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ERC Research Shocase presentation January 2020

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Master Presentation slides from the ERC Research Showcase January 2020

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ERC Research Shocase presentation January 2020

  1. 1. ERC Research Showcase Monday January 20th 2020
  2. 2. Welcome  Stephen Roper, Director, ERC
  3. 3. IP, innovation and performance: A sectoral analysis Joanne Turner, ERC
  4. 4. Main aim of the research project • To understand the contribution IP has to innovation, business growth and productivity Preliminary research questions • How does IP protection influence the probability of innovation in firms? • How does IP protection influence the proportion of firm sales from innovation?
  5. 5. Descriptive analysis • Intellectual Property Office (IPO) data detailing patents granted, trade marks registered and designs registered during the 1998-2018 period • IP histories built for UK-located firms using renewal data and the unique company reference number (CRN) attached to each successful application • Matched with Business Structure Database (BSD) (1998- 2018) • Examined use of IP across 2-digit sectors in 2012 and 2016
  6. 6. Figure 1: Proportion of firms within a sector using some IP protection (a) 2012 (b) 2016
  7. 7. Figure 2: Proportion of firms within a sector using at least one patent (a) 2012 (b) 2016
  8. 8. Figure 3: Proportion of firms within a sector using at least one trade mark (a) 2012 (b) 2016
  9. 9. Figure 4: Proportion of firms within a sector using at least one registered design (a) 2012 (b) 2016
  10. 10. • IP histories are matched with the UK Community Innovation Survey (CIS) (2002-2016) to explore innovation/IP synergies • We examine how the stock, or number, of each IP instrument at the beginning of each wave of data affects 1. The probability that a firm will carry out product innovation during the 3-year period of each wave 2. The proportion of firm turnover attributable to product innovation at the end of each wave Econometric analysis
  11. 11. Probability of product innovation Probability of NTF product innovation Probability of NTM product innovation Patents Trade marks Registered Designs Patents Trade marks Registered Designs Patents Trade marks Registered Designs All firms (N>50K) - - + ** - + - + + + *** Manufacturing firms (N>10K) - + ** + * - + + + + * + ** Service firms (N>40K) - + + - - - + + + *** High-tech / knowledge- intensive firms (N>20K) - + + - + - + - + ** Low-tech /less knowledge- Intensive firms (N>30K) + + + ** - - - + + + *** The effect of IP protection on the probability of product/service innovation *= significant at the 10 per cent level **=significant at the 5 per cent level ***=significant at the 1 per cent level
  12. 12. Proportion of turnover from NTF product innovation Proportion of turnover from NTM and NTF product innovation Patents Trade marks Registered Designs Patents Trade marks Registered Designs All firms (N>50K) - - ** + - - ** + ** Manufacturing firms (N>10K) - - + - - ** + ** Service firms (N>40K) - - ** + - - + ** High-tech / knowledge-intensive firms (N>20K) - - - + - ** + * Low-tech /less knowledge- Intensive firms (N>30K) - - + - - + * The effect of IP protection on the proportion of firm turnover attributable to product/service innovation *= significant at the 10 per cent level **=significant at the 5 per cent level ***=significant at the 1 per cent level
  13. 13. Descriptive points • A relatively small proportion of firms use formal IP protection mechanisms (less than 10 per cent in most sectors) • There is widespread use of trade marks across sectors, although use is more concentrated in manufacturing firms • The use of patents and registered designs is concentrated in manufacturing firms with a higher proportion of firms using registered designs than patents • In sectors with more than 10 per cent of firms using IP, non parametric tests suggest that future productivity is likely to be higher for IP users compared with non-IP users Preliminary findings
  14. 14. The probability of innovating • Patents and trade marks have no significant effect on the probability of product innovation, with the exception of trade marks and NTM product innovation in manufacturing firms where the effect is significant and positive • Registered designs have a significant, positive effect on the probability of NTM product innovation in all groups of firms Innovative sales • Patents have no significant effect on the proportion of firm turnover from product innovation • Trade marks have a significant, negative effect on the proportion of firm turnover from NTF product innovation in service firms and a significant, negative effect on the proportion of firm turnover from NTF+NTM product innovation in manufacturing and high-technology/knowledge-intensive firms • Registered designs have a significant, positive effect on the proportion of firm turnover from NTF+NTM product innovation in all groups of firms Preliminary findings – ctd.
  15. 15. Next stages • More detailed econometric analysis of the effect IP has on innovation • An econometric analysis of the effect IP has on business growth and productivity
  16. 16. Thank you!
  17. 17. The Collaboration Paradox in SMEs – UK Foundries and Metal Forming Firms Temitope Akinremi, ERC
  18. 18. • Why don’t SMEs engage in innovation collaboration???? • Innovation plays a pivotal role in firm growth and profitability • Innovation adoption and strategy is influenced by firm-level factors • Smallness can be a disadvantage……..Capability constraint • Small firms have more to gain from collaboration but may find implementation difficult - • In this project we focus on barriers to collaborative innovation due to limited and asymmetric information on the benefits of the collaboration; the capability of potential partners; and trustworthiness of potential partners (Hewitt-Dundas and Roper 2018) Collaboration Paradox Setting the Scene
  19. 19. • SME dominated sectors • Unique supply chain position • Challenging production processes • Evident gap in knowledge creation • Mixed Method (Qualitative & Quantitative); • 25 In-depth Interviews • Surveyed 170 firms (75 Foundries, 95 Metal-Forming Firms) Foundry Metal-Forming Sector • Our research focuses on two industry sectors in the UK; Industry Sector & Methodology
  20. 20. Key Findings • Informational Market Failure 1: Firms not understanding the benefit of innovation and collaboration • Innovation Activities in the last three years (2016-2019) 62% 60% “.. New products and improved performance for other products. …pushing the boundaries of what processes are capable of ” “…you get the benefit of 30, 40, 50 years experience immediately, without having to go through 50 years to get the knowledge, you get to take it immediately.”
