ERC Research Shocase presentation January 2020

ERC Research Showcase
Monday January 20th 2020

Welcome
 Stephen Roper, Director, ERC

IP, innovation and performance:
A sectoral analysis
Joanne Turner, ERC

Main aim of the research project
• To understand the contribution IP has to
innovation, business growth and productivity
Preliminary research questions
• How does IP protection influence the
probability of innovation in firms?
• How does IP protection influence the
proportion of firm sales from innovation?

Descriptive analysis
• Intellectual Property Office (IPO) data detailing patents
granted, trade marks registered and designs registered
during the 1998-2018 period
• IP histories built for UK-located firms using renewal data
and the unique company reference number (CRN)
attached to each successful application
• Matched with Business Structure Database (BSD) (1998-
2018)
• Examined use of IP across 2-digit sectors in 2012 and
2016

Figure 1: Proportion of firms within a sector using some IP protection
(a) 2012 (b) 2016

Figure 2: Proportion of firms within a sector using at least one patent
(a) 2012 (b) 2016

Figure 3: Proportion of firms within a sector using at least one trade mark
(a) 2012 (b) 2016

Figure 4: Proportion of firms within a sector using at least one
registered design
(a) 2012 (b) 2016

• IP histories are matched with the UK Community
Innovation Survey (CIS) (2002-2016) to explore
innovation/IP synergies
• We examine how the stock, or number, of each IP
instrument at the beginning of each wave of data
affects
1. The probability that a firm will carry out product
innovation during the 3-year period of each wave
2. The proportion of firm turnover attributable to product
innovation at the end of each wave
Econometric analysis

Probability of product innovation Probability of NTF product innovation Probability of NTM product innovation
Patents Trade marks Registered
Designs
Designs
Designs
All firms
(N>50K)
- - + ** - + - + + + ***
Manufacturing
firms
(N>10K) - + ** + * - + + + + * + **
Service firms
(N>40K)
- + + - - - + + + ***
High-tech /
knowledge-
intensive firms
(N>20K)
- + + - + - + - + **
Low-tech /less
knowledge-
Intensive firms
(N>30K)
+ + + ** - - - + + + ***
The effect of IP protection on the probability of
product/service innovation
*= significant at the 10 per cent level **=significant at the 5 per cent level
***=significant at the 1 per cent level

Proportion of turnover from NTF product innovation Proportion of turnover from NTM and NTF product
innovation
Designs
Designs
All firms (N>50K)
- - ** + - - ** + **
Manufacturing firms
(N>10K) - - + - - ** + **
Service firms
(N>40K)
- - ** + - - + **
High-tech /
knowledge-intensive
firms
(N>20K)
- - - + - ** + *
Low-tech /less
knowledge-
Intensive firms
(N>30K)
- - + - - + *
The effect of IP protection on the proportion of firm
turnover attributable to product/service innovation
*= significant at the 10 per cent level **=significant at the 5 per cent level
***=significant at the 1 per cent level

Descriptive points
• A relatively small proportion of firms use formal IP protection
mechanisms (less than 10 per cent in most sectors)
• There is widespread use of trade marks across sectors,
although use is more concentrated in manufacturing firms
• The use of patents and registered designs is concentrated in
manufacturing firms with a higher proportion of firms using
registered designs than patents
• In sectors with more than 10 per cent of firms using IP, non
parametric tests suggest that future productivity is likely to
be higher for IP users compared with non-IP users
Preliminary findings

The probability of innovating
• Patents and trade marks have no significant effect on the probability of
product innovation, with the exception of trade marks and NTM product
innovation in manufacturing firms where the effect is significant and positive
• Registered designs have a significant, positive effect on the probability of
NTM product innovation in all groups of firms
Innovative sales
• Patents have no significant effect on the proportion of firm turnover from
product innovation
• Trade marks have a significant, negative effect on the proportion of firm
turnover from NTF product innovation in service firms and a significant,
negative effect on the proportion of firm turnover from NTF+NTM product
innovation in manufacturing and high-technology/knowledge-intensive firms
• Registered designs have a significant, positive effect on the proportion of
firm turnover from NTF+NTM product innovation in all groups of firms
Preliminary findings – ctd.

Next stages
• More detailed econometric analysis of the effect
IP has on innovation
• An econometric analysis of the effect IP has on
business growth and productivity

The Collaboration Paradox in SMEs – UK
Foundries and Metal Forming Firms
Temitope Akinremi, ERC

• Why don’t SMEs engage in innovation collaboration????
• Innovation plays a pivotal role in firm growth and profitability
• Innovation adoption and strategy is influenced by firm-level factors
• Smallness can be a disadvantage……..Capability constraint
• Small firms have more to gain from collaboration but may find
implementation difficult -
• In this project we focus on barriers to collaborative innovation due to
limited and asymmetric information on the benefits of the collaboration;
the capability of potential partners; and trustworthiness of potential
partners (Hewitt-Dundas and Roper 2018)
Collaboration Paradox
Setting the Scene

• SME dominated sectors
• Unique supply chain position
• Challenging production processes
• Evident gap in knowledge creation
• Mixed Method (Qualitative & Quantitative);
• 25 In-depth Interviews
• Surveyed 170 firms (75 Foundries, 95 Metal-Forming Firms)
Foundry
Metal-Forming
Sector
• Our research focuses on two industry sectors in the UK;
Industry Sector & Methodology

Key Findings
• Informational Market Failure 1: Firms not understanding the benefit of innovation and collaboration
• Innovation Activities in the last three years (2016-2019)
62%
60%
“.. New products and improved performance for other products. …pushing the boundaries of
what processes are capable of ”
“…you get the benefit of 30, 40, 50 years experience immediately, without having to go
through 50 years to get the knowledge, you get to take it immediately.”

