Growth Processes Of High- Growth Firms In The UK - Nesta

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Nesta Working Paper No. 12/10Growth Processes of HighGrowth Firms in the UKAlex CoadMarc CowlingJoshua Siepel

Growth Processes of High-Growth Firms in the UKAlex CoadSPRU, University of Sussex &Aalborg University, DenmarkMarc CowlingUniversity of ExeterJosh SiepelSPRU, University of Sussex &Aalborg University, DenmarkNesta Working Paper 12/10October 2012www.nesta.org.uk/wp12-10AbstractWe seek to complement existing research on High-Growth Firms (HGFs) byapplying relatively advanced econometric techniques to the analysis of HGFgrowth processes. Structural Vector Autoregressions (SVARs) show that thegrowth processes of firms start with employment growth, then sales growth, thenassets growth, then profits growth, while the growth processes of HGFs put moreemphasis on sales growth driving the other dimensions. We then investigate thepossibility of interdependence or ‘spillovers’ between the growth of HGFs andnon-HGFs. Peer-effects econometrics dispel concerns that HGFs should be seenas ‘cannibals’ that exploit growth opportunities that would otherwise be exploitedby other firms.JEL Classification: L25Keywords: High growth firms, growth process, SVAR, rivalry, firm growthWe are very much indebted to Albert Bravo Biosca, Louise Marston and Alessio Moneta for many helpful comments, andparticipants at Nesta workshops for many helpful comments and discussions. Any remaining errors are ours alone.Corresponding Author: Alex Coad, Freeman Centre, SPRU, University of Sussex, Falmer, Brighton, BN1 9QE, UK.A.Coad@sussex.ac.ukThe Nesta Working Paper Series is intended to make available early results of research undertaken or supported byNesta and its partners in order to elicit comments and suggestions for revisions and to encourage discussion and furtherdebate prior to publication (ISSN 2050-9820). 2012 by the authors. Short sections of text, tables and figures may bereproduced without explicit permission provided that full credit is given to the source. The views expressed in thisworking paper are those of the author(s) and do not necessarily represent those of Nesta.

1IntroductionInterest in high-growth firms (HGFs) has exploded in recent years, once the job-creatingprowess of a minority of fast-growing firms became recognized – roughly 4% of firmscan be expected to generate 50% of jobs (Storey, 1994, p. 117). Research into highgrowth firms has itself undergone high-growth. However, the level of analysis has often remained rather simplistic, focusing on either the relative numbers of high-growthfirms across countries, or the sectors in which HGFs are relatively abundant, or the determinants and characteristics of HGFs in contradistinction to non-HGFs consideringvariables such as size and age (Henrekson and Johansson, 2010). Previous work hastypically found it extremely hard to predict which firms will become HGFs, and hasobserved that high-growth episodes are not persistent (a HGF in one year need not be aHGF in the next). HGFs are found in all sectors, especially the services sector (but theyare not over-represented in high-tech sectors – if anything, they are under-representedhere (Henrekson and Johansson, 2010; Mason and Brown, 2010)). Henrekson and Johansson (2010, p. 227) also observe that “it is young age more than small size that isassociated with rapid growth." More generally, however, it is difficult to predict whichfirms will be HGFs. In this paper, we do not seek to predict who will become a HGF.Instead, we seek to complement existing work by applying advanced econometric techniques to get new insights into the processes of high-growth firms.First, we investigate how growth processes of HGFs unfold, by applying data-driventechniques based on Independent Component Analysis (ICA) for establishing causality,that exploit the non-Gaussian structure of residuals to infer causal relationships. In particular, we build upon the LiNGAM model (Linear Non-Gaussian Acyclic Model) introduced in a cross-sectional context by Shimizu et al. (2006), and extended to a SVAR(Structural Vector Autoregression) context, by introducing lagged effects, by Monetaet al. (2012). This VAR-LiNGAM approach to obtaining causal estimates from observational data is often applied in the neuroimaging literature, although it has recentlybeen introduced into the econometrics literature by Moneta et al. (2012).Second, we investigate whether HGFs are seen as rivals or sources of beneficial‘spillovers’ by other firms in the same industry, by applying peer-effects econometrics.On the one hand, it could be that HGFs rush in to take advantage of economic opportuni2

