Exploring the incidence and spatial distribution of high growth firms in the UK and their contribution to job creation

Nesta Working Paper No. 13/05

Michael Anyadike-Danes Karen Bonner Mark Hart

Exploring the incidence and spatial distribution of high growth firms in the UK

and their contribution to job creation

Michael Anyadike-Danes Aston Business School

Karen Bonner Aston Business School

Mark Hart Aston Business School

Nesta Working Paper 13/05

March 2013

www.nesta.org.uk/wp13-05

Abstract

High growth firms have been attracting increasing attention from policymakers interested in promoting job growth. Researchers have responded by exploiting newly available large firm-level datasets to study the role of high growth firms in the dynamics of job creation and destruction. This paper uses the recently agreed OECD definition of a high growth firm – requiring sustained growth in employment, averaging 20 per cent per year over three years -- to provide benchmark numbers on high growth firms for the UK compiled from the ONS Business Structure Database over the period from 1997 to 2010. It reports counts of high growth firms and measures of incidence (high growth firm numbers relative to the size of the business population) across a set of characteristics: age; size; industrial sector; and location. It concludes with an investigation of job growth which confirms that in the UK high growth firms are, indeed, disproportionately prolific job creators.

JEL Classification: F23, L25, L26, L53, R12

Keywords: High growth firms, job creation

We thank Albert Bravo-Biosca for his support and encouragement, an anonymous referee for comments which improved the paper, and the ONS VML team for their assistance. Corresponding author: Michael Anyadike-Danes. Economics and Strategy Group. Aston Business School. Aston University, Birmingham B1 7ET. Email: m.anyadike-danes@aston.ac.uk. The Nesta Working Paper Series is intended to make available early results of research undertaken or supported by Nesta and its partners in order to elicit comments and suggestions for revisions and to encourage discussion and further debate prior to publication (ISSN 2050-9820). © 2012 by the author(s). Short sections of text, tables and figures may be reproduced without explicit permission provided that full credit is given to the source. The views expressed in this working paper are those of the author(s) and do not necessarily represent those of Nesta.

Contents

introduction 2context and motivation . . . . . . . . . . . . . . . . . . . . . . . . . 2data sources & definitions . . . . . . . . . . . . . . . . . . . . . . . 2

1 HGF incidence and characteristics 81.1 HGFs: age, size & sector . . . . . . . . . . . . . . . . . . . . . 8

1.1.1 Headline figures . . . . . . . . . . . . . . . . . . . . . 81.1.2 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.1.3 Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.1.4 Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2 The distribution of HGFs across characteristics . . . . . . . . 131.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 hgf geography 192.1 Introduction and summary statistics . . . . . . . . . . . . . . 192.2 Spatial distribution of HGFs . . . . . . . . . . . . . . . . . . . 21

2.2.1 London . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.2.2 The Greater South East . . . . . . . . . . . . . . . . . . 232.2.3 Scotland . . . . . . . . . . . . . . . . . . . . . . . . . . 242.2.4 The north of England . . . . . . . . . . . . . . . . . . . 252.2.5 South West England and Wales . . . . . . . . . . . . . 252.2.6 The Midlands . . . . . . . . . . . . . . . . . . . . . . . 26

3 Job creation by HGFs 273.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.2 The accounting framework . . . . . . . . . . . . . . . . . . . 283.3 Job creating firms, 1998/2001 – 2007/10 . . . . . . . . . . . . 293.4 Job creation, 1998/01 – 2007/10 . . . . . . . . . . . . . . . . . 31

1

4 Drawing the threads together 344.1 UK HGFs: incidence and characteristics . . . . . . . . . . . . 344.2 Geography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3 HGFs contribution to job creation . . . . . . . . . . . . . . . . 364.4 The bigger picture . . . . . . . . . . . . . . . . . . . . . . . . . 37

5 Tables and Figures 42

Appendices 69

Appendix A Foreign firms in the UK: headline numbers, the inci-dence of high growth and its dynamic 70A.1 Headline numbers . . . . . . . . . . . . . . . . . . . . . . . . 71

A.1.1 Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71A.1.2 Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72A.1.3 Average size . . . . . . . . . . . . . . . . . . . . . . . . 72A.1.4 Net job creation . . . . . . . . . . . . . . . . . . . . . . 72A.1.5 Firms by country . . . . . . . . . . . . . . . . . . . . . 73A.1.6 Jobs by country . . . . . . . . . . . . . . . . . . . . . . 74A.1.7 Firms by sector . . . . . . . . . . . . . . . . . . . . . . 74A.1.8 Jobs by sector . . . . . . . . . . . . . . . . . . . . . . . 74

A.2 Foreign high growth firms: incidence and characteristics . . 75A.2.1 numbers and incidence . . . . . . . . . . . . . . . . . 75A.2.2 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77A.2.3 Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

A.3 The turbulent history of the UK’s foreign firms born intocohort98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78A.3.1 Becoming foreign . . . . . . . . . . . . . . . . . . . . . 78A.3.2 Understanding the evolution of the foreign share . . 80A.3.3 Is foreign ownership protective? . . . . . . . . . . . . 81

A.4 Foreign firms and high growth . . . . . . . . . . . . . . . . . 82A.4.1 Foreign firms in the rolling balanced panel . . . . . . 82A.4.2 Foreign firms and the incidence of high growth . . . 83A.4.3 Foreign ownership and the timing of high growth

instances . . . . . . . . . . . . . . . . . . . . . . . . . . 84A.5 Caveats, conjectures and future work . . . . . . . . . . . . . . 86

2

Introduction

Context and motivation

1. Over the last thirty years considerable evidence has accumulated (al-beit of widely varying quality) which supports a ’pareto-type’ con-jecture that a relatively small proportion of firms – disproportion-ately small firms – account for a relatively large proportion of jobcreation. David Birch is generally credited with having first formu-lated this conjecture (for an accessible summary see Birch [1981]),and although his claim about the extent of the small firm contribu-tion proved controversial (see for example Davis et al. [1996b]), theconjecture itself became widely accepted quite quickly (see for ex-ample the discussion in Storey and Johnson [1987]), and interest in itcontinues (for two recent contributions see Haltiwanger et al. [2012]and Neumark et al. [2011]1 ).

2. Systematic work on the conjecture was long hindered by a paucityof appropriate firm-level data but, particularly since the mid-1990s,as the data deficiency was made good, policy makers began to takean increasingly active interest in this research,

”[We have] the empirical observation that there is typicallya small group of firms that are responsible for a large shareof new jobs created. These rapidly expanding firms, byway of their supposed or actual potential to generate jobs,have attracted the attention of policy makers, eager to re-duce unemployment.”Schreyer [2000, p. 6]

This growing interest, in turn, motivated the OECD to initiate aprogramme of work which aimed both to measure the contribu-tion to job creation of these ’rapidly expanding firms’ – christened

1Most young firms are small, and the current focus is on distringuishing the relativeimportance of age and size

3

high growth firms (HGFs) – and to investigate their differentiat-ing characteristics (see Schreyer [2000], OECD [2002]). One of theby-products of this work was an internationally agreed definitionof an HGF (set out in the next section) and a chapter dedicatedto HGFs in the Manual of Business Demography (see EUROSTAT-OECD [2007, Chapter 8]). Although measuring the contribution tojob creation (i.e. calibrating the pareto-type conjecture) played a rolein the choice of HGF definition, its potential for use in internationalcomparisons appears to have been decisive in preferring it to the al-ternative high growth metric proposed by Birch (see the discussionin Ahmad [2006, p. 57], and for Birch’s ”growth index”, see Birch[1987, pp.36–38]).2

3. In 2008, a year after the publication of the Manual of Business De-mography, the OECD began publishing data on HGFs (see OECD[2008, Section B]), though not for the UK. However, there have as yetbeen relatively few studies of HGF incidence which make use of theOECD definition (amongst them are: Anyadike-Danes et al. [2009],Bravo-Biosca [2011] and Teruel and de Wit [2011]). Of course, therehad been studies of HGFs in the period before the OECD definitionwas agreed (for a comprehensive survey see Henrekson and Johans-son [2010]), nonetheless it seems that with respect to HGFs, policymakers have been running somewhat ahead of the evidence – HGF-oriented policy has been enthusiastically promoted even though it isaccepted that the evidence base is very weak (for a policy-orientedoverview of the evidence see Lilischkis [2011]).

4. Of course the newness of the definition may in part explain thescarcity of research.3 Certainly there has been virtually no systematicinvestigation of HGFs over time, 4 and this is the first gap in the evi-dence base which we address in the first chapter of the report, using atimes series of cross-sections to track the evolution of HGF incidenceover time by age, size and sector and the distribution of HGFs across

2However the ”growth index” continues to be used, see for example the recent studyof ”high-impact” firms and job creation (Acs et al. [2008]) for the US Small Business Ad-ministration.

3But there also seems to be some resistance to the adoption of the OECD definition. Avery clear example can be found in a recent discussion of financing innovative HGFs inFinland, see Murray et al. [2009, p. 148]

4Holzl [2011] is an (isolated) exception, but its focus is on survival and persistence anddoes not, for example, report basic descriptives on the number or incidence rate of HGFsin Austria.

4

these characteristics. The aim is an improved understanding of the’nature’ of HGFs and to provide ’benchmark’ numbers for UK policymakers with an interest in the HGF agenda. The two further chap-ters of the report address equally under-researched topics. Chap-ter two examines the distribution of HGF incidence across the 380Unitary Authorities and Local Authority Districts of Great Britain,whilst chapter three tackles the geography of HGFs and, finally, per-haps a rather more fundamental issue, the contribution of HGFs tojob creation. A fourth chapter sums up and a substantial appendixinvestigates foreign firms, their characteristics and the incidence ofhigh growth amongst them.

Data sources & definitions

1. We use the recently released UK Business Structures Database5 (com-piled by the Office for National Statistics)6 which records annualdata on employees for the entire population of firms in the UK. Thisdata is compiled from a series of annual ’snapshots’ of the Inter-Departmental Business Register, an administrative database whichcaptures information from a range of sources, amongst them VAT re-turns and employer Pay As You Earn (PAYE) tax and social securityrecords. The unit of analysis is an ”employer enterprise” – a businesswith at least one employee7 – which we refer to as a firm. Firms maycomprise a number of distinct local units (establishments or plants)but our data refer to firm-level employee numbers.

2. We have linked together the annual ’snapshots’ from the BSD us-ing firm-level identifiers to form a longitudinal firm-level database(LFLD) for the UK and have devised algorithms to produce firm-level demographic markers for ’birth’ and ’death’. The birth of a

5The statistical data used here is from the Office of National Statistics (ONS) and isCrown copyright and reproduced with the permission of the controller of HMSO andQueens Printer for Scotland.The use of the ONS statistical data in this work does notimply the endorsement of the ONS in relation to the interpretation or analysis of thestatistical data. The analysis upon which this report is based uses research datasets whichmay not exactly reproduce National Statistics aggregates.

6For a full, official, account of the Business Structures Database and its compilation,see Evans and Welpton [2009]

7Since an employee can work for more than one firm summing over firms producesan estimate of jobs rather than employment, we ignore this distinction here and use theterms employment and jobs interchangeably.

5

firm is dated by the first appearance of non-zero employment andits death is treated symmetrically and dated by the disappearance ofthe last employee. The data do not distinguish between de novo birthsand those which result from the break-up of an existing firm, simi-larly the data do not distinguish between the closure of a firm and itsdisappearance due to merger. Although the data start in 1997, firmsalive in 1997 could have been born in any previous year, so the firstbirth year we can identify with certainty is 1998.

3. Firms are classified as either ’private’ or ’public’ sectors and we makethis split using the classification by industrial sector. All employeesin: public administration and defence; education; and health andsocial work (SIC928 sections L, M, N); are classified as public sector.Of course, some firms in these sectors (in health and/or educationfor example) are private, and some firms in our private sector arepublic, but ours is a reasonable approximation and ensures that mosttypically longer lived public entities (like schools and hospitals) donot distort our calculations.

4. The first stage in the OECD metric for identifying an HGF (seeEUROSTAT-OECD [2007, Chapter 8]) requires that we consider onlyfirms which,

• are born before the beginning of the period

• are alive at the end of the period

These two requirements imply that in each period we will havea ’balanced panel’ of firms – the same firms are always presentthroughout the period (often referred to as ’continuing firms’).

An HGF is a firm in the balanced panel which,

• has at least 10 employees at the beginning of the period

• records an annual average growth of 20% in employment9 overthe period

8the UK version of the EU NACE rev.19Alternatively, an annual average growth of 20% in turnover over the period can be

used as the criterion, but only employment is used here.

6

5. Finally, we define HGF incidence10 and the ’incidence rate’ as thenumber of HGFs divided by the number of firms (in the balancedpanel) with 10+ employees11 We use three years as our ’period’: so,starting with 1998, there are ten 3-year periods: from 1998/2001 to2007/2010 – is is the ’rolling balanced panel’ (RBP) used throughoutthis report.

10We use the term ’incidence’ here by analogy with epidemiology, to serve as a re-minder that HGF status is time-dependent – in the present framework a firm which is anHGF in one three year period may, or may not, be an HGF in some other period.

11The ’incidence rate’ is similar to the term ”share” used by the OECD. However theOECD (for reasons which are obscure) uses a denominator in this calculation wider thanthe balanced panel: ”The share of high-growth enterprises is compiled as the number ofhigh-growth enterprises as a percentage of the population of enterprises with ten or moreemployees.” OECD [2011, p. 74]. In other words their denominator also includes all firmswhich are alive at the beginning of the period (so adding in those born in the first year ofthe period), whether or not they survive the period. The use of incidence rate instead of’share’ also allows us to distinguish clearly between conclusions about the incidence ofHGFs by characteristics and their distribution across characteristics, see below Section 4.

7

Chapter 1

HGF incidence and characteristics

1.1 HGFs: age, size & sector

1.1.1 Headline figures

1. The basic data on HGFs for periods 1998/2001 to 2007/2010 are dis-played on Figure 5.1. The HGF numbers are plotted by bars (scale onthe left hand side) and the incidence rate measured as a percentageis plotted by the line (scale on the right hand side).

2. There is quite a clear pattern in the HGF numbers – a ’bulge’ begin-ning in 1999/02 and lasting for three periods with close to 13,000HGFs in each period – then, for the six periods 2002/2005 onwards,numbers have been around 10,000 per period. The incidence ratefollowed a similar course to the numbers – during the bulge the inci-dence rate was close to 9%, since 2002/2005 it has fluctuated withina narrow range, just below 7.5% .1

1In a previous report (Anyadike-Danes et al. [2009]) we looked at HGF incidence forjust two periods, 2002/2005 and 2005/2008, using an earlier version of this dataset. Thenewer version of the dataset has, amongst other improvements, a better algorithm for de-termining births and deaths and produces smaller numbers of HGFs and slightly higherestimates of the incidence rate. In the older study there were 11,369 HGFs in 2002/2005and 11,530 in 2005/2008, the corresponding estimates of the incidence rate were 6.3% and5.8%. Using an entirely different dataset (derived from company accounts data and withlimited coverage) Hoffmann and Junge [2006, p. 9] estimated the HGF incidence rate forfirms with 15 or more employees in the UK to be between 3% and 4% (in 1999, 2000, and2001).

8

1.1.2 Age

3. Each period includes firms born a varying number of years previ-ously and we can classify them2, using their date of birth, into ’birthcohorts’: cohort98, those born in 1998; cohort99, those born in 1999;and so on. There is one important exception: the firms alive in 1997(the first year of our dataset), are a unknown mixture of firms born inevery preceding year and, although these firms are not a ’true’ birthcohort, for convenience we refer to them as cohort97*3.

4. From Figure 5.2, where the incidence rate is plotted against periodas before, we can see the effect of distinguishing incidence by date ofbirth. Evidently the incidence rate for cohort97* is very much lowerthan that for the other cohorts (and the average of the other cohorts).Moreover we can see, as might have been anticipated, that the over-all ’average’ incidence rate we saw in Figure 5.1 is largely driven bycohort97*.

