LSC South East - lsc.gov.uk - Learning and Skills Council

A report to the Learning and Skills Council (South
East), for the Regional Infrastructure Group
(Capital Bids)
Learning Planning & Performance Team
LSC South East
Projections of postcompulsory
education
learner numbers in the
South East of England.
May 2008

Report prepared for the Learning and Skills Council by Michael Barrow,
Department of Economics, University of Sussex
Researchers: Ray Bachan, Annelle Bellony, Alvaro Monge Zegarra,
University of Sussex.
For more information, please contact:
Jan Jackson
Learning and Skills Council
Price House
53 Queens Road
Brighton
BN1 3XB
Jan.jackson@lsc.gov.uk

Projections of post-compulsory education learner numbers in the
South East of England.
1. Introduction
We were commissioned by the LSC (South East) in March 2008 to develop a model
to project forward participation in post-compulsory education, building on some
existing work within the LSC (see the Literature Review section below). The brief
asked for projections of population, participation rates and learner numbers to
2020, with a system of warnings where the forecasts were thought to carry a great
deal of uncertainty. The purpose of the report is to provide a framework within
which to make judgements about capital bids coming forward from schools and
colleges in the coming years. This is pertinent because of the recent
announcement of the raising of the compulsory participation age to 17 in 2013 and
to 18 in 2015. Subject to trends in the age cohort, this will increase the demand on
schools and colleges for education post-16. This raising of the school-leaving age
is the main factor driving our forecasts of demand for post-16 education though, as
we will show, this is moderated by anticipated changes in the population age
cohort.
We have constructed a spreadsheet model to calculate projections of learner
numbers at the level of district and unitary local authorities, and allow different
assumptions or scenarios to be tested. This report explains the workings of the
model and provides forecasts on the basis of different scenarios. One is a central
or baseline forecast, examining the anticipated effect of raising the compulsory
participation age (where we still assume a small element of non-participation, i.e.
truancy). One alternative then examines a different assumption about the growth of
work-based learning and its effect upon the forecast values. This alternative is a
realistic one but part of the purpose of including it is to show how the model can be
used to look at different scenarios. We also (in an appendix) present a third
scenario, which assumes 100% participation everywhere and thus illustrates the
(estimated) maximum possible effect of the government’s policy changes.
It is important to be aware of the purpose of the model and also of its limitations. It
is intended to provide a consistent framework for evaluating capital bids and
therefore applies the same methodology to all districts in the South East, using the
same data sources for all. It is quite possible therefore that our projections may
differ from others produced by (e.g.) local councils or colleges, where different
assumptions have been made and/or different data sources used. (The LSC is
fully aware that in some areas of the South East, some local authorities are in
discussions with central government about the reliability (or validity) of ONS data for
their particular circumstances.) However, it should then be possible to explore the
reasons for any differences and gain a better appreciation of likely future outcomes.
For example, it is possible that several colleges in a district are all independently
projecting an increase in market share and hence growth in learner numbers.
However, aggregating these would result in an unattainable overall outcome, and
this should be observable by comparison with forecasts given by our model.
2

Another limitation is the quality and consistency of the data. Where different data
sources are used there is always a danger that differences in definitions, etc. will
cause inconsistencies between different sets of figures. Even within a single data
source there can be shortcomings; for example, the DCSF tables for participation
rates report that “due to rounding error” the participation rates for Wokingham and
Reading in 2005 amounted to over 100%. Given that the figures for both 2004 and
2006 record participation rates of 91% or less, this is some rounding error. We
have endeavoured to ensure the use of consistent data and methods and we do
know that the results are at least internally consistent within our model, something
which may not occur if projections are being made by a number of different parties.
Another point to note is that our forecasts apply to 16 and 17 year olds in the year in
question. Many of the 17 year olds will reach 18 during the year and, even after the
raising of the compulsory participation age, will then be entitled to leave school or
college. Hence we will have some attrition of these students during their final year.
We do not model this effect, on the grounds that provision would have to be made
for their participation at the start of the year and this determines the required
capacity of schools and colleges.
In addition to the model, we have done a range of other research into the
background of post-compulsory education, which will be useful in interpreting the
output of the model. For example, part of this work illustrates the flows of students
across local authority boundaries (including into and out of the South East as a
whole), demonstrating that it is not sufficient only to forecast the growth of resident
learners. Some of this is possibly as valuable as the model itself, and it reveals
that although a statistical model can produce forecasts, there is still an important
element of judgement that is required when deciding upon the implications for a
particular district.
The structure of our report is as follows.
• Section 2 provides a review of relevant economics literature, identifying
some of the factors that have been found to influence the decision to stay on
beyond the compulsory school leaving age.
• Section 3 provides a brief account of our main data sources.
• Section 4 then explores a range of contextual issues using a variety of
information sources. This provides useful background material for
interpreting the output of the model.
• Section 5 then explains the methodology we have used in our model and
how we have used the model to generate forecasts.
• Section 6 reveals the results of our modelling exercise and, importantly,
shows how these may be interpreted in the light of the contextual information
reported in section 4 above.
• Section 7 provides a conclusion.
3

• Section 8 provides some appendices, with references and fuller versions of
some of the tables in the text.
A separate Executive Briefing document provides a shorter version of this report,
focussing on the contextual background, methodology and results.
4

2. Literature Review
There is a large body of UK empirical research that examines the factors that
influence the decision to participate in post-compulsory education between the
ages of 16-18. Most of these studies, often carried out by educationalists,
psychologists and economists, using different data and methodologies, find some
agreement on the factors that influence this decision (Appendix A1 provides the list
of references to the studies reported in this section. Appendix A2 provides a
summary of data and methodologies used in selected studies reviewed below). It
is important to note that there is almost universal agreement in the studies
reviewed here, inter alia, that educational attainment at 16, measured by either O
level or GCSE grades, provide an important indicator of an individual’s academic
skills and aptitude for post-compulsory education and is found to be a significant
determinant of participation rates. For ease of exposition we review the literature
under four headings that convey the general influences on the decision to
participate in post-compulsory education commonly found. These are: expected
future earnings, family background characteristics, school effects, and the effect of
government policy changes.
2.1 Expected Future Earnings
The theory of human capital is often used as a framework of analysis in many
empirical studies (see Becker, 1993). Post-compulsory education is assumed to
be an investment good and the returns to such investment, as measured by the
future or lifetime discounted earnings stream associated with post-compulsory
qualifications, is assumed, a priori, to be an important determinant of participation
in education beyond the school leaving age. Researchers using labour market
information from a variety of data sources (see Appendix A2), have constructed
variables that proxy pupils’ expected lifetime earnings streams. These include: the
future discounted earnings associated with graduate study; the future discounted
earnings associated with different occupations; and the future discounted earnings
received by different ethnic groups (see, for example, Leslie and Drinkwater, 1991;
Andrews and Bradley, 1997; Rice, 1999). The results from this literature support the
view that a pupil’s expected future earnings are significant in determining the
decision to participate in post-compulsory education. Against this, studies using
time series data over various periods since the 1950s, have suggested that higher
levels of present and future youth and adult unemployment are associated with
greater uncertainty regarding future earning streams and thereby discourage
participation (see, for example, Pissarides, 1981; Whitfield and Wilson 1991; Rice,
1999; McVicar and Rice, 2001; Clark, 2002). However, Micklewright et al (1988)
found no evidence of an association between participation rates and
unemployment in the period 1974-78.
2.2 Family Background Characteristics
A consistent finding in cross-sectional studies is that the parental and socioeconomic
background of a student has an important effect on the probability of
participating in post-compulsory education. Such influences can be transmitted
through parents’ ability to finance, encourage, and support their children in further
5

education. For instance, there is evidence that children from middle class families
are more likely to stay on than their lower working class counterparts. Halsey et al
(1980) suggests that there are two channels of influence. First, a direct route by
which professional families encourage or coerce their children to stay on. Second,
there is an indirect channel by which high ability children, who tend to be middle
class, have a greater propensity to stay on. Access to capital markets may also be
an important influence on participation with less wealthy families being constrained
in this respect. Thus socio-economic factors can indeed influence participation and
these influences may also differ according to household structure and size (e.g.
single parent household and the number of siblings), household income, ethnicity,
parental occupation and education (post-compulsory). Researchers have also
used various measures of deprivation such as local income levels, health status
and the receipt of means tested benefits, as further indicators of the socioeconomic
status of the household. Rice (1987), Micklewright (1989), Leslie and
Drinkwater (1991), Gray et al (1993), Andrews and Bradley (1997), Dearden et al
(2006) and Gorard and Smith, 2007 provide evidence on these issues. Similar
evidence is found in the time series studies cited above. Foskett and Hesketh
(1997) found evidence that children from working class backgrounds who stayed
on opted for vocational rather than academic programmes. However, it is
interesting to note that Thomas et al (2003) found that local income levels had no
significant affect on participation.
2.3 School Effects
The type of school attended (private, grant maintained comprehensive, special and
single sex), the size of the year 11 cohort, the quality of current school/college
provision (e.g. position in school ‘league tables’), and the academic reputation of
the recipient institution are often cited as key influences on the decision to
participate in post-compulsory education. These influences can manifest
themselves in the degree of support and information that that teachers give to their
pupils regarding post-compulsory education pathways or ‘trajectories’. The
available evidence also suggests that pupils from public/grant maintained schools
and single sex schools (particularly for girls) are more likely to stay on than their
counterparts without such attributes. Micklewright (1989), Cheng (1995), Andrews
and Bradley (1997), Foskett and Hesketh (1997), Rice (1999) and Clark (2002),
provide empirical evidence on these issues. Several studies have focused on peer
group effects and find a significant influence on the desire to continue (see,
Thomas and Webber 2001; Thomas et al 2003). Mangan et al (2001) found
evidence that the nature of the curriculum offered by post-compulsory providers and
the cost of transport to the nearest provider are significant in determining whether
the student remains in the same school, or switches to a new provider.
2.4 Government Policy
Participation in post-compulsory education has increased in the UK in the post war
period. The abolition of fees for state secondary education by the Education Act
1944 was a key influence on the increase in participation in post-compulsory
education in the early post WWII period. Successive changes in education policy
have been acknowledged as driving up participation rates in post-compulsory
education and training. These policy initiatives include: the increase in the school
6

leaving age from 14 to 15 in 1946 and from 15 to 16 in 1972 (Micklewright, 1989);
the provision of youth training schemes (Whitfield and Wilson 1991; Andrews and
Bradley, 1997); the introduction of GCSEs in England and Wales in 1986 and the
introduction of the national curriculum in 1988 under the Education Reform Act
1988. For instance, the introduction of the GCSEs was geared towards raising
educational achievement at 16 and improving participation in post-compulsory
education (Ashford et al. 1993; Gray et al. 1993). National targets for education and
training were also reinforced in the Dearing Report (1996) which provided further
impetus for the increase in participation in the early 2000s.
The expansion of the Higher Education sector has resulted in lower entry
requirements for higher education programmes and ‘role model’ effects across
successive cohorts have been cited as significant influences on post-compulsory
staying on rates (see, for example, McVicar and Rice, 2001; Gorand and Smith,
2007). More recently the introduction of Curriculum 2000 in England and Wales,
that gives students more choice and flexibility regarding their A level choice may
have also contributed to the recent rise in participation.
Finally, the introduction of the educational maintenance allowance (EMA) nationally
in England and Wales in 2004, for economically disadvantaged students or for
students experiencing some degree of social deprivation, has had a positive
impact on the participation rates of students with low socio-economic status
(Dearden, 2006), but may have impacted unfavourably on other learning
‘trajectories’ such as youth training (Maguire and Thompson, 2006). These
benefits are means tested and payable weekly, during school term only (for 2 years
or 3 for people with special education needs). There is also a
retention/achievement ‘bonus’ payable to those who are good attendees and meet
agreed learning targets.
2.5 Other analyses of learner numbers in the South East
We are aware of other analyses of learner numbers that have been made by or on
behalf of the LSC. Simon Winkworth for Hampshire LSC has produced a similar
analysis to ours, but differing in some details (e.g. it includes 18 year olds whereas
we include only 16 and 17 year olds) and not investigating such a wide range of
scenarios.
Sussex LSC has also commissioned work which is complementary to ours,
calculating numbers from a ‘bottom up’ approach. This is a model which is used
by Sussex LSC and individual colleges to forecast learner numbers, by using
assumptions about participation rates, progression rates, numbers of new
learners and market share. The tool allows different scenarios to be modelled
using historical evidence on specific patterns of participation in a local area and
travel to learn patterns. It includes data from FE Colleges, schools and work based
learning providers. In principle, if these were aggregated for all colleges, the
answers ought to be similar to ours. The model is not in use by all colleges so
cannot at this stage be used as a consistency check across all colleges in the
region.
7

3. Data sources
We use four main sources of data in our research, which we briefly describe here.
Other sources are noted in the text, as appropriate. The four sources are:
1. Population figures and projections. We use the ONS sub-national
population projections (SNPP) as our baseline data. See
http://www.statistics.gov.uk/statbase/Product.asp?vlnk=997 for details of
these data. This provides a consistent set of data across the South East
although it may not take account of developments contained in local plans,
etc. We discuss the implications of these omissions in section 4.2. These
data are available at county, unitary authority and district level, annually
through to 2020 and beyond. Only broad age groups are given, the relevant
one for our purposes being the 15-19 age group. We have relied on LSC
calculations of the breakdown of this broad age group into individual year
cohorts, in particular, 16 and 17 year olds.
2. Participation rates. Again we rely on official figures, specifically those
published by the Department for Children, Schools and Families in their
Statistical First Release series. These give participation rates (by different
types of provider: schools, colleges, etc) by local education authority (LEA,
i.e. the counties and unitary authorities) for the years up to 2006, which we
take as our base year. Participation rates are not provided at district level.
3. The Individual Learning Record (ILR). We use this data source to obtain
data about the numbers of learners (in colleges) within each district and
hence to estimate district level participation rates. As the database contains
both the district of the learner and of the education provider, we can also use
this source to examine movement across district borders, where a student
resident in one district attends school in another.
4. The Pupil Level Annual School Census (PLASC) serves a similar role as the
ILR, but for schools. Again, we can estimate participation rates and crossborder
flows.
In most of our analyses we focus attention on the 16 and 17 year old age groups
though in places we include 18 year olds where this is relevant or unavoidable.
Supplementary data sources are described when encountered.
8

4. Contextual information relevant to the forecasts
4.1 Population
The population projections from the ONS suggest that overall there will be a
modest reduction in the population between now and 2013, continuing to decline
thereafter to 2020. The trend for 16 and 17 year olds is shown in Figure 1:
Figure 1: Trend in the 16 and 17 year old population in South East England (000s).
225.0
220.0
215.0
210.0
205.0
200.0
195.0
190.0
185.0
180.0
175.0
Source: ONS and LSC
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Thus it can be seen that 2008 is expected be a peak in terms of population for this
age group with a more or less continuous decline through to 2020. The population
declines by 4.7% between 2006 (our baseline year) and 2013 when it is expected
that the school leaving age will be raised to 17. By 2015, the population will have
declined by 6.1% and by 2020 the decline is 7.7%.
Thus even if the participation rate rises substantially, the increase in student
numbers may be modest. For example, supposing that the 16 year old
participation rate rises from 86% in 2006 to 98% in 2013, the number of students
should rise by only 98 _ (1 – 0.047) – 86 _ 1 = 7.4%. If the 98% participation rate
holds true through to 2020, the number of 16 year olds in the system will only be
6.0% above the level in 2006. Individual authorities will, of course, vary around this
average.
The dispersion of population trends by counties is shown in Figure 2 below, where
it is evident that the trends are similar across all the counties of the SE but differ
from the experience of unitary authorities.
9

Figure 2: Trends in 16 and 17 year old population in SE Counties (000s).
45000
40000
35000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
30000
25000
20000
15000
10000
5000
0
10
Buckinghamshire
East Sussex
Hampshire
Kent
Oxfordshire
Surrey
West Sussex
Up until the year 2013, the population of 16 and 17 year olds in counties declines
gently by an average of 3.5%, the range being from West Sussex, with a roughly
constant population, to Oxfordshire, with an expected fall of 6.1%. From there
through to 2020 the population is expected to decline with all being below their
2006 level of population. Concentrating on the change in population suggests a
greater degree of disparity than suggested by the graph, which focuses on the
levels of population, and hence all counties tend to look similar.
Figure 3: Trends in 16 and 17 year old population in SE unitary authorities (000s).
8000
7000
6000
5000
4000
3000
2000
1000
0
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Bracknell Forest
Brighton and Hove
Isle of Wight
Medway
Milton Keynes
Portsmouth
Reading
Slough
Southampton
West Berkshire
Windsor and Maidenhead
Wokingham
For the unitary authorities (Figure 3) we see a different picture, with consistent falls
in population. By 2013 the population (16-17) of the unitaries is expected to drop by
8.1% on average, with falls as large as 16.6% in Reading and 13.8% in

Portsmouth. Subsequently, the fall is generally more modest, of the order of an
additional 3% points on average.
On the basis of this evidence therefore, we would expect growth in student
numbers to occur more outside the unitary authorities.
Once we look at the district level we might expect greater divergences (by the law of
large numbers), and this is true to some extent. Line charts such as those above
cannot adequately show the large number of district authorities, so we present the
population growth rates for districts in the form of bar charts. Figure 4 below shows
the histograms of the growth rates for 2013 and 2020 (both relative to 2006).
Figure 4: Trends in 16 and 17 year old population in SE districts and unitary
authorities.
(a) to 2013 (b) to 2020
Density
0 2 4 6 8 10
-.15 -.1 -.05 0 .05 .1
Population growth rate to 2013
Density
11
0 2 4 6
-.2 -.1 0 .1 .2
Population growth rate to 2020
In Figure 5 below we show a map of the South East district and unitary authorities 1 ,
coloured according to the absolute growth in population of 16 and 17 year olds over
the period 2006 to 2013. The map reveals that there are a few more authorities
with decreases (38) than with increases (25).
1 Appendix 5 contains a map with an associated list of district and unitary authorities, to assist identification.

