A comparison of Coast to Capital with comparator …...Unlike Cheshire & Warrington or Berkshire,...
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A comparison of Coast to Capital with comparator Local Enterprise Partnerships Report prepared for Coast to Capital LEP by Michael Barrow Guillermo Larbalastier University of Sussex March 2018 Contact: [email protected]
Transcript of A comparison of Coast to Capital with comparator …...Unlike Cheshire & Warrington or Berkshire,...
A comparison of Coast to Capital with comparator Local Enterprise Partnerships
Report prepared for Coast to Capital LEP by
Michael Barrow
Guillermo Larbalastier
University of Sussex
March 2018
Contact: [email protected]
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A Comparison of the Coast to Capital area with comparator
Local EnterprisePartnerships
Introduction
We were asked to consider questions around the performance of the Coast to Capital (C2C)
local enterprise partnership (LEP) in support of their Strategic Plan. In particular we were
tasked with looking at suitable comparator LEPs and to compare performance in terms of
Gross value added
Employment
Business start-ups
Large Businesses
We have presented our findings in the text in the form of graphs, with the underlying tables in
an appendix. We provide interpretation of the graphs as we go along.
We have made use of a loose unifying framework, decomposing gross value added per capita
(GVA pc) into constituent parts of GVA per hour worked (productivity), hours per worker,
the employment rate and the ratio of the working population to total population.
For business structure, we have looked at the growth in business numbers and separated it
into births and deaths, with a look also at survival of new firms. We have also dug down, as
far as we can currently go, into the structure of industry in C2C and comparator LEPs
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Executive summary
We made comparisons between Coast to Capital and five comparator LEPs:
Buckinghamshire Thames Valley (Bucks)
Cheshire and Warrington (Ch & Warr)
Enterprise M3 (Ent M3)
South East (SE)
Thames Valley Berkshire (Berks)
These were chosen on the basis of Local Enterprise Partnerships (LEPs) with high levels of
productivity (gross value added per hour) and/or geographical similarity to C2C, e.g.
bordering London.
Comparisons were made in terms of income per head, productivity, employment, business
structure and start-ups. We included historical data where possible, going back as far as 1997
in some cases, in order to see what trends exist. This complements comparisons made across
LEPs at a particular moment in time, usually the latest year for which data are available.
All data were sourced from public sources, either NOMIS or the Office of National Statistics
(ONS) more generally.
Income – similar slow growth across all LEPs
Across the six LEPs (C2C and its five comparators) growth of household income (per capita)
is about 3% p.a. over the period 1997-2015. This is similar to the overall average for all
LEPs, though the six all have higher levels of income than the average for all LEPs.
Once adjusted for inflation, growth averages just 1% p.a. and all the growth occurred before
2007; incomes since then have slightly declined.
Productivity – stagnant, particularly since 2007
Household income is not an accurate measure of a region’s productivity, for a variety of
reasons. Some residents migrate out of an LEP for work elsewhere and also incomes include
sources such as pensions and transfer payments that are unrelated to local economic
performance.
Hence we measure gross value added (GVA) per hour worked as a more fundamental
measure of local productivity. After adjusting for inflation, we find this has grown at an
average rate of 0.5% p.a. and all of the growth occurred before the financial crisis of 2007.
Afterwards, productivity growth has been stagnant. C2C is in the middle of the pack of
comparators as far as productivity is concerned, a similar position as for income.
For both incomes and productivity, differences in growth rates between the comparator LEPs
are not statistically significant and there is limited evidence of a regression towards the mean.
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Employment – strong recent growth in C2C but much of it in part-time jobs
C2C has a relatively low value for hours worked per employee, which has been falling over
the period 2004-16, in contrast to many of the other LEPs. Part of the reason for this is that
C2C has (2016) a relatively high proportion of part-time jobs, 29%, the highest of the
comparator group. The average for all LEPs is 25% and two of the comparators (Berkshire,
Cheshire and Warrington) are at 23%.
The employment rate in all the comparator LEPs is above the overall average and C2C in
particular has seen rapid recent growth in the employment rate, though much of this
represents part-time jobs.
There are also changes in the working age population, with this declining since around 2007.
This might be due to increasing net out-migration of workers as a result of the economic
downturn in addition to any ‘natural’ changes due to an ageing population. C2C has typically
had a low ratio of working population to overall population relative to its comparators but the
recent fall in the ratio has been modest in C2C compared to other areas.
Business Start-ups – C2C similar to comparators but less dynamic than the rest
of the country
Data limitations mean it is difficult to follow the trend of births of new firms over time.
However, it is apparent that C2C follows the pattern of its comparators with similar birth and
death rates of firms. These six LEPs tend to be less dynamic than much of the rest of the
country, where the birth rate and death rate tends to be higher.
When it comes to survival rates, C2C is once again in the middle of the comparator pack, its
one-year survival rate of 94% being equal highest, its five-year rate being 46.5%, nearer the
middle of the group.
Industry Structure – dominated by micro-businesses but not so important for
employment
C2C does not have one stand-out sector, unlike Berkshire (Communications) or Cheshire and
Warrington (Manufacturing). It is a leader in Finance and Insurance though this is still only a
small part of GVA in the area.
In terms of size of business, C2C has a relatively high share of micro-businesses (1-9
employees), second only to Buckinghamshire. However, if we measure in terms of
employment, the micro-business sector is not so dominant. So micro-businesses account for
about 90% of firms, but 40% of employment. This is still the largest share of any type of
business; large businesses, for example, have about a 24% share of employment.
In conclusion
It is easy to focus in on particular statistics and identify differences between areas but the
general message is that Coast to Capital is not so different from its comparators. It is often in
the middle of the pack as far as a number of comparisons we have made are concerned.
Unlike Cheshire & Warrington or Berkshire, where a fairly obvious economic strategy
presents itself, C2C is more of an all-rounder, which is perhaps advantageous in rendering it
less vulnerable to downturns in a particular sector.
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Any important differences between LEPs will therefore lie at a higher level of disaggregation
than we have used. Unfortunately, less data is available at disaggregated levels, with
employment probably the best available indicator.
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Incomes and productivity – a framework
The economic performance of an area can be examined in terms of its income and
productivity. These are obviously related but are not the same, as will be shown below. We
can summarise the relationships we examine by means of a fairly simple accounting identity.
