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ERC-BEIS Longitudinal Small Business Survey Dissemination Event Slides

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ERC-DBT Longitudinal Small
Business Survey
Dissemination Event
16 November 2023, Shard
November 2023
Longitudinal Small
Business Survey 2022 –
Key findings
Jayshree Varsani
Longitudinal Small Business Survey (LSBS)
Published annually (in August and September):
https://www.gov.uk/government/collections/small-business-survey-reports
• Telephone survey of c.10k UK businesses, with fewer than 250 employees
(registered and unregistered businesses)
• Majority of questions are repeated year-to-year for longitudinal analysis – nearly
700 businesses have taken part in all 8 waves of the survey
3
LSBS: Content
Survey content:
• SECTION A: ABOUT THE BUSINESS
• SECTION B: EMPLOYMENT
• SECTION C: EXPORTS
• SECTION E: ENERGY USAGE
• SECTION F: TAXATION
• SECTION G: OBSTACLES
• SECTION H: FINANCE
• SECTION J: INNOVATION
• SECTION K: BUSINESS SUPPORT
• SECTION M: PAYMENT
• SECTION N: TRAINING
• SECTION P: TURNOVER
• SECTION Q: ENVIRONMENTAL ACTION
• SECTION R: FUTURE INTENTIONS
4
Longitudinal Small Business Survey (LSBS 2022): Key Analysismall
Business Survey (LSBS 2022): Key Analysis
5
Change in employment compared to 12 months previously (2015 to 2022 ; based
on all SME employers trading for at least one year)
0%
20%
40%
60%
80%
100%
2015 2016 2017 2018 2019 2020 2021 2022
Increase in employment No change Decrease in employment
Longitudinal Small Business Survey (LSBS 2022): Key Analysismall
Business Survey (LSBS 2022): Key Analysis
6
Current turnover compared to 12 months previously, by year (based on SME
employers trading for at least one year)
0%
10%
20%
30%
40%
50%
60%
2015 2016 2017 2018 2019 2020 2021 2022
Increase in turnover No change Decrease in turnover

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ERC-BEIS Longitudinal Small Business Survey Dissemination Event Slides

  • 1. ERC-DBT Longitudinal Small Business Survey Dissemination Event 16 November 2023, Shard
  • 2. November 2023 Longitudinal Small Business Survey 2022 – Key findings Jayshree Varsani
  • 3. Longitudinal Small Business Survey (LSBS) Published annually (in August and September): https://www.gov.uk/government/collections/small-business-survey-reports • Telephone survey of c.10k UK businesses, with fewer than 250 employees (registered and unregistered businesses) • Majority of questions are repeated year-to-year for longitudinal analysis – nearly 700 businesses have taken part in all 8 waves of the survey 3
  • 4. LSBS: Content Survey content: • SECTION A: ABOUT THE BUSINESS • SECTION B: EMPLOYMENT • SECTION C: EXPORTS • SECTION E: ENERGY USAGE • SECTION F: TAXATION • SECTION G: OBSTACLES • SECTION H: FINANCE • SECTION J: INNOVATION • SECTION K: BUSINESS SUPPORT • SECTION M: PAYMENT • SECTION N: TRAINING • SECTION P: TURNOVER • SECTION Q: ENVIRONMENTAL ACTION • SECTION R: FUTURE INTENTIONS 4
  • 5. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 5 Change in employment compared to 12 months previously (2015 to 2022 ; based on all SME employers trading for at least one year) 0% 20% 40% 60% 80% 100% 2015 2016 2017 2018 2019 2020 2021 2022 Increase in employment No change Decrease in employment
  • 6. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 6 Current turnover compared to 12 months previously, by year (based on SME employers trading for at least one year) 0% 10% 20% 30% 40% 50% 60% 2015 2016 2017 2018 2019 2020 2021 2022 Increase in turnover No change Decrease in turnover
  • 7. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 7 Expectations of turnover in 12 months’ time, by year 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 2015 2016 2017 2018 2019 2020 2021 2022 Increase in turnover No change Decrease in turnover
  • 8. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 8 Generated a profit or surplus in the last financial year, by year 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 2015 2016 2017 2018 2019 2020 2021 2022
  • 9. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 9 Innovation - Percentage of SME employers which have introduced new or significantly improved goods, services or processes in the last three years - by size of business 0% 5% 10% 15% 20% 25% 30% 35% All SMEs Micro businesses Small businesses Medium-sized businesses Goods Services Processes
  • 10. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 10 Access to finance Forms of external finance currently used by SME employers - percentages 0% 10% 20% 30% 40% 50% 60% 70% 80% Other finance Equity finance P2P loan Factoring/invoice discounting Loan from family etc. Government/LA grant/scheme Commercial mortgage Loan from bank etc. Loan from business etc. Government/LA grant/schemes directly related to… Loan from bank etc. directly related to coronavirus* Leasing/hire purchase Bank overdraft Credit cards Any finance 2022 2021 2020 2019 2018
  • 11. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 11 Major obstacles to the success of the business Percentages of SME employers citing each major obstacle to the success of the business (cohort B only) 0% 10% 20% 30% 40% 50% 60% 70% 80% Workplace pensions Obtaining finance Availability/cost of premises National living wage Late payment EU exit Other issues relating to costs Coronavirus (COVID-19) pandemic Regulations/red tape Staff recruitment and skills Taxation, VAT, PAYE etc. Competition in the market Level of energy prices 2022 2021 2020 2019 2018
  • 12. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 12 Use of business support Percentage of SME employers that sought external information or advice in the last year, by employment size and year 0% 10% 20% 30% 40% 50% 60% 2015 2016 2017 2018 2019 2020 2021 2022 Total micro (1-9 employees) small (10-49 employees) medium (50-249 employees)
  • 13. Longitudinal Small Business Survey (LSBS 2022): Key Analysismall Business Survey (LSBS 2022): Key Analysis 13 Exporting Whether sold goods or services, or licensed products outside the UK in the last 12 months, by year 0% 5% 10% 15% 20% 25% 2015 2016 2017 2018 2019 2020 2021 2022 Exported goods or services Exported goods Exported services
  • 14. Managing Minds at Work: Next steps • Next Steps: • Explore how to best implemented in organisations to ensure integration with other strategies and policies to maximise effectiveness. • Examine access issues for sectors, employers and employees who we haven’t engaged. • Examine how to engage line managers that have not engaged.
  • 15. Longitudinal Small Business Survey: panel report – UK, 2019-2022 - Overview Stephen Roper
  • 16. Background • The panel report covers firms which responded to four consecutive waves of the LSBS – a balanced panel covering 1437 firms. (A slightly smaller number than in previous panel reports. • The four years covered by this year’s report were dramatically different being before (2019), during (2020 and 2021) and after (2022) the most disruptive periods of the COVID-19 pandemic • Responses are weighted to be representative of the base year of the panel – here 2019. Panel firms have stronger growth ambition and are more likely to be exporting, innovating and using advice and finance than non-panel firms. Less likely to be providing training though …
  • 17. Innovation • Businesses are asked a series of questions about their innovation activities including their adoption of product or service innovations. This question is aligned with the UK Innovation Survey and the OECD Oslo Manual • The proportion of firms reporting either product or service innovation was 33.1% in 2019, fell to 28.3% in 2020 before recovering to reach 29.7% in 2021 and 29.5% in 2022 Innovation rates fell sharply and then have recovered a little – reflects other survey results
  • 18. Importing and exporting • Exporting • The weighted data shows that in 2019, 22.8% of firms in the panel reported exporting either goods or services. • This proportion fell marginally to 20.9% in 2020, 19.8% in 2021 and 18.9% in 2022. • Importing • More than three quarters (75.4%) of business in the panel had no imports from either EU or non- EU countries in 2019. • Out of the 11.6% of businesses in the panel which reported imports exclusively from EU countries in 2019, approximately a third of them (35.6%) continued to import goods and services only from the EU in 2022, while 15.6% of them had diversified and sought goods and/or services from both within and outside the EU by 2022. A proportion of firms stepped out of importing from the EU – are SMEs generally dis-engaging from international markets?
  • 19. External finance • Excluding COVID related finance, 12.4% of businesses in the longitudinal panel acquired external finance in 2019, falling to 9.8% in 2020 and 7.5% in 2021, before recovering somewhat to 10.4% in 2022 • Risk appetite has declined somewhat Risk of rejection is being replaced by uncertainty as a reason for non-application for external finance
  • 20. Growth related business behaviours 2022 data looks a bit more like that of 2019,. A return to more ‘normal’ trading conditions?
  • 21. Behaviours and outcomes Training and innovation provide consistent returns, less consistent evidence for other types of activities…
  • 22. Sales growth objective over the next three years Growth objectives remained rather similar throughout the pandemic and beyond…
  • 23. Anticipated and actual turnover growth And… the profile of achieved growth ambition has been improving since 2021….
  • 24. Final remarks • Post-pandemic the panel data report proved very interesting – 2022 data suggests a return to business as usual • Some more detailed industry and regional contrasts proved interesting but samples here are relatively small (so care needed) • For the future - are there things which we should cover in the panel report? Other issues or areas of the questionnaire?
  • 25. Mapping Schumpeterian Outcomes in the UK Small Business Population over Time – The Effect of Social and Environmental Orientation on Innovation, Exporting & Growth ERC-DBT LSBS Dissemination Event 16 November 2023. Ines Alvarez-Boulton, Anna Rebmann, Ute Stephan, King’s Business School, King’s College London. Saul Estrin, London School of Economics and Political Science.
  • 26. Why ? • Small businesses → backbone of the UK economy • Schumpeterian entrepreneurship: innovation, exporting, and job creation (employment growth). • A growing number of small businesses are socially orientated
  • 27. Average distribution of business goals in UK small businesses in 2017 17% 35% 34% 14% 6% 5% 25% 65% 0% 20% 40% 60% 80% 100% Not relevant Low importance Medium importance High importance Social goals Financial goals
  • 28. Average distribution of business goals grouped by importance rating 30% 56% 5% 9% 0% 20% 40% 60% 80% 100% Low importance to both goals High importance to financial goals only High importance to social goals only High importance to both goals
  • 29. 20% of socially orientated small businesses located in most deprived decile
  • 30. 55% 61% 88% 76% 0% 20% 40% 60% 80% 100% Low importance to both goals High importance to financial goals only High importance to social goals only High importance to both goals Average proportion of businesses facing COVID as an obstacle by business goals
  • 31. Innovation and radical innovation • Overall decrease in innovation and radical innovation from 2017 to 2021. • Probably due to period of exogenous economic shocks and economic slowdown in the UK. • Both social and financial goals benefit innovation, effect increases across time. • Jointly high levels of social and financial goals are beneficial for innovation. • Socially orientated small businesses are not more likely to introduce radical innovation.