Key Findings (Contd.)
• Motivation for Innovation
25.8
11.7
21.5
35.9
35.3
52.0
26.9
10.9
11.1
58.5
83.8
69.5
53.5
50.2
31.9
61.5
86.3
88.9
0% 20% 40% 60% 80% 100%
Process Improvement
Remaining Competitive
Customer Demand
Cost Reduction
Stricter Standards
Improving Time to Market
Increasing Margins
Improving H&S
Improving Quality
Percentage Importance of Innovation Motivation in
Foundries
Low Importance (%) Medium Importance (%)
High Importance (%) Not Applicable (%)
33.6
15.0
21.3
40.2
39.7
33.4
34.3
34.8
19.9
51.9
69.5
69.2
42.5
40.7
38.9
52.9
55.2
76.0
0% 20% 40% 60% 80% 100%
Process Improvement
Remaining Competitive
Customer Demand
Cost Reduction
Stricter Standards
Improving Time to Market
Increasing Margins
Improving H&S
Improving Quality
Percentage Importance of Innovation Motivation in Metal-
Forming Firms
Low Importance (%) Medium Importance (%)
High Importance (%) Not Applicable (%)
“without innovation, you can't compete on price, as it were, so
you have to compete in different ways, through technology and
offering something different.”
Improving quality and remaining competitive; similar motivation for innovation across both
industry sectors

• Collaboration with External Partners
33%
67%
Percentage of SME Foundries that Collaborated with
External Partners for Innovation
Collaborators Non Collaborators
28%
72%
Percentage of Metal-Forming SME Firms that
Collaborated with External Partners for Innovation
Collaborators Non Collaborators
More than two-thirds of SME firms conducted their innovation in-house

• Collaboration Partners
9%
17%
20%
8%
18%
11%
7%
2%
8%
Total Percentage of Collaboration in SME Foundries
Firms within Enterprise Group
Customers
Suppliers
Competitors/Firms within Sector
Firms outside Industry Sector
Consultants/R&D Institutes
Universities/Higher Education
Government/Public Research
Trade Associations
12%
25%
19%
10%
10%
11%
5%
3%
5%
Total Percentage of Collaboration in Metal-Forming SMEs
Other Firms within Enterprise
Customers
Suppliers
Competitors/Firms within
Sector
Firms outside Industry Sector
Consultants/R&D Institutes
Universities/Higher Education
Government/Public Research
Trade Associations
“When it comes to innovation; I mean obviously a lot of it comes from the customer”“Suppliers, play a key role in terms of developing innovative new technologies and its
application”
Most common collaboration; Firm-Supplier & Firm-Customer Collaboration

• Beneficial Collaboration
3.8
11.1
12.3
4.5
6.7
2.0
3.7
0.0
2.2
0 2 4 6 8 10 12 14
Other Firms within
Enterprise
Customers
Suppliers
Competitors/Firms
within Sector
Firms outside Industry
Sector
Consultants/R&D
Institutes
Universities/Higher
Education
Government/Public
Research
Trade Associations
Percentage Benefit of Collaborations in Foundries
Hight Benefit (%) Some Benefit (%) Low Benefit (%)
1.8
12.1
5.4
2.7
1.3
4.9
0.9
0.9
0.9
0 5 10 15
Other Firms within
Enterprise
Customers
Suppliers
Competitors/Firms
within Sector
Firms outside Industry
Sector
Consultants/R&D
Institutes
Universities/Higher
Education
Government/Public
Research
Trade Associations
Percentage Benefit of Collaborations in Metal-Forming Firms
High Benefit (%) Some Benefit (%) Low Benefit (%)
Firm-government, firm-university and firm-competitor collaboration are the least beneficial
collaborations

• Informational Market Failure 2: Lack of information for assessing the capability of potential partners
• Barriers to Innovation Collaboration
18.5
22.3
11.9
16.5
25.8
22.8
13.5
22.9
17.9
21.6
20.6
35.2
18.8
8.2
20.6
20.3
16.8
31.0
20.5
21.5
25.3
42.6
46.4
25.4
40.1
20.9
26.7
0% 20% 40% 60% 80% 100%
New Product/Process Risk
Firm Size
Time Constraint
Lack of Funds
Lack of Trust & Openness
IP Ownership/Protection
Insufficienct Knowledge on
Capability
Corporate Culture
Competitive Environment
Influence (%)
Percentage Influence of Barriers to Innovation
Collaboration in Foundries
No Influence (%) Low Influence (%) Medium Influence (%)
High Influence (%) Not Applicable (%)
“…as an SME, time, money, resources, brainpower you know, they are all working against
us.”
17.2
21.7
20.8
18.1
17.2
17.6
16.7
18.9
17.1
25.7
21.7
27.6
19.9
17.6
24.4
23.1
22.1
31.2
12.7
12.7
24.4
28.4
35.3
19.9
32.1
11.8
17.2
0% 20% 40% 60% 80% 100%
New Product/Process Risk
Firm Size
Time Constraint
Lack of Funds
Lack of Trust & Openness
IP Ownership/Protection
Insufficienct Knowledge on Capability
Corporate Culture
Competitive Environment
Influence (%)
Percentage Influence of Barriers to Innovation
Collaboration in Metal-Forming Firms
No Influence (%) Low Influence (%) Medium Influence (%)
• Most Influential Barriers: Lack of Trust & Openness, Insufficient Knowledge about Capability
& Lack of Funds

• Importance of Capability Knowledge
6%
10%
13%
22%
49%
Percentage Importance of Capability
Knowledge in Foundries
Very Unimportant (%)
Unimportant (%)
Neither Important nor
Unimportant (%)
Important (%)
Very Important (%)
8%
6%
86%
Percentage Importance of Trust for
Collaboration in Foundries
Unimportant
Unimportant (%)
Important (%)
Very Important (%)
“I guess you do not always know what people can do…so you do not know what is available”
“… knowing more about capabilities is important. I mean often you do not know exactly what
such a partner may be able to offer or what technology or understanding or knowledge they
may have behind them”