The relationship between many of the variables can be summarised in the following equation:
GDHI GDHI GVA H LF WAPop
Pop GVA H LF WAPop Pop
where
GDHI = gross domestic household income
GVA = gross value added
Pop = population
H = hours worked (in total)
LF = labour force
WAPop = working age population
In non-mathematical terms this shows that prosperity (GDHI/Pop) depends upon
productivity (GVA/H),
hours worked by workers (H/LF) which largely reflects part-time and full-time work
the employment rate (LF/WAPop) and
the age profile (WAPop/Pop)
There is also an adjustment term (GDHI/GVA) which in aggregate is approximately one
(these are two ways of measuring output or income) but which can differ at the regional or
LEP level, for various reasons. For example, there are various transfers through the tax-
benefit system and there are patterns of work migration across local boundaries.
Hence we examine all of these ratios in order to gain some understanding of the economic
performance of the Coast to Capital region.
Income – similar slow growth across all LEPs
We start off by looking at gross domestic household income, measured per capita (GDHI)
over the period for which we have data, 1997-2017. We compare Coast to Capital (C2C)
with a set of comparator authorities:
Buckinghamshire Thames Valley (Bucks)
Cheshire and Warrington (Ch & Warr)
Enterprise M3 (Ent M3)
South East (SE)
Thames Valley Berkshire (Berks)
chosen because they are amongst those with high levels of productivity and/or are in some
sense geographically similar, bordering the London region.
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Figure 1: Gross domestic household income per capita (£ nominal)
We have chosen to present most data in nominal terms, i.e. not correcting for inflation. This
avoids slightly tricky questions of deflators and, since the task is to compare LEPs, does not
invalidate the analysis.
Figure 1 shows all the comparator authorities growing at a similar rate, about 3% per annum.
C2C lies in the midst of the comparator group, all of which are above the average GDHI of
all LEPs. There is little evidence of a ‘regression to the mean’ or convergence of the LEPs.
There is no statistically significance evidence of any difference between the six LEPs’ rates
of growth over this period.
Figure 2 shows the effect of inflation adjustment, using the CPI. The ranking of LEPs is
obviously unchanged but one can now see the sustained effect of the 2007 financial crisis and
its aftermath. In real terms, growth is about 1% per annum over the whole 18 year period.
These two charts suggest LEPs’ performance is substantially determined by macroeconomic
factors rather than individual policies. Henceforth we use nominal figures rather than real in
our charts, unless otherwise stated.
10,000
12,000
14,000
16,000
18,000
20,000
22,000
24,000
26,000
28,000
GDHI per capita
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Figure 2: Gross domestic household income per capita (2015 prices)
GDHI and GVA per capita
GDHI per capita provides a measure of how well off the residents of an area are. However,
this does not necessarily reflect the economic performance of the area, due to several factors,
such as not measuring company profits, the effects of taxes and benefits, and also work
migration patterns. For economic performance within the area, it is better to examine gross
value added (GVA), which is a production based measure.
Figure 3 illustrates GVA per capita across the comparator group in a similar manner to
GDHI. The growth rate is 3.3% p.a. slightly higher than for GDHI, reflecting the squeeze on
household incomes over the period. One can also note that C2C has fallen, over time, below
the level of the all LEP average for GVA per capita.
15,000
17,000
19,000
21,000
23,000
25,000
27,000
29,000
GDHI per capita, inflation adjusted
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Figure 3: Gross value added per capita (£ nominal)
However, this measure of productivity, GVA per capita, is misleading since the numerator
includes all the value added of the area but the denominator is residents rather than workers.
Hence an area with intrinsically high productivity might have a low GVA per capita if it has,
say, a high proportion of retired residents and of out-migrating workers (e.g. commuting to
London).
Productivity – very slow growth, particularly since 2007
From our accounting equation we now examine GVA per hour to give a more precise
measure of workplace productivity. This is shown in Figure 4.
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
GVA per capita
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Figure 4: GVA per hour worked
Once again we see similar performance across the LEPs, with annual growth of
approximately 2.2% p.a. (nominal). This is less than the growth rate of GVA per capita
(3.3%), but over a different timescale (due to data availability). If we measure both over
2004-2015 we find growth rates of 2.3% and 2.4% (GVA per capita growing faster), which is
trivially different. This implies that hours worked per capita have not changed much over
time.
If we deflate the GVA per hour series to real terms we obtain the result shown in Figure 5.
This reveals scarcely any real productivity improvement over the whole period, less than
0.5% per annum, averaged over all the LEPs. This can be put down to the financial crash of
2007; before then there was some improvement in productivity but none afterwards.
20.0
25.0
30.0
35.0
40.0
45.0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
GVA per hour worked
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Figure 5: GVA per hour worked, real terms
Employment and hours of work - hours worked per worker declined in C2C in contrast to other LEPs
There remain the final three elements of our accounting equation, looking at labour input
over the period:
GDHI GDHI GVA H LF WAPop
Pop GVA H LF WAPop Pop
___________________
Hours worked per worker are shown in Figure 6 below. Here we see an interesting difference
between C2C and some of the other LEPs, with C2C having shorter working hours and, what
is more, these have been declining for much of the period since 2004. In the other
comparator LEPs average working hours have increased, sometimes significantly.
25.0
27.0
29.0
31.0
33.0
35.0
37.0
39.0
41.0
43.0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
GVA per hour, real terms
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Figure 6: Hours worked per employee
Part of the change in hours reflects the balance of part-time and full-time work, shown in
Figure 7
Figure 7: Part-time workers as a percentage of all workers
20.0
25.0
30.0
35.0
40.0
45.0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Hours worked per employee (weekly average)
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
20
22
24
26
28
30
32
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Percentage of part-time workers
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Not unexpectedly we note the rise in part-time work in Coast to Capital, in contrast to some
other LEPs. There is a surprising amount of change year to year apparent in the graph, with
changes often reversed. We are not sure if this is genuine or due to some statistical artefact in
the collection of the data. Hence results might be treated with some caution. It is perhaps
easier to summarise the changes in a table, averaging over time.
Table 1: Growth in part-time work and in hours (average annual percentage change, 2004-16)
Part-time Hours
Bucks -0.26 0.30
Ch & Warr -0.67 1.36
C2C 0.35 -0.11
Ent M3 0.53 0.73
SE -0.08 0.33
Berks 0.65 0.40
All LEPs 0.23 0.30
Coast to Capital has a shift towards part-time employment and a fall in average hours. In
contrast, Enterprise M3 has an even bigger shift towards part-time employment yet average
hours are increasing.
Employment
The employment rate (the number employed divided by the working age population) is
shown in Figure 8 and once again shows the importance of the 2007 crash. Since that time,
employment rate growth in Coast to Capital has been strong, indeed it has the largest increase
in the employment rate over the period since 2004.
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Figure 8: Employment rate (%)
The final element is to look at how the working age population (16-64) has changed over
time. This is shown below.
