LSBS event presentation slides

BEIS-ERC Longitudinal Small Business
Survey Research Showcase Event
19 September 2019
WBS London, the Shard
Funded by

LONDON’S EVENING UNIVERSITY BBK.AC.UK
EXPLORING THE LINK BETWEEN TRAINING
AND INNOVATION
Marion Frenz and Ray Lambert
3

• Rationale and research questions
• Data and methods
• Results
• Discussion
• Policy implications
4
OUTLINE

Impact of investments in different types of on- and off-the-job
training on a range of innovation outcomes in the small business
sector.
5
RESEARCH TOPIC

Why is this important?
• Size of the small business sector and the relative lack of empirical evidence
that stems from that sector
• Much of the evidence is based on CIS-type data, which does not include
micro-firms
• Innovation literature focuses on technology and knowledge
• Measures
• Emphasis on R&D and related investments
• Measures of human capital: formal qualifications, e.g. share of staff qualified to
degree level and above
• Tentative evidence that training is also important (Freel, 2005 and McGuirk, Lenihan
and Hart, 2015)
6
RATIONALE

1. Does general employee training enhance innovation capabilities and is the
strength of this relationship influenced by firm size?
2. Do innovation impacts vary according to whether the training was on- or
off-the-job?
3. Does manager training enhance innovation capability? Do effects vary by
type of manager training?
7
RESEARCH QUESTIONS

• Mainly the panel dataset of 4,165 businesses, and from that panel the 3,102
businesses with one or more employees.
• A comparison with UKIS shows that the LSBS reports a higher share of
produce and process innovators among comparable parts of the UK economy
(SMEs).
• These differences cannot be explained by the sectoral coverage, but possibly
by the survey method (CIS is postal vs LSBS telephone interviews).
8
DATA AND METHOD

Dependent variables
• Product innovation, new-to-market product innovation, process innovation,
new-to-industry process innovation in the last three years
Independent variables
• General training, on- and off-the-job training, manager training, as well as 6
specific areas of manager within a particular calendar year
Controls: past innovation activity, size, sector and region
9
DATA AND METHOD

• Dynamic probit regressions
• Dependent variables are measured in 2017 wave. Reference period is the last
3 years.
• The main independent variables (training) are taken from the 2015 wave.
Reference period is the last year.
• Control for past innovation activity, including the lagged dependent
variables, measured in the 2015 wave
• The dependent variables, and most of the independent variables, are binary
variables
• Interaction between training and three size bands (1-9 empl., 10-49 empl.,
50-249 empl.)
10
DATA AND METHOD

• Overall, positive link between training that is not explicitly for innovation and
innovation outcomes.
• The link is more pronounced for micro businesses.
• This relationship is strongest for product innovation, compared with process
or new-to-market/industry innovations.
• The positive link is similar for on- and off-the-job training
13
DISCUSSION AND CONCLUSIONS

• Training that links to formal qualifications, likely to be less employment
specific, is not positively linked to innovation propensity. Hence, training that
‘fits’ the firm may be more effective.
• Manager training in IT and financial management is linked with product and
process innovation.
• Manager training in leadership skills is linked with novel product and process
innovation.
14
DISCUSSION AND CONCLUSIONS

The tentative policy implications are that promoting training both of workforce
and managers seems likely to stimulate innovation, with the potential effects
appearing to be more pronounced in micro firms than in other SMEs, although
positive in all cases.
15
POLICY IMPLICATIONS

The UK’s European university
Presentation for the BEIS-ERC LSBS Showcase Event, The Shard, 19th
September, 2019
The Role of Innovation in Small Business Performance
– A regional perspective
Catherine Robinson, Marian Garcia, Jeremy Howells and Guihan Ko,
University of Kent

Acknowledgements:
• We are grateful to the ERC for providing support for this work
• Data used in this paper are accessed via the UK Data Service.
The Longitudinal Small Business Survey(LSBS), Department for Business, Innovation and Skills. (2018) Longitudinal Small Business Survey, 2015-2017:
Secure Access. [data collection]. 2nd
Edition. UK Data Service. SN:8261, http://doi.org/10.5255/UKDA-SN-8261-2. The Business Structure Database
(BSD), Office for National Statistics. (2019) Business Structure Database, 1997-2018: Secure Access. [data collection]. 10th
Edition. UK Data Services.
SN:6697, http://doi.org/10.5255/UKDA-SN-6697-10. The British Enterprise, Research and Development (BERD) dataset, Office for National Statistics.
(2019). Business Expenditure on Research and Development, 1995-2017: Secure Access. [data collection]. 8th
Edition. UK Data Service. SN: 6690,
http://doi.org/10.5255/UKDA-SN-6690-8. 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.
The UK’s European UniversityPage 17

Aim of the presentation
• Background
• Innovation and small firms
• Regional context
• Research questions
• Methodology
• Data – LSBS and…
• Preliminary findings
• Summary and next steps.

Background/motivation
• Small firm* performance and innovation
• SMEs considered to be nimble (Cowling et al, 2014)
• New SMEs bring new ideas to market (Coad et al, 2016)
• SMEs perceived as being resource constrained
• Drivers
• International involvement (Love and Roper, 2015; Knight and Cavusgil, 2004)
• Diversity – in teams and leadership (Carter et al, 2015; Kloosterman, 2010 – ‘mixed
embeddedness’)
• Human capital (Coad et al, 2014)
• Supply chain linkages (agglomerations – Van Oort, 2015)
• Public support for innovation can help level the playing field for SMEs
*small firms/SMEs/firms using LSBS definition

To what extent is small business performance affected by firm level and/or
regional innovation contextual factors?
• SMEs likely to be more dependent on the environment in which they are
situated, both industrially and geographically (Aarstad and Kvitastein,
2019; Tojeiro-Rivero et al, 2019)
• But what level of geography is relevant?
• City-regions are an economically meaningful unit based on the commuting patterns of
high skilled workers (Robson, 2006; Coombes 2014)
• Used in previous analyses (Mason et al, 2013)
• And what variables are important at the regional level?
• Local labour markets
• Diverse or specialised regions?
• Regional R&D activity

