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LSBS event presentation slides

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LSBS event . Full presentation slides 19.09.19

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LSBS event presentation slides

  1. 1. BEIS-ERC Longitudinal Small Business Survey Research Showcase Event 19 September 2019 WBS London, the Shard Funded by
  2. 2. LONDON’S EVENING UNIVERSITY BBK.AC.UK EXPLORING THE LINK BETWEEN TRAINING AND INNOVATION Marion Frenz and Ray Lambert 3
  3. 3. • Rationale and research questions • Data and methods • Results • Discussion • Policy implications 4 OUTLINE
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. • 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
  8. 8. 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
  9. 9. • 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
  10. 10. 11 RESULTS
  11. 11. 12
  12. 12. • 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
  13. 13. • 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. Aim of the presentation • Background • Innovation and small firms • Regional context • Research questions • Methodology • Data – LSBS and… • Preliminary findings • Summary and next steps. The UK’s European UniversityPage 18
  18. 18. 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 The UK’s European UniversityPage 19
  19. 19. 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 The UK’s European UniversityPage 20
  20. 20. 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 The UK’s European UniversityPage 21
  21. 21. 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 The UK’s European UniversityPage 22
  22. 22. 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 The UK’s European UniversityPage 23
  23. 23. Findings (1) Random Effects The UK’s European UniversityPage 24 (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]
  24. 24. Findings (2) Random Effects The UK’s European UniversityPage 25 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
  25. 25. Findings (3) Multi-level modelling The UK’s European UniversityPage 26 (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]
  26. 26. Findings (4) Multi- level modelling The UK’s European UniversityPage 27 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
  27. 27. 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 UniversityPage 28
  28. 28. THE UK’S EUROPEAN UNIVERSITY www.kent.ac.uk
  29. 29. 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.
  30. 30. 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
  31. 31. 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
  32. 32. 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)
  33. 33. 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
  34. 34. 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 ProbabilityofCollaboratingwithaUniversity ProbabilityofCollaboratingwithaUniversity Productivity Productivity
  35. 35. 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
  36. 36. 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)
  37. 37. Results: Probability of collaboration with universities occurring
  38. 38. 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
  39. 39. Results: Productivity Changes
  40. 40. 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
  41. 41. 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.
  42. 42. 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
  43. 43. 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
  44. 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
  45. 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
  46. 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
  47. 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)
  48. 48. 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]
  49. 49. 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
  50. 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
  51. 51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 57. 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?
  58. 58. 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
  59. 59. 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
  60. 60. 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.
  61. 61. 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.
  62. 62. 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
  63. 63. 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)
  64. 64. 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”
  65. 65. 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
  66. 66. Business Support Model for Stages of SME Finance (source OI, SQW, Owen et al, 2019)
  67. 67. 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
  68. 68. 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
  69. 69. Slide 70 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 2018 SME Employers
  70. 70. Slide 71 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
  71. 71. Slide 72 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. BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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
  72. 72. Slide 73 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 2018 SME EMPLOYERS
  73. 73. Slide 74 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). BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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
  74. 74. Slide 75 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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
  75. 75. Slide 76 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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.
  76. 76. Slide 77 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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%).
  77. 77. Slide 78 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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.
  78. 78. Slide 79 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. BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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
  79. 79. Slide 80 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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.
  80. 80. Slide 81 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). BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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
  81. 81. Slide 82 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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.
  82. 82. Slide 83 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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
  83. 83. Slide 84 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 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 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.
  84. 84. Slide 85 • 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
  85. 85. Slide 86 BEIS LONGITUDINAL SMALL BUSINESS SURVEY 2017 SME Employers
  86. 86. Thank you

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