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he Role of Innovation in Small

usiness Performance:

Regional Perspective

RC Research Paper 82

ebruary 2020

1

The Collaboration Paradox: Understanding

the Barriers to Small Firms’ Innovation

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The Role of Innovation in Small Business

Performance: A Regional Perspective

Catherine Robinson*, Marian Garcia, Jeremy Howells and Guihan Ko

Kent Business School University of Kent

Chatham Historic Dockyard Gillingham

Kent ME4 4TE

*Corresponding Author c.robinson-501@kent.ac.uk

The Enterprise Research Centre is an independent research centre which focusses on SME growth and productivity. ERC is a partnership between Warwick Business School, Aston Business School, Queen’s University School of Management, Leeds University Business School and University College Cork. The Centre is funded by the Economic and Social Research Council (ESRC); Department for Business, Energy & Industrial Strategy (BEIS); Innovate UK, the British Business Bank and the Intellectual Property Office. The support of the funders is acknowledged. The views expressed in this report are those of the authors and do not necessarily represent those of the funders.

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ABSTRACT

The small and medium sized enterprise sector is seen as the engine of growth for an

economy, in terms of generating innovation and employment growth. Firm entry can

create pressure on incumbent firms and yet research on the transmission mechanisms,

as they apply to small firms is less well understood, in part because of small firm ‘churn’

but also because they are less well represented in firm level survey data. The advent of

the Longitudinal Small Business Survey (LSBS) goes a considerable way in allowing us

to address this knowledge gap. This paper presents evidence using the latest waves of

the LSBS data (2015-2017) combined with data on the regional environment in which

small firms are located. We argue that city regional factors influence firm growth and

performance and in particular the innovative environment of the firm. We find evidence

of City Regional level effects but weak evidence in relation to specific channels for these

effects, specialisation agglomerations appear to be positively associated with higher

levels of labour productivity. Our findings suggest that more work is needed to

understand what it is about the regional environment that fosters productivity

improvements in small firms particularly in relation to innovation.

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ACKNOWLEDGEMENTS

Research undertaken as part of the Enterprise Research Centre, whose financial support

is gratefully acknowledged. We would particularly like to thank Jiao Liu for her support

and patience. Data used in this paper are accessed via the UK Data Service. The paper

uses the following datasets: 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 authors gratefully acknowledge helpful suggestions from the

funders review group at BEIS and comments on earlier drafts from Stephen Drinkwater,

and participants in the ERC-BEIS LSBS Showcase Event, 19th September, 2019,

London.

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CONTENTS

Acknowledgements ...................................................................................... 4

1. Introduction ............................................................................................... 6

2. Small business performance and innovation ......................................... 8

3. Data sources ........................................................................................... 13

3.1 Firm level data ....................................................................................... 15

3.2 City-Region data ................................................................................... 17

4. Empirical methodology .......................................................................... 19

5. Results ..................................................................................................... 22

5.1 Multilevel results ................................................................................... 24

6. Conclusions ............................................................................................ 29

Conclusions and Discussion ..................................................................... 29

Possibilities for future research ................................................................ 30

References .................................................................................................. 32

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1. INTRODUCTION

Collectively, small and medium sized enterprises1 (SMEs) account for around 60% of all

private sector employment (BEIS, 2019) and drive radical innovation by bringing new

services and products to the market (Spencer and Kirchhoff, 2006). From a

Schumpeterian perspective, the pressure small and new firms create on incumbents is

also recognised as driving productivity growth (Acemoglu et al, 2018). Moreover, by

engendering an entrepreneurial culture new firms are seen as positively contributing to

wider economic growth and prosperity (Beugelsdijk, 2007). The role that small firms play

in the economy has been extensively researched (e.g., Storey 2008; Roper and Hart,

2019) and yet, the dynamic nature of the small firm sector varies from nation to nation,

in part a result of differences in institutional settings and over time and this hampers

general policy conclusions being drawn. Innovation amongst small firms is widely found

to be positively associated with survival and productivity performance but research

largely focuses on the firm level engagement with innovation. This paper explores the

role of the regional innovative environment on firm level performance.

The definition of a small firm is not internationally uniform; in China for example, a firm

employing less than 500 employees is considered small (Zheng et al, 2009). In Europe,

the definition of a small firm is usually determined by the number of employees (as being

less than 250) but some studies define small firms in terms of a maximum level of

turnover. The definition adopted here is that of the Small to Medium Sized Enterprise

(SME), used in the longitudinal Small Business Survey (LSBS). That is, a firm with less

than 250 employees (BEIS, 2018).

The growth of SMEs is often analysed in terms of their business characteristics (Cowling

et al, 2015), and considered to be largely defined in terms of their sector, age and size

(Berisha and Pula, 2015). Certain sectors are more prone to SME presence than others

as a result of their market characteristics (Mulhern, 1995). Size may be regarded as a

proxy for resource availability, linked to the existence of economies of scale. The

relationship between age and size is also something that has received considerable

1 Defined as enterprises with between 0 and 249 employees.

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attention; Gibrat’s law, first published in 1931, famously suggests that the two are

independent but this may be an oversimplification of the relationship (Lotti et al, 2003).

It is recognised that there are clear linkages between investments in innovation at the

firm level and subsequent performance (Mason et al, 2009; Holzl, 2009). Broader

regional effects on firm performance have also been explored extensively (Crescenzi

and Rodriguez-Pose, 2012). However, less clear cut is an understanding of the wider

spatial environmental conditions and the channels through which they affect small firm

productivity. McCann (2018) identifies the persistence of the regional productivity puzzle

in the UK as productivity growth is slow to diffuse beyond the South East and London

regions. Evidence points to this being driven by a lack of technology diffusion to an

extent unprecedented in other OECD countries (McCann, 2018). Given the resource

constraints that small firms in particular experience, it is argued here that local and

regional environmental conditions can play a significant role in firm performance

particularly in relation to their engagement with innovation (Sternberg and Arndt, 2001)

and this paper aims to contribute empirically to the literature in this area.