  21. 21. Key Findings (Contd.) • Motivation for Innovation 25.8 11.7 21.5 35.9 35.3 52.0 26.9 10.9 11.1 58.5 83.8 69.5 53.5 50.2 31.9 61.5 86.3 88.9 0% 20% 40% 60% 80% 100% Process Improvement Remaining Competitive Customer Demand Cost Reduction Stricter Standards Improving Time to Market Increasing Margins Improving H&S Improving Quality Percentage Importance of Innovation Motivation in Foundries Low Importance (%) Medium Importance (%) High Importance (%) Not Applicable (%) 33.6 15.0 21.3 40.2 39.7 33.4 34.3 34.8 19.9 51.9 69.5 69.2 42.5 40.7 38.9 52.9 55.2 76.0 0% 20% 40% 60% 80% 100% Process Improvement Remaining Competitive Customer Demand Cost Reduction Stricter Standards Improving Time to Market Increasing Margins Improving H&S Improving Quality Percentage Importance of Innovation Motivation in Metal- Forming Firms Low Importance (%) Medium Importance (%) High Importance (%) Not Applicable (%) “without innovation, you can't compete on price, as it were, so you have to compete in different ways, through technology and offering something different.” Improving quality and remaining competitive; similar motivation for innovation across both industry sectors
  22. 22. Key Findings (Contd.) • Collaboration with External Partners 33% 67% Percentage of SME Foundries that Collaborated with External Partners for Innovation Collaborators Non Collaborators 28% 72% Percentage of Metal-Forming SME Firms that Collaborated with External Partners for Innovation Collaborators Non Collaborators More than two-thirds of SME firms conducted their innovation in-house
  23. 23. Key Findings (Contd.) • Collaboration Partners 9% 17% 20% 8% 18% 11% 7% 2% 8% Total Percentage of Collaboration in SME Foundries Firms within Enterprise Group Customers Suppliers Competitors/Firms within Sector Firms outside Industry Sector Consultants/R&D Institutes Universities/Higher Education Government/Public Research Trade Associations 12% 25% 19% 10% 10% 11% 5% 3% 5% Total Percentage of Collaboration in Metal-Forming SMEs Other Firms within Enterprise Customers Suppliers Competitors/Firms within Sector Firms outside Industry Sector Consultants/R&D Institutes Universities/Higher Education Government/Public Research Trade Associations “When it comes to innovation; I mean obviously a lot of it comes from the customer”“Suppliers, play a key role in terms of developing innovative new technologies and its application” Most common collaboration; Firm-Supplier & Firm-Customer Collaboration
  24. 24. Key Findings (Contd.) • Beneficial Collaboration 3.8 11.1 12.3 4.5 6.7 2.0 3.7 0.0 2.2 0 2 4 6 8 10 12 14 Other Firms within Enterprise Customers Suppliers Competitors/Firms within Sector Firms outside Industry Sector Consultants/R&D Institutes Universities/Higher Education Government/Public Research Trade Associations Percentage Benefit of Collaborations in Foundries Hight Benefit (%) Some Benefit (%) Low Benefit (%) 1.8 12.1 5.4 2.7 1.3 4.9 0.9 0.9 0.9 0 5 10 15 Other Firms within Enterprise Customers Suppliers Competitors/Firms within Sector Firms outside Industry Sector Consultants/R&D Institutes Universities/Higher Education Government/Public Research Trade Associations Percentage Benefit of Collaborations in Metal-Forming Firms High Benefit (%) Some Benefit (%) Low Benefit (%) Firm-government, firm-university and firm-competitor collaboration are the least beneficial collaborations
  25. 25. Key Findings (Contd.) • Informational Market Failure 2: Lack of information for assessing the capability of potential partners • Barriers to Innovation Collaboration 18.5 22.3 11.9 16.5 25.8 22.8 13.5 22.9 17.9 21.6 20.6 35.2 18.8 8.2 20.6 20.3 16.8 31.0 20.5 21.5 25.3 42.6 46.4 25.4 40.1 20.9 26.7 0% 20% 40% 60% 80% 100% New Product/Process Risk Firm Size Time Constraint Lack of Funds Lack of Trust & Openness IP Ownership/Protection Insufficienct Knowledge on Capability Corporate Culture Competitive Environment Influence (%) Percentage Influence of Barriers to Innovation Collaboration in Foundries No Influence (%) Low Influence (%) Medium Influence (%) High Influence (%) Not Applicable (%) “…as an SME, time, money, resources, brainpower you know, they are all working against us.” 17.2 21.7 20.8 18.1 17.2 17.6 16.7 18.9 17.1 25.7 21.7 27.6 19.9 17.6 24.4 23.1 22.1 31.2 12.7 12.7 24.4 28.4 35.3 19.9 32.1 11.8 17.2 0% 20% 40% 60% 80% 100% New Product/Process Risk Firm Size Time Constraint Lack of Funds Lack of Trust & Openness IP Ownership/Protection Insufficienct Knowledge on Capability Corporate Culture Competitive Environment Influence (%) Percentage Influence of Barriers to Innovation Collaboration in Metal-Forming Firms No Influence (%) Low Influence (%) Medium Influence (%) • Most Influential Barriers: Lack of Trust & Openness, Insufficient Knowledge about Capability & Lack of Funds
  26. 26. Key Findings (Contd.) • Importance of Capability Knowledge 6% 10% 13% 22% 49% Percentage Importance of Capability Knowledge in Foundries Very Unimportant (%) Unimportant (%) Neither Important nor Unimportant (%) Important (%) Very Important (%) 8% 6% 86% Percentage Importance of Trust for Collaboration in Foundries Very Unimportant (%) Unimportant Neither Important nor Unimportant (%) Important (%) Very Important (%) “I guess you do not always know what people can do…so you do not know what is available” “… knowing more about capabilities is important. I mean often you do not know exactly what such a partner may be able to offer or what technology or understanding or knowledge they may have behind them”