Methodology
• Dependent variable(Y)=labour productivity (turnover per employee)
• First stage – random effects models over two years of data
Yijt = β0 + β1lempit-1 + β3age3it + β4export4it + β5wom5it + β6meg6it + β7rnd_supp7it + β8radicalit +
β9incrementit + β10diversjt + β11specialisationtj + β12chbctj + β14lagrndtj + uitj
• Second stage – multilevel models
• To take account of regional factors appropriately
• Not looking to ‘net out’ regional influences but understand them
• Random intercept model allows for intercepts to vary across CR
Yij = γ00 + γ10lemp_11j + γ20age2j + γ30export3j + γ40wom4j + γ50meg5j + γ60rnd_supp6j + γ70radical7j +
γ80increment8j + γ01divers1j + γ02specialisation2j + γ03chbc3j + γ04lagrnd4j + u0j + rij

Data used
• LSBS, waves 1-3 – accessed also via the UK data service
• 2015-2017
• Variables of interest:
• Turnover*
• Employment*
• Sector
• Exporter
• Radical/ incremental innovator
• Success in finding R&D support
• Whether female-led or minority ethnic group-led
• City-region (location)
• In addition, LSBS data were supplemented and matched to:
• City-region data constructed from NOMIS for labour market data
• BERD data for research and development at the city region level
• BSD data for more accurate turnover and employment data

Data construction – city region
• Diversity
• ‘herfindahl’ index based on employment shares
• Specialisation
• Own industry specialisation (employment shares)
• Regional R&D spend
• Labour market conditions
• Share of high skilled labour
• Employment rate
• Population growth
• Change in business count
• Picking up regional growth

Findings (1) Random Effects
(6)
Ln(labour productivity)
Ln (lagged employment) 0.0182*
[0.010]
Age of business (years) 0.0038***
[0.001]
Export (dummy) 0.2926***
[0.027]
Women-led (dummy) -0.1581***
[0.029]
Minority Ethnic led (dummy) 0.0548
[0.062]
Undertaking radical innovation (dummy) 0.1105***
[0.032]
Undertaking incremental innovation (dummy) 0.0423*
[0.023]
In receipt of government R&D support -0.0059
[0.015]

Findings (2)
Random Effects
Sector (Manufacturing) 0.1523***
[0.037]
Sector (Agriculture, fishing) -0.0335
[0.073]
Sector (Mining, energy) 0.5312***
[0.041]
Labour market conditions (factor) 0.0174
[0.018]
Lagged R&D spend 0.0000***
[0.000]
Diversity 0.3371
[0.229]
Own Specialisation 0.0164**
[0.008]
Change in Business Count 0.006
[0.004]
Constant 3.0729***
[0.390]
Observations 11,107
Number of serial 7,327
Wald 529.3
SEE 0.404
Robust standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1

Findings (3) Multi-level
modelling
(8) (9)
VARIABLES Ln(labour productivity)
Constant 3.8786*** 3.6113***
[0.028] [0.465]
Ln (lagged employment) -0.0023 -0.0038
[0.007] [0.007]
Age of business (years) 0.0052*** 0.0045***
[0.001] [0.001]
Export (dummy) 0.5096*** 0.5039***
[0.025] [0.026]
Women-led (dummy) -0.1480*** -0.1482***
[0.025] [0.026]
Minority Ethnic led (dummy) 0.0648 0.0536
[0.049] [0.050]
In receipt of government R&D support 0.1578*** 0.1662***
[0.040] [0.042]
Undertaking radical innovation (dummy) -0.0086 -0.0123
[0.032] [0.033]
Undertaking incremental innovation (dummy) -0.0341 -0.0333
[0.022] [0.023]

Findings (4) Multi-
level modelling
Sector (Manufacturing) 0.0689** 0.0883***
[0.031] [0.033]
Sector (Agriculture, fishing) -0.0736 -0.015
[0.050] [0.053]
Sector (Mining, energy) 0.5457*** 0.5730***
[0.034] [0.035]
Labour market conditions (factor) 0.0212
[0.022]
Diversity 0.2895
[0.327]
Own Specialisation 0.0036
[0.010]
Change in Business Count 0.0137*
[0.008]
Lagged R&D spend 0
[0.000]
Variance (city region) 0.0106 0.0077
[0.0040] [0.0040]
Variance (business-city region) 1.1213 1.1267
[0.0146] 0.0152]
LR test versus linear model 65.20*** 10.89***
Log likelihood -17609.931 -16442.043
Observations 11,912 11,107
Number of groups 55 52
Wald 905.8 837.7
Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

Conclusions
• Firm level findings are consistent with expectations
• Exporting positively associated with labour productivity
• Radical innovation/R&D support positively associated with labour productivity
• Women-led organisations negatively associated with labour productivity
• Regional factors play a significant role in SME performance
• But the channels of transmission are not clear
• Specialisation appears to have a positive impact, albeit weakly significant
• Findings are not out of line with other multilevel studies
Next Steps
• Is the City region the most appropriate geography?
• What about LEPs?
• So far we have only estimated a random intercept model; a slope model might offer further insights but is
more complex
• Explore CIS links further