• Capability Information to Know
• Capability Information to Know; Quality Standards, Operational & Financial Capability
76.2
79.1
55.4
78.9
81.6
78.3
79.4
77.7
77.5
75.0
71.0
73.7
44.8
71.4
77.3
73.3
69.7
59.7
65.2
68.3
0 10 20 30 40 50 60 70 80 90
Innovation Cost
Financial Capability
Growth Strategy
Operational Capability
Quality Standards
Reputation
Case Study/Past Projects
Expectations
Product/Process Type
Feasibility of Innovation
Percentage of Firms
CapabilityInformationtoKnow
Capability Information to Know
Metal-Forming Firms Foundries

• Capability Information Sources
• Trade Associations and Supply Chain Partners are useful information sources
58.8
60.7
52.1
45.9
36.1
29.0
74.3
52.1
48.4
28.1
30.3
25.3
14.5
65.1
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0
Friends/Family
SC Partners
Trade Associations
Business Networks/Clubs
Exhibitions
Conferences
Internal Research
Capability Knowledge Information Sources
Metal-Forming Firms Foundries

• Informational Market Failure 3: Difficulty in assessing trustworthiness of potential partners
• Importance of Trust Knowledge
8%
6%
86%
Percentage Importanceof Trust for Collaboration in
Foundries
Unimportant
Unimportant (%)
Important (%)
Very Important (%)
3%
2% 2%
8%
85%
Percentage Importanceof Trust for Collaboration in Metal-
Forming SMEs
Unimportant
Neither Important nor Unimportant
(%)
Important (%)
Very Important (%)
“…and there has to be that trust. With trust
then comes the more, with the more comes more
….It is a spiral that builds…
• Trust is very important in the decision to collaborate

• Informational Market Failure 3: Difficulty in assessing trustworthiness of potential partners
• Assessing Trustworthiness
53.19
44.06
80.57
38.67
56.45
66.64
66.56
61.99
0% 20% 40% 60% 80% 100%
Assessments & Visits
Financial Strategy
Meetings/Relationships
NDAs
Data Protection
Reputation
Openness to Info Sharing
References/Recommendations
Assessing Trustworthiness in Foundries
Never Effective (%) Rarely Effective (%) Sometimes Effective (%) Most Effective (%)
48.42
32.10
72.30
28.82
47.93
57.49
53.41
56.58
0% 20% 40% 60% 80% 100%
Assessments & Visits
Financial Strategy
Meetings/Relationships
NDAs
Data Protection
Reputation
Openness to Info Sharing
References/Recommendations
Assessing Trustworthiness in Metal-Forming Firms
Never Effective (%) Rarely Effective (%)
Sometimes Effective (%) Most Effective (%)

Conclusion
• Less than half of SME firms introduced new products/services in the last 3
years
• SME firms understand the benefits of innovation and innovation
collaboration
• Firms are motivated to innovate to remain competitive, and for quality
improvements
• Supplier and Customer collaboration is the most adopted collaboration type
• Lack of trust and insufficient knowledge about capability are limiting factors
in the decision to collaborate
• Trade bodies and supply chain partners can play a useful role in helping firms
overcome some of the informational barriers

Next Steps
• Pilot Innovation Action with Firms
 Working with firms as an ‘innovation agent’ to help firms overcome capability
and trust barriers
 Action Research
• Develop a process model for innovation collaboration across industry sectors

ERC Research Shocase presentation January 2020

• Aim of Collaboration
6.1
6.1
9.1
3.0
3.0
3.0
3.0
3.0
11.1
14.8
33.3
18.5
25.9
14.8
7.4
7.4
20.0
40.0
33.3
20.0
20.0
26.7
13.3
40.0
9.6
13.7
19.9
10.1
12.3
10.0
5.9
9.8
0.0 20.0 40.0 60.0 80.0 100.0 120.0
Supply Chain Efficiency (%)
Business Re-Engineering (%)
New/Improved Products (%)
Lean Production (%)
Quality Mangement (%)
Robotics/Automation (%)
Data Connectivity (%)
Process/Product Simulation (%)
Aim of Collaboration in SME Foundries
0-9 Firmsize 10-49 Firmsize 50-249 Firmsize Total Industry SMEs
• Aim of Collaboration
3.13
3.13
10.94
1.56
4.69
1.56
0.00
3.13
6.45
16.13
32.26
25.81
19.35
6.45
9.68
9.68
4.51
8.53
19.80
11.64
10.79
3.60
4.02
5.85
0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00
Supply Chain Efficiency (%)
Business Re-Engineering (%)
New/Improved Products (%)
Lean Production (%)
Quality Mangement (%)
Robotics/Automation (%)
Data Connectivity (%)
Process/Product Simulation (%)
Aim of Collaboration in Metal-Forming SMEs
0-9 Firmsize 10-249 Firmsize Total Industry SMEs
Collaboration focused on product development and quality improvement

Learning from the Best SMEs
Carol Stanfield, ERC

Contents
• The question
• Quantitative analysis
• Qualitative research
• Conclusions and implications

The question
• Productivity growth is significantly more rapid among ‘frontier’ firms
in the top decile of the productivity distribution.
• Usually large firms
• What about smaller firms ‘behind the frontier’?

Method
• Analysis of Companies House data – detailed financial data and
access to contact details
• Analysis of value added and turnover per employee growth in twelve
4-digit sectors (six in manufacturing and six in services)
• Analysis by key characteristics
• Qualitative research in selected sectors

Are most productive SMEs also fastest
growing? Manufacturing

growing? Services

growing? Manufacturing sub-sectors.

growing? Services sub-sectors.