  27. 27. Key Findings (Contd.) • Capability Information to Know • Capability Information to Know; Quality Standards, Operational & Financial Capability 76.2 79.1 55.4 78.9 81.6 78.3 79.4 77.7 77.5 75.0 71.0 73.7 44.8 71.4 77.3 73.3 69.7 59.7 65.2 68.3 0 10 20 30 40 50 60 70 80 90 Innovation Cost Financial Capability Growth Strategy Operational Capability Quality Standards Reputation Case Study/Past Projects Expectations Product/Process Type Feasibility of Innovation Percentage of Firms CapabilityInformationtoKnow Capability Information to Know Metal-Forming Firms Foundries
  28. 28. Key Findings (Contd.) • Capability Information Sources • Trade Associations and Supply Chain Partners are useful information sources 58.8 60.7 52.1 45.9 36.1 29.0 74.3 52.1 48.4 28.1 30.3 25.3 14.5 65.1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 Friends/Family SC Partners Trade Associations Business Networks/Clubs Exhibitions Conferences Internal Research Capability Knowledge Information Sources Metal-Forming Firms Foundries
  29. 29. Key Findings (Contd.) • Informational Market Failure 3: Difficulty in assessing trustworthiness of potential partners • Importance of Trust Knowledge 8% 6% 86% Percentage Importanceof Trust for Collaboration in Foundries Very Unimportant (%) Unimportant Neither Important nor Unimportant (%) Important (%) Very Important (%) 3% 2% 2% 8% 85% Percentage Importanceof Trust for Collaboration in Metal- Forming SMEs Very Unimportant (%) Unimportant Neither Important nor Unimportant (%) Important (%) Very Important (%) “…and there has to be that trust. With trust then comes the more, with the more comes more ….It is a spiral that builds… • Trust is very important in the decision to collaborate
  30. 30. Key Findings (Contd.) • Informational Market Failure 3: Difficulty in assessing trustworthiness of potential partners • Assessing Trustworthiness 53.19 44.06 80.57 38.67 56.45 66.64 66.56 61.99 0% 20% 40% 60% 80% 100% Assessments & Visits Financial Strategy Meetings/Relationships NDAs Data Protection Reputation Openness to Info Sharing References/Recommendations Assessing Trustworthiness in Foundries Never Effective (%) Rarely Effective (%) Sometimes Effective (%) Most Effective (%) 48.42 32.10 72.30 28.82 47.93 57.49 53.41 56.58 0% 20% 40% 60% 80% 100% Assessments & Visits Financial Strategy Meetings/Relationships NDAs Data Protection Reputation Openness to Info Sharing References/Recommendations Assessing Trustworthiness in Metal-Forming Firms Never Effective (%) Rarely Effective (%) Sometimes Effective (%) Most Effective (%)
  31. 31. Conclusion • Less than half of SME firms introduced new products/services in the last 3 years • SME firms understand the benefits of innovation and innovation collaboration • Firms are motivated to innovate to remain competitive, and for quality improvements • Supplier and Customer collaboration is the most adopted collaboration type • Lack of trust and insufficient knowledge about capability are limiting factors in the decision to collaborate • Trade bodies and supply chain partners can play a useful role in helping firms overcome some of the informational barriers
  32. 32. Next Steps • Pilot Innovation Action with Firms  Working with firms as an ‘innovation agent’ to help firms overcome capability and trust barriers  Action Research • Develop a process model for innovation collaboration across industry sectors
  33. 33. Key Findings (Contd.) • Aim of Collaboration 6.1 6.1 9.1 3.0 3.0 3.0 3.0 3.0 11.1 14.8 33.3 18.5 25.9 14.8 7.4 7.4 20.0 40.0 33.3 20.0 20.0 26.7 13.3 40.0 9.6 13.7 19.9 10.1 12.3 10.0 5.9 9.8 0.0 20.0 40.0 60.0 80.0 100.0 120.0 Supply Chain Efficiency (%) Business Re-Engineering (%) New/Improved Products (%) Lean Production (%) Quality Mangement (%) Robotics/Automation (%) Data Connectivity (%) Process/Product Simulation (%) Aim of Collaboration in SME Foundries 0-9 Firmsize 10-49 Firmsize 50-249 Firmsize Total Industry SMEs • Aim of Collaboration 3.13 3.13 10.94 1.56 4.69 1.56 0.00 3.13 6.45 16.13 32.26 25.81 19.35 6.45 9.68 9.68 4.51 8.53 19.80 11.64 10.79 3.60 4.02 5.85 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 Supply Chain Efficiency (%) Business Re-Engineering (%) New/Improved Products (%) Lean Production (%) Quality Mangement (%) Robotics/Automation (%) Data Connectivity (%) Process/Product Simulation (%) Aim of Collaboration in Metal-Forming SMEs 0-9 Firmsize 10-249 Firmsize Total Industry SMEs Collaboration focused on product development and quality improvement
  34. 34. Learning from the Best SMEs Carol Stanfield, ERC
  35. 35. Contents • The question • Quantitative analysis • Qualitative research • Conclusions and implications
  36. 36. The question • Productivity growth is significantly more rapid among ‘frontier’ firms in the top decile of the productivity distribution. • Usually large firms • What about smaller firms ‘behind the frontier’?
  37. 37. Method • Analysis of Companies House data – detailed financial data and access to contact details • Analysis of value added and turnover per employee growth in twelve 4-digit sectors (six in manufacturing and six in services) • Analysis by key characteristics • Qualitative research in selected sectors
  38. 38. Are most productive SMEs also fastest growing? Manufacturing
  39. 39. Are most productive SMEs also fastest growing? Services
  40. 40. Are most productive SMEs also fastest growing? Manufacturing sub-sectors.
  41. 41. Are most productive SMEs also fastest growing? Services sub-sectors.