The regional environment is seen as a source of external agglomeration spillovers which

may be transmitted either through similar organisations (i.e. firms within the same

industry) or through supply chains or technology proximity. The former are often referred

to as ‘Marshallian spillovers’, while the latter were described in great detail in Jacobs

(1968). While the two sources of external spillovers are not mutually exclusive, debates

in the literature have often concluded one or other dominates. Moreover, the level of

geography used clearly matters. The macroeconomic environment is also relevant

although there is some research to suggest that SMEs are thought to be less sensitive

to economic downturns given their agility (Cowling et al, 2015) and resource-light nature.

The purpose in this paper is to explore the determinants of small firm level performance

by considering a combination of both firm level and regional level factors. We focus

attention on the role of British City Regions (CR), particularly the innovative environment,

in determining firm growth and performance, using the latest available longitudinal data

on small businesses in Great Britain2, which contains panel data on small businesses for

2 Analysis is undertaken at the UK level however, the inclusion of NOMIS data results in a focus on Great Britain, given data constraints.

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the period 2015-2017. This paper utilises a multilevel modelling approach as discussed

in van Oort (2015) and applied in Norway and in Spain by Aarstad and Kvitastein (2019)

and Tojeiro-Rivero and Moreno (2019), respectively. Here, however, we focus on

performance, as measured by labour productivity, rather than the propensity to innovate.

In this way, the paper applies a relatively novel approach to linked microdata on SMEs

for the UK, enhancing the scope for analysis.

This paper is structured as follows. Section 2 reviews the current evidence in relation to

the drivers of innovation in small businesses as well as the empirical evidence on firm

performance for SME innovators, outlining the specific hypotheses tested in this paper.

Section 3 provides a detailed description of the methodological approach used and

Section 4 describes the data sources required to undertake the analyses are presented.

Section 5 presents the findings and Section 6 provides a conclusion and provides a

discussion of the implications of our findings and limitations of our analysis.

2. SMALL BUSINESS PERFORMANCE AND INNOVATION

Much of the literature in relation to small firms assumes that smallness is a temporary

state and that they will, given time, grow (O’Farrell and Hitchins, 1988). In reality, the

growth paths of firms are quite lumpy and may not always be desired by firms (Mason et

al, 2009). Moreover, survival rates for new SME ventures are known to be lower than for

larger firms (Huggins et al, 2017). Many studies have focussed on the barriers to growth

that small firms face (Drinkwater et al, 2018). A considerable amount of discussion

relates to challenges of access to finance (see, for example, Hall, 1989; Irwin and Scott,

2010; Lee et al., 2015). Another strand of the literature engages with the

internationalisation of firm activity for SMEs (Love et al 2016; Cowling et al, 2015). In

this paper, we present our discussion of the literature around firm level factors and

regional level factors that affect SME performance directly and indirectly through

innovation.

As with large organisations, not all small firms innovate (Hyvärinen, 1990; Hausman,

2005). Those that do, however, are thought to be more ‘successful’, in terms of survival,

productivity and growth. Innovation itself is thought to play a significant part in small firm

survival (Cefis and Marsili, 2006). SMEs are thought to be nimble, responsive and

adaptable, allowing them to take advantage of new innovative opportunities (Rhee et al.,

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2010). At the same time, they face a number of disadvantages in terms of access to

finance and internal capabilities which are found to be critical for successful innovation

(Cowling, 2016). Forés and Camisón (2016) argue that the characterisation of innovation

is quite confused in the literature but identify this form of categorisation as a useful way

of distinguishing between different types of innovative activity, associating product and

service innovations with radical innovation and more procedural and structural

innovations as incremental to the firm. A number of factors have been cited as enabling

innovation amongst SMEs (Love and Roper, 2015), including complementary exporting

behaviour as well as high levels of human capital (McGuirk et al, 2015). Other workplace

practices associated with higher performance are often correlated with innovation (see

Kmieciak et al. 2012; Dunn et al., 2016), creating multicollinarity in estimation. Love and

Roper (2015) review the literature on the relationship between exporting, innovation and

small firm performance. They highlight recent research using the small business survey

for the UK which found significant interdependence issues between exporting and

innovation (Anon-Higon and Driffield, 2011) and thus establishing the direction of

causality has proven to be problematic when using cross sectional data.

Love and Roper (2015) distinguish between internal factors that relate to SME

advantages in behavioural strengths (compared with large firms that have greater

resource advantages), and external factors. The external factors include skills, R&D

capabilities derived primarily from external sources and relationships with other

organisations, internal sources of financing and potentially publicly supported

investments to overcome natural barriers to finance experienced by SMEs. They also

highlight the role for three external enablers of innovation and exporting: firstly, firms

being where they are located, secondly being open to partner with others in the market

or supply chain and thirdly, learning from exporting, whereby firms have the potential to

learn and subsequently innovate on the basis of knowledge gained from the wider

market.

Small firms are often characterised as young firms on the path to greater scale but, while

correlated, size and age are distinctly different characteristics. Age is generally found to

have a positive influence on firm performance, although it is often reported as being non-

linear (Coad et al, 2018). The relationship between innovation and age is more complex.