Figure 9: Proportion of the population of working age
70
72
74
76
78
80
82
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Employment Rate
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
ALL LEPs
60
61
62
63
64
65
66
67
68
Working age population as percentage of overall population
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Here we see the proportion of working age falling everywhere, particularly since around
2004. Coast to Capital has a relatively low proportion (i.e. high proportion of juveniles and
elderly) compared to other areas, though it has been falling less rapidly than most. Of course,
in the modern economy the concept of ‘working age’ has less relevance as many choose, or
need, to work on beyond the age of 65.
Summary of findings
We have covered a lot of ground so far and it may be useful to sum up the findings in a broad
brush way.
Table 2: Qualitative description of findings
Item Overall finding Coast to Capital finding
GDHI pc (prosperity) Similar growth (1% real
p.a.) across all LEPs.
GVA pc Similar growth to GDHI. C2C growing slightly more
slowly than average for
LEPs
GVA/hour (productivity) Very little growth since
1997, approx. 0.5% p.a. No
growth from 2008-16.
Negative productivity
growth since 2008.
Hours/worker Growing modestly since
2004
Declining since 2004, the
only one in the comparator
group to do so. Partly due
to shift towards part-time
work
Employed/working age
population
U-shaped trend since 2004,
bottoming in 2011
Particularly strong growth
since 2011, especially part-
time work
Working age
population/Population
Falling, especially since
2008
Low ratio but falling more
slowly than elsewhere
Business Start-ups
This section uses the business demography data available from the Office for National
Statistics to examine differences in birth rates, death rates and survival rates of new
enterprises for the period 2010-2016. The performance of Coast to Capital (C2C) is again
compared to the same five LEPs as before.
We look beyond the business count data and its rate of change to the underlying births and
deaths of companies and the pattern of survival. The basic accounting relationship is
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Businesses = Births – Deaths
where indicates the annual change in the variable. Hence, pretty obviously, if births exceed
deaths in any year then the number of businesses is increasing.
Birth Rates
The birth rate of new enterprises is calculated using the number of business births as a
proportion of active businesses, i.e. the number per 100.
Figure 10: Birth Rate of New Enterprises (%)
Figure 10 shows that the rate of business births follows a similar pattern across the six LEPs
with C2C in the middle of the pack. The sharp increase in business birth rates in 2013
coincides with the growth in PAYE schemes following the introduction of HMRC’s Real
Time PAYE reporting system in 2013, hence the figures pre- and post-2012 are not strictly
comparable. The period 2013-15 shows relatively stable birth rates across the six LEPs. It is
interesting to note that the six LEPs have not kept up with the rate increase in other LEPs
though the difference is not particularly large.
With the exception of the Cheshire and Warrington region, the fall in birth rate in 2016 was
less pronounced in the Coast to Capital region, which nevertheless experienced an overall
growth in the business birth rate of 3 percentage points from 2010 to 2016 (but not
accounting for the PAYE effect). It is speculation but perhaps the fall in start-ups in 2016
reflects uncertainty engendered by the Brexit vote.
The overall increasing trend in birth rates may be attributable to the following
macroeconomic factors:
- Continued economic improvements since the economic downturn of 2008 and 2009.
- Increased employment and a stronger labour market.
- Low interest rates that have reduced financing costs for businesses.
8
9
10
11
12
13
14
15
16
2010 2011 2012 2013 2014 2015 2016
Birth Rate of New Enterprises
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
ALL LEPs
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Death Rate
The death rate of new enterprises is calculated using the number of business deaths as a
proportion of active businesses.
Figure 11: Death Rate of New Enterprises (%)
Figure 11 show that the rate of business deaths again follows a similar pattern across the six
LEPs though the pattern is inverse to that for births, not unexpectedly. The increasing gap
between birth rates and death rates for 2010-14 may be the consequence of the positive
macroeconomic factors outlined previously.
On the other hand, the increase in death rates after 2014 may be explained by the following
macroeconomic factors:
- Increases in consumer price inflation and the depreciation of the pound sterling,
which would have both translated to higher raw materials and import prices (new
businesses are less likely to be exporters who would gain from depreciation).
- The earlier increase in company births. Some insight into this comes from our
survival analysis below.
Taking the difference between births and deaths we obtain the picture in Figure 12. The
problem interpreting this figure is that it is distorted by the change in 2013, for which we
have not been able to find an appropriate adjustment.
8
9
10
11
12
13
14
15
16
2010 2011 2012 2013 2014 2015 2016
Death Rate of New Enterprises
Bucks
Ch & Warr
C2C
Ent
SE
Berks
ALL LEPs
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Figure 12: Births minus deaths (%)
Business survival rates Underlying the births and deaths of businesses lies the survival rate. The one-year survival
rate for businesses is defined as the proportion of new enterprises that are still in operation
one year after their birth. Two- and higher-year survival rates are defined analogously. The
chart below shows the 5-year survival rates of businesses born in 2011 across the six LEPs:
Figure 13: Survival of Newly Born Enterprises (2011)
Note: values denote the survival rate for Coast to Capital.
-2
-1
0
1
2
3
4
5
6
2010 2011 2012 2013 2014 2015 2016
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
ALL LEPs
94.0
77.7
62.9
53.6
46.5
30
40
50
60
70
80
90
100
1-year 2-year 3-year 4-year 5-year
Survival of Newly Born Enterprises (birth units in 2011)
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
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Across the comparator LEPs, Coast to Capital has the highest one-year survival rate (94%,
equal with Buckinghamsire) for businesses born in 2011. This figure is almost one percentage
point higher than the survival rate for the whole country and increases further as the year-
survival rate considered increases. In fact, the five-year survival rate gap between C2C and
All LEPs is 2.5 percentage points. For all other survival rates, C2C consistently ranks in the
middle.
The changes in survival profiles over time are shown by the charts below. Figure 14 shows
that the one-year survival rate for enterprises in the Coast 2 Capital LEP has fluctuated
between 94% and 91%. It is currently at its lowest point, 91.1%, for businesses born in 2015.
This fall is in accordance to the increase in death rates of businesses and the influence of the
aforementioned macroeconomic factors. The two-year survival rate shows an overall
increase over time, while the remaining survival-rates exhibit relatively stable figures.
Figure 14: C2C New Enterprise Survival
This overall trend is also apparent when aggregating the comparator LEPs and when
considering the country as a whole. Figures 15 and 16 show that the one-year survival rate
has fallen over time in a similar manner to Coast to Capital. The two-year survival rates show
small increases but are less pronounced than those exhibited by C2C. The remaining survival
rates show miniscule changes over time.
