The Economics of Education:
is it Profitable to be Ignorant?
Michael Quigley
Salford Business School CORAS
Room 714 Maxwell Building, University of Salford, UK, M5 4WT
+44 161 295 2000
michael@ michael-quigley.com
Abstract
In the UK, formal tertiary education comes at monetary cost to those who choose to
participate. Currently 43% of young people enter universities in the belief that they
will recoup the expense in higher salaries, later in life. Whilst this is a historically
true model for the majority of graduates, many analysts believe that the Government’ s
push for 50% participation, coupled with rising costs to attend universities, could alter
the profitability of higher education and instead lead to a situation where ignorance is
not only bliss but also an economically sound decision.
This paper explores the current situation conceming the economic benefit of higher
education: building a model to represent this situation. Key variables and values are
then highlighted and discussed, in this current research project, to test whether they
could take the current situation over the tipping-point.
1, Introduction
The United Kingdom (UK) has undergone radical changes to its economy over the
last 4 decades, from a predominantly manufacturing economy to one based on the
service sector. As the UK’s knowledge economy grows so does the requirement for a
skilled, higher educated, workforce. Higher education institutions (HEIs) are tasked
with providing the skilled labour that the economy requires, at the correct output rate.
The changes in the economy have, through this direct interaction, led to changes
occurring in the composition and capacity of HEIs.
The UK Government's policy for higher education is to reach a [higher education
initial] participation rate (HEIPR) of 50% for 18 to 30 year olds by 2010 (Clarke,
2003). This increase is from the current 42% (Cook, 2006), an increase in real-terms
of 16%. The rationale behind the policy is embedded in the perceived changes to the
economy of the UK. In the White Paper the Government quotes “that 80 per cent of
the 1.7 million new jobs which are expected to be created by the end of the decade
[2010] will be in occupations which normally recruit those with higher educated
qualifications” (Wilson and Green, 2001).
More worryingly, for the UK economy and universities, is the expected fall in the
number of UK students, due to population dynamics. The universities will need to
recruit larger numbers of European (EU) and overseas students to maintain their
current workload (Fazackerley, 2006), whilst this adds little direct benefit to the UK
economy after these students have completed their study.
The annual budget speech often contains statements echoing the need for more skilled
workers (Treasury, 1998-2008), only 2000 and 2005 not containing a similar
statement. The budget statement of 2007 announced 4 million new skilled jobs would
be created by 2020. Although critics argue that the numbers stated are
misrepresentative; being derived from supply-side models rather than demand-side
models. Critics state that the 4 million jobs are actually the up-skilling of previously
unskilled jobs, due to an over-supply of skilled labour (Kingston, 2008).
Despite, positive reception for and criticism against this objective, there has been
limited modelling carried out to examine the resulting outcomes for this and other
higher education policies. A human resource management (HRM) tool that could
produce time-based, dynamic, analysis would allow greater understanding of this
problem and lead to better policy decisions.
Criticisms of the policy focus on the current number of students graduating but not
using their degree, asking whether the knowledge economy is as large as reported
(Brown and Hesketh, 2004).
The debate on skilled human resources is not a recent phenomenon. In 1995,
Robinson, stated:
The attainment of qualifications threatens to run ahead of the economy’s
ability to absorb those qualifications.
- (Robinson, 1995)
Robinson argued that if the targets for training were met there would flood the
economy with skills that are not required, as the employment market was already
over-educated. This view was later backed up by a research showing that any
perceived gaps in the labour market are filled by existing skilled employees rather
than graduates (Alpin et al., 1998). Earlier research on the highly skilled doctoral
students showed an over-saturation in certain research subjects (Pearson et al., 1993).
However, the long-term economic benefits for HE students indicate that overall
demand for skilled labour is greater than the supply, though this varies dependent on
subject area (Machin and Vignoles, 2005).
This paper explores the economics of education, examining the costs of obtaining a
degree weighed against the potential monetary returns. Highlighting how forces such
as demographics and later retirement age may affect graduates entering the
employment market as skilled employees.