  • 32. -2 -0.5 0.5 1.5 0 1 2 1.5 1 0.5 0 -0.5 -1.5 -2 X ( Social goals) Z (Likelihood of innovation) Y (Financial goals) Alignment effect of goals on innovation 2021
  • 33. Innovation and radical innovation in most deprived decile • Location → relative deprivation does not impact business’ social and commercial orientation on innovation. • But regional disparities matter for radical innovation. 9
  • 34. Innovation: impact of COVID pandemic • The positive effect of social and financial goals on innovation only exists for businesses not affected by COVID. 34
  • 35. Radical innovation: impact of COVID pandemic • We see an increased likelihood of radical innovation for socially orientated businesses not affected by COVID. • But a decrease in the likelihood of radical innovation for businesses negatively impacted by COVID. 35
  • 36. Exporting • The likelihood a firm export also shows a slight downward trend in 2021 vs. in 2017 and 2019. • Socially orientated UK small businesses have a lower likelihood of exporting over time. • But business’ commercial orientation increases the likelihood of exporting over time. • Jointly high levels of social and financial goals are detrimental to exporting. • Exporting is highest for businesses with strong financial but low social goals. • We do not find evidence that small businesses located in deprived areas have either a higher or lower likelihood of exporting. 36
  • 37. -2 -0.5 0.5 1.5 0 1 2 1.5 1 0.5 0 -0.5 -1.5 -2 X (Social goals) Z (Likelihood of exporting) Y (Financial goals) Misalignment effect of goals on exporting 2021 37
  • 38. Exporting impact of COVID pandemic • COVID reduced the likelihood of exporting for socially orientated businesses. • For businesses that report not to have been negatively affected by COVID, social goals appear to benefit exporting. 38
  • 39. Employment growth • On average UK small businesses grew by 0.96% across five years (2017 – 2021). • This is a very low growth rate considering a five-year span. • There is evidence that socially orientated businesses help create employment growth, highest employment growth in the last five years, i.e., 46%. • If employment growth is negative, socially orientated businesses shrink less (i.e. are less likely to lay off employees) but also grow more slowly than commercially orientated businesses. 39
  • 40. Average proportion of employment growth and shrinkage in the last five years (2017-2021) by business goals in UK small businesses 30% 44% 26% 35% 27% 38% 39% 35% 26% 34% 20% 46% 0% 20% 40% 60% 80% 100% Shrinkage Zero growth Growth Low importance to both goals High importance to financial goals only High importance to social goals only High importance to both goals 40
  • 41. Conditional change graph by social and financial goals across years 41
  • 42. Policy recommendations UK small businesses innovated less, exported less, and stagnated in terms of employment in this period. The social orientation of UK small businesses on its own and in concert with their commercial orientation strengthens innovation. Important for increasing economic inclusivity. Resource-support for Socially oriented businesses in deprived areas may yield high dividends in terms of new and socially inclusive offerings. Results highlight the need for export support policies specifically targeted at socially orientated businesses. A policy mix supporting both growth in existing firms and new entrants may help lower the shrinkage rates in existing businesses. 4 2
  • 44. SME PERFORMANCE IN CORE AND PERIPHERAL UK REGIONS: EXPLORING THE ROLE OF INNOVATION AND FIRM NETWORKS DURING TIMES OF FINANCIAL DISTRESS* George Saridakis University of Kent, Kent Business School (co-authored with Yazid Abubakar Abdullahi, Bochra Idris, Tapas Mishra and Mamata Parhi) The full paper is available at the following link: https://www.enterpriseresearch.ac.uk/publications/sme-performance-in-core-and-peripheral-uk- regions-exploring-the-role-of-innovation-and-firm-networks-during-times-of-financial-distress/ *This paper sets out preliminary analysis and discussion of data and findings as part of the funded project awarded earlier this year by the ERC. The paper is still ongoing, and comments and suggestions are welcome.
  • 45. What is the purpose of and the approach to this study? • Purpose: The global financial crisis triggered by the Covid-19 pandemic sparked the closure of many small and medium sized enterprises (SMEs) across the globe. Studies (e.g., Brown and Cowling, 2021) has found that Covid- 19 had an uneven impact on firms across different UK regions. Given that innovation and networking can be used as strategies to improve firm performance, this study examines their (i) direct effects, and (ii) interaction effects with the Covid-19 recession, on the performance of SMEs in the peripheral and core regions of the UK. • Design/methodology/approach: We carried out panel data analysis on firm-level data from the ‘UK Longitudinal Small Business Survey 2015-2021’, collected by the Department for Business, Energy, and Industrial Strategy (BEIS). The survey provides rich information on firm characteristics up to and including the Covid-19 pandemic period.
  • 46. What is the contribution of this study to the existing work? • Contribution: The paper contributes to the theoretical and empirical literature on pandemic-driven/financial crisis and the resilience of SMEs. We generate our findings by empirically testing the direct link between innovation, networking, financial obstacles, and several other variables, and SME performance, and by examining the potential interaction effects of the key explanatory variables with a Covid-19 recession dummy.
  • 47. What do theories suggest in times of crises? • There are two principal theoretical perspectives about the effects of financial crisis on enterprises: the ‘resilience’ and ‘vulnerability' views (e.g., Kitching et al., 2009; Yaya et al., 2022). • The vulnerability view sees firms as being highly vulnerable to external shocks, which are considered to have significant negative effects on their performance. Shocks result in declining profits, increasing the probability of exit for financially constrained firms (Smallbone et al., 1997). • Resilience adherents view enterprises as having the resilience to survive financial crisis because they are able to diversify their product and service offerings through innovations, and they can also build resilience through formal and informal networks (e.g., Love and Roper, 1999; Florin et al., 2003; Idris and Saridakis, 2018; Yaya et al., 2022). Periods of economic shock can therefore create opportunities for firms (Davidsson and Gordon, 2016).
  • 48. What are the key questions to be answered? • A number of questions remain unanswered: • Does innovation weaken the negative effect of Covid-19? This is important because innovation, along with other potential strategies such as networking, can be a way of keeping firms competitive and minimizing their probability of exiting the market during times of uncertainty (Yaya et al., 2022). • Also, how did different regions withstand the effects of Covid-19 recession? Again, this is important because innovation and networking across regions can differ and it is important to capture regional effects by controlling for these variables in the specification (see Acs, 2002; Abubakar and Mitra, 2017). In Figure 1, we outline the proposed theoretical framework of this paper, which we now discuss by reference to the five identified effect paths.
  • 49. Conceptual framework Covid-19 recession SME Performance SME Innovation SME Networking Path A Path B Path C Path E Path D Control Variables (e.g., exporting, firm size, firm age) Regional dummies
  • 50. The expected effects of Covid-19, innovation and networking on firm performance • Covid-19 (-): Crises such as Covid-19 can impose several financial constraints on firms, including cash flow interruption and a lack of access to capital (Runyan, 2006). Economic recessions can also negatively affect enterprises because of the greater instability and economic uncertainty in the business environment, which can threaten the survival of firms (Herbane, 2010). • Innovation (+): SMEs often employ innovation as a tool for creating new demand and generating revenue and temporary monopolistic profits, which can boost their resilience and especially their chances of surviving a recession (Mangani and Tarrini, 2017;Yaya et al., 2022). • Networking (+): Through networking, SMEs can gain access to resources, suppliers, customers, competitors, and collaborate in R&D activities, which can in turn enhance their competitive advantages and the likelihood of their survival during uncertainty (e.g., Mole, 2016; Idris and Saridakis, 2018; Jibril et al., 2022).
  • 51. Reducing the negative effect of Covid-19 through innovation and networking • The interaction term between Covid-19 and innovation (+): Schumpeter sees external shocks as periods of ‘creative destruction’, which are times when old industries, technologies, and products go into decline, and new industries, technologies, and products emerge (Schumpeter, 1934). Generally, economic crisis tend to push SMEs to look for new innovations as a means of survival (e.g., Yaya et al., 2022; Kabir and Abubakar, 2022). Previous research considers that innovation is a critical driver of firms’ success through a recession (e.g., Ebersberger and Kuckertz, 2021). • The interaction term between Covid-19 with networking (+): Small firm may push itself to seek advice when it is facing significant challenges for which its own experience, expertise, and internal resources might not be beneficial, efficient, or effective (Johnson et al., 2007). Through networking opportunities, firms can gain access to ‘valuable and specialized knowledge’ which can complement their lack of resources (Adler and Kwon, 2002; Parida et al., 2010: 1).
  • 52. Hypotheses • H1: The Covid-19 recession dummy is negatively associated with SME performance. • H2a: Innovation is positively associated with SME performance. • H2b: The interaction effect of innovation with the Covid-19 recession dummy will be positive and strongly associated with SMEs’ performance. • H3a: Network advice is positively associated with SME performance. • H3b: The interaction effects of network advice with the Covid-19 recession dummy will be positive and strongly related to SME performance.
  • 53. Data • We use data from the ‘UK Longitudinal Small Business Survey 2015-2021’, produced by the UK’s Department for Business, Energy, and Industrial Strategy (BEIS, 2022a). There are seven years in the longitudinal data, and it covers SMEs located across four regions in the UK. • The questionnaire requests considerable information regarding the operations of SMEs (see BEIS, 2022b technical report for more information). For example, it surveys firms on their turnover, number of employees, the constraints they face, trading activities, innovation activities, the effect of Covid-19 on the business, their networking activities (in the relevant years), and their future intention. More specifically, the survey asks owner-mangers of small firms if they are facing any obstacles to obtaining finance, if they have introduced innovation in the past 3 years, if they sought external advice from outside sources, and their annual turnover. This information can be extracted at the regional level.
  • 54. The dependent variable: firm performance In this paper we measure firm performance by growth in turnover (e.g., Bartel, 1994; Robson and Bennett, 2000). We use the natural log of turnover as a proxy of firms’ performance, and deal with zero values by using a log (y+1) transformation. The survey asks owner-mangers the following question: ‘Can you please tell me the approximate turnover of your [business name] in the past 12 months across all your UK sites? – To clarify, turnover is the total income received by the business from all sales of goods and services charged to third parties’ (BEIS, 2022b: 89).
  • 55. The independent variables: Covid-19, innovation and networks • We capture such potential effects of Covid-19 on firm performance by creating a dummy variable, which takes the value of one during the Covid-19 years (i.e., 2020-2021), and zero otherwise. • We follow previous research in the field (e.g., Abubakar et al., 2019; Saridakis et al., 2019; Idris et al., 2022), and create an index variable that captures whether firms have introduced any type of innovation (i.e., a good, service, or process). Hence, our independent variable is a binary variable that takes the value of one if the firm introduced any type of innovation, and zero otherwise. • We follow previous research in the field and use ‘external advice’ as a proxy of firms’ networking activities (Hoang and Antoncic, 2003; Idris and Saridakis, 2018). This explanatory variable is a binary variable which takes the value of one if owner-managers of small firms sought external advice, and zero if not. Our variable captures both formal and informal networks.