Hyytinen et al. (2015, 565) state that “pursuing innovation entails a more complex start

up process”. Coad et al. (2016) explore the relationship between R&D investment and

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firm age, using Spanish PITEC innovation data, 2004-2012, focussing on growth in sales,

labour productivity and employment for firms of all sizes but the sample is skewed

towards larger firms. However, their quantile regression analysis reveals that innovation

is more risky for younger firms, in part attributed to a lack of experience. Love et al.

(2016) consider the importance of age in determining exporting performance. In their

study of UK SMEs, they look in some detail at the reach of small firms in their exporting

activities, finding the balance of argument supports the ‘process theory’ of exporting

rather than the more recently proposed ‘born global’ phenomenon (see, for example,

Andersson and Wictor, 2003). Indeed, they argue that SMEs are more likely to be ‘born

regional’ than global, learning as they extend their reach within the domestic market first.

They also identify complementarity between internationalisation and innovation,

consistent with other findings (Fillis, 2001; Chetty and Campbell-Hunt, 2003).

Formal management practices have also been identified in recent firm level research in

the context of SMEs (Bryson and Forth, 2019). Bryson and Forth (2019) extend the firm

level work of Bloom et al. (2016) to consider specifically SMEs and find that the

probability of engaging in management practices is indeed lower for small firms, but

those that did engage saw a statistically significant and positive correlation with

productivity. This is an important finding as previous studies had cautioned against the

relevance of such practices for firms considered to be too small to benefit or, at the very

least, are more nuanced in terms of implementation at the SME level (Lai, 2016).

We know that in terms of teams, diversity is seen as a positive influence on innovative

activities (Garcia Martinez et al, 2017); however, evidence in relation to diversity more

generally on the fortunes of a firm are more mixed. Carter et al. (2015) review the

divergent literature in relation to women and ethnic minority entrepreneurship. They

highlight that while the literature for these two groups have developed largely separately,

often the same or similar policy vehicles are deployed to support greater

entrepreneurship amongst these - arguably underrepresented - groups. While the

entrepreneurship literature is somewhat tangential to the question of SME leaders, it

does provide evidence on the barriers faced. Crucially, Carter et al. (2015) explore the

concept of ‘mixed embeddedness’ proposed by Kloosterman (2010), which highlights the

importance of political, spatial, economic and regulatory contexts in which minority and

women led entrepreneurship takes place.

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The entrepreneurship literature makes clear the importance of the regional environment.

In their discussion of national systems of entrepreneurship, Acs et al. (2016) highlight

that context matters in terms of who starts a business, what form of business they start

and whether they will pursue a growth agenda. Carter et al (2015) and Kloosterman

(2010) acknowledge that entrepreneurship takes place within an ecosystem that brings

together locational factors, including innovation. Ohmae (1995) argues that the ‘real work

gets done and real markets flourish’ at the region-state level (Ohmae, 1995, p.4). In a

study of entrepreneurship in Wales, Huggins et al (2017) identify locational factors as

affecting both the rate of enterprise as well as the likelihood of survival over time. Giner

et al (2017) find that the probability of becoming a high growth firm is enhanced by firms

belonging to technological districts and large urban areas indicating that region continues

to be relevant once the firm is established.

Agglomeration economies (Hanson, 2001) are recognised as being important in certain

industries (Krugman, 1991), through certain supply chains (Venables, 1996) and in

situations where skilled labour may be particularly relevant (Black and Henderson, 1999).

Despite this, much of the analysis of entrepreneurship has focussed on the

characteristics of the individuals (Gartner, 1988; Korunka et al., 2003; Onnetti et al. 2016;

Blumberg and Pfann, 2016). The same is true for SMEs and their performance; the focus

has been on factors internal to the firm and yet much of what shapes a firm will be

external to the firm, particularly if we consider SMEs to be comparatively resource

constrained.

Agglomeration economies are identified as being persistent despite the growth in

boundary-challenging technologies that exist in the digital age. Explanations for this

revolve around the multifaceted nature of proximity, in which geography accounts for

only one dimension (Rodgriguez Pose and Crescenzi, 2008). In addition to geography,

organisational, institional and social proximities also matter and these all coelese in

urban and metropolitan areas. Thus, while the digital space eases some of the barriers,

it is not sufficient to overcome them all; geography still matters (Thisse, 2019).

Van Oort (2015) provides an extensive review of the new economic geography literature

with regard to the tensions between diversity (heterogeneity) versus specialisation

(homogeneity) at the regional level as a means of gaining agglomeration economies.

Duranton and Puga (1999) provide an early discussion on how this might be incorporated

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into firm level studies of agglomeration economies, utilising measures of specialisation

and diversification, where diversity accounts for Jacobs-type spillovers and

specialisation is more akin to Marshall’s source of positive externalities. The review

highlights what van Oort (2015) argues is a false dichotomy between the two sources of

agglomeration economies; rather, different regions, at different points in time, with

different industrial and institutional settings will benefit from either diversity or

specialisation (DeGroot et al, 2009; Melo et al, 2009). The most appropriate level of

geography is therefore not always clear. Data availability often dictates the unit of

analysis and yet, these are defined by administrative boundaries and as such, capturing

economic effects may be difficult. Moreover, research by Liang and Goetz (2018) in the

U.S. suggest that agglomeration economies affect high and low technology industries

differently. Liang and Goetz (2018) argue that high tech firms benefit from related

diversity (Jacob type spillovers) whereas low tech firms benefit from specialisation

(Marshallian type spillovers). However, more generally agglomerations provide certain

clear benefits, such as reduced search costs and more opportunities to partner with other

firms (Feldman 1999; c.f. Love and Roper, 2015).

