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

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Presentation Plan 1.Project Outline 2. Data 3. Methodological Issues 4. Incidence of High-growth firms

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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.

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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

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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.

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Methodological Issues 1.Number of Firms (10+ employees) extracted from Orbis are significantly smaller than those obtained from the population data for each country. 2.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) 3.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)

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HGFs (2006-09)non-HGFs (2006-09) MeanSDMeanSD Size (avg. no of employees)1689201511541 Age18172721 Turnover (mill)8138776770 K/L ratio (thousands)135991117815 Intangibles/ Total Assets (%)4.38.92.36.4 Cash Flow (mill)3.9363.660 Debt (mill)11829.8206 Avg. Wage (thousands)7364 161 Comparison HGFs vs non-HGFs 2006-09

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Employment Size Distribution of HGFs in the UK No. of HGFs (percentage) HGFs (2006-09) Orbis (BvD) HGFs (2005-08) ONS 10-1925.953.4 20-4930.630.8 50-9919.28.2 100-24914.14.6 250+10.13.1

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Methodological Issues (contd.) 1.Calls into question the ability to undertake sub- national analysis at NUTS 2 or NUTS 3 level 2...the incidence of HGFs for these geographical areas will be based on very small numbers 3.Robust econometric models will be difficult to estimate.

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Incidence of HGFs Selected Countries 2006-09 HGF - OECD Employment DefinitionBelgiumDenmarkGermanyItalyUK UK (ONS) 2005-08 Manufacturing2.83.22.83.32.73.5 Construction3.33.02.35.93.96.0 Wholesale and Retail Trade3.7 6.34.65.2 Hotels and Restaurants3.86.55.06.45.05.1 Transport, Storage and Communication5.38.14.97.56.36.5 Financial Intermediation4.82.63.44.410.39.1 Real Estate, Renting and Business Activities10.78.97.79.69.87.8 All4.74.84.05.06.05.8

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Incidence of HGFs 2006-09 BelgiumDenmarkGermanyItalyUK High-tech Manufacturing 7.94.93.44.93.2 Low-tech Manufacturing2.43.12.73.22.9 High-tech Services11.417.39.58.111.6 Low-tech Services5.54.84.76.97.1

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Belgium: 4.7% (947 firms/20,310) Top 5 NUTS-2 (11 regions in total)% NUTS-3 ( 44 regions in total)% Rgion de Bruxelles- Capitale / Brussels Hoofdstedelijk Gewest6.5 Arr. Arlon (PR) 9.6 Prov. Brabant Wallon5.8Arr. Oostende (PU)7.4 Prov. Vlaams-Brabant5.4 Arr. Neufchteau (PR) 7.1 Prov. Antwerpen5.2 Arr. de Bruxelles-Capitale / Arr. van Brussel- Hoofdstad (PU)6.5 Prov. Luxembourg (BE)5.2Arr. Antwerpen (PU)6.1 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

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Denmark: 4.8% (475 firms/9,950) Top 5 NUTS-2 (5 regions in total)% NUTS-3 (11 regions in total)% Hovedstaden5.8 Byen Kbenhavn (PU) 7.5 Midtjylland4.5 Kbenhavns omegn (PU ) 5.1 Syddanmark4.3 Sydjylland (PR) 4.9 Nordjylland4.2 stjylland (IN) 4.9 Sjlland2.6 Nordsjlland (IN) 4.3 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

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Italy: 5.0% (2,548 firms/50,458) Top 5 NUTS-2 (21 regions in total)% NUTS-3 (107 regions in total)% Basilicata9.0 Brindisi (IN) 12.8 Lazio7.9 Matera (PR) 12.5 Calabria7.9 Caltanissetta (IN) 10.3 Sicilia6.7 Oristano (PR) 10.2 Valle d'Aosta/Valle d'Aoste6.5 Messina (IN) 9.9 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

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United Kingdom: 6.0% (1,607 firms/26,599) Top 5 NUTS-2 (37 regions in total)% NUTS-3 (133 regions in total)% Inner London9.1 Portsmouth (PU) 14.7 Outer London8.4 Perth & Kinross and Stirling (IN) 12.1 Cornwall and Isles of Scilly8.3 Isle of Wight (IN) 11..8 Gloucestershire, Wiltshire and Bristol/Bath area7.3 West Cumbria (IN) 11.7 Berkshire, Buckinghamshire and Oxfordshire7.0 Outer London - West and North West (PU) 10.4 PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

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Germany: 4.0% (1,787 firms/44,867) Top 5 NUTS-2 (39 regions in total)% Berlin6.6 Hamburg6.5 Bremen6.2 Darmstadt6.0 Sachsen-Anhalt5.4 NUTS 3 Analysis not yet completed for Germany PU = predominantly urban; IN = intermediate; PR = predominantly rural Source: European Commission (DG REGIO and DG AGRI)

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Thank you