0
25
50
75
100
Births 2011 Births 2012 Births 2013 Births 2014 Births 2015
C2C New Enterprise Survival
1-year survival
2-year survival
3-year survival
4-year survival
5-year survival
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Figure 15: Comparator LEPs New Enterprise Survival
Figure 16: England New Enterprise Survival
0
25
50
75
100
Births 2011 Births 2012 Births 2013 Births 2014 Births 2015
Comparator LEPs New Enterprise Survival
1-year survival
2-year survival
3-year survival
4-year survival
5-year survival
0
25
50
75
100
Births 2011 Births 2012 Births 2013 Births 2014 Births 2015
England New Enterprise Survival
1-year survival
2-year survival
3-year survival
4-year survival
5-year survival
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Industry structure
In this section we compare the industry structures of the 6 chosen LEPs to see where their
respective strengths lie. This is first shown in Figure 17, which plots GVA by industry for
1997 and 2015. A few conclusions can be drawn from the graphs:
1. These authorities are generally similar in their industry structure.
2. There has been a decline in the importance of manufacturing almost everywhere,
apart from Cheshire & Warrington. The latter is an important area for aerospace
engineering, with the Airbus plant located just outside Chester, with its affiliated
supply chain. Otherwise, all these LEPs have manufacturing contributing less than
10% of their GVA.
3. The other outlier is Berkshire, specialising in Information and Communications
services. Many of the major IT companies are based in the county, many around the
Reading area.
C2C leads the comparator group in the fields of Finance & Insurance (though still only a
small share of GVA) and also Property Services, Public Administration, Education and
Health and has a small share of its GVA from manufacturing.
Apart from these obvious differences, we need to drill down further to identify the dominant
sub-sectors in each LEP.
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Figure 17: GVA by industry (% share), 1997 and 2015
Note: The radar graph shows industrial sectors around the periphery. Each LEP is a coloured line within the
‘web’. The further out is a line, the more important is that sector for that LEP. For example, manufacturing is
important for Cheshire and Warrington, comprising about 23% of local GVA. The Agriculture and the Mining
& Quarrying industries have been omitted, being of very small size in all LEPs.
0
5
10
15
20
25Mfg
Constrn
W'sale & transport
Comms
Fin & InsProperty Svcs
Admin & Prof
Pub admin, ed & health
Ents & Other
GVA by sector, 1997
Bucks Ch &Warr C2C Ent M3 SE Berks
0
5
10
15
20
25Mfg
Constrn
W'sale & transport
Comms
Fin & InsProperty Svcs
Admin & Prof
Pub admin, ed &health
Ents & Other
GVA by sector, 2015
Bucks Ch &Warr C2C Ent M3 SE Berks
Page | 23
We have not been able to drill down further using the GVA measure but we can make a little
progress by looking at employment by industry. This is available at LA level, which we have
aggregated to LEP level. Using this one can calculate location quotients for each LEP, for
each industry. The location quotient is defined as follows:
ij iij
j
E ELQ
E E
Where
LQij is the location quotient of industry i in location j.
Eij is employment in industry i in location j
Ei is total employment in industry i (nationally)
E is total employment (nationally)
As an example, a location quotient of 1.5 for the construction industry in the Coast to Capital
area would mean the share of C2C employment that is in the construction industry is 50%
higher than the national average. The higher a location quotient, the more employment is
concentrated in that industry.
Figure 18 illustrates the results in the form of graphs for each industry (we have omitted both
Agriculture and Mining & Quarrying as unimportant).
Figure 18: Location quotients for each of the 6 LEPs, by industry, 2015
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
C: Manufacturing
0.0 0.5 1.0 1.5 2.0
BucksCh & Warr
C2CEnt M3
SEBerks
D: Electricity, gas, steam and air con supply
0.0 0.5 1.0 1.5 2.0
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
E: Water supply, sewerage and waste
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
F: Construction
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0.0 0.5 1.0 1.5
BucksCh & Warr
C2CEnt M3
SEBerks
G: Wholesale, retail, vehicle repair
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
H: Transportation and storage
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
I: Accommodation, food services
0.0 1.0 2.0 3.0 4.0
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
J: Information and Communication
0.0 0.5 1.0 1.5
Bucks
Ch & WarrC2C
Ent M3SE
Berks
K: Finance and Insurance
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
L: Real estate
0.0 0.5 1.0 1.5 2.0
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
M: Prof, Scientific & tech activities
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
N: Admin & support activities
Page | 25
From all of this information we can note high location quotients for C2C the fields of
Finance and Insurance
Real estate
Public administration, defence and social security
Health and social work
And low values in
Manufacturing
Electricity and gas supply
Water supply
Share of Business by Size
This section examines the businesses size share across different LEPs. Enterprises are
categorized according to an employment size band as follows:
0.0 0.2 0.4 0.6 0.8 1.0
BucksCh & Warr
C2CEnt M3
SEBerks
O: Public admin & defence, social security
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
P: Education
0.0 0.5 1.0 1.5
BucksCh & Warr
C2CEnt M3
SEBerks
Q: Health and social work
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
R: Arts and entertainment
0.0 0.5 1.0 1.5
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
Other services
Page | 26
Micro (0-9 employees),
Small (10-49 employees),
Medium (50-249 employees) and
Large (250+ employees).
A business size share is calculated using the number of businesses in a given employment
size band as a proportion of total businesses. Data on the count of enterprises by LEP is made
available by the Office for National Statistics. The business size shares of Coast to Capital
are compared to the same five comparator LEPs.
Trends over Time
For the period 2010-17 there are no substantial differences between LEPs in the pattern of
firms by employment size band. Generally, they all seem to follow similar patterns, which
suggests that macroeconomic factors dictate how these shares change over time.
Figures 19 to 22: shows that the share of the four different size enterprises, as a percentage of
all enterprises. Looking at them together there is a pattern whereby since 2013 there has been
an increase in micro enterprises but falls in the other three types. However, we noted above
the rapid increase in company births in 2013 (most of which will initially be micro
enterprises) and how this may be a statistical artefact due to changes in the system of PAYE
reporting. Hence we cannot lend too much credence to the observed changes over time.