THE UK’S
EUROPEAN
UNIVERSITY
www.kent.ac.uk

References:
• Aarstad, J. And O. A. Kvitastein (2019) ‘Enterprise R&D investments, product innovation and the regional industry structure, Regional Studies, online
first, available at https://doi.org/10.1080/00343404.2019.1624712
• Carter, S. S. Mwaura, M. Ram, K. Trehan and T. Jones (2015) ‘Barriers to ethnic minority and women’s enterprise: Existing evidence, policy tensions
and unsettled questions’, International Small Business Journal, 33(1), 49-69.
• Coad, A., A. Segarra and M. Teruel (2016) ‘Innovation and firm growth,: Does age play a role?’, Research Policy, 45, 387-400.
• Coombes, M. (2014) ‘From City-region Concept to Boundaries for Governance: The English Case’, Urban Studies, 51(11), 2426-2443.
• Cowling, M., W. Liu, A. Ledger and N. Zhang (2015) ‘What really happens to small and medium sized enterprises in a global economic recession? UK
evidence on sales and job dynamics’, International Small Business Journal: Researching Entrepreneurship, 33(5), 488-513.
• Love, J. and S. Roper (2015) ‘SME innovation, exporting and growth: A review of existing evidence’, International Small Business Journal, 33(1), 28-48.
• Mason, G., C. Robinson, C. Rozassa-Bondibene (2013) Firm growth and innovation in UK City-Regions, NESTA Working Paper 13/11
• Robson, B., R. Barr, M. Coombes, K. Lymperopoulou and J. Rees (2006) A Framework for City-Regions: Working Paper 1, Mapping City-regions,
London: Office of the Deputy Prime Minister (ODPM).
• Tojeiro-Rivero, D. and R. Moreno (2019) ‘Technological cooperation, R&D outsourcing and innovation performance at the firm level: The role of the
Regional Context’, Research Policy, 48, 1798-1808.
• Van Oort, F. (2015) ‘Unity in variety? Agglomeration economics beyond the specialization-diversity controversy’, Chapter 12, 259-271, in Handbook of
Research Methods and Application in Economic Geography, Karlsson, C., M. Andersson and T. Norman (eds), Elgar, London.

University Engagement and
Productivity in Innovative SMEs:
An Empirical Assessment
Daniel Prokop
Cardiff University
prokopd@Cardiff.ac.uk
Andrew Johnston
Sheffield Hallam University
a.Johnston@shu.ac.uk

SMEs and Innovation
• Influences
• SMEs and open innovation
• Resource-based view of the firm
• Why Collaborate with Universities?
• Increased resources/capabilities
• Increased R&D
• Increased patenting
• Higher sales
• Greater scope
• Firm characteristics and U-I Collaboration
• Openness
• R&D intensity
• Size
• Search, screen, and signal capabilities
• Physical closeness to a university

University-Industry Collaboration
• Why Universities?
• Safer partners
• Not competitors
• Centres of (frontier) knowledge creation
• Broad choice – 159 HEIs in UK
Five Decades of Policy Making
Docksey Report (1971) Wilson Review (2012)
Jarratt Report (1985) Witty Report (2013)
Dearing Report (1997) McMillan Report (2016)
Lambert Review (2003) Industrial Strategy White Paper (2017)

Research Questions & Research Approach
• Two pronged analysis:
• Does the productivity of SMEs influence their propensity to
collaborate with universities?
• 4289 SMEs from 2015 LSBS
• Logistic regression model to capture influence of characteristics on SME’s
propensity to engage with a university
• Does university collaboration influence the subsequent productivity
of SMEs?
• 258/154 SMEs from 2016/2017 LSBS
• OLS Regression with end productivity as dependent variable

Productivity and U-I Collaboration: Three
Scenarios
Probability increases linearly with performance Probability highest for ‘average’ performers Probability highest for leading and lagging firms
ProbabilityofCollaboratingwithaUniversity
Productivity
Productivity
Productivity

Starting vs End Productivity Following U-I
Collaboration: Three Scenarios
No effect on end productivity End productivity boosted for average performers End productivity Transformed/Maintained for
lagging/leading firms
EndProductivity
Starting Productivity
EndProductivity
EndProductivity
Starting Productivity Starting Productivity

SMEs’
Collaborative
Partners
Partner in Collaboration
Proportion of
firms
Suppliers of equipment, materials, services or
software 57.99%
Clients or customers from the private sector 42.19%
Clients or customers from the public sector 27.44%
Other businesses within enterprise group 27.08%
Competitors or other businesses in the same
industry 21.24%
Consultants, commercial labs or private R&D
institutes 19.38%
Universities or other higher education
institutions 13.55%
Government or public research institutes 6.60%
N=4406 (Source: LSBS 2015)

Results:
Probability of
collaboration
with
universities
occurring

Results
Summary:
Probability of
collaboration
with
universities
occurring
Variable Effect on U-I collaboration
Productivity No effect
Employment Positive
No. of sites No Effect
Age No Effect
Legal status No Effect
Sector: production and construction Negative/No Effect
Sector: transport retail and food sectors Negative
Sector: business services Negative/No Effect
Exporter Positive
Family business Negative
No. of directors and partners No Effect
Women-led No Effect
MEG-led No Effect
Urban-based No Effect
Regional GVA per capita No Effect
Regional Employment No Effect
Regional GERD per capita Negative
Industrial Specialisation No Effect
People management No Effect
Developing and implementing a business plan and strategy No Effect
Developing and introducing new products or services No Effect
Accessing external finance No Effect
Operational improvement No Effect
Social media Positive
Local Chamber of Commerce No Effect
Formal business network No Effect
Informal business network No Effect

Results
Summary:
Changes in
Productivity
Variable
Effect on End
Productivity
Productivity U Shaped effect
Employment Positive
No. of sites No Effect
Age No Effect
Legal status No Effect
Sector: production and construction Positive
Sector: transport retail and food sectors Positive
Sector: business services Positive
Exporter (2017 only) Positive
Family business Negative
No. of directors and partners No Effect
Women-led No Effect
MEG-led No Effect
Urban-based No Effect
Regional GVA per capita No Effect
Regional Employment No Effect
Regional GERD per capita No Effect
Industrial Specialisation No Effect

Conclusions
• Does the productivity of SMEs influence their propensity to
collaborate with universities?
• No
• Positively influenced by size of workforce, exporting, engagement in social
networks, and openness.
• Negatively influenced by being a family firm and located in region with higher
levels of GERD.
• Does university collaboration influence the subsequent productivity
of SMEs?
• Yes
• Transforms the productivity of less productive firms and maintains the
productivity of more productive firms.