1.1 Over-educated Workforce View
A number of case studies compare the qualifications obtained by a population
compared with the qualifications required to complete a job. One of the most
influential studies was the Job-Completion Model, found in Economics of
Overeducation (Thurow, 1972).
Using Thurow’s model, Alpin et al (1998), identified particular employment sectors
that had supply/demand gaps. Over- and Undereducation in the UK Graduate Labour
Market [sic] (Alpin et al., 1998) cites that many of the perceived gaps in the labour
market's knowledge are filled by existing employees rather than graduates, who lack
the experience.
Concer over the throughput of students, in HEI, has been recorded in case studies for
over a decade. In 1995, Robinson, examined the characteristics of employees in the
job market and concluded that the employment market is already over-educated and
that “the attainment of qualifications threatens to run ahead of the economy’s ability
to absorb those qualifications”.
Most of the above studies do not refute the idea of attaining higher educational
qualifications as over-qualified workers tend to be more productive and offer addition
skills, which may help them throughout their career (Mason, 1996).
2. The UK Higher Education System
To meet the Governments (50% HEIPR) target the universities are going to have to
encourage more people to enter higher education. One population that has historically
been under-represented in HEIs is people from lower socio-economic backgrounds.
The Government passed legislation that allowed HEIs to charge fees to students
(Great_Britain, 2004). To make sure students from lower socio-economic
backgrounds are not discouraged from attending university they are exempt from the
fees. All students are entitled to a tuition loan to cover the course cost and a
maintenance loan to cover living expenses, where the first 75 per cent of the
maintenance loan is available to all and the remaining 25 per cent means-tested.
These loans are repaid at 9% of earnings when students finish study and eam above
£15,000. Additional to the loans students can apply for grants, which are provided by
the Government, and bursaries are provided by the Universities on a case-by-case
basis. Grants and bursaries are means-tested.
Despite the exemption and maintenance loans, the number of students enrolling at
universities from lower socio-economic backgrounds fell from 90,000 to 87,900 (a
1.25 per cent reduction) immediately following 2005 introduction (Qureshi, 2008).
2.1 Demographics
The inflow of students into universities can be divided by age. The largest percentage
belongs to 18-year olds; the age at which the majority of students complete further
education (FE).
In the UK, after the Second World War (1945), there was a period of approximately
20 years of above average births (Skidmore and Huber, 2003). This generation,
known as the “baby boomers” (figure 1) caused a second wave of births between 1972
and 1992.
Age (yrs)
aeuBRSs ABH BAA A BRS
mm wm MD DW MW 0 Mm Mw wT A A a
Population (thousands)
Figure 1: Baby Boomers in Population Pyramid
The higher number of young people (18-30 year olds) in the 1990’s and early 21*
Century, combined with a growing number of HEI’s led to unprecedented rises in the
number of university participants (figure 2).
Higher Education Enrolment
600 —Male Undergraduates
— Female Undergraduates
on ai —Male Postgraduates
— Female Postgraduates
Enroiment(000's)
o
3
8
1970/71 1980/81 1990/91 2000/01
Year
Figure 2: University Enrolment Boom in the 1990's
The boom from the 1990’s was down to both a higher number of young people and a
higher percentage of you people choosing to go to university. However, the academic
years 2006/7-2008/9 have seen the growth sustained almost entirely off population
dynamics, as participation rates levelled off at 42%.
From 2011, the population dynamics change again as the overall number of young
people begins to decrease. This decrease in real terms is 60.000 students between
2011 and 2014, which equates to approximately six universities closing. This
situation can be avoided if the participation rates were to increase and counterattack
the fall in the demographic cohort.
2.2 Higher Education Institutions
There are 168 active universities, colleges of higher education and other HEIs in the
UK, in 2006-2007 (HESA, 2007). HEIs are predominantly places of study for levels
5 and 6 of the international standard classification of education (ISCE) (table 1).