  42. 42. Age, size, investment, no. of subsidiaries? Value added per employ growth Value added per employ growth Turnover per employee growth Turnover per employee growth 3rd decile -6.632 -6.608 -3.224 -3.242 (7.775) (7.796) (5.201) (5.204) 4th decile 0.335 0.324 -3.046 -3.044 (7.736) (7.754) (5.179) (5.185) 5th decile 2.255 2.232 -7.545 -7.552 (7.810) (7.826) (5.192) (5.194) 6th decile -15.055** -15.070** -6.043 -6.296 (7.660) (7.673) (5.162) (5.168) 7th decile -6.955 -6.929 -2.173 -2.022 (7.632) (7.644) (5.178) (5.188) 8th decile -16.128** -16.149** -8.488 -8.234 (7.652) (7.662) (5.170) (5.181) 9th decile -7.843 -7.914 -1.384 -1.638 (7.663) (7.681) (5.140) (5.150) Business age (years) -0.027 -0.11 (0.110) (0.075) Employment -0.003 0.026 (0.035) (0.024) No. of subsidiaries -0.008 -0.502 (0.887) (0.636) Constant term 1.908 2.829 8.098 8.322 (10.804) (11.729) (6.974) (7.537) N 1195 1195 1337 1337 P 0.324 0.508 0.715 0.675 Bic 13486.278 13507.464 14215.968 14234.15
  43. 43. Quantitative conclusions • Little consistent relationship between firms’ position in deciles of the productivity distribution and subsequent productivity growth. This suggests that behind the frontier productivity growth is not characterised by the difference in productivity levels which characterises the frontier v non-frontier firm distinction. • Few significant observable differences between firms behind the frontier experiencing rapid productivity growth and those experiencing slower growth. Firm age, size, number of subsidiaries and investment seem only weakly related to productivity growth at least in the short-term.
  44. 44. Qualitative sample & characteristics • SMEs with high productivity and high growth • Sectors: Manufacture of instruments and appliances for measuring, testing and navigation Manufacture of metal structures and parts of structures Computer consultancy activities Temporary employment agency activities Legal services • Size: 20 – 200 (500) employees • Age: 11 – 120 years • Ownership – family to multinational
  45. 45. Complex interplay of factors in SMEs Leader Product and Market Customer focussed innovation Investment Operational management Valuing staff Government activity Reactive, responsive Strategic, tactical Data driven, routinised Performanc e culture Market conditions Autonomy, agility
  46. 46. Sector nuances Manufacturing Services: ComputerConsultancy Services: Legal Niche products/markets and exporters Broad market Niche/specialist Innovation essential to deliver customer demand Innovation gains an advantage when market growing Staff engagement more likely to be within ‘operational management processes’ – focus on expertise in product development Staff engagement more likely to be within ‘people management’ Company wide rewards Individual rewards
  47. 47. Conclusions and Implications Good practice can happen in smaller firms too • Continue to aid the dissemination of the message that good management and leadership matters • Selecting and developing leaders: what do Boards need to know? • Tapping into the passion of small business leaders • Emphasising good management practices in Government procurement criteria • Government can also ‘Learn from the Best’
  48. 48. Understanding local productivity Disparities: Size and Sector Neha Prashar
  49. 49. Background • We know that global frontier firms have seen significantly more rapid productivity growth when compared with non-frontier firms. This has remained robust since 2000s. • The same can be said about UK frontier firms, where the top 10% in the productivity distribution are ten times more productive than those in the bottom 10%. • This dispersion remains persistent and impacts the way current measures of productivity, both aggregate and firm-level, should be interpreted….with caution! • This combination of dispersion plus persistence would yield meaningless average (mean) productivity estimates using firm-level data.
  50. 50. Main Research Question • What explains productivity differences between the 38 local LEP areas in England? 1. Differently shaped productivity distributions? 2. Firm size distribution? 3. Sectoral composition? • We need to consider points across the distribution during comparative analysis over time and area. • Using productivity (turnover per job) to estimate quantiles (2.5% intervals) for each LEP to obtain a distribution.
  51. 51. Data • Using data from the ONS IDBR (BSD), we compare productivity at the: 25%; 50% and 75% points of the distribution for firms in each LEP area. • Whilst such an approach will generate a considerable volume of data, the gains from taking a more nuanced view will allow us to form a more accurate and robust picture of the extent of productivity differences between LEPs. • Firm vs local unit data at this level of spatial analysis will be a challenge.
  52. 52. Method (1) • Once 2.5% quantiles were estimated, LEP’s were then ordered by the 50th, 25th and 75th percentile to give an overview of high and low productivity LEP areas. • We look at the medians (this is the average median due to disclosure issues which meant that we couldn’t output the true median itself but rather +/- 5 observations averaged) for each year between 2013-2018. • We want to know whether LEPs next to each other (i.e., London and Thames Valley), once ordered, have significantly different distributions, as well as, LEPs on opposite ends of the productivity order (i.e., London and Cumbria).
  53. 53. Method (2) • A simple way to view this is by plotting the quantiles against productivity levels and do a visual inspection, however, statistical testing is better. • We use the two sample Kolmogorov-Smirnov (K-S) test which compares whether the empirical distribution function of the same variable from two datasets differ significantly. • This was the chosen test as there is no prior assumption on the distribution of the data and is non-parametric. • We do this analysis for LEP pairs for each year between 2013-2018 and results to not vary over time.
  54. 54. Quantiles Productivity(TurnoverperEmployee) 2.5% 5% 7.5% 10% 12.5% 15% 17.5% 20% 22.5% 25% 27.5% 30% 32.5% 35% 37.5% 40% 42.5% 45% 47.5% 50% 52.5% 55% 57.5% 60% 62.5% 65% 67.5% 70% 72.5% 75% 77.5% 80% 82.5% 85% 87.5% 90% 92.5% 95% 97.5% 0 50 100 150 200 250 300 350 400 450 500 550 600 650 Worcestershire LEP Cornwall LEP Buckinghamshire LEP London LEP This is the top two performing LEPs (Buckinghamshire and London) and the two bottom performing LEPs (Worcestershire and Cornwall) for 2018. Distributions look very similar, which is confirmed through the KS test. Figure 1: Quantile plot of productivity (2018)
  55. 55. Previously…. • As Figure 1 shows, we found no significant distributional differences between LEPs - the top performing LEPs (London, Thames Valley etc) had overall higher productivity medians than the low performing LEPs (Cornwall, Cumbria, North East etc) – on visual observation, there is a clear left-side skew in most LEPs moving away from a log-normal distribution. • What does this mean? – There are more higher productive firms in high preforming LEPs. – There is no overall difference in distribution but does this hold when looking at subset of firms (size and sector) – If there are differences, what conditions could be causing this? Competition, infrastructure, spillovers, local demand etc.