Spatial disparities in SME productivity
in England
Sara Maioli Pattanapong Tiwasing Matthew Gorton
Jeremy Phillipson Robert Newbery
Rural Enterprise UK
Centre for Rural Economy & Newcastle University Business School
Newcastle University
44

The UK as a Spatially Unequal Country
• A long tail of low productivity businesses and significant spatial variations
in productivity characterise the UK economy.
• The disparities in firm productivity are large and growing across sub-
regions and regions and widened after the 2008 global financial crisis (Gal
and Egeland, 2018).
• The UK is one of the most inter-regionally unequal countries in the
industrialised world (Gal and Egeland, 2018; McCann, 2019): London has a
level of productivity at 33% above the UK average in 2017 (ONS 2019).
• Using the LSBS for 2015-17 we analyse the determinants of labour
productivity for a sample of 2,203 English SMEs, with a particular focus on
how place and productivity interact.
45

Why Location Matters?
• The literature draws largely on four main theoretical perspectives to
explain regional variation in business performance:
• theories of industrial organisation
• the ‘New Economic Geography’
• the Resource-Based View (RBV) of the firm
• institutional perspectives.
• These regional and sub-regional business performance disparities depend
on differences in both firms’ internal characteristics and locational effects.
• We identify the firm and locality, as captured by Local Enterprise
Partnerships (LEPs), determinants of SME productivity using nested
multilevel regression analysis.
46

Interaction between Productivity and Place:
How to Model It?
• Multilevel (also called mixed-effects or hierarchical) analysis (MA) allows us
to capture the nested structure of our data (firms located into 38 LEPs) and
effectively accounts for the contextual environment in which SMEs
operate.
• Standard regression models, such as OLS or GLS, are inappropriate because
they do not allow for residual components at each level in the hierarchy
and treat the firms as independent observations, so the standard errors of
regression coefficients will be underestimated, leading to an overstatement
of statistical significance.
• MA instead relaxes the assumption of zero intra-group correlation, crucially
important when dealing with economic geography.
47

Empirical Model
• Adapting the specification from Rebe-Hesketh and Skrondal (2004), Fazio and Piacentino
(2010), and Rebe-Hesketh and Skrondal (2012), our multilevel model is a longitudinal
two-level model with random intercept and random slopes:
• where Yij is firm’s productivity (measured in terms of the natural logarithm of turnover
per employee) of i-th firm nested within j-th LEP;
• Xhi is the h-th explanatory variable at firm-level, whose βh coefficient does not change
across LEPs; Wgij is the g-th explanatory variable at firm-level, whose β coefficient is
allowed to vary across LEPs;
• Z j is the -th explanatory variable at LEP level, whose coefficient does not change across
LEPs.
• Hence, βh and and β are deterministic coefficients, whilst the intercept β0j and the slopes
βgj are LEP-specific random coefficients
48
Yij= β0j+ βhXhi
H
h=1
+ βgjWgij
G
g=1
β Z j
L
=1
+ εij εij~N(0, 2
) (1)

Empirical Model
49
• Equation (1) can be re-written as
• Labour productivity is assumed to be the result of both fixed effects (first
bracket) and random effects (the latter bracket). So the first bracket is the
deterministic part of the model, while the second bracket is the stochastic
part of the model, because it allows both the intercept and slopes to vary
spatially.
• Thus, the multilevel analysis comprises a fixed-effects part (at firm level or
level one) and a random-effects part (at LEP level or level two).
(2)Yij= [γ00+ βhX i+γg0W ij+β Z j ]+[u0j +ugjW ij + εij]

Fitting the Model
• For the firm-level analysis, we include:
- business age - innovation capability - use of own website
- legal status - external finance capability - use of a third-party website
- industrial code - operational capability - use of social-media networks
- women-led business - strategic capability - Chamber of Commerce membership
- rural location - business size indicators - support received
• At LEP level, we merge LSBS and other datasets through the LEP codes to
identify locality-related determinants:
• broadband speeds from Ofcom, measured as average percentage of premises that
are unable to receive broadband speeds of 2 Megabits per second (Mbps), and
• educational attainment as measured by the achievement of National Vocation
Qualifications at level 4 (NVQ4) from NOMIS.
• In addition to a random intercept, we let the coefficients for financial and
wholesale/retail sectors dummies vary by LEP (random slopes).
50