Age, size, investment, no. of subsidiaries?
Value added per employ growth Value added per employ growth Turnover per employee growth Turnover per employee growth
3rd decile -6.632 -6.608 -3.224 -3.242
(7.775) (7.796) (5.201) (5.204)
4th decile 0.335 0.324 -3.046 -3.044
(7.736) (7.754) (5.179) (5.185)
5th decile 2.255 2.232 -7.545 -7.552
(7.810) (7.826) (5.192) (5.194)
6th decile -15.055** -15.070** -6.043 -6.296
(7.660) (7.673) (5.162) (5.168)
7th decile -6.955 -6.929 -2.173 -2.022
(7.632) (7.644) (5.178) (5.188)
8th decile -16.128** -16.149** -8.488 -8.234
(7.652) (7.662) (5.170) (5.181)
9th decile -7.843 -7.914 -1.384 -1.638
(7.663) (7.681) (5.140) (5.150)
Business age (years) -0.027 -0.11
(0.110) (0.075)
Employment -0.003 0.026
(0.035) (0.024)
No. of subsidiaries -0.008 -0.502
(0.887) (0.636)
Constant term 1.908 2.829 8.098 8.322
(10.804) (11.729) (6.974) (7.537)
N 1195 1195 1337 1337
P 0.324 0.508 0.715 0.675
Bic 13486.278 13507.464 14215.968 14234.15

Quantitative conclusions
• Little consistent relationship between firms’ position in deciles of the
productivity distribution and subsequent productivity growth. This
suggests that behind the frontier productivity growth is not
characterised by the difference in productivity levels which
characterises the frontier v non-frontier firm distinction.
• Few significant observable differences between firms behind the
frontier experiencing rapid productivity growth and those
experiencing slower growth. Firm age, size, number of subsidiaries
and investment seem only weakly related to productivity growth at
least in the short-term.

Qualitative sample & characteristics
• SMEs with high productivity and high growth
• Sectors:
Manufacture of instruments and appliances for measuring, testing and navigation
Manufacture of metal structures and parts of structures
Computer consultancy activities
Temporary employment agency activities
Legal services
• Size: 20 – 200 (500) employees
• Age: 11 – 120 years
• Ownership – family to multinational

Complex interplay of factors in SMEs
Leader
Product and
Market
Customer
focussed
innovation
Investment
Operational
management
Valuing staff
Government
activity
Reactive, responsive
Strategic, tactical
Data driven, routinised
Performanc
e culture
Market
conditions
Autonomy, agility

Sector nuances
Manufacturing Services: ComputerConsultancy Services: Legal
Niche products/markets and exporters Broad market Niche/specialist
Innovation essential to deliver customer
demand
Innovation gains an advantage when market
growing
Staff engagement more likely to be within
‘operational management processes’ – focus on
expertise in product development
Staff engagement more likely to be within ‘people management’
Company wide rewards Individual rewards

Conclusions and Implications
Good practice can happen in smaller firms too
• Continue to aid the dissemination of the message that good
management and leadership matters
• Selecting and developing leaders: what do Boards need to know?
• Tapping into the passion of small business leaders
• Emphasising good management practices in Government
procurement criteria
• Government can also ‘Learn from the Best’

Understanding local productivity
Disparities:
Size and Sector
Neha Prashar

Background
• We know that global frontier firms have seen significantly more rapid
productivity growth when compared with non-frontier firms. This has
remained robust since 2000s.
• The same can be said about UK frontier firms, where the top 10% in the
productivity distribution are ten times more productive than those in the
bottom 10%.
• This dispersion remains persistent and impacts the way current
measures of productivity, both aggregate and firm-level, should be
interpreted….with caution!
• This combination of dispersion plus persistence would yield
meaningless average (mean) productivity estimates using firm-level
data.

Main Research Question
• What explains productivity differences between the 38 local
LEP areas in England?
1. Differently shaped productivity distributions?
2. Firm size distribution?
3. Sectoral composition?
• We need to consider points across the distribution during
comparative analysis over time and area.
• Using productivity (turnover per job) to estimate quantiles
(2.5% intervals) for each LEP to obtain a distribution.

Data
• Using data from the ONS IDBR (BSD), we compare productivity
at the: 25%; 50% and 75% points of the distribution for firms
in each LEP area.
• Whilst such an approach will generate a considerable volume
of data, the gains from taking a more nuanced view will allow
us to form a more accurate and robust picture of the extent of
productivity differences between LEPs.
• Firm vs local unit data at this level of spatial analysis will be a
challenge.

Method (1)
• Once 2.5% quantiles were estimated, LEP’s were then ordered
by the 50th, 25th and 75th percentile to give an overview of
high and low productivity LEP areas.
• We look at the medians (this is the average median due to
disclosure issues which meant that we couldn’t output the
true median itself but rather +/- 5 observations averaged) for
each year between 2013-2018.
• We want to know whether LEPs next to each other (i.e.,
London and Thames Valley), once ordered, have significantly
different distributions, as well as, LEPs on opposite ends of
the productivity order (i.e., London and Cumbria).

Method (2)
• A simple way to view this is by plotting the quantiles against
productivity levels and do a visual inspection, however, statistical
testing is better.
• We use the two sample Kolmogorov-Smirnov (K-S) test which
compares whether the empirical distribution function of the same
variable from two datasets differ significantly.
• This was the chosen test as there is no prior assumption on the
distribution of the data and is non-parametric.
• We do this analysis for LEP pairs for each year between 2013-2018
and results to not vary over time.

Quantiles
Productivity(TurnoverperEmployee)
2.5%
5%
7.5%
10%
12.5%
15%
17.5%
20%
22.5%
25%
27.5%
30%
32.5%
35%
37.5%
40%
42.5%
45%
47.5%
50%
52.5%
55%
57.5%
60%
62.5%
65%
67.5%
70%
72.5%
75%
77.5%
80%
82.5%
85%
87.5%
90%
92.5%
95%
97.5%
0
50
100
150
200
250
300
350
400
450
500
550
600
650
Worcestershire LEP
Cornwall LEP
Buckinghamshire LEP
London LEP
This is the top two
performing LEPs
(Buckinghamshire
and London) and
the two bottom
performing LEPs
(Worcestershire
and Cornwall) for
2018.
Distributions look
very similar, which
is confirmed
through the KS
test.
Figure 1: Quantile plot of productivity (2018)

Previously….
• As Figure 1 shows, we found no significant distributional
differences between LEPs - the top performing LEPs (London,
Thames Valley etc) had overall higher productivity medians
than the low performing LEPs (Cornwall, Cumbria, North East
etc) – on visual observation, there is a clear left-side skew in
most LEPs moving away from a log-normal distribution.
• What does this mean?
– There are more higher productive firms in high preforming LEPs.
– There is no overall difference in distribution but does this hold when
looking at subset of firms (size and sector)
– If there are differences, what conditions could be causing this?
Competition, infrastructure, spillovers, local demand etc.