  • 56. Kernel density estimated distribution of turnover for innovative and non-innovative SMEs during Covid-19
  • 57. SME performance with and without network advice before and during Covid-19 No network Network Difference Before Covid-19 12.566 (0.027) 13.344 (0.039) 0.778*** (0.048) During Covid-19 12.431 (0.047) 13.126 (0.072) 0.694*** (0.088) Difference -0.134** (0.052) -0.217*** (0.079) Notes: *** denotes significant at 1%, and ** at 5%. Standard errors are reported in parenthesis. Performance is measured as turnover (in log).
  • 58. Control variables • The survey also allows us to control whether a firm faced a financial obstacle. Specifically, the survey asks, ‘Which of the following would you say are major obstacles to the success of your [business name] in general?’ (BEIS, 2022b: 66). From this question, a binary variable is created that takes the value of one if firms face financial obstacles, and zero otherwise. We include this variable in the model and also interact it with the Covid-19 recession to examine whether financial obstacles were magnified during the Covid-19 recession. • Moreover, we control for the following variables that might affect firm performance: the size of the firm measured as the natural log of the number of employees (Idris et al., 2023), the age of the business measured as the natural log of the number of years the business has been in operation (Bennett and Robson, 1999), and whether the firm is engaged in exporting activities, which is a binary variable that takes the value of one if the firm exports any goods and/or services outside the UK, and zero otherwise. We also control for the legal status of the firm, its sector, and the region. Finally, we include a variable for whether the firm is located in a rural or urban area.
  • 59. Note: *Significant at the 5% level or better. Correlation matrix between key variables
  • 60. Statistical model • We model SME firm performance, log(𝑡𝑢𝑟𝑛𝑜𝑣𝑒𝑟)𝑖,𝑡 (for i-th firm at time t) by Covid-19 (a dummy variable that takes the value of one in the years 2020 and 2021), innovation (a dummy variable that takes the value of one if the firm innovates), networks (a dummy variable that takes the value of one if the firm makes use of any type of formal and/or informal advice), and their various interactions. We use several control variables (𝑋). The firm fixed effects are captured by 𝜇𝑖. The term, 𝑒𝑖,𝑡 is assumed to be white noise (normally distributed with no serial correlation and no heteroscedasticity). Thus: log 𝑡𝑢𝑟𝑛𝑜𝑣𝑒𝑟 𝑖,𝑡 = 𝛽1𝐶𝑜𝑣𝑖𝑑𝑡 + 𝛽2𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝛽3𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑠𝑖,𝑡 + 𝛽4𝐶𝑜𝑣𝑖𝑑𝑡 ∗ 𝑍𝑖,𝑡 + 𝛽5𝑋𝑖,𝑡 + 𝜇𝑖 + 𝑒𝑖,𝑡 (1) • We estimate equation (1) using both fixed and random effects methods. A Hausman specification guides our choice between the two. Moreover, separate models are estimated for various regions to account for regional differences in the SME performance model. We also estimate a model that allows for a dynamic adjustment. Hence, a lagged value of the dependent variable is included in the model, and a generalized method of moment (GMM) estimator is used to estimate the model and to deal with potential endogeneity issues.
  • 61. Empirical results: UK model • All models suggest that Covid-19 recession is negatively and statistically significant associated with SME performance; hence we find evidence for our H1. Our results are in line with previous literature which indicates that a negative external economic shock has an adverse effect on small firms (Bundy et al., 2017; Klöckner et al., 2023). • The results show that innovation has a statistically insignificant effect on SME performance (see Model I); hence we do not find support for our H2a. (The results from PSM show that the difference in performance between treated and control groups is found to be small but positive, suggesting that firms that undertook innovation perform better than firms that did not innovate during Covid-19. When we restricted the sample to English regions, the difference remained small and positive.) • When innovation interacts with Covid-19 (see Model II), the interaction effect is found to be positive and statistically significant. This supports our H2b by indicating that firms that were innovative during Covid-19 outperformed those that did not introduce any type of innovation
  • 62. Empirical results • Moreover, the results tend to suggest that network advice is positively associated with SMEs (this finding is supported by the pooled and random effect models), which offers support for our H3a. (We also estimate the model using lagged firm networks to address potential endogeneity between the ‘firm networks’ variable and the ‘firm performance’ measure.) • When the network variable is interacted with the Covid-19 variable (Model III), the interaction effect is found to be statistically insignificant, providing no support for H3b. (Our measure of networks is very specific and does not capture the strength of network collaboration. We therefore cannot capture how the intensity of collaboration between networks changed over the Covid-19 pandemic and whether or not this is associated with SME performance.)
  • 63. Empirical results • We extract other interesting findings. Financial obstacles carry a negative and statistically significant coefficient. Our results are in line with previous research that finance is considered to be a critical factor for small firms’ growth and performance (Cook and Nixson, 2000; Guariglia et al., 2011; Moscalu et al., 2020). • We also interact this variable with the Covid-19 recession dummy, but the interaction coefficient is found to be negative and statistically insignificant. Perhaps this counter-intuitive finding can be explained by the UK government’s introduction of financial support schemes such as small business grants, Bounce Back Loans, etc. (see Pope et al., 2020; Rostamkalaei et al., 2023). • We also find some statistically significant coefficients for several other variables (e.g., exporting, firm age, firm size, the type of firm, industry) confirming the association with SME performance that prior literature has noted
  • 64. Impact of Covid-19 on SME firm performance in the UK Model I Model II Model III Variables OLS RE FE OLS RE FE OLS RE FE OLS Covid-19 -0.246*** -0.214*** -0.202*** -0.269*** -0.252*** -0.252*** -0.248*** -0.205*** -0.182*** -0.261*** (-8.147) (-7.913) (-5.353) (-6.706) (-6.801) (-4.910) (-6.804) (-6.676) (-4.568) (-8.165) Innovation -0.017 0.024 0.051 -0.034 -0.003 0.013 -0.017 0.024 0.051 -0.017 (-0.675) (1.057) -1.596 (-1.176) (-0.129) (0.397) (-0.675) (1.057) (1.595) (-0.677) Covid- 19xInnovation 0.063 0.101** 0.137** (1.073) (1.996) (2.109) Network advice 0.125*** 0.064** -0.028 0.125*** 0.064** -0.029 0.123*** 0.071*** -0.008 0.124*** (4.548) (2.444) (-0.725) (4.543) (2.442) (-0.737) (3.988) (2.783) (-0.244) (4.524) Covid-19xNetwork advice 0.008 -0.029 -0.074 (0.123) (-0.516) (-1.011) Financial obstacle -0.247*** -0.199*** -0.134*** -0.247*** -0.199*** -0.135*** -0.247*** -0.199*** -0.134*** -0.270*** (-6.620) (-5.789) (-2.764) (-6.610) (-5.780) (-2.777) (-6.618) (-5.788) (-2.761) (-6.522) Covid- 19xFinancial obstacle 0.106 -1.178 Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 9.127*** 9.117*** 11.199*** 9.148*** 9.167*** 11.326*** 9.127*** 9.115*** 11.179*** 9.130*** (56.129) (53.563) (18.968) (54.958) (52.257) (17.832) (56.191) (53.584) (18.879) (56.242) Observations 13325 13325 13325 13325 13325 13325 13325 13325 13325 13325
  • 65. Empirical model: English sub-sample • To unpack some regional differences, we control for England’s regions in the specification (with the reference category being London). • The Covid-19 recession is negatively associated with SME performance for both the peripheral and core regions (i.e., London and the South East). • Using an OLS, we find the interaction coefficient between Covid-19 recession and innovation to be positive and statistically significant for the peripheral regions only. Using a GMM estimator, the interaction term between innovation and Covid-19 recession dummy is found to be positive and statistically significant in both models. • We also find that several peripheral regions (e.g. East Midlands; West Midlands, South West), underperformed compared with London (which is one of the core regions). • We also estimate a model interacting the Covid-19 recession with regions. However, the model is not presented here because the interactions offer no additional insight into our model, being jointly equal to zero (p= 0.455). • Also, in these models, we find an association between firm’s networks and SME performance, and the effect seems to be stronger in core regions.
  • 66. OLS regression results for England Regression All Peripheral regions Core regions ln(Turnovert-1) 0.735*** 0.694*** 0.815*** (18.002) (13.513) (13.507) Covid-19 -0.190*** -0.191*** -0.182** (-4.563) (-3.805) (-2.480) Innovation 0.002 0.006 -0.002 (0.074) (0.191) (-0.042) Covid-19xInnovation 0.168*** 0.179*** 0.162 (2.996) (2.715) (1.524) Network advice 0.080*** 0.057* 0.121*** (3.234) (1.877) (2.674) Financial obstacle -0.049 -0.038 -0.058 (-1.400) (-0.831) (-1.235) Regions Yes Controls Yes Yes Yes Industry dummies Yes Yes Yes Constant 2.646*** 2.852*** 2.048** (5.562) (5.541) (2.233) Observations 5897 4012 1885 Notes: t-statistics in parentheses. *p<0.1, ** p<0.05, *** p<0.01.
  • 67. Policy implications • The managers of SMEs should encourage innovation during times of economic downturn as a way of remaining competitive and boosting performance. • Although both innovation and networking are important for SME performance, our study suggests that innovation is a more powerful resilience strategy for countering the negative effects of pandemic-driven financial crisis. • Government policy makers should encourage and support innovation during periods of financial crisis. • Government policy makers should pay more attention to the more vulnerable peripheral regions by giving the SMEs in those regions extra support to boost their innovation and cushion the adverse effects of pandemic- driven financial crisis. • Policy makers should also consider how to enhance firm networking and collaboration between the peripheral and core regions. This can be done by, say, promoting knowledge creation, resource sharing, and information exchange between the regions. • Such policies can promote regional growth and economic equality and prosperity.