5. The display in Figure 5.2 also puts the ’bulge’ in interesting per-spective. Whilst it appears that the cohort97* rate of incidence isits (proximate) cause, the incidence rate excluding 1998/2001 ’slopes’downward (albeit from a much lower level) and looks rather moresimilar to that of other cohorts. It appears that the regular flow ofnew cohorts – each of which records initially a relatively high inci-dence rate – serves to offset the slow decline of cohort97* incidence,and keeps the all-age incidence rate broadly flat.

6. It is easier to visualise the relationship between age and HGF inci-dence if we re-plot the data with age rather than period on the hor-izontal axis. On Figure 5.3 cohort97* now looks even more similarto the subsequent cohorts. Although the cohort97* incidence rate isalways very much lower, with the exception of the ’jump’ betweenage 1 and age 2, incidence declines steadily with age. All the othercohorts record incidence rates between 14% and 16% at age 1, withthe earlier cohorts (98, 99, 00) rather closer to 16%, later cohorts aretypically closer to 14%. On average, the incidence rate declines byabout 0.5 percentage points per year of age.4

2The convention adopted here is to measure age of HGFs at the first year of each 3-year growth period. Alternatively age could be measured to the middle year or the lastyear. Of course, by virtue of the OECD definition, HGFs must be at least one year old).

3The * is attached to cohort97 to differentiate it from the ’true’ birth cohorts4Indeed we might almost be able to ’tack’ the cohort97* series on to the end of the

9

1.1.3 Size

7. We have classified HGFs into size-bands using size in the year pre-ceding the beginning of the period (i.e. the same year used to deter-mine whether a firm has 10 or more employees as required by theHGF definition). So, for example, the allocation into size-bands forthe 1998/2001 period is based on firm size in 1997. The size-bandsare: 10 to 19; 20 to 49; 50 to 99; 100 to 249; and 250+.

8. It is difficult to discern any simple pattern in the display of incidenceover periods by size-band in Figure 5.4. It is clear that the steep risebetween 1998/2001 and 1999/2002 noted earlier is proximately dueto the two smallest size-bands – 10 to 19 and 20 to 49 – most of theothers either decline or are flat between the first two periods.

9. After 2002/05 the incidence rate curves all flatten, and there is nostable ranking by size, although the two smallest size-bands seem tobe consistently above the others – with the 20 to 49 size-band almostalways recording the largest incidence rate.

10. In order to probe the incidence/age relationship we need to split offcohort97*, because of its weight it dominates the overall incidencefigure. However it is not possible to distinguish each of the birthcohorts because some of the age/size-band combinations fall belowthe permitted disclosure limit,5 So we have an all cohort – except co-hort97* – incidence rate series by size-band, which is plotted againstage in Figure 5.5.

11. We can now see a relatively clear pattern – most size-bands declinewith age – except 250+ which is essentially flat. The 20 to 49 size-band has the largest incidence rate, but its incidence rate becomesmore similar to those of the other size-bands as the cohorts age. Byage 9 (by which date, admittedly, we have only observations fromcohort98) all the size-bands, including 250+, are recording about thesame rates of incidence.

12. Evidently size effects on HGF incidence are second order (leaving

avex97 series. From age 2 onward the cohort97* incidence rate declines about 0.5 per-centage points per year – the same as the avex97 series – over the following nine periods.

5The rules regulating use of the BSD prohibit the publication of any table which hascell counts less than 10.

10

the 250+ size-band to one side) when compared to age effects.6 Ofcourse, we might have inferred that size was unimportant from theoverall incidence figure alone, but we can now see its relative unim-portance more clearly.

1.1.4 Sector

13. We have computed incidence for a reasonably fine-grained industrialclassification – 47 categories of the 2-digit UK standard industrialclassification(SIC927). Although firms are very unevenly distributedacross sectors, in only one sector does the number of 10+ firms fallbelow the lower disclosure limit (the denominator for the HGF in-cidence rate calculation). Incidence rates vary widely, by a factor often, from a minimum under 2% 8 to a maximum of 25%.

14. The simplest way to approach detecting a pattern in incidence acrosssectors is to rank them. We have ten sets of ranks, one for each period(1998/2001 to 2007/2010) and we then compute an average rank foreach sector. The ’top 10’ sectors turn out to form quite a compactgroup: there are just ten sectors with an average rank of ten or higher,and all of them are typically ranked ten or higher in each period. Justfive sectors outside this top 10 are ever in the top 10. For example,the sector at 11th place in the list of average ranks, records a top 10rank in only two periods.

15. The top 10 sectors by average rank are plotted on Figure 5, from 1 to10 they are,

• 64 – Posts and telecommunications (KIS–HT)

• 72 – Computer and related activities (KIS–HT)

• 66 – Insurance and pension funding (KIS)

• 73 – Research and development (KIS–HT)

• 90 – Sewage and refuse disposal (LKIS)

• 67 – Activities auxiliary to financial intermediation (KIS)

6Although their study did not deal with the role of HGFs specifically, ? reached asimilar conclusion about the relative importance of age and size in their recent paper onjob creation in the US.

7The UK version of the EU NACE Rev 1.8If we ignore the sectors with no HGFs, typically those with very few 10+ firms.

11

• 65 – Financial intermediation (KIS)

• 74 – Other business activities (KIS)

• 71 – Renting of machinery and equipment and household goods(KIS)

• 93 – Other service activities (e.g. dry cleaning, hairdressing, fu-nerals, fitness) (LKIS)

Three of the top four places on the list are occupied by the(only) three 2-digit sectors which EUROSTAT classify as ”High-tech Knowledge Intensive services” (KIS-HT)9, another five are also”Knowledge Instensive Services”(KIS), whilst the final two are ”LessKnowledge Intensive Services” (LKIS). So ’the top 10’ are all services,and nearly all are knowledge intensive services.10

16. It is worth noticing too, that the bulge between 1999/2002 and2002/2005 is very pronounced in only a handful of the top ten. Butthe two sectors most affected – ’posts and telecommunications’ and’computers and related services’ (both of them hi-tech knowledgeintensive services) – are very strongly affected.11 In both cases theincidence rate is between 10 and 15 percentage points higher in thethree bulge periods than it is later on. For most of the other sectorsthe differential is much closer to five percentage points.

17. To confirm that focusing on the ’average’ top 10 is not too mislead-ing we can look at the other five sectors which are ever ranked inthe top 10: two are knowledge intensive services (Recreational, cul-tural and sporting activities (92), Real Estate Activities (70)); one is aless knowledge-intensive service (Travel Support Consumer Services(63)); one is hi-tech manufacturing (Manufacturing of office machin-ery and computers (30)); and finally Construction (45), which (oddly)seems not to appear at all in the EUROSTAT classification.

9For EUROSTAT’s account of statistics on ”hi-tech” sectors see http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/High-tech_statistics; and for a 2-digit level sector list see http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/Annexes/htec_esms_an2.pdf.

10In our earlier UK study, with just two periods (2002/2005 and 2005/2008), we had acoarser, eight (broad) sector, classification, but the incidence rate was apparently aboveaverage in Financial Intermediation (sic65 to sic67) and Real Estate, Renting and BusinessServices (sic 70 to sic74), see Anyadike-Danes et al. [2009, p. 28].

11It seems reasonable to suppose that the bulge in these two sectors is associated withthe ’dot-com boom’.

12

18. It is difficult to investigate the sectoral story in much more detail. Weknow already that the combined sample is dominated by cohort97*,especially in the early years and it is not possible to compute re-liable estimates of incidence (even leaving disclosure issues aside)for a classification as fine-grained as 2-digit sectors if cohort97* isexcluded. However, if we apply the same (admittedly rough-and-ready) ranking procedure to the data – ranks of incidence by 2-digitsector averaged for periods 2002/2005 to 2007/2010 – we get broadlysimilar results for the top 10 for cohort97* and for not-cohort97 as wehad for the pooled data (though with not precisely the same order-ing).

1.2 The distribution of HGFs across characteris-tics

1. Using the OECD definition of an HGF we have focused on the inci-dence rate. This is a measure which answers a question like: whatproportion of one year old 10+ firms record an instance of highgrowth? However there is an alternative measure for summarisingthe importance of HGFs that answers a different question, one like:

13

what proportion of HGFs are one year old?12 Here we will examinethe distribution of HGFs across age, size and sector.

2. As Table 5.1 confirms most HGFs are relatively old (it is a patternmight have been anticipated given what we know about the relativesize of cohort97*) – in 2007/2010 almost 50% are more than 10 yearsold. Otherwise the distribution looks relatively flat, with shares be-tween 5% and 7% at each age, but with a slight tendency to declinepast age 6. Of course, data for previous periods becomes less andless informative the further we go back in time because we are un-able to distinguish the ages of firms born before 1998, and so we havedisplayed just the 2007/2010 period.13 Evidently, although the inci-dence rate declines with age, the ’weight of the past’ ensures thatmost HGFs in any particular period are quite elderly.

3. Figure 5.7 displays the distribution of HGFs by size-band. The moststriking feature of the data is that half of all HGFs are found in thesmallest, 10 to 19 size-band, and very, very, few – less than 5% – arein the largest, 250+ size-band. The bulge in the first few years is onceagain clearly evident and it is concentrated in the 10 to 19 category

12These two measures are, in fact, quite closely (algebraically) related and the link be-tween them is a ”location quotient”(LQ)-type statistic. To see this, first write the incidencerate (inc) for an instance i of some characteristic (like age) of an HGF as the number ofHGFs divided by the number of firms with 10 employees or more (f10+),

inci =HGFi

f10+i

Now ’standardise’ the incidence rate for characteristic i by expressing it as a ratio to theaverage incidence rate (inc),

(inciinc

) =

HGFi

f10+i∑HGFi∑f10+i

We can re-arrange this expression into a LQ-type statistic for an incidence rate i of somecharacteristic (like age) of an HGF relative to the population of f10+ with that same char-acteristic as,

(inciinc

) = LQi =

HGFi∑HGFi

f10+i∑f10+i

The numerator of the LQ is the distribution of HGFs across different values of the char-acteristic (e.g. the ratio of one year old HGFs to all HGFs) and the denominator is theanalogous distribution for 10+ firms.

13For example, in 1999/2002 the split is 5.5% one year old and 94.5% greater than oneyear old; in 2000/2003 it is 4.9% one year old, 6.0% two years old, and 89.1% more thantwo years old; and so on.

14

which for three periods (1999/2002 to 2001/2004) accounts for 60%of all HGFs. From 2002/2005 onwards the shares change very little,with the 10 to 19 share at 54%, the 20 to 49 share at 30%, and thelarger size-bands sharing the rest.

4. HGFs are quite heavily concentrated by sector: together the top fivesectors – whose shares are displayed on Figure 5.8 – account formore than half of all HGFs in each period. Remarkably, around onefifth of all HGFs are in business services (sic74), with construction(sic45), second on the list, having just a 10% share. Notice too thatthe share of business services grows quite markedly in the post-bulgeperiod: in 2001/2004 it was 18%, by 2007/2010 it was seven percent-age points larger at 25%.

5. Finally, it is worth noting that there is no overlap between the top fiveon the shares list and the top five on the incidence rate list. Indeed, ofthe top five by share only one, business services, is in the incidencerate top ten (it is ranked seven). Moreover, the other sectors withshares in the top five – construction (sic45), wholesale distribution(sic51); and retail distribution (sic52); hotels and restaurants (sic55) –taken together account for about one third of all HGFs, even thoughthe HGF incidence rate in most of these sectors is typically quitemodest. Evidently, with the partial exception of business services,we may infer that the distribution of 10+ firms by sector looks quitedifferent to the sectoral distribution of HGFs: the other members ofthe top five must account for a disproportionate share of larger busi-nesses.

1.3 Discussion

1. As mentioned earlier, research on HGF incidence using the OECDdefinition is relatively scarce. However, at roughly the same timeas the OECD definition was agreed, Henrekson and Johansson[2010](H&J) surveyed the empirical literature on HGFs, and theirfindings provide a natural context for our results on the UK. Aftera systematic search of bibliographic databases (from 1990 to 2008)H&J compiled a list of 20 studies which analysed data from a rangeof countries (though not the UK). They organised their results about

15

the characteristics of HGFs – which they refer to as ”Gazelles”14 –around three propositions (Henrekson and Johansson [2010, p. 228]),

• ”On average, Gazelles are younger”• ”On average, Gazelles are smaller than other firms”• ”Gazelles are over-represented in high-technology industries”

2. As H&J recognise there is considerable variation across the studiesthey survey in just about every dimension (definition of HGFs,15

measurement of growth, choice of time period) and in the classifi-cation of HGF characteristics (sectors, size-bands, age range). How-ever,

” Sometimes this [heterogeneity] is a drawback since com-parability may be impaired. However in this case the largevariation should be seen as an advantage, since the resultsregarding the importance of Gazelles turn out to be quiterobust. Regardless of method, definition, time period etc.some findings emerge.” Henrekson and Johansson [2010,p. 240]

3. With respect to age, H&J’s answer was clear,

”The results regarding age are unambiguous. All studiesreporting on age find that Gazelles tend to be younger onaverage.”Henrekson and Johansson [2010, p. 240]

We found that the incidence rate declined with age – a larger propor-tion of younger firms are HGFs; but we also found that in 2007/2010almost half HGFs were ten or more years old. Since H&J seem to bereferring to the first, the greater propensity of young firms to grow,the UK evidence appears to be consistent with their proposition.

4. H&J’s conclusion about size is more nuanced:

” ... the results are ambiguous. Gazelles can be of all sizes...It appears that newness is a more important factor thansmall size.” Henrekson and Johansson [2010, p. 240]

14”In what follows we will use the terms Gazelles and high-growth firms synony-mously.” Henrekson and Johansson [2010, p. 229]. However, they do note (Henreksonand Johansson [2010, p. 228]) that the OECD was formulating a proposal that the termgazelle be used to denote a young (less than five year old) HGF, for the ”official” OECDdefinition see EUROSTAT-OECD [2007, p. 63]

15Only one of the studies included used the OECD definition.

16

This also fits with our findings: incidence by size-band showed littlevariation by period, and when incidence by size-band is displayedagainst age the picture is dominated by the decline with age: sizeplays only a secondary role.16

5. H&J find the proposition about the distribution of HGFs by sector tobe decisively rejected:

”There is no evidence that Gazelles are overrepresentedin high-technology industries. Gazelles exist in all indus-tries... [However] they appear to be overrepresented in ser-vices.” Henrekson and Johansson [2010, p. 240]

If ”overrepresented” is interpreted as an LQ-type proposition: thatHGFs are a larger proportion of 10+ firms in hi-tech sectors than theyare in non-hi-tech sectors (see footnote 12), then the UK is different.However, if ”overrepresented” means concentrated, then our evi-dence is consistent with H&J’s proposition: since a relatively smallproportion of the UK’s HGFs are found in hi-tech sectors.

6. There are two more recent studies which relied on the OECD defini-tion: Bravo-Biosca [2011] and Teruel and de Wit [2011]). The secondof these – a cross country investigation of the determinants of inci-dence used the turnover variant of the HGF definition and was re-stricted to firms with more than 50 employees (see Teruel and de Wit[2011, p. 9]) – and does not report results which are comparable withours. Bravo-Biosca [2011] is also a cross-country study which covers(amongst other things) HGF incidence rates and, like us, uses the em-ployment variant of the OECD definition, in his case computed forthe three year period 2002/2005. A section on HGFs records someheadline results Bravo-Biosca [2011, pp. 18–20] for a varying selec-tion of countries,

• the incidence rate17is higher for younger firms, but most high-growth firms are older (5 countries: Norway, Austria, Nether-lands, Italy, Finland)

• most high-growth firms are small, but large firms can achievehigh growth (9 countries: Norway, Austria, Netherlands, Den-mark, Italy, Finland, Spain, United States, UK)

16It is worth noting that Haltiwanger et al. [2012] emphasised the importance of con-trolling for age when discussing the signifiance of size in a job creation context .