ties by spoiling these opportunities for others, and stealing the market in a ‘cannibalistic’sense, in such a hasty way that these opportunities are exploited rather inefficiently. Onthe other hand, it could be that HGFs play a more complementary role, spotting opportunities that would otherwise remain undeveloped, and generating a number of spillovers(such as knowledge spillovers, productivity spillovers (‘red queen effects’), boostingeconomic growth through new wealth and new demand, etc) that benefit other firms.Theory is not clear, and so this issue needs to be addressed with empirical evidence.The structure of the paper is the following. The next section briefly summarizes therelevant literature. Section 2 gives an introduction to firm growth processes in the UKby presenting some simple vector autoregression results based on official ONS data.Section 3 presents the FAME database that will be the focus of our subsequent analysis.In Section 4 we apply some SVAR models to analyze firm growth, first presenting oureconometric methodology and then discussing the results. In Section 5 we apply peereffects econometrics to investigate whether HGFs can be seen as rivals or whether theyplay a complementary role with regards to other firms. The final section (Section 6)contains our conclusions, where we discuss policy implications of our results.2Preliminary findingsTo begin with, we present some simple vector autoregression models on firm growthprocesses, using the available data on sales growth and employment growth, from Office of National Statistics (ONS) data, using the Business Structure Database (BSD)files (for more information on the BSD, see Evans and Welpton (2009)). BSD provides a detailed record of the performance of UK firms, using VAT figures collectedby HM Treasury and employment records from National Insurance. Growth rates ofsales and employment are calculated by taking log-differences of total sales and totalemployment. Table 1 looks at vector autoregression models with either 2 or 3 lags. Tobegin with, we see plenty of evidence of negative autocorrelation in the time series ofSales growth and Employment growth. This negative autocorrelation means that, ceteris paribus, Sales (Employment) growth in any one year is not likely to be followed bySales (Employment) growth in the following year.3

Another interesting result concerns the relationship between size and growth – arelationship often referred to as ‘Gibrat’s Law’. We proxy size by taking the laggednatural logarithm of the number of employees. With respect to sales growth, we seethat lagged size has a small positive association with subsequent sales growth. Withrespect to employment growth, however, a larger size is associated with slower growth– and the effect is much larger than for sales growth. Taken together, the evidencesuggests that firms with many employees are less likely than their smaller counterpartsto experience subsequent employment growth, and that instead they can benefit fromgrowth in a different dimension – sales growth.With these vector autoregression models, however, we are primarily interested inthe interplay of sales and employment growth. The results in Table 1 show that laggedemployment helps predict sales growth, and lagged sales growth helps predict subsequent employment growth. Although the results are statistically significant (no doubtbolstered by the large number of observations), the magnitudes of the effects are notespecially high. Moreover, the R2 statistic remains low, indicating that most of the variation in growth rates remains unexplained. The low R2 of growth rate regressions hasbeen observed in many other studies and has been taken as evidence that firm growthis essentially a ‘random walk’ process.1 Looking at the coefficient magnitudes, it appears that lagged employment growth has a slightly larger contribution to subsequentsales growth than vice versa (lagged sales growth on subsequent employment growth),because the respective coefficients are 0.13-0.14 versus 0.05-0.06 at the first lag. Themagnitude of the effects fades as the number of lags increases.Table 2 presents the results of a size disaggregation exercise. These results showthat, for smaller firms, employment growth plays a more important role with regardsto subsequent sales growth, although there are also significant effects of sales growthon subsequent employment growth. As our focus shifts towards larger firms (250 employees), the co-evolutionary link between sales growth and employment growthbecomes weaker.Sales growth and employment growth appear to be more ‘mutually reinforcing’ inthe case of smaller firms. The growth of small firms is qualitatively different from the1For a survey see e.g. (Coad, 2009, Table 7.1).4