Estimation results
51
One-level
GLS
Two-level mixed effects
Firm productivity Model 0 Model I Model II Model III Model IV Model V
rural 0.0171 0.0789**
0.0623**
0.0112 0.0129
(0.34) (2.48) (1.98) (0.26) (0.30)
support 0.0158 0.0690**
0.0634**
0.0624**
0.0625**
(0.79) (2.49) (2.31) (2.27) (2.28)
family -0.0235 -0.0252 -0.0253 -0.0254 -0.0245
(-0.67) (-0.83) (-0.83) (-0.84) (-0.81)
age≤ 5years 0.00597 -0.0853*
-0.0739 -0.0727 -0.0731
(0.12) (-1.88) (-1.64) (-1.62) (-1.63)
sole trader -0.352***
-0.302***
-0.301***
-0.301***
-0.303***
(-5.60) (-6.98) (-7.02) (-7.02) (-7.06)
micro -0.199***
-0.0618*
-0.0589*
-0.0603*
-0.0604*
(-7.02) (-1.77) (-1.71) (-1.75) (-1.75)
small -0.0538 0.153***
0.141***
0.141***
0.140***
(-1.39) (3.97) (3.72) (3.72) (3.70)
medium -0.164***
0.136***
0.131***
0.131***
0.133***
(-3.48) (2.87) (2.80) (2.80) (2.85)
primary 0.724***
0.765***
0.777***
0.773***
0.772***
(4.79) (7.62) (7.86) (7.83) (7.82)
manufacturing 0.983***
0.980***
0.986***
0.983***
0.983***
(8.41) (12.69) (12.98) (12.94) (12.95)
construction 0.837***
0.914***
0.915***
0.913***
0.912***
(6.93) (11.46) (11.67) (11.65) (11.65)
wholesale & retail 1.229***
1.270***
1.358***
1.358***
1.362***
(10.85) (16.96) (14.05) (13.99) (13.97)
transport 0.422***
0.499***
0.509***
0.506***
0.503***
(2.76) (4.88) (5.06) (5.03) (5.00)
accommodation 0.148 0.167*
0.170**
0.170**
0.170**
(1.11) (1.91) (1.97) (1.98) (1.97)
information 0.514***
0.539***
0.532***
0.530***
0.530***
(3.96) (6.29) (6.32) (6.31) (6.30)
financial 0.965***
1.006***
0.943***
0.941***
0.946***
(7.01) (11.06) (5.34) (5.37) (5.39)
professional 0.412***
0.498***
0.494***
0.492***
0.490***
(3.74) (6.84) (6.90) (6.88) (6.85)
admin 0.291**
0.365***
0.367***
0.363***
0.361***
(2.34) (4.43) (4.53) (4.49) (4.45)
education -0.0120 0.0163 0.0219 0.0205 0.0196
(-0.09) (0.18) (0.24) (0.23) (0.22)
health -0.519***
-0.466***
-0.458***
-0.462***
-0.464***
(-4.43) (-5.91) (-5.91) (-5.95) (-5.99)
arts -0.0971 0.0130 0.0180 0.0152 0.0126
(-0.64) (0.13) (0.18) (0.15) (0.13)
capability
operation
0.0459 0.0316 0.0313 0.0320 0.0333
(1.00) (1.08) (1.08) (1.10) (1.15)
capability finance 0.132***
0.118***
0.0950***
0.0940***
0.0939***
(3.12) (4.31) (3.49) (3.46) (3.45)
(continued) Model 0 Model I Model II Model III Model IV Model V
capability
innovation
-0.0458 -0.0377 -0.0292 -0.0304 -0.0309
(-1.06) (-1.36) (-1.06) (-1.11) (-1.13)
capability strategy 0.130***
0.104***
0.110***
0.111***
0.112***
(2.89) (3.58) (3.80) (3.85) (3.88)
media network 0.0231 0.0340 0.0485*
0.0486*
0.0476*
(0.53) (1.21) (1.73) (1.74) (1.70)
Chamber network 0.106**
0.0524 0.0547*
0.0552*
0.0543*
(2.11) (1.60) (1.67) (1.69) (1.66)
women-led
business
-0.155***
-0.258***
-0.258***
-0.257***
-0.257***
(-3.66) (-7.42) (-7.48) (-7.47) (-7.45)
own website 0.188***
0.151***
0.132***
0.132***
0.133***
(3.15) (3.94) (3.46) (3.46) (3.48)
third party website -0.0708 -0.0409 -0.0508 -0.0516 -0.0525
(-1.36) (-1.21) (-1.51) (-1.54) (-1.56)
broadband -0.0432 -0.0469 -0.0403 -0.0803**
-0.0725*
(-1.51) (-1.46) (-1.28) (-2.10) (-1.92)
education nvq4 0.00848***
0.0103***
0.00971***
0.00980***
0.00848***
(2.98) (3.28) (3.23) (3.37) (3.08)
year 2016 0.0950***
0.0929***
0.0949***
0.0943***
0.0967***
(5.23) (2.85) (2.96) (2.94) (3.02)
year 2017 0.0857***
0.102***
0.103***
0.102***
0.105***
(4.83) (3.20) (3.29) (3.27) (3.35)
rural*broadband 0.0554 0.0896*
0.0892*
(1.37) (1.73) (1.73)
LSE 0.124**
0.122**
(2.26) (2.11)
constant 9.614***
10.634***
9.420***
9.452***
9.468***
9.496***
(60.34) (444.20) (65.43) (67.51) (68.68) (72.18)
RE Var at LEP
Random intercept - 0.015***
0.008***
0.006***
0.005*
0.003
Financial sector - - - 0.616***
0.604***
0.653***
Wholesale-Retail - - - 0.120***
0.122***
0.125***
IntraClass Cor.
ICC
- 1.14% 0.80% 0.64% 0.55% 0.32%
LR Test (one-
level)
- 89.09***
15.71***
134.94***
132.66***
131.23***
LR Test (model II) - - - 119.22***
122.14***
125.93***
Nr. observations 5,831 9591 5,831 5,831 5,831 5,831
Nr. of groups - 38 38 38 38 38
Observations per
group min-max
- 69 - 1,186 15 - 714 15 - 714 15 - 714 15 - 714
AIC - 29,842.23 16,355.83 16,240.61 16,239.69 16,237.9
z-score statistics in parentheses. -*, **,
*** denote significance at 10%, 5% and 1% level respectively.
RE is random effects. Var is variance.
LR test results (one-level) are obtained comparing all models with one-level linear regression
LR test (model II) results are obtained comparing models III-V with model II.
AIC is the Akaike’s Bayesian information criteria

Findings: firm-level drivers
Using an unbalanced panel of 5,831 firm-year observations drawn from
the LSBS for the 2015-17 period and focusing on England our findings for
firm-specific characteristics affecting SMEs’ productivity are:
• Larger SMEs (rather than micro-businesses) are significantly more
productive, while sole traders are significantly less productive.
• Women-led businesses record significantly lower productivity.
• The sectoral composition of the economy matters for SMEs’
productivity: the health and social work sector is negatively associated
with productivity.
• Firms located in rural areas perform at least as well as urban firms.
52

Findings: firm-level drivers
• Digital capabilities matter, as SMEs that have their own website are
significantly more productive, whereas using third-party websites to
promote or sell products or services is not statistically associated with
productivity.
• Capabilities for implementing and developing a business plan and strategy
positively contribute to productivity.
• Capabilities to obtain external finance are a driver of productivity, whereas
operational capability and innovation capability are statistically
insignificant (for innovation there is a huge gap however between
capability and actual realised innovation).
• Being a member of a local Chamber of Commerce or using social-media-
based business networks improves somewhat productivity.
53

Findings: LEP-level drivers
• SMEs located in LEPs with a greater proportion of high-skilled population
(measured in terms of NVQ at level 4 or above qualifications) are positively
associated with higher productivity.
• SMEs located in LEPs with access to reasonable broadband speeds (at least
2Mbit/s) also improve their productivity, suggesting that the digital
infrastructure also matters.
• Location matters also in terms of industrial structure, as the spatial
contribution of some sectors (financial, wholesale and retail but not
manufacturing) to productivity changes by LEP.
• The analysis confirms the presence of regional disparities in the UK, as we
find firms located in London and the South East, overall, are significantly
more productive.
54