17Bravo-Biosca [2011] uses the term ”share” where we use ’incidence rate’.

17

• high-growth firms are everywhere, not only in hi-tech or ”in-novative” sectors (9 countries, Norway, Austria, Netherlands,Denmark, Italy, Finland, Spain, United States, UK)

7. The findings for age and size match ours, on both the incidence rateand the distribution measure, however the findings on sectors donot. Certainly we found HGFs to be ubiquitous, and also agreethat ”A majority of ... high growth firms are found in the servicesectors...”Bravo-Biosca [2011, p. 19] but, using data for 2-digit sectorswe found that the incidence rate was highest in hi-tech and knowl-edge intensive services. Bravo-Biosca’s data was more highly aggre-gated – 4 broad sectors – and he found no detectable cross-sectorpattern in incidence.

18

Chapter 2

hgf geography

2.1 Introduction and summary statistics

1. This chapter describes the spatial distribution of hgf incidence ratesacross the Unitary Authorities and Local Authority Districts (UAL-ADs)1 in Great Britain. It is organised into two sections. This firstsection provides an introduction to the distribution and its evolutionover time and then introduces the summary statistic we use here: thedistribution of the median HGF incidence rate of each UALAD. Thesecond section maps the data for six broad regions of GB and pro-vides a running commentary on the pattern in the location of placesin the top 25% of the distribution.

2. Our starting point is the 380 local authorities on which we have HGFincidence rate observations for each of the ten three year periods inthe ’rolling balanced panel’2 – 2002/05 to 2007/10. A set of period-by-period boxplots displayed on Figure 5.9 provides a useful sum-mary of the data. Evidently the dataset is reasonably compact – thereare relatively few outliers (given that there are almost 400 observa-tions per period). It is also worth noting that the median of the UAL-ADs incidence rates, period-by-period, tracks the national HGF inci-dence rate quite closely, as you will see from Figure 5.10.

3. Obviously since we wish to investigate the spatial pattern of the HGFincidence rate it is impractical to work with 380 observations for each

1For simplicity the local authority units are referred to throughout as UALADs or localauthorities. However, in London these are ”boroughs” and in Scotland ”council areas”.

2For details of the construction of the ’rolling balanced panel’ see the Introduction,section 2

19

of ten periods, we need a summary statistic. As HGF incidence ratesin the early periods – from 1999/02 to 2001/04 – look atypically high(and this equally true of the national figure as of the median UALADrate), we have focused on the later period 2002/05 to 2007/10. So werepresent each UALAD by the median of its incidence rates in the sixperiods between 2002/05 to 2007/10.

4. Our basic dataset – an HGF incidence rate for each UALAD in GB –is plotted on Figure 5.11. The 380 observations have been organisedin ascending order, from the minimum (3.35%, Conwy in Wales) tothe maximum (12.2%, Rushmoor, South East England). The x-axisof the figure is labelled: ”SQ1”, ”SQ2”, through to ”SQ8”. These la-bels indicate the upper boundaries of the semi-quartiles of the data –as quartiles divide the data into quarters, so semi quartiles divide itinto eighths – quartiles refer to the 25th, 50th, and 75th percentiles,by analogy semi-quartiles refer to 12.5th, the 25th, the 37.5th etc per-centiles. SQ1 is the bottom semi-quartile and SQ8 is the top andthe HGF incidence rates at the semi-quartile boundaries have beenrecorded on the plot just above the x-axis tick marks. The number ofUALADs in each semi-quartile varies slightly because there are tiedvalues,3 and because 380 is not exactly divisible by eight.

5. The most immediately striking feature of Figure 5.11 is the limitedextent of variation across a very large proportion of the local author-ities. Across the middle half of the distribution – 213 observations– from the upper bound of SQ2 to the upper bound of SQ6, the in-crease is from 5.95% to 7.66%, just 1.71 percentage points and thedifference between successive semi-quartiles is typically around halfa percentage point. After SQ6 the ’slope’ of the curve increases andthe difference between the SQ6 upper boundary and the SQ7 up-per boundary is twice as large, and beyond SQ7 the rate moves upsharply: the difference between the SQ7 and SQ8 upper boundariesis almost four percentage points.

6. Our focus in what follows is on the top end of the distribution: locat-ing the UALADs which record HGF incidence rates in the top quar-tile of the distribution and, because the top semi-quartile stands outso clearly, we also distinguish SQ8 from SQ7.

3Since the number of HGFs in many UALADs is less than 100, it is not appropriate tomeasure HGF incidence rates to more than two decimal places.

20

2.2 Spatial distribution of HGFs

7. With a relatively large number of datapoints – 380 in all – a practi-cable approach to investigating the spatial pattern incidence requiresthat we group the the UALADs into broader spatial aggregates. Herewe use regions as building blocks: the nine (formerly governmentoffice) regions of England; and the two devolved administrations,Scotland and Wales. The first step is to tabulate the frequency dis-tribution of HGF incidence rates by semi-quartile across these areas,the counts are displayed in Table 5.2.

8. If the distribution of incidence rates were uniform across regionsthen, of course, the frequency in each cell across a region’s rowwould be the same. So for example the East Midland (EM) in thefirst row with 40 local authoritiess would record five local authori-ties in each semi-quartile. Evidently it does not – the count for SQ3(at the lower end of the distribution) is 11, over 25% of all its localauthorities. By contrast, the count for SQ8 is two, half as many asmight be ’expected’ in the top semi-quartile.

9. Whilst there is no particular reason to expect each region’s UALADsto be distributed uniformity across the semi-quartiles, the ’indepen-dence of rows and columns’ hypothesis (although it is a traditional”null” for a table of counts like this one with fixed margins), doesprovide a benchmark from which to start. The ’excess’ recorded asthe last column of the table is the difference between the SQ8 columnand 12.5% of the regional total (for example -2 for the East Midlands)and the most striking, and extraordinary, departure from this bench-mark is London: 21 of its 33 UALADs are in the top semi-quartile,where we might have expected about 4, so the ’excess’ is +17. Lon-don has 64% of its UALADs in SQ8 rather than 12.5%

10. Necessarily if London is over-represented – it alone accounts for al-most half of SQ8 local authorities – then at least one other region isunder-represented and in fact most are, as we can see from the lastcolumn of the table. However two other regions still do stand outas relatively well-endowed: Scotland exceeds the benchmark 12.5%,whilst the South East matches it. Nonetheless there is a spatial ra-tionale for including the second top semi-quartile (SQ7): London sodominates SQ8 that we need to consider SQ7 too if we are to be ableto differentiate between places in regions outside London.

21

11. In the following commentary, organised by region and groups of re-gions, we look at the places which populate the top end of the distri-bution of HGF incidence rates. We start with London itself and thenconsider the Greater South East, which comprises the two regions –the South East and East of England – which surround London. Nextwe move north, to Scotland and then discuss in turn: the North ofEngland – comprising the North East, the North West, and Yorkshireand the Humber; followed by Wales and South West England; andfinish with the Midlands – the East Midlands and the West Midlandstaken together.

12. In each case we plot local authorities at their centroids using the Ord-nance Survey National Grid (1 unit of easting or northing on the scaleis equal to 1 km).4 All but one of the grids are on much the same scaleand display an area of about 300× 300 kilometres. The London gridis much more detailed covering just 55× 55 kilometres. Distinguish-ing symbols are used to differentiate SQ8 places and SQ7 places fromeach other, and from the places with rates lower down the incidencerate distribution.

2.2.1 London

13. The location of London’s 33 local authorities are plotted on Figure5.12 which is (approximately) centred on the City of London (gridreference (E532, N181)). Indeed, using a euclidean distance5 mea-sure, all UALADs are less than 25km from the City of London.

14. We saw on Table 5.2 that 21 of the 33 London UALADS are in SQ8(and one SQ7) and these places are noticeably more concentrated –relative to the City of London – quite close to the centre and stretch-ing to its west, whilst there are rather fewer towards the east andthe south. Figure 5.13, a circular plot6 – with places represented bytheir angle (relative to a vertical axis centred on the City of London)– provides a graphical method for visualising data on orientation.

4For details, see the Ordnance Survey website:http://www.ordnancesurvey.co.uk/oswebsite/gps/information/coordinatesystemsinfo

5Where the euclidean distance is calculated as the square root of the sum of squaredeasting and northing distances from grid reference (E531, N181).

6For a discussion of this plot, its construction and related concepts see Mardia andJupp [2000]

22

15. Figure 5.13 incorporates an arrow – the ’mean direction’ of the points– this is a summary measure of orientation. It is very slightly northof west (at 285 ◦ from due north). Extending the data to include thesingle SQ7 point which, as we can see, is to the south of the Cityof London in the calculation of the mean direction changes the re-sult, but only very slightly (to 279 ◦ from due north). So this sum-mary measure further confirms the visual impression about the pre-ponderantly westerly location of London places with high incidencerates.

2.2.2 The Greater South East

16. Next we widen our focus to consider the Greater South East (GSE)(comprising the English regions South East and East of England)which surrounds London, and Figure 5.14 displays the location ofthese 114 places.7 Most of them are within 100km (euclidean dis-tance) of the City of London, and only seven – all in northern Nor-folk – are further than 125km. More notably, eight of the 39 places inthe GSE within 50km of the City of London are SQ8 (one of the twoSQ8 ’outliers’, Rushmoor in Surrey, is just 53km away the other, WestBerkshire is rather further). So the places in the GSE with the highestincidence rates are disproportionately concentrated relatively closeto London, or to put it conversely: almost no SQ8 places in the GSEare very far from London. Moreover, as we can also see from Figure5.14, the SQ8 places are typically to the west of the City of London,sharing a similar orientation as the SQ8 places in London (indeedthe mean direction, including all SQ8 places in the GSE, is 249 ◦ fromdue north).

17. The overall visual impression of the pattern changes very little whenwe include the 18 SQ7 places in the GSE, because about half of them(eight of 18) are within 50km of the City of London, scattered acrossthe ’home counties’ lying around the borders of London, particu-larly Surrey, Berkshire, Middlesex, Buckinghamshire and Hertford-shire. However the more remote locations (beyond 50km) also forman interesting group of (mainly) small cities. To the north of Lon-

7To simplify the visual interpretation of the pattern in Figure 5.14 the London UAL-ADs have not been plotted (with the exception of the City of London), although, as weknow from Figure 5.12, they occupy the ’white space’ in the middle of Figure 5.14 be-tween E500 to E550 and N150 to N200.

23

don: Cambridge, and more remotely the city of Norfolk (and closeby Great Yarmouth); to the north west, we have Oxford and (andnearby Cherwell); and in the south a string of towns, Brighton, Hor-sham, Chichester and Winchester. 8

18. Turning now from those places which are represented on either theSQ8 and SQ7 lists and looking for those places which are not on thelist, even though they are relatively close to London: we have, almostentirely missing, local authorities located in Kent, Essex and Suffolk.Evidently London casts a ’shadow’, particularly towards the east, inwhich relatively few places from the top end of the HGF incidencerate distribution are to be found.

2.2.3 Scotland

19. Of course the further we move away from London the less likelyis distance or direction from central London to be an influence onthe concentration of the top semi-quartile of HGF incidence rates.Indeed, as we saw earlier, Scotland’s share of SQ8 locations is slightlylarger than the ’norm’. There are 32 UALADs in Scotland so fourwould have been expected in SQ8, and there are six, however thepattern in their location is easier to appreciate if we consider the fiveSQ7 places too.

20. Figure 5.15 displays the location of 29 of Scotland’s UALADs (thethree missing are ’off the grid’ and very remote: Eilean Siar; theOrkney Islands; and the Shetland Islands). The plot is centred ap-proximately on Stirling, one of Scotland’s SQ8 locations. Just to thesouth of Stirling we can see a ’line’ of SQ7/SQ8 locations lying alongthe ’central belt’ of Scotland – seven contiguous UALADs stretchingfrom Renfrewshire in the east to the City of Edinburgh in the west,and comprising: East Renfrewshire; City of Glasgow; North Lanark-shire; South Lanarkshire; and West Lothian.

21. There is another group of three SQ8 locations, more northerly, andmuch further from central Scotland: Aberdeen City, Aberdeenshireand Moray UALADs. Activities in the first two of these at least, itseems reasonable to suppose, are connected with the oil industry.

8Hart in Surrey, at 60km from London, is just outside the 50km ring but is betterregarded as a very slightly remote ’home counties location.

24

2.2.4 The north of England

22. The north of England – taking together the North East, the NorthWest, and Yorkshire and the Humber – includes 72 local authori-ties, and with just five SQ8 locations between them, they are clearlyunder-represented at the top end of the incidence rate distribution.9

The local authorities are plotted on Figure 5.16.

23. Most of the places in the top two categories are either urban places,or peripheral to them: in the top right hand we have North andSouth Tyneside (neighbouring Newcastle upon Tyne) and Middles-brough; towards the middle of the map we have Leeds; and in thelower left of the grid a Manchester group of six local authorities(four of these local authorities are in Greater Manchester Metropoli-tan County), with Manchester towards the south and South Ribbleand Hyndburn to the north; and finally, to the west, Liverpool. Fur-ther south, and a little remote, but possibly associated with Manch-ester/Liverpool, is Cheshire West and Chester. The only real ’outlier’in this pattern is, right in the middle of the map, Richmondshire inthe Yorkshire Dales

2.2.5 South West England and Wales

24. South West England and Wales taken together, have 59 local author-ities but just four SQ8 locations between them, but including theeight SQ7 places gives a 20% share for the top two semi-quartiles.The locations are plotted on Figure 5.17. It may appear initiallythat urban places, or near-urban places are rather less importantthan in the north of England. However in Wales we have the cap-ital city of Cardiff (with Blaenau Gwent and Rhondda, Cynon Taffnearby), though the other two are quite remote, relatively rural,places (Gwynedd and Pembrokeshire); whilst in the South West,Bristol, Bath and South Gloucestershire form a contiguous ’cluster’,with Gloucester not far away. There are also two substantial towns:Torbay (essentially Torquay) and Plymouth, leaving West Somersetas the South West’s only remote and sparsely populated ’outlier’.

9Though it is worth noticing that the North East, taken alone, has one SQ8 and twoSQ7 places: which equals the ’expected’ 25% of its 12 UALADs.

25

2.2.6 The Midlands

25. The Midlands is the most under-represented of all the areas: thereare 70 local authorities, and only two of them are SQ8 places. Evenmore strikingly, neither of those SQ8 places are in the 30 UALADs inthe West Midlands – the West Midlands might have been expectedto have four places at the top end of the incidence rate distribution,but it has none. Nor does taking SQ8 and SQ7 together improvethe picture very much: the combined Midlands share is 13%, ninelocations about half the ’expected’ 18.

26. The locations of local authorities in the Midlands are plotted on Fig-ure 5.18. Almost half of the SQ7/SQ8 places are relatively ruralplaces on the northern periphery of the grid. Of the two more cen-tral places: Ashfield is a suburban extension of nearby Nottingham;whilst North West Leicestershire, it may be worth noting, containsthe East Midlands airport. Of the other three places, lower downthe map, Warwick and Rugby UALADs are contiguous, but NorthWarwickshire is better regarded as a rather more rural ’outlier’.

26

Chapter 3

Job creation by HGFs

3.1 Background

1. Whilst the OECD definition of HGFs does seem broadly acceptedthere has been little discussion of how to measure the HGFs contri-bution to job creation, and certainly there is no agreed methodology.This lack of agreement is all the more puzzling because the initialrationale for the identification of HGFs was, in fact, their role as pro-lific job creators. So our motivation here is simple, to investigate howbest to answer the question: ”what proportion of job creation is con-tributed by high growth firms?”