Figure 19: Share of Micro Enterprises 2010-17
88
89
90
91
92
2010 2011 2012 2013 2014 2015 2016 2017
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
Page | 27
Figure 20: Share of Small Enterprises 2010-17
Figure 21: Share of Medium Enterprises 2010-17
6
7
8
9
10
2010 2011 2012 2013 2014 2015 2016 2017
Percentage of all businesses
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
1.0
1.2
1.4
1.6
1.8
2.0
2010 2011 2012 2013 2014 2015 2016 2017
Percentage of all businesses
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
Page | 28
Figure 22: Share of Large Enterprises 2010-17
What does appear from these figures, and seems reliable, is that Coast to Coast has a higher
proportion of micro enterprises and lower proportions of the largest size firms, compared to
most of the comparator group. Buckinghamshire seems to be the closest in structure to C2C.
Share of Business by Size in 2017
We can look at the structure for 2017 in isolation, particularly since the time trends above
might not be reliable. Figures 23 and 24 summarise the situation in different ways. Looking
at them we note the overall message of C2C being home for many micro businesses, more so
than four out of five comparators, and also compared to the average for all LEPs. Note that
the horizontal axis in the first panel is truncated, starting at 89%, so the picture should be
interpreted in the light of this.
Figure 23: Share of Businesses by Size 2017
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2010 2011 2012 2013 2014 2015 2016 2017
Percentage of all businesses
Bucks
Ch & Warr
C2C
Ent M3
SE
Berks
All LEPs
89 89 90 90 91 91 92
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Share of Micro Enterprises
0 2 4 6 8 10
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Share of Small Enterprises
Page | 29
Table 24 illustrates how the business landscape is dominated by micro enterprises,
approximately 90% being of this type.
Figure 24: Share of Businesses by Size 2017
Businesses by size: employment and GVA shares
Simply counting the number of firms does not necessarily measure the importance of a sector
(micro, small, etc) since one large firm can employ as many people as hundreds of micro
firms, for example. Hence we can try to measure the total number employed in each size
band of firm. In the absence of precise data available to us on this, we estimate the figures by
assuming a firm in a size band employs a number of workers equal to the midpoint of the
interval. Hence each micro enterprise is assumed to have 5 employees (mid-point of 1-9) and
similarly for the other size bands.
This calculation results in estimates of employment shares (rather than numbers of firms) by
size band of firm , for each LEP. It should be remembered that this is an estimate rather than
0.0 0.5 1.0 1.5 2.0
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Share of Medium Enterprises
0.0 0.1 0.2 0.3 0.4 0.5 0.6
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Share of Large Enterprises
0 10 20 30 40 50 60 70 80 90 100
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Share of Business by Size
Micro (0-9) Small (10-49) Medium (50-249) Large (250+)
Page | 30
a precise count. We illustrate this just for the years 2010 and 2015 as the time trends above
suggested little more can be learned by looking at all of the years.
Figure 25: Share of Employment in each business size band 2015
Figure 26: Share of Employment in each business size band 2010
Visually, the importance of micro enterprises is substantially reduced, to between 40-50%,
when we base our measurement on total employment. Interestingly, C2C no longer stands
out as exceptional in terms of its micro business sector and, indeed, much of its employment
is in fact based in large enterprises.
If we were to assume a similar level of productivity for each size band of business then the
figures above would also show the contribution to GVA of each type of business as well. To
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Employment shares by firm size band 2015
Micro
Small
Medium
Large
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
All LEPs
Berks
SE
Ent M3
C2C
Ch & Warr
Bucks
Employment shares by firm size band 2010
Micro
Small
Medium
Large
Page | 31
the extent that larger firms have higher levels of productivity then the graphs in Figures 25
and 26 will tend to over-estimate the contribution of the smaller firms to GVA.
In conclusion
We have provided a fairly terse account, much of our time has been spent in gathering and
manipulating the data, so we have provided fairly brief accounts of the main features. We
have tried not to speculate too much, or indicated where we do so.