  56. 56. Let’s start looking at size • We look at the productivity distributions for micro (1-9 employees), small (10-49 employees), medium (50-249 employees) and large (250+ employees) firms in each LEP. • We find that larger firms exhibit different productivity distributions in high performing compared with low performing LEPs.
  57. 57. 0 20 40 60 80 100 120 140 160 180 WORC HUMB CUMB CORN NORE COVE GLIN SHEF TEES GBIR BLAC DERB YORK HEAR STOK LIVE NEWA GMAN LANC LEED LEIC THEM WEST DORS GLOU CHES GCAM SOLE SWIN SOUM OXFO SOUH COAS ENTE THAM HERT BUCK LOND AVERAGEMEDIANPRODUCTIVITY LEP Figure 2: Average Productivity Medians by Firm Size (LEP is in ascending order using 1-9 employees) 1-9 employees 10-49 employees 50-249 employees 250+ employees
  58. 58. 0 20 40 60 80 100 120 140 160 180 CORN SOUH YORK LIVE TEES DORS CHES HEAR NEWA GMAN LANC LEED WEST GLIN OXFO HERT SHEF DERB NORE COAS LOND GLOU WORC STOK GBIR CUMB BLAC LEIC HUMB ENTE GCAM SWIN SOLE COVE SOUM THAM THEM BUCK AVERAGEPRODUCTIVITYMEDIAN LEP Figure 3: Average Productivity Medians by Firm Size (LEPs are in ascending order using 250+ employees) 1-9 employees 10-49 employees 50-249 employees 250+ employees
  59. 59. What is this telling us? • Micro-firms (1-9 employees) have no statistical difference in productivity distribution between LEPs (Figure 2). • Medium and large-sized firms, however, appear to have a statistical difference when looking at the opposite ends of the LEP distribution - The Marches and Buckinghamshire vs Cornwall and South East for 250+ employee firms (Figure 3) • When looking at the probability density functions, there is clearly a left skew in the distribution in low preforming LEPs with a high peak. High performing LEPs have a lower peak and are more dispersed in productivity.
  60. 60. What about Sector? • We look at 1 digit SIC codes (2003 classification) – Figures 3 and 4 • Generally find there are no statistical differences between productivity distributions in most sectors except the Transport, storage and communication sector and financial intermediation sector in each LEP • Financial intermediation is interesting when looking at the median values….
  61. 61. 0 20 40 60 80 100 120 140 CORN CUMB DORS HEAR TEES HUMB NEWA GLIN WEST NORE DERB LANC SHEF YORK GLOU SOLE GMAN THEM WORC LEIC LIVE COAS CHES STOK BLAC COVE SWIN LEED GBIR SOUH ENTE GCAM LOND OXFO SOUM BUCK HERT THAM AVERAGEPRODUCTIVITYMEDIAN LEP Figure 3: Average Productivity Medians by Firm Size (LEPs are in ascending order using Wholesale and Retail sector) manufacturing construction wholesale and retail hotels and restaurants transport, storage and communication financial intermediation real estate other activities
  62. 62. 0 20 40 60 80 100 120 WORC GBIR HUMB BLAC COVE SOUM YORK SHEF STOK GLIN WEST LEIC SWIN LIVE GMAN DERB GCAM LEED CHES OXFO TEES HERT CORN SOLE GLOU NEWA SOUH LANC CUMB NORE HEAR THAM THEM DORS BUCK LOND ENTE COAS Figure 4: Average Productivity Medians by Firm Size (LEPs are in ascending order using Transport, Storage and Communication sector) transport, storage and communication financial intermediation Financial intermediation has the same median productivity in nearly all LEPs except London. This is also persistent across time (2013 onwards). The dispersion is higher in London.
  63. 63. What is all this telling us? • London and the home counties clearly have higher median productivity levels but are not statistically different from other LEPs overall. • However, there are statistically significant differences when looking at firms with 50+ employees where productivity is much more dispersed between the high and low preforming LEPs. • In the financial sector, there is statistical difference in distributions between London and low performing LEPs (Liverpool and Sheffield), where London has a higher level of dispersion and lower peak. • Literature (Syverson (2004a); Okubo, Tomiura (2011); Du and Vanino (2019)) suggests this could be down to agglomeration, competition and spillover effects – low productive firms who set up near large productive firms are able to survive by proximity to their customer and benefit from positive externalities. • Equally – in agglomerated areas such as London – higher local demand can fuel product differentiation allowing low productive and heterogenous firms to survive.
  64. 64. Policy Implications • Looking at productivity distributions is much more informative than looking at average productivity effects for local economic areas – reveals issues which permit a more nuanced policy response. • Firms’ productivity and potential is heavily influenced by the proximity to highly productive firms in the same market/area. • Alongside programmes to target an individual firm’s productivity, the current crop of Local Industrial Strategies (LIS) might also consider attracting and fostering highly productive firms in their geographical area, especially in larger firms. • This could change the productivity distribution and head towards a log- normal distribution where productivity is evenly dispersed.
  65. 65. Going Forward • The focus here has been on the 38 English LEPs – this could be extended to look at NUTS2 areas in Scotland, Wales and NI to examine if similar distributional differences occur. • This project follows on well to the next phase of project which looks at local competition and productivity growth • We will take a closer look at the competition and agglomeration effects to empirically assess whether this is driving the differences we have observed in the productivity distributions.