2. Right at the start we face a difficulty. The number of firms is a stock– measured at a single time point, whereas job creation is a flow –the difference between the stock of jobs at two different time points.Consequently the relationship between the job creation flow and thestock of firms (and by extension the stock of high growth firms) de-pends on the length of the measurement period. This dependence isimportant because many firms have relatively short lives and so, asthe measurement period lengthens, larger numbers of firms do notsurvive. Equally, as the measurement period lengthens, larger num-bers of new firms are born within the period (indeed firms may beborn and die within the measurement period). These side-effects of alengthening measurement period render the short period dynamicsof labour market flows increasingly invisible and serve to blur thedistinction ’new’ and ’continuing’ firms and their relative contribu-tions to job creation

27

3.2 The accounting framework

3. Using the three year variant of the OECD definition of HGFs effec-tively commits us to a three year measurement period (t to (t+3))for our investigation of job creation. An obvious starting point is todistinguish between job creation by HGFs from t to (t+3) and job cre-ation by not-high growth firms (nonHGFs) from t to (t+3). However,there is a complication the OECD definition of HGFs covers onlyfirms which are at least one year old (that is born in (t-1) or earlier),so the OECD HGF definition does not cover:

• any firms born in period t and alive in period (t+3)

• any firm born after period t up to and including period (t+3)

Firms in the first category may have jobs at time t and (t+3), whilstthose in the second category may only have jobs at (t+3). So if we areto have a complete accounting for all jobs created between t and (t+3)consistency requires that we include these firms which may createjobs but are not classified as either HGFs or nonHGFs.

4. For these two reasons – the three year measurement period and theprecise character of the HGF definition – we need to adapt the con-ventional (annual) job creation and destruction accounts.1 Here wefocus on job creating firms only, and we distinguish four categories,

(a) HGFs, born before t, and alive (t+3), at least 10 jobs in t and 20%average annual growth between t and (t+3) – HGF

(b) nonHGFs, born before t and alive in (t+3) with more jobs in (t+3)than t, but not HGF – nonHGF

(c) firms born in period t and alive (t+3) with more jobs in (t+3)than t – young

(d) firms born after period t and alive (t+3) with jobs in (t+3) – new

5. We can lay out the accounting framework more formally startingwith the relationship between the stock of all job creating firms alive attime (t+3) (firmst+3) and its components, where the first in the pairof subscripts refer to the year of birth,

1For a discussion of the construction job creation accounts see the Technical Appendixto Davis et al. [1996a].

28

firmst+3 ≡ HGF(beforet),t+3 + nonHGF(beforet),t+3 +

+ youngt,t+3 + new(aftert),t+3 (3.1)

6. HGFs and nonHGFs will be referred to below (as elsewhere) as mem-bers of the ’balanced panel’ of firms which comprises all firms bornbefore period t and surviving to (t+3)). It is also helpful, again aswe shall see below, to distinguish between those relatively large non-HGFs which (like HGFs) have 10 or more employees (nonHGFla) andthose that do not, that is relatively small nonHGFs (nonHGFsm) – thelarger nonHGF category is a useful comparator for the HGF categorybecause it is so similar (by construction).

7. This framework has been designed to account for job creation, andfirms which do not create jobs have simply been left out of the picture– some of these will have the same number of jobs at period (t+3) asthey had at t, some will have fewer, others will have died (so no jobsat (t+3)).

3.3 Job creating firms, 1998/2001 – 2007/10

8. The first three columns of Table 5.3 provides some context for ourdiscussion of job creating firms. Column (1) records the overall num-ber of firms at the end of each three year period, it starts at 1.34 mil-lion in 1998/01 and is 200,000 larger by 2007/10, whilst the secondcolumn reports the number of job creating firms, it also expandedand by 2007/10 it was up by 150,000 from 0.69 million in 1998/01.However after 2005/08 both series drop, and the fall of 100,000 injob creating firms by 2007/10 matching the fall in the all firm total.The change in both series is broadly in parallel and the ratio betweenthem recorded in column (3), which fluctuates (except for 1998/01)in quite a narrow range between 55% and 60% shows no definitetrend.

9. The right hand side of 5.3 displays data on the numbers in eachcategory of job creating firm, and the numbers of new firms – in therange 500 to 650,000 – considerably exceed all the other categoriesyaken together. There are about half as many nonHGFs as there arenew firms, but the small nonHGFs are clearly the more numerous.

29

The number of larger nonHGFs – typically in the 40 to 50,000 range– are similar to those of the young, whilst HGFs – generally around10,000 – are the smallest of the categories of job creating firms by avery large margin.

10. As we saw above the two most notable features of the job creatingfirm numbers are the rise of 250,000 between 1998/01 and 2005/08and the subsequent fall of 100,000 from 2005/08 to 2007/10. Whilstthe rise is almost equally shared by smaller nonHGFs and new firms,the fall is entirely accounted for by the number of new job creatingfirms. The numbers (in column (8) dropped by 50,000 per period inboth 2006/09 and 2007/10. It seems reasonable to suppose that thispattern may well be cyclical, during an expansion phase (1999/02to 2005/08) the numbers of new firms are likely to rise but with theonset of recession the number of new job creating firms is likely tobe depressed. The mechanics here are in part a by-product of thedefinition: even if the number of new firms being born in each yearof the period (remember new firms are born after period t) remainrelatively unchanged, an increase in deaths of the new-born over theperiod (between t and t+3) will reduce numbers in the new category.The numbers of small nonHGFs are not affected by the downturn,and the other three categories – HGFs, large nonHGFs and young –have similar numbers in 2007/10 as they had in 1998/01.

11. Figure 5.19 displays the data on the different categories of job cre-ating firms as shares of the total, and serves to reinforce what hasjust been learned from Table 5.3. Notice that in order to plot the newfirm share on the same chart as the other categories it has its own(right hand) scale, however the scales have been arranged so thatthe tick marks for both the left and right hand scales are the samedistance apart (five percentage points) to make change comparisonseasier. The share of new firms fluctuates quite widely, but averagesabout two thirds and we can see that fluctuations in new firms isalmost exactly offset by fluctuations in the share of the smaller non-HGFs about a much smaller average share, around one fifth. Theconjunctural interpretation looks even more plausible. The shares ofthe other three categories are essentially constant – young firms andthe larger nonHGFs at 5%, and HGFs at a little above 1%.

12. The choice of denominators used in HGF job creation comparisonsis not covered by any agreed standard (not surprising perhaps since,as mentioned earlier, the subject seems to be discussed so rarely).

30

In an earlier study of HGF numbers (Anyadike-Danes et al. [2009,p. 18]) we used the ratio of HGFs to all larger firms in the balancedpanel to measure the contribution of HGFs to job creation, that isthe number of firms with 10 or more employees whether or not theywere job creating firms – this yields an HGF ratio between 6% and7%. The OECD seem to suggest a slightly different denominator:”the population of enterprises with ten or more employees”, OECD[2011, p. 74] – apparently including young firms with 10 or moreemployees as well, and this is of course all firms not just job creatingfirms, and using this denominator produces an HGF ratio less thanhalf a percentage points smaller than 6%.

3.4 Job creation, 1998/01 – 2007/10

13. The first two columns of Table 5.4 record the total number of jobs inthe initial and terminal years of each three year period. We can seethat employment has generally been rising over the period, with anoverall average level around 18.5 million. From column (3), whichrecords the difference between them – the net increase in jobs in eachperiod – it is clear that the pace of change was typically quite slow,and that in 2007/10 total private sector jobs actually fell. As wecan see from columns (4) to (6) the comparable data for job creatingfirms is strikingly different – the initial stock of jobs in each periodis around 6.5 million (about one third of all firm jobs, from column(7)), but by the terminal period it is around 12 million (two thirds ofall firm jobs, from column (8)), so between five and six million jobsare being created each period by job creating firms (and rememberjob creating firms are just half of all firms in the balanced panel). Thejob creation series peaks at just over 6 million in 1999/02, and thenfalls fairly steadily, on average by around 200,000 per period, until2007/10 when it drops more steeply, by 400,000 to 4.7 million (this isthe period when the number of jobs actually fell).

14. Our particular interest here is the role of different categories of jobcreating firms. The data on job numbers are displayed in Figure 5.20and there is a very clear hierarchy,

• new firms are at the top, in slow decline from about 2.25 millionin 2002/05 to 2.1 million in 2005/08 and then a steeper drop to1.7 million in 2007/10

31

• HGFs are next, again in slow, uneven, decline from 1.5 mil-lion to 1.4 million 2004/07, then a steeper drop to 1 million in2007/10

• the larger nonHGFs are virtually constant at around 1 millionper period

• the smaller nonHGF series is more volatile but typically around0.75 million

• finally, the young firms job creation rate is more or less constantbut just 250,000 per period

15. New firms are the principal contributors to job creation, but theyshare the responsibility for the overall decline in the rate of job cre-ation with HGFs. The pattern of change is easier to judge if we trans-form the data into shares, and these are plotted on Figure 5.21. Thedisproportionate fall in jobs created by new firms and HGFs trans-lates into a fall of around three percentage points in each of theirshares in the last few periods, and the matching rise shows up in thetwo categories of nonHGFs, each of which rise by three percentagepoints. Interestingly, from 2005/08 onwards, the larger nonHGFs ac-counted for the same proportion of job creation as did HGFs.

16. What proportion of job creation is contributed by high growth firms?If we assume a three year measurement period there are four plausi-ble alternatives, following from different choices of denominator,

• all job creating firms

• all job creating firms alive in period t

• all job creating firms alive in period (t-1) (the OECD balancedpanel)

• all job creating firms alive in period (t-1) with 10 or more em-ployees in period t (10+ members of the OECD balanced panel)

17. These four measures are plotted on Figure 5.22. We have seen thefirst, broadest, measure before (see Figure 5.21) – the HGF contri-bution averages around 27% from 1998/01 to 2004/07, and from2005/08 to 2007/10 the average is 22%, five percentage points lower.The second measure, which excludes new firms, follows a similarpath over time, with a 44% average in the early period and in thelater period almost 10 percentage points down, at around 35%. Thetime path for the share of HGFs in job creation by the OECD balanced

32

pane is very similar, essentially parallel to the second measure, andthe HGF share drops from an average of 47% in the early period to38%. Finally we have HGF job creation as a share of jobs created by10+ members of the balanced panel, and again the share is down tenpercentage points, from 60% in the years up to 2002/05 and 50% inmore recent years.2

18. In brief, across a range of plausible alternative denominators, thecontribution of HGFs to job creation varies by a factor of two – 60%versus 30% in the early period, 50% versus 25% in the last few years.It is also clear that the contribution of HGFs to to job creation hasfallen irrespective of the measure, though the extent of the fall doesdepend on the measure.

2We reported this fourth measure for 2002/05 and 2005/08 in our earlier report onHGFs, see Anyadike-Danes et al. [2009, p. 19]

33

Chapter 4

Drawing the threads together

4.1 UK HGFs: incidence and characteristics

1. we have some reasonably clear findings about HGFs in the UK overthe last decade. In summary,

• About 12,500 HGFs were identified in each of the six three yearperiods from 2003/2007 to 2007/2010 and the incidence ratewas about 7%.

• The incidence rate declines as firms age, at one year old it isabout 15% and then falls at around 0.5 percentage points eachyear. In 2007/2010 almost half of all HGFs were more than 10years old, and it is the ’weight’ of the old firms (with their lowerincidence) which contribute largely to producing an average in-cidence rate of 7.5%

• The incidence/age relationship is largely invariant to size, mostsize-bands decline at the all size average of 0.5 percentagepoints per year. Firms with more than 250 employees are dif-ferent, their incidence rate – about 10% – is essentially indepen-dent of age. Most HGFs are relatively small – more than halfhave less than 20 employees, 80% have less than 50 employees

• The (2-digit) sectors which record the highest incidence rates areall services, some hi-tech, all knowledge intensive. Most HGFsare found in service sectors too, in 2007/2010 one quarter werein business services; the other (2-digit) sectors with the mostHGFs (each between 5% and 10%) were construction, wholesaleand retail trade and hotels and restaurants

34

2. The incidence and distribution of HGFs in the UK by age, size andsector are largely consistent with the general pattern found else-where in the (relatively sparse) previous studies. Having been de-rived from a much more comprehensive dataset – ten successivecross-sections – our results seem rather more clear cut (and likelymore robust). The one area where our results differ most markedlyis with respect to sectors. We have such a large dataset and are ableto work at a much finer grain and have detected, contrary to whatseems to have been reported previously, that although HGFs are sec-torally ubiquitous, incidence rates are detectably higher in a numberof hi-tech and knowledge intensive services.

4.2 Geography

3. Some potentially useful generalisations about the variation of HGFincidence across UALADs are worth re-iterating.

• the first, and most striking, finding is the extraordinarily dom-inant position of parts of London and the home counties at thetop end of the distribution of HGF incidence rates: taken to-gether these UALADs account for 60% of the top semi-quartile(eightth) of the distribution

• not all of London’s UALADs are at the top end of the distribu-tion, the performance of outer London to the east and south isless impressive, equally the ’halo’ effect on the UALADs bor-dering London is weakest in these same directions

• outside London cities are important: in Scotland a major role isplayed by the its two principal cities – Edinburgh and Glasgow– and the places between them. In the north of England citiesalso stand out: Newcastle (or at least nearby North and SouthTyneside); Middlesbrough; Leeds; Manchester; and Liverpool;and in Wales, Cardiff (and areas close to it); and the South Westof England, Bristol and Bath all appear in the top quarter of thedistribution of HGF incidence

• there is, though, one very large and important exception tothe prominent role of cities at the upper end of the HGF inci-dence rate distribution – Birmingham. Moreover this city is oneof seven UALADs in the West Midlands metropolitan county(Coventry and Wolverhampton are also amongst the seven),

35

and none of these urban locations appear in the top half ofthe distribution. But Birmingham is not the only large ’north-ern’ city that is ’missing’ – in the North, Sheffield and Bradfordare as well; as Derby and Nottingham in the Midlands; and inWales, Swansea is a notable omission

• of course, the top end of the HGF incidence rate distribution itis not just about large cities. There is a substantial collection oftowns (amongst them, Chichester, Winchester, Plymouth) at thetop end of the distribution. There are also a number of relativelyremote, relatively sparsely populated, largely rural places thatrecord impressively large HGF incidence rates (for example,Richmondshire and Pembrokeshire), but these perhaps mightbe regarded as ’outliers’

4.3 HGFs contribution to job creation

4. The proposition which originally motivated interest in HGFs is thecomparison between the proportion of job creating firms and theproportion of job creation they contribute. Focusing on the broad-est measure (all job creating firms) and using 2007/10 data we have,

• new (firms born between 2008 and 2010): 61.2% of job creatingfirms and 35.5% of job creation

• young (firms born in 2007): 5.1% of job creating firms and 5.4%of job creation

• small nonHGF (firms born before 2007 with less than 10 em-ployees in 2007): 26.6% of job creating firms and 14.8% of jobcreation

• large nonHGF (firms born before 2007 with 10 or more employ-ees in 2007): 5.9% of job creating firms and 22.5% of job creation

• HGF: 1.2% of job creating firms and 21.9% of job creation

5. Clearly, of the five categories distinguished here, HGFs are relativelythe most prolific category of job creating firms, however their closestcomparators – the larger nonHGFs – are quite prolific too. The pointis, surely, that definitions are important, and that summary state-ments which gloss over the detail of the definitions may seriouslymislead.