Let’s start looking at size
• We look at the productivity distributions for micro (1-9
employees), small (10-49 employees), medium (50-249
employees) and large (250+ employees) firms in each LEP.
• We find that larger firms exhibit different productivity
distributions in high performing compared with low
performing LEPs.

0
20
40
60
80
100
120
140
160
180 WORC
HUMB
CUMB
CORN
NORE
COVE
GLIN
SHEF
TEES
GBIR
BLAC
DERB
YORK
HEAR
STOK
LIVE
NEWA
GMAN
LANC
LEED
LEIC
THEM
WEST
DORS
GLOU
CHES
GCAM
SOLE
SWIN
SOUM
OXFO
SOUH
COAS
ENTE
THAM
HERT
BUCK
LOND
AVERAGEMEDIANPRODUCTIVITY
LEP
Figure 2: Average Productivity Medians by Firm Size
(LEP is in ascending order using 1-9 employees)
1-9 employees 10-49 employees 50-249 employees 250+ employees

0
20
40
60
80
100
120
140
160
180 CORN
SOUH
YORK
LIVE
TEES
DORS
CHES
HEAR
NEWA
GMAN
LANC
LEED
WEST
GLIN
OXFO
HERT
SHEF
DERB
NORE
COAS
LOND
GLOU
WORC
STOK
GBIR
CUMB
BLAC
LEIC
HUMB
ENTE
GCAM
SWIN
SOLE
COVE
SOUM
THAM
THEM
BUCK
AVERAGEPRODUCTIVITYMEDIAN
LEP
(LEPs are in ascending order using 250+ employees)
1-9 employees 10-49 employees 50-249 employees 250+ employees

What is this telling us?
• Micro-firms (1-9 employees) have no statistical difference in productivity
distribution between LEPs (Figure 2).
• Medium and large-sized firms, however, appear to have a statistical
difference when looking at the opposite ends of the LEP distribution - The
Marches and Buckinghamshire vs Cornwall and South East for 250+
employee firms (Figure 3)
• When looking at the probability density functions, there is clearly a left
skew in the distribution in low preforming LEPs with a high peak. High
performing LEPs have a lower peak and are more dispersed in productivity.

What about Sector?
• We look at 1 digit SIC codes (2003 classification) – Figures 3 and 4
• Generally find there are no statistical differences between productivity
distributions in most sectors except the Transport, storage and
communication sector and financial intermediation sector in each LEP
• Financial intermediation is interesting when looking at the median
values….

0
20
40
60
80
100
120
140
CORN
CUMB
DORS
HEAR
TEES
HUMB
NEWA
GLIN
WEST
NORE
DERB
LANC
SHEF
YORK
GLOU
SOLE
GMAN
THEM
WORC
LEIC
LIVE
COAS
CHES
STOK
BLAC
COVE
SWIN
LEED
GBIR
SOUH
ENTE
GCAM
LOND
OXFO
SOUM
BUCK
HERT
THAM
AVERAGEPRODUCTIVITYMEDIAN
LEP
(LEPs are in ascending order using Wholesale and Retail sector)
manufacturing construction
wholesale and retail hotels and restaurants
transport, storage and communication financial intermediation
real estate other activities

0
20
40
60
80
100
120
WORC
GBIR
HUMB
BLAC
COVE
SOUM
YORK
SHEF
STOK
GLIN
WEST
LEIC
SWIN
LIVE
GMAN
DERB
GCAM
LEED
CHES
OXFO
TEES
HERT
CORN
SOLE
GLOU
NEWA
SOUH
LANC
CUMB
NORE
HEAR
THAM
THEM
DORS
BUCK
LOND
ENTE
COAS
(LEPs are in ascending order using Transport, Storage and
Communication sector)
transport, storage and communication financial intermediation
Financial intermediation has the
same median productivity in nearly
all LEPs except London. This is also
persistent across time (2013
onwards). The dispersion is higher
in London.

What is all this telling us?
• London and the home counties clearly have higher median productivity levels but are
not statistically different from other LEPs overall.
• However, there are statistically significant differences when looking at firms with 50+
employees where productivity is much more dispersed between the high and low
preforming LEPs.
• In the financial sector, there is statistical difference in distributions between London
and low performing LEPs (Liverpool and Sheffield), where London has a higher level of
dispersion and lower peak.
• Literature (Syverson (2004a); Okubo, Tomiura (2011); Du and Vanino (2019)) suggests
this could be down to agglomeration, competition and spillover effects – low productive
firms who set up near large productive firms are able to survive by proximity to their
customer and benefit from positive externalities.
• Equally – in agglomerated areas such as London – higher local demand can fuel product
differentiation allowing low productive and heterogenous firms to survive.

Policy Implications
• Looking at productivity distributions is much more informative than
looking at average productivity effects for local economic areas – reveals
issues which permit a more nuanced policy response.
• Firms’ productivity and potential is heavily influenced by the proximity to
highly productive firms in the same market/area.
• Alongside programmes to target an individual firm’s productivity, the
current crop of Local Industrial Strategies (LIS) might also consider
attracting and fostering highly productive firms in their geographical area,
especially in larger firms.
• This could change the productivity distribution and head towards a log-
normal distribution where productivity is evenly dispersed.

Going Forward
• The focus here has been on the 38 English LEPs – this could be extended
to look at NUTS2 areas in Scotland, Wales and NI to examine if similar
distributional differences occur.
• This project follows on well to the next phase of project which looks at
local competition and productivity growth
• We will take a closer look at the competition and agglomeration effects to
empirically assess whether this is driving the differences we have
observed in the productivity distributions.