  • 68. The impact of Brexit on the internationalisation, innovation and turnover of UK SMEs: Implications for the UK’s industrial strategy and the ‘levelling up’ agenda José M. Liñares-Zegarra (Essex Business School and CRBF) John O.S. Wilson (Centre for Responsible Banking & Finance (CRBF), University of St Andrews) ERC-DBT Longitudinal Small Business Survey Dissemination Event 16th November 2023
  • 69. Overview of the report • Purpose • Analyse the impact of Brexit on internationalization, innovation, and turnover of UK SMEs • Examine sectoral and regional heterogeneity in Brexit's consequences • Relevance • SMEs are crucial to UK economy (99% of firms, 60% of employment) • Brexit introduced uncertainty and challenges for SMEs • Findings inform policy to support SMEs in post-Brexit landscape • Data and methods • UK Longitudinal Small Business Survey (LSBS) • Four most recent LSBS waves from 2018 to 2021, due to inconsistencies in the questions • Univariate analysis, recursive bivariate probit models, Heckman models, Multinomial probit models • Cross-sectional regional analysis
  • 70. Preview of key findings • Widespread business challenges: Brexit is viewed as a major obstacle by a significant number of UK SMEs, especially in transport, retail, and hospitality sectors • Sector-Specific impact: Varied effects across sectors, with notable influence on investment, development, and operational plans • Turnover and growth concerns: SMEs anticipate reduced turnover and growth due to Brexit, impacting financial expectations and future strategies • Innovative SMEs face increased challenges: Brexit presents greater challenges for innovative SMEs, affecting investment, market access, and operational costs • Regional Policy Needs: The diverse regional impacts of Brexit highlight the necessity for tailored policy measures to support SMEs and align with the 'levelling up' agenda
  • 71. SMEs increasingly view Brexit as a business challenge • Changes in operational costs and administrative burdens • Disruptions in supply chain and logistics • Impact on workforce availability due to immigration policy changes Figure 1: Brexit as a major business obstacle by survey wave
  • 72. Transport, Retail, and Food Service/Accommodation sectors report the highest impact from Brexit • Specific challenges faced by each sector: • Transport, Retail, and Food Service sectors hardest hit by Brexit, affecting 26.6% of SMEs • Business Services sector close behind with a 25.2% Brexit impact on SMEs • Production and Construction less affected at 20.7%, with Other Services at the lowest impact of 15.0% • Variations in impact intensity across different industries Figure 2: Brexit as a major business obstacle by sector (% of SMEs)
  • 73. 33% of SMEs considers Brexit as a factor in projected sales revenue reductions • Substantial geographical and sectoral variations Figure 3: Extent to which SME considers the UK's exit from the EU to be a factor in the decrease in turnover that is expected in the next 12 months (% of SMEs)
  • 74. Brexit drives fluctuating impact on SMEs' R&D and Product Development Plans, with notable increases in 2021 Figure 4. Whether plans over the next three years have been affected by Brexit: Invest in R&D (% of SMEs) Figure 5. Whether plans over the next three years have been affected by Brexit: Develop and launch new products/services (% of SMEs) Brexit influences future capital investment plans and the development plans of new products, particularly in production and construction industries.
  • 75. Brexit’s influence on SME export and market expansion plans increased in 2021 • Brexit's influence on SMEs' export/market expansion plans decreased from 30.2% in 2018 to 25.7% in 2020 but increased to 34.7% in 2021 • The production and business services sector felt the most significant impact Figure 6. Whether plans over the next three years have been affected by Brexit: Increase export sales or begin selling to new overseas markets (% of SMEs)
  • 76. Brexit perceived as major obstacle for innovative and export-oriented SMEs • Brexit's perception as a significant obstacle has negative implications on SMEs' future operations: • A potential 15% reduction in growth objectives • A 20% decrease in future capital investments • Brexit presents greater challenges for innovative SMEs, which include increased capital raising difficulties, and shifts in import/export costs • Both innovative and export-oriented SMEs associate expected changes in their turnover (sales revenue) following Brexit, albeit to varying degrees
  • 77. Brexit impact and economic productivity reveal UK’s complex business landscape • Gross Value Added (GVA) per hour worked (£) as a measure for differences in productivity between regions • London SMEs perceive Brexit as a major obstacle the most (35.17%), with the highest GVA per hour (£50.7) • The North-East reports the lowest Brexit concern among SMEs (16.69%) with a moderate GVA per hour (£32.47) • Significant regional variations in Brexit impact and economic productivity underscore the complex UK business landscape post-Brexit Figure 7: Brexit as a major business obstacle and Gross Value Added (GVA) per hour worked (£)
  • 78. Conclusions • Brexit has had varied impacts across different industries and regions of the UK • This highlights the need for tailored industrial and regional development policies to support the sectors (i.e., transport, retail, hospitality) most affected • There are concerns over decreased investment and innovation across regions • Strategies are needed to encourage investment and innovation, targeting struggling areas. • Brexit is perceived as an obstacle for exporters • Policies with a focus on supporting SME exporting activities through trade agreements and export promotion • Regional disparities are evident in the impacts of Brexit on SMEs • Coordinated industrial and regional policies to support impacted sectors and struggling areas • This can ensure resilience and growth in a post-Brexit economy
  • 80. Empirical Findings (I): Impact on Future Operations of SMEs • Brexit's perception as a significant obstacle has negative implications on various aspects of SMEs' future operations: a potential 15% reduction in growth objectives and a 20% decrease in future capital investments • Uncertainty and its effect on strategic planning • Need for agility and adaptability in operations • Long-term strategic shifts among UK SMEs =1 Aim to grow sales =1 if Brexit is a major obstacle Brexit is a major obstaclet-1 -0.150** (-2.35) SME innovatort-1 0.063*** -0.003 (5.65) (-0.15) SME exportert-1 0.057*** 0.244*** (3.19) (76.44) =1 Capital investment (in premises, machinery etc.) in the UK =1 if Brexit is a major obstacle Brexit is a major obstaclet-1 -0.200*** (-7.17) SME innovatort-1 0.105*** -0.005 (12.10) (-0.27) SME exportert-1 0.024* 0.243*** (1.78) (60.10) Table 1: Impact of Brexit on future grow sales of SMEs Table 2: Impact of Brexit on future capital investment (in premises, machinery etc.) of SMEs in the UK
  • 81. Empirical Findings (II): Challenges for Innovative SMEs • Innovative SMEs perceive greater challenges due to Brexit, which include increased capital raising difficulties, and shifts in import/export costs • Access to talent and skilled workforce • Funding challenges for innovation and growth • Regulatory hurdles and intellectual property concerns Average marginal effects Selection Outcome SME innovatort-1 0.036*** 0.050*** (4.12) (3.02) SME exportert-1 0.112*** 0.012 (19.10) (0.79) Table 3: Major obstacles relating to UK exit from EU: Decrease in investment/greater difficulty in raising capital for SMEs in the UK Table 4: Major obstacles relating to UK exit from EU: Increase in cost of imports from the EU for SMEs in the UK Average marginal effects Selection Outcome SME innovatort-1 0.036*** 0.052 (4.02) (1.33) SME exportert-1 0.111*** 0.034 (18.71) (0.63)
  • 82. Empirical Findings (III): Brexit's Influence on Financial Expectations • Both innovative and export- oriented SMEs associate expected changes in their turnover following Brexit, albeit to varying degrees • Adjusted financial expectations in the post-Brexit era • Variations in financial outlook across different sectors Table 5: Extent to which SME considers the UK's exit from the EU to be a factor in the decrease in turnover that is expected in the next 12 months for SMEs in the UK Not a factor Minor factor Major factor SME innovatort-1 -0.086*** -0.003 0.089*** (-4.53) (-0.11) (10.15) SME exportert-1 -0.208*** 0.052** 0.156*** (-13.07) (2.04) (6.33) Table 6: Extent to which SME considers the UK's exit from the EU to be a factor in the increase in turnover that is expected in the next 12 months for SMEs in the UK Not a factor Minor factor Major factor SME innovatort-1 -0.001 -0.007 0.008* (-0.16) (-0.73) (1.91) SME exportert-1 -0.042*** 0.016 0.026** (-3.51) (1.59) (2.24)
  • 83. Empirical Findings (IV): Regional Disparities and the 'Levelling Up' Agenda • The index of multiple deprivation (IMD) based on measures involving productivity, employment, vacancy rates, lack of skills and transport links • London: Highest Brexit concern among SMEs (35.17%), moderate deprivation score (11.10215) • South-East: Lower Brexit impact (22.53%), highest Index of Multiple Deprivation (IMD) score (13.64864) • North-East: Least Brexit concern (16.69%), highest deprivation with the lowest IMD score (8.794727) • Illustrates complex relationship between regional Brexit challenges and socio-economic deprivation in the UK Figure 8. Brexit as a major business obstacle and Index of multiple deprivation (IMD)
  • 84. Brexit and Digital Technology Adoption of UK SMEs Martina Pardy* and David Ampudia† *London School of Economics and Political Science †Innovation Growth Lab at Nesta ERC-DBT LSBS Dissemination Event Nov 16, 2023
  • 85. Research Gap Brexit increased (potential) trade costs ▶ UK’s exit from the European Union (EU) led to a large increase in barriers to trade with the country’s largest trading partner (Bakker et al., 2022) ▶ Trade and Cooperation Agreement (TCA) the regulatory and customs framework changed ▶ Brexit led to a fall in EU imports and exports (Crowley et al., 2018; Krena and Lawless, 2022) ▶ Before TCA: SMEs reducing trade and investment (Brown et al., 2018)
  • 86. Research Gap Lacking evidence of the impact of Brexit on digital technology adoption on SMEs ▶ Substantial impact of Brexit on the UK economy ▶ overall conclusion that Brexit will make the UK economy poorer (Sampson, 2017) ▶ Effect of Brexit on UK firms ▶ Decrease in investment and productivity among larger firms between 2016-2019 (Bloom et al., 2019) ▶ Survey evidence response: SMEs are scaling back on capital investment, innovation and exports (Brown et al., 2018; Liñares-Zegarra and Wilson, 2023) ▶ Insufficient evidence of the actual impact on SMEs’ digital behaviour ▶ How Small and Medium Enterprises (SMEs) adjust their digital technology adoption after the Brexit referendum ▶ the role of digitalisation
