire University	1800	21.3
108	University Campus Suffolk	365	19.2
109	The University of Sunderland	1750	25.7
110	The University of Surrey	1590	7.3
111	The University of Sussex	1855	6.8
112	The University of Teesside	1475	26.7
113	Thames Valley University	735	7.7
114	Trinity Laban Conservatoire of Music and Dance(#2)	125	7.4
115	University College London(#3)	2205	3.4
116	The University of Warwick	2560	5.5
117	University of the West of England, Bristol	4345	9.3
118	The University of Westminster	2610	5.9
119	The University of Winchester	1135	11.9
120	The University of Wolverhampton	2125	21.6
121	The University of Worcester	975	9.5
122	Writtle College	130	12.4
123	York St John University	1155	12.3
124	The University of York	2245	7.2
125	Aberystwyth University	1865	9.6
126	Bangor University	1835	11.2
127	Cardiff University	4025	6.2
128	University of Wales Institute, Cardiff	1675	10.3
129	University of Glamorgan	2080	14.5
130	Glyndŵr University	320	18.6
131	The University of Wales, Lampeter	130	13.2
132	The University of Wales, Newport	670	16.2
133	Swansea Metropolitan University	750	12.3
134	Swansea University	2405	9.3
135	Trinity University College(#2)	310	8.1
154	The Queen’s University of Belfast	3340	5.3
155	St Mary’s University College	195	8.9
156	Stranmillis University College	195	4.1
157	University of Ulster	4050	7.9

N.B.

FTFDY = Full-time, first degree, young entrants

PLPN = Percentage from low-participation neighborhoods

The SPSS output shows, first of all, that PLPN does skew low since the calculated mean (Table 2 below) stands at just 10.2%. This is highly variable, as indicated by the standard deviation of 5.5.

Table 2.

Descriptive Statistics
	Mean	Std. Deviation	N
First-time, 1st degree, young	1826.19	1446.935	139
Part. from low-participation neighbourhoods	10.189	5.5141	137

The first indicator of a weak relationship between the two HEI performance indicator is the finding (Table 3) that Pearson’s r is not only marginal, it is negative. Had a trend line been drawn in the scatter plot (above, page 9), it would have been virtually flat, suggesting the lack of a relationship between the two variables.

Table 3.

Correlations
		First-time, 1st degree, young	Part. from low-participation neighbourhoods
First-time, 1st degree, young	Pearson Correlation	1.000	-.031
	Sig. (2-tailed)		.723
	N	139.000	137
Part. from low-participation neighbourhoods	Pearson Correlation	-.031	1.000
	Sig. (2-tailed)	.723
	N	137	137.000

When a linear regression is nevertheless run, the estimation equation (values derived from Table 4 below) would read as follows:

Y = a + b₁X₁= 10.4 -.03 X₁

Fundamentally, this means that – given the prevailing situation in all UK HEI’s (except Scotland) – typical participation is at 10% and percentage participation from the adjacent LPN’s declines by 0.03 for every unit increase in the student body of the typical HEI. The presumed teaching and physical plant capacity of the HEI is irrelevant to the ability to recruit and retain more first-time, first degree and young entrants from surrounding LPN’s. Other variables must enable successful HEI’s to progress the egalitarian goals of government and reduce or eliminate deprivation-induced failure to attend university.

Table 4.

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95% Confidence Interval for B
			Std. Error	Beta			Lower Bound	Upper Bound
1	(Constant)	10.405	0.772		13.481	0.000	8.879	11.932
	First-time, 1st degree, young	0.000	0.000	-0.031	-0.355	0.723	-0.001	0.001
a. Dependent Variable: Part. from low-participation neighbourhoods

Bibliography

Anderson, G. (n.d.) Performance indicators. Paper IGS-SFC (150). Web.

Blanden, J. Hansen, K. & Machin, S. (2008). The GDP cost of the lost earning potential of adults who grew up in poverty. Joseph Rowntree Foundation. Web.

Communities and Local Government (2010) Indices of deprivation. Web.

Davenport, M. (2004) Menu must change for FSM. TES Connect. Web.

Feinstein, L. (2003) Inequality in the early cognitive development of British children in the 1970 cohort. Economica. 70, pp. 73-97.

Feinstein, L., Budge, D., Vorhaus, J. & Duckworth, K. (2008). The social and personal benefits of learning: a summary of key research findings. Centre for Research on the Wider Benefits of Learning. Web.

Higher Education Funding Council for England (2008) Updated young participation area classification (POLAR2). Web.

HM Treasury (2008) Ending child poverty: everybody’s business. Web.

OECD (2000) Programme for International Student Assessment (PISA).

Schools Analysis and Research Division (2009) Deprivation and education: the evidence on pupils in England, Foundation Stage to Key Stage 4. London/HMSO: Department for Children, Schools and Families.

Times Higher Education (2008) There may be trouble ahead. TLS Education Ltd. Web.

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