Figure 5: Growth in numbers of 16 and 17 year olds, 2006-2013
Population growth 2006-2013
12
50 - 266
0 - 50 50 -266
-80 - 0-50 0
-100 -80 - -80 -0
-150 -100 - -100 --80
-200 -150 - -150 --100
-275 -200 - -200 --150
-420 -275 - -275 --200
-660 -420 - -420 --275
-660 --420
Table 1 below lists the authorities with the highest absolute growths of population.
Table 1: Authorities with the largest predicted increase in the 16 and 17 year old
population, 2006-2013
Authority 2006 Growth
%
growth
Ashford 2843 266 9%
Chichester 2365 136 6%
Elmbridge 3033 156 5%
Horsham
Basingstoke and
3331 128 4%
Deane 3810 103 3%
Isle of Wight 3494 92 3%
Thanet 3461 86 2%
Rother 2070 48 2%
Dartford 2360 54 2%
Runnymede 1656 33 2%
Chiltern 2373 29 1%
South Bucks 1618 11 1%
Arun 3408 -5 0%
Eastbourne 2296 -6 0%
Surrey Heath 2079 -8 0%

The full table is given in Appendix 3 as Table A3.1.
As well as individual authorities, it is useful to look at broader areas of increase
since, as we discuss later, there is much travelling across boundaries to go to
school or college. For example, Ashford and Rother are neighbouring authorities
which appear in the above table, as do Chiltern and South Buckinghamshire. It
should be noted however, that the absolute sizes of the increases are not very large
in most cases, only five districts have numbers increasing by more than 100.
In addition, we note that some of these areas are ones identified by SEEDA in its
regional plan as ‘growth diamonds’. Although this does not necessarily imply
increased population but rather areas with potential for investment and economic
growth, this may in turn draw in people from other areas. Relevant to the above list
in this context are the growth diamonds in Basingstoke, and Gatwick/Crawley. We
say more about the growth diamonds and related issues later on.
It is also interesting to note that many of the areas on (but within) the South East
border, have quite low predicted increases in population, particularly around
London. Only Dartford, Elmbridge and South Bucks are listed in Table 1 above.
Authorities on the border are often recipients of students from outside the South
East and can therefore face additional pressures, especially on the borders of
London. We analyse cross-border movements in a later section.
4.2 Other sources of information regarding population projections
The ONS figures for population are trends which may not reflect all the information
available at a more local level, such as planned new towns or housing
developments, etc. (The ONS figures do take account of population migration both
internationally and internally, though it is recognised that there can be a great deal
of uncertainty about some of these data.) We have therefore researched local
authority web sites, the South East Plan, SEEDA’s Regional Economic Strategy and
other possible sources of information to supplement the ONS projections.
From the South East plan
(http://www.seeda.co.uk/Publications/Developments_&_Infrastructure/docs/
RegionalHousingStrategy2006.pdf we have obtained data on the planned average
annual growth in the housing stock. Multiplying this by 11 we get the growth up to
2015 (from our base year of 2004 for which we have the housing stock). We can
therefore calculate the expected growth rate of the housing stock, with which we
can compare to our growth rate of population. Where the former is significantly
greater than the latter, we might have cause to doubt the ONS population
projections.
Table 2 shows the authorities which have the largest anticipated growth of the
housing stock and compares this to their projected population growth figures. (The
full table is given in Appendix 3, Table A3.2.)
13

than those projected by ONS. The figures for Milton Keynes and Aylesbury Vale are
also consistent with information in SEEDA’s Regional Economic Strategy 2006-
2016, which suggests additional housing of 70,000 by 2031 (MK) and 18,300 by
2021 (AV). We will make use of the information in this table as a form of ‘traffic
light’ warning when interpreting the results from our modelling, which only uses the
ONS projections.
The above information relates to the population (particularly 16-17 year olds) rather
than the number of learners. Hence we also need to examine the participation rate
and how this is expected to unfold in the future. We therefore now turn our attention
to a review of participation rates.
4.3 Participation rates
The main policy driver of likely future changes in post-16 participation rates is
legislation, with the government recently (May 2008) announcing an increase in the
legal school-leaving age, to 17 in 2013 and 18 in 2015. These proposals therefore
form the centrepiece of our forecast and these are the main drivers of changes in
participation rates. First however, we look at some of the evidence regarding
trends in participation rates in the recent past.
From a rate of around 10% in 1950, post-compulsory participation increased
steadily until reaching a rate of around 85% by 2006 2 . The experience since 1985
is shown in Figure 6 below.
Figure 6: Participation rate (1985-2006) in education and training of 16 and 17
year olds in England
90.0
85.0
80.0
75.0
70.0
65.0
60.0
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
15
2001
2002
2003
2004
2005
2006
The main features are the steady growth from 1985 to 1995, but a level
performance since then. Clark suggests that rising unemployment prior to 1993
2 See Clark (2002) for more detail.

could explain part of the growth, with falling unemployment after 1993 offsetting the
positive effects on participation of increasing examination success (at GCSE level).
The perception of generally increasing economic returns to education is also likely
to encourage participation (see our literature review above) and it is perhaps
surprising that the participation rate has stagnated.
To obtain a more nuanced picture of the recent trends in participation we examine
briefly the data at education authority level across the whole of England. For this we
use data from the DCSF/DIUS website
(http://www.dfes.gov.uk/rsgateway/DB/SFR/s000734/index.shtml). We will look at
the full-time, part-time and WBL participation rates but we start off with the total
participation rate, aggregating all three of these categories. We examine the data
from 1998 to 2005 (for which the data are complete and consistent).
4.4 The total participation rate
We begin by looking at 16 year old students. The overall participation rate rises
only slightly, from 83.5% to 86.0%, over the seven years. The distribution across
authorities tells an interesting and not unexpected story: the distribution is
squeezed from below as the poorer performers catch up and, of course, the rate is
limited above by 100%.
Figure 7: Distribution of growth rates of participation by 16 year olds, English
education authorities, 1998 and 2005
(a) 1998
Density
Density
0 .02
0
.02 .04 .04 .06 .06 .08
(b) 2005
60
60
70
70
80
tt16_98
80
tt16_98
90
90
100
100
16

Density
.05 .1 .15
0
60 70 80 90 100
tt16_05
An alternative way of looking at the same data is via a multiple box plot. From left to
right (1998 to 2005) we observe a fluctuating average participation rate but with a
steadily declining spread (Figure 8 below). (Note: The central box of the box plot
shows the central 50% of the data, i.e. between the first and third quartiles. For
1998 this can be seen as lying between 79 and 88, approximately. The horizontal
line within the box represents the median – the value at the centre of the
distribution. This would be the local authority in the middle of the distribution of
participation rates. The ‘whiskers’ extending above and below the box contain all
‘reasonable’ values and beyond the whiskers lie the extreme values or outliers,
represented by dots. The height of each whisker is, by convention, 1.5 times the
height of the box.)
Figure 8: Box plots of 16 year old total participation rate, 1998-2005
60 70 80 90 100
tt16_98 tt16_99
tt16_00 tt16_01
tt16_02 tt16_03
tt16_04 tt16_05
17

We can perform the same analysis for 17 year olds, capturing the relevant features
of the data in similar box plots:
Figure 9: Box plots of 17 year old total participation rate, 1998-2005
50 60 70 80 90 100
tt17_98 tt17_99
tt17_00 tt17_01
tt17_02 tt17_03
tt17_04 tt17_05
We observe a similar pattern, though the average participation rate is lower: 75.8%
in 1998 and 76.2% in 2005. It is interesting that, for both ages, the distributions are
fairly symmetric (the horizontal bar representing the median is roughly in the middle
of the box). One might have expected many authorities clustered at a high level,
then a longer tail of lower achieving authorities, but this does not appear to be the
case.
We can look in the same way at the individual components of the overall figure.
18

4.5 Full time participation rates
Figures 10 and 11: Box plots of 16 and 17 year old full time participation rate,
1998-2005
50 60 70 80 90
ft16_98 ft16_99
ft16_00 ft16_01
ft16_02 ft16_03
ft16_04 ft16_05
19
40 50 60 70 80 90
ft17_98 ft17_99
ft17_00 ft17_01
ft17_02 ft17_03
ft17_04 ft17_05
There is a more clearly discernable upward trend to the full time figures and there
is a consistent difference of around 12% points between the figures for 16 year
olds and 17 year olds. Since the total participation rate drops by 8-10% between 17
and 16 year olds, it follows that much of the decline in full-time participation results
in non-participation rather than a switch to part-time or WBL.
4.6 Part-time participation rates
Similar box plots are drawn below to illustrate the trends in part-time participation
rates.
Figure 12 and 13: Box plots of 16 and 17 year old part-time participation rate,
1998-2005
0 10 20 30
pt16_98 pt16_99
pt16_00 pt16_01
pt16_02 pt16_03
pt16_04 pt16_05
0 5 10 15 20 25
pt17_98 pt17_99
pt17_00 pt17_01
pt17_02 pt17_03
pt17_04 pt17_05
Here we can observe the small shift out of full-time into part-time education at age
17, where the part-time participation rate is 1.5% to 2% points higher than the rate
at 16. The trend over time is clearly downwards which is partly due to the
disappearance of very high rates (over 20%) in some areas in the late 90s.
(Kingston upon Thames and Sutton are the only such areas in the south east
region.)

4.7 Work-based learning
These graphs demonstrate a similar pattern to part-time learning, with a decline
over time and a small jump up from the rate at 16 to that at 17. The jump is again
of the order of 2% points. WBL is slightly more popular than other forms of parttime
learning, though the orders of magnitude are fairly similar. It may be
reasonable to consider these two categories together for some purposes, as there
is likely to be considerable variation within each category (e.g. in terms on numbers
of hours in education).
Figures 14 and 15: Box plots of 16 and 17 year old WBL participation rate, 1998-
2005
0 5 10 15 20 25
wb16_98 wb16_99
wb16_00 wb16_01
wb16_02 wb16_03
wb16_04 wb16_05
20
0 5 10 15 20 25
wb17_98 wb17_99
wb17_00 wb17_01
wb17_02 wb17_03
wb17_04 wb17_05
4.8 Summary of findings for population and participation trends
For the purposes of our modelling exercises we have learned the following stylised
facts, which will inform the assumptions we make for the future regarding
participation rates:
The overall participation rate has not changed much over the recent past
There has been an increase in full-time participation and a fall in parttime
participation, the latter due largely to falls from high levels in some
authorities.
There is a fall in participation between 16 and 17, a small part of which is
a switch from full-time to part-time education.
Combining these findings with the falling size of the 16-18 cohort over time, it
suggests that without a change in external factors we would not expect the
numbers of students participating post-16 to increase. Increases in numbers must
come from increases in participation driven by exogenous events, such as
changes in legislation (the school-leaving age) or the curriculum (the new
diplomas), etc. The legislation regarding the school leaving clearly dominates
other events so this is the focus of our forecast.

4.9 Travel to learn patterns
The participation rates we will calculate from our model are on the basis of
residence, but these figures do not indicate where students actually attend school
or college as there may be substantial cross-border movement. We can gain
insight into such movements by analysis of the ILR (college) and PLASC (school)
databases, using data for 2006-7. These contain, for each student, both the local
authority of the residence and the local authority of the education provider. We can
therefore work out how many students each authority is ‘exporting’ and ‘importing’
as well as the numbers attending school or college within their own local authority.
Not only can we observe cross-border transfers within the South East, we can also
observe transfers across the South East boundary with the rest of the country.
Large numbers of students crossing district boundaries imply a need to be more
careful about translating any increases in participation by residents into a need for
additional supply within the same authority, especially as this pattern of transfers
may change over time.
Transfers across the South East boundary
First we look at the transfers into and out of the South East as a whole. The
numbers of transfers can be seen in Table 3 below.
Table 3: Numbers of students crossing the South East border – 16 and 17 year
olds
Number
of
learners
resident
in the SE Exports Imports
21
Net
imports
Number
taught
in SE
Net
imports
as % of
learners
Full-time -
school 56,772 46 3,196 3,150 59,922 6%
Full-time -
college 74,392 2,119 7,560 5,441 79,833 7%
Part-time 22,015 3,033 2,294 -739 21,276 -3%
Total 153,179 5,198 13,050 7,852 161,031 5%
We see that the SE region is a net importer of full-time students, to the order of
6.5%. This adds a total of about 8,600 full time students to the total taught, with
slightly more than half of these in college rather than in secondary school. For parttime
education, there is modest export of about 3% of the students. Not
surprisingly, colleges engage in more trade than schools.
The authorities which are the biggest importers across the SE boundary are shown
in Table 4 below.

Table 4: Numbers of students imported from outside the South East – 16 and 17
year olds (authorities with 5% or more imports)
LA
Schools
ILR
imports
- full
time
22
ILR
imports
- part
time
Total -
full
time Total - all
Population,
16 & 17 yo Imports Imports Imports Imports Imports
% of
population
Epsom and
Ewell 1863 340 679 203 1019 1222 66%
Dartford 2360 438 729 150 1167 1317 56%
Elmbridge 3033 75 1077 85 1152 1237 41%
New Forest 4196 180 1066 172 1246 1418 34%
Reigate
and
Banstead 3165 78 662 113 740 853 27%
Tandridge 2362 549 8 0 557 557 24%
Slough 3165 241 269 139 510 649 21%
Chiltern 2373 198 138 37 336 373 16%
Chichester 2365 0 237 111 237 348 15%
West
Oxfordshire 2628 56 321 3 377 380 14%
Runnymede 1656 1 174 14 175 189 11%
Spelthorne 2115 75 122 22 197 219 10%
Gravesham 2750 19 118 101 137 238 9%
Hastings 2361 6 17 179 23 202 9%
Guildford 3493 10 130 157 140 297 9%
Swale 3605 53 225 2 278 280 8%
Thanet 3461 16 224 7 240 247 7%
Milton
Keynes 6096 72 243 79 315 394 6%
Cherwell 3587 20 131 37 151 188 5%
Tunbridge
Wells 3102 141 4 11 145 156 5%
Tonbridge
and Malling 3362 50 49 70 99 169 5%
Aylesbury
Vale 4867 103 62 45 165 210 4%
Figure 16 illustrates these figures on a map of the South East, for schools and
colleges separately. Note that the map illustrates the actual number of students
imported, not imports as a percentage of the resident population.
Figure 16: Students (16 and 17) received from outside the South East
(a) Schools (all full time)

Figure 16: Students (16 and 17) received from outside the South East
School students received from outside SE
(a) Schools (all full time)
Schoolstudentsreceived from outside SE
23
Numbers
45 - 549
11 - 45
2 - 11
0 - 2
Numbers
45 -549
11 -45
2-11
0-2

(b) Colleges (full time and part time)
College students received from outside SE
24
Numbers
Numbers 168 - 1238
168 42 -1238 - 168
42 16 -168 - 42
16 0 -42 - 16
0-16
Imports into schools are heaviest on the northern borders of the SE, especially
around south west London. The college map shows a more even geographical
distribution, with fewer transfers into the SE amongst some of the boroughs
bordering London. Boroughs which are not on the boundary generally do not
receive large numbers of students from outside the SE (one exception is
Chichester, where large numbers from outside the SE attend Chichester College).
In contrast, exports (which are almost exclusively from colleges, not schools) are
more evenly spread and no authority exports more than 7% of 16-17 year olds
outside the SE, apart from Spelthorne (12%). Figure 17 below shows the map of
exports (again, this is numbers of students).