  • 87. Research Gap The importance of understanding how Brexit affects SMEs ▶ SMEs as backbone of the economy ▶ Firms with less than 250 employees ▶ 99.9% of private business population in the UK (Nesta, 2017) ▶ Providing 60% of all jobs, even more - 70% - for local economies in South West England and Wales (BEIS, 2021) ▶ Drivers of productivity (Schneider, 2010) ▶ Digitalisation as key priority for policy makers (OECD, 2020; European Parliament, 2023) ▶ Innovation & technological change as long run driver of economic development (Solow, 1957; Romer, 1986) ▶ Reduce search costs, replication costs, transportation costs and tracking costs (Goldfarb and Tucker, 2019)
  • 88. Research Aim Research Aim To what extent does the Brexit-induced trade policy shock affect the digital behaviour of SMEs? This paper ▶ contributes to existing work on the effect of Brexit on SMEs by focusing on digital technologies ▶ links existing survey-measures with novel measures on digital technology adoption ▶ uses a differences-in-differences design exploring the Brexit referendum as trade policy shock ▶ finds that affected firms reduce e-commerce-related, but also other basic digital technologies ▶ explores heterogeneities by sector and location
  • 89. Literature and Theoretical Framework Contribution: the effect of Brexit on UK firms ▶ The first study on the effect of Brexit on Digitalisation of SMEs Author Year Outcome Method Unit Bakker et al. 2022 Consumer prices DD UK Firms Brown et al. 2018 Exports and Innovation Survey UK SMEs Brown et al. 2020 Exports and Innovation Survey UK SMEs Bloom et al. 2019 Investment and productivity DD UK Firms Casadei and Iammarino 2021 Sales, Turnover and trade Survey UK Textile firms Crowley et al. 2018 Exports DD UK Firms Gornicka 2018 Investment DD UK Firms Liñares-Zegarra and Wilson 2023 Invest., turnover and internat. Survey UK SMEs Pardy and Ampudia 2023 Digital technologies DD UK SMEs
  • 90. Literature and Theoretical Framework The Brexit referendum as trade policy uncertainty shock ▶ Brexit (will) increase trade barriers ▶ Higher potential future trade costs ▶ (Expectations of) lower future trade with the EU ▶ More pessimistic expectations about the future business environment ▶ Expectations of higher future costs, lower future profits ▶ Increased importance of Brexit-related uncertainty ▶ Uncertainty about future access to EU markets ▶ Uncertainty about future regulatory changes
  • 91. Literature and Theoretical Framework Firms’ potential digital response ▶ Reduction of e-commerce-related technologies ▶ Firms reduce trade amount, discouraged starting trading or exiting trade with the EU (Brown et al., 2018; Crowley et al., 2018) ▶ SMEs cut costs on digital technologies ▶ Firms more cautious and uncertain in the future (Bloom et al., 2019) ▶ SMEs reduce capital and R&D investment (Brown et al., 2018) ▶ SMEs increase spending on particular digital technologies ▶ To account for lower future profits firms spend more on advertising ▶ Less time on planning on future-growth strategies ▶ SMEs/their management spend more time to Brexit planning (Bloom et al., 2019) ▶ Less time on expanding digital strategies ▶ Fewer adoption of digital technologies
  • 92. Data Firm-level data: Longitudinal Small Business Survey ▶ LSBS - Longitudinal Small Business Survey ▶ Compiled by the UK Department for Business Energy and Industrial Strategy (BEIS) ▶ Nationally representative survey of SMEs ▶ Sample size: 39,177 from 2015-2021 ▶ Firm level characteristics ▶ Size, sector, age, exporting, turnover, innovation, employment, etc. ▶ Specific questions on Brexit, for example: ▶ Whether Brexit is perceived as major obstacle ▶ What are the obstacles that your firm faces because of the UK’s forthcoming exit from the EU? Sampling Brexit questions
  • 93. Data Firm-level data: linking survey data to novel measures of digital technology adoption ▶ Obtain Longitudinal Small Business Survey data for 39,177 SMEs ▶ 32,139 agreed to data linkage, get company names and addresses ▶ Get URL for SME’s homepage ▶ Internet search for name and address ▶ Multiple URLS, select homepage ▶ 9,685 homepages ▶ Verification firm website: name and address ▶ 4423 homepages ▶ Use BuiltWith to get partial tech stack ▶ Technologies, languages and frameworks from a firm’s website
  • 94. Data Firm level-data: Digital Technologies ▶ Digital technology = representation of information in bits (Goldfarb and Tucker, 2019) ▶ Information about firms’ digital behaviour ▶ From firm’s websites: software solutions as observed through their website ▶ Using BuiltWith: technologies from their page body, cookies or server headers ▶ For every firm with a website ▶ Wealth of information on technologies ▶ 33 tech categories ▶ Historical data, from 2000 onwards ▶ Observe when a technology has been first and last detected ▶ Focus on categories related to trading ▶ Digital categories: count the number of technologies per firm and year Tech categories Measurement
  • 95. Data Relevant digital technologies: e-commerce-related ▶ Payment: Visa, Mastercard ▶ Javascript: interactive elements like shopping carts and login ▶ Secure Socket Layer: LetsEncrypt, secure payment ▶ Language: English, Spanish ▶ Analytics: Google Analytics ▶ Shipping: UPS, DHL ▶ eCommerce: Shopify, OpenCart ▶ Content Delivery Network: Cloudflare, often for e-commerce
  • 96. Data Regional data: the Brexit vote ▶ Estimated Brexit referendum vote by constituency from Norris (2019) ▶ Substantial differences in voting across space: interest in“Leave” vs “Remain” areas ▶ Aggregate to NUTS-3 level Figure: Spatial differences in Brexit vote, Source: BBC (2021)
  • 97. Methods 2x2 Diff-in-Diff model: Firm-level shock EU Trade shock yit = βEUi ∗ Postt + vi + vt + εit (1) ▶ yit: digital technology count of firm i in year t ▶ EUi : dummy for firms whether they depend on the EU or not ▶ Postt: time dummy, 1 from 2016 onwards ▶ vi : firm fixed effects ▶ vt: year fixed effects ▶ εit: error term Identification
  • 99. Results Results: Main Table Dep. Var.: Payment Secure Layer Analytics Javascript Content Delivery Netw. Model: (1) (2) (3) (4) (5) Variables Post × EU dep. -0.2361∗∗ -0.1593∗∗∗ -0.2865∗∗∗ -0.2177∗∗∗ -0.1343∗∗ (0.0982) (0.0572) (0.0538) (0.0465) (0.0676) Fixed-effects Firm Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Fit statistics Observations 10,080 16,667 18,730 24,690 19,780 Squared Corr 0.57778 0.45019 0.59882 0.69353 0.68425 Pseudo R2 0.31312 0.22252 0.32426 0.51405 0.44744 BIC 22,509.8 54,973.8 56,275.1 101,997.0 47,514.8 Clustered firm standard-errors in parentheses Signif. Codes: ***: 0.01, **: 0.05, *: 0.1
  • 100. Results Further analysis ▶ Sectoral Analysis ▶ Significant differences by sector Sector ▶ Results driven by multiple sectors: Primary, Manufacturing, Wholesale/Retail, Accommodation/Food, Information/Communication, Professional/ Scientific, Education, Other Service ▶ Size ▶ No significant differences by size Size ▶ Other significant technologies ▶ Reduction also in other technologies, including mobile technologies or media Other Tech ▶ Differences“Leave” vs “Remain” areas ▶ Spatial differences in the Brexit vote share
  • 101. Results Local Economic Effects: Brexit Vote Dependent Variables: Payment Secure Layer Analytics Javascript Content Del. Net. Model: (1) (2) (3) (4) (5) Variables Post × Brexit Vote 0.010∗∗∗ 0.004∗ 0.006∗∗∗ 0.005∗∗ 0.007∗∗ (0.004) (0.003) (0.002) (0.002) (0.003) Fixed-effects Firm Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Region Yes Yes Yes Yes Yes Fit statistics Observations 8,792 20,853 15,953 21,427 17,185 Squared Correlation 0.602 0.442 0.592 0.658 0.632 Pseudo R2 0.251 0.219 0.238 0.409 0.330 BIC 26,765.0 71,099.3 62,536.1 116,951.9 58,941.3 Clustered firm standard-errors in parentheses Signif. Codes: ***: 0.01, **: 0.05, *: 0.1
  • 102. Conclusion and Discussion Conclusion ▶ Link existing survey-measures with novel measures on digital technology adoption from firms’ websites ▶ Analyse to what extent the Brexit-induced trade shock affects the digital behaviour of UK SMEs, 2016-2019 ▶ Lower adoption of digital technologies for firms trading with the EU compared to comparison group ▶ SMEs reduced their adoption of trade-enhancing technologies ▶ Significant decline in other technologies, including basic website functionalities ▶ Decline in digital technologies in multiple sectors ▶ Lower adoption in “Remain” areas
  • 103. Conclusion and Discussion Policy implications ▶ Addressing uncertainties and potential trade costs to mitigate the adverse effects on SMEs ▶ Targeted policies that provide guidance and financial support for SMEs ▶ To navigate the changing trade landscape and encourage investment in digital technologies websites ▶ Encourage investment in digital technologies ▶ Sector-specific interventions to assist industries, such as manufacturing, education, retail, and information services ▶ Training and collaboration programmes with digital platforms, educational institutions, and non-profit organisations
  • 104. Conclusion and Discussion Discussion and next steps ▶ Explore more heterogeneity ▶ Differentiate between premium vs. free technologies ▶ Novel vs older firms? ▶ Understanding mechanisms ▶ Trade channel ▶ Decrease in technology investments ▶ Strategic realignment ▶ Getting more data? predictions of full technology stack ▶ Robustness checks ▶ Information on supply chain participation
  • 105. Conclusion and Discussion Thank you for your attention! m.l.pardy@lse.ac.uk
  • 107. Conclusion and Discussion Conceptualisation: Brexit referendum as firm-level shock ▶ Brexit referendum affects firms in multiple ways ▶ Changed expectations about future UK-EU relations and regulations ▶ Brexit affects trading firms due to higher uncertainty and higher import costs Figure: Brexit-related obstacles for SMEs, LSBS 2017
  • 108. Conclusion and Discussion Brexit: LSBS Questions ▶ Which of the following would you say are major obstacles to the success of your business in general?: UK exit from the EU, 2016-2021, All ▶ Overall, how beneficial or detrimental would UK exit from the EU be to your business? (scale 1-5), 2016-2021, All ▶ Which of these, if any, are the obstacles that you firm faces because of the UK’s forthcoming exit from the EU? 2017-2021, All ▶ Have any of these plans been affected by the UK exit from the EU? IF YES: Which plans? 2017-2021, Cohort B ▶ How has the scale of these plans been affected by UK exit from the EU? For each that I read out, please tell me whether they have been scaled down or scaled up, or do they remain at the same level? 2017-2021, Cohort B ▶ How has the timing of these plans been affected? For each that I read out, please tell me whether they have been brought forward pushed back or is the timing unaffected? 2017-2021, Cohort B ▶ How prepared do you feel your [ANSWER AT A-2] is currently for the UK’s exit from the EU? (scale 1-5), 2017, All Go Back