  66. 66. Thank you! Questions/Comments? Professor Mark Hart (mark.hart@aston.ac.uk) Dr Neha Prashar (n.prashar14@aston.ac.uk) The data used here is from the Jobs and Turnover version of the Longitudinal Business Structure Database which can be accessed through the Secure Lab. The use of these data does not imply the endorsement of the data owner or the UK Data Service at the UK Data Archive in relation to the interpretation or analysis of the data. This work uses research datasets which may not exactly reproduce National Statistics aggregates
  67. 67. What’s coming up from the Enterprise Research Centre ? Stephen Roper, ERC
  68. 68. Core projects – Jan-Aug 2020 1. How are SMEs responding to the climate emergency? RQ1: What factors shape firms’ environmental ambition? RQ2: What organisational and financial responses are SMEs making to the climate emergency? RQ3: What drives environmentally relevant innovation? Is this different from other types of innovative activity? 2. Competition, local growth and productivity RQ1: How does the strength of local competition vary across the UK? Geographical analysis by sector, location. RQ2 How does the intensity of competition and concentration influence the growth and productivity of local firms? RQ3: Are there spillover or displacement effects which arise from more intensive local competition? 3. Measuring digital diffusion RQ1: What metrics have prior studies used to capture the adoption and implementation of digital technologies? RQ2: What are the internal and external challenges and benefits of digital adoption and implementation for SMEs? RQ3: Developing measurement scales for ‘digital readiness’, ‘digital adoption level’ and ‘digital implementation’. 4. Local knowledge innovation, exporting and productivity RQ1: What local eco-system factors most strongly support innovation and exporting, growth and productivity? RQ2: How do business capabilities influence firms’ ability to draw strength from their local environment? RQ3: Are there spillover effects from eco-system strengths for firms in other local areas? Or are such effects purely ‘local’.
  69. 69. Commissioned projects On-going projects (selected) • Mental health and productivity in Midlands firms (ME) • Geographical Indications of Origin/PFNs and their benefits (ESRC) • Innovation in micro-businesses in Northern Ireland (DFE NI) • R&D in creative industries (DCMS) New projects for next quarter • NIPF fellowship with Warwick Manufacturing Group (ESRC) • SME support evaluation partners for JP Morgan Foundation (JPM) • Innovation readiness in foundation industries (ESRC)
  70. 70. SOTA Reviews on Women’s Enterprise (ERC/ISBE Gender and Enterprise Network) Aim: To ask insightful questions about women’s experiences of enterprise, and gender differences in entrepreneurship Themes: • Is Time Up for The Hero Male Entrepreneur? A Review of Enterprise Discourse and its Effects • Is Expanding Women’s Self-employment a Good Thing? A Review of Evidence • A Review of Assumptions Underpinning Women’s Enterprise Policy Initiatives • How Does Gender Shape Entrepreneurial Resources and Practices? A Review of Evidence • What Do We Know About Ethnic and Migrant Women Entrepreneurs? A Review of Evidence HOLD THE DATE – Seminar/workshop event on 12th March – Where next for Women’s Enterprise Policy?
  71. 71. Flying kites… (projects seeking funders!) • Business crime – its dynamic effects on entrepreneurship and ambition • Predicting local productivity growth using remote sensing satellite data • Tracking innovations beyond the grant – what’s the long-term story?
  72. 72. Thanks … it must be time for lunch!
  73. 73. Lunch
  74. 74. Building Better Business Resilience Final Report Launch
  75. 75. Welcome and introduction  Lee Hopley, ERC  Delphine Poschmann, Global Philanthropy, J.P. Morgan
  76. 76. Maria Wishart, ERC Building resilience in under- represented entrepreneurs: A European comparative study
  77. 77. Background • SMEs account for 99% of businesses, 68% of jobs and 58% of value- added in the European Union (EU, 2017) but how they experience, plan for and respond to adversity is not well understood • Female and ethnic minority groups are under-represented in entrepreneurial activities, and little is known about their resilience • Research aims: – Explore the range of resilience challenges faced by SMEs, with a focus on female and ethnic minority-led businesses – Identify the characteristics and strategies that foster resilience, survival and growth in these firms – Develop practical tools to help SMEs strengthen their resilience to shocks in challenging times
  78. 78. The study • Five city, two year project • Quantitative survey of 2,975 firms – 3 to 99 employees, variety of sectors, in low and middle income boroughs – Computer Aided Telephone Interviews • Qualitative in-depth interviews • Four key phases: 1. Review & synthesis of previous research: Feb-July 2018 2. London fieldwork: Oct-Dec 2018 3. Paris, Frankfurt, Madrid & Milan fieldwork: Jan-May 2019 4. Data integration, comparative analysis, toolkit development & dissemination of findings June-Dec 2019
  79. 79. Survey sample Low income borough Middle income borough London Total 301 300 Female-led 146 150 Ethnic-led 92 91 Paris Total 300 300 Female-led 114 147 Ethnic-led 99 75 Frankfurt Total 268 259 Female-led 129 103 Ethnic-led 73 65 Milan Total 300 300 Female-led 156 139 Ethnic-led 79 67 Madrid Total 330 317 Female-led 152 156 Ethnic-led 104 79
  80. 80. Profile of firms surveyed Size distribution of firms by city
  81. 81. Profile of firms surveyed Sectoral composition of city respondent firms
  82. 82. Survey headlines • Ethnic-led firms and female-led firms tend to have different goals • Ethnic leaders are less likely to seek external advice for their businesses • Female and ethnic leaders tend to consult different kinds of external advice than their counterparts • Overall, ethnic-led firms more likely to experience crisis, but considerable variation by city • Variation by city and firm type in kinds of crisis anticipated and experienced