In many ways, C2C is unremarkable. Looking at a number of measures, the region does not
appear quantitatively different from it comparator group. Hence it is somewhat difficult to
point to obvious lessons for an economic strategy. Hence to progress further would require a
look at more disaggregated data, with the drawback that some of this might be commercially
confidential and hence unavailable.
.
Page | 32
Appendix tables
The tables below underlie the graphs in the main text, following the same order as the graphs.
GDHI per capita
LEP Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
1997 15,463 11,655 13,196 15,214 11,926 14,070 11,276
1998 15,913 11,970 13,688 15,777 12,169 14,506 11,676
1999 16,435 12,578 14,087 16,261 12,675 14,814 12,149
2000 17,464 13,200 14,979 17,144 13,427 15,903 12,904
2001 18,444 13,700 15,732 17,815 14,153 16,227 13,519
2002 18,699 14,383 16,087 18,202 14,540 16,638 13,840
2003 18,982 15,181 16,505 18,436 14,893 17,034 14,283
2004 19,408 15,650 16,858 18,866 15,192 17,174 14,620
2005 19,884 16,034 17,629 19,557 15,646 17,714 15,101
2006 21,255 16,478 18,278 20,459 16,095 18,393 15,656
2007 21,975 17,027 18,908 21,175 16,698 19,480 16,277
2008 22,304 17,224 19,107 21,432 17,082 19,674 16,596
2009 22,928 17,724 19,375 21,954 17,688 19,891 17,021
2010 23,297 18,108 19,847 22,388 18,073 20,114 17,399
2011 23,318 18,158 19,995 22,539 18,164 20,398 17,482
2012 23,722 18,838 20,736 23,498 18,705 21,093 18,101
2013 24,029 19,140 21,124 23,913 18,957 21,550 18,382
2014 24,661 19,583 21,486 24,414 19,301 21,924 18,840
2015 25,312 19,898 22,104 25,095 19,861 22,679 19,389
Real GDHI per capita
year Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
1997 22,058 16,626 18,825 21,703 17,013 20,071 16,086
1998 22,350 16,812 19,225 22,159 17,091 20,374 16,399
1999 22,795 17,445 19,538 22,553 17,580 20,546 16,850
2000 24,022 18,157 20,604 23,582 18,469 21,875 17,750
2001 25,060 18,614 21,375 24,205 19,230 22,048 18,368
2002 25,099 19,306 21,593 24,432 19,517 22,333 18,577
2003 25,142 20,107 21,861 24,419 19,726 22,562 18,918
2004 25,370 20,458 22,037 24,661 19,859 22,450 19,111
2005 25,460 20,530 22,572 25,041 20,033 22,681 19,335
2006 26,602 20,623 22,876 25,606 20,144 23,020 19,594
2007 26,864 20,815 23,115 25,886 20,413 23,814 19,899
2008 26,333 20,335 22,558 25,303 20,168 23,228 19,594
2009 26,476 20,467 22,373 25,351 20,425 22,969 19,655
Page | 33
2010 26,059 20,255 22,200 25,043 20,216 22,499 19,462
2011 24,966 19,441 21,408 24,132 19,448 21,839 18,717
2012 24,685 19,602 21,578 24,452 19,464 21,949 18,836
2013 24,395 19,431 21,446 24,277 19,246 21,878 18,662
2014 24,661 19,583 21,486 24,414 19,301 21,924 18,840
2015 25,312 19,898 22,104 25,095 19,861 22,679 19,389
GVA per capita (nominal)
Bucks Ch & Warr C2C Ent M3 SE Berks
1997 17,731 15,396 15,158 17,182 12,625 22,570
1998 18,960 15,928 15,917 18,207 12,985 24,282
1999 19,134 16,709 16,546 19,516 13,149 26,339
2000 20,768 16,624 17,508 20,702 13,767 27,536
2001 20,866 18,792 18,299 21,420 14,516 27,843
2002 21,144 19,799 19,094 22,264 15,286 30,205
2003 22,102 20,517 19,855 22,876 16,252 31,740
2004 23,196 21,142 20,124 23,085 16,562 31,696
2005 23,226 22,541 20,676 23,777 17,210 34,110
2006 23,763 24,456 22,016 25,103 17,900 34,783
2007 24,565 25,306 22,688 26,476 18,480 35,755
2008 25,782 25,913 22,903 27,118 18,844 35,990
2009 25,078 25,429 22,078 26,525 17,993 34,215
2010 25,441 25,497 22,410 27,406 18,432 34,691
2011 25,771 25,285 22,033 27,649 18,562 36,166
2012 26,907 26,985 23,079 28,917 18,836 37,049
2013 27,675 28,065 23,697 29,980 19,397 37,796
2014 28,510 29,217 24,610 31,170 20,190 38,850
2015 28,825 30,099 24,941 31,821 20,762 40,248
GVA per hour (nominal)
Bucks Ch &
Warr C2C Ent M3 SE Berks All LEPs
2004 29.1 26.8 26.1 27.5 23.9 32.0 24.9
2005 30.0 27.9 27.1 28.4 24.8 33.1 25.7
2006 31.1 29.4 28.5 29.8 25.9 34.4 26.9
2007 32.0 30.7 29.8 31.2 26.9 35.6 28.0
2008 33.0 31.4 30.8 32.5 27.6 36.6 28.9
2009 33.5 31.4 30.7 32.9 27.6 36.8 28.8
2010 34.9 31.9 31.2 33.7 28.1 37.4 29.6
2011 35.4 32.3 31.5 33.9 28.2 37.6 29.9
Page | 34
2012 35.7 32.7 32.0 34.3 28.3 38.1 30.2
2013 36.0 33.1 32.9 35.2 28.8 39.0 30.9
2014 36.4 33.4 33.5 35.9 29.4 39.8 31.6
2015 37.0 33.7 34.1 36.7 30.2 40.6 32.2
2016 37.3 33.9 34.2 37.0 30.6 41.0 32.6
GVA per hour, real terms
Bucks Ch &
Warr C2C Ent M3 SE Berks All LEPs
UK Deflator
2004 35.4 32.6 31.8 33.4 29.1 39.0 30.3 29.9 82.1
2005 35.8 33.3 32.3 33.9 29.5 39.5 30.7 30.3 83.8
2006 36.2 34.2 33.2 34.6 30.2 40.1 31.4 30.9 85.9
2007 36.3 34.8 33.8 35.4 30.5 40.5 31.8 31.4 88.1
2008 36.5 34.7 34.0 35.9 30.5 40.5 31.9 31.5 90.5
2009 36.0 33.7 33.1 35.3 29.7 39.5 31.0 30.8 93.0
2010 37.4 34.3 33.5 36.1 30.2 40.2 31.8 31.4 93.1
2011 37.7 34.4 33.5 36.1 30.0 40.1 31.8 31.5 93.9