Aarstad and Kvitastein (2019) and Tojeiro-Rivero and Moreno (2019) explore various

aspects of regional conditions on the propensity to innovate. Both studies adopt a multi-

level modelling approach, illustrating the benefits of adopting a methodology that allows

for regional variation in intercepts and gradients. In contrast to the approach used here,

their dependent variable is the dichotomous variable of whether a firm innovates. They

find evidence of regional effects on the probability of innovating but the variables

themselves are perhaps more weakly significant than anticipated.

In summary, as with large firms, successful SMEs engage in innovation and exporting

as part of a suite of high-performance workplace practices. As such, these are often

complementary and self-enforcing (Roper and Love, 2015). From a methodological

perspective, this makes disentangling the causality difficult but the growth in the

availability of panel data offers scope for the use of lagged variables to address this, if

not yet allowing for the application of extensive panel techniques. Additionally, in

comparatively resource constrained SMEs, the importance of the regional environment

in facilitating such high-performance workplace practices is magnified.

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This paper aims to test whether the City-Region level of geography, which offers an

economically meaningful unit of analysis, offers a clear association with firm-level

productivity performance and specifically, through which mechanism. In light of the

existing literature and theoretical evidence (as summarised previously in this section),

there are a number of hypotheses relating to firm level performance that we seek to test

using the UK Small Business Survey data (2015-2017) and the characteristics of the

regions in which the firms operate. Specifically,

H1: Controlling for other factors, the City-Region is a significant determinant of firm-level

labour productivity for UK SMEs.

H2: Controlling for other factors, City-Region labour market conditions have a positive

association with firm level labour productivity for SMEs

H3: Controlling for other factors, City-Region R&D spend has a positive association with

firm level labour productivity for UK SMEs.

H4: Controlling for other factors, City-Region business enterprise growth is positively

associated with firm level labour productivity for UK SMEs.

H5: Controlling for other factors, City-Region specialisation (homogeneity) is positively

associated with firm-level labour productivity for UK SMEs.

H6: Controlling for other factors, City-Region diversity (heterogeneity) is positively

associated with firm-level labour productivity for UK SMEs

3. DATA SOURCES

Individual data on small firms are provided by the Longitudinal Small business Survey

(LSBS), accessed both locally and via the secure data laboratory. The LSBS is a

telephone survey undertaken on behalf of the Department for Business, Energy and

Industrial Strategy (BEIS) on an annual basis since 2015, as part of the wider SBS

survey. Full details of the third wave of the data are available from the technical report

(BEIS, 2018). Three years of small business data are available from the LSBS at the

time of writing. The panel used here is unbalanced, with attrition over the period 2015-

17, despite some replacement (Table 1). In terms of national representativeness, this

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compares with official data reported in the national accounts of around 14% in the

production sector during the period of February to April 2019 (Booth, 2019).

Manufacturing has been declining in the UK for decades as part of the deindustrialisation

process, which offers some explanation for the discrepancy. It is anticipated that survey

will naturally underrepresent manufacturing compared with national totals because the

minimum efficient scale required for manufacturing will often be higher than for service

sectors.

Table 1: Broad sector breakdown of Businesses (counts) in the LSBS (2015-2017)

Sector 2015 % 2016 % 2017 %

Services 11,998 77.2 7,044 76.1 5,032 75.9

Production 1,553 10.0 976 10.5 679 10.3

Agriculture, Fishing and Forestry 492 3.2 327 3.5 268 4.0

Construction 1,502 9.7 911 9.8 648 9.8

Total 15,545 100 9,258 100 6,627 100 Note: Booth (2019) provides data from Feb-Apr 2019, estimating 79.4% of GDP is generated by services, 14% production, 6% construction and 1% agriculture.

LSBS data for the first three waves, 2015-17, were available directly from the data survey

providers; however, because these data are derived from the common survey frame

used by government, the Interdepartmental Business Register (IDBR), linkage of data

across databases is possible, giving potentially greater breadth of questions. Variables

captured in the survey relate to the characteristics of SME leaders, training undertaken

by the workforce, the international scope of the firm, energy usage, taxation, barriers to

SME growth and finance, innovation and business support as well as information on

employment, industry, location and approximate turnover. Primarily, data are coded as

dichotomous variables, indicating whether a specific business characteristic is relevant

or not. As such there are few continuous variables beyond turnover, employment and

age3.

3 Further details of the survey are available from the LSBS Technical Report (BEIS, 2018).

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The scope for data matching with a number of additional datasets was explored. In the

first instance, LSBS data were linked to the Business Structure Database which permits

the enhancement of a number of key variables. The Business Structure Database (BSD)

is the panel of snapshots of annual IDBRs, comprising of almost all businesses in the

UK that have either PAYE or VAT registration numbers. As such, the overall matching is

excellent although a small proposition of the LSBS firms (around 13% of firm-year

observations) do not have the linking variable. Thus, all but the very smallest firms are

included. It contains relatively limited information on the birth and death of firms, their

location, their primary Standard Industrial Classification (SIC) and data on employment

and turnover. Data on turnover and employees is collected for the BSD as a matter of

law and so the data are generally regarded as the most accurate when compared to the

LSBS which asks the respondent to approximate levels of turnover in the firm over the

past year. The BSD also has comprehensive data on firm demographics, improving

information on firm birth, death and age.

In addition, other firm level data matches were explored but yielded a significantly

reduced sample for analysis over the full period. Specifically, firm level variables from

the Community Innovation Survey (CIS) were explored but the level for matching is more

challenging given that the CIS is collected at the reporting unit level, rather than the

enterprise level. Moreover, with the exception of Mason et al. (2013), there are few

applications of regional CIS aggregates in the literature. In part, this reflects the focus

of the CIS on innovative firms, suggesting that regionally aggregated data may not be

truly reflective of the firm population.