  83. 83. Survey findings • Running their businesses: – Objectives – Likelihood of firms to seek external advice – Who they consult • Experiencing adversity: – Overall likelihood of firms to experience a crisis – City-level analysis of crises by ethnicity and gender of leader: • Likelihood of crisis • Top perceived threats • Top causes of crisis – Responses to crisis by city
  84. 84. Objectives Firm objectives by city & ethnicity 65% 55% 76% 59% 78% 60% 66% 50% 57% 47% 67% 47% 77% 57% 74% 44% 54% 50% 51% 42% 71% 65% 90% 85% 91% 77% 77% 74% 71% 72% 41% 47% 70% 53% 45% 67% 56% 35% 36% 17% Migrant Non migrant Migrant Non migrant Migrant Non migrant Migrant Non migrant Migrant Non migrant London Madrid Milan Frankfurt Paris To increase the social and environmental benefits of the business To make a contribution to the local community To keep my business similar to how it operates now To build a national and/or international business
  85. 85. Objectives Firm objectives by city & gender 65% 52% 63% 63% 66% 63% 57% 53% 52% 48% 62% 44% 65% 60% 54% 48% 55% 47% 47% 43% 70% 63% 88% 86% 81% 80% 79% 72% 72% 71% 43% 46% 58% 57% 59% 64% 34% 46% 23% 22% Female Male Female Male Female Male Female Male Female Male London Madrid Milan Frankfurt Paris To increase the social and environmental benefits of the business To make a contribution to the local community To keep my business similar to how it operates now To build a national and/or international business
  86. 86. Seeking external advice Firms seeking external advice in past 12 months by city & ethnicity 33% 38% 12% 32% 49% 37% 37% 50% 57% 53% Paris Frankfurt Milan Madrid London Non-Ethnic Ethnic
  87. 87. Seeking external advice Firms seeking external advice in past 12 months by city & gender 39% 35% 40% 53% 51% 34% 38% 41% 49% 52% Paris Frankfurt Milan Madrid London Male Female
  88. 88. Who they consult – all cities Sources of advice by ethnicity 21% 22% 33% 34% 42% 54% 69% 13% 18% 31% 37% 30% 62% 66% LA Government Network Mentor Family Legal Accountant Non ethnic Ethnic
  89. 89. Who they consult – all cities Sources of advice by gender 15% 20% 32% 33% 38% 59% 64% 14% 17% 31% 32% 36% 62% 68% LA Government Network Family Mentor Legal Accountant Male Female
  90. 90. Experiencing adversity Firms that had experienced a crisis in the preceding 5 years by ethnicity and city 29% 40% 4% 27% 48% 32% 27% 29% 41% 33% Paris Frankfurt Milan Madrid London Ethnic-led Non ethnic-led
  91. 91. Experiencing adversity Firms that had experienced a crisis in the preceding 5 years by gender and city 33% 30% 21% 38% 37% 29% 30% 26% 36% 38% Paris Frankfurt Milan Madrid London Female-led Male-led
  92. 92. Experiencing adversity 32% 32% 23% 34% 39% 30% 29% 23% 40% 36% Paris Frankfurt Milan Madrid London Low income Middle income Firms that had experienced a crisis in the preceding 5 years by borough and city
  93. 93. 37% 18% 10% 10% 9% Customers Cashflow Strike Costs Regulations Cause of crisis female-led 31% 19% 10% 8% 8% Customers Cashflow Strike Premises Staff Cause of crisis ethnic-led 51% 50% 49% 49% 49% Staff Illness Inc comp Cashflow Costs Perceived threats ethnic-led 57% 53% 51% 51% 49% Staff Illness Inc comp Regulations Cashflow Perceived threats female-led Paris 33% 29% Female-led Male-led Experience of adversity by gender 29% 32% Ethnic-led Non ethnic-led Experience of adversity by ethnicity
  94. 94. Frankfurt 30% 30% Female-led Male-led Experience of adversity by gender 40% 27% Ethnic-led Non ethnic-led Experience of adversity by ethnicity 41% 20% 12% 10% 9% Customers Staff Illness Cashflow Premises Causes of crisis female-led 37% 17% 15% 9% 9% Customers Staff Cashflow Materials Other Causes of crisis ethnic-led 51% 38% 36% 36% 30% Staff Regulations Costs Customer Cyber Perceived threats ethnic-led 48% 42% 39% 38% 32% Staff Regulations Illness Cyber Customers Perceived threats female-led
  95. 95. Milan 4% 29% Ethnic-led Non ethnic-led Experience of adversity by ethnicity 33% 18% 10% 8% 5% Customers Strike New comp Cashflow Weather Causes of crisis female-led 33% 17% 17% 17% 17% Strike Regulations New comp Inc comp Premises Causes of crisis ethnic-led 51% 51% 46% 46% 45% Costs Customers Materials Inc comp Staff Perceived threats female-led 64% 62% 62% 62% 59% Costs New comp Materials Technical Inc comp Perceived threats ethnic-led 21% 26% Female-led Male-led Experience of adversity by gender
  96. 96. Madrid 27% 41% Ethnic-led Non ethnic-led Experience of adversity by ethnicity 36% 19% 11% 9% 7% Customers Strike Staff Cashflow Inc comp Causes of adversity female-led 33% 17% 14% 7% 7% Customers Strike Other Inc comp Cashflow Causes of adversity ethnic-led 65% 63% 63% 62% 61% Illness Costs Regulations Cashflow Inc comp Perceived threats female-led 75% 74% 65% 62% 62% Illness Cashflow Regulations Inc comp Costs Perceived threats ethnic-led 38% 36% Female-led Male-led Experience of adversity by gender
  97. 97. London 37% 38% Female-led Male-led Experience of adversity by gender 48% 33% Ethnic-led Non ethnic-led Experience of adversity by ethnicity 30% 18% 16% 15% 14% Costs Staff Strike Cashflow Premises Causes of crisis female 25% 15% 15% 14% 12% Costs Customers Cashflow Inc comp Strike Causes of crisis ethnic-led 57% 56% 54% 52% 47% Costs Staff Regulations Cyber Cashflow Perceived threats ethnic-led 60% 58% 50% 49% 48% Staff Costs Illness Regulations Cyber Perceived threats female-led
  98. 98. Response to crisis by city 53% 61% 41% 53% 55% 39% 72% 30% 51% 73% 32% 30% 17% 21% 58% Paris Frankfurt Milan Madrid London Middle income boroughs Financial Developed plan Informal advice 40% 52% 46% 55% 43% 50% 67% 38% 36% 75% 40% 54% 4% 21% 70% Paris Frankfurt Milan Madrid London Low income boroughs Financial Developed plan Informal advice
  99. 99. Experiencing adversity Sector differences – all cities • The sample includes firms from the following sectors: primary, manufacturing, construction, trade transport & hospitality, information and communication, business services and other services sectors. • Overall, firms in manufacturing, construction and business services are significantly more likely than those in other sectors to have had a crisis in the preceding five years. • Overall, firms in manufacturing and trade transport & hospitality are significantly less likely than those in other sectors to have crisis plans in place.