Thank you!
Questions/Comments?
Professor Mark Hart (mark.hart@aston.ac.uk)
Dr Neha Prashar (n.prashar14@aston.ac.uk)
The data used here is from the Jobs and Turnover version of the Longitudinal Business Structure Database which can be
accessed through the Secure Lab. The use of these data does not imply the endorsement of the data owner or the UK
Data Service at the UK Data Archive in relation to the interpretation or analysis of the data. This work uses research
datasets which may not exactly reproduce National Statistics aggregates

What’s coming up from the Enterprise
Research Centre ?
Stephen Roper, ERC

Core projects – Jan-Aug 2020
1. How are SMEs responding to the
climate emergency?
RQ1: What factors shape firms’ environmental
ambition?
RQ2: What organisational and financial responses
are SMEs making to the climate emergency?
RQ3: What drives environmentally relevant
innovation? Is this different from other types of
innovative activity?
2. Competition, local growth and
productivity
RQ1: How does the strength of local competition
vary across the UK? Geographical analysis by sector,
location.
RQ2 How does the intensity of competition and
concentration influence the growth and productivity
of local firms?
RQ3: Are there spillover or displacement effects
which arise from more intensive local competition?
3. Measuring digital diffusion
RQ1: What metrics have prior studies used to capture
the adoption and implementation of digital
technologies?
RQ2: What are the internal and external challenges
and benefits of digital adoption and implementation
for SMEs?
RQ3: Developing measurement scales for ‘digital
readiness’, ‘digital adoption level’ and ‘digital
implementation’.
4. Local knowledge innovation, exporting
and productivity
RQ1: What local eco-system factors most strongly
support innovation and exporting, growth and
productivity? RQ2: How do business capabilities
influence firms’ ability to draw strength from their
local environment?
RQ3: Are there spillover effects from eco-system
strengths for firms in other local areas? Or are such
effects purely ‘local’.

Commissioned projects
On-going projects (selected)
• Mental health and productivity in
Midlands firms (ME)
• Geographical Indications of
Origin/PFNs and their benefits
(ESRC)
• Innovation in micro-businesses in
Northern Ireland (DFE NI)
• R&D in creative industries (DCMS)
New projects for next quarter
• NIPF fellowship with Warwick
Manufacturing Group (ESRC)
• SME support evaluation
partners for JP Morgan
Foundation (JPM)
• Innovation readiness in
foundation industries (ESRC)

SOTA Reviews on Women’s Enterprise
(ERC/ISBE Gender and Enterprise Network)
Aim: To ask insightful questions about women’s experiences of enterprise,
and gender differences in entrepreneurship
Themes:
• Is Time Up for The Hero Male Entrepreneur? A Review of Enterprise Discourse and
its Effects
• Is Expanding Women’s Self-employment a Good Thing? A Review of Evidence
• A Review of Assumptions Underpinning Women’s Enterprise Policy Initiatives
• How Does Gender Shape Entrepreneurial Resources and Practices? A Review of
Evidence
• What Do We Know About Ethnic and Migrant Women Entrepreneurs? A Review of
Evidence
HOLD THE DATE – Seminar/workshop event on 12th March – Where next
for Women’s Enterprise Policy?

Flying kites… (projects seeking funders!)
• Business crime – its dynamic
effects on entrepreneurship and
ambition
• Predicting local productivity
growth using remote sensing
satellite data
• Tracking innovations beyond the
grant – what’s the long-term
story?

Thanks … it must be time for
lunch!

Building Better Business
Resilience Final Report
Launch

Welcome and introduction
 Lee Hopley, ERC
 Delphine Poschmann, Global Philanthropy, J.P. Morgan

Maria Wishart, ERC
Building resilience in under-
represented entrepreneurs: A
European comparative study

Background
• SMEs account for 99% of businesses, 68% of jobs and 58% of value-
added in the European Union (EU, 2017) but how they experience,
plan for and respond to adversity is not well understood
• Female and ethnic minority groups are under-represented in
entrepreneurial activities, and little is known about their resilience
• Research aims:
– Explore the range of resilience challenges faced by SMEs, with a
focus on female and ethnic minority-led businesses
– Identify the characteristics and strategies that foster resilience,
survival and growth in these firms
– Develop practical tools to help SMEs strengthen their resilience to
shocks in challenging times

The study
• Five city, two year project
• Quantitative survey of 2,975 firms
– 3 to 99 employees, variety of sectors, in low and middle income
boroughs
– Computer Aided Telephone Interviews
• Qualitative in-depth interviews
• Four key phases:
1. Review & synthesis of previous research: Feb-July 2018
2. London fieldwork: Oct-Dec 2018
3. Paris, Frankfurt, Madrid & Milan fieldwork: Jan-May 2019
4. Data integration, comparative analysis, toolkit development &
dissemination of findings June-Dec 2019

Survey sample
Low income
borough
Middle income
borough
London Total 301 300
Female-led 146 150
Ethnic-led 92 91
Paris Total 300 300
Female-led 114 147
Ethnic-led 99 75
Frankfurt Total 268 259
Female-led 129 103
Ethnic-led 73 65
Milan Total 300 300
Female-led 156 139
Ethnic-led 79 67
Madrid Total 330 317
Female-led 152 156
Ethnic-led 104 79

Profile of firms surveyed
Size distribution of firms by city

Profile of firms surveyed
Sectoral composition of city respondent firms

Survey headlines
• Ethnic-led firms and female-led firms tend to have different
goals
• Ethnic leaders are less likely to seek external advice for their
businesses
• Female and ethnic leaders tend to consult different kinds of
external advice than their counterparts
• Overall, ethnic-led firms more likely to experience crisis, but
considerable variation by city
• Variation by city and firm type in kinds of crisis anticipated and
experienced

Survey findings
• Running their businesses:
– Objectives
– Likelihood of firms to seek external advice
– Who they consult
• Experiencing adversity:
– Overall likelihood of firms to experience a crisis
– City-level analysis of crises by ethnicity and gender of leader:
• Likelihood of crisis
• Top perceived threats
• Top causes of crisis
– Responses to crisis by city

