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The Local Distribution and Determinants of HGFs in selected OECD countries

Explore the distribution and determinants of High-Growth Firms (HGFs) in selected OECD countries, analyzing firm age, size, sector, and ownership types. Investigate the impact of local factors on HGF incidence and develop a model for their occurrence. Utilize Orbis and Eurostat data to assess the robustness of the dataset. Address methodological issues related to firm size reporting and sub-national analysis challenges. Compare HGF incidence across Belgium, Denmark, Italy, United Kingdom, and Germany.

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The Local Distribution and Determinants of HGFs in selected OECD countries

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  1. The Local Distribution and Determinants of HGFs in selected OECD countries Professor Mark Hart & Dr Yama Temouri Economics & Strategy Group OECD International workshop: 28th March 2012

  2. Presentation Plan • Project Outline • 2. Data • 3. Methodological Issues • 4. Incidence of High-growth firms

  3. Project Outline • Task 1: Show distributions of HGF locations for a selected number of OECD countries. • By age of firms • By size of firms • By industrial sector of firms (2-digit industries, high-tech versus low-tech) • By ownership types (foreign versus domestic) • Task 2: Estimate the determinants for the incidence of HGFs, with special emphasis on local factors. • Firm level variables • Local factors at NUTS-2/3 level.

  4. Research Questions - Stage 2 • Does locality matter in determining the number of HGFs? • We know from previous research that there are key local drivers of small firm growth in the UK (Hart and McGuinness, 2003) • Towards a model of the Incidence of HGF • HGF = Population Density (proxy for Urban/agglomeration effects) + GDP measure (in the base year for the 3-year HGF metric) + change in inactivity/unemployment + educ/skills (social capital measure). • However, priority is to establish how robust the Orbis (BvD) dataset is to enable us to undertake this work

  5. Data & Sources • Orbis (Bureau van Dijk) • Focus on the most recent 3 year period  2006 – 2009 • Firm-level data (Orbis) : Turnover; Employees, Assets, Business Age, Cash Flow, industry affiliation, location, ownership • Local determinants (Eurostat): Labour market characteristics, Human Capital (education/skills), inactivity/unemployment, local demand conditions (cost of land & labour), stock and dynamics of existing enterprise activity, population density.

  6. Methodological Issues • Number of Firms (10+ employees) extracted from Orbis are significantly smaller than those obtained from the population data for each country. • For example, in the UK for the 2006-09 period there are ~12,000 HGFs based on ONS data (see Anyadike-Danes; Bonner and Hart, 2011) – whereas from Orbis there are 1,607 HGFs (both use the employment definition) • We know that smaller firms may not report full accounts  leading to bias towards larger firms - which is in fact what we observe (insert chart on next slide)

  7. Comparison HGFs vsnon-HGFs – 2006-09

  8. Employment Size Distribution of HGFs in the UK

  9. Methodological Issues (contd.) • Calls into question the ability to undertake sub-national analysis at NUTS 2 or NUTS 3 level – • …..the incidence of HGFs for these geographical areas will be based on very small numbers • Robust econometric models will be difficult to estimate.

  10. Incidence of HGFs – Selected Countries 2006-09

  11. Incidence of HGFs – 2006-09

  12. Belgium: 4.7% (947 firms/20,310) – Top 5 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

  13. Denmark: 4.8% (475 firms/9,950) – Top 5 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

  14. Italy: 5.0% (2,548 firms/50,458) – Top 5 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

  15. United Kingdom: 6.0% (1,607 firms/26,599) – Top 5 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

  16. Germany: 4.0% (1,787 firms/44,867) – Top 5 NUTS 3 Analysis not yet completed for Germany PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

  17. Thank you

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