  • 109. Conclusion and Discussion BuiltWith Technology Categories Advertising Analytics & Tracking Audio/Video Media Content Delivery Network Content Management System Copyright Widgets Document Standards Domain Parking e-Commerce Email Hosting Providers Framework Javascript Library Language Mapping Mobile Name Server Operating Systems & Servers Payment Registrar Services Robots Seo Header Tag SEO Meta Tag Seo Title Tag Shipping providers SSL certificates Verified Link Web hosting providers Web Master Registration Web Servers Go Back
  • 110. Conclusion and Discussion Measuring digital technologies of firms Measurement ▶ Capturing partial tech stack, not the full tech stack Proxy for ▶ Digital technology adoption (Ragoussis and Timmis, 2022; Apostol and Hernández Rodrı́guez, 2023) ▶ How firms position themselves ▶ Firms performance (Tadelis et al., 2023) Rationale ▶ Most survey measures lack detailed and timely information on digital technologies, in particular SMEs Limitations ▶ No information on who built the website - outsourced or internally ▶ Only for firms with a website - selective sample Go Back
  • 111. Conclusion and Discussion Comparison Group & Identification Challenges ▶ Comparison group ▶ All firms not trading at all and firms that trade with countries ̸= EU ▶ More similar in terms of digital technologies than firms that trade with countries ̸= EU ▶ Key identifying assumption: treated firms have similar trends to the control firms in the absence of treatment ▶ Plot average outcome changes before and after treatment ▶ Testing for Parallel Trends: differences in the trend for the two groups prior the time treatment ▶ Language and Shipping not used because of lack of change in outcome variable ▶ Anticipation of the Brexit vote? ▶ Other shocks affecting digitalisation of firms ▶ Covid in 2020 ⇒ limit observation period until 2019 Brexit Timeline Comparison Group
  • 112. Conclusion and Discussion Trends in average outcome changes 0.00 0.25 0.50 0.75 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average change EU dependence 0 1 Figure: Javascript 0.0 0.1 0.2 0.3 0.4 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average change EU dependence 0 1 Figure: Content Delivery Network 0.00 0.02 0.04 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average change EU dependence 0 1 Figure: e-Commerce Levels
  • 113. Conclusion and Discussion Trends in average outcome changes 0.00 0.03 0.06 0.09 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average change EU dependence 0 1 Figure: Payment 0.0 0.1 0.2 0.3 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average change EU dependence 0 1 Figure: Secure Layer 0.00 0.05 0.10 0.15 0.20 0.25 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average change EU dependence 0 1 Figure: Analytics Levels
  • 114. Conclusion and Discussion Comparison group: Brexit as EU trade policy shock ▶ Challenge to find a proper control group ▶ Europe comparison: UK most digitally advanced firms (Apostol and Hernández Rodrı́guez, 2023) ▶ Control firms in the UK (Bakker et al., 2022; and Bloom et al. 2019) ▶ Increase in trade barriers for those firms directly trading with the EU ▶ Comparison group ▶ All firms not trading at all and firms that trade with countries ̸= EU ▶ More similar in terms of digital technologies than firms that trade with countries ̸= EU ▶ Robustness check: remove firms with an indirect link ⇒ supplying goods to EU trading firms Go Back
  • 115. Conclusion and Discussion Brexit: Timeline Figure: Brexit events timeline, Source: Javorcik et al., (2022)
  • 116. Conclusion and Discussion Development of the Uncertainty Index over time Figure: Development of Brexit Uncertainty Index, Source: Bloom et al. (2018)
  • 117. Conclusion and Discussion LSBS data: sampling ▶ Cross-section: Designed to be representative of the SME population ▶ Weighting the balanced panel to be representative of the SME population in 2018 and retained those weights for 2019 and 2020 ▶ Panel firms are seen to differ from non-panel firms: ▶ more likely to be aiming to grow their business ▶ more likely be exporting ▶ more likely be innovating ▶ more likely be accessing business support Go Back
  • 118. Conclusion and Discussion Trends in average outcome levels: Payment, Secure Layer and Analytics 0.1 0.2 0.3 0.4 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average count EU dependence 0 1 Figure: Payment 0.25 0.50 0.75 1.00 1.25 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average count EU dependence 0 1 Figure: Secure Layer 0.5 1.0 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average count EU dependence 0 1 Figure: Analytics Go Back
  • 119. Conclusion and Discussion Trends in average outcome levels: Javascript, Content Delivery Network and e-Commerce 1 2 3 4 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average count EU dependence 0 1 Figure: Javascript 0.25 0.50 0.75 1.00 1.25 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average count EU dependence 0 1 Figure: Content Delivery Network 0.00 0.05 0.10 0.15 0.20 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 Year Average count EU dependence 0 1 Figure: e-Commerce Go Back
  • 120. Conclusion and Discussion Sectoral Analysis Dependent Variables: Payment Secure Layer Analytics Javascript Content Delivery Network Model: (1) (2) (3) (4) (5) Variables DD*Primary -0.8798∗∗ 0.0883 -0.6358∗∗ -0.5909∗∗ -0.1852 (0.3660) (0.3603) (0.2793) (0.2877) (0.3121) DD*manufacturing -0.1795 -0.2439∗∗∗ -0.3599∗∗∗ -0.2355∗∗∗ 0.0436 (0.1644) (0.0915) (0.0833) (0.0751) (0.1378) DD*construction -0.1867 0.1207 0.0745 -0.0040 0.3649 (0.4012) (0.2511) (0.2704) (0.1849) (0.3403) DD*Wholesale -0.3468∗∗ -0.1972∗∗ -0.2425∗∗∗ -0.1899∗∗ -0.2116∗ (0.1405) (0.0933) (0.0847) (0.0815) (0.1247) DD*Transport 0.5255 -0.1591 -0.0953 -0.2879∗ -0.1997 (0.6179) (0.1777) (0.2004) (0.1485) (0.2102) DD*Accommodation 0.1488 -0.0345 -0.2015 -0.1449 -0.3972∗∗ (0.4329) (0.2398) (0.2245) (0.1701) (0.1953) DD*Information -0.4221 -0.2945∗∗ -0.2746∗ -0.3015∗∗ -0.0247 (0.3949) (0.1228) (0.1492) (0.1358) (0.2715) DD*Financial -0.2259 0.5388 -0.2436 -0.2971 -0.0167 (0.4786) (0.4735) (0.2423) (0.2108) (0.2458) DD*Professional -0.0924 0.0030 -0.3049∗∗∗ -0.3259∗∗∗ -0.3820∗∗∗ (0.2261) (0.1429) (0.1074) (0.1127) (0.1306) DD*Administrative -0.2835 -0.2132 -0.3657∗∗ -0.0515 -0.1758 (0.2297) (0.1767) (0.1551) (0.1532) (0.1615) DD*Education -0.1614 -0.5202∗ 0.5046 0.2359 -0.6773∗∗ (0.5031) (0.2839) (0.5272) (0.2969) (0.3289) DD*Health -0.1808 -0.1391 0.4075 0.1886 -0.0668 (0.4120) (0.3982) (0.4392) (0.2595) (0.2887) DD*Arts -0.3313 -0.1926 -0.3164 0.1734 -0.3777 (0.4320) (0.2664) (0.3367) (0.3919) (0.3368) DD*Other service -1.044∗∗∗ -0.1078 -0.2874 0.0384 0.1130 (0.3955) (0.4518) (0.2926) (0.2510) (0.1853) Go Back
  • 121. Conclusion and Discussion Analysis by size Dependent Variables: Payment Secure Layer Analytics Javascript Content Delivery Network Model: (1) (2) (3) (4) (5) Variables DD*No employees 0.1818 -0.3486 2.274 0.2070 -0.1530 (0.2400) (63,517.0) (18,071.1) (2,080.8) (12,247.9) DD*Micro (1-9 employees) 0.1142 -0.2053 2.223 0.1721 -0.4136 (0.2006) (63,517.0) (18,071.0) (2,080.8) (12,247.9) DD*Small (10-49 employees) 0.1930 -0.0743 2.343 0.6468 -0.2200 (0.1659) (63,517.0) (18,071.0) (2,080.8) (12,247.8) DD*MEdium (Medium 50 - 249) -0.0330 2.468 0.8734 -0.1082 (63,517.0) (18,070.9) (2,080.8) (12,247.8) Fixed-effects Firm Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Fit statistics Observations 1,437 3,432 2,684 3,765 2,855 Squared Correlation 0.79443 0.70908 0.77212 0.86325 0.83937 Pseudo R2 0.15367 0.16521 0.15678 0.40310 0.24988 BIC 8,561.4 22,566.8 18,793.9 30,997.4 19,109.0 Clustered (Firm) standard-errors in parentheses Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 Go Back
  • 122. Conclusion and Discussion Results: other technologies Dep. Var.: Media Content MS Framework Hosting Mobile Web Server Name Server Model: (1) (2) (3) (4) (5) (6) (7) Variables EU trade*post -0.324∗∗ -0.100∗ -0.138∗∗∗ -0.147∗∗∗ -0.196∗∗∗ -0.144∗∗∗ -0.195∗∗∗ (0.133) (0.058) (0.043) (0.040) (0.068) (0.033) (0.042) Fixed-effects Firm Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Fit statistics Observations 4,473 15,246 17,899 22,351 18,284 22,253 16,289 Sq. Corr. 0.49014 0.49862 0.50910 0.47799 0.66084 0.49089 0.42823 Pseudo R2 0.21126 0.22576 0.19403 0.17533 0.35746 0.15366 0.12347 BIC 11,641.1 50,373.4 61,848.7 72,028.2 60,724.1 77,810.1 48,433.8 Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 Clustered (firm) standard-errors in parentheses. Content MS refers to Content Management System. The table presents the effect of other technologies that are not e-commerce related. Different number of observations as those with only 0 in the outcomes have been removed. Go Back
  • 123. Conclusion and Discussion Description: other technologies ▶ Media: Youtube, Vimeo, Zoom ▶ CMS: Wordpress, Slack ▶ Framework: PHP, GlobalSign Domain Verification ▶ Hosting: Cloudflare Hosting, Amazon ▶ Mobile: IPhone/Mobile Compatible, MS Mobile Optimized ▶ Web Master: Google Webmaster, structured information for search engines to index websites better Go Back
  • 124. Conclusion and Discussion General Data Protection Regulation as concern? ▶ Processing of personal data ▶ Applies since 25 May 2018 in the UK ▶ Affect treatment and control group in a different way? ▶ Affect certain technology groups: linked to sensitive data, such as customer relationship management
  • 125. Demand for external finance by environmentally-motivated SMEs: An exploration of geographical disparities Sylvia Gottschalk and Robyn Owen Middlesex University Business School ERC-DBT - 16 November 2023
  • 126. Outline Motivation UK SMEs characteristics Accessibility index Econometric Analysis Results Conclusions
  • 127. Motivation How do geographical disparities and different amounts/types of external finance impact on the green growth of UK SMEs? ▶ How is SME/SE green external financing related to SME skills and capabilities, and future business intentions? (referencing also sections K, R and N) ▶ How is SME/SE green financing related to industrial sectors and other business environment characteristics (urban versus rural location, local deprivation index)? (referencing also section J)
  • 128. Importance of location in bank lending ▶ Physical distance between informationally opaque borrowers, e.g. SMEs, and lenders leads to informational frictions and allocative inefficiencies ▶ Geographical proximity to SMEs allows banks to mitigate asymetric information: 1. at the screening stage by visiting credit applicants’ plants, talking to their managers, and studying their business plans. 2. at the monitoring stage, by requiring a constant flow of information from its borrowers, verifying and analysing it, and taking prompt action when there are indications of mismanagement.