Objectives
Firm objectives by city & ethnicity
65%
55%
76%
59%
78%
60%
66%
50%
57%
47%
67%
47%
77%
57%
74%
44%
54%
50% 51%
42%
71%
65%
90%
85%
91%
77% 77%
74%
71% 72%
41%
47%
70%
53%
45%
67%
56%
35% 36%
17%
Migrant Non migrant Migrant Non migrant Migrant Non migrant Migrant Non migrant Migrant Non migrant
London Madrid Milan Frankfurt Paris
To increase the social and environmental benefits of the business
To make a contribution to the local community
To keep my business similar to how it operates now
To build a national and/or international business

Objectives
Firm objectives by city & gender
65%
52%
63% 63%
66%
63%
57%
53% 52%
48%
62%
44%
65%
60%
54%
48%
55%
47% 47%
43%
70%
63%
88% 86%
81% 80% 79%
72% 72% 71%
43%
46%
58% 57% 59%
64%
34%
46%
23% 22%
Female Male Female Male Female Male Female Male Female Male
London Madrid Milan Frankfurt Paris
To increase the social and environmental benefits of the business
To make a contribution to the local community
To keep my business similar to how it operates now
To build a national and/or international business

Seeking external advice
Firms seeking external advice in past 12 months by city & ethnicity
33%
38%
12%
32%
49%
37%
37%
50%
57%
53%
Paris
Frankfurt
Milan
Madrid
London
Non-Ethnic Ethnic

Seeking external advice
Firms seeking external advice in past 12 months by city & gender
39%
35%
40%
53%
51%
34%
38%
41%
49%
52%
Paris
Frankfurt
Milan
Madrid
London
Male Female

Who they consult – all cities
Sources of advice by ethnicity
21%
22%
33%
34%
42%
54%
69%
13%
18%
31%
37%
30%
62%
66%
LA
Government
Network
Mentor
Family
Legal
Accountant
Non ethnic Ethnic

Who they consult – all cities
Sources of advice by gender
15%
20%
32%
33%
38%
59%
64%
14%
17%
31%
32%
36%
62%
68%
LA
Government
Network
Family
Mentor
Legal
Accountant
Male Female

Experiencing adversity
Firms that had experienced a crisis in the preceding 5 years by
ethnicity and city
29%
40%
4%
27%
48%
32%
27%
29%
41%
33%
Paris Frankfurt Milan Madrid London
Ethnic-led Non ethnic-led

Firms that had experienced a crisis in the preceding 5 years by
gender and city
33%
30%
21%
38% 37%
29% 30%
26%
36%
38%
Female-led Male-led

32% 32%
23%
34%
39%
30% 29%
23%
40%
36%
Low income Middle income
Firms that had experienced a crisis in the preceding 5 years
by borough and city

37%
18%
10% 10% 9%
Customers Cashflow Strike Costs Regulations
Cause of crisis female-led
31%
19%
10%
8% 8%
Customers Cashflow Strike Premises Staff
Cause of crisis ethnic-led
51% 50% 49% 49% 49%
Staff Illness Inc comp Cashflow Costs
Perceived threats ethnic-led
57%
53% 51% 51% 49%
Staff Illness Inc comp Regulations Cashflow
Perceived threats female-led
Paris
33%
29%
Female-led Male-led
Experience of adversity by gender
29%
32%
Experience of adversity by ethnicity

Frankfurt
30% 30%
Female-led Male-led
40%
27%
41%
20%
12% 10% 9%
Customers Staff Illness Cashflow Premises
Causes of crisis female-led
37%
17% 15%
9% 9%
Customers Staff Cashflow Materials Other
Causes of crisis ethnic-led
51%
38% 36% 36%
30%
Staff Regulations Costs Customer Cyber
48%
42%
39% 38%
32%
Staff Regulations Illness Cyber Customers

Milan
4%
29%
33%
18%
10% 8%
5%
Customers Strike New comp Cashflow Weather
Causes of crisis female-led
33%
17% 17% 17% 17%
Strike Regulations New comp Inc comp Premises
51%
51%
46% 46%
45%
Costs Customers Materials Inc comp Staff
64%
62% 62% 62%
59%
Costs New comp Materials Technical Inc comp
21%
26%
Female-led Male-led

Madrid
27%
41%
36%
19%
11%
9%
7%
Customers Strike Staff Cashflow Inc comp
Causes of adversity female-led
33%
17%
14%
7% 7%
Customers Strike Other Inc comp Cashflow
Causes of adversity ethnic-led
65%
63%
63%
62% 61%
Illness Costs Regulations Cashflow Inc comp
75% 74%
65% 62% 62%
Illness Cashflow Regulations Inc comp Costs
38% 36%
Female-led Male-led

London
37% 38%
Female-led Male-led
48%
33%
30%
18%
16% 15% 14%
Costs Staff Strike Cashflow Premises
Causes of crisis female
25%
15% 15% 14%
12%
Costs Customers Cashflow Inc comp Strike
57% 56% 54% 52%
47%
Costs Staff Regulations Cyber Cashflow
60% 58%
50% 49% 48%
Staff Costs Illness Regulations Cyber

Response to crisis by city
53%
61%
41%
53% 55%
39%
72%
30%
51%
73%
32% 30%
17%
21%
58%
Middle income boroughs
Financial Developed plan Informal advice
40%
52%
46%
55%
43%
50%
67%
38% 36%
75%
40%
54%
4%
21%
70%
Low income boroughs
Financial Developed plan Informal advice

Sector differences – all cities
• The sample includes firms from the following sectors: primary,
manufacturing, construction, trade transport & hospitality, information
and communication, business services and other services sectors.
• Overall, firms in manufacturing, construction and business services
are significantly more likely than those in other sectors to have had a
crisis in the preceding five years.
• Overall, firms in manufacturing and trade transport & hospitality are
significantly less likely than those in other sectors to have crisis plans in
place.