36

4.4 The bigger picture

6. In a deliberately provocative paper: ”Why encouraging more peo-ple to become entrepreneurs is bad public policy”, Shane arguedforcefully for a shift in policy priorities: ”It is about encouraging theformation of high quality, high growth companies. Policy makersshould stop subsidizing the formation of the typical start-up and fo-cus on the subset of businesses with growth potential.”Shane [2009,p. 141] However, even if the negative argument is accepted (stopsubsidizing start-ups), it still not at all clear what the positive argu-ment (encouraging formation of high growth companies) entails byway of policy (see Mason and Brown [2013]). Indeed, a recent policybrief for the European Commission listed as one of its policy im-plications: ”Since substantial evaluations of policies are apparentlymissing so far, it remains unclear what instruments of policies forinnovative high-growth SMEs are particularly successful or unsuc-cessful.”Lilischkis [2011, p. 94]

7. Indeed it may seem somewhat ironic, especially given the stimulusthat David Birch’s work has given to the high growth ’agenda’ , thathe was himself somewhat sceptical about the practical policy impli-cations of his ’discovery’ of the prolific job creation performance ofHGFs,

”We know that smaller, volatile firms are the major replac-ers of lost jobs, but we have no experience in identifyingand assisting them in large numbers. Because they aresmall, we must reach many of them to have a measureableeffect. Because they are volatile, we must monitor eachindividual firm’s performance carefully if we are to gainmaximum benefit from our invested dollars (on the highside) and avoid scandal (on the low side). From this re-searcher’s viewpoint it seems like a very difficult problemto solve administratively. A massive bureaucracy wouldbe required to monitor individual small businesses on thescale required ...” Birch [1979, p. 4p]

8. It has become commonplace to suggest that a researcher’s answer tomost questions is to call for more research, but it may nonetheless beappropriate in respect of HGFs. Whilst there is widespread accep-tance of the proposition that a relatively small proportion of firms

37

are responsible for a disproportionate share of job creation, there isnot yet complete agreement, despite the efforts of the OECD and EU-ROSTAT, about how such firms might be identified. Whilst evidenceabout the basic characteristics of these firms is still quite scarce, thefindings reported here provide a solid base for an improved under-standing of HGFs in the UK.

38

Bibliography

Acs, Zoltan, William Parsons, and Spencer Tracy (2008) “High-ImpactFirms: Gazelles Revisited,” Research Report 328, SBA Office of Advo-cacy.

Ahmad, Nadim (2006) “A Proposed Framework For business Demogra-phy Statistics,” OECD Statistics Working Papers 2006/3, OECD Pub-lishing.

Anyadike-Danes, Michael, Karen Bonner, Mark Hart, and Colin Mason(2009) “Measuring Business Growth: High-growth firms and their con-tribution to employment in the UK,” research report, NESTA.

Birch, David L (1979) “The Job Generation Process,” research report, MITProgram on Neighborhood and Regional Change, Cambridge, MA.

(1981) “Who Creates Jobs?” Public Interest, Vol. 65, pp. 3–14.

(1987) Job Creation in America, New York: New York: Free Press.

Bravo-Biosca, Albert (2011) “A look at business growth and contraction inEurope,” October, presented 3rd European Conference on Corporate R& D and Innovation, CONCORD-2011, Seville (Spain).

Davis, S J, J C Haltiwanger, and S Schuh (1996a) Job Creation and Destruc-tion, Cambridge, MA: MIT Press.

Davis, Steven J, John Haltiwanger, and Scott Schuh (1996b) “Small Busi-ness and Job Creation: Dissecting the Myth and Reassessing the Facts,”Small Business Economics, Vol. 8, No. 4, pp. 297–315.

EUROSTAT-OECD (2007) EUROSTAT OECD Manual on Business Demog-raphy Statistics, Luxembourg: EUROSTAT.

39

Evans, Peter and Richard Welpton (2009) “Methods explained - BusinessStructure Database,” Economic and Labour Market Review, Vol. 3, No. 6,pp. 71–75.

Haltiwanger, John, Ron Jarmin, and Javier Miranda (2012) “Who createsjobs? Small vs Large vs Young,” Review of Economics and Statistics, pp.1–52, just accepted ms, on website.

Henrekson, Magnus and Dan Johansson (2010) “Gazelles as job creators:a survey and interpretation of the evidence,” Small Business Economics,Vol. 35, No. 2, pp. 227–244.

Hoffmann, Anders N. and Martin Junge (2006) “Documenting Data onHigh-growth Firms and Entrepreneurs across 17 Countries,” workingpaper, FORA.

Holzl, Werner (2011) “Persistence, Survival and Growth: A Closer Look at20 Years of High-Growth Firms in Austria,” WIFO Working Papers 403,WIFO.

Lilischkis, Stefan (2011) “Policies in support of high-growth innovativeSMEs,” An INNO-Grips Policy Brief 2, European Commission.

Mardia, Kanti and P. Jupp (2000) Directional Statistics, Chichester, England:Wiley.

Mason, Colin and Ross Brown (2013) “Creating good public policy to sup-port high-gowth firms,” Small Business Economics, Vol. forthcoming, justaccepted ms, on website.

Murray, Gordon, Ari Hyytinen, and Markku MaulaIn (2009) “Growth En-trepreneurship and Finance,” in Ministry of Employment and the Econ-omy (Finland) eds. Evaluation of the Finnish National Innovation SystemFull Report, Helsinki University: Helsinki University Print.

Neumark, David, Brandon Wall, and Junfu Zhang (2011) “Do Small Busi-nesses Create More Jobs? New Evidence for the United States from theNational Establishment Time Series,” The Review of Economics and Statis-tics, Vol. 93, No. 1, pp. 16–29.

OECD (2002) “High-Growth SMEs and Employment,” oecd report,OECD.

40

(2008) “Measuring Entrepreneurship A Collection of Indicators2008 Edition,” OECD-EUROSTAT Entrepreneurship Indicators Pro-gramme Publication, OECD Statistics Directorate.

(2011) “Entrepreneurship at a Glance, 2011,” OECD-EUROSTATEntrepreneurship Indicators Programme Publication, OECD.

ONS (2011) “Foreign Ownership of Businesses in the United Kingdom,”briefing note, Office of National Statistics.

Schreyer, Paul (2000) “High-Growth Firms and Employment,” OECD Sci-ence, Technology and Industry Working Papers 2000/3, OECD Publish-ing.

Shane, Scott (2009) “Why encouraging more people to become en-trepreneurs is bad public policy,” Small Business Economics, Vol. 33, No.2, pp. 141–149.

Storey, David and Steven Johnson (1987) Job Generation and Labour MarketChange, Basingstoke, Hants: Macmillan.

Teruel, Mercedes and Gerrit de Wit (2011) “Determinants of high-growthfirms,” Scales Research Reports H201107, EIM Business and Policy Re-search.

41

Chapter 5

Tables and Figures

42

Table 5.1: HGF distribution by age, 2007–2010

age share

1 5.92 6.83 6.94 7.45 6.96 6.77 5.88 4.79 4.810+ 44.1

Note: age, years since birth

43

Table 5.2: distribution of HGF incidence rates by semi-quartile (SQ) acrossUK UALADS by region: counts

SQ SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SQ7 SQ8 sum ’excess’(upper (5.42) (5.95) (6.35) (6.80) (7.20) (7.66) (8.33) (12.20)bound)

region

EM 5 6 11 7 4 3 2 2 40 -3EN 8 9 2 3 6 11 6 2 47 -4GL 0 0 1 2 2 6 1 21 33 +17NE 2 0 0 3 1 3 2 1 12 -1NW 4 8 8 3 3 5 5 3 39 -2SC 5 2 5 1 5 3 5 6 32 +2SE 6 11 8 5 9 8 12 8 67 0SW 4 5 4 6 8 3 4 3 37 -2WA 3 1 1 6 3 3 4 1 22 -2WM 9 4 3 5 2 2 5 0 30 -4YH 3 3 5 5 3 0 1 1 21 -2

GB 49 49 48 46 46 47 47 48 380

Notes:1. rows are regions: EM, East Midlands; EN, East of England; GL, London; NE,North East; NW, North West; SC, Scotland; SE, South East; South West; WA,Wales; WM, West Midlands; YH, Yorkshire and the Humber2. columns are semi-quartiles (SQ1 lowest incidence rate, SQ8 highest incidencerate)3. the ’excess’ column is the difference between the SQ8 column and 12.5% of theregional total, see text

44

Tabl

e5.

3:fir

ms

and

job

crea

ting

firm

sby

cate

gory

,199

8/01

–20

07/1

0

firm

sjc

firm

sra

tio

firm

s’0

00jo

bcr

eati

ngfir

ms

’000

rati

os’0

00’0

00(2

)/(1

)H

GF

nHG

Fla

nHG

Fsm

youn

gne

wH

GF

nHG

Fla

nHG

Fsm

youn

gne

w(1

0)/(

5)(1

1)/(

6)(1

2)/(

7)(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)(1

1)(1

2)(1

3)(1

4)(1

5)(1

6)

1998

/01

1337

.268

5.6

0.51

8.9

131.

658

0.3

114.

650

1.9

8.9

33.4

100.

041

.550

1.9

25.4

17.5

36.2

1999

/02

1332

.878

9.5

0.59

14.2

125.

960

2.2

92.7

497.

814

.243

.619

8.2

35.7

497.

834

.733

.738

.520

00/0

313

67.0

806.

40.

5913

.612

4.3

609.

910

4.3

514.

913

.639

.319

9.2

39.5

514.

9331

.633

.437

.920

01/0

414

25.0

856.

60.

6013

.112

4.0

613.

510

1.2

573.

113

.140

.019

3.9

36.4

573.

32.3

32.3

36.0

2002

/05

1470

.682

7.2

0.56

10.3

139.

061

0.2

101.

360

9.8

10.3

43.9

128.

035

.260

9.8

31.6

21.3

34.8

2003

/06

1520

.986

7.5

0.57

10.8

138.

762

5.7

112.

263

3.4

10.8

46.1

136.

640

.663

3.4

33.2

22.2

36.2

2004

/07

1589

.192

7.0

0.58

10.8

136.

065

8.9

150.

463

3.0

10.8

47.5

188.

147

.663

3.0

34.9

29.0

31.6

2005

/08

1617

.694

4.5

0.58

10.7

135.

570

3.0

131.

663

6.7

10.7

48.6

204.

643

.963

6.7

35.8

29.6

33.4

2006

/09

1573

.891

0.1

0.58

11.0

136.

972

6.8

123.

457

5.7

11.0

50.8

227.

644

.957

5.7

37.1

31.8

36.4

2007

/10

1523

.984

4.6

0.55

10.4

141.

073

6.0

119.

651

6.9

10.4

49.9

224.

842

.751

6.35

.431

.035

.7

Not

es:

1.fo

rde

finit

ions

see

text

2.al

lfirm

s,co

l(1)

,att

erm

inal

year

ofpe

riod

,sum

ofco

ls(4

)to

(8),

may

diff

erdu

eto

roun

ding

3.al

lHG

Fan

dne

war

ejo

bcr

eati

ngfir

ms

45

Table 5.4: job creation, 1998/01 – 2007/10

jobs in all firms, mill jobs in jc firms, mill ratiosinit term (2)-(1) init term (5)-(4) (4)/(1) (5)/(2)(1) (2) (3) (4) (5) (6) (7) (8)

1998/01 17.32 17.95 0.63 6.26 11.86 5.60 0.361 0.6611999/02 17.35 18.46 1.11 6.72 13.03 6.32 0.387 0.7062000/03 17.61 18.51 0.90 6.67 12.87 6.20 0.379 0.6952001/04 17.95 18.44 0.49 6.26 12.17 5.91 0.349 0.6602002/05 18.46 18.63 0.17 6.26 11.82 5.57 0.339 0.6342003/06 18.51 18.83 0.32 6.45 11.95 5.50 0.348 0.6352004/07 18.44 18.95 0.51 6.38 11.87 5.50 0.346 0.6262005/08 18.63 19.18 0.55 6.94 12.28 5.34 0.373 0.6402006/09 18.83 19.21 0.38 7.27 12.43 5.16 0.386 0.6472007/10 18.95 18.64 -0.31 6.93 11.63 4.70 0.366 0.624

46

Figure 5.1: HGF numbers (’000) and HGF incidence (%) vs period0.

02.

55.

07.

510

.012

.515

.0

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

period

0.0

2.5

5.0

7.5

10.0

12.5

15.0

num

ber

'000

(ba

r, lh

s)

47

Figure 5.2: HGF incidence vs period by cohort, cohort97* to cohort07 (%)0

510

1520

period

inci

denc

e

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

avavex97

48

Figure 5.3: HGF incidence vs age by cohort, cohort97* to cohort07 (%)0

510

1520

age (years)

inci

denc

e

1 2 3 4 5 6 7 8 9 10

coh97avex97

49

Figure 5.4: HGF incidence vs period, by size-band (%)0

510

15

period

inci

denc

e (%

)

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

10−1920−4950−99100−249250+

50

Figure 5.5: HGF incidence vs age, all cohorts (excl. cohort97*), by size-band (%)

05

1015

20

age (years)

inci

denc

e

1 2 3 4 5 6 7 8 9

10−1920−4950−99100−249250+

51

Figure 5.6: HGF incidence vs period, by 2-digit sic (%)0

510

1520

25

period

inci

denc

e

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

sic64sic72sic66sic67sic90

key:the top5 are labelled: sic64, posts and telecommunications; sic72,computer and related activities; sic66,insurance and pension funding;sic73, research and development; sic90, sewage and refuse disposal; sic67,activities auxiliary to financial intermediationsectors ranked 6 to 10 (plotted but not labelled) are: sic65, financialintermediation; sic74, other business activities; sic71, renting ofmachinery and equipment and household goods; sic93, other serviceactivities

52

Figure 5.7: HGF distribution by size-band vs period (%)0

1020

3040

5060

70

period

shar

e (%

)

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

10−19

20−49

50−99

100−249

250+

53

Figure 5.8: HGF distribution by sector vs period, top 5 sectors(%)0

510

1520

2530

period

shar

e (%

)

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

sic74sic45sic51sic55sic52

key: sic74, business services; sic45, construction; sic51, wholesale dis-tribution; sic52, retail distribution; sic55, hotels and restaurants

54

Figure 5.9: HGF incidence rate for UALADs, boxplots by period, 1998/01to 2007/10

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

0

5

10

15

20

period

rate

%

Note: For each boxplot,

• the line in the middle of a box is the median for that period

• the box is drawn from the first to the third quartile, that is its heightis the inter-quartile range (IQR)

• the lines extending from the box and ending in a bar are each1.5×IQR

• the points beyond the bar at the end of the lines are outliers –observations which exceed the 1.5×IQR

55

Figure 5.10: HGF incidence rate: national and median of UALADs2002/051 to 2007/10

24

68

1012

period

rate

%

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

nationalmedian

56

Figure 5.11: HGF incidence rate for UALADs, median of 2002/051 to2007/10, ordered plot with semi-quartile boundaries

UALADs

hgf i

ncid

ence

rat

e %

SQ

1

SQ

2

SQ

3

SQ

4

SQ

5

SQ

6

SQ

7

SQ

8

0

2

4

6

8

10

12

14

5.42

5.95

6.35

6.80

7.20

7.66

8.33

12.2

0

Note: semi-quartile tick mark labels are at the upper bound of thesemi-quartile

57

Figure 5.12: London (GL) UALADs, plotted on the Ordnance Survey Na-tional grid

500

510

520

530

540

550

560

150

160

170

180

190

200

210

easting

nort

hing

SQ8SQ7

58

Figure 5.13: London (GL) UALADs from SQ8, circular plot

N

E

S

W +

Note: the arrow points to the mean direction of the points, see text fordetails

59

Figure 5.14: Greater South East (South East and East) UALADs, plotted onthe Ordnance Survey National grid

400

450

500

550

600

650

700

50

100

150

200

250

300

350

easting

nort

hing

SQ8SQ7City of London

C

N G

B

H

Z

W

O

X

Y

Key:”B”, Brighton; ”C”, Cambridge; ”G”, Great Yarmouth; ”H”, Horsham;”N”, Norwich; ”O”, Oxford; ”W”, Winchester; ”X”, Cherwell; ”Y”, WestBerkshire; ”Z”, Chichester.Note: London is in the middle of the map, only the City of London(E532,N181) is marked.