Figure 17: South East students (16 and 17 full time and part time) taught outside
the region
SE students taught outside SE
25
N
Numbers
um bers
94
94
-451
- 451
55 - 94
55 -94
31
31
-55
- 55
14
14
-31
- 31
Once again we see, not surprisingly, most exports from the border authorities.
(Note that exports are smaller than imports – the dark blue areas imply exports of
between 94 and 451 students, whereas for college imports the dark blue areas
represent 168 to 1238.) The authorities with the largest exports are as follows
(remember these are all from colleges, not schools):
Table 5: Exports of 16-17 year old pupils to outside the South East
Population
Full
time
Part
time
All
exports
% of
population
Milton Keynes 6096 209 242 451 7%
Portsmouth 4726 185 120 305 6%
Spelthorne 2115 194 68 262 12%
New Forest 4196 129 106 235 6%
Cherwell 3587 105 66 171 5%
Slough 3165 103 67 170 5%
Vale of White
Horse 3468 110 49 159 5%
Aylesbury Vale 4867 71 74 145 3%
Havant 3244 89 51 140 4%

Southampton 5035 9 121 130 3%
Elmbridge 3033 91 33 124 4%
Dartford 2360 41 75 116 5%
Sevenoaks 2984 62 52 114 4%
Epsom and Ewell 1863 63 39 102 5%
We conclude that imports are a more important feature than exports when
forecasting student numbers; not only are imports larger, they are also more
concentrated in a few authorities. It is also noticeable that, unsurprisingly, it is the
colleges which have higher proportions of students from outside the SE.
These transfers do not have large implications for our forecasts if the patterns of
transfer remain constant, e.g. if the growth of population and participation rates of
authorities outside the South East are similar to those inside. However, if these
rates diverged or, for instance, if one of those authorities outside the South East
were to build a new college, then we might see a change in the patterns of transfer.
Transfers within the South East
We now turn to patterns of movement within the South East. Table 6 shows the
authorities who are most affected by trade across their borders, either importing or
exporting within the SE authorities (not across the SE border).
Table 6: Numbers of students moving between local authorities – 16 and 17 year
olds (extremes of the distribution)
Schools Colleges
Resident Net Resident Net
Total
net % of Number
LA
learners imports learners imports imports learners taught
Rushmoor 113 -113 1610 2697 2584 150% 4307
Elmbridge 422 253 1190 1885 2138 133% 3750
Havant 144 31 2327 2805 2836 115% 5307
Dartford 738 603 783 959 1562 103% 3083
Chichester 654 159 1249 1759 1918 101% 3821
Epsom and Ewell 621 330 507 794 1124 100% 2252
Canterbury 1515 220 1056 1691 1911 74% 4482
Guildford 895 257 1219 1263 1520 72% 3634
Winchester 18 -18 2313 1689 1671 72% 4002
Tonbridge and Malling 1435 221 850 960 1181 52% 3466
Eastbourne 20 -20 1536 819 799 51% 2355
Chiltern 1387 508 468 424 932 50% 2787
M M M M M M M M
Portsmouth 27 -27 3325 -1777 -1804 -54% 1548
Spelthorne 183 28 1366 -897 -869 -56% 680
Adur 378 -82 720 -561 -643 -59% 455
Surrey Heath 648 -39 986 -962 -1001 -61% 633
Arun 1010 -116 1635 -1594 -1710 -65% 935
Test Valley 8 -7 2272 -1533 -1540 -68% 740
Sevenoaks 1190 -885 693 -688 -1573 -84% 310
Hart 228 17 1552 -1541 -1524 -86% 256
26

Thus Rushmoor, proportionately the largest importer, has 1723 resident learners,
most in colleges somewhere. There are also net imports of 2584 (the balance of
those residents educated outside Rushmoor and non-residents educated within
the borough), which amount to 150% of the resident learners, making the number
taught much larger than the number of resident learners. In contrast, Hart exports
most of its learners, i.e. most go to college outside the borough. The full table is
given in Appendix 3, table A3.6(a) and A3.6(b).
It might be thought that the percentage exported would be inversely proportional to
the number of resident 16-17 year olds, on the grounds that ‘small’ authorities are
less able to provide sufficient educational opportunities within their boundaries.
However, this is not the case as there appears to be no relationship between the
variables (correlation coefficient = 0.018).
The average value of trade (i.e. the average value of net imports, ignoring the minus
signs) is 17% and gives the number of students crossing the LA boundary (in
either direction), as a proportion of the resident population. This figure is larger if
we compare it to the proportion of 16-17 year olds in full-time education (61% on
average). Thus in the typical authority, about 28% of full time learners are crossing
LA boundaries.
In terms of the need to supply education, it matters little if the imports to an authority
are from another authority inside or outside the SE. Hence Figure 18 shows the
figures in terms of numbers of imports from whatever district, with the darker blue
areas indicating a higher level of net imports. This shows a variegated pattern, with
less of a ‘SE border effect’.
27

Figure 18: Net imports of students by local authorities
Net imports between LAs
4.10 Implications of travel to learn patterns for forecasting
28
Numbers
800 800 - 3200
0
0-800
- 800
-550 -550 - 0
-2000 - -550
The overall evidence suggests that calculating population growth rates and
participation rates for district level authorities is only part of the information
required. We also need to take account of travel patterns, as illustrated in Figure 18
above. The interpretation could be ambiguous however. If we observe a projected
increase in learners in a district that typically exports many of its learners, we could
either conclude that the increase will likely be met by provision outside the borough,
or that there is an additional argument for provision within the borough itself. This
is, of course, a policy judgement. For authorities with large imports, the number
taught will be larger than the resident population, possibly significantly so. Hence a
large percentage increase relative to resident learners might not be so large when
compared to numbers taught.

5. Modelling
Having written in some detail about the context of future participation rates we now
move on to describe how we have modelled future events and then present our
results. First we briefly describe the data used.
5.1 Population data
As described earlier, we rely upon ONS sub-national population projections. As far
as data availability goes, published figures give population from the present up to
2020, at the level of the district, by broad age bands (15-19 is the relevant age band
in this case). From this we need to extract estimates of the numbers aged 16,17
and 18. Unpublished ONS figures giving this breakdown were provided by the LSC
and we use these estimates without further adjustment. It is worth noting that the
ONS census date is in January whereas the census date for participation data is
August 31 st . However, this is likely to impart only a small bias to the numbers and,
in any case, would cancel out when calculating growth figures.
5. 2 Participation rates data
We described earlier that we use data from DCSF on participation rates, which are
only available at county/UA level, not district level. Hence part of our modelling
procedure is to disaggregate this to district level.
Future numbers of learners are by definition equal to the relevant population
multiplied by the participation rate. Since we have population projections, our task
is to forecast the participation rates.
5. 3 Forecasting participation rates
For participation rates, we have data for 2006 and earlier at the level of counties
and unitary authorities, but not for the districts. We therefore need to (a) find a way
of estimating 2006 participation rates for districts and (b) projecting forward the
participation rates.
Our methodology to calculate future participation rates is as follows:
1. Estimate participation rates for districts in 2006, varying around the relevant
county averages.
2. Disaggregate those participation rates into full time school, full time college,
part time, work-based learning and the independent sector.
3. Estimate the overall participation rates for 2013/2015 for each district
4. Estimate the overall participation rate for intervening years
5. Project forward the component parts (full-time, etc) of the overall
participation rate, consistent with the overall rate in each year.
6. Project forward beyond 2013/2015.
29

At various stages of the calculations we need to ensure consistency, e.g. that the
participation rates estimated for districts in 2006 are consistent with the published
overall participation rates for the counties, or that the projections for full-time, parttime,
etc. participation rates add up to the overall rate in each year of the forecast.
In effect, this means that one category must always be a residual, i.e. the amount
needed to be consistent with the total for all categories. This implies that any
errors in estimating one category will have corresponding off-setting errors in
another. A further implication is that we can generally have greater confidence in
the estimate of a total than in the estimate for a component category. This should
be borne in mind when interpreting the results of the model. We now explain in
more detail each stage of the forecasting procedure.
1. Estimating district participation rates in the South East in 2006
After some experimentation with different methods we decided the best approach
is to use information from the ILR and PLASC databases. Using these, we obtain
a count of the numbers of full time and part time learners (in both schools and
colleges) in each district. Dividing these by our population estimates gives an
estimate of the district participation rate. Our method can be verified by the fact that
it gives estimates for counties and unitary authorities which are close to those
reported by the DCSF. We can therefore have some confidence that the method
provides reasonably accurate estimates at the district level also.
Hence the participation rate for a district can be calculated as
p
p
district = county
2006
2006
p
p
district
county
PLASC
This is probably best explained by example. To calculate Aylesbury Vale’s
2006
participation rate, we first take the published rate for Buckinghamshire ( p county =
0.720). We then look at the PLASC and ILR data which suggest the district
participation rate for Aylesbury Vale is 0.635 and the county rate is 0.692. Note that
there is a slight discrepancy between the DCSF figure and the PLASC figure for the
county. The PLASC/ILR data suggest that Aylesbury Vale’s figure is about 92% of
the county figure (0.635/0.692). We therefore estimate the district participation rate
0.
635
as 0 . 72
= 0.
660 or 66%. This gives us a figure which is consistent with the
0.
692
published DCSF figure for the county. (This example is for 16 year olds in full time
education.)
Note that, if the DSCF and PLASC/ILR data give the same county participation rate,
the estimated participation rate we use is precisely the PLASC/ILR figure. We only
make adjustments because there are discrepancies between the two sources, and
these are generally quite minor.
Applying this method to all districts in turn, we can estimate the overall district
participation rates. Note that, although the PLASC.ILR data covers full time and part
time learners, it does not include the independent sector, which is included in the
DCSF figure. We assume the full time rates as calculated from PLASC/ILR are the
30

est measure for estimating the overall participation rate, although there will be a
small degree of inaccuracy using this method.
2. Disaggregating the overall participation rate for 2006
For full time students either in school or college, we calculate the participation rate
in a similar manner to step 1 above. We know the full time school participation rate
for Buckinghamshire from the DCSF data. Using the PLASC or ILR data we know
that the district rate for Aylesbury Vale differs from the county average by a certain
amount. We can therefore work out the district participation rate, consistent with the
DCSF county figure.
For part time students we apply the same method, except that we use part time
participation rates in the ILR data (there are virtually no part-time students in the
PLASC data).
For work-based learning we assume the patterns of WBL are similar to patterns of
part time learning. We use the same methodology as for full time and part time,
except that we have to use ILR data from part time data to generate the district
participation rates consistent with (and varying around) the county WBL
participation rate reported by DCSF.
There only remains the independent sector, about which we have very little
information. We therefore calculate the independent school participation rate as a
residual. It is calculated as:
Independent rate = overall rate – full time rate – part-time rate – WBL rate
Since it is calculated as a residual, any errors in the calculation of the other
participation rates will affect the independent rate. Since the full time rate in
particular is much larger than the independent sector rate, a small error in the
former could result in a relatively large error in the latter. For example:
True rate Estimated
rate
Full time rate 65 63
Part time 5 5
WBL 5 5
Independent 10 12
The underestimate of the full time rate is just 3% (=63/65 – 1) but the resulting
overestimate of the independent rate is 20% (=12/10 – 1). However, the
independent sector is not the central focus of this study, so we are not so
concerned about errors (which are probably quite small in absolute value) in this
sector.
We comment more generally on some of the approximations in our data at the end
of this section.
31

3. Estimate the overall participation rates for 2013/2015 for each district
For modelling purposes, we presume that the school leaving age is raised to 17 in
2013 and to 18 in 2015, in line with government proposals. We interpret
‘compulsory schooling’ to mean a participation rate of 98% rather than 100%, on
the grounds that even if compulsory, there will be a minority who will not participate.
Evidence from DCFS (Statistical Release: Pupil Absence – Autumn 2008 Term
Report (Provisional), 6 May 2008) suggests that 0.7% of pupils in the South East
may be regarded as persistent truants, this figure ranging from 0.4% in
Buckinghamshire to 1.3% in Southampton. This figure applies across the
secondary sector and it is therefore likely that the figure is higher towards the end of
compulsory schooling. We therefore feel justified in assuming a figure of around
2% once the age is raised to 17 and then 18.
(We also report the outcome of assuming a 100% participation rate in all
authorities as one alternative scenario, though we believe this is less realistic than
our central projection.)
Not all authorities will be at precisely 98% however, so we allow authorities to vary
around this figure in 2013/2015. We calculate the future overall participation rates
as follows. We have the 2006 participation rates for district and unitary authorities
from steps 1 and 2. We then assume that participation rates grow in line with the
patterns identified earlier for 1998-2005, with lower participation authorities (in
2006) following a faster rate of growth. There is a convergence of participation
rates from the bottom up. Hence we specify a narrowing of the range of
participation rates over the period. For 2006 we have a mean participation rate of
86% and a range of 35% points (between 65% and 100%). For 20013, we set the
mean to 98% and the range to 17% points (half its value in 2006) for 16 year olds.
In schematic form we have the following mapping of participation rates:
2006
100%
86%
65%
100%
98%
2013
83%
The halving of the range is a matter of judgement, based on the observed
diminution of the range between 1998 and 2005 and the need to be spread around
a higher average of 98%. Again, this assumption can be adjusted within the
spreadsheet to explore alternative scenarios.
32

The precise formula we use is
2013
pi = 0.
98 + i
2006 ( p 0.
86)
0.
17
0.
34
2006
pi 0.
86 gives the deviation of authority i from the SE average in 2006.
This is then scaled by 0.17/0.35, which is the shrinking of the range, to
give a smaller deviation…
… around the 2013 mean of 0.98.
Example: Aylesbury Vale, with a 2006 participation rate of 0.798 gets mapped onto:
0.
17
0 . 98 +
= .
0.
35
( 0.
798 0.
86)
0 942
We apply this formula to each authority to determine its 2013 participation rate for
16 year olds. For 17 year olds we adopt the same methodology, except that we
assume the 98% rate is achieved in 2015.
4. Estimate the overall participation rate for intervening years
In our original proposal we modelled the progression of participation rates over
time as a logistic curve (an S-shape). Our implementation is actually slightly
different, and simpler. One reason for this is that participation rates for most
authorities are already fairly high (they are near the top of the S) and the logistic
curve is fairly straight in this region. Therefore there is very little difference between
a logistic curve and a straight line interpolation. The second reason is that our new
method is easier to implement and adjust in a spreadsheet model.
The difference between the two methods is quite small in practice. Compared to
the logistic method, our implementation has a slower increase in the early years
after 2006 but speeding up later on. This might turn out to be a more accurate
method if the main upward pressure on the participation rate comes from the
change in legislation around 2013.
In our implementation therefore, the participation rate grows at a constant rate each
year, between 2006 and 2103 (2015 for 17 year olds). The actual formula used is:
t+
1
i
t
i
( 1 g )
p = p +
i
Where g i is the rate of growth for authority i each year, calculated as
Example:
33
1 7
2013 p i
g =
2006
i
.
pi

Buckinghamshire’s participation rate goes from 87% to 97.2%. The average rate of
1 7
0.
972
growth of the participation rate is therefore = 1.
016 , or by 1.6% each year
0.
87
over the seven year period. Hence the participation rate in 2007 is calculated as
0.87 1.016 = 0.884. For 2008 it becomes 0.886 1.032 = 0.899, and so on.
5. Project forward the component parts of the overall participation rate
We now need to project forward the component parts of the overall participation
rate, in such a way as to be consistent with the overall rate. The simplest method
would be to assume that each component retains its share of the total, however
this is unlikely to be correct and we can do better than this.
We make a number of (we believe) reasonable assumptions about future trends in
the components of participation. These can obviously be challenged and it is
simple to examine the effects of alternative assumptions in the spreadsheet
model. In particular, we assume:
a) The independent share of learners remains constant. The policy driver of an
increase in the school leaving age is unlikely to affect the independent
sector.
b) The work-based learning participation rate increases by 50%. We base this
assumption on the government’s response to the Leitch Report 3 which sets
a target of a doubling of apprenticeships by 2020. If the implied rate of
growth is taken to 2013 it implies an increase of 50%.
c) We assume the part time participation rate remains constant. There is not
much basis for any particular assumption, but it is in line with the data from
1998-2005 and it is unlikely to grow given the policy driver makes education
compulsory and presumably full time for most.
d) The full time participation rate therefore becomes the residual component
and accounts for most of the growth (apart from WBL) in overall participation.
6. Projections post 2013/2015
These years are simply set to be the same as for 2013 (for 16 year olds) or 2015
(for 17 year olds). It is possible that the participation rate will eventually climb
above 98% and there might be continuing convergence, but the differences are
likely to be small and any estimates rely on a substantial degree of speculation.
Commentary on the general quality of the data and caveats for our results
Undertaking this project we encountered difficulties with some aspects of the data
and with combining it with our methodology, which suggest some limits to the
3 Prosperity for all in the global economy – world class skills (HM Treasury 2006).
34

accuracy of the results. This is not unique to our chosen methodology, but applies
generally as long as we have the current data.
We make our remarks under three headings:
Missing data
Accuracy and compatibility of the data
Use of the data with our methodology
Missing data
In some places we would want to have more information, but it is simply
unavailable. For example, we have no data on numbers in private education (by
residence), or in work-based learning at the district level. We therefore have to
make estimates, which may be inappropriate. For example, for work-based
learning we assume that it is similar in distribution to the pattern of part time
learning. This is an untested assumption. As a significant part of growth is
expected to come from WBL, this uncertainty about the starting point is problematic.
Accuracy and compatibility
We make use of information from various sources and it is not always clear that
they are compatible (e.g. whether they use the same definition of variables). We
might illustrate this problem by noting that the DCSF’s own participation figures are
not without fault. The published rates for 2005 include a statement that says “Due
to the margin of error surrounding local level participation estimates and the use of
school level data for independent schools, participation rates can be over 100 per
cent. For these areas, an asterisk is placed in the table.” This is the case for both
Wokingham and Reading and is especially surprising the published participation
rates for 2004 are 83% and 90%, and for 2006 are 91% and 91%. Hence it is
remarkable that a margin of error seems to lead to a change of over 10% points.
These are unitary authorities with education departments, so the task of estimating
such rates for districts which are not themselves education authorities should be
much harder.
As explained earlier, we use PLASC and ILR data to disaggregate participation
rates to district level. We believe that there is a risk here that these data sources
exaggerate the variability of the participation rate within counties, but we have no
other data to test this hypothesis against. The reason for our suspicion is that the
variability suggested by the PLASC/ILR data implies some districts have
participation rates greater than 100% (this is because other districts have low
rates, and we need them to average out at the known county rate). We found this
applied particularly in Oxfordshire and may be due to the unusual nature of the
education ‘market’ in that county, with the presence of the university and perhaps,
private schools. Since participation rates cannot rise above 100% we have had to
make ad hoc adjustments to our data to ensure our numbers are feasible. (We do
this, for example, by assuming one district within a county has a similar
participation pattern to another of similar size). We have only had to do this for
Oxfordshire but the problem may also occur on a lesser scale in a few other
districts.
35