2012 37.3 34.2 33.4 35.9 29.6 39.8 31.6 31.4 95.7
2013 36.8 33.8 33.6 35.9 29.4 39.8 31.6 31.4 97.9
2014 36.7 33.6 33.7 36.1 29.6 40.1 31.8 31.6 99.3
2015 37.0 33.7 34.1 36.7 30.2 40.6 32.2 32.2 100.0
2016 37.3 33.2 33.5 36.3 29.9 40.2 31.9 31.9 102.1
Hours worked per employee
Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
2004 31.3 33.1 30.9 35.2 28.1 37.8 32.4
2005 28.4 32.5 31.3 36.0 28.0 37.2 32.7
2006 29.6 33.1 31.1 35.7 27.6 37.4 32.6
2007 30.8 32.9 30.4 36.5 27.8 37.7 32.6
2008 30.7 32.7 30.2 35.9 28.0 37.2 32.3
2009 31.2 33.6 29.5 35.7 27.4 36.1 32.3
2010 28.7 33.5 29.6 35.5 27.3 36.6 32.3
2011 29.4 33.5 29.9 37.0 28.0 38.7 32.7
2012 31.5 34.5 29.6 38.0 29.2 38.8 33.1
2013 31.6 36.2 28.8 37.5 28.7 37.9 33.2
2014 31.5 36.5 29.9 39.8 28.7 39.0 33.6
2015 31.7 39.0 28.9 38.5 28.9 38.3 33.2
2016 32.4 38.9 30.5 38.4 29.3 39.6 33.6
Page | 35
Percentage of part-time workers
Bucks Ch & Warr C2C Ent M3 SE Berks All LEPs
2004 28.5 24.5 27.6 26.2 28.0 20.9 24.6
2005 28.8 26.4 26.8 26.4 28.5 21.7 24.8
2006 29.3 24.6 27.5 26.3 28.8 22.3 24.6
2007 27.8 26.2 27.7 26.7 28.6 20.6 24.6
2008 25.1 24.3 27.6 25.7 29.0 20.7 24.6
2009 27.7 24.9 27.4 26.7 29.5 21.9 25.5
2010 27.5 26.3 28.4 28.2 30.2 24.0 25.8
2011 28.5 27.5 28.4 26.8 29.1 22.5 25.9
2012 30.9 26.0 28.3 27.4 29.0 22.5 26.0
2013 30.5 26.3 28.6 28.1 28.1 25.3 25.8
2014 30.1 25.6 27.8 26.6 28.1 23.1 25.6
2015 28.7 23.1 30.2 27.8 29.1 24.1 25.6
2016 27.6 22.6 28.7 27.9 27.7 22.6 25.3
Employment rate (%)
Bucks Ch &
Warr C2C Ent M3 SE Berks ALL
LEPs
2004 79.4 76 76.3 79.1 75.6 78 73.0
2005 80.2 75.9 75.8 79.6 75.8 78.8 73.0
2006 77.8 75.5 75.1 80 74.6 78.7 72.8
2007 76.6 75.7 76 78.9 75 78.8 72.7
2008 77.6 75.3 76.6 78.7 73.8 78.4 72.5
2009 76.6 73.1 74.1 76.6 73.6 76.6 71.0
2010 75.4 74.6 75.6 76.2 72.6 76.2 70.5
2011 75.8 73.7 73.1 76 72 75.2 70.2
2012 76.4 74.5 75.3 77.5 72.1 75.9 71.0
2013 76.3 73.7 76.8 77.4 73.3 77.3 71.7
2014 78.6 75.5 76.3 76.9 73.7 77.2 72.7
2015 79.5 75.1 76.7 77.9 74.5 78.5 74.0
2016 81.2 75 79.1 79.4 75 78.8 74.4
Working age population as percentage of overall population
Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
1997 65.2 64.1 62.5 64.4 62.3 66.5 63.6
1998 65.0 64.1 62.6 64.4 62.3 66.4 63.6
Page | 36
1999 64.9 64.0 62.7 64.5 62.2 66.5 63.7
2000 64.7 64.0 62.8 64.4 62.3 66.6 63.9
2001 64.5 63.9 62.9 64.4 62.4 66.7 64.1
2002 64.4 64.0 63.0 64.4 62.4 66.7 64.2
2003 64.2 64.0 63.1 64.3 62.5 66.7 64.3
2004 64.1 64.0 63.2 64.2 62.6 66.7 64.5
2005 64.2 64.1 63.4 64.3 62.9 66.9 64.7
2006 64.3 64.3 63.8 64.4 63.1 67.0 64.9
2007 64.2 64.4 64.0 64.4 63.2 67.0 65.0
2008 63.9 64.3 64.0 64.2 63.2 66.8 65.0
2009 63.6 64.1 64.0 63.9 63.0 66.5 64.9
2010 63.3 63.9 63.9 63.6 62.9 66.1 64.7
2011 63.1 63.6 63.7 63.3 62.7 65.7 64.6
2012 62.4 62.8 63.1 62.6 62.0 65.1 64.0
2013 62.0 62.3 62.8 62.1 61.6 64.6 63.7
2014 61.7 61.9 62.5 61.8 61.3 64.2 63.4
2015 61.5 61.6 62.4 61.5 61.1 63.9 63.2
2016 61.3 61.2 62.2 61.2 60.9 63.6 62.9
Birth rate of new enterprises (%)
Bucks Ch & Warr C2C Ent M3 SE Berks
ALL LEPs
2010 95.8 99.2 101.7 96.8 97.1 111.3 101.4
2011 101.2 109.5 110.2 110.4 108.4 124.0 113.9
2012 98.4 113.7 109.6 109.3 110.7 120.2 115.9
2013 120.1 138.2 135.0 126.0 135.1 142.3 144.2
2014 120.9 130.6 131.0 122.4 128.9 140.1 140.1
2015 120.8 131.3 135.2 126.2 137.0 147.9 146.7
2016 118.4 134.1 131.1 118.2 131.4 136.7 149.7
Death rate of new enterprises
Bucks Ch & Warr C2C Ent SE Berks
ALL LEPs
2010 98.0 100.9 103.7 98.2 105.5 98.8 107.0
2011 92.9 100.6 97.0 91.6 98.0 93.1 99.2
2012 97.1 104.6 104.9 102.1 106.7 103.4 107.1
2013 89.2 94.8 96.0 91.5 98.9 91.9 97.7
2014 85.3 90.0 91.7 84.8 95.9 96.5 97.5
2015 91.6 100.6 101.9 98.2 103.1 103.7 106.7
2016 106.5 107.6 110.2 111.3 110.0 114.7 116.5
Page | 37
Births minus deaths (%)
Bucks Ch & Warr C2C Ent M3 SE Berks ALL LEPs
2010 -0.2 -0.2 -0.2 -0.1 -0.8 1.3 -0.6
2011 0.8 0.9 1.3 1.9 1.0 3.1 1.5
2012 0.1 0.9 0.5 0.7 0.4 1.7 0.9
2013 3.1 4.3 3.9 3.5 3.6 5.0 4.6
2014 3.6 4.1 3.9 3.8 3.3 4.4 4.3
2015 2.9 3.1 3.3 2.8 3.4 4.4 4.0
2016 1.2 2.6 2.1 0.7 2.1 2.2 3.3
Survival rates of newly born enterprises (2011)
Bucks Ch & Warr C2C Ent SE Berks ALL LEPs
1-year 94.0 93.6 94.0 93.5 93.6 93.4 93.1
2-year 78.1 78.2 77.7 76.8 76.8 77.5 75.5
3-year 63.3 63.5 62.9 63.2 61.0 62.7 60.4
4-year 55.4 53.0 53.6 54.5 51.4 52.9 50.8
5-year 48.1 46.2 46.5 47.1 44.5 46.2 44.0
Coast to Capital new enterprise survival rates 1-year
survival 2-year
survival 3-year
survival 4-year
survival 5-year
survival
Births 2011 94.0 77.7 62.9 53.6 46.5 Births 2012 91.9 75.7 61.9 52.2
Births 2013 94.0 77.4 62.1
Births 2014 93.0 78.2
Births 2015 91.1
Comparator LEPs new enterprise survival rates 1-year
survival 2-year
survival 3-year
survival 4-year
survival 5-year
survival