60

Figure 5.15: Scotland UALADs, plotted on the Ordnance Survey Nationalgrid

100

150

200

250

300

350

400

550

600

650

700

750

800

850

easting

nort

hing

SQ8SQ7

A

M

R

S

E

L

Z

Key:”A”, Aberdeen City;”E”, Edinburgh; ”L”, South Lanarkshire; ”M”,Moray; ”R”, Renfrewshire; ”S”, Stirling; ”Z”, Aberdeenshire.Note: Eilean Siar; the Orkney Islands; and the Shetland Islands arebeyond the grid in this figure.

61

Figure 5.16: North of England (North East, North West, Yorkshire and theHumber) UALADs, plotted on the Ordnance Survey National grid

300

350

400

450

500

550

350

400

450

500

550

600

easting

nort

hing

SQ8SQ7

C

T

L

RB

M

S

X

Key:”C”, Cheshire West; ”B”, Middlesbrough; ”H”, Hyndburn; ”L”, Leeds;”M”, Manchester; ”R”, Richmondshire; ”S”, South Ribble; ”T”, North andSouth Tyneside; ”X”, Liverpool.

62

Figure 5.17: South West England and Wales UALADs, plotted on the Ord-nance Survey National grid

100

200

300

400

0

100

200

300

400

easting

nort

hing

SQ8SQ7

G

P

Y T

C

W

B

X

Key:”B”, Bath and North Somerset; ”C”, Cardiff;”G”, Gwynedd; ”P”,Pembrokeshire;”T”, Torbay; ”W”, West Somerset; ”X”, Gloucester; ”Y”,Plymouth.

63

Figure 5.18: The Midlands UALADs, plotted on the Ordnance Survey Na-tional grid

350

400

450

500

550

200

250

300

350

400

easting

nort

hing

SQ8SQ7

T

N

H

X

RW

Y

A

B

Key:”A”, Ashfield; ”B”, Bassetlaw; ”H”, High Peak; ”N”,Newcastle-under-Lyme; ”R”, Rugby; ”T”, The Wrekin; ”W”, Warwick;”X”, North Warwickshire; ”Y”, North West Leicestershire.

64

Figure 5.19: job creating firms by category, 1998/01 – 2007/10, share (%)

period

shar

e (%

)

05

1015

2025

3035

40

1998

/01

1999

/02

2000

/03

2001

/04

2002

/05

2003

/06

2004

/07

2005

/08

2006

/09

2007

/10

hgfnonhgflanonhgfsmyoungnew(rhs)

3540

4550

5560

6570

75

'new

' sha

re %

65

Figure 5.20: job creation by category of job creating firm, 1998/01 –2007/10, million

period

num

ber

mill

ion

00.

51

1.5

22.

53

1998

/01

1999

/02

2000

/03

2001

/04

2002

/05

2003

/06

2004

/07

2005

/08

2006

/09

2007

/10

hgfnonhgflanonhgfsmyoungnew

66

Figure 5.21: job creation by category of job creating firm, 1998/01 –2007/10, share (%)

period

shar

e (%

)

05

1015

2025

3035

4045

1998

/01

1999

/02

2000

/03

2001

/04

2002

/05

2003

/06

2004

/07

2005

/08

2006

/09

2007

/10

hgfnonhgflanonhgfsmyoungnew

67

Figure 5.22: job creation by HGFs, ratio to alternative denominators,1998/01 – 2007/10, ratio (%)

period

ratio

(%

)

010

2030

4050

6070

1998

/01

1999

/02

2000

/03

2001

/04

2002

/05

2003

/06

2004

/07

2005

/08

2006

/09

2007

/10

aliveborn(t)born(t−1)born(t−1)10+

68

Appendices

69

Appendix A

Foreign firms in the UK: headlinenumbers, the incidence of highgrowth and its dynamic

1. This appendix explores the place of foreign firms in the UK econ-omy and, more particularly, aims to illuminate the role of foreignhigh growth firms. It is organised into five sections. The first sectionprovides some summary information on the number of foreign firmsand jobs by size, country and sector. This serves as a backgroundto the second section which reports findings on the incidence andcharacteristics of foreign high growth firms. Sections three and fourlook in more detail at a single cohort of firms – the quarter of a mil-lion private sector firms born in 1998. Using cohort data allows usto abstract from the confounding effect of age (because firm mortal-ity rates are large and strongly age-related), and so allows us to chartmore clearly the evolution of the stock of firms. The discussion startswith a demographic account of foreign firms and then un-picks theinter-related sequences of entry into foreign-ownership and the ex-perience of high growth. The brief final section records some caveatsand conjectures.

2. The foreign ownership information is provided to the ONS by Dunand Bradstreet. There are two variables ”immediate” and ”ultimate”ownership, and we have combined these two. We give primacyto the ultimate foreign ownership variable , and then, if there is afirm which has immediate foreign ownership but the ultimate for-eign ownership field is blank (i.e. not the UK) then it is also labelledas foreign, and owned by the country of immediate ownership. This

70

pattern – immediate but not ultimate – is quite rare. In 1997 and 1998less than one per cent, in later years less than 25 cases per year. Thetwo ownership categories are defined in an ONS briefing note,

” The IDBR holds information on the immediate and ulti-mate country of ownership of an enterprise. An enterpriseor group of enterprises may be owned at the enterprise orgroup level or by a separate holding company or legal en-tity. This entity may in turn be owned by a series of furthercompanies or legal entities, the highest level of which isthe ultimate owner. The immediate and ultimate ownermay be located inside or outside the UK. The ultimate isthe enterprise or enterprise group that is the final ownerin the chain which could either be inside or outside of theUK. If there is only one owner of the enterprise or enter-prise group, then this would be both the immediate andultimate owner, because there is no further ownership inthe chain. The country of ultimate and immediate foreignownership may therefore differ ... Only the first immediateforeign owner and the highest level of foreign ownershipor ultimate owner are used in this analysis.”ONS [2011, p.2 ]

3. Our foreign ownership variable is available for every year from 1997to 2010 and each firm has a ’string’ of foreign ownership flags as-sociated with it. So, for example, the count of foreign firms in aparticular year (and the associated jobs) include only firms whichare foreign-owned in that year. Some firms change backwards andforwards between domestic and foreign owners (and some foreign-owned firms change their country of ownership). It should also benoted that not all foreign firms are flagged, so the ONS recommendsthat the foreign figures be regarded as minimum estimates.

A.1 Headline numbers

A.1.1 Firms

Between 1997 and 2010 the stock of domestically-owned firms roseby 25% from 1.2m to 1.5m (Table A.1), but the stock of foreign-ownedfirms grew much more – it almost tripled from just 8,500 in 1997 to

71

23,600 by 2010.1 As a result the share of foreign firms in the UK stockmore than doubled from 0.71% in 1997 to 1.55% in 2010 (see FigureA.1).

A.1.2 Jobs

The number of private sector jobs in the UK grew by about 7% be-tween 1997 and 2010, from 17.39m to 18.64m, an increase of 1.24m(see Table A.1). However, the number of jobs in domestically-ownedfirms actually fell by 350,000 whilst, in contrast, foreign-owned firmsadded 1.6m jobs over the same thirteen years – a rise of 83%. Thisshift in the composition of the stock of jobs began in the year 2000,and in the subsequent decade the share of jobs in foreign-ownedfirms doubled from 10% to about 20% in 2010 (see Figure A.1).

A.1.3 Average size

The foreign share of the stock of jobs is larger by a factor of ten thanthe foreign share of the stock of firms. This implies that the averagenumber of jobs per firm in foreign firms is about ten times the UKaverage number of jobs per firm. If we turn this into a comparisonbetween foreign and domestic firms, the ratio (in 2010) is about 15(Figure A.2): an average of 150 jobs per firm in foreign firms; anaverage of ten in domestic firms. We can also see from Figure A.2that the average size of foreign firms, from 1999 at least, changesvery little. By contrast, the average job per firm figure for domesticfirms drifts slowly down, as we know it must, since the number ofdomestic firms is growing and the number of jobs in domestic firmsis shrinking.

A.1.4 Net job creation

’Net job creation’ from the job creation and destruction accounts pro-vides an alternative perspective on the contrasting contributions ofdomestic and foreign firms to the evolution of the stock of jobs (Fig-ure A.3). Both the domestic and foreign rates displayed here have

1The terms ’domestic’ and ’domestically-owned’ firms will be used inter-changeably,as will the terms ’foreign’ and ’foreign-owned’.

72

been expressed as ratios to the same, UK, employment denomina-tor 2 – so the UK figure for net job creation can be computed as thesum of the domestic and foreign components displayed on the chart.Since 2001 foreign firms have made often large, and always (withthe exception of 2010), positive contributions to job creation, whilstover the same decade the contribution of (the much larger) stock ofdomestic firms was quite small, and typically negative. In 2010 do-mestic firms accounted for almost three quarters of the 3 percentagepoint contraction in UK net job creation. It is important to recognisethat, here, net job creation is equal by construction to the change injob numbers. So foreign net job creation in a period includes jobsin firms which have become foreign-owned in the period as well asjobs added by firms which were foreign-owned throughout the pe-riod. Of course it is also true, again by construction, that domesticnet job creation is equal to negative foreign net job creation.

A.1.5 Firms by country

In every year (since 1997) firms from more than 50 countries hademployees in the UK (in 2002 more than 100 countries). Howeverthe bulk of foreign firms and foreign jobs are accounted for by a rel-atively small group of countries. The distribution of firms acrosscountries has a long thin tail with (typically) two thirds of the coun-tries with less than ten firms. Here we focus on the top end of thedistribution: the 13 countries which in 2010 had more than 500 firmsin the UK. Collectively they accounted for 17,706 firms (column (2)of Table A.2), which represented 75% (column(3)) of all foreign firmsin the UK. The United States is by far the most heavily representedwith 5,994 firms, 25% of the total. The next three taken together –Germany, France and the Netherlands – account for a fifth. At eighthplace in the list (perhaps a little surprisingly) is Jersey with a shareof 3.5%. Indeed there are two other countries in the top 13, perhapsnot typically thought of as major foreign investors, Luxembourg andthe British Virgin Islands.

2As is conventional, the denominator for a particular year is the average of employ-ment in that year and the preceding year.

73

A.1.6 Jobs by country

The top 13 countries also account for the bulk of jobs in foreign firms– 2.82 million (Table A.2, column (5)), almost 80% of the foreign to-tal (column (6)). Moreover, the ranking of countries by jobs coin-cides quite closely with the ranking of countries by firms (column(4)). Again the United States stands out at the top of the list with onethird of all foreign jobs, and the next three – Germany, France andthe Netherlands– account for one quarter between them.

A.1.7 Firms by sector

Although foreign firms are distributed across all 2-digit sectors, aswith the distribution across countries, a relatively small group of in-dustries account for a large proportion of firms. If we (again) takea 500 firm cut-off in 2010, there are 11 sectors (see Table A.3, col-umn (1)) which collectively account for 17,300 firms, 74% of all for-eign firms. At the top of the list is business services which alonecontributes more than 20%, and if we add in wholesale distribution,the second sector on the list, these two account for 40% of all for-eign firms. We also report (column (4)), the ’lq’, a measure of thesimilarity between the distribution of foreign firms by sector andthe sectoral distribution of all UK firms.3 Evidently foreign firmsare much more heavily represented in some sectors – notably finan-cial services, manufacture of machinery, and wholesale distribution– than are domestic firms.

A.1.8 Jobs by sector

Since we might expect that the average number of jobs per firm couldvary quite systematically by sector, it is hardly surprising that theranking of sectors by number of jobs (Table A.3 column (5)) is a littledifferent from the ranking for firms. Nevertheless, the 11 sectors wehave identified in the firm ranking account for almost two millionjobs (column (6)), more than half of all jobs in foreign firms (the sec-tors in the top 11 by jobs but not in the top 11 by firms have beenrecorded in the ’memo’ item at the bottom of the table). The three

3The ’lq’ is defined as the ratio of a sector’s share in foreign firms to its share in UKfirms, a value of unity indicates the two distributions are identical.

74

sectors with the largest share of jobs are (in descending order) – re-tail, business services and wholesale – together contributing morethan one third of all jobs in foreign firms.

A.2 Foreign high growth firms: incidenceand characteristics

4. The base dataset here is drawn from the ’rolling balanced panel’ (seeChapter 1, data sources and methods) and is further restricted to in-clude only firms which were foreign-owned in the year immediatelypreceding the three year period in which high growth ’episodes’ arecounted. The foreign high growth firm (HGF) incidence rate for eachperiod is defined as the ratio of foreign HGFs to the number of for-eign firms with 10 or more employees.

5. Section A.3 below provides a more fine-grained treatment of foreignHGFs, examining the relationship between foreign ownership andhigh growth episodes for the 250,000 UK firms which were born in1998. Here we look at data on overall numbers, and variations inthe incidence rate by age and size. Unfortunately there are too fewforeign HGFs to allow us to analyse their sectoral distribution since itis necessary to work at the SIC 2-digit level to draw any meaningfulconclusions.

A.2.1 numbers and incidence

6. From the bars on Figure A.4 we can see that the number of foreignHGFs has fluctuated quite widely over the ten three year periods1998/01 to 2007/10, but exhibits a rising trend, at least since 2002/05.Numbers in 2007/10 are almost double those in 1998/01, they risefrom around 400 to almost 700. A rather more informative guide tothe relative importance of HGFs in the population of foreign firms isthe incidence rate, the line on Figure A.4 , which after a ’bulge’ in thefirst few periods, seems to have varied within quite a narrow range,between 7% and 8% since 2002/05, albeit with a very slightly risingtrend.

7. An obvious benchmark for the HGF incidence rate for foreign firmsis the comparable figure for domestically-owned firms, and the two

75

are plotted together on Figure A.5. Whilst the two rates are ratherdifferent in the early periods – the ’bulge’ in foreign is rather morepronounced than it is in domestic4 – from 2002/2005 onwards therates are less than half a percentage point apart until 2007/2010,when their paths diverge, the domestic rate falling by about a per-centage point and the foreign rate rising by a similar amount.

8. An alternative means of providing some additional perspective onthe number of foreign high growth firms is to decompose the nu-merator and the denominator of the HGF incidence rate: the ratioof incidence rates can be written as the foreign share of all HGFs tothe foreign share of all 10+ firms. Taking the ratio of the foreign rate(HGFratefor) to the UK rate (HGFrateuk) we can re-arrange termsas follows,

HGFrateforHGFrateuk

=

HGFfor

HGFuk

firm10+for

firm10+uk

(A.1)

9. The ratio of incidence rates and these two components are plottedon Figure A.6. Leaving aside 1998/01 which, as we have seen, looksto be something of an outlier, the ratio of incidence rates (right handscale) fluctuates within quite narrow bounds, as we might have an-ticipated from Figure A.5. What this relative stability disguises isthe trend-like increase (described in Section A.1) for the proportionof firms in the population of UK firms to rise. We can see that boththe shares of HGFs and of 10+ firms rose steadily: between 1999/02and 2006/09 both shares roughly doubled. The widening of the gapbetween the HGF rates in 2007/10 can now be seen as (proximately)caused by the foreign HGF share continuing to rise whilst the foreignshare of the 10+ population did not.

10. In Chapter 1 Figure 5.1, we saw that since 2002/05 the number ofHGFs in each period has varied within quite a narrow range between10 and 11,000. What we now know is that the foreign share of thosenumbers increased substantially as the number of foreign HGFs rosefrom about 450 to over 700.

4Given its timing, it seems a plausible conjecture that the ’bulge’ might be a side-effectof the ’hi-tech’ boom. Notice, though, that the three datapoints in the ’bulge’ are firmsalive in 1998, 1999, 2000 which survive to 2001, 2002 and 2003, respectively. So whilst itmay be a ’side-effect’, the firms which died in the ’bust’ would not be included.