  • 129. Impact of distance between SME borrowers and banks ▶ spatial price discimination 1. the lower the distance the more likely it is that SME will receive external credit. 2. SMEs pays higher rates. (Agarwal and Hauswald, 2010; Bellucci et al., 2013; Cowling et al., 2020; Degryse and Ongena, 2005) 3. spatial discrimination is reduced by competition from other banks ▶ no impact of distance on bank lending (Carling and Lundberg, 2005) or increase in distance between lenders and borrowers (Petersen and Rajan, 2002) ▶ Marginal cost pricing for large firms irrespective of location (Bellucci et al., 2013)
  • 130. Geo-accessibility: Empirical evidence ▶ retail banking evidence in Germany: residents in sparsely populated rural regions have both low physical and below-average digital access (Conrad et al., 2018) ▶ no substitutatibility of soft information and credit scores in Germany: banks’ internal rating systems do not replace “real” soft information, which can only be validated in long-term relationships with clients (Flögel and Beckamp, 2020) ▶ UK firms in peripheral areas are more likely to have their applications for finance rejected, and this potentially reinforces regional disparities (Lee and Brown, 2017) ▶ region where UK SMEs are located matters for the probability of SMEs facing bank credit constraints (Zhao and Jones-Evans, 2017)
  • 131. Green SMEs taxonomy- 2017-2019-2021 (Owen et al., 2019) Year Major Minor None Refused Unweighted Base 2017 10.7% 56.3% 29.3% 3.8% 5618 2019 11.7% 63.1% 21.7% 3.4% 9490 2021 15.4% 72% 10% 2.6% 6102 Dataset: LSBS 2021 Wave. Questions on environmentally-oriented SMEs only since 2017 and are biannual. Green objectives are: 1. Only concern or major priority: Major 2. Equal or lesser goal relative to financial goals: Minor 3. Not a concern: None: 4. Don’t know/refused to answer: Refused
  • 132. Characteristics: production, age and location - 2021 row% Major Minor None Refused Broad Sector Production and construction 8.50% 76.80% 12.20% 2.50% Transport, retail and food service/ accommodation 11.50% 76.90% 10.30% 1.30% Business services 14.00% 75.20% 7.80% 3.10% Other services 24.50% 62.30% 10.20% 2.90% Age established 0 - 5 years 15.90% 73.30% 7.80% 3.00% 6 - 10 years 19.90% 72.20% 6.40% 1.50% 11 - 20 years 13.00% 77.10% 7.30% 2.60% More than 20 years 15.60% 67.80% 13.80% 2.80% Location England 15.70% 71.30% 10.40% 2.60% Scotland 11.80% 77.20% 8.60% 2.40% Wales 13.50% 78.80% 4.10% 3.60% Northern Ireland 18.60% 72.10% 7.00% 2.30% Urban 17.00% 69.90% 10.10% 3.00% Rural 11.40% 76.90% 10.00% 1.80%
  • 133. Characteristics: management - 2021 row % Major Minor None Refused Management Woman-led Yes 17.00% 72.50% 7.00% 3.60% Woman-led No 13.90% 73.10% 11.00% 2.00% MEG-led Yes 15.10% 77.90% 6.30% 0.60% MEG-led No 15.50% 71.70% 10.20% 2.60% Family-owned: Yes 13.20% 74.20% 10.30% 2.30% Family-owned: No 27.50% 12.90% 12.40% 25.40% Employment Zero 15.80% 70.10% 11.30% 2.80% 1 to 9 15.00% 75.50% 7.50% 2.00% 10 to 49 11.60% 80.90% 5.20% 2.20% 50 to 249 10.60% 83.00% 4.30% 2.10%
  • 134. Major obstacles to the success of your business in general? Major Minor None UB Obtaining finance 22.8a 12.9b 18.0a, b 463 Taxation, VAT, PAYE, National In- surance, business rates 21.1a 27.3a 24.6a 1083 Staff recruitment and skills 14.2a 28.0b 19.8a, b 1510 Regulations/red tape 32.5a 32.9a 29.5a 1289 Availability/cost of suitable premises 24.7a 15.9b 16.2a, b 500 Competition in the market 31.7a 44.9b 41.9a, b 1342 Workplace pensions 7.3a 5.3a 5.4a 305 Late payment 25.1a, b 24.7a, b 19.2b 804 UK exit from the EU 27.6a 27.4a 25.1a 1046 National Living Wage 12.1a 12.4a 19.2a 676 Columns with shared subscript letters denote a subset of column categories whose proportions do not differ significantly from each other at the .05 level.
  • 135. Use of external finance - 2021 Types of finance currently being used Major Minor None Bank overdraft facility 10.8%a 18.9%b 24.3%c Commercial mortgage 2.4%a 3.7%a, b 2.8%a, b Credit cards 17.9%a 24.1%b 25.1%b Equity Finance 0.8%a 0.6%a 0.2%a Factoring/invoice discounting 0.8%a 2.5%b 0.4%a Government grants not including any directly related to Coronavirus 5.0%a 3.6%a 0.7%b Government grants directly related to Coronavirus 21.7%a 24.4%a 21.1%a Leasing or hire purchase 6.5%a 12.5%b 9.9%a, b Loan from a bank,or other financial institution not dir- ectly related to Coronavirus 2.7%a 6.5%b 3.2%a Loan from a bank,or other financial institution directly related to Coronavirus 11.1%a 18.9%b 18.4%b Loan from family/friend 7.6%a 5.6%a 6.9%a Loan from business partner/directors/owner 12.7%a 11.5%a 11.0%a Loan from a peer to peer platform 0.8%a 0.8%a 0.4%a Other finance 2.1%a 1.1%a 1.3%a None of these 46.2%a 36.1%b 38.9%b Columns with shared subscript letters denote a subset of column categories whose proportions do not differ significantly from each other at the .05 level.
  • 136. Demand for external finance - 2021 Types of finance applied for Major Minor None Bank overdraft facility 14.8%a 38.1%b 7.1%a Commercial mortgage 2.8%a Credit cards 4.9%a 17.0%a 3.6%a Equity Finance Factoring/invoice discounting 2.3%a Leasing or hire purchase 16.1%a 22.5%a 14.3%a Other government finance grants 36.1%a 5.5%b 25.0%a Loan from a bank, or other financial institution 24.2%a 29.4%a 39.3%a Loan from family/friend 8.1%a 21.7%a 3.6%a Loan from a Peer to peer platform 0.8% 3.7%a 0.3% Loan from business partner/directors/owner 8.2%a 13.8%a Other finance 13.1%a 7.8%a 3.6%a Coronavirus COVID-19 Government-backed accred- ited loans or finance agreements 3.3%a 11.0%a Coronavirus COVID-19 business grants funded by government 3.2%a 3.7%a 3.6%a Columns with shared subscript letters denote a subset of column categories whose proportions do not differ significantly from each other at the .05 level.
  • 137. Major green SMEs geographical location - 2017-2019-2021
  • 138. Minor SMEs geographical location - 2017-2019-2021
  • 139. Prevalence of SE and SME employers in areas of deprivation
  • 140. Accessibility models ▶ combination of two functions (ESPON, 2015; Schürmann et al., 1997; Spiekermann and Wegener, 2007): Ai = n X j=1 g(Wj)f(cij) (1) ▶ Ai: accessibility of area i, ▶ Wj: activity to be reached in area j, ▶ cij: generalised cost of reaching area j from area i. ▶ g(Wj) and f(cij) are called activity and impedance functions, respectively. ▶ The greater the number of attractive destinations in regions j and the more accessible regions j are from region i, the greater is the accessibility of region i.
  • 141. Accessibility definitions Types of accessibility Activity function g(Wj) Impedance function f(cij) Travel distance/cost Wj = ( 1 if Wj ≥ Wmin 0 if Wj ≤ Wmin cij Daily accessibility Wj cij = ( 1 if cij ≤ cmax 0 if cij ≥ cmax Potential Wα j exp(−βcij) Source: Schürmann et al. (1997). Ait = n X j=1 GVAjt distancei,j (2) DAit = GVAtBBi,t (3) for t= 2015,..,2021, i= 1,..,39177
  • 142. Data Variables Definition Source Reference UK postcodes NUTS3 codes Mapping of UK postcodes to EU NUTS3 regional codes European Spatial Development Perspective (ESPON)- European Observation Network for Territorial Development and Cohesion https://www.espon.eu/tools- maps/espon-database GVA Gross value added by local au- thority, chained volume meas- ures in 2019 money value, pounds million Office for National Statistics (ONS) See footnote 1 UK postcodes geo coordinates longitude and latitude of UK postcodes Free Map Tools See footnote 2 distances routes driving distances between UK postcodes in km Open Source Routing Machine (OSRM) https://project-osrm.org/ distance matrices driving distances between UK postcodes in km Open Street Map https://www.openstreetmap.org SME UK postcodes postcode of SMEs surveyed LSBS Wave 2021 Department for Business, Energy & Industrial Strategy (BEIS) SFBB Percentage of premises in each postcode that has Superfast Broadband (greater than 30 Mbit/s and less than 300Mbit/s) coverage. Connected Nations Annual Report 2015 to 2017 OFCOM (See footnote 3) UFBB Percentage of premises in each postcode that has Ultrafast Broadband (greater than 300 Mbit/s) coverage. Connected Nations Annual Report 2015 to 2017 OFCOM (See footnote 3) 1 https://www.ons.gov.uk/economy/grossdomesticproductgdp/datasets/regionalgrossvalueaddedbalancedbyindustr 2 https://www.freemaptools.com/download-uk-postcode-lat-lng.htm 3 https://www.ofcom.org.uk/research-and-data/data/opendata
  • 143. Econometric analysis ▶ panel probit model: estimation of probability of demanding external finance ▶ dependent variable: demand for different types external finance ▶ explanatory variables: firm-level charateristics and accessibility measures ▶ time fixed effects: capture impact of events that occur in non-sampled years
  • 144. Dependent Variables Variable Values Description External Finance used H3A 0=No;1=Yes. Bank overdraft facility H3C 0=No;1=Yes. Credit cards H3F 0=No;1=Yes. Government or local authority grants or schemes exc. related to Coronavirus H3H 0=No;1=Yes. Loan from any financial institution, exc. related to Coronavirus H3J 0=No;1=Yes. Loan from P2P platform External Finance applied for H51 0=No;1=Yes. Bank overdraft facility H53 0=No;1=Yes. Credit cards H56 0=No;1=Yes. Government or local authority grants or schemes, exc. related to Coronaviruss H58 0=No;1=Yes. Loan from any financial institution exc. related to Coronavirus H510 0=No;1=Yes. Loan from P2P platform Source: BEIS - Longitudinal Survey of Small Businesses (LSBS) -2021