  100. 100. Summary • Complex picture • Significant variation in the ways that firms anticipate and experience adversity by city and firm type, implying that initiatives to build resilience should take account of local environmental, regulatory and cultural factors • Differences between perceived threats and actual causes of crisis suggests that firm leaders lack skills and resources to distinguish and prioritise threats • Ethnic-led firms take less external advice and, along with female-led firms, consult more informal sources: need to understand why, and consider this when communicating & delivering initiatives
  101. 101. Implications Interventions to help small firms to anticipate threats and plan for future crisis may help them to improve their resilience Why? Many firms are not identifying the most potent future risks for their firms Who? Particular focus on female and ethnic-led firms, who differ in their approach to managing their businesses, in their experiences of adversity and in their ability to identify key risks. How? Develop accessible tools which are relevant to these firms. City variations suggest that localised factors may be impacting, and findings indicate key differences between mainstream and under represented leaders, so tools must be flexible.
  102. 102. Implications • Interventions should take account of the complex picture that this research has identified, and so any tools must: – acknowledge that some challenges are common to all SMEs, but that some are unique to under represented entrepreneurs, and offer tailored solutions – accommodate firm and location-level variations – be simple to use, time efficient and relevant – be delivered in partnership with support organisations that have links to under represented groups
  103. 103. Lee Hopley, ERC Building resilience in under- represented entrepreneurs Toolkit overview
  104. 104. Background • Under-represented entrepreneurs’ firms experience lower turnover and higher failure rates • Differences between perceived threats and actual causes of crisis • Diversity in approach towards, and experience of, adversity, with borough and city-level variation also evident • Some challenges that are common to all SMEs, some that are unique to their firm type, and usually a combination • Available resilience resources tend to focus on a single issue (e.g., weather events) and to be targeted at large firms • Need for flexible interventions to help build resilience, that can accommodate firm and location-level variations
  105. 105. Resilience toolkit - principles • Developed specifically for small firms, focus on under-represented groups • Three-phase toolkit, elements can work together or as stand-alone tools • Core elements with option for additional resources so they can be tailored for different markets/user groups • Evidence-based: All have supporting framework based on – Academic and grey literature – Findings from Building entrepreneur and small business resilience project fieldwork • Adopts accessible language • Straightforward to use, time efficient, delivers immediate responses
  106. 106. Resilience toolkit
  107. 107. • Encourages small firms to consider their resilience status. • Sees SME resilience as influenced by individual leader factors and firm- level factors, and evaluates both. • Individual resilience seen as a product of experience, skills set and networks. • Business resilience seen as a product of planning activities, people, and values. • Simple self-completion grid for each element. 1. Resilience health check tool
  108. 108. Health check self-completion grids
  109. 109. Output of resilience health check in a combined radar chart 72% 77% 69% 40%84% 57% Experience Skills & Attributes Networks Planning & Resources People Values My Score
  110. 110. • Designed to encourage SMEs to reflect on possible future risks. • Asks leaders to consider two elements of risk: the likelihood of it happening and the severity of the impact it would have. This is an approach rooted in academic literature. • Divides risk factors into those internal and external to a firm. • Internal: those which the firm can influence – financial, people & plant/technical. • External: those which the firm has less control over – supply chain, environmental and political/legal. 2. Risk analysis tool
  111. 111. Risk analysis self-completion grids
  112. 112. Output of risk analysis tool in a risk matrix with colour-coded scores: Likelihoodofhappening High 3 6 9 Medium 2 4 6 Low 1 2 3 Low Medium High Seriousness
  113. 113. MY SCORE 1 2 3 1 2 3 S X L Low Medium High Low Medium High Total loss or failure of one supplier 1 2 3 1 2 3 1 Increase in cost of materials from one supplier 1 2 3 1 2 3 3 Disruption of supply of materials 1 2 3 1 2 3 4 Reduction in quality of materials 1 2 3 1 2 3 6 Short or inaccurate delivery of supplies 1 2 3 1 2 3 6 Loss of one customer 1 2 3 1 2 3 3 Loss of several customers 1 2 3 1 2 3 9 Failure of a customer to pay 1 2 3 1 2 3 2 Appearance of a new competitor 1 2 3 1 2 3 6 Competitor undercutting on price 1 2 3 1 2 3 4 Appearance of a substitute product 1 2 3 1 2 3 9 Loss of a distributor 1 2 3 1 2 3 3 SERIOUSNESS (S) LIKELIHOOD (L) Example of output from risk analysis tool
  114. 114. • Aims to allow small firms to continue to function during and after adversity. • Template - based tool designed to encourage SMEs to embrace a contingency planning mind-set. • Has two foci: the creation of an essential data file and the establishment of clear plans for specific critical risks. • Data file offers quick access to essential information without recourse to firm’s usual systems. • Critical risk plan identifies actions, assigns ownership and allocates resources necessary in the event of an identified potential crisis. 3. Crisis planning tool
  115. 115. Output of crisis planning tool data file templates
  116. 116. Critical risk plan template
  117. 117. Resilience toolkit - testing • Initial testing with London-based small business leaders from under represented groups across a range of sectors found: – User-friendly: quick and straightforward to use – Wide range of scores from healthcheck tool reflecting diverse leader and firm characteristics – Risk analysis tool delivered broad spread of scores, highlighting different key threats for different firms – Planning templates well-received
  118. 118. Resilience toolkit - summary • Format and content of three main tools, and evidence base for each, now established • Planning for ‘additional resources’ elements in progress • Testing of toolkit with entrepreneurs from under represented groups started in July 2019 and ongoing • Launch due early 2020
  119. 119. What can we do to build more resilient businesses? Panel discussion  Mark Hart, ERC  Harriet Walker Director, Business Emergency Resilience (Business in the Community  Sonali Parekh Head of Policy, Federation of Small Business (TBC),  Olu Orugboh CEO and Founder Synergy Solutions
  120. 120. Final remarks and event close Lee Hopley, ERC
  121. 121. Thank you For further details please visit : www.enterpriseresearch.ac.uk @ERC_Uk ERC Funded by

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