3.1 Firm level data

The Small Business Survey is a survey that has traditionally taken place every two years.

Standalone cross-sectional data are available for 2012 and 2014, however, the

longitudinal data are available from 2015-2017 - an interesting period in the UK; following

the financial crisis and straddling the Brexit vote. The data offer three years for

longitudinal analysis, however in order to address concerns of endogeneity, lagging key

variables results in a reduced panel dimension of two years.

In terms of dependent variables, we use a number of alternative measures of

performance. We consider the log of turnover, employment and labour productivity

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(measured as real turnover per employee4). Independent variables possible for inclusion

are largely restricted to those available in the LSBS. Building on those factors identified

in the literature; we include age, exporting activity and broad sector. Innovation is

captured at the firm level using three measures. Firstly, firms indicated whether they

introduced product or process that was new to the market in the last three years. We

have coded this as a radical innovation. Secondly, incremental innovation is measured

as firms declaring they introduced a product or process that is new to their firm, following

Forés and Camisón (2016). Finally, we include a measure of whether firms were

successful in receiving R&D support, but this is only available for 2017.

Table 2 presents descriptive statistics for the firm level variables, included. The number

of observations represents firm level observations pooled over the three years of the

survey. The panel is unbalanced and the sample size shrinks over time, so that it is

approximately half what it was by the end of the period. In terms of data cleaning,

observations were dropped if neither employment nor turnover data were available.

Financial data were deflated using the GDP deflator. Data on age were also derived

from the BSD, as data in the LSBS on age were only available in banded form for 2015

and 2016. The average age of firms in the data used was over 19 years. Data are

censored as information on service sector firms only become available from 1997, thus

this is deemed the year of birth for many of the firms. Other continuous variable are

reported in log form.

In terms of the pairwise correlations, Table 2 shows that, as expected, (log of) turnover

and employment show a high and significant correlation with each other. All variables

save the minority ethnic led indicator are statistically significant at the 1% level and all

except the women led indicator are positively correlated. This is broadly true also for

employment, although the associations are weaker. Exporting is positively associated

with all other variables except women led and minority ethnic led indicators. If we

consider innovation indicators, we see that there are positive and significant associations

with exporting and negative associations with age, which is consistent with the findings

explored in greater detail elsewhere in the literature (Love et al, 2016). The associations

4 Measures for turnover and employment growth offered weak explanatory findings and so are not reported.

17

with turnover and employment for the innovation measures reveal that incremental

innovation indicator has a stronger association with (natural log of) employment than it

does turnover, but that radical innovation is statistically significantly associated with (the

natural log of) turnover.

3.2 City-Region data

Our level of regional analysis for localised interactions is the British City-Region. In

contrast to administratively determined measures of geography, the City-Region,

developed by Robson et al. (2009) offers an economically meaningful level of analysis,

as it is based on the travel to work patterns of the high skilled (Mason et al., 2013). A

number of variables have been derived from the NOMIS data on business counts and

skills at the local authority district (LAD) level and with the BSD which can be aggregated

to the City-Region level. There are 56 City-Regions (CRs) in the UK, although a number

of these CRs are composite areas, such as “other north east”. A full list of CRs is provided

in the Appendix. Arguably, such regions are less economically meaningful than other,

more clearly defined regions. Moreover, data from NOMIS was available primarily at the

GB level, excluding Northern Ireland from our analysis. Yet nonetheless the CR provides

a robust measure of the regional environment that firms located within them face and

experience.

We incorporate measures to capture labour market and business conditions in the CR.

The labour market measures included the proportion of high skilled workers (of total

employment), employment density and growth in employment in a CR5. These data are

obtained from NOMIS labour market statistics and aggregated to the CR level using

Local Authority District codes. In addition, we use the UK Business Expenditure

Research and Development (BERD) data via the secure data lab to create total

Research and Development (R&D) spending at the CR level6. This is collected at the

firm level but is used here to calculate the share of R&D spend per year at the CR level.

5 For the labour market and skills measure, factor analysis was conducted to derive a single measure to address the correlation between variables. 6 UK Innovation Survey data was also explored but matched sample sizes were considered too small for meaningful analysis at the firm level.

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In line with the literature (Duranton and Puga, 1999; Mason et al., 2013), a measure of

diversity is calculated as one minus the squared sum of shares of employment in each

two-digit industry, as a share of employment in the CR. In addition, we include a measure

for specialisation which is calculated as the maximum one-digit sector share in total city

region employment. This value is adjusted in terms of national share (by dividing the

share of each sector in local employment by its share in national employment) to identify

a CR’s relative specialisation. A dummy variable is interacted with the specialisation

measure to pick up own sector specialisation in addition to sector concentration.

Table 3 presents descriptive statistics for the CR variables in the dataset constructed.

Note that the observations represent firm-year observation numbers as in Table 2.

Correlations amongst the CR variables are generally significant but are a mixture of

positive and negative associations. Labour market conditions are negatively correlated

with all other variables.

Table 2: Descriptive statistics for firm level variables

Notes: N represents firm-year observations; The panel is unbalanced and thus the number of firms differs across the years. Although the LSBS is an SME survey, some SMEs in wave 1 have grown beyond the 249 definition of SME. Correlation coefficients presented with significance at the 5% level indicated by *.

19

Table 3: Descriptive statistics for City-Region variables

Variable Obs Mean Std. Dev.

Labour market conditions

Regional R&D spend (share of total) Diversity Specialisation

Labour market conditions 27,191 0.314 1.109 Regional R&D spend (share of total) 27,702 307778 539592 0.5336*

Diversity 27,191 0.234 0.081 -0.7078* -0.4454*

Specialisation 26,846 1.692 1.123 -0.1358* -0.1404* 0.4841* Business count growth 27,191 5.853 3.025 -0.0160* 0.2380* -0.2009* -0.3577*

Notes: Observations represent firm-year counts. Correlation coefficients presented with significance at the 5% level indicated by *.