76

A.2.2 Age

11. The HGF incidence rate for the UK declines with the age of the firm:at one year old it is typically around 15%, by age nine it is lower byfive percentage points5: high growth is a ’game for young players’.Of course, there is no a priori reason to expect the incidence/age rela-tionship to have the same shape for foreign-owned firms since it willbe affected by the (as yet unknown) relationship between age andforeign ownership.

12. Figure A.7 displays the HGF incidence rate for foreign and domesticfirms plotted against the age of the firm, where the rates by age areaveraged over firms born between 1998 and 2006. We can see that theforeign and domestic paths do look a little different. In particular,incidence rates for foreign firms are lower immediately after birth,but they exceed those of domestic firms by about two percentagepoints from age three onwards, after which both rates fall more orless in parallel. There is a more detailed investigation of the inter-relationship between foreign ownership and high growth over timeusing cohort data in the next section.

A.2.3 Size

13. The incidence rate of high growth in the UK is typically inverselyrelated to size (see Chapter 1, Section 1.1.3) – the smaller the firmthe larger incidence rate – and the relationship is generally similarfor foreign firms as we can see from Figure A.8 (although the 50 to99 and the 100 to 249 size-bands do occasionally switch rank order).The average (all age) relationship between foreign and domestic in-cidence rates – foreign exceeds domestic – also holds typically, size-band by size-band. Table A.4 records data on rates (averaged overthe periods 2002/05 to 2007/10), and the foreign/domestic differen-tial declines with size: it is largest for firms in the smallest, 10 to 19size-band. Exceptionally, the HGF incidence rate for foreign firmsin the largest 250+ size-band is considerably smaller than that fordomestic firms in the same size-band.

5These figures refer to firms born 1998 and after, the precise age of pre-1998 firms isnot known – see Chapter 1, Section 1.1.2.

77

A.3 The turbulent history of the UK’s for-eign firms born into cohort98

14. The most important fact of firm demography is that firms die, mostof them quite soon after birth – in the UK the 10 year survival rateis less than 20%.6 Whilst the survival rate does vary a little by size,the overwhelming force of mortality guarantees that firm age willbe a hugely significant conditioning factor when judging firm per-formance. One relatively simple way to ensure that empirical con-clusions are appropriately conditioned on age is to work with birthcohort data – as we do here.

A.3.1 Becoming foreign

15. We start with the 239,649 firms born in 19987 – cohort98. Of these,just 327 – that is, 0.14% – were born foreign. By 2010 85% of cohort98were dead, just 33,673 firms survived, and of them 771 – 2.3% wereforeign (and a further 300 of those alive had, at some stage, beenforeign-owned). So whilst the number of foreign firms roughly dou-bled between birth and 2010, the foreign-owned share of the stock offirms rose hugely more, by a factor of 16.

16. It might seem reasonable to expect that the stock of foreign firmssimply grew, more or less steadily, over the life of the cohort, but thatis entirely wrong. In the 12 years 1998 to 2010 a considerable number– 2,716 firms (1.13% of cohort98 at birth) – were foreign-owned forat least one year. A summary of the ownership history of these 2,716ever foreign firms is set out in Table A.5. Of the 327 which were bornforeign, just 166 remained foreign (and only 16 of the 166 surviveduntil 2010), the other 161 became domestically-owned. However, 22of the 161 subsequently returned to foreign ownership on more thanone occasion. The 2,389 firms which were born domestic, but becameforeign, display a similarly turbulent history: whilst 1,460 remainedforeign after having become so, 929 did not, and almost 100 of themhad more than one further period of domestic ownership.

6As calculated from data on cohorts of firms born in 1998, 1999 and 2000.7Although our database has firm-level records for 1997, as noted earlier 1998 is the

earliest year for which a firm’s birth date can be determined.

78

17. Clearly we cannot track the evolution of the stock of foreign firmsby simply labelling a firm ’foreign’ on the first occasion it becomesforeign-owned. The demographic accounts displayed in Table A.6decompose the change into components which can inform our un-derstanding of the process of change from birth to 2010. It displaysthe stock of foreign firms ’alive’ in the middle row, with the con-stituent inflows above the ’alive’ row and constituent outflows be-low the ’alive’ row. The entries connect the 327 firms ’born’ foreign(in the first row) through to the final, 2010, stock of 771.

18. The accounting framework which links the years from 1999 onwardscan be summarised in a pair of equations. The first starts with firmswhich were foreign at the end of the previous year (alivet−1) fromwhich are subtracted the outflow to domestic (transout) and death(death) to arrive at the number which remain foreign at the end ofthe current year (remain). The other equation adds to those remain-ing the two categories of inflows from the stock of domestic firms,those becoming foreign for the first time (becoming) and those previ-ously foreign, now coming back to foreign after a period in domesticownership (reverting), yielding the overall end-period stock (alivet).

alivet−1 − transoutt − deatht = remaint

and,

remaint + becomingt + revertingt = alivet

19. One way to understand the time-path of the foreign stock is by plot-ting its three components from the second (inflow) equation, andthe data are displayed on Figure A.9. You will notice immediatelythat numbers peak in 2002 when there were more than 1,200 foreignfirms (from the ’alive’ row of Table A.6 we can see the exact num-ber is 1,257), that is about four times the 1998 number and one and ahalf times the number in 2010. Indeed in 2002 about half of all everforeign firms were alive and foreign. We can also see clearly that theinflow of firms ’becoming’ foreign was particularly strong in the firstyears of the cohort’s life, but after 2003 the inflow largely dried upand most foreign firms were those which simply remained foreign.

79

A.3.2 Understanding the evolution of the foreignshare

20. Using a decomposition based on the first (outflow) equation, we canrepresent the numbers as proportions of the foreign stock, scalingthem by the numbers ’alive’ at the end of the previous year. Fig-ure A.10 displays the time series of these three proportions: remain-ing foreign (remaining); moving from foreign to domestic (transout);and the proportion dying (death). The most striking feature of thedata is the steady, and parallel, decline in both the proportion trans-ferring back into domestic ownership and in the proportion dying –at age 2, for example, about 40% died or reverted to domestic own-ership (in roughly equal proportions), with the other 60% of foreignfirms remaining foreign. But by age 12 both death and outwardtransfer rates are less than 10% each (death, 7.2%, transout, 5.9%)and 85% of those foreign at the beginning of the year remained for-eign at the end. By 2010 the transfers within the stock of firms –two categories of inflow (becoming and reverting) and one categoryof outflow (transout) – more or less match, leaving mortality as theprincipal (proximate) influence on the size of the stock of foreignfirms.

21. Even though the number of foreign firms peaked in 2002 and thenfell more or less continuously, the share of foreign firms in the stockof all firms did not fall – as we can see from the ’alive share’ row ofTable A.6 – it continued to rise until 2004 when it paused, after whichit continued to rise (albeit at a slower pace). To better understandthe factors at work here we can use a simple decomposition of thegrowth in the foreign firm share. In obvious notation, we can re-arrange it as follows,

forsharetforsharet−1

≡ fort ÷ fort−1

allt ÷ allt−1

22. In Table A.7 the period has been divided into two: 1998 to 2003;and 2004 to 2010. In the first period the foreign share increases by afactor of about 10, in the second just over 1.58. The decompositionshows that in the first period (the ’growth’ period) the foreign stockitself expanded by a factor of 3.6, whilst the overall stock shrunk

8The product of these two is close to the whole period 16-fold increase in the foreignshare noted at the beginning of the discussion of cohort98.

80

by a factor of one third, producing the 11-fold increase in the for-eign share. In the second period, as we know, the stock of foreignfirms declined, the ratio of 0.79% shown in the decomposition tableimplies a rate of about 20%. Here again though the continuing con-traction of the stock of firms, by one half, produces an expansion inthe foreign share, here by 1.5. The key finding, then, is that foreignownership share is driven early in the life of the cohort by acquisi-tions, and when that slows, the rise in the foreign share continuesbut is driven most importantly by the continuing (and substantial)contraction in the overall stock of firms.

A.3.3 Is foreign ownership protective?

23. As we have just seen the firm mortality rate plays an important rolein interpreting the data on firm performance (indeed this is the ratio-nale for studying cohort data), so it is worth investigating whetherforeign owned firms have a better chance of survival – in epidemio-logical terms, whether it is ’protective’. The simplest way to answerthis question is to compare the death rate of foreign firms (the deathproportion from Figure A.10), with the corresponding death rate fordomestic firms. Our death rate is, in fact, equivalent to a conven-tionally defined ’hazard rate’ (here the hazard of mortality) familiarfrom event history analysis. The two death rates are plotted on Fig-ure A.11 and it appears that the domestic rate is larger (or about thesame) at every age, but with both declining from 20% in the first yearto just below 10% at year 12.

24. A striking feature of the plot is the pronounced ’spike’ in the hazardfor domestic firms at age two which foreign firms do not share, butthis datapoint, like the drop in the foreign hazard at age 4, may justrepresent idiosyncratic departures from the general shape of the twohazards.9 Certainly, after the surge in the number of domestic firmsbecoming foreign passed, the two hazard rates become more similar.We can see even more clearly now that although the domestic mor-tality rate is a huge influence on the increase in the foreign firm shareof the stock of firms it was not mainly attributable to foreign owner-ship being protective. Rather it seems that the impact of mortality on

9Although the downward displacement of the foreign hazard at age 4 may be con-nected with the extraordinary numbers of firms ’becoming’ foreign in 2001 and 2002, seeFigure A.9.

81

the foreign stock was offset, initially to a great extent, later to a lesserextent, by firms transferring from foreign to domestic ownership.

A.4 Foreign firms and high growth

A.4.1 Foreign firms in the rolling balanced panel

25. If we are to uncover the incidence of high growth amongst foreignfirms in cohort98 we need an appropriate dataset, again it is a rollingbalanced panel (RBP) but restricted to the firms born into cohort98and it covers the nine three year periods from 1999/02 to 2007/10.At the outset it is worth checking the extent to which the RBP resem-bles the cohort as a whole, at least in respect of foreign ownership,and the obvious place to start is with a summary of the ownershiphistory of foreign firms in the RBP. The bottom panel of Table A.7provides a display of RBP data, below the cohort98 data we lookedat earlier. A side-effect of the selection criteria is that there are manyfewer firms in the RBP: the first period is 1999/02 (all firms must beat least one year old) and we exclude from each three year periodfirms which do not survive that period. So, for example, the 92,000firms in the first period are, by construction, firms that have surviveduntil at least 2002. Overall the RBP is about 40% of cohort98 and thenumber of foreign firms is smaller in much the same proportion, sothe proportion of ’ever foreign’ is similar in both panels of the table.Importantly, the shares of the ’dynamic’ components (”remained”,”turned” etc) are virtually identical: it appears that the properties ofthe RBP resemble those of cohort98 – at least in respect of foreignownership and its turbulent history.

26. We can check this impression in more detail using the demographicaccounts for foreign firms in the RBP which are set out in Table A.8.A casual inspection of the table suggests that most of the entries inthe RBP accounts are about half the size of those of the correspondingitems of the cohort98 accounts, and the dynamics look rather similar.However the timing does look a little different and these differencesmay be, to some extent, artefacts, by-products of the RBP selectioncriteria: first, for the RBP we assign foreign ownership in the yearpreceding the growth period; and second it is not entirely clear howto align the three year periods of the RBP with the annual cohort98

82

data. More specifically, the peak figure for the number of foreignfirms occurs in 2003/06, after which the stock declines (though notsmoothly), and here too the decline coincides with the tailing off inthe number of firms ’becoming’ foreign.

27. Finally we can look at the evolution of the foreign share. First noticefrom the last row of Table A.7 that over the periods covered by theRBP, there is a ten-fold rise in the foreign share of firms ’alive’: from0.18% in 1999/2002 to 1.82% in 2007/10, roughly matching the risein the alive share for cohort98. A decomposition of this rise is pro-vided in the lower panel of Table A.7 and we can see a pattern whichresembles the cohort98 case. The contraction of the overall stock is amajor factor in both periods (a 50% fall in both), what differentiatesthe periods is the fall in the growth in foreign firm numbers.

A.4.2 Foreign firms and the incidence of high growth

28. The data displayed in Table A.9 report the frequency distribution ofHGF instances for the RBP as a whole, as well as for foreign firms. Ofthe 2,999 firms in the RBP which recorded an instance of high growth5% – 155 firms – were foreign-owned at the time of their high growthepisode, five times the proportion of ’ever foreign’ firms in the RBPpopulation. However, this disproportion is quite readily explicable.Firstly, as we shall very soon see, the HGF incidence rate (the ratioof HGF instances to the number of firms with 10 or more employ-ees) for foreign and domestic firms is relatively similar. Secondly,foreign firms are, on average, considerably larger than firms overall(see Section 2.3), in particular in the RBP about 50% of foreign firmshave more than 10 employees, whilst in the general firm populationthe figure is closer to 5%. So if the ratio of HGFs to 10+ firms is sim-ilar, but the proportion of 10+ firms in the population differs by afactor of five, the share of foreign HGFs in the all HGF total will alsobe larger by a factor of five.

29. The table also shows the frequency distribution of HGF instancesacross firms. From column (3), we see that one and two instances aremore common than average for firms whilst foreign-owned, whilstforeign firms are relatively under-represented (just 2%) amongstfirms that record three or more instances of high growth. But as wecan see from the ’memo’ item at the foot of the table, foreign firmsrecorded 1.8 instances per firm (275 in total), around 20% less than

83

the corresponding all firm average of 2.0 (total, 6097). As we shallsee below this appearance has to be quite carefully interpreted.

30. Figure A.12 displays the HGF incidence rate for domestic and for-eign firms and there are some features of this plot which are alreadyfamiliar,

• the two incidence rate curves are (generally) downward sloping– we saw earlier that the incidence rate declines with age (seeabove Figure A.7 and Chapter 1, section 1.1.3)

• there is a an early ’bulge’ in the foreign rate, but after 2002/05it becomes more similar to the domestic rate, diverging slightlyin 2007/10 (see Figure A.12)

Whilst foreign firms are disproportionately represented amongstHGFs in cohort98, nonetheless foreign HGFs appear to be rather lesslikely to record multiple instances of high growth. This may accountfor our finding that the incidence rate for foreign firms seems quitesimilar, much of the time, to the domestic rate. Investigating furtherrequires us to dig a little below the surface and to consider in moredetail the timing of movements in and out of foreign ownership andof instances of high growth.

A.4.3 Foreign ownership and the timing of highgrowth instances

31. We start our more detailed examination of the timing of events witha sequence index plot for the 155 foreign HGF firms in the RBP:this provides a visual impression of the inter-relationships over timebetween ownership and high growth. The data are displayed onFigure A.13. Each firm is represented by a horizontal ’strip’ al-locating each of the nine periods into one of a five-fold classifica-tion of states – two ownership categories (foreign and domestic)cross-classified by two growth categories (HGF and nonHGF), plusa the fifth ’dead’ category and where each state has been differentlycoloured. The firm-level sequences of states (’strips’) have been or-ganised so that those with an early experience of high growth whilstforeign-owned (coloured blue) appear towards the bottom, whilstthose with an early experience of high growth whilst domestically-owned (coloured red) appear towards the top. In between we have

84

the foreign (but nonHGF) at birth (coloured pink) and the domestic(but nonHGF) at birth (coloured white). The dashed horizontal linesdivide the vertical axis into groups of 25 firms.

32. A striking feature of the chart is the complexity of the sequences:not one of the firms remains in the same state throughout its life.10

Our intention here is to find some order in these sequences, whichmeans finding a meaningful summary of the patterns whose onlyobvious common denominator is that (by virtue of the selection cri-terion) they experienced an instance of high growth whilst foreign-owned. As we know already from Table A.5 most ’ever foreign’ firmsin cohort98 are domestic earlier in life, and from the sequence plotwe can see that the same is true of the foreign HGFs too – 134 firms(86%) were domestic in the first period, either nonHGFs (white) orHGFs (red) – so only 21 were born foreign, almost equal numbers ofnonHGF (pink) and HGF (blue).