Table 1: International Standard Classification of Education
Level Description
Level 0 Pre-Primary Education
Level 1 Primary Education or First Stage of Basic Education
Level 2 Lower Secondary or Second Stage of Basic Education
Level 3 (Upper) Secondary Education
Level 4 Post-Secondary Non-Tertiary Education
First Stage of Tertiary Education (Not leading directly to
Level 5 an advanced research qualification)
Second Stage of Tertiary Education (Leading to an
Level 6 advanced research qualification)
3. Current Policy and Background - Under-educated Workforce View
The United Kingdom’s Department for Education and Skills (DfES) published a
White Paper in January 2003 entitled The Future of Higher Education (Clarke, 2003),
which explains the Government's higher education policies. The background reports
and investigations, that lead up to the White Paper’s publication, began almost 7 years
earlier with The Dearing Report (Dearing, 1997b). It is therefore beneficial to
understand the background to the White Paper and the political climate at that time,
before examining the policies contained in the White Paper.
A timeline from the NCIHE appointment, which produced the Dearing Report, to the
publication of The Future of Higher Education is produced below (figure 3).
Timeline
10% May 1996 — National Committee of Inquiry into Higher Education
(NCIHE) appointed.
1st October 1996 — Tony Blair, addresses the Labour Party conference,
states that his three top priorities on coming to office were “education,
education and education”.
24 May 1997 — Labour Party win UK General Election with historic
“landslide” victory.
234 July 1997 — NCIHE publishes The Dearing Report.
October 1997 — The Higher Education Funding Council for England
response to The Dearing Report.
October 1997 — Royal Society response to The Dearing Report.
January 2003 — The Future of Higher Education White Paper
published by Department for Education Skills.
January 2004 — The Higher Education Act was given royal assent.
Figure 3 - Timeline Showing Significant Political and Educational Events from May
1996 to January 2004.
3.1 The Dearing Report
On 10 May 1996 the National Committee of Inquiry into Higher Education (NCIHE)
was “appointed with bipartisan support by the Secretaries of State for Education and
Employment, Wales, Scotland, and Norther Ireland” (Dearing, 1997a). The NCIHE
was assigned with producing a report by the summer of 1997. The report was to make
recommendations on how the purposes, shape, structure, size and funding of higher
education, including support for students, should develop to meet the needs of the
United Kingdom over the next 20 years. The full terms of reference (Dearing, 1997c)
can be found in “Appendix B - Public, Private and Government Reports”. The terms
of reference that are of interest here are:
e there should be maximum participation in initial higher education by young
and mature students and in lifetime leaming by adults, having regard to the
needs of individuals, the nation and the future labour market;
e leaming should be increasingly responsive to employment needs and include
the development of general skills, widely valued in employment.
The NCIHE had support from both of the major political parties, so although the
investigation began under the Conservative Party and was completed under the
Labour Party there was no perceived motive to bias the findings. As such it can be
said that the NCIHE had no political agenda. The NCIHE aims for this report were to
aim for long-term over short-term goals, though immediate concems about research
must be addressed.
The Dearing report made nearly 90 recommendations, including an increase in the
number of people attending HEI and the use conclusion that graduates should
contribute to the cost of their university education.
4. SD Modelling in Higher Education
SD models are simulations built to improve policy (Forrester, 1961, Sterman, 2000),
rather than make forecasts, which has led to SD modelling of higher education to lend
itself to several stand alone human resource management (HRM) models (Rodrigues
and Martis, 2004). There are also numerous occasions where HE has made up a
smaller section of a much larger model, usually one examining economic policy
decisions (Dangerfield, 2005).
Galbraith, 1984, believes that models should be able to monitor both the level of
employment requiring tertiary education and the level of its use, for any given subject
area. This HRM approach has been used to examine engineers (Rodrigues and
Martis, 2004).
Rodrigues and Martis, 2004, argued that human resources (HR) and knowledge
management (KM) - the propagation of knowledge - can be viewed as a
supply/demand model. They believe that effective control of the supply of engineers,
to match demand, can be achieved through controlling the KM, i.e. varying the length
of engineering university courses.
5. Model Process and C onstruction
It is not within the remit of this paper to go into detail about the model building
process. A brief description and is produced below, due to this research being an
ongoing piece of work.
It is not usually possible to build a simulation that contains all of the variables and
relationships present in the system, one is attempting to replicate. Models, by
definition, are a representation of the key variables and significant relationships
present in that system. The modeller must make decisions on the scope of the
simulation by setting model boundaries. The model boundary is dependent on the
model objective, key variables and time horizon. For this reason, it is often preferred
to follow a systemic modelling process.