4. EMPIRICAL METHODOLOGY

In order to address our hypotheses, the data described in section 3 was analysed as a

panel using a random effects (GLS) estimator in Stata. Panel or longitudinal data offer

greater capacity to control for unobservable characteristics in the sample. However, in

the case of the LSBS, the panel itself is very short with only three years of data, this is

limited to two years when lagged variables are included. The full range of observations

are used and it is therefore an unbalanced panel. In addition, data were cleaned by

replacing employment and turnover for with the more accurate BSD data, where

available, and also correcting age using the BSD data, as age was found to be banded

for the 2017 wave.

Equation [1] estimates the generalised form of the model:

���� = �� + ∑��� + ∑��� + ���� [1]

Where Y is the performance measure, either turnover, employment or labour productivity

in log form. X is a vector of firm level characteristics for each i firm and Z is a vector of

city-region characteristics in j regions. Eijt is the random error term. Data also vary over

time t. Equation [2] expands the general form to specify the variables included:

Yijt = β0 + β1lempit-1 + β2ageit + β3exportit + β4womit + β5megit + β6rnd_suppit + β7radicalit +

β8incrementit + β9diversjt + β 10specialisationtj + β11chbctj + β12lagrndtj + uitj [2]

20

Where lemp is lagged employment7, age is the age of the firm, export is a dummy

variable that takes the value of 1 if the firm has exported either a good or service this

year, wom is a dummy variable that takes a value of 1 if the firm is led by a woman. The

variable meg is a dummy variable, taking the value of 1 if the firm is led by an individual

identifying as from minority ethnic group. Finally, the firm level data contain three

variables to capture innovation activity. Rnd_supp is a dummy variable that takes the

value of 1 if the firm has received public funding for its R&D activities. This information

was only available for 2016 and 2017. Radical takes the value of 1 if the firm has

identified itself as introducing a good that is new to market. Incremental takes the value

of 1 if the firm has identified itself as introducing a product or process that is new to the

sector or firm, and a new process to the market. In addition, there were controls for

sector at the broad level (See Table 1, with the service sector as the reference category).

CR variables were constructed as discussed in section 38. Divers is a herfindahl style

measure of 2-digit sector employment within a CR. Specialisation captures own sector

specialisation at the 1 digit sector level based on employment shares. Chbc represents

the change in business counts within a city-region and lagrnd is the lagged value of R&D

spend within a city-region derived from the BERD dataset.

While the random effects approach has been utilised in many studies (Mason et al.,

2013), it fails to sufficiently deal with the interdependence between observations (and

spatial autocorrelation effects) within clusters which may lead to an overstatement of

statistical significance (Raudenbush and Bryk, 2002). Moreover, while utilising the

random effects with clustering can ‘net out’ the effects the information contained at the

cluster level (in our case, CR), in this paper, the nature of the interaction between the

CR and the firm is precisely of interest. An alternative approach is to utilise a multilevel

modelling approach, as used in Aarstad and Kvitastein (2019) and Tojeiro-Rivero and

Moreno (2019). In both these models, they utilise innovation success as the dependent

variable and utilise multilevel mixed-effects logistic regression which controls for both

regional and industry effects. Here we use performance variables as dependent

variables and examine how regional factors including innovation influence these. Our

7 In the case of the employment equations, this is replaced with sizeband. 8 A list of city regions is provided in appendix A.

21

data are organised in a hierarchical structure and firms cluster within city-regions. The

use of multilevel models enables us to consider the group effects (i.e. city region) at the

same time as considering the firm specific effects and quantify them.

Multi-level mixed modelling is introduced to explore whether the variability between CR

is associated with turnover, employment or the labour productivity of small firms. In this

case we include level-2 predictors into the means-as-outcomes model (varying-intercept)

model. This can be written as a varying-intercept model with both level one and level

two predictors as follows:

Yij = γ00 + γ10X1j + γ20X2j + … + γQ0XQj + γ01W1j + γ02W2j + … + γ0SWSj + u0j + rij [3]

Where

γ00 = overall mean intercept adjusted for W;

γ01 = regression coefficient associated with W relative to level-1 slope;

u0j = random effects of the jth level-2 unit adjusted for W on the intercept;

XQj = level-1 predictors (firm level)

WSj = level-2 predictors (CR level)

With this approach, the combined model in this study can be written as follows;

Yij = γ00 + γ10lemp_11i + γ20age2i + γ30export3i + γ40wom4i + γ50meg5i + γ60rnd_supp6i + γ70radical7i

+ γ80increment8i + γ01divers1j + γ02specialisation2j + γ03chbc3j + γ04lagrnd4j + u0i + rij [4]

Where Y is the performance measure (natural log of turnover, employment and labour

productivity). The coefficients in level one predictors are fixed and we only treat CR as a

random effect. The result provides information about the outcome variability at each of

the two levels. σ2, variance of residual, represents the within group variability, while τ00

captures the between-group variability. We can derive the intraclass correlation

coefficient, by measuring the proportion of the variance in the outcome that is between

the level-2 units (Raudenbush and Bryk, 2002).

22

5. RESULTS

Random effects estimates are presented in Table 4, with natural log of turnover,

employment and labour productivity as dependent variables. By separately estimating

the models for turnover and employment, we are able to consider in greater detail which

part of labour productivity is associated with the variables of interest. Initially, in models

(1) to (3), we include only individual level variables – our baseline models. Broad sector

controls are included in all specifications (relative to the service sector), regional controls

at the government office region (GOR) level (relative to the East Midlands) and sizeband

dummies are included in the employment model (2) to account for size effects. By

logging the dependent variable we are able to interpret coefficients as partial elasticities.