The implication of this is that there are off-setting errors: if one district’s rate is
under-estimated, another’s must be overestimated. Since, over time, these are
converging towards compulsory participation in 2023/2015, this implies off-setting
forecasts of growth in numbers: if it is under-estimated in one district, it is likely to
be over-estimated in another within the county.
Use of the data with our methodology
Any methodology of forecasting, not just ours, has to be internally consistent when
it comes to the forecast. The numbers for a district must add up to the total for the
county, and that the sub-components must add up to the overall participation rate.
This means that, inevitably, some items have to be treated as a residual, and
calculated as such. This can result in large proportionate errors in this residual,
particularly if it is a small item. This may not matter too much if the item is not of
interest per se, such as the numbers in independent schools. However, the growth
of this from the estimated value in 2006 will then influence the values of other
variables and lead to uncertainty in the forecast.
Overall, we believe we have made the best use that we can of the data available.
Our model’s estimates are at least internally consistent. However, it would be
useful if further research could shed light on some of the issues where data are
missing or where there are puzzling anomalies between data series.
A comment on NET/NEET data
It would be useful to make use of NET/NEET data (Not in Education or Training/Not
in Education, Employment or Training) in construction of the participation rates, or
as a check. However there are some formidable problems of consistency between
the various series. The figures are published by DCFS in the Statistical First
Release Series but are not disaggregated to the local level. This can be done
using data from the Connexions service, but this is not directly comparable to the
SFR series due to differences of definition of the series and because Connexions
is only aware of some, not all, persons within the NET category. The local
breakdown is available for the NEET series, but to be compatible with our series
we should use the NET series, which is the complement of our participation rate.
Furthermore, the NEET rates are based on the numbers in schools and colleges in
each district, not on the basis of residence, which is what is used in this report.
36

6. Results
The result of our modelling are contained in our spreadsheet model and there are
many outputs which could be produced (e.g. for different age groups, or exploring
different scenarios), so we will present a ‘central’ forecast here. Alternative outputs
can be explored by the user of the spreadsheet model.
Table 7 below presents our estimates of learner numbers (16 and 17 year olds
combined), by district and unitary authority, for the years 2006 and 2013. We break
these figures down by school (full time), college (full time), independent school,
part time (in colleges, not schools) and WBL. It is difficult to comment on particular
details as there are so many different numbers; however, there are a couple of
points worth noting:
1. The greatest reliance can be placed upon the figures for total learners. Once
we break this down into its component parts we introduce another element
of estimation and hence of uncertainty. Hence the work-based learning
numbers increase proportionately more than the other categories because
of the government’s targets for increasing this category. The independent
and part-time learner numbers decline in some districts, because we
assume a constant participation rate for these, coupled with a declining
population size. The school and college numbers then make up the
residual numbers. Any errors in our assumptions (e.g. regarding WBL
participation) will affect all the categories.
2. The growth figures should be read in the context of the travel to learn
numbers that we derived earlier and also the earlier comments about
housing. To incorporate these, Table 8 reports the authorities with the
largest projected increases in learner numbers, along with the figure for net
imports calculated earlier, plus the figure for projected new housing, where
this was significantly higher than the population projection. Thus it is
interesting to compare Milton Keynes and Elmbridge for example, at the top
of the table. The projected increases in school plus college numbers are
fairly similar, but Elmbridge also has a large net import of students, whilst
Milton Keynes has a very slight net export of its resident students. The
increased demand in Elmbridge could be met therefore by increased
provision, or it could be met by increased provision in neighbouring
boroughs, reducing the imports into Elmbridge, which would leave room in
Elmbridge for its own increased numbers. (Note that the Net imports figure
is a per annum figure, while the other numbers refer to increases over time.
Hence care has to be taken in interpretation, one cannot simply compare
these numbers.) Note also that our projection for Milton Keynes might be an
underestimate if the figure for housing growth proves accurate.
Thus we see that judgement is needed when interpreting the figures and that, in
particular, one cannot simply look at a district in isolation.
37

2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Aylesbury Vale 2046 852 116 232 319 3565 2440 1016 111 222 437 4226 394 164 -5 -10 117 661
Chiltern 1412 341 246 68 92 2158 1545 373 248 69 134 2369 133 32 3 1 42 211
South Bucks 831 277 91 70 96 1364 961 320 91 70 138 1581 130 43 1 0 42 217
Wycombe 2160 917 338 157 217 3789 2255 957 315 147 289 3962 95 40 -23 -11 72 173
Bracknell Forest 906 914 528 188 103 2639 1019 1029 463 164 126 2802 113 114 -65 -24 23 163
Milton Keynes 2572 1563 61 214 367 4778 3033 1843 60 210 512 5657 460 280 -1 -4 145 879
Cherwell 1118 1008 709 183 169 3188 1203 1083 681 176 232 3375 84 76 -28 -7 62 187
Oxford 1165 514 535 155 144 2513 1295 571 511 147 194 2718 130 57 -25 -7 50 205
South Oxfordshire 745 1168 398 287 267 2866 789 1237 364 262 346 2999 44 69 -34 -25 79 133
Vale of White Horse 1159 760 486 247 225 2877 1272 834 451 227 298 3082 113 74 -35 -20 72 205
West Oxfordshire 861 496 362 172 158 2048 1030 594 342 163 214 2342 169 98 -20 -9 56 294
Reading 1212 901 416 200 183 2912 1145 852 347 167 216 2727 -67 -50 -69 -33 33 -185
Slough 1308 1031 0 222 159 2720 1308 1031 0 195 199 2733 0 0 0 -27 40 13
West Berkshire 1812 672 631 216 216 3549 1969 730 567 196 278 3740 156 58 -64 -21 62 191
Windsor and
Maidenhead 1527 655 916 131 130 3359 1765 758 812 116 164 3614 238 102 -104 -15 33 255
Wokingham 1669 853 497 239 221 3479 1850 945 485 234 302 3816 181 92 -11 -6 82 337
Brighton & Hove 757 2538 746 266 346 4652 782 2624 687 245 459 4796 26 86 -59 -21 113 144
Eastbourne 20 1371 146 74 111 1721 24 1709 145 74 157 2110 5 339 0 0 46 389
Hastings 416 1168 147 90 136 1957 477 1338 145 88 188 2236 61 170 -2 -2 52 279
Lewes 205 1326 160 81 121 1893 238 1536 150 76 161 2162 33 210 -9 -5 40 268
Rother 203 1182 141 75 112 1713 244 1417 146 76 161 2043 41 236 4 1 48 330
Wealden 1455 1165 446 70 105 3241 1615 1294 427 67 142 3544 160 128 -18 -3 37 303
Adur 378 588 115 65 65 1211 436 678 103 58 83 1359 58 90 -12 -6 18 148
Arun 1016 1272 243 169 169 2870 1173 1468 243 169 242 3294 156 196 -1 0 73 424
Chichester 671 1013 205 113 113 2114 776 1170 217 119 171 2453 105 158 12 6 58 339
Crawley 989 701 156 137 137 2120 1129 800 156 136 195 2416 140 99 0 -1 58 296
Horsham 704 1544 326 125 125 2824 845 1855 341 131 186 3358 141 310 15 6 62 534
Table 7: Numbers of 16 and 17 year old resident learners, by district, 2006 and 2013

39
2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Worthing 34 1621 181 119 119 2075 36 1725 174 114 163 2212 2 104 -8 -5 44 137
Basingstoke and
Deane 162 2700 37 197 345 3441 176 2942 38 203 504 3865 15 243 1 6 159 424
East Hampshire 37 2078 132 105 184 2537 42 2366 126 99 246 2880 5 288 -6 -6 62 343
Eastleigh 13 2325 112 130 228 2808 13 2383 104 121 300 2921 0 58 -9 -9 72 113
Fareham 110 1986 133 106 186 2521 112 2020 120 96 238 2586 2 34 -13 -10 52 65
Gosport 261 1061 12 93 162 1590 282 1147 11 83 207 1730 21 86 -1 -10 44 141
Hart 226 1470 189 56 97 2039 266 1733 188 55 137 2379 40 262 -1 -1 40 341
Havant 152 2060 79 131 229 2651 158 2135 71 117 290 2770 6 75 -8 -14 61 119
New Forest 623 2180 118 164 287 3371 714 2501 114 160 396 3885 92 321 -3 -4 109 514
Rushmoor 105 1443 82 83 145 1860 115 1569 77 77 192 2029 9 126 -6 -6 46 169
Test Valley 9 2045 76 121 212 2463 10 2338 75 116 287 2825 1 293 -2 -5 75 363
Winchester 19 1917 23 183 323 2466 23 2263 21 176 435 2917 3 346 -2 -7 112 451
Isle of Wight 1311 940 122 123 315 2812 1577 1131 125 125 460 3418 266 191 3 2 146 606
Portsmouth 28 2988 331 143 285 3775 29 3088 285 122 348 3873 1 100 -46 -21 63 98
Southampton 60 3078 252 228 355 3973 64 3271 223 203 446 4207 4 193 -28 -26 91 234
Ashford 1315 745 250 95 158 2563 1560 884 273 106 248 3070 245 139 23 10 89 507
Canterbury 1580 774 281 109 183 2927 1882 922 263 102 243 3413 302 148 -18 -7 61 486
Dartford 774 594 130 77 127 1702 1009 774 132 80 186 2181 235 180 2 3 59 479
Dover 1333 646 231 93 155 2458 1467 712 213 86 204 2681 134 65 -18 -7 48 223
Gravesham 1168 682 229 82 136 2297 1226 716 209 75 175 2400 58 34 -20 -8 39 102
Maidstone 1706 717 315 103 174 3015 1932 812 300 98 237 3379 226 95 -15 -5 63 364
Sevenoaks 1254 512 237 73 120 2197 1483 606 221 69 160 2539 229 94 -16 -4 40 342
Shepway 940 648 188 75 125 1977 1144 789 186 74 176 2371 205 141 -2 -1 51 394
Swale 1544 847 276 113 189 2970 1795 985 274 114 269 3436 251 138 -3 0 79 466
Thanet 1248 929 108 157 264 2706 1529 1139 111 161 385 3325 281 210 3 3 121 618
Tonbridge and Malling 1508 610 272 91 153 2634 1737 703 258 87 207 2992 229 93 -14 -4 54 358
Tunbridge Wells 1517 480 323 62 106 2487 1734 548 306 59 143 2789 217 69 -17 -3 37 302
Medway 2930 1743 185 258 406 5522 3340 1987 170 238 530 6266 411 244 -15 -20 124 744
Elmbridge 441 1063 449 78 91 2121 631 1521 471 82 137 2842 190 458 23 4 46 720
Epsom and Ewell 659 399 319 52 61 1490 779 472 306 50 83 1689 120 73 -14 -2 23 200
Mid Sussex 714 1526 307 134 134 2815 840 1795 302 131 187 3255 126 269 -5 -2 53 440

40
(e) Please note also the caution about census dates mentioned on page 27.
(d) Participation rates for 2013 are based on projections forwards from 2006 rates, consistent with the target of full participation in 2013 or 2015
(16 and 17 year olds respectively). More detail is in the Methodology section.
(c) Participation rates for 2006 are based on DCFS data (for counties and unitary authorities). Disaggregating to district level is done using
information from PLASC and ILR databases, as described in the Methodology section.
(b) Population figures for each district and year are obtained from the Office for National Statistics.
(a) Learner numbers are calculated as number of 16/17 year olds in the local population participation rate.
Notes on sources of data used to construct this table:
2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Reigate and Banstead 621 1403 546 131 156 2858 654 1477 522 125 212 2991 33 74 -24 -6 55 133
Runnymede 359 676 291 61 71 1458 406 765 297 62 104 1635 48 90 6 1 32 177
Spelthorne 188 1240 406 82 97 2013 183 1208 384 78 132 1985 -5 -32 -22 -4 35 -27
Surrey Heath 670 910 380 54 65 2079 655 890 380 54 92 2071 -15 -20 0 0 27 -8
Tandridge 697 527 371 61 71 1727 865 655 348 57 95 2021 168 128 -23 -4 24 294
Waverley 86 1613 528 79 91 2398 114 2133 471 70 116 2904 28 520 -57 -9 25 507
Woking 366 1146 402 76 88 2077 349 1094 378 71 118 2010 -17 -52 -24 -5 30 -67
Guildford 947 994 551 116 131 2739 1149 1207 537 113 184 3190 202 212 -14 -3 53 451
Mole Valley 842 475 438 48 56 1859 948 534 421 46 77 2026 106 60 -17 -2 21 167

Table 8: Districts with the largest projected increases in learners
District School College
Increase 2006-2013
Total
school
plus
college Indep
Part
time WBL Total
Net
imports
% growth
of
housing
stock
Milton Keynes 460 280 740 -1 -4 145 879 -46 25%
Medway 411 244 655 -15 -20 124 744 185
Elmbridge 190 458 648 23 4 46 720 3124
Aylesbury Vale 394 164 559 -5 -10 117 661 111 14%
Waverley 28 520 548 -57 -9 25 507 256
Thanet 281 210 491 3 3 121 618 -186
Isle of Wight 266 191 457 3 2 146 606 -117 8%
Horsham 141 310 452 15 6 62 534 -52 11%
Canterbury 302 148 451 -18 -7 61 486 1943
Dartford 235 180 416 2 3 59 479 2250 20%
Guildford 202 212 415 -14 -3 53 451 1646
New Forest 92 321 412 -3 -4 109 514 2062
Mid Sussex 126 269 394 -5 -2 53 440 -891 12%
Swale 251 138 389 -3 0 79 466 -801
Ashford 245 139 385 23 10 89 507 -385 23%
Arun 156 196 352 -1 0 73 424 -1714
Winchester 3 346 349 -2 -7 112 451 1983 11%
Shepway 205 141 346 -2 -1 51 394 -189
Eastbourne 5 339 343 0 0 46 389 834
Windsor and Maidenhead 238 102 340 -104 -15 33 255 228
Sevenoaks 229 94 323 -16 -4 40 342 -1635
Tonbridge and Malling 229 93 322 -14 -4 54 358 1398 9%
Maidstone 226 95 321 -15 -5 63 364 514
Hart 40 262 302 -1 -1 40 341 -1530
Tandridge 168 128 296 -23 -4 24 294 -280
Test Valley 1 293 294 -2 -5 75 363 -1352 9%
East Hampshire 5 288 293 -6 -6 62 343 -567
Wealden 160 128 288 -18 -3 37 303 -1335
Tunbridge Wells 217 69 285 -17 -3 37 302 146
Rother 41 236 276 4 1 48 330 -172
Wokingham 181 92 273 -11 -6 82 337 -1327 8%
West Oxfordshire 169 98 267 -20 -9 56 294 -65 8%
Chichester 105 158 262 12 6 58 339 2151 8%
Basingstoke and Deane 15 243 257 1 6 159 424 506 12%
Lewes 33 210 243 -9 -5 40 268 787
Crawley 140 99 239 0 -1 58 296 684 8%
Hastings 61 170 231 -2 -2 52 279 -217
Bracknell Forest 113 114 228 -65 -24 23 163 -576 11%
West Berkshire 156 58 214 -64 -21 62 191 92 8%
Dover 134 65 200 -18 -7 48 223 -498
Because we should go beyond looking at a particular district in isolation, it may be
helpful to look at a map of the projected increases in numbers, shown in Figure 19

elow. This shows nine separate categories, with approximately equal numbers of
districts in each. The darker colours represent larger increases in learner numbers.
Figure 19: Map of growth in total learner numbers
Growth in total learner numbers
42
Numbers
Num bers
500
500
-
-880
880
450 - 500
450 -500
400
400
-
-450
450
350
350
-
-400
400
300 - 350
300 -350
250
250
-
-300
300
200
200
-
-250
250
0 - 200
0 -200
-185
-185
-
-0
0
Note that this map includes learners in the independent sector as well as those
following a WBL route. Using this map we get an idea of where there are groups of
neighbouring authorities with large increases in numbers, for example in North Kent and
in the north of Buckinghamshire.
Figure 20 shows a similar map, but includes only full time learners (in both schools and
colleges). This leaves out part time learners, WBL and students in the independent
sector. This gives a similar pattern to the figure above (note that the colours represent
different values in the two graphs), but again suggests the northern districts of Kent as
areas of growth, as well as Milton Keynes and Aylesbury Vale together in the north of the
region.

