The Local Distribution and Determinants of HGFs in selected OECD countries

Professor Mark Hart & Dr Yama TemouriEconomics & Strategy Group

OECD International workshop:28th March 2012

Presentation Plan

1. Project Outline

2. Data

3. Methodological Issues

4. Incidence of High-growth firms

 

Project OutlineTask 1: Show distributions of HGF locations for a selected

number of OECD countries.By age of firmsBy size of firmsBy 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 variablesLocal factors at NUTS-2/3 level.

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

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.

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)

HGFs (2006-09) non-HGFs (2006-09)

Mean SD Mean SDSize (avg. no of employees) 168 920 151 1541

Age 18 17 27 21

Turnover (mill) 81 387 76 770K/L ratio (thousands) 135 991 117 815Intangibles/Total Assets (%) 4.3 8.9 2.3 6.4

Cash Flow (mill) 3.9 36 3.6 60

Debt (mill) 11 82 9.8 206Avg. Wage (thousands) 73 64 64 161

Comparison HGFs vs non-HGFs – 2006-09

Employment Size Distribution of HGFs in the UK

No. of HGFs(percentage)

HGFs (2006-09)Orbis (BvD)

HGFs (2005-08)ONS

10-19 25.9 53.420-49 30.6 30.850-99 19.2 8.2100-249 14.1 4.6250+ 10.1 3.1

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.

Incidence of HGFs – Selected Countries 2006-09HGF - OECD Employment Definition Belgium Denmark Germany Italy UK

UK (ONS)

2005-08

Manufacturing 2.8 3.2 2.8 3.3 2.7 3.5

Construction 3.3 3.0 2.3 5.9 3.9 6.0Wholesale and Retail Trade 3.7 3.7 3.7 6.3 4.6 5.2Hotels and Restaurants 3.8 6.5 5.0 6.4 5.0 5.1Transport, Storage and Communication 5.3 8.1 4.9 7.5 6.3 6.5Financial Intermediation 4.8 2.6 3.4 4.4 10.3 9.1

Real Estate, Renting and Business Activities 10.7 8.9 7.7 9.6 9.8 7.8

All 4.7 4.8 4.0 5.0 6.0 5.8

Incidence of HGFs – 2006-09

Belgium Denmark Germany Italy UK

High-tech Manufacturing7.9 4.9 3.4 4.9 3.2

Low-tech Manufacturing 2.4 3.1 2.7 3.2 2.9

High-tech Services 11.4 17.3 9.5 8.1 11.6

Low-tech Services 5.5 4.8 4.7 6.9 7.1

Belgium: 4.7% (947 firms/20,310) – Top 5

NUTS-2 (11 regions in total) %

NUTS-3( 44 regions in total) %

Région de Bruxelles-Capitale / Brussels Hoofdstedelijk Gewest 6.5 Arr. Arlon (PR) 9.6

Prov. Brabant Wallon 5.8 Arr. Oostende (PU) 7.4

Prov. Vlaams-Brabant 5.4 Arr. Neufchâteau (PR) 7.1

Prov. Antwerpen 5.2

Arr. de Bruxelles-Capitale / Arr. van Brussel-Hoofdstad (PU) 6.5

Prov. Luxembourg (BE) 5.2 Arr. Antwerpen (PU) 6.1

PU = predominantly urban; IN = intermediate; PR = predominantly ruralSource: European Commission (DG REGIO and DG AGRI)

Denmark: 4.8% (475 firms/9,950) – Top 5

NUTS-2 (5 regions in total) %

NUTS-3(11 regions in total) %

Hovedstaden 5.8 Byen København (PU) 7.5

Midtjylland 4.5 Københavns omegn (PU) 5.1

Syddanmark 4.3 Sydjylland (PR) 4.9

Nordjylland 4.2 Østjylland (IN) 4.9

Sjælland 2.6 Nordsjælland (IN) 4.3

PU = predominantly urban; IN = intermediate; PR = predominantly ruralSource: European Commission (DG REGIO and DG AGRI)

Italy: 5.0% (2,548 firms/50,458) – Top 5

NUTS-2 (21 regions in total) %

NUTS-3(107 regions in total) %

Basilicata 9.0 Brindisi (IN) 12.8

Lazio 7.9 Matera (PR) 12.5

Calabria 7.9 Caltanissetta (IN) 10.3

Sicilia 6.7 Oristano (PR) 10.2

Valle d'Aosta/Vallée d'Aoste 6.5 Messina (IN) 9.9

PU = predominantly urban; IN = intermediate; PR = predominantly ruralSource: European Commission (DG REGIO and DG AGRI)

United Kingdom: 6.0% (1,607 firms/26,599) – Top 5

NUTS-2 (37 regions in total) %

NUTS-3(133 regions in total) %

Inner London 9.1 Portsmouth (PU) 14.7

Outer London 8.4Perth & Kinross and Stirling (IN) 12.1

Cornwall and Isles of Scilly 8.3 Isle of Wight (IN) 11..8Gloucestershire, Wiltshire and Bristol/Bath area 7.3 West Cumbria (IN) 11.7Berkshire, Buckinghamshire and Oxfordshire 7.0

Outer London - West and North West (PU) 10.4

PU = predominantly urban; IN = intermediate; PR = predominantly ruralSource: European Commission (DG REGIO and DG AGRI)

Germany: 4.0% (1,787 firms/44,867) – Top 5

NUTS-2 (39 regions in total) %

Berlin 6.6

Hamburg 6.5

Bremen 6.2

Darmstadt 6.0

Sachsen-Anhalt 5.4

NUTS 3 Analysis not yet completed for GermanyPU = predominantly urban; IN = intermediate; PR = predominantly ruralSource: European Commission (DG REGIO and DG AGRI)

Thank you