5.1 Systemic Building Processes
HRM planning involves five stages; analysis of the system, deciding the time horizon
of the model, forecasting the demand for or supply of the human resources,
reconciliation and preparation of the action plans (Tripathi, 2002). This research does
not aim to forecast but produce behavioural patterns one would expect to see under a
given set of circumstances. For this reason, SD planning is geared more towards the
building and validation of any model, thus producing a robust policy model.
In Industrial Dynamics (Forrester, 1961) a framework for designing a policy model
was produced and later adapted for a HRM model (Rodrigues and Martis, 2004),
depicted in figure 4. This methodological framework includes a step for policy and
scenario building. At this step, a number of scenarios should be examined, to enable
contingency planning, rather than a restricted number of forecasts produced.
Situation Analysis Steps:
1. Problem Identification
Statement of the Problem &
Situation Definition
Causal Loop Diagram 2. System Conceptualisation
Flow Diagram
3. Model Formulation
Equations
4. Simulation &
Simulation and Validation Validation
5. Policy Analysis
Policy & Scenario Building &
Improvement
Policy Improvement
Figure 4: Rodrigues and Martis HRM Modelling Methodology
5.2 High-level and Causal Loop Maps
To help define and articulate the problem, a high-level map can be produced, which
shows how skilled labour demands can only be met by university graduates (figure 5)
or the up-skilling of jobs (employment being re-classified as skilled, from a status of
unskilled).
The cost of High School and Further Education is free, in the UK, but Higher
Education must be paid for. The cost is dependent on socio-economic circumstances.
As people filter down the map they can choose to start employment or carry on with
their education. For simplicity vocational qualifications are not discussed in this
paper.
High School Leavers
Unskilled Employment = P
And Unemployment om [auiaiteretlisctita
Skilled Employment | | ees
Figure 5: High-level Map of Main System Flows
If a person chooses to enter employment, they will start to eam a wage and pension
contributions. The downside of entering employment is a slower progression in their
employment and typically lower starting role. These variables are sensitive to
particular subject/employment areas. The model (figure 6) runs a number of
subscripts, to allow individual subject areas to be analysed.
Any person opting to enter tertiary education will incur the costs of the course and
living expenses. They will also lose 3-7 years income and pension contributions. For
this monetary loss to be recouped the subject area chosen must be one where there is a
sufficient gap in the market to allow graduates to start at a higher salary level, than
those opting out of HE.
Years employed Population
ol
degree
Expected salary
( me with a eo Degree demand
pe
Total cost of Economic benefit of
f degree obtaining degree Spa dean
\ Se expect slay ot
Cost of degree without degree Degree supply
per year
Years employed
without degree
Figure 6: Causal Loop Diagram of Economic Benefits Model
The causal loop diagram shows that the total cost of obtaining the degree (currently
£8,500/year) has little long-term affects on the economic benefits of obtaining a
degree.
This is interesting, because there is discontent amongst the population that higher
tuition fees will lead to a lifetime of debt. The model indicates that any reasonable
increase in tuition or living costs will not lead to these problems. The key drivers that
can increase/decrease the economic benefits of a degree are the job availability.
A low demand for a skill obtained at university will lead to the economic benefits
being marginal, if not negative. The subjects that offer negative or marginal monetary
benefit are art, history, sociology and languages.
Accountancy, medicine and engineering offer the best long-term payout for students.
5.3 Stock-and-Flow Diagrams
By expanding the causal loop model to incorporate the employment sector, a more
sophisticated model can help clarify the situation. The stock-and-flow diagram can
also incorporate variables relating to migration of labour and prestige value of
degrees. Subject areas, such as art, are often chosen for reasons other than job
prospects/economic benefits.
A replication of a small section of the stock-and-flow model has been produced in
figure 7. This model is continuing to be
— | hiversity
enrolments graduates
Komal oa
Economic Benefits entering unskilled ae
= work
Prestiege Factor
entering skilled Skilled Employment
work
6. Further Work
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