Policy Implications
• The results indicate that digital capabilities matter as well as those
regarding implementing and developing a business plan and strategy.
• However, over one-third of SMEs describe themselves as having poor
capabilities in e-marketing and only around one-quarter describe themselves
as having a strong capability to create or develop their own website (BIS
2015)
• Support programmes to upgrade digital capabilities appear warranted, and
should focus on enabling firms to create and sell through their own websites.
• Gender issues receive little attention in the Industrial Strategy beyond
improving girls’ uptake of STEM subjects in schools.
• Greater attention should be paid to gender issues, considering the reasons
for, and potential strategies to overcome, gender biases in SME performance.
55

Policy Implications
• Support for start-ups and established SMEs often pays little attention to business
networks, but rather focuses on internal considerations and this appears
misguided.
• SMEs located in LEPs with a greater proportion of highly-skilled people are
positively associated with higher productivity, supporting theories that proximity
to higher-skilled workforce improves firm performance.
• The UK’s problems of a long tail of poor productivity businesses and, in certain
areas, weak educational attainment are often treated separately, with the former
the preserve of business and the latter a “schools issue”. However, the analysis
indicates their interconnectedness.
• As long as educational attainment in terms of NVQ level 4 is more than double in the best
performing LEP territory compared with the weakest, significant spatial variations in business
productivity are likely to persist.
• Solving the productivity puzzle is not merely a business policy question, but
requires progress in educational outcomes.
56

Investigating SME Access to Finance, Growth and
Productivity 2015-17
Presentation to the ERC LSBS Conference
London, September, 2019
Robyn Owen, Theresia Harrer, Suman Lodh, Tiago Botelho* & Osman Anwar**
CEEDR, Middlesex University, *Norwich Business School UEA, **SQW
Robynowen63@gmail.com
All views expressed in this presentation are those of the authors only
CEEDR

Introduction
• Follow-up to LSBS 2015 study (Owen et al, 2017) access to finance
• UK Government Industrial Strategy (2017) focus on competitiveness through improved
Productivity
• Government policy supports SME finance: e.g. British Business Bank - Regional Investment
Funds (£690m), equity funds etc.
• Brexit impacts – loss of EU Regional Funds, underpinning Regional Funds in UK – Future
Prosperity Fund?
Three RQs:
RQ1 – What are the characteristics of SMEs that determine their funding and discouragement?
RQ2 – What are the impacts of external finance on SME performance and productivity?
RQ3 – What are the implications for policy and future research?

Methodology
• Quantitative analysis of 3 annual waves of UK LSBS 2015-17
• Focus on remaining panel of 4,165 SMEs responding in all 3 waves
• Descriptive panel analysis of SME characteristics associated with: External Finance Access,
Discouragement, Growth
• Confirm findings of LSBS 2015 baseline study (Owen et al, 2017)
• Productivity crude measure: SME change in sales turnover per employee between Autumn
2015 and Autumn 2017
• Conduct Binary Logit regression sifts examining % Productivity Change (2015-17) using
upper quartile (UQ), upper median (UM) & lower quartile (LQ) as dependent variables
• Qualitative test of findings with 6 Oxford Innovation (OI) specialist SME finance advisors & 3
senior OI and St John’s Innovation staff

Descriptive Findings 1:
• 31.5% (1,313) sought finance; 20% (2015), 14% (2016 & 2017).
• Success rates rise if seeking finance every year (97%) - others (83%).
• 3.8% accessed finance every year, receiving 2x median (£200k).
• Rising use of business support & specialist business finance
• Annual applicants were significantly (<.001) more likely to use general and specialist access
to finance advice and to be successful
Access to Finance (2015-17):
• Confirmed baseline: smallest (self-emp) & youngest (<6yrs) sig (<.001) less successful.
• RBV (firm resources) key: fewer managers, perceived poor capabilities to access finance sig
(<.01) associated with less success.
• Lesser (firm level) innovators sig less successful (<.05).
• Most successful annual applicants sig (<.05): larger (50-249 emp), support users, more
managers, good perceived ability to access finance

Findings 2:
Non Financed:
• Happy non seekers sig (<.001) more likely to be self-employed, not using business advice, no
business plan, not innovative.
• Discouraged non finance seeking sig (<.001) less likely large SMEs, more likely poor perceived
capabilities to access finance, no business plan, younger (<6 yrs <.01).
• Unsuccessful seekers sig (<.01) more likely to be younger, innovative & use business support,
but have no business plan.

Findings 3: Growth & Productivity
• No sig difference in employment (37.9% up) and sales (49.3%) growth of externally financed and
non-financed SMEs.
• High % of external finance was for premises, equipment, working capital and R&D – unlikely to
render shorter term changes.
• Productivity rise eg from investment in more efficient machinery and working practices, may not
create short-term employment increase.
• Half (50.8%) increased productivity; 8.9% static; 40.3% declined
• Median % rise highest amongst successful finance seekers (5%) and lowest for contented non
seekers (0%).
• Successful access to finance correlated (<.1) to productivity rise - relating to more frequent
applications by larger SMEs.
• Younger SMEs (<10 years) more likely productivity rise (6-9 years).
• Where external finance: smaller self-employed and micro SMEs struggle to raise productivity;
older SMEs (20+ years) show least impact; larger SMEs (50-249) less productivity rise than non-
financed counterparts.