33. Rather more surprising is that there are 26 domestic HGFs in thefirst period, twice the number of foreign HGFs. Indeed as we lookdown the plot we find that for 60 of the firms the first instance ofhigh growth is recorded under domestic ownership (26 in the firstperiod, the other 34 after a varying number of periods as domesticnonHGFs). In many cases the instance of high growth under for-eign ownership follows immediately the domestic instance. The re-verse is clearly much rarer, comparatively few instances of domestichigh growth are recorded immediately subsequent to foreign highgrowth.

34. We can also see that for firms starting with periods as a domesticnonHGF not followed by a domestic high growth instance – the 74firms in the lower middle of the plot – it is around 50% more likelythat the firm will become foreign (white to pink, 45 firms) before itbecomes a foreign HGF, rather than moving directly from domesticnonHGF to foreign HGF (white to blue, 29 firms). This evolution –a move from foreign nonHGF to foreign HGF (pink to blue) – is alsothe path followed by virtually all of the 11 firms which were foreignnonHGFs in the first period.

35. To investigate further the relationship between domestic and foreign

10Of course, we might have inferred this from Table A.9: every firm on the sequenceindex plot is a foreign HGF in at least one period and no foreign HGF recorded more thansix HGF instances out of a possible nine.

85

instances of high growth in foreign high growth firms we display inTable A.10 a cross-classification of the firm-level distribution of in-stances: the rows record the HGF foreign instances, and the ’all’ col-umn reproduces the ’foreign’ column familiar from Table A.9; andthe columns record the domestic instances. We can see from the ’all’row that more than half foreign HGF firms (155 – 74 = 81) report atleast one instance of high growth when the firm was domestically-owned and half report multiple instances. Moreover, a consider-able number report more domestic than foreign instances, in the firstrow 24 firms recorded two or more instances of high growth whilstdomestically-owned and just one whilst foreign-owned. If we in-clude the domestic instances at least 81 (more than half) of thesefirms record more instances per firm than our original calculation in-cluded. We saw earlier that the 155 firms recorded 275 instances be-tween them, it turns out that in total there are 125 instances recordedwhilst the firms were domestically-owned, so the average per firmfor firms which are ever foreign HGFs becomes 2.7 (=420/155) ratherthan 1.8 – one third larger than overall cohort98 average.

36. The (somewhat) unexpected importance of domestic HGF instancesto foreign HGFs suggests it might be worth investigating the HGFexperience whilst domestically-owned of other foreign but not foreignHGF firms.11 The numbers have been recorded in the ’Memo’ rowof Table A.10 and there are 118 firms in all. Almost half, 55 of them,foreign at some stage, recorded just a single instance of high growth,and did so whilst they were domestically-owned; similarly, 63 ’ever’ for-eign firms recorded two or more instances of high growth but didso whilst they were domestically-owned. In summary, 118 firmswhich were at some stage foreign-owned recorded instances of highgrowth whilst they were not foreign owned. Evidently many highgrowth firms become foreign owned only after their experience ofhigh growth is behind them.

A.5 Caveats, conjectures and future work

37. This is the first study to have added foreign ownership markers tothe firm-level records from the BSD, so the findings should be treated

11Of course these firms do not appear on the sequence index plot because they do notmeet the selection criterion of having recorded an instance of high growth whilst foreign-owned.

86

cautiously. One particular (and slightly worrying) feature of the datais that there are some indications that the coverage of the ownershipmarker might be less complete in the first two years (1997 and 1998)than it is later on. For example, the 1997 and 1998 datapoints on Fig-ure A.1 for the foreign share of the stock of firms, and on Figure A.2for the average number of jobs per firm for foreign firms, look a lit-tle out of line. Conversely, though, there appears to be no reason tosuspect any discontinuous variation in coverage from 1999 onwards.

38. Another feature of the data which might be regarded as a little unex-pected is the extent of the turbulence in the stock of foreign firms. Inparticular, the scale of the movement out of foreign ownership andthen back into it is a characteristic which does not previously seemedto have been reported. But there is no reason to suppose, other thanits novelty, that this is an artefact or a by-product of measurement er-ror. Needless to say, it might worth taking a sample of cases from analternative, independent, dataset like FAME (even though it is muchless comprehensive) to investigate this phenomenon.

39. A larger issue worthy of further investigation is the dynamics ofthe relationship between foreign ownership and instances of highgrowth. First, we know that the incidence of high growth declineswith age. Second, it seems plausible that the chance of a firm becom-ing foreign-owned may increase with age – in epidemiological terms,the older a firm the more years it is ’at risk’ of foreign-ownership –as we found for cohort98. Taken together these two propositions im-ply (as we found) that some of the (relatively numerous) firms withan early high growth experience will become foreign-owned. Whatwe have not yet determined is whether early high growth plays acausal role in this context. More work is required to first of all ensurethat the cohort98 findings do generalise and are not cohort-specificor period-specific, and then, with some more broadly based results itwould be worth attempting to untangle the sequence of events moredefinitively.

87

Table A.1: Firms and jobs: 1997 & 2010

firms jobsyear uk domestic foreign uk domestic foreign

1997 1208121 1199584 8537 17394400 15461140 19332602010 1523882 1500255 23627 18636923 15108587 3528336

2010/1997difference 315761 300671 15090 1242523 -352553 1595076ratio 1.261 1.251 2.768 1.071 0.977 1.825

88

Table A.2: Foreign firms and jobs by country, 2010 (more than 500 firms)

firms jobsCountry (1) (2) (3) (4) (5) (6)

rank number share(%) rank number share(%)United States 1 5994 25.4 1 1148389 32.5Germany 2 2044 8.7 2 345178 9.8France 3 1585 6.7 3 339214 9.6Netherlands 4 1496 6.3 4 176401 5.0Ireland 5 1055 4.5 9 98682 2.8Japan 6 921 3.9 8 112196 3.2Switzerland 7 891 3.8 5 143993 4.1Jersey 8 825 3.5 7 123423 3.5Australia 9 681 2.9 14 54989 1.6Luxembourg 10 568 2.4 6 134218 3.8Italy 11 567 2.4 20 37539 1.1Sweden 12 541 2.3 16 51097 1.4British Virgin Islands 13 538 2.3 17 50998 1.4

Sum 17706 74.9 2816317 79.8

Total 23627 100 3528336 100

89

Table A.3: Foreign firms and jobs by 2-digit sector, 2010 (more than 500firms)

firms jobsSector (1) (2) (3) (4) (5) (6) (7) (8)

sic92 rank firms share lq rank jobs share lqBusiness Services 74 1 5186 22.0 0.83 2 478789 13.6 0.69Wholesale 51 2 4153 17.6 3.35 3 279185 7.9 1.42Computer Services 72 3 1978 8.4 1.21 7 124236 3.5 1.38Real Estate 70 4 1274 5.4 1.03 25 36227 1.0 0.36Retail 52 5 1184 5.0 0.50 1 532981 15.1 1.01Machinery, other 29 6 650 2.8 4.19 11 91864 2.6 1.96Personal Services 93 7 621 2.6 0.60 29 29154 0.8 0.38Financial services 65 8 603 2.6 4.95 5 152641 4.3 1.28Auxiliary to Finance 67 9 595 2.5 2.73 13 83209 2.4 2.06Travel 63 10 571 2.4 2.72 12 87890 2.5 1.46Recreational services 92 11 529 2.2 0.67 14 78131 2.2 0.62

Sum 17344 73.5 1974307 55.9

Total 23627 100 3528336 100

Memo:Hotels 55 16 359 1.5 0.20 4 208851 5.9 0.72Food Manufacturing 15 23 258 1.1 2.65 6 125253 3.5 1.80Vehicle Manufacturing 34 25 201 0.9 5.03 10 96277 2.7 3.22Chemicals 24 13 446 1.9 8.39 9 98851 2.8 2.89

Note: ’lq’ in columns (4) and (8) are sector shares for foreign firms dividedby the corresponding sector shares for all firms.

90

Table A.4: HGF incidence rate by size-band, average 2002/05 to 2007/10,foreign and domestic, %

size-band foreign domestic difference

10-19 10.6 7.6 3.020-49 8.8 6.9 1.950-99 6.9 6.5 0.4100-249 6.5 6.3 0.2250+ 4.9 6.6 -1.7

all 7.8 7.2 0.6

91

Table A.5: cohort98, summary of firm ownership histories

all RBP

all 239649 93146

number share % number share %never foreign 236933 98.9 91806 98.6ever foreign 2716 1.1 1340 1.4

components of ever foreign

foreign at birth 327 12.0 170 12.7

remain foreign 166 6.1 89 6.6turned domestic 161 5.9 81 6.1

of which,one turn 139 5.1 71 5.3

domestic at birth 2389 88.0 1170 87.3

remain foreign 1460 53.8 733 54.7reversion to domestic 929 34.2 437 32.6

of which,one reversion 831 30.6 413 30.8

92

Tabl

eA

.6:c

ohor

t98

firm

s,fo

reig

n,de

mog

raph

icac

coun

ts

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

born

327

beco

min

g24

443

244

750

521

371

157

5786

9157

29re

mai

ning

170

263

499

686

917

851

698

789

736

724

748

719

reve

rtin

g16

3166

5956

8443

7247

2323

aliv

e32

741

471

197

712

5711

8997

893

988

989

486

282

877

1

tran

sout

9170

117

168

155

153

138

5568

8367

49de

ath

6681

9512

318

518

514

295

8587

4760

aliv

esh

are

%0.

140.

220.

500.

851.

351.

511.

481.

651.

751.

952.

112.

242.

29

Not

es:

1.se

ete

xtfo

rde

finit

ions

2.de

nom

inat

orfo

rsh

ares

isal

lfirm

s(f

orei

gnpl

usdo

mes

tic)

aliv

e

93

Table A.7: cohort98 and cohort98 RBP firms, foreign share of alive, decom-position

(a) cohort98 1998 to 2003 2004 to 20101998 2003 ratio 2004 2010 ratio

foreign 327 1189 3.64 978 771 0.79all 239649 78855 0.33 65968 33673 0.51

share % 0.14 1.51 10.79 1.48 2.29 1.54

(b) RBP 1999/02 to 2004/07 2005/08 to 2007/101999/02 2004/07 ratio 2005/08 2007/10 ratio

foreign 170 709 4.17 606 614 1.01all 93146 45752 0.49 40836 33673 0.51

share % 0.18 1.55 8.61 1.48 1.82 1.23

94

Tabl

eA

.8:c

ohor

t98

RBP

firm

s,fo

reig

n,de

mog

raph

icac

coun

ts

1999

/02

2000

/03

2001

/04

2002

/05

2003

/06

2004

/07

2005

/08

2006

/09

2007

/10

beco

min

g17

012

224

224

927

411

737

9930

rem

aini

ng0

9415

727

840

856

153

748

555

1re

vert

ing

00

*20

3731

3262

33al

ive

170

216

399

547

719

709

606

646

614

tran

sout

050

3659

9089

9789

43de

ath

026

2962

4969

7532

52

aliv

esh

are

%0.

180.

270.

600.

961.

421.

551.

481.

751.

82

Not

es:

1.se

ete

xtfo

rde

finit

ions

2.*

coun

tles

sth

an10

,bel

owdi

sclo

sure

thre

shol

d,co

mbi

ned

wit

h’r

emai

ning

’2.

deno

min

ator

for

shar

esis

allfi

rms

(for

eign

plus

dom

esti

c)al

ive

95

Table A.9: cohort98 firms: HGF, all and ever foreign instance, frequency ofHGF instances per firm, 19998/02 to 2007/10

frequency all foreign foreignshare %

(1) (2) (3)

all 2999 155 5

1 1316 73 62 775 56 73 + 908 26 2

Memo:instances: all, 6097; foreign, 275average per firm: all, 2.0; foreign, 1.8

96

Table A.10: cohort98 firms, ever foreign HGF: frequency of foreign vs do-mestic instances per firm, 1999/02 to 2007/10

domestic instances

foreign 0 1 2+ allinstances

1 32 17 24 732+ 42 23 17 82

all 74 40 41 155

Memo:zero foreign 0 55 63 118instances

Note: foreign HGFs instances: 275 whilst foreign-owned;145 whilstdomestically-owned; total 420

97

Figure A.1: share of foreign firms and foreign jobs in uk total, 1997–2010(%)

1998

2000

2002

2004

2006

2008

2010

0.0

0.5

1.0

1.5

2.0

2.5

year

firm

s sh

are

%

0

5

10

15

20

25

jobs

sha

re %

firmsjobs

98

Figure A.2: average jobs per firm, domestic and foreign, 1997–2010

1998

2000

2002

2004

2006

2008

2010

5

10

15

20

25

year

dom

estic

50

100

150

200

250

fore

ign

domesticforeign

99

Figure A.3: domestic and foreign components of net job creation rate,1998–2010 (%)

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

njc

%

−3

−2

−1

0

1

2

3foreigndomestic

Note:The domestic and foreign contributions to uk net job creation have beenexpressed as ratios to uk employment. As is conventional in job creationand destruction accounting, the denominator is an average ofemployment in the current and the preceding year.

100

Figure A.4: HGF: foreign owned, number and incidence rate by period,1998/01 to 2007/10

020

040

060

080

0

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

period

02

46

810

1214

16in

cide

nce

% (

line,

rhs

)

num

ber

(bar

, lhs

)

101

Figure A.5: HGF: foreign and domestic owned, incidence by period,1998/01 to 2007/10, %

05

1015

period

inci

denc

e %

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

foreigndomestic

102

Figure A.6: HGF foreign incidence rate, component ratios, 1998/01 to2007/10, %

02

46

8

periodperiod

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

hgfsh(lhs)10+sh(lhs)hgfratio(rhs)

0.8

1.2

1.6

2.0

2.4

hgfs

h, 1

0+sh

, sha

re %

hgfr

atio

, rat

io

103

Figure A.7: HGF: foreign and domestic, incidence rate by age, averageover cohorts, 1998 to 2006, %

05

1015

20

years since birth

inci

denc

e %

1 2 3 4 5 6 7 8 9

foreigndomestic

104

Figure A.8: HGF: foreign by size-band by period, 1998/01 to 2007/10, %0

510

1520

25

period

inci

denc

e %

9801

9902

0003

0104

0205

0306

0407

0508

0609

0710

10−1920−4950−99100−249250+all

105

Figure A.9: cohort98 firms, foreign alive, 1998-2010, by inflow component,number

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

revertingremainingbecoming

years

num

bers

0

200

400

600

800

1000

1200

Notes:1. in 1998 ”becoming” is firms born foreign2. for data see Table A.6

106

Figure A.10: cohort98 firms, foreign alive, 1998-2010, by outflow compo-nent, ratio to alivet−1

2000

2002

2004

2006

2008

2010

0.0

0.2

0.4

0.6

0.8

1.0

years

ratio

to o

peni

ng s

tock

remainingtransoutdeath

Note: for data see Table A.6

107

Figure A.11: cohort98, deaths, foreign and domestic, years since birth, cal-culated hazard rates

2 4 6 8 10 12

0.0

0.1

0.2

0.3

0.4

0.5

years since birth

ratio

to o

peni

ng s

tock

foreigndomestic

Notes:1. ratio of death to alivet−1

2. for data see Table A.6

108

Figure A.12: cohort98 firms, HGF incidence rate, domestic and foreign, %,1999/02 to 2007/10

0

5

10

15

20

25

periods

rate

%

domesticforeign

99/0

2

00/0

3

01/0

4

02/0

5

03/0

6

04/0

7

05/0

8

06/0

9

07/1

0

Note: foreign-owned HGF, foreign in the year before the high growthepisode

109

Figure A.13: cohort98 firms, ever foreign HGF, sequence index plot

years

155

firm

s

99/0

2

00/0

3

01/0

4

02/0

5

03/0

6

04/0

7

05/0

8

06/0

9

07/1

0

Key: red, domestic HGF; blue, foreign HGF; white, domestic nonHGF;pink, foreign nonHGF; black, dead

110