Figure 20: Map of growth of full time learners
Growth in full time learner numbers
43
Numbers
400 - 740
Numbers 350 - 400
400 300 - 740 - 350
350 250 - 400 - 300
300 200 - 350 - 250
250 100 - 300 - 200
200 0 - 100 250
100 -117 - 200 - 0
0 - 100
-117 - 0
From our model we can also measure the growth to the year 2015, by which time all 17
year olds should be in education or training. These numbers obviously reveal a little
more growth, and are shown in Table 9 below. Note that the numbers of students in
education or training in 2015 are fairly similar to the numbers in 2013 (on average about
1% higher) but that when measured as the growth from 2006, the 2015 figure is about
11% larger than the 2013 figure. (Example: Horsham’s 2013 number is estimated to be
3358 and in 2015 to be 3410, an increase of 1.6%. The growth from 2006’s figure of
2824 is 534 (to 2013) and 587 (to 2015), which is an increase of 9.8%.) There are wide
variations around this average and this illustrates the difficulty of accurately measuring
the rate of growth, rather than the level, of a variable.

2006 2015 Increase 2006-2015
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Aylesbury Vale 2046 852 116 232 319 3565 2546 1060 112 223 460 4401 500 208 -5 -9 141 835
Chiltern 1412 341 246 68 92 2158 1593 385 254 69 142 2442 181 44 8 2 50 284
South Bucks 831 277 91 70 96 1364 954 318 88 68 141 1569 123 41 -2 -2 44 205
Wycombe 2160 917 338 157 217 3789 2286 970 315 147 304 4022 126 54 -23 -11 87 233
Bracknell Forest 906 914 528 188 103 2639 1043 1052 457 163 134 2849 137 138 -71 -25 31 210
Milton Keynes 2572 1563 61 214 367 4778 3080 1871 59 207 532 5749 507 308 -2 -7 165 971
Cherwell 1118 1008 709 183 169 3188 1252 1128 692 179 248 3498 134 120 -18 -5 78 310
Oxford 1165 514 535 155 144 2513 1330 586 506 146 204 2772 165 73 -30 -9 60 259
South Oxfordshire 745 1168 398 287 267 2866 794 1245 357 255 356 3007 49 77 -41 -32 88 141
Vale of White Horse 1159 760 486 247 225 2877 1271 833 439 221 304 3068 112 73 -47 -26 79 191
West Oxfordshire 861 496 362 172 158 2048 1045 603 344 162 224 2377 184 106 -18 -10 66 329
Reading 1212 901 416 200 183 2912 1115 829 332 160 222 2658 -97 -72 -84 -40 39 -254
Slough 1308 1031 0 222 159 2720 1242 979 0 183 196 2601 -65 -52 0 -39 37 -119
West Berkshire 1812 672 631 216 216 3549 2005 744 559 193 289 3789 192 71 -72 -24 72 241
Windsor and
Maidenhead 1527 655 916 131 130 3359 1857 797 823 118 178 3772 330 142 -93 -13 48 414
Wokingham 1669 853 497 239 221 3479 1843 941 475 229 318 3807 174 89 -21 -10 97 328
Brighton & Hove 757 2538 746 266 346 4652 786 2636 673 240 468 4803 29 98 -73 -26 122 151
Eastbourne 20 1371 146 74 111 1721 26 1801 148 76 172 2223 6 430 3 2 61 502
Hastings 416 1168 147 90 136 1957 479 1344 141 88 198 2250 63 176 -7 -2 62 292
Lewes 205 1326 160 81 121 1893 240 1548 146 74 167 2175 34 221 -14 -6 46 282
Rother 203 1182 141 75 112 1713 243 1415 141 75 169 2043 40 233 -1 0 57 330
Wealden 1455 1165 446 70 105 3241 1674 1341 432 68 153 3667 219 176 -14 -2 47 426
Adur 378 588 115 65 65 1211 440 684 100 56 84 1365 62 96 -15 -9 19 154
Arun 1016 1272 243 169 169 2870 1180 1477 238 166 250 3311 164 205 -6 -3 80 441
Chichester 671 1013 205 113 113 2114 769 1160 211 116 174 2430 98 148 6 3 61 316
Crawley 989 701 156 137 137 2120 1119 793 149 131 196 2388 130 92 -7 -6 59 268
Horsham 704 1544 326 125 125 2824 859 1886 340 130 195 3410 155 341 14 5 70 586
Mid Sussex 714 1526 307 134 134 2815 840 1797 294 128 192 3251 126 270 -13 -6 58 436
Worthing 34 1621 181 119 119 2075 37 1784 174 116 173 2284 3 163 -7 -4 54 209
Basingstoke and 162 2700 37 197 345 3441 177 2945 38 202 529 3891 15 246 1 4 184 450
Table 9: Numbers of 16 and 17 year old resident learners, by district, 2006 and 2015

45
2006 2015 Increase 2006-2015
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
East Hampshire 37 2078 132 105 184 2537 43 2383 123 98 258 2904 5 305 -10 -7 74 367
Eastleigh 13 2325 112 130 228 2808 13 2355 101 118 310 2897 0 30 -11 -12 83 89
Fareham 110 1986 133 106 186 2521 114 2061 120 96 251 2641 4 74 -13 -10 65 120
Gosport 261 1061 12 93 162 1590 280 1137 10 79 208 1714 19 75 -1 -14 45 124
Hart 226 1470 189 56 97 2039 278 1813 189 56 146 2482 53 342 0 0 48 443
Havant 152 2060 79 131 229 2651 157 2126 68 114 299 2764 5 66 -11 -17 70 114
New Forest 623 2180 118 164 287 3371 719 2516 112 157 411 3915 96 336 -5 -7 124 544
Rushmoor 105 1443 82 83 145 1860 114 1566 74 74 194 2023 9 123 -9 -9 49 163
Test Valley 9 2045 76 121 212 2463 10 2306 71 111 293 2792 1 261 -5 -9 82 329
Winchester 19 1917 23 183 323 2466 23 2313 20 176 466 2997 4 395 -3 -8 143 531
Isle of Wight 1311 940 122 123 315 2812 1597 1145 124 125 480 3470 285 205 2 2 165 658
Portsmouth 28 2988 331 143 285 3775 29 3108 279 121 361 3899 1 120 -52 -22 76 124
Southampton 60 3078 252 228 355 3973 64 3248 215 194 453 4175 3 170 -37 -34 99 201
Ashford 1315 745 250 95 158 2563 1566 887 271 105 260 3089 251 142 21 10 102 526
Canterbury 1580 774 281 109 183 2927 1954 957 263 102 256 3533 375 183 -19 -7 74 606
Dartford 774 594 130 77 127 1702 1000 768 127 76 187 2158 227 174 -3 -1 60 456
Dover 1333 646 231 93 155 2458 1476 716 207 84 210 2693 144 70 -24 -9 55 235
Gravesham 1168 682 229 82 136 2297 1265 739 209 75 186 2474 97 57 -20 -7 50 176
Maidstone 1706 717 315 103 174 3015 1950 820 294 96 243 3404 244 103 -21 -7 70 389
Sevenoaks 1254 512 237 73 120 2197 1539 629 221 68 169 2626 285 116 -16 -5 48 429
Shepway 940 648 188 75 125 1977 1171 808 185 74 184 2420 231 159 -4 -2 59 444
Swale 1544 847 276 113 189 2970 1824 1000 270 112 280 3487 280 153 -6 -1 91 517
Thanet 1248 929 108 157 264 2706 1515 1129 105 157 392 3298 267 199 -3 -1 129 592
Tonbridge and Malling 1508 610 272 91 153 2634 1781 720 256 87 217 3060 273 110 -16 -5 64 426
Tunbridge Wells 1517 480 323 62 106 2487 1700 538 294 56 143 2731 183 58 -29 -6 38 244
Medway 2930 1743 185 258 406 5522 3345 1990 165 231 544 6276 416 247 -20 -27 138 754
Elmbridge 441 1063 449 78 91 2121 681 1642 487 84 148 3042 240 579 38 7 57 920
Epsom and Ewell 659 399 319 52 61 1490 823 498 312 51 89 1773 164 99 -8 -1 28 283
Guildford 947 994 551 116 131 2739 1196 1256 537 112 191 3292 249 262 -15 -3 60 553
Mole Valley 842 475 438 48 56 1859 969 546 420 46 80 2061 126 71 -18 -2 24 202
Reigate and Banstead 621 1403 546 131 156 2858 668 1508 528 126 225 3055 47 106 -19 -5 68 197
Runnymede 359 676 291 61 71 1458 403 760 285 60 105 1613 45 84 -6 -1 34 155
Deane

46
2006 2015 Increase 2006-2015
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Tandridge 697 527 371 61 71 1727 892 675 346 57 100 2069 195 148 -25 -4 29 342
Waverley 86 1613 528 79 91 2398 120 2235 465 69 121 3010 33 622 -63 -9 30 612
Woking 366 1146 402 76 88 2077 344 1079 369 70 121 1983 -21 -67 -33 -6 33 -94
Spelthorne 188 1240 406 82 97 2013 181 1196 377 77 136 1967 -7 -44 -29 -6 39 -46
Surrey Heath 670 910 380 54 65 2079 642 873 375 53 97 2041 -27 -37 -5 -1 32 -38

The figures in Table 9 are illustrated in Figure 21 below.
Figure 21: Growth in full time learner numbers to 2015
Growth in full time learner numbers
Numbers
Numbers
497
497
-
-
819
819
402
402
-
-
497
497
383
383
-
-
402
402
274
274
-
-
383
383
245 - 274
245 - 274
197
197
-
-
245
245
152
152
-
-
197
197
78
78
-
-
152
152
-169
-169
-
-
78
78
This yields a similar map to that of Figure 20 for the growth to 2013. Alternative
scenarios
It is relatively easy to explore alternative scenarios using the model. We explore two in
this report:
(a) Assuming work-based learner numbers double by 2013 (instead of increasing
by 50%)
(b) Assuming 100% participation in some form of education or training by
2013/2015
The first of these increases the proportion of growth that is accounted for by WBL and
may be a reasonable alternative to our central assumption, given that we must expect
growth to come from students who have traditionally dropped out and are unlikely to
want to follow more academic routes. However, it should be noted that this is a
significantly larger increase than is implicit in government policy (which implies growth
of about 50% by 2013). The impact of this alternative is to increase the numbers in WBL,
obviously, and to decrease the numbers in school and college. The overall participation
rate remains the same.
These results are illustrated in Table 10 and it is perhaps most useful in seeing how
changing this assumption affects the various projections. For example, in Aylesbury
Vale (the first district in the table), the WBL number increases by 127 (we are comparing

the figure of 244 with that of 117 in Table 7), resulting in fall in school growth from 394 to
305 (-89) and college growth to be reduced from 164 to 127 (-37). The independent
sector is unchanged, as we believe it is unlikely to be involved in the same client group
as WBL. The part-time sector is also unchanged, again by assumption, though the
borderline between part-time learning and WBL may be a fine one, so we could interpret
this scenario as an increase in either WBL or part-time learning.
48

2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Aylesbury Vale 2046 852 116 232 319 3565 2350 979 111 222 564 4226 305 127 -5 -10 244 661
Chiltern 1412 341 246 68 92 2158 1513 365 248 69 174 2369 101 24 3 1 82 211
South Bucks 831 277 91 70 96 1364 931 310 91 70 178 1581 100 33 1 0 82 217
Wycombe 2160 917 338 157 217 3789 2196 932 315 147 372 3962 36 15 -23 -11 155 173
Bracknell Forest 906 914 528 188 103 2639 1002 1011 463 164 161 2802 96 97 -65 -24 59 163
Milton Keynes 2572 1563 61 214 367 4778 2941 1787 60 210 659 5657 369 224 -1 -4 292 879
Cherwell 1118 1008 709 183 169 3188 1168 1052 681 176 298 3375 49 44 -28 -7 129 187
Oxford 1165 514 535 155 144 2513 1257 554 511 147 249 2718 92 40 -25 -7 105 205
South Oxfordshire 745 1168 398 287 267 2866 751 1177 364 262 444 2999 6 9 -34 -25 177 133
Vale of White Horse 1159 760 486 247 225 2877 1220 800 451 227 384 3082 61 40 -35 -20 159 205
West Oxfordshire 861 496 362 172 158 2048 990 571 342 163 276 2342 130 75 -20 -9 118 294
Reading 1212 901 416 200 183 2912 1111 826 347 167 275 2727 -101 -75 -69 -33 92 -185
Slough 1308 1031 0 222 159 2720 1276 1006 0 195 255 2733 -32 -25 0 -27 97 13
West Berkshire 1812 672 631 216 216 3549 1911 709 567 196 358 3740 98 36 -64 -21 141 191
Windsor and
Maidenhead 1527 655 916 131 130 3359 1732 744 812 116 210 3614 206 88 -104 -15 79 255
Wokingham 1669 853 497 239 221 3479 1795 917 485 234 385 3816 126 64 -11 -6 165 337
Brighton & Hove 757 2538 746 266 346 4652 751 2519 687 245 595 4796 -6 -19 -59 -21 249 144
Eastbourne 20 1371 146 74 111 1721 24 1666 145 74 202 2110 4 295 0 0 91 389
Hastings 416 1168 147 90 136 1957 463 1299 145 88 240 2236 47 131 -2 -2 105 279
Lewes 205 1326 160 81 121 1893 232 1498 150 76 206 2162 27 171 -9 -5 84 268
Rother 203 1182 141 75 112 1713 237 1379 146 76 206 2043 34 197 4 1 93 330
Wealden 1455 1165 446 70 105 3241 1593 1276 427 67 182 3544 138 111 -18 -3 77 303
Adur 378 588 115 65 65 1211 427 663 103 58 107 1359 48 75 -12 -6 42 148
Arun 1016 1272 243 169 169 2870 1142 1429 243 169 312 3294 125 157 -1 0 143 424
Chichester 671 1013 205 113 113 2114 756 1140 217 119 221 2453 85 128 12 6 108 339
Crawley 989 701 156 137 137 2120 1095 776 156 136 252 2416 106 75 0 -1 115 296
Horsham 704 1544 326 125 125 2824 828 1818 341 131 240 3358 124 273 15 6 115 534
Mid Sussex 714 1526 307 134 134 2815 823 1759 302 131 240 3255 109 232 -5 -2 107 440
Worthing 34 1621 181 119 119 2075 35 1679 174 114 211 2212 1 57 -8 -5 91 137
Basingstoke and 162 2700 37 197 345 3441 168 2807 38 203 647 3865 6 108 1 6 303 424
Table 10: Learner number projections using an alternative scenario: doubling the WBL participation rate

50
2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
East Hampshire 37 2078 132 105 184 2537 41 2298 126 99 315 2880 4 220 -6 -6 131 343
Eastleigh 13 2325 112 130 228 2808 13 2300 104 121 384 2921 0 -25 -9 -9 156 113
Fareham 110 1986 133 106 186 2521 108 1957 120 96 305 2586 -2 -30 -13 -10 119 65
Gosport 261 1061 12 93 162 1590 271 1100 11 83 265 1730 10 39 -1 -10 103 141
Hart 226 1470 189 56 97 2039 261 1699 188 55 176 2379 35 228 -1 -1 79 341
Havant 152 2060 79 131 229 2651 152 2058 71 117 372 2770 0 -2 -8 -14 143 119
New Forest 623 2180 118 164 287 3371 689 2413 114 160 509 3885 67 233 -3 -4 222 514
Rushmoor 105 1443 82 83 145 1860 111 1518 77 77 246 2029 5 75 -6 -6 101 169
Test Valley 9 2045 76 121 212 2463 9 2259 75 116 367 2825 1 213 -2 -5 155 363
Winchester 19 1917 23 183 323 2466 21 2146 21 176 553 2917 2 228 -2 -7 230 451
Isle of Wight 1311 940 122 123 315 2812 1500 1075 125 125 594 3418 188 135 3 2 279 606
Portsmouth 28 2988 331 143 285 3775 28 2990 285 122 448 3873 0 1 -46 -21 163 98
Southampton 60 3078 252 228 355 3973 62 3148 223 203 571 4207 1 70 -28 -26 216 234
Ashford 1315 745 250 95 158 2563 1516 858 273 106 318 3070 201 114 23 10 159 507
Canterbury 1580 774 281 109 183 2927 1835 899 263 102 313 3413 256 125 -18 -7 130 486
Dartford 774 594 130 77 127 1702 979 751 132 80 238 2181 206 158 2 3 111 479
Dover 1333 646 231 93 155 2458 1428 693 213 86 262 2681 95 46 -18 -7 107 223
Gravesham 1168 682 229 82 136 2297 1194 698 209 75 224 2400 27 16 -20 -8 88 102
Maidstone 1706 717 315 103 174 3015 1884 792 300 98 305 3379 178 75 -15 -5 131 364
Sevenoaks 1254 512 237 73 120 2197 1451 593 221 69 205 2539 197 81 -16 -4 85 342
Shepway 940 648 188 75 125 1977 1115 769 186 74 227 2371 175 121 -2 -1 101 394
Swale 1544 847 276 113 189 2970 1746 958 274 114 345 3436 202 111 -3 0 156 466
Thanet 1248 929 108 157 264 2706 1465 1092 111 161 496 3325 218 162 3 3 232 618
Tonbridge and Malling 1508 610 272 91 153 2634 1695 685 258 87 266 2992 187 76 -14 -4 113 358
Tunbridge Wells 1517 480 323 62 106 2487 1702 538 306 59 185 2789 185 58 -17 -3 79 302
Medway 2930 1743 185 258 406 5522 3246 1931 170 238 680 6266 317 188 -15 -20 274 744
Elmbridge 441 1063 449 78 91 2121 619 1493 471 82 176 2842 178 430 23 4 85 720
Epsom and Ewell 659 399 319 52 61 1490 764 462 306 50 108 1689 105 64 -14 -2 47 200
Guildford 947 994 551 116 131 2739 1123 1179 537 113 238 3190 176 184 -14 -3 108 451
Mole Valley 842 475 438 48 56 1859 934 526 421 46 99 2026 92 52 -17 -2 43 167
Reigate and Banstead 621 1403 546 131 156 2858 636 1436 522 125 271 2991 15 33 -24 -6 115 133
Runnymede 359 676 291 61 71 1458 396 746 297 62 134 1635 37 70 6 1 62 177
Deane