Considering models (1) to (3) we see that the dependent performance variable is

positively and significantly associated with the size variable, which is consistent with our

expectations. Age has a positive and significant association, indicative of experience

more than compensating for any negative associations associated with firm age (when

other factors are also controlled for). Initial explorations with non-linear transformations

of age were omitted from the analysis due to multicollinearity. In the case of exporting

behaviour, we find a positive and significant association in the case of turnover and

labour productivity, in line with previous findings. If we consider the coefficients attached

to our leadership diversity measures, there is a significant and negative association

between being female led and financial performance measures. However, when

considering the female led organisations in the case of employment, we see a positive

and significant association (note that broad sector differences have been controlled for).

It appears therefore that in the case of female-led SMEs their impact lies in growing

employment faster than turnover which results in a negative association with labour

productivity. In the case of minority ethnic led firms, there are no statistically significant

differences.

Considering our findings for the innovation indicators, we note that radical innovation is

significant and positive for labour productivity only. Incremental innovation is not

significantly different from zero for any models based on individual data only. Those

firms in receipt of public R&D funds show a significantly positive association with all

measures of performance. This is in line with expectations as those that are successful

23

at competitively winning support for R&D are likely to be those most committed to

innovation, although the direction of causality is not established here.

Regional dummy variables have been included in models (1) to (3) at the GOR level of

disaggregation. There is some indication that, relative to the base of the East Midlands,

there are characteristics associated with London that have a positive influence on firm

level performance, thus offering some limited support for H1 (Controlling for other factors,

the City-Region is a significant determinant of firm-level labour productivity for UK

SMEs.), although this level of geography is aggregated and based on an administrative

rationale. Moreover, these dummy variables offer us little information on specifically

what it is about the region that makes the difference.

Turning to columns (4), (5) and (6) in Table 3, specific regional variables have now been

incorporated at the CR level of geography into the specifications (therefore regional

dummy variables are excluded). The magnitude and significance levels of the firm level

variables are consistent with the initial firm level only regressions, although radical

innovation has a higher level of significance in the labour productivity specification (6)

and R&D support becomes insignificant. This may be due to the fact that R&D support

is regionally influenced in terms of programme design.

The coefficients of interest here are those relating to CR level variables. We find that

local labour market factors as measured here are not significant, thus we are unable to

accept H2 (Controlling for other factors, City-Region labour market conditions have a

positive association with firm level labour productivity for SMEs). Diversity is not found

to be significant either, offering little evidence in support of H6 (Controlling for other

factors, City-Region diversity (heterogeneity) is positively associated with firm-level

labour productivity for UK SMEs); however, we do find that own industry specialisation

has a positive and significant association with labour productivity, thus we find evidence

in support of H5 (Controlling for other factors, City-Region specialisation (homogeneity)

is positively associated with firm-level labour productivity for UK SMEs). CR levels of

R&D appear to be significant and positive in the case of labour productivity and turnover

models. but appear small as a result of the scale, providing some support for H3

(Controlling for other factors, City-Region R&D spend has a positive association with firm

level labour productivity for UK SMEs). Business dynamics in the CR are not found to be

significant, leading us not to accept H4(Controlling for other factors, City-Region

24

business enterprise growth is positively associated with firm level labour productivity for

UK SMEs) at this stage. Weak CR effects are not out of line with the existing literature,

but our regional dummy variables from specifications 1 to 3 would suggest that

geography does have a significant role to play. In order to enhance our estimation, we

turn to the multilevel modelling approach.

Multilevel results

The choice of multilevel models is dictated in part by the nature of the dependent

variable, the number of levels in the data and the nature of the ‘level’. Previous studies

that have used this approach in relation to innovation have adopted logistic models (c.f.

Aarstad and Kvitastein, 2019) because of the dichotomous nature of the dependent

variable. Here, we estimate a two-level random-intercept multilevel mixed-effects

regression, following the methodology section.

All null models, columns (1), (4), and (7) in Table 5, are included to demonstrate the

significance of the random intercepts; that is, CRs matter, offering further support for H1.

We see that all are significant, indicating multilevel modelling is an appropriate approach

to take (McCulloch et al., 2008). Even though the model fits of these null models are

good (except the Model 1), intraclass correlation coefficients, which explains the

variability of dependent variables between city-region level, are close to zero (not

reported here).

The results of adding level-1 predictors to the null model (i.e. firm level) are presented in

columns (2), (5), and (8) in Table 5. It can be seen that the intercept is still significant,

indicating the possibility of variability at the CR level. Specifically, when using (natural

log of) turnover as a dependent variable, lagged employment, age of business, export,

and government R&D support show significantly positive relationship, while women-led

a negative coefficient, as before (in Table 4). When using (natural log of) employment as

the dependent variable (model 5), radical and incremental innovation show negative and

significant coefficients, perhaps indicative of the choice firms have to make in which

inputs to invest (capital or labour). Interestingly, we note that the dummy variable

indicating those firms that export is negative in relation to employment, whereas women-

led firms have a positive and significant coefficient. When we turn to labour productivity,

the results are consistent with those observed in relation to turnover.

25

Moving on to level-2 predictors (columns 3, 6, 9 in Table 5), most CR variables are not

significantly associated with the dependent variables, with the exception of firms

operating in regions with high levels of own industry specialization (which has a negative

association). This may suggest that firms are competing for labour resources. In

addition, the change in business count has a positive and significant association in the

case of the employment model (6), significant at the 10% level. Also, we note that the

change in business count is also positively significant (at the 10% level) with labour

productivity, suggesting that a more dynamic environment with a growing business base

is likely to foster improvements in labour productivity and therefore offering some support

for H4. Overall however, we see that hypotheses H2, H3, H5 and H6 are not supported

by our multilevel analysis.