Births 2011 93.6 77.1 62.2 52.8 45.8 Births 2012 91.2 74.7 60.9 51.9
Births 2013 94.0 76.8 62.9
Births 2014 93.3 77.8
Births 2015 91.1
Page | 38
England new enterprise survival rates 1-year
survival 2-year
survival 3-year
survival 4-year
survival 5-year
survival
Births 2011 93.1 75.5 60.4 50.8 44.0 Births 2012 91.1 73.7 59.2 50.2
Births 2013 93.5 75.1 60.8
Births 2014 92.2 75.8
Births 2015 89.6
GVA by industry (% share), 1997
Mfg Constrn W'sale & transport Comms
Fin & Ins
Property Svcs
Admin & Prof
Pub admin, ed &
health Ents & Other
Bucks 18 5 18 7 4 19 10 13 3 Ch &Warr 25 5 19 4 5 13 9 12 3
C2C 10 6 21 6 8 18 11 15 4
Ent M3 13 5 19 8 4 18 12 14 3
SE 18 7 20 3 5 17 8 15 3
Berks 14 4 21 17 4 12 11 10 3
16 5 20 7 5 16 10 13 3
GVA by industry (% share), 2015
Mfg Constrn W'sale & transport Comms
Fin & Ins
Property Svcs
Admin & Prof
Pub admin, ed &
health Ents & Other
Bucks 6 7 22 9 3 17 13 16 5 Ch &Warr 23 5 18 5 6 10 14 13 3
C2C 6 7 19 6 8 17 13 18 4
Ent M3 8 6 18 11 5 14 15 15 5
SE 8 9 21 4 4 16 11 18 4
Berks 7 4 19 24 3 10 12 11 4
Page | 39
Location quotients by LEP by industry, 2015
Bucks Ch & Warr C2C Ent M3 SE Berks
A : Agriculture, forestry and fishing 0.0 0.0 0.0 0.0 0.0 0.0
B : Mining and quarrying 0.0 0.2 0.0 0.1 0.0 0.0
C : Manufacturing 0.8 1.0 0.6 0.7 0.8 0.6
D : Electricity, gas, steam and air conditioning supply 0.1 0.8 0.4 1.8 0.2 0.8 E : Water supply; sewerage, waste management and remediation activities 1.3 1.3 0.8 0.7 1.4 1.4
F : Construction 1.1 0.9 1.0 1.1 1.4 0.7 G : Wholesale and retail trade; repair of motor vehicles and motorcycles 1.3 1.0 1.1 1.1 1.2 1.1
H : Transportation and storage 0.6 1.0 1.1 0.7 1.1 0.9
I : Accommodation and food service activities 0.9 1.1 1.1 1.1 1.0 0.8
J : Information and communication 1.7 1.0 1.1 1.8 0.8 3.4
K : Financial and insurance activities 0.6 1.0 1.3 0.8 0.8 0.6
L : Real estate activities 1.3 0.8 1.2 1.1 0.9 0.9
M : Professional, scientific and technical activities 1.2 1.1 1.0 1.3 0.8 1.4
N : Administrative and support service activities 0.9 1.3 0.9 0.9 0.9 0.9
O : Public administration and defence; compulsory social security 0.6 0.7 0.9 0.5 0.8 0.5
P : Education 1.1 0.8 1.1 1.0 1.1 1.0
Q : Human health and social work activities 0.9 0.9 1.1 0.9 1.1 0.7 R : Arts, entertainment and recreation 0.9 1.1 1.0 1.3 1.0 1.0
S : Other service activities 1.3 1.2 1.1 1.3 1.1 1.2
Share of micro enterprises (number of micro enterprises as percentage of all enterprises)
Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
2010 91.1 89.5 90.2 89.9 89.6 89.0 88.8
2011 91.3 89.7 90.4 90.1 89.7 89.0 88.9
2012 91.1 89.1 90.3 90.1 89.5 89.2 88.7
2013 90.7 88.8 90.0 89.8 89.0 88.7 88.3
2014 90.6 88.7 90.0 89.9 89.0 89.0 88.4
2015 90.7 89.0 90.2 90.0 89.4 89.4 88.8
2016 91.2 89.2 90.6 90.3 89.9 89.9 89.3
2017 91.3 89.5 90.9 90.5 90.1 90.2 89.6
Page | 40
Share of small enterprises
Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
2010 7.3 8.7 8.2 8.2 8.7 8.6 9.2
2011 7.0 8.4 7.9 8.0 8.6 8.5 9.1
2012 7.2 8.9 8.1 8.0 8.8 8.5 9.2
2013 7.6 9.2 8.3 8.2 9.1 8.8 9.6
2014 7.6 9.3 8.3 8.2 9.2 8.6 9.5
2015 7.5 9.0 8.2 8.1 8.8 8.3 9.2
2016 7.1 8.7 7.8 7.8 8.4 7.9 8.8
2017 7.0 8.5 7.6 7.7 8.2 7.6 8.6
Share of medium enterprises
Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
2010 1.4 1.5 1.3 1.5 1.3 1.8 1.6
2011 1.4 1.5 1.3 1.5 1.4 1.8 1.6
2012 1.4 1.5 1.3 1.5 1.4 1.7 1.6
2013 1.4 1.5 1.3 1.6 1.5 1.8 1.7
2014 1.5 1.6 1.3 1.5 1.5 1.8 1.7
2015 1.4 1.6 1.3 1.5 1.4 1.7 1.6
2016 1.4 1.6 1.3 1.5 1.4 1.7 1.6
2017 1.4 1.6 1.2 1.5 1.4 1.7 1.5
Share of large enterprises
Bucks Ch &
Warr C2C Ent M3 SE Berks All
LEPs
2010 0.33 0.42 0.33 0.42 0.31 0.64 0.41
2011 0.31 0.42 0.35 0.40 0.32 0.65 0.41
2012 0.30 0.41 0.36 0.40 0.32 0.65 0.41
2013 0.30 0.43 0.35 0.41 0.33 0.61 0.41
2014 0.31 0.41 0.34 0.38 0.31 0.60 0.40
2015 0.29 0.38 0.32 0.38 0.31 0.59 0.38
2016 0.30 0.41 0.30 0.38 0.31 0.56 0.38
2017 0.29 0.38 0.29 0.37 0.30 0.55 0.37
Page | 41
Share of Business by Size 2017
Micro Small Medium Large
Buckinghamshire Thames Valley 91.3 7.0 1.4 0.3 Cheshire and Warrington 89.5 8.5 1.6 0.4
Coast to Capital 90.9 7.6 1.2 0.3
Enterprise M3 90.5 7.7 1.5 0.4
South East 90.1 8.2 1.4 0.3
Thames Valley Berkshire 90.2 7.6 1.7 0.5
All LEPs 89.6 8.6 1.5 0.4
Employment shares by firm size band 2015
LEP Micro Small Medium Large
All LEPs 37.0 19.2 19.9 23.9
Berks 41.4 19.3 24.1 15.2
SE 37.6 18.6 18.1 25.7
Ent M3 47.6 21.4 23.6 7.4
C2C 40.0 18.1 17.1 24.7 Ch & Warr 41.1 20.8 22.0 16.1
Bucks 51.8 21.5 24.4 2.3
Employment shares by firm size band 2010
LEP Micro Small Medium Large
All LEPs 33.6 17.4 18.0 30.9
Berks 36.0 17.4 21.8 24.8
SE 34.4 16.8 15.4 33.4
Ent M3 42.3 19.4 20.9 17.4
C2C 35.7 16.2 15.6 32.6 Ch & Warr 35.2 17.0 17.4 30.3
Bucks 49.6 19.8 22.4 8.2