51
2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Tandridge 697 527 371 61 71 1727 850 643 348 57 123 2021 153 116 -23 -4 52 294
Waverley 86 1613 528 79 91 2398 112 2101 471 70 150 2904 26 488 -57 -9 59 507
Woking 366 1146 402 76 88 2077 341 1068 378 71 152 2010 -25 -78 -24 -5 64 -67
Spelthorne 188 1240 406 82 97 2013 178 1176 384 78 169 1985 -10 -64 -22 -4 72 -27
Surrey Heath 670 910 380 54 65 2079 644 875 380 54 118 2071 -26 -35 0 0 53 -8

The second scenario, of 100% participation, represents the maximum possible out-turn.
It is therefore interesting to explore this scenario, even if we think it is less likely than our
central projection. These tables (for the changes to 2013 and to 2015) are contained in
Appendix 4, Table A4.1 and A4.2. Because we are now adding an additional 2% points
to the participation rate, we need to modify our assumption about the growth of WBL
(otherwise, the extension from 98% to 100% participation is assumed all to occur in
schools and colleges. Since these are the most marginal learners, this outcome
seems unlikely and it is more reasonable to assume some increase in WBL.). We
therefore assume a growth of 75% in WBL (rather than the 50% in our central scenario),
although other figures could reasonably be used. This illustrates again the difficulty of
estimating the individual components of the overall growth.
Looking at the growth numbers in this scenario, it now predicts around 24,100 additional
learners in the South East, compared to an estimate of 19,880 in our central projection,
a difference of 21.4%. Again, there are wide variations around the average: where the
overall participation rate was equal or near to 100% already in Table 7, there will be little
difference reported in Table A4.1. For some authorities (e.g. Medway) there is a big
difference because in our central projection we had them falling short of our average
figure of 98% participation.

7. Conclusions
Our work has provided a mass of contextual information regarding post-16 participation
and has derived a methodology for making projections of learner numbers (with various
degrees of disaggregation) up to the year 2020. We have identified possible reasons for
adjustments to our basic specification and we have demonstrated how the results may
be interpreted. In particular we noted that transfers across local authority borders mean
it is important to take these into account when interpreting the results of the model. We
have examined two different scenarios to illustrate how the model might be used.
In the light of this, it would be dangerous simply to take the predicted increase in learner
numbers in a district as a guide to the need for additional provision. That decision
would need to take account of other factors such as:
the flows of students between districts in the vicinity
the plans of neighbouring districts and colleges
the split of students into the various components such as full-time or part-time
learners
It is also important to continue monitoring changes as time goes on. For example, it
may become apparent that the assumptions made in this model regarding the growth of
WBL participation turn out to be wrong. In this case the forecasts should be revised in
the light of new information.
There are areas where the model could possibly be improved and hence where further
research might be worthwhile. We have already discussed some of the issues around
the data at the end of section 5 above. We were unable to obtain much useful
information about the independent sector and this is calculated as a residual in our
model. The DCSF does provide participation rates for the independent sector at the
country/unitary authority level and, depending upon the source of that data, it might be
possible to obtain that at district level. There are also some inconsistencies, we believe,
between the DCSF data and the PLASC/ILR data, which we have had to circumvent.
There is also some debate over population forecasts, as the ONS figures are
projections based upon birth and death rates, coupled with estimates of migration. They
do not take account of information contained in local plans such as new housing
developments, which might prove to be more accurate. However, it might be difficult to
integrate such figures into our model and maintain the consistency that is given by using
the ONS figures.
53

8. Appendices
Appendix 1: References for literature review
Andrews, M.J. and Bradley, S. (1997). ‘Modelling the transition from school and the
demand for training in the UK’, Economica, 64, 387-413.
Ashford, S., Gray, J., and Tranmer, M. (1993). ‘The introduction of GCSE examinations
and changes in post-16 participation’, Youth Cohort Report No 23, Department for
Education and Employment, Sheffield.
Becker, G. S. (1993), Human Capital: A Theoretical and Empirical Analysis, with Special
Reference to Education. 3rd Edn (University of Chicago Press).
Cheng, Y. (1995). ‘Staying on in full-time education after 16: do schools make a
difference?’ Youth Cohort Report No 37, Department for Education and Employment,
Sheffield.
Clark, D. (2002). ‘Participation in post-compulsory education in England: what explains
the boom and bust?’ Discussion Paper no. 24, Centre for the Economics of Education,
London School of Economics.
Dearden, L., Emmerson, C., Frayne, C., and Meghir, C. (2006) ‘Education subsidies and
school drop-out rates’. Discussion Paper no. 53, Centre for the Economics of Education,
London School of Economics.
Dearing, R. (1996). Review of qualifications for 16-19 year olds, Summary Report,
(London, SCAA).
Foskett, N., and Hesketh, A. (1997). ‘Constructing choice in contiguous and parallel
markets: institutional and school leavers’ responses to the new post-16 marketplace’,
Oxford Review of Education, 23, 299-319.
Gray, J., Jesson, D. and Tranmer, M. (1993). ‘Boosting post-16 participation in full-time
education’, Youth Cohort Report No 20, Department for Education and Employment,
Sheffield.
Gorard, S., and Smith, E. (2007). ‘Do barriers get in the way? A review of the
determinants of post-16 participation’, Research in Post-Compulsory Education, 12,
141- 158.
Halsey, A., Heath, A., and Ridge, S. M. (1980). Origins and Destinations. Oxford: Oxford
University Press.
Leslie, D., and Drinkwater, S. (1991). ‘Staying on in full-time education: reasons for
higher participation rates among ethnic minority males and females’, Economica, 66,
63-77.
McVicar, D. and Rice, P. (2001). ‘Participation in further education in England and Wales:
an analysis of post-war trends’, Oxford Economic Papers, 53, 47-66.
54

Maguire, S., and Thompson, J. (2006). ‘Paying for people to stay at school – does it
work? Evidence from the evaluation of the piloting of the Education Maintenance
Allowance (EMA)’. SKOPE research paper 16. University of Oxford.
Mangan, J., Adnett, N., and Davies P. (2001). ‘Movers and stayers: determinants of post-
16 educational choice’, Research in Post-Compulsory Education, 6, 31-50.
Micklewright, J. (1989). ‘Choice at sixteen’, Economica, 56, 25-40.
Micklewright J, Pearson M, and Smith S. (1988). ‘Unemployment and Early School
Leaving’, Economic Journal, 100, Conference Papers (1990), 163-169.
Pissarides, C.A. (1981). ‘Staying-on at school in England and Wales’, Economica, 48,
345-63.
Rice, P. (1987). ‘The demand for post-compulsory education in the U.K. and the effects
of educational maintenance allowances’, Economica, 54, 465-76.
Rice, P. (1999). ‘The impact of local labour markets on investment in further education:
Evidence from the England and Wales Youth Cohort Studies’, Journal of Population
Economics, 12, 287-312.
Thomas, W., and Webber, D. (2001) ‘Because my friends are’: the impact of peer groups
on the intention to stay on at sixteen’, Research in Post-Compulsory Education, 6, 339-
354.
Thomas, W., Webber, D., and Walton, F. (2003) ‘School effects that shape students’
intentions to stay-on in education’, Research in Post-Compulsory Education, 8, 197-
211.
Whitfield, K., and Wilson, R.A. (1991). ‘Staying-on in full-time education: the educational
participation rate of 16 year-olds’, Economica, 58, 391-404.
55

Andrews & Bradley
(1997)
England Analysis of demand for youth training at 16+
(amongst other choices inc. non-vocational
and vocational post compulsory education).
Choices post compulsory, determined by
individual, school, and labour market factors.
Main findings: academic ability (GCSE score),
ethnicity, gender, size of institution,
institution’s academic performance (league
table ranking), and expected lifetime earnings
determine 16+ choice. Different effects by
gender.
Human
capital
Lancashire careers service records for 14000
students who finished compulsory schooling in
1991 (6 months after leaving). Regressors used
fall into 5 categories: Personal (health status,
gender, ethnicity, educational qualifications),
School (size (proxied by size of 5 th form (year
11)), type (e.g. single-sex), league table ranking),
Socioeconomic (individual level data unavailable
– used ‘ward’ level data relating to housing,
family composition, occupation of head of
household), Occupation (students’ desired
occupation), labour market (industrial structure
Multinomial logit
Whitfield & Wilson
(1991)
England
& Wales
Paper focused on time series analysis of
socio-economic factors influencing post
compulsory participation rates at a time when
there was growth in youth training
programmes (YTS). Main findings: the rate of
return to education, social class,
unemployment rate and the availability of
training schemes influence choice. Cohort
academic attainment and cohort size were
found insignificant. Note dummy used to
account for the raising of the school leaving
age in 1972 found to be significant.
Human
Capital
Time series (annual data) 1956/7-1985/6.
Aggregate data used: participation rate, GDP
per capita (used to proxy consumption aspect of
education), return to HE degrees (proxy returns
to stay on beyond 16), changes in social class
structure (proxied by proportion of population in
certain occupational categories over time), the
unemployment rate, and the availability of
training schemes (YTS or equivalent).
ECM (OLS)
Binary logit
Mickelwright (1989) England
& Wales
Paper focused on probability of completing
education at minimum legal age (low by
OECD standards at the time). Sample period
includes 1972 when school leaving age rose
from 15 to 16. Main findings: social class and
parental education have significant effects on
the participation rate after controlling for ability
and school type. Different effects by gender.
Human
Capital
NCDS (children born in 1958) 4 th sweep (1974).
Regressors include: student’s family background
characteristics (parents educational background,
social class, no of siblings, net household
income, receipt of free school meals), area of
residence, school type (grammar/comp/private/
sec mod) and ability scores (maths and
communication)
Binary logit
Author(s) (year) country Conclusions/Empirical results Theory Data Methodology
Appendix 2: Summary table of findings of studies reported in the literature review

57
Mangan, Adnett &
Davies (2001)
UK This paper looks at why students stay in the
same school post 16 (year 12). Search and
‘switching’ costs (e.g. attending meetings,
finding info, availability of career advisors),
‘familiar surroundings’ , transport costs and
the similarity in provision between providers
leads to ‘student inertia’. Movers
predominantly reject A-levels and enrol at a
FE college. An important finding is that
curriculum matters. (These decisions may be
influenced by parents)
Consumer
choice
theory
Survey of year 11 students in 2 English
(anonymous) towns in 1997 using questionnaire
based on results from focus group
Statistical
analysis and
binary logit
McVicar & Rice
(2001)
England
& Wales
Similar focus to previous research (Rice
1999), but considers the LR relationship. Main
findings: local labour market conditions,
(youth unemployment rates in early 1990s),
educational attainment, expansion of the HE
sector all matter. Note dummies used to
account for the raising of the school leaving
age in 1972.
Human
Capital
Pooled time series (1954-1994). Data on
individual characteristics (gender) socioeconomic
status (household income, occupation of head of
household) and external conditions (rate of
unemployment among 18 and 20 year olds,
proportion of young students (

58
Thomas et al
(2003)
UK
Clark (2002) England Focus on explaining rise of participation from
45% in 1988 to 70% in 1993 and its relative
stability thereafter (till 1999/2000) even after a
rise in the level of examination success.
Considers individual level factors
(qualifications, social class, etc) and
aggregate level factors (e.g. local
unemployment rate): Main findings: local
unemployment rate (particularly for boys),
supply constraints (school leaving cohort),
peer group effects, unemployment and peer
effects have a large effect on the decision to
participate in FE college vs staying on in
school sixth form/sixth form college.
FES, LFS, GHS used to construct a twenty year
panel of regional-level data 1976-1996.
Regressors include controls for academic
attainment, parental income, returns to
participation and measures of youth
unemployment.
Panel model
(fixed or
random
effects?)
Thomas & Webber
(2001)
England Paper focused on peer group effects on the
decision to stay continue in post compulsory
education. Main findings peer group effects
strong for males but not for females. Other
significant factors include: teacher advice,
ethnicity, academic ability, future intentions to
live in area, and socio-economic status.
(These decisions may be influenced by
parents). Differing effects by gender.
Human
Capital
Bradford youth cohort data 1998 (2766
students). Data school level: ethnicity, school
type, predicted attainment, work experience
(assumed to reduce participation), teacher
effects, socio-economic status (proportion in
district/ward earning

59
Gorard & Smith
(2007)
UK
Maguire (2006) England Focus on the introduction of EMA on
participation. Evidence reviewed suggest that
EMA had impacted on post-compulsory
education participation and retention possibly
drawing students who would have otherwise
opted for youth based work/training
programmes.
Generally a literature review of longitudinal
studies looking at the impact of EMA and
description of surveys.
Dearden et al
(2006)
England Focused on concern over dropping out of
post compulsory education. The impact of
providing students with subsidies –
educational maintenance allowances (EMA) –
(first piloted in England in September 1999
and went national in 2004) is analysed i.e.
addresses the effects of financial constraints
on participation. Main findings: EMA is
important in determining decision to continue
particularly amongst those who receive the
max (means tested) allowance, effects large
amongst poorer socio-economic groups. Does
not affect participation rates for those who
were not eligible. Retention and attendance is
also improved
Students who became eligible for EMA in Sept
1999. Four cases considered where the levels of
EMA varied between urban and rural areas
(weekly payment of between £30 or £40),
benefits tapered for family income (£13,000-
£30,000). Data constructed from surveys - using
face to face interviews with non eligible and noneligible
students. Variables included info on:
family income/background (education and
occupation), child’s health status, special needs,
ethnicity, educational attainment, and
administrative data (distance to post 16
education provider, school and neighbourhood
quality (e.g. deprivation index, access to
services))
OLS
Author(s) (year) country Conclusions/Empirical results Theory Data Methodology

Appendix 3: Full versions of selected tables in the main text
Table A3.1: Authorities with the largest predicted increase in the 16 and 17 year old
population, 2006-2013 (Full version of Table 1 in text)
Authority 2006 Growth
%
growth
Ashford 2843 266 9%
Chichester 2365 136 6%
Elmbridge 3033 156 5%
Horsham 3331 128 4%
Basingstoke and Deane 3810 103 3%
Isle of Wight 3494 92 3%
Thanet 3461 86 2%
Rother 2070 48 2%
Dartford 2360 54 2%
Runnymede 1656 33 2%
Chiltern 2373 29 1%
South Bucks 1618 11 1%
Arun 3408 -5 0%
Eastbourne 2296 -6 0%
Surrey Heath 2079 -8 0%
Crawley 2545 -13 -1%
Swale 3605 -26 -1%
Shepway 2577 -28 -1%
Hart 2552 -32 -1%
Hastings 2361 -39 -2%
Mid Sussex 3464 -61 -2%
Milton Keynes 6096 -110 -2%
Wokingham 3978 -92 -2%
Guildford 3493 -90 -3%
New Forest 4196 -116 -3%
Test Valley 3102 -108 -3%
Mole Valley 2148 -82 -4%
Cherwell 3587 -144 -4%
Epsom and Ewell 1863 -77 -4%
Wealden 3790 -157 -4%
Worthing 2383 -100 -4%
Aylesbury Vale 4867 -213 -4%
Reigate and Banstead 3165 -141 -4%
Oxford 2984 -140 -5%
Maidstone 3718 -177 -5%
West Oxfordshire 2628 -131 -5%
East Hampshire 3235 -163 -5%
Tonbridge and Malling 3362 -170 -5%
Spelthorne 2115 -108 -5%
Tunbridge Wells 3102 -173 -6%
Winchester 3420 -195 -6%