26

Table 4: Performance of SMEs (Random effects)

27

28

Table 5: Multilevel mixed effects regression with performance measures as the

dependent variable

29

6. CONCLUSIONS

Conclusions and Discussion

The positive relationship between performance and innovation is well established in

industrial economics and yet, increasing the level of innovation is a challenge for policy

makers. Indeed, the recent Industrial Strategy (BEIS, 2017) puts innovation is at the

heart of the strategy, mentioning innovation 259 times in 254 pages, and committing to

a target of 2.4% of GDP for R&D spending by 2027. Historically, this level is

unprecedented (1.68% achieved in 2015) and has already been identified as being

unrealistic with the potential to divert resources from more fruitful ways of supporting

innovation (Rae et al, 2017). However, SMEs will have a significant role to play if such

innovation ambitions are to be met.

This paper considers how small firm success is influenced, not only by their internal

practices and capabilities but by the broader regional environment in which they operate.

Taking a CR view, and controlling for a number of firm level factors, this analysis finds

that regional factors have a significant role to play in the productivity of SMEs.

Identification of the precise nature of these channels using a multi-level modelling

approach is more difficult but not out of line with existing findings elsewhere (Aarstad

and Kvitastein, 2019).

The analysis presented in this paper has found that there is considerable consistency

across factors that are associated with firm level labour productivity (and its component

parts) across both estimators used and in line with the extant literature for SMEs.

Exporting and age are found to have significant positive associations with productivity.

Evidence on innovation at the firm level is more sensitive to measurement but

productivity is positively associated with radical innovation. Those SMEs led by women

are found to have lower levels of labour productivity, but here we see that this primarily

a result of the positive contribution such organisations make to employment. This finding

at the firm level warrants further research.

Firm level effects clearly dominate, but the role of the regional environment in fostering

productivity and growth is thought to be more significant for smaller firms since these are

more resource constrained compared to larger firms. Having controlled for firm level

30

factors, the paper contributes to the growing literature on regional influences on firm

performance for SMEs. Initially, using GORs as the unit of geography there is some

indication that regional differences exist, but this paper seeks to understand these and

looks to use a more economically meaningful level of geography, using the City Region.

Our random effects estimates do not pinpoint a clear mechanism for CR effects on firm

level performance, with no significance attached to local labour market conditions, the

degree of industry diversity nor the dynamic growth of the region as captured by business

growth. CR levels of R&D expenditure appear to offer some direct link with SME

success, as does industry specialisation. Mulitlevel modelling confirms the relevance of

CR effects, but our measures introduced a the CR level fail to capture fully what it is

about the CR that benefits SMEs, with only a weakly significant finding for business

dynamics in the CR.

Possibilities for future research

This paper has thrown light on a number of issues that might be fruitful for further

research. Firstly, the level of geography explored has focussed on City Regions. While

we would argue that these are more economically meaningful units of geography than

those that are administratively determined, they lack the dynamic development that

economic areas undoubtedly witness. One alternative would be to utilise the Local

Enterprise Partnership (LEPs) areas since these are active, sub-regional entities defined

in terms of business activity. However, they have not been discrete until recently and

vary significantly in size (Coombes, 2014). Travel To Work Areas (TTWAs), which are

another popular alternative and economically meaningful disaggregation are perhaps too

disaggregated to create a clear picture for policy purposes. Therefore, although some

further analysis of geography may produce different results, City Regions may indeed be

the most appropriate spatial indicator in the UK context.

With new waves of the LSBS it will be increasingly possible to extend this analysis and

make more of the dynamic properties within the panel. A number of refinements to the

specification should also help our understanding of the channels through which regional

factors influence small firm performance and innovation activity. Specifically, exploring

alternative measures of specialisation and diversification to better capture agglomeration

spillovers. Moreover, the analysis could extend to include regional indicators of

31

innovation such as a measure of enterprise zones within a region, or the use of UK-CIS

derived variables.

32

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Appendix 1: List of City Regions

City region name Code City region name Code

Birmingham/Sandwell/Wolves 1 Plymouth 29

Bournemouth/Poole 2 Portsmouth/Southampton 30

Brighton&Hove 3 Preston 31

Bristol/S.Gloucester 4 Reading 32

Cambridge 5 Sheffield 33

Carlisle 6 Stoke-on-Trent 34

Chester 7 Swindon 35

Colchester 8 Telford and Wrekin 36

Coventry 9 Worcester 37

Exeter 10 York 38

Greater London 11 Cardiff 39

Gloucester/Cheltenham 12 Swansea 40

Ipswich 13 Aberdeen 41

Kingston upon Hull 14 Dundee 42

Leeds/Bradford 15 Edinburgh 43

Leicester 16 Glasgow 44

Lincoln 17 Belfast* 45

Liverpool 18 Other North East 46

Luton 19 Other North West 47

Manchester/Salford/Trafford 20 Other Yorkshire and Humber 48

Middlesbrough/Stockton 21 Other East Midlands 49

Milton Keynes 22 Other West Midlands 50

Newcastle/Gateshead/Sunderland 23 Other Eastern 51

Northampton 24 Other South East 52

Norwich 25 Other South West 53

Nottingham/Derby 26 Other Wales 54

Oxford 27 Other Scotland 55

Peterborough 28 Other Northern Ireland* 56

*not available in NOMIS Data and so excluded from analysis

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