Findings 4: Binary Logit (4 sift models)
Control Variables (Model 1):
• Smaller SMEs sig (<.01) less (Upper Median (UM); zero <.01 less in UQ
• Younger (<20yrs) sig more UM (<.01); <10yrs <.001 more in UQ
Management/Capabilities/Innovation & Performance Variables
(Model 2):
• Positive associations at UM: sales rise (<.001), R&DTC (<.05)
• Negative associations at UM: emp rise (<.001), no plan, family (<.01); women, avg ability to
access (<.05); also no plan (<.001) for UQ.
• Associations with LQ: emp & avg ability to access (<.001), 1-2 managers (<.01), women (<.05)
Access to Finance variables (Model 3):
• Positive associations at UM: leasing (<.05)
• Negative sig (<.05) at LQ: some success applying one year only, or annual access, grant &
commercial mortgage

Findings 5: Binary Logit Summary Model 4
Controls:
• Positive sig at UQ: Younger SMEs, <6yrs (<.05), 6-9 (<.01)
• Positive sig at LQ: smaller SMEs <50emp (<.001)
• Neg sig at UQ: zero emp (<.001); at UM <10emps (<.001)
Access to Finance:
• Positive sig at UQ: non seeking discouraged borrower (<.05); at UM: £100k+ & Lease (<.05)
• Negative sig at UM: factoring (<.01): at LQ: non seeker (<.01), some success & where applied 1
year only (<.05)
Management Types/Capabilities & Performance:
• Positive sig at UM: sales rise (<.001); at LQ: emp rise (<.001). Avg capability to access (<.05)
• Negative sig at UQ: emp rise (<.001), no plan (<.01); at UM: emp rise (<.001), women (<.05); at
LQ: sales rise (<.001)

Discussion 1: Advisors’ Views
• Antecedent investment (2010-14) – evidence of absorptive learning, prior seekers -
more likely support users and successful applicants
• Possible lagged investment impact due to finance reason/type:
- Leasing “…more rapid equipment efficiency…”
- R&D TaxC positive driver “…where digitech shorter horizon”
Business support a likely explanator:
• Young, inexperienced entrepreneurs “lack financial know-how”
• “Smaller SMEs are not strategic” – sub-optimal use of investment
• “Older SME problems if bank finance unavailable, seek help too late…”
• High profile A2F in Cornwall reduced discouragement, raised financial know-how, improved
IR for applications, presentations, access to finance networks
• Local regional service nuances: “M4 corridor digitechs different from Cornish rural services”

Discussion 2:
“What is often absent is the resourcing to provide continuing support once the finance has been raised.
This is a problem in all EU funded and Government funded programmes. The issue is that:
(a) programmes typically receive funding for 3 years and there will not be any resources to track
outcomes after this period and
(b) the funders specify the outputs/outcomes that they want and we primarily track these. Funders
seem to work on the basis that a company requires one fixed term intervention to achieve a specific
output (e.g. 4 days support to support external finance or 3 days support to develop a business plan).
They do not fund holistic, business-centred support that can be provided on an ongoing basis.”
Jane Galsworthy CEO of Oxford Innovation

Business Support Model for Stages of SME Finance
(source OI, SQW, Owen et al, 2019)

Conclusions
• Confirmatory findings of (Owen et al, 2017): support RBV
• Higher level Productivity growth associated with external finance where less lag, larger
finance, larger SMEs - linked to business support and SME absorptive capacity – optimises
in 6-9 years category
• Visible A2F programmes can help reduce discouragement, raise access to suitable external
finance – but lack finance aftercare which may be critical for the young, small under
resourced
• Local/regional sectoral variation needs to be addressed by policy
• Optimal investment productivity outcomes more likely to take place where ongoing
business support takes place

Approach: RBV Dynamic Capabilities (Teece, 2018)
Sense -> Seize -> Transform
External finance requirement Search and access external
finance
Grow sales, employment and
increase productivity
Internal resources -> Strategy <- External resources
Number of managers, Management
characteristics Innovative
Business plan, perceived skills to
access finance, previous
experiences access finance
External finance finder, general
business support
Firm age and employment size Controls
Regions, rural/urban, deprived
area, sectors
External business environment in
UK e.g. Brexit factor

BEIS LONGITUDINAL SMALL
BUSINESS SURVEY
2018
SME Employers

Background
• ASBS and subsequently SBS run by SBS, DTI, BERR, BIS and subsequently BEIS
since 2003.
• Designed to provide data on SME performance and the factors that affect this.
• Decision taken in 2015 to establish a longitudinal SBS, a resurvey of the same
businesses each year for five years.
• Separate reports for SMEs with and without employees are published here:
• https://www.gov.uk/government/collections/small-business-survey-reports
BEIS LONGITUDINAL SMALL BUSINESS SURVEY
2018 SME EMPLOYERS

Survey design
• Sampled from IDBR (registered business/employers) and D&B (unregistered businesses with no
employees). Sample stratified by country (x4), size of business (x6) and sector (x14).
• Unregistered non-employers 11%
• Registered non-employers 13%
• Micro (1-9) 35%
• Small (10-49) 28%
• Medium (50-249) 13%
• Telephone survey (average length 25 minutes). Fieldwork undertaken between July 2018 and
January 2019.
2018 SME EMPLOYERS
Type of respondent Employment
Total sample size Panel Top-ups Employers Non-employers
2015 15,501 15,501 N/A 11,146 4,355
2016 9,221 7,252 1,969 6,987 2,234
2017*
2018*
6,596
15,015
5,292
4,486
1,304
10,529
4,771
11,497
1,825
3,509
*Panel number includes those interviewed in 2015 and 2017, but not in 2016

Survey content
• SECTION A: ABOUT THE BUSINESS
• SECTION B: EMPLOYMENT
• SECTION C: EXPORTS
• SECTION D: SOCIAL ENTERPRISES
• SECTION E: ENERGY USAGE
• SECTION F: TAXATION
• SECTION G: OBSTACLES
• SECTION H: FINANCE
• SECTION I: NATIONAL LIVING WAGE
• SECTION J: INNOVATION
• SECTION K: BUSINESS SUPPORT
• SECTION M: PAYMENT
• SECTION N: TRAINING
• SECTION P: TURNOVER
• SECTION R: FUTURE INTENTIONS
2018 SME EMPLOYERS

Change in employment compared with 12 months earlier
• Overall net increase in employment among panelists, but the proportion with an
increase in employment has reduced significantly since 2017 (22% increase
employment in 2018, compared with 37% in 2017), continuing a downward trend.
• Two thirds of SME Employers had no change in the number of staff since a year
previously, whilst 13% had fewer staff (compared to 31% in 2017).
2018 SME EMPLOYERS
45%
27% 28%
37%
32% 31%
22%
64%
13%
0%
10%
20%
30%
40%
50%
60%
70%
Increase in employment No change Decrease in employment
2016
2017
2018