Summary
• Complex picture
• Significant variation in the ways that firms anticipate and
experience adversity by city and firm type, implying that initiatives
to build resilience should take account of local environmental,
regulatory and cultural factors
• Differences between perceived threats and actual causes of crisis
suggests that firm leaders lack skills and resources to distinguish
and prioritise threats
• Ethnic-led firms take less external advice and, along with female-led
firms, consult more informal sources: need to understand why, and
consider this when communicating & delivering initiatives

Implications
Interventions to help small firms to anticipate threats and plan
for future crisis may help them to improve their resilience
Why? Many firms are not identifying the most potent future risks for
their firms
Who? Particular focus on female and ethnic-led firms, who differ in their
approach to managing their businesses, in their experiences of adversity
and in their ability to identify key risks.
How? Develop accessible tools which are relevant to these firms. City
variations suggest that localised factors may be impacting, and findings
indicate key differences between mainstream and under represented
leaders, so tools must be flexible.

Implications
• Interventions should take account of the complex picture
that this research has identified, and so any tools must:
– acknowledge that some challenges are common to all SMEs, but
that some are unique to under represented entrepreneurs, and
offer tailored solutions
– accommodate firm and location-level variations
– be simple to use, time efficient and relevant
– be delivered in partnership with support organisations that have
links to under represented groups

Lee Hopley, ERC
Building resilience in under-
represented entrepreneurs
Toolkit overview

Background
• Under-represented entrepreneurs’ firms experience lower
turnover and higher failure rates
• Differences between perceived threats and actual causes of
crisis
• Diversity in approach towards, and experience of, adversity,
with borough and city-level variation also evident
• Some challenges that are common to all SMEs, some that are
unique to their firm type, and usually a combination
• Available resilience resources tend to focus on a single issue
(e.g., weather events) and to be targeted at large firms
• Need for flexible interventions to help build resilience, that
can accommodate firm and location-level variations

Resilience toolkit - principles
• Developed specifically for small firms, focus on under-represented
groups
• Three-phase toolkit, elements can work together or as stand-alone
tools
• Core elements with option for additional resources so they can be
tailored for different markets/user groups
• Evidence-based: All have supporting framework based on
– Academic and grey literature
– Findings from Building entrepreneur and small business resilience project
fieldwork
• Adopts accessible language
• Straightforward to use, time efficient, delivers immediate responses

• Encourages small firms to consider
their resilience status.
• Sees SME resilience as influenced by
individual leader factors and firm-
level factors, and evaluates both.
• Individual resilience seen as a product
of experience, skills set and networks.
• Business resilience seen as a product
of planning activities, people, and
values.
• Simple self-completion grid for each
element.
1. Resilience health check tool

Health check self-completion grids

Output of resilience health check in a combined radar
chart
72%
77%
69%
40%84%
57%
Experience
Skills & Attributes
Networks
Planning & Resources
People
Values
My Score

• Designed to encourage SMEs to
reflect on possible future risks.
• Asks leaders to consider two
elements of risk: the likelihood
of it happening and the severity
of the impact it would have. This
is an approach rooted in
academic literature.
• Divides risk factors into those
internal and external to a firm.
• Internal: those which the firm
can influence – financial, people
& plant/technical.
• External: those which the firm
has less control over – supply
chain, environmental and
political/legal.
2. Risk analysis tool

Risk analysis self-completion grids

Output of risk analysis tool in a risk matrix with
colour-coded scores:
Likelihoodofhappening
High 3 6 9
Medium 2 4 6
Low 1 2 3
Low Medium High
Seriousness

MY SCORE
1 2 3 1 2 3 S X L
Low Medium High Low Medium High
Total loss or failure of one supplier
1 2 3 1 2 3 1
Increase in cost of materials from one supplier 1 2 3 1 2 3 3
Disruption of supply of materials 1 2 3 1 2 3 4
Reduction in quality of materials 1 2 3 1 2 3 6
Short or inaccurate delivery of supplies 1 2 3 1 2 3 6
Loss of one customer 1 2 3 1 2 3 3
Loss of several customers 1 2 3 1 2 3 9
Failure of a customer to pay 1 2 3 1 2 3 2
Appearance of a new competitor 1 2 3 1 2 3 6
Competitor undercutting on price 1 2 3 1 2 3 4
Appearance of a substitute product 1 2 3 1 2 3 9
Loss of a distributor 1 2 3 1 2 3 3
SERIOUSNESS (S) LIKELIHOOD (L)
Example of output from risk analysis tool

• Aims to allow small firms to
continue to function during and
after adversity.
• Template - based tool designed
to encourage SMEs to embrace a
contingency planning mind-set.
• Has two foci: the creation of an
essential data file and the
establishment of clear plans for
specific critical risks.
• Data file offers quick access to
essential information without
recourse to firm’s usual systems.
• Critical risk plan identifies
actions, assigns ownership and
allocates resources necessary in
the event of an identified
potential crisis.
3. Crisis planning tool

Output of crisis planning tool data file templates

Resilience toolkit - testing
• Initial testing with London-based small business
leaders from under represented groups across a
range of sectors found:
– User-friendly: quick and straightforward to use
– Wide range of scores from healthcheck tool reflecting
diverse leader and firm characteristics
– Risk analysis tool delivered broad spread of scores,
highlighting different key threats for different firms
– Planning templates well-received

Resilience toolkit - summary
• Format and content of three main tools, and
evidence base for each, now established
• Planning for ‘additional resources’ elements in
progress
• Testing of toolkit with entrepreneurs from under
represented groups started in July 2019 and ongoing
• Launch due early 2020

What can we do to build more resilient
businesses?
Panel discussion
 Mark Hart, ERC
 Harriet Walker Director, Business Emergency Resilience (Business in the Community
 Sonali Parekh Head of Policy, Federation of Small Business (TBC),
 Olu Orugboh CEO and Founder Synergy Solutions

Final remarks and event close
Lee Hopley, ERC

Thank you
For further details please visit :
www.enterpriseresearch.ac.uk
@ERC_Uk
ERC Funded by

ERC Research Shocase presentation January 2020

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ERC Research Shocase presentation January 2020