Lewes 2479 -145 -6%
%
Authority 2006 Growth growth
Woking 2155 -128 -6%
Tandridge 2362 -145 -6%
Canterbury 4030 -258 -6%
Sevenoaks 2984 -195 -7%
Wycombe 4322 -294 -7%
Eastleigh 3210 -236 -7%
Medway 7367 -554 -8%
Rushmoor 2330 -179 -8%
Dover 3078 -240 -8%
Brighton and Hove 5322 -419 -8%
Vale of White Horse 3468 -276 -8%
South Oxfordshire 3396 -292 -9%
Gravesham 2750 -250 -9%
Fareham 2917 -279 -10%
West Berkshire 4349 -439 -10%
Adur 1683 -170 -10%
Gosport 2092 -216 -10%
Havant 3244 -347 -11%
Waverley 3853 -417 -11%
Southampton 5035 -566 -11%
Windsor and Maidenhead 4360 -494 -11%
Slough 3165 -369 -12%
Bracknell Forest 3408 -420 -12%
Portsmouth 4726 -654 -14%
Reading 3328 -551 -17%
Table A3.2: Growth in housing stock and growth of population* (full version of Table 2
in main text).
Authority
Housing
Stock
2004
Housing
stock
2015
%
growth
in
housing
stock
61
Population
2006
(000)
Population
2015
(000)
% growth
in
population
Berkshire
Bracknell Forest 44000 48851 11% 3408 2952 -13%
Reading 57000 61689 8% 3328 2658 -20%
Slough 45000 47115 5% 3165 2601 -18%
West Berkshire 58000 62725 8% 4349 3857 -11%
Windsor and
Maidenhead 55000 57529 5% 4360 3919 -10%
Wokingham 58000 62707 8% 3978 3807 -4%

Buckinghamshire
Authority
Housing
Stock
2004
Housing
stock
2015
%
growth
in
housing
stock
62
Population
2006
(000)
Population
2015
(000)
% growth
in
population
Aylesbury Vale 67000 76540 14% 4867 4674 -4%
Chiltern 36000 37080 3% 2373 2442 3%
Milton Keynes 89000 110960 25% 6096 5912 -3%
South Bucks 25000 25810 3% 1618 1577 -3%
Wycombe 64000 66970 5% 4322 4022 -7%
East Sussex
Brighton and
Hove 115000 119950 4% 5322 4803 -10%
Eastbourne 43000 45160 5% 2296 2340 2%
Hastings 39000 40890 5% 2361 2276 -4%
Lewes 41000 42980 5% 2479 2273 -8%
Rother 39000 41520 6% 2070 2066 0%
Wealden 60000 63600 6% 3790 3671 -3%
Hampshire
Basingstoke and
Deane 64000 71425 12% 3810 3891 2%
East Hampshire 44000 46340 5% 3235 3002 -7%
Eastleigh 48000 51186 7% 3210 2897 -10%
Fareham 44000 45674 4% 2917 2641 -9%
Gosport 32000 33125 4% 2092 1792 -14%
Hart 34000 35800 5% 2552 2544 0%
Havant 49000 51835 6% 3244 2812 -13%
New Forest 74000 75863 3% 4196 4008 -4%
Portsmouth 81000 87615 8% 4726 3989 -16%
Rushmoor 35000 37790 8% 2330 2075 -11%
Southampton 94000 101335 8% 5035 4304 -15%
Test Valley 45000 49014 9% 3102 2871 -7%
Winchester 44000 48698 11% 3420 3199 -6%
Isle of Wight 60000 64680 8% 3494 3550 2%
Kent
Ashford 44000 54215 23% 2843 3089 9%
Canterbury 59000 62240 5% 4030 3765 -7%
Dartford 36000 43065 20% 2360 2307 -2%
Dover 46000 48745 6% 3078 2765 -10%
Gravesham 39000 43185 11% 2750 2508 -9%

Maidstone 58000 61690 6% 3718 3472 -7%
Medway 102000 109335 7% 7367 6606 -10%
Sevenoaks 45000 46395 3% 2984 2784 -7%
Authority
Housing
Stock
2004
Housing
stock
2015
%
growth
in
housing
stock
63
Population
2006
(000)
Population
2015
(000)
% growth
in
population
Shepway 43000 45295 5% 2577 2525 -2%
Swale 52000 55735 7% 3605 3536 -2%
Thanet 57000 59925 5% 3461 3416 -1%
Tonbridge and
Malling 45000 48825 9% 3362 3165 -6%
Tunbridge Wells 43000 45250 5% 3102 2798 -10%
Oxfordshire
Cherwell 56000 61310 9% 3587 3498 -2%
Oxford 54000 57150 6% 2984 2817 -6%
South
Oxfordshire 52000 56590 9% 3396 3032 -11%
Vale of White
Horse 47000 52175 11% 3468 3104 -10%
West Oxfordshire 40000 43015 8% 2628 2478 -6%
Surrey
Elmbridge 52000 54079 4% 3033 3290 8%
Epsom and Ewell 28000 29629 6% 1863 1819 -2%
Guildford 53000 55898 5% 3493 3400 -3%
Mole Valley 34000 35539 5% 2148 2061 -4%
Reigate and
Banstead 53000 56483 7% 3165 3055 -3%
Runnymede 33000 34314 4% 1656 1624 -2%
Spelthorne 39000 40359 3% 2115 1967 -7%
Surrey Heath 32000 33683 5% 2079 2041 -2%
Tandridge 32000 33008 3% 2362 2200 -7%
Waverley 47000 49070 4% 3853 3394 -12%
Woking 38000 40178 6% 2155 1983 -8%
West Sussex
Adur 26000 27170 5% 1683 1460 -13%
Arun 65000 69185 6% 3408 3335 -2%
Chichester 47000 50870 8% 2365 2430 3%
Crawley 41000 44150 8% 2545 2432 -4%
Horsham 52000 57580 11% 3331 3449 4%
Mid Sussex 53000 59345 12% 3464 3314 -4%
Worthing 45000 46800 4% 2383 2299 -4%

* Note to table: The population growth figures are based on ONS projections.
Table A3.6a: Imports and exports of school pupils across LA boundaries (full version
of Table 6 in the main text)
Number
of
learners
Number
who
stay
64
% of
learners
taught
Number Number Net Number
exported imported imports taught
Adur 378 281 97 15 -82 296 78
Arun 1010 851 159 43 -116 894 89
Ashford 1264 1052 212 106 -106 1158 92
Aylesbury Vale
Basingstoke and
2024 1798 226 440 214 2238 111
Deane 154 0 154 0 -154 0 0
Bracknell Forest 926 625 301 165 -136 790 85
Brighton and Hove 780 770 10 97 87 867 111
Canterbury 1515 1345 170 390 220 1735 115
Cherwell 1011 745 266 35 -231 780 77
Chichester 654 584 70 229 159 813 124
Chiltern 1387 1228 159 667 508 1895 137
Crawley 999 990 9 106 97 1096 110
Dartford 738 601 137 740 603 1341 182
Dover 1274 1157 117 136 19 1293 101
East Hampshire 38 0 38 0 -38 0 0
Eastbourne 20 0 20 0 -20 0 0
Eastleigh 13 1 12 0 -12 1 8
Elmbridge 422 319 103 356 253 675 160
Epsom and Ewell 621 437 184 514 330 951 153
Fareham 105 0 105 0 -105 0 0
Gosport 248 248 73 73 321 129
Gravesham 1129 1018 111 254 143 1272 113
Guildford 895 812 83 340 257 1152 129
Hart 228 201 27 44 17 245 107
Hastings 405 396 9 28 19 424 105
Havant 144 96 48 79 31 175 122
Horsham 691 603 88 97 9 700 101
Isle of Wight 1310 1309 1 12 11 1321 101
Lewes 199 144 55 6 -49 150 75
Maidstone 1633 1399 234 553 319 1952 120
Medway 2887 2753 134 332 198 3085 107
Mid Sussex 716 671 45 173 128 844 118
Milton Keynes 2462 2257 205 94 -111 2351 95
Mole Valley 801 534 267 263 -4 797 100
New Forest 613 605 8 184 176 789 129
Oxford 1108 868 240 98 -142 966 87
Portsmouth 27 0 27 0 -27 0 0
Reading
Reigate and
1133 603 530 422 -108 1025 90
Banstead 587 328 259 194 -65 522 89
Rother 194 0 194 0 -194 0 0
Runnymede 336 231 105 181 76 412 123
Rushmoor 113 0 113 0 -113 0 0
Sevenoaks 1190 219 971 86 -885 305 26
Shepway 914 760 154 51 -103 811 89
Slough 1306 1012 294 510 216 1522 117

South Bucks 814 323 491 352 -139 675 83
South Oxfordshire 1049 920 129 342 213 1262 120
Southampton 60 55 5 44 39 99 165
Spelthorne 183 132 51 79 28 211 115
Number
of
learners
Number
who
stay
65
% of
learners
taught
Number Number Net Number
exported imported imports taught
Surrey Heath 648 540 108 69 -39 609 94
Swale 1493 1316 177 196 19 1512 101
Tandridge 661 489 172 592 420 1081 164
Test Valley 8 1 7 0 -7 1 13
Thanet 1192 1107 85 81 -4 1188 100
Tonbridge and Malling 1435 739 696 917 221 1656 115
Tunbridge Wells 1450 973 477 935 458 1908 132
Vale of White Horse 1057 845 212 215 3 1060 100
Waverley 84 32 52 100 48 132 157
Wealden 1388 1100 288 249 -39 1349 97
West Berkshire 1710 1624 86 566 480 2190 128
West Oxfordshire 1018 988 30 304 274 1292 127
Winchester
Windsor and
18 18 0 -18 0 0
Maidenhead 1474 1140 334 446 112 1586 108
Woking 344 165 179 43 -136 208 60
Wokingham 1860 1476 384 314 -70 1790 96
Worthing 33 0 33 0 -33 0 0
Wycombe 2145 1699 446 450 4 2149 100
Table A3.6b: Imports and exports of college pupils across LA boundaries (full version
of Table 6 in the main text)
Number
of
learners
Number
who
stay
% of
learners
taught
Number Number Net Number
exported imported imports taught
Adur 720 37 683 122 -561 159 22
Arun 1635 37 1,598 4 -1594 41 3
Ashford 983 483 500 213 -287 696 71
Aylesbury Vale
Basingstoke and
1298 771 527 433 -94 1204 93
Deane 3131 2,623 508 1,133 625 3756 120
Bracknell Forest 1225 586 639 202 -437 788 64
Brighton and Hove 3145 2,533 612 1,360 748 3893 124
Canterbury 1056 923 133 1,824 1691 2747 260
Cherwell 1182 699 483 305 -178 1004 85
Chichester 1249 800 449 2,208 1759 3008 241
Chiltern 468 211 257 681 424 892 191
Crawley 1009 657 352 930 578 1587 157
Dartford 783 437 346 1,305 959 1742 222
Dover 887 235 652 132 -520 367 41
East Hampshire 2257 1,106 1,151 624 -527 1730 77
Eastbourne 1536 1,336 200 1,019 819 2355 153
Eastleigh 2565 1,509 1,056 1,828 772 3337 130
Elmbridge 1190 792 398 2,283 1885 3075 258
Epsom and Ewell 507 227 280 1,074 794 1301 257

Fareham 2181 626 1,555 720 -835 1346 62
Gosport 1274 643 631 351 -280 994 78
Gravesham 880 511 369 716 347 1227 139
Guildford 1219 550 669 1,932 1263 2482 204
Hart 1552 8 1,544 3 -1541 11 1
Hastings 1400 692 708 544 -164 1236 88
Number
of
learners
Number
who
stay
66
% of
learners
taught
Number Number Net Number
exported imported imports taught
Havant 2327 1,891 436 3,241 2805 5132 221
Horsham 1786 1,118 668 590 -78 1708 96
Isle of Wight 1253 1,083 170 26 -144 1109 89
Lewes 1505 844 661 1,454 793 2298 153
Maidstone 994 615 379 569 190 1184 119
Medway 2411 1,849 562 540 -22 2389 99
Mid Sussex 1786 622 1,164 149 -1015 771 43
Milton Keynes 2207 1,741 466 497 31 2238 101
Mole Valley 564 2 562 11 -551 13 2
New Forest 2540 1,910 630 1,579 949 3489 137
Oxford 697 571 126 1,003 877 1574 226
Portsmouth 3325 1,200 2,125 348 -1777 1548 47
Reading 1209 784 425 1,371 946 2155 178
Reigate and Banstead 1643 1,188 455 1,436 981 2624 160
Rother 1352 754 598 610 12 1364 101
Runnymede 785 207 578 812 234 1019 130
Rushmoor 1610 1,262 348 3,045 2697 4307 268
Sevenoaks 693 3 690 2 -688 5 1
Shepway 834 390 444 359 -85 749 90
Slough 1348 671 677 769 92 1440 107
South Bucks 410 1 409 1 -408 2 0
South Oxfordshire 2051 1,408 643 1,049 406 2457 120
Southampton 3784 2,632 1,152 1,034 -118 3666 97
Spelthorne 1366 290 1,076 179 -897 469 34
Surrey Heath 986 19 967 5 -962 24 2
Swale 1138 309 829 7 -822 316 28
Tandridge 647 2 645 -645 2 0
Test Valley 2272 390 1,882 349 -1533 739 33
Thanet 1353 989 364 176 -188 1165 86
Tonbridge and Malling 850 336 514 1,474 960 1810 213
Tunbridge Wells 649 89 560 204 -356 293 45
Vale of White Horse 1065 604 461 323 -138 927 87
Waverley 1700 907 793 979 186 1886 111
Wealden 1323 29 1,294 9 -1285 38 3
West Berkshire 966 485 481 99 -382 584 60
West Oxfordshire 853 410 443 131 -312 541 63
Winchester
Windsor and
2313 1,488 825 2,514 1689 4002 173
Maidenhead 893 227 666 734 68 961 108
Woking 1259 420 839 193 -646 613 49
Wokingham 1278 17 1,261 12 -1249 29 2
Worthing 1841 1,275 566 844 278 2119 115
Wycombe 1209 247 962 125 -837 372 31

2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Aylesbury Vale 2046 852 116 232 319 3565 2553 1063 111 222 501 4451 508 211 -5 -10 181 885
Chiltern 1412 341 246 68 92 2158 1529 369 248 69 154 2369 117 28 3 1 62 211
South Bucks 831 277 91 70 96 1364 951 317 91 70 158 1587 120 40 1 0 62 223
Wycombe 2160 917 338 157 217 3789 2225 944 315 147 331 3962 65 28 -23 -11 114 173
Bracknell Forest 906 914 528 188 103 2639 1056 1066 463 164 144 2893 150 151 -65 -24 41 254
Milton Keynes 2572 1563 61 214 367 4778 3075 1868 60 210 586 5799 502 305 -1 -4 219 1021
Cherwell 1118 1008 709 183 169 3188 1185 1067 681 176 265 3375 66 60 -28 -7 96 187
Oxford 1165 514 535 155 144 2513 1299 572 511 147 222 2751 134 59 -25 -7 78 238
South Oxfordshire 745 1168 398 287 267 2866 777 1219 364 262 396 3018 32 50 -34 -25 128 152
Vale of White Horse 1159 760 486 247 225 2877 1268 831 451 227 341 3119 109 71 -35 -20 116 242
West Oxfordshire 861 496 362 172 158 2048 1077 621 342 163 245 2449 217 125 -20 -9 87 401
Reading 1212 901 416 200 183 2912 1128 839 347 167 246 2727 -84 -62 -69 -33 63 -185
Slough 1308 1031 0 222 159 2720 1292 1019 0 195 227 2733 -16 -12 0 -27 68 13
West Berkshire 1812 672 631 216 216 3549 1982 735 567 196 318 3799 170 63 -64 -21 102 250
Windsor and
Maidenhead 1527 655 916 131 130 3359 1838 789 812 116 187 3741 311 134 -104 -15 57 383
Wokingham 1669 853 497 239 221 3479 1822 931 485 234 344 3816 153 78 -11 -6 123 337
Brighton & Hove 757 2538 746 266 346 4652 766 2571 687 245 527 4796 10 33 -59 -21 181 144
Eastbourne 20 1371 146 74 111 1721 26 1790 145 74 180 2214 6 419 0 0 68 493
Hastings 416 1168 147 90 136 1957 475 1332 145 88 214 2255 59 164 -2 -2 79 298
Lewes 205 1326 160 81 121 1893 246 1589 150 76 183 2245 41 262 -9 -5 62 351
Rother 203 1182 141 75 112 1713 243 1415 146 76 183 2063 40 233 4 1 71 350
Wealden 1455 1165 446 70 105 3241 1605 1286 427 67 162 3547 151 121 -18 -3 57 306
Adur 378 588 115 65 65 1211 462 718 103 58 95 1437 84 130 -12 -6 30 226
Arun 1016 1272 243 169 169 2870 1165 1458 243 169 277 3312 148 186 -1 0 108 442
Chichester 671 1013 205 113 113 2114 766 1155 217 119 196 2453 94 143 12 6 83 339
Crawley 989 701 156 137 137 2120 1131 802 156 136 224 2449 142 101 0 -1 87 329
Horsham 704 1544 326 125 125 2824 849 1863 341 131 214 3397 145 318 15 6 89 573
Table A4.1: Growth in learner numbers to 2013 (100% participation)
Appendix 4: Tables illustrating 100% participation