2017 SME EMPLOYERS
Turnover compared with 12 months previously
• Similar proportions to 2017; 34% reported increasing turnover during the preceding
year with 18% reporting a decline. 43% reported no change in turnover.
40% 41%
44% 43% 43%
39% 38% 34%
36%
34%
18% 17%
20% 19% 18%
0%
10%
20%
30%
40%
50%
2014 2015 2016 2017 2018
Increase in
turnover
No change
Decrease in
turnover

39% 37%
46%
55%
45% 46%
39%
32%
10% 11%
7% 6%
0%
10%
20%
30%
40%
50%
60%
Total 1 to 9 employees 10 to 49 employees 50 to 249 employees
Expect turnover to increase Expect turnover to stay the same Expect turnover to decrease
2018 SME EMPLOYERS
Expectation of future performance – turnover
• Two fifths of SME Employers (39%) expected to increase their turnover in the coming
year.
• The proportion is the same as those seen in 2017 (40%), but lower than in 2014-2015.

74%
71%
68%
71%
73%
69%
66%
62%
71%
56%
58%
60%
62%
64%
66%
68%
70%
72%
74%
76%
2010 2011 2012 2013 2014 2015 2016 2017 2018
2018 SME EMPLOYERS
Ambitions for growing future sales
• 71% reported that they aimed to increase their sales over the next three years, a
significant increase since 2017, returning close to seen in 2014.
• The decline in growth ambition is most evident among the micros (69%), although this
is significantly higher than 2017 (59%).

2018 SME EMPLOYERS
20%
18%
25%
34%
11% 10%
17%
25%
12% 11%
13%
16%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Total 1 to 9 employees 10 to 40 employees 50 to 249 employees
Export goods or services Export goods Export services
Exporting
• One in five SME Employers (20%) had exported goods or services in 2018, in line with
2017.

Innovation
• One in five (21%) SME employers reported introducing new or significantly improved
processes in the last three years, in line with 2017, and rising to over a third (36%) of
employers with 50 to 249 employees.
2018 SME EMPLOYERS
25%
24%
31%
39%
20%
18%
26%
33%
20%
19%
26%
35%
21%
20%
27%
36%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Total 1-9 employees 10-49 employees 50-249
employees
2015
2016
2017
2018

26% 25% 24%
22%
19%
17%
13% 13% 12%
0%
5%
10%
15%
20%
25%
30%
2010 2011 2012 2013 2014 2015 2016 2017 2018
2018 SME EMPLOYERS
Access to finance
• In 2018, just 12 percent of SME Employers sought finance over the year, down 1ppt
on 2017, and continuing the longer term decline.
• Compared to 2016, businesses were more likely to seek finance for investment (46%
vs. 41%) than working capital (57% vs. 66%).
• 76% obtained any finance compared with 77% in 2017.

Major obstacles to the success of the business
• Taxation, VAT, PAYE, NI and rates remain the most commonly mentioned obstacle to the
success of the business, albeit to a lesser extent than in 2017 (46% compared to 51%).
• The only increase since 2017 was in the proportion mentioning the National Living Wage
(+2ppts).
2018 SME EMPLOYERS
15%
17%
18%
20%
21%
30%
30%
36%
42%
47%
18%
18%
20%
21%
27%
33%
37%
41%
46%
51%
17%
20%
18%
17%
29%
33%
33%
40%
43%
46%
0% 10% 20% 30% 40% 50% 60%
Other spontaneous mentions
No major obstacles
Obtaining finance
Availability/cost of premises
National Living Wage
Workplace pensions
UK exit from the EU
Late payment
Staff recruitment and skills
Taxation, VAT, PAYE, NI, rates
2018
2017
2016

49% 47% 45% 45% 44%
33%
26%
29%
26%
46% 44% 42% 43% 43%
31%
24%
27% 25%
59% 59% 59%
55%
51%
40%
34%
38%
31%
68% 68% 68%
65%
61%
50%
45% 46%
40%
0%
10%
20%
30%
40%
50%
60%
70%
80%
2010 2011 2012 2013 2014 2015 2016 2017 2018
2018 SME EMPLOYERS
Total
10-49
employees
1-9 employees
50-249
employees
Use of business support
• 26% of SME employers sought information or advice.
• This proportion is 3ppts lower than in 2017, continuing the longer-term downward
trend.

Training
• Just under half of SME Employers had arranged some form of training over the
previous year, and one in three had provided any management training.
• Over time, there is a downward trend in the proportions of businesses providing
training
2018 SME EMPLOYERS
60% 60% 60% 59% 57% 55% 55%
49%
56% 55% 54% 53% 52% 50% 48%
43%
85% 86% 86% 83% 80% 80% 82%
77%
94% 93% 92% 91% 89% 89% 91%
85%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011 2012 2013 2014 2015 2016 2017
Total
50-249
employees
10-49
employees
1-9
employees

60% 60% 60% 59% 57% 55% 55%
49% 47%
56% 55% 54% 53% 52% 50% 48%
43% 41%
85% 86% 86%
83%
80% 80% 82%
77%
71%
94% 93% 92% 91% 89% 89% 91%
85% 83%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011 2012 2013 2014 2015 2016 2017 2018
2018 SME EMPLOYERS
Total
10-49
employees
1-9 employees
50-249
employees
Training
• Just under half of SME Employers had arranged some form of training over the
previous year.
• Over time, there continues to be a downward trend in the proportions of businesses
providing training.

• Overall, there is a notable standstill with SME in terms of
performance i.e. numbers employed and turnover.
• Previous concerns that many SMEs are not investing in the future –
for example, downward trends over time in innovation, use of
business support, seeking finance and training, continue to be seen
throughout 2018
• However, there appears to be signs of confident optimism with an
clear upturn in the ambition for future growth,
• The 2019 (Year 5) LSBS will started fieldwork in July. With a
significant boost of new top-ups, and the overall sample size will be
15,000.
•
BEIS LONGITUDINAL SMALL BUSINESS SURVEY 2018
SME EMPLOYERS

BEIS LONGITUDINAL SMALL
BUSINESS SURVEY
2017
SME Employers

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