  • 145. Independent Variables Independent variables techSF digital accessibility (Ultra-fast broadband coverage) techUF digital accessibility (Super-fast broadband coverage) access index accessibility indicator Control variables A2SPSS2 Age of business URBRUR2 Broad urban/rural categorisation from postcode A12 Is your business a family owned business WLED Whether business is women-led MLED Whether business is MEG-led C1 C2 Whether export goods or services P1 approximate turnover of your business in the past 12 months R1 Aim to grow sales. IMD 20 Index of multiple deprivation from postcode (20%) IMD 40 Index of multiple deprivation from postcode (40%) IMD 60 Index of multiple deprivation from postcode (60%) IMD 80 Index of multiple deprivation from postcode (80%) IMD 100 Index of multiple deprivation from postcode (100%) H4 Have you tried to obtain external finance for your business in the past 12 months? Growth Summary of growth in last year. SECTOR Broad industrial sector Age Age of business - summary Region Location in UK regions Source: BEIS - Longitudinal Survey of Small Businesses (LSBS) -2021
  • 146. Current use of external finance (Major green SMEs) Dependent variable: Govt grants Bank loans P2P Variables Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME access index -0.01967597 0.00 *** -0.00771 0.02307 0.00 *** 0.00881 -0.01968 0.00 *** -0.00771 techSF 0.00032834 0.600821 0.000129 0.00079 0.390701 0.000302 0.000328 0.600821 0.000129 A2SPSS2 -0.06795273 0.00 *** -0.02661 -0.10271 0.00 *** -0.03924 -0.06795 0.00 *** -0.02661 C1 C2 -0.0075553 0.074981 * -0.00296 -0.01176 0.0589779 * -0.00449 -0.00756 0.074981 * -0.00296 R1 -0.0041572 0.202232 -0.00163 -0.00569 0.234083 -0.00217 -0.00416 0.202232 -0.00163 R2a 0.09033905 0.00 *** 0.035377 0.203353 0.00 *** 0.077678 0.090339 0.00 *** 0.035377 R2b 0.02055622 0.00 *** 0.00805 0.099243 0.00 *** 0.037909 0.020556 0.00 *** 0.00805 H4 0.00075285 0.905463 0.000295 0.176537 0.00 *** 0.067435 0.000753 0.905463 0.000295 WLED 0.00307347 0.421988 0.001204 -0.01889 0.0007713 *** -0.00722 0.003073 0.421988 0.001204 IMD quintiles 40 0.01128678 0.003589 *** 0.00442 -0.00492 0.3866724 -0.00188 0.011287 0.003589 *** 0.00442 Growtha 0.00637256 0.109066 0.002496 -0.00566 0.3317005 -0.00216 0.006373 0.109066 0.002496 Growthb 0.01204938 0.001672 *** 0.004719 0.008236 0.1430979 0.003146 0.012049 0.001672 *** 0.004719 R-Squared 0.030542 0.10785 0.030542 Adj. R-Squared 0.029696 0.10707 0.029696 F(12,16028) 123.772 239.43 123.772 p-value 0.000 0.000 0.000 Fixed Effects Estimate Pr(> |t|) Estimate Pr(> |t|) Estimate Pr(> |t|) 2017 0.1623434 0.000 0.224186 0.000 0.162343 0.000 2019 0.1220058 0.000 0.21934 0.000 0.122006 0.000 2021 0.1277824 0.000 0.188161 0.000 0.127782 0.000
  • 147. Application for external finance (Major green SMEs) Dependent variable: Govt grants Bank loans P2P Variables Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME access indexa 0.004898 0.652258 0.00189407 0.007151 0.6654782 0.002573 0.004898 0.652258 0.001894 techSFa 0.002697 0.297507 0.00104273 -0.00821 0.0371438 * -0.00295 0.002697 0.297507 0.001043 A2SPSS2a 0.007523 0.462597 0.00290905 -0.00626 0.6878533 -0.00225 0.007523 0.462597 0.002909 C1 C2a -0.02097 0.229876 -0.0081111 -0.00586 0.8256455 -0.00211 -0.02097 0.229876 -0.00811 R1a -0.00349 0.83746 -0.0013499 0.072804 0.0049678 ** 0.026193 -0.00349 0.83746 -0.00135 R2a 0.129183 4.478e-14 *** 0.04995577 0.164618 2.426e-10 *** 0.059226 0.129183 4.478e-14 *** 0.049956 R2b 0.037823 0.027923 * 0.01462638 0.088261 0.0008 *** 0.031755 0.037823 0.027923 * 0.014626 H4a -0.12431 0.00 *** -0.0480693 0.034891 0.1431871 0.012553 -0.12431 0.00 *** -0.04807 WLEDa 0.037304 0.061345 . 0.01442559 -0.01334 0.6600951 -0.0048 0.037304 0.061345 . 0.014426 IMD quintiles 40 0.068349 0.00*** 0.02643077 -0.0797 0.0016239 ** -0.02867 0.068349 0.00*** 0.026431 Growtha 0.023768 0.177926 0.00919102 -0.04103 0.1264363 -0.01476 0.023768 R-Squared 0.045891 0.00029311 0.045891 Adj. R-Squared 0.039783 0.0061065 0.039783 F-statistic(12, 2187) 19.6951 8.59 19.7 p-value 0.00 0.00 0.00 Fixed Effects Estimate Pr(> |t|) Estimate Pr(> |t|) Estimate Pr(> |t|) 2017 0.105895 0.0135054 0.2425134 2.04E-04 0.105895 0.013505 2019 0.068827 0.08904312 0.2567646 3.16E-05 0.068827 0.089043 2021 0.068363 0.14439463 0.281013 8.26E-05 0.068363 0.144395
  • 148. Use of external finance (Minor green SMEs) Dependent variable: Govt grants Bank loans P2P Variables Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME access indexa -0.00868898 0.0892648 * -0.00344 -0.02812 0.003193 ** -0.01086 -0.00869 0.0892648* -0.00344 techSFa 0.00064808 0.5257697 0.000257 0.001017 0.593182 0.000393 0.000648 0.5257697 0.000257 A2SPSS2a -0.02760977 0.00 *** -0.01093 -0.10189 0.00 *** -0.03935 -0.02761 0.00 *** -0.01093 C1 C2a 0.00312387 0.6955567 0.001237 -0.01158 0.436585 -0.00447 0.003124 0.6955567 0.001237 R1a 0.00547837 0.3321981 0.002169 0.021769 0.038782 * 0.008407 0.005478 0.3321981 0.002169 R2a 0.04888569 0.0003779 *** 0.019358 0.073019 0.004376 ** 0.028199 0.048886 0.0004 *** 0.019358 R2b 0.03534719 0.0011336 ** 0.013997 0.061292 0.002460 ** 0.02367 0.035347 0.0011336 ** 0.013997 H4a -0.00876953 0.5187497 -0.00347 0.242825 0.00*** 0.093776 -0.00877 0.5187497 -0.00347 WLEDa -0.00528869 0.4236735 -0.00209 -0.02801 0.023085** -0.01082 -0.00529 0.4236735 -0.00209 IMD quintiles 40 0.02000361 0.0027627 ** 0.007921 0.040473 0.0012 ** 0.01563 0.020004 0.0028 ** 0.007921 Growtha 0.01609391 0.0278** 0.006373 -0.00497 0.715108 -0.00192 0.016094 0.0277199 * 0.006373 Growthb 0.00623492 0.3286072 0.002469 -0.01075 0.366153 -0.00415 0.006235 0.3286072 0.002469 R-Squared 0.018245 0.077096 0.018245 Adj. R-Squared 0.013328 0.072474 0.013328 F-statistic(12,2795) 10.1772 38.3008 10.1772 p-value 0.00 0.00 0.00 Estimate Pr(> |t|) Estimate Pr(> |t|) Estimate Pr(> |t|) 2017 0.05942655 0.00142032 0.221063 2.16E-10 0.059427 0.00142032 2019 0.04162561 0.02029935 0.221239 4.27E-11 0.041626 0.02029935 2021 0.04281285 0.04534225 0.180163 6.43E-06 0.042813 0.04534225
  • 149. External finance applied for (Minor green SMEs) Dependent variable: Govt grants Bank loans P2P Variables Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME Estimate Pr(> |t|) ME access indexa -0.05383 0.1103835 -0.0211815 0.0284979 0.6240189 0.010057 -0.053835 0.110384 -0.02118 techSFa -0.00881 0.2092665 -0.0034679 0.0015955 0.8951975 0.000563 -0.008814 0.209267 -0.00347 A2SPSS2a 0.010089 0.744077 0.00396946 -0.0872793 0.103009 -0.0308 0.010089 0.744077 0.003969 C1 C2a 0.045119 0.2962931 0.01775242 -0.1186559 0.1124363 -0.04188 0.045119 0.296293 0.017752 R1a -0.19676 0.00 *** -0.0774161 0.1632676 0.0134156 * 0.057619 -0.19676 0.00 *** -0.07742 R2a 0.151802 0.0005 *** 0.05972709 -0.1242632 0.0983418 . -0.04385 0.151802 0.0005 *** 0.059727 R2b 0.182434 0.00 *** 0.07177977 -0.0302601 0.6960688 -0.01068 0.182434 0.00 *** 0.07178 H4a 0.031995 0.4396005 0.01258856 -0.1228089 0.0867219 . -0.04334 0.031995 0.439601 0.012589 WLEDa 0.236821 0.00 *** 0.09317855 -0.3277493 0.000*** -0.11567 0.236821 0.00 *** 0.093179 IMD quintiles 40 -0.00619 0.8950754 -0.0024346 -0.1781877 0.0286827 * -0.06288 -0.006188 0.895075 -0.00243 Growtha 0.02798 0.526906 0.01100877 0.0384304 0.614954 0.013563 0.02798 0.526906 0.011009 Growthb 0.074013 0.1069039 0.02912067 0.012431 0.8751661 0.004387 0.074013 0.106904 0.029121 R-Squared 0.040786 0.01483 0.040786 Adj. R-Squared -0.01067 -0.038014 -0.010666 F-statistic(12 and 261) 6.86126 3.25456 6.86126 0.00 0.00021674 0.00 Estimate Pr(> |t|) Estimate Pr(> |t|) Estimate Pr(> |t|) 2017 0.248089 0.04935798 0.4531514 0.03782504 0.248089 0.049358 2019 0.191351 0.11382327 0.4360871 0.03736061 0.191351 0.113823 2021 0.352045 0.01503553 0.3579741 0.15095526 0.352045 0.015036
  • 150. Conclusions: Geography and access to finance 1. Geographical peripherality (i.e. low geo-accessibility) is a hurdle for the use of -and applications for- bank-based external finance (loans, overdrafts and credit cards). It increases the likelihood that SMEs will use government and local authority grants. 2. SMEs that prioritise environmental aims rely more on government grants and less on financial services than those that prioritise profit-making. 3. Access to a bank is still important for green SMEs. Broadband access has a significant role to play in diversifying their sources of external finance, but so far it has not replaced the need for physical access to financial services.
  • 151. Conclusions: Green awareness 1. More SMEs have become green between 2017-2021. Only 10% of all SMEs have no environmental objective in 2021, against 30% in 2017. 2. The proportion of SMEs that prioritise financial goals over environmental ones (i.e., SMEs with minor green mission) has increased significantly between 2017 and 2021 (56.3% in 2017 and 72% in 2021). This suggests that higher environmental awareness did not translate into a higher proportion of SMEs having mainly green objectives. 3. More than half of the SMEs without any green objective have no awareness of energy saving schemes. As Owen et al (2022b) suggest, this underscores the need for public policy to raise awareness and access to finance to encourage green change.
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