69
Mid Sussex 714 1526 307 134 134 2815 850 1818 302 131 214 3315 136 291 -5 -2 80 500
2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Worthing 34 1621 181 119 119 2075 35 1712 174 114 187 2223 2 91 -8 -5 68 148
Basingstoke and
Deane 162 2700 37 197 345 3441 172 2874 38 203 577 3865 10 175 1 6 232 424
East Hampshire 37 2078 132 105 184 2537 43 2408 126 99 281 2958 6 330 -6 -6 97 421
Eastleigh 13 2325 112 130 228 2808 13 2341 104 121 342 2921 0 16 -9 -9 114 113
Fareham 110 1986 133 106 186 2521 110 1988 120 96 272 2586 0 2 -13 -10 86 65
Gosport 261 1061 12 93 162 1590 289 1176 11 83 236 1796 28 115 -1 -10 74 206
Hart 226 1470 189 56 97 2039 270 1758 188 55 157 2428 44 287 -1 -1 60 389
Havant 152 2060 79 131 229 2651 158 2130 71 117 331 2806 5 70 -8 -14 103 155
New Forest 623 2180 118 164 287 3371 722 2527 114 160 453 3976 99 347 -3 -4 166 605
Rushmoor 105 1443 82 83 145 1860 116 1582 77 77 219 2071 10 139 -6 -6 74 211
Test Valley 9 2045 76 121 212 2463 10 2363 75 116 327 2890 1 317 -2 -5 115 427
Winchester 19 1917 23 183 323 2466 24 2382 21 176 495 3098 5 465 -2 -7 172 632
Isle of Wight 1311 940 122 123 315 2812 1583 1135 125 125 528 3496 272 195 3 2 213 684
Portsmouth 28 2988 331 143 285 3775 30 3117 285 122 398 3952 1 129 -46 -21 113 176
Southampton 60 3078 252 228 355 3973 65 3321 223 203 509 4321 5 242 -28 -26 154 348
Ashford 1315 745 250 95 158 2563 1538 871 273 106 283 3070 223 126 23 10 125 507
Canterbury 1580 774 281 109 183 2927 1989 974 263 102 278 3607 409 200 -18 -7 96 680
Dartford 774 594 130 77 127 1702 1071 822 132 80 212 2317 297 228 2 3 85 615
Dover 1333 646 231 93 155 2458 1485 720 213 86 233 2737 152 74 -18 -7 78 279
Gravesham 1168 682 229 82 136 2297 1225 716 209 75 200 2424 58 34 -20 -8 64 127
Maidstone 1706 717 315 103 174 3015 1950 820 300 98 271 3440 244 103 -15 -5 97 425
Sevenoaks 1254 512 237 73 120 2197 1562 638 221 69 183 2673 308 126 -16 -4 62 476
Shepway 940 648 188 75 125 1977 1184 817 186 74 202 2463 244 168 -2 -1 76 486
Swale 1544 847 276 113 189 2970 1796 985 274 114 307 3475 252 138 -3 0 118 505
Thanet 1248 929 108 157 264 2706 1555 1158 111 161 441 3426 307 229 3 3 177 719
Tonbridge and Malling 1508 610 272 91 153 2634 1778 719 258 87 237 3078 270 109 -14 -4 84 444
Tunbridge Wells 1517 480 323 62 106 2487 1771 560 306 59 164 2860 254 80 -17 -3 58 373
Medway 2930 1743 185 258 406 5522 3475 2067 170 238 606 6556 545 324 -15 -20 199 1034
Elmbridge 441 1063 449 78 91 2121 686 1654 471 82 156 3050 245 591 23 4 66 929
Epsom and Ewell 659 399 319 52 61 1490 794 481 306 50 96 1726 135 82 -14 -2 35 236
Guildford 947 994 551 116 131 2739 1177 1236 537 113 211 3275 230 242 -14 -3 80 536

70
Note: It is assumed that WBL will increase by 75% in this scenario.
2006 2013 Increase 2006-2013
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Runnymede 359 676 291 61 71 1458 404 761 297 62 119 1643 45 85 6 1 48 185
Spelthorne 188 1240 406 82 97 2013 180 1192 384 78 151 1985 -7 -48 -22 -4 53 -27
Surrey Heath 670 910 380 54 65 2079 649 883 380 54 105 2071 -20 -27 0 0 40 -8
Tandridge 697 527 371 61 71 1727 926 701 348 57 109 2141 229 173 -23 -4 38 414
Waverley 86 1613 528 79 91 2398 130 2427 471 70 133 3231 44 814 -57 -9 42 834
Woking 366 1146 402 76 88 2077 345 1081 378 71 135 2010 -21 -65 -24 -5 47 -67
Mole Valley 842 475 438 48 56 1859 941 530 421 46 88 2026 99 56 -17 -2 32 167
Reigate and Banstead 621 1403 546 131 156 2858 645 1456 522 125 242 2991 24 54 -24 -6 86 133

71
2006 2015 Increase 2006-2015
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Aylesbury Vale 2046 852 116 232 319 3565 2685 1118 112 223 537 4674 639 266 -5 -9 217 1109
Chiltern 1412 341 246 68 92 2158 1574 380 254 69 165 2442 162 39 8 2 73 284
South Bucks 831 277 91 70 96 1364 942 314 88 68 164 1577 111 37 -2 -2 68 213
Wycombe 2160 917 338 157 217 3789 2251 955 315 147 354 4022 91 39 -23 -11 138 233
Bracknell Forest 906 914 528 188 103 2639 1083 1093 457 163 156 2952 177 179 -71 -25 53 313
Milton Keynes 2572 1563 61 214 367 4778 3126 1899 59 207 621 5912 553 336 -2 -7 254 1134
Cherwell 1118 1008 709 183 169 3188 1230 1108 692 179 289 3498 112 101 -18 -5 120 310
Oxford 1165 514 535 155 144 2513 1338 590 506 146 238 2817 173 76 -30 -9 94 304
South Oxfordshire 745 1168 398 287 267 2866 781 1224 357 255 415 3032 35 55 -41 -32 147 166
Vale of White Horse 1159 760 486 247 225 2877 1262 828 439 221 354 3104 103 68 -47 -26 129 227
West Oxfordshire 861 496 362 172 158 2048 1085 626 344 162 261 2478 225 130 -18 -10 103 430
Reading 1212 901 416 200 183 2912 1094 813 332 160 259 2658 -118 -88 -84 -40 76 -254
Slough 1308 1031 0 222 159 2720 1224 965 0 183 229 2601 -84 -66 0 -39 70 -119
West Berkshire 1812 672 631 216 216 3549 2019 749 559 193 337 3857 206 77 -72 -24 121 308
Windsor and
Maidenhead 1527 655 916 131 130 3359 1939 832 823 118 208 3919 412 177 -93 -13 77 560
Wokingham 1669 853 497 239 221 3479 1808 923 475 229 370 3807 139 71 -21 -10 150 328
Brighton & Hove 757 2538 746 266 346 4652 768 2576 673 240 546 4803 11 38 -73 -26 200 151
Eastbourne 20 1371 146 74 111 1721 27 1888 148 76 200 2340 7 517 3 2 89 619
Hastings 416 1168 147 90 136 1957 477 1339 141 88 231 2276 61 171 -7 -2 95 319
Lewes 205 1326 160 81 121 1893 249 1608 146 74 195 2273 44 282 -14 -6 74 380
Rother 203 1182 141 75 112 1713 243 1411 141 75 197 2066 39 229 -1 0 85 353
Wealden 1455 1165 446 70 105 3241 1662 1332 432 68 178 3671 207 166 -14 -2 73 430
Adur 378 588 115 65 65 1211 472 734 100 56 98 1460 94 145 -15 -9 33 249
Arun 1016 1272 243 169 169 2870 1172 1467 238 166 291 3335 156 195 -6 -3 122 465
Chichester 671 1013 205 113 113 2114 758 1143 211 116 203 2430 86 130 6 3 90 316
Crawley 989 701 156 137 137 2120 1125 798 149 131 229 2432 136 97 -7 -6 92 312
Horsham 704 1544 326 125 125 2824 861 1890 340 130 228 3449 158 346 14 5 103 625
Mid Sussex 714 1526 307 134 134 2815 850 1818 294 128 224 3314 136 291 -13 -6 90 499
Table A4.2: Growth in learner numbers to 2015 (100% participation)

72
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Basingstoke and
Deane 162 2700 37 197 345 3441 172 2862 38 202 618 3891 10 162 1 4 273 450
East Hampshire 37 2078 132 105 184 2537 44 2437 123 98 301 3002 6 359 -10 -7 117 465
Eastleigh 13 2325 112 130 228 2808 13 2303 101 118 362 2897 0 -22 -11 -12 134 89
Fareham 110 1986 133 106 186 2521 112 2021 120 96 293 2641 2 35 -13 -10 107 120
Gosport 261 1061 12 93 162 1590 288 1172 10 79 242 1792 27 111 -1 -14 80 202
Hart 226 1470 189 56 97 2039 283 1846 189 56 170 2544 58 375 0 0 73 505
Havant 152 2060 79 131 229 2651 157 2124 68 114 349 2812 5 64 -11 -17 120 161
New Forest 623 2180 118 164 287 3371 724 2535 112 157 480 4008 101 355 -5 -7 193 637
Rushmoor 105 1443 82 83 145 1860 116 1584 74 74 227 2075 10 141 -9 -9 82 215
Test Valley 9 2045 76 121 212 2463 10 2337 71 111 342 2871 1 291 -5 -9 130 408
Winchester 19 1917 23 183 323 2466 24 2435 20 176 543 3199 5 518 -3 -8 220 733
Isle of Wight 1311 940 122 123 315 2812 1597 1145 124 125 559 3550 285 205 2 2 245 738
Portsmouth 28 2988 331 143 285 3775 30 3137 279 121 421 3989 1 149 -52 -22 137 214
Southampton 60 3078 252 228 355 3973 65 3301 215 194 529 4304 4 223 -37 -34 174 331
Ashford 1315 745 250 95 158 2563 1538 871 271 105 304 3089 223 126 21 10 145 526
Canterbury 1580 774 281 109 183 2927 2081 1020 263 102 299 3765 502 246 -19 -7 116 838
Dartford 774 594 130 77 127 1702 1067 819 127 76 218 2307 294 225 -3 -1 91 605
Dover 1333 646 231 93 155 2458 1501 728 207 84 245 2765 168 82 -24 -9 90 307
Gravesham 1168 682 229 82 136 2297 1267 740 209 75 217 2508 99 58 -20 -7 81 211
Maidstone 1706 717 315 103 174 3015 1970 828 294 96 284 3472 264 111 -21 -7 110 457
Sevenoaks 1254 512 237 73 120 2197 1631 667 221 68 197 2784 378 154 -16 -5 76 587
Shepway 940 648 188 75 125 1977 1214 838 185 74 215 2525 275 189 -4 -2 89 548
Swale 1544 847 276 113 189 2970 1825 1001 270 112 327 3536 282 154 -6 -1 137 566
Thanet 1248 929 108 157 264 2706 1545 1151 105 157 458 3416 297 222 -3 -1 194 710
Tonbridge and Malling 1508 610 272 91 153 2634 1830 740 256 87 253 3165 322 130 -16 -5 100 531
Tunbridge Wells 1517 480 323 62 106 2487 1733 548 294 56 167 2798 216 68 -29 -6 62 311
Medway 2930 1743 185 258 406 5522 3495 2080 165 231 635 6606 566 337 -20 -27 229 1085
Elmbridge 441 1063 449 78 91 2121 747 1800 487 84 173 3290 306 737 38 7 82 1169
Epsom and Ewell 659 399 319 52 61 1490 842 510 312 51 104 1819 184 111 -8 -1 43 329
Guildford 947 994 551 116 131 2739 1233 1295 537 112 223 3400 286 301 -15 -3 92 661
Mole Valley 842 475 438 48 56 1859 960 541 420 46 94 2061 118 66 -18 -2 38 202
2006 2015 Increase 2006-2015
Worthing 34 1621 181 119 119 2075 37 1770 174 116 202 2299 3 149 -7 -4 83 224

73
2006 2015 Increase 2006-2015
Part
Part
Part
District School College Indep time WBL Total School College Indep time WBL Total School College Indep time WBL Total
Spelthorne 188 1240 406 82 97 2013 178 1176 377 77 159 1967 -10 -64 -29 -6 62 -46
Surrey Heath 670 910 380 54 65 2079 636 864 375 53 113 2041 -34 -46 -5 -1 48 -38
Tandridge 697 527 371 61 71 1727 957 724 346 57 117 2200 260 197 -25 -4 45 473
Waverley 86 1613 528 79 91 2398 138 2581 465 69 141 3394 52 968 -63 -9 50 996
Woking 366 1146 402 76 88 2077 339 1064 369 70 141 1983 -26 -83 -33 -6 53 -94
Reigate and Banstead 621 1403 546 131 156 2858 657 1482 528 126 262 3055 35 80 -19 -5 106 197
Runnymede 359 676 291 61 71 1458 401 755 285 60 122 1624 42 79 -6 -1 51 166

74
30
29
43
33
20
42
23
22
17
31
40
24
41
21
39
25
26
19 18
32
34
28
38
66
56
45
52
60
65
37
35
59
61
51
50
48
67
55
64
46
57
62
53
54
36
3
1
2
4
47
49
44
5
63
7
6
58
27
15
14
9
8
12
16
13
10
11
Key to map of South East
Appendix 5: Map of the South East with key to districts

75
Windsor and
Reigate and
Maidenhead 5 East Hampshire 34
Banstead 61
Slough 6 Hart 35 Runnymede 62
Buckinghamshire South Bucks 7 Rushmoor 36 Spelthorne 63
Chiltern 8 Basingstoke and Deane 37 Surrey Heath 64
Wycombe 9 Test Valley 38 Tandridge 65
Aylesbury Vale 10 Eastleigh 39 Waverley 66
Milton Keynes 11 New Forest 40 Woking 67
Oxfordshire Oxford 12 Southampton 41
Cherwell 13 Portsmouth 42
South Oxfordshire 14 Isle of Wight 43
Vale of White Horse 15 Medway 44
West Oxfordshire 16 Kent Ashford 45
East Sussex Hastings 17 Canterbury 46
Rother 18 Dartford 47
Wealden 19 Dover 48
Eastbourne 20 Gravesham 49
Lewes 21 Maidstone 50
Brighton & Hove 22 Sevenoaks 51
West Sussex Worthing 23 Shepway 52
Arun 24 Swale 53
Chichester 25 Thanet 54
Horsham 26 Tonbridge and Malling 55
Crawley 27 Tunbridge Wells 56
Mid Sussex 28
Adur 29
Berkshire West Berkshire 1 Hampshire Gosport 30 Surrey Elmbridge 57
Reading 2 Fareham 31 Epsom and Ewell 58
Wokingham 3 Winchester 32 Guildford 59
Bracknell Forest 4 Havant 33 Mole Valley 60
Key to map:

Projections of post-compulsory education learner numbers in the
South East of England.
1. Introduction
We were commissioned by the LSC (South East) in March 2008 to develop a model
to project forward participation in post-compulsory education, building on some
existing work within the LSC (see the Literature Review section below). The brief
asked for projections of population, participation rates and learner numbers to
2020, with a system of warnings where the forecasts were thought to carry a great
deal of uncertainty. The purpose of the report is to provide a framework within
which to make judgements about capital bids coming forward from schools and
colleges in the coming years. This is pertinent because of the recent
announcement of the raising of the compulsory participation age to 17 in 2013 and
to 18 in 2015. Subject to trends in the age cohort, this will increase the demand on
schools and colleges for education post-16. This raising of the school-leaving age
is the main factor driving our forecasts of demand for post-16 education though, as
we will show, this is moderated by anticipated changes in the population age
cohort.
We have constructed a spreadsheet model to calculate projections of learner
numbers at the level of district and unitary local authorities, and allow different
assumptions or scenarios to be tested. This report explains the workings of the
model Report and provides prepared forecasts for on the basis Learning of different and scenarios. Skills Council One is by a central
or baseline forecast, examining the anticipated effect of raising the compulsory
participation Michael age (where Barrow, we still assume Department a small element of Economics,
of non-participation, i.e.
truancy). One alternative then University examines a of different Sussex assumption about the growth of
work-based learning and its effect upon the forecast values. This alternative is a
realistic one but Researchers: part of the purpose Ray of Bachan, including Annelle it is to show Bellony, how the model can be
used to look at different scenarios. Alvaro Monge We also (in Zegarra, an appendix) present a third
scenario, which assumes 100% participation everywhere and thus illustrates the
(estimated) maximum possible University effect of the of government’s Sussex. policy changes.
It is important to be aware of the purpose of the model and also of its limitations. It
is intended to provide For more a consistent information, framework please for evaluating contact: capital bids and
therefore applies the same methodology to all districts in the South East, using the
Jan Jackson
same data sources for all. It is quite possible therefore that our projections may
differ from others produced Learning by (e.g.) and local Skills councils Council or colleges, where different
assumptions have been made and/or different data sources used. (The LSC is
fully aware that in some areas of Price the South House East, some local authorities are in
discussions with central government 53 Queens about the Road reliability (or validity) of ONS data for
their particular circumstances.) However, it should then be possible to explore the
reasons for any differences and gain Brighton a better appreciation of likely future outcomes.
For example, it is possible that several BN1 colleges 3XB in a district are all independently
projecting an increase in market share and hence growth in learner numbers.
However, aggregating these Jan.jackson@lsc.gov.uk
would result in an unattainable overall outcome, and
this should be observable by comparison with forecasts given by our model.
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