growth of larger firms – smaller firms must struggle through the ‘liability of newness’ toachieve economies of scale, and the ‘grow or die’ dilemma is especially acute for thesefirms. Smaller firms also enjoy a more flexible organizational structure, and so canrespond better to new human resources to put them to work on new tasks in imaginativeways. For larger firms, in constrast, sales and employment growth appear to be morerandom and less inter-related, perhaps because selection pressures are less severe forthese established firms who have reached the ‘minimum efficient scale’ (MES).Table 2 also contains evidence on the relationship between size and growth. Thecoefficients on lagged size in Table 2 indicate that larger firms tend to experience slowergrowth in terms of both sales and employment – a finding often referred to as Galtonian ‘reversion to the mean’.2 This negative dependence of growth on size has beenobserved in previous empirical literature (see for example Sutton, 1997; Caves, 1998;Coad, 2009). While small firms must struggle to grow to overcome their size disadvantage, larger firms that have achieved a minimum efficient scale are under less pressureto grow.Table 3 looks specifically at the growth processes of the firms that are growing fastestin terms of sales or employment. For the subsample of Sales HGFs, we see that salesgrowth has a slightly larger association with subsequent employment growth than inthe case of Employment HGFs. For the subsample of Employment HGFs, we see thatemployment has a considerably larger association with subsequent sales growth than inthe case of Sales HGFs. In other words, firms with the 5% fastest employment growthare seen to efficiently ‘convert’ this employment growth into sales growth – in the sensethat employment growth in these firms makes an especially visible impact on subsequent sales growth. These firms appear to be more capable of internalizing new humanresources to fuel subsequent growth of sales.These results can be broadly interpreted as follows: employment growth and salesgrowth are two related but distinct dimensions of firm size and growth. To be sure, largefirms are large in terms of both sales and employment, but during their growth there maywell be stages in the growth of sales and employment where one variable has a larger2The results in Table 2 (focusing on firms above a minimum size threshold) appear to contrast slightlywith those in Table 1, where we looked at all firms taken together. This is presumably due to the samplescontaining firms of different sizes.5

Table 1: Vector autoregression models on ONS/ABS data for sales and employmentgrowth, for VAR models including either 2 or 3 lags. Coefficients and t-statistics.Sales gr.Sales gr. (lagged)Empl gr. (lagged)Sales gr. (2nd lag)Empl gr. (2nd lag)Sales gr. (2nd lag)Empl gr. (2nd lag)Sales/Empl(Sales/Empl)2log(Empl), .0399t-statEmpl gr.2 210.0830530149950.0242t-statSales 0422455660.0468-6.704.14-136.54148.28t-statEmpl gr.3 5.79impact on the other, where one leads and the other follows. We would like to know thecausal ordering of these firm growth variables – not just intertemporal associations fromone year to the next, but ideally see how sales growth and employment growth affecteach other in the shorter term – within the same period. If we focus only on lags ofone year or more (in the context of reduced-form vector autoregressions) then we mightmiss out on some important within-the-period effects that fade out in the longer-term.To get a better understanding of the processes of firm growth, we need to peer inside the‘black box’ of instantaneous causal effects – how sales growth and employment growth(and perhaps other facets of firm growth not covered in the ONS ABS data) causallyaffect each other within the same year, also considering lagged effects.3DatabaseFor our advanced econometric analysis, which requires a relatively large number of variables as well as recent developments in econometric theory and software, we use dataon UK businesses from FAME (Financial Analysis Made Easy). FAME data has someadvantages over Census data in that it provides information on a number of variables not just sales and employment, but also other variables such as financial performanceand growth of assets. We take growth of operating surplus as an indicator of the finan-6

7Sales gr. (lagged)Empl gr. (lagged)Sales gr. (second lag)Empl gr. (second lag)Sales/Empl(Sales/Empl)2log(Empl), laggedConstant .000183-7.03E-10-0.026260.1058878207700.0401Sales gr.t-statEmpl gr.10 les gr.t-statEmpl gr.50 -31.7433.57t-statt-statEmpl gr.250 55940.8196935.661.162584209520950.12250.1422Sales le 2: Vector autoregression models on ONS/ABS data for sales and employment growth, for three groups of firms (meansize of 10 , 50 and 250 employees respectively). Coefficients and t-statistics.

Table 3: Vector autoregression models on ONS/ABS data for sales and employmentgrowth, for subsamples of High-Growth Firms (measured as 5% fastest growing firmsin terms of sales or employment, respectively). Coefficients and t-statistics.Sales gr. (lagged)Empl gr. (lagged)Sales gr. (second lag)Empl gr. (second lag)Sales/Empl(Sales/Empl)2log(Empl), laggedConstant termObservationsR2Sales .42E-100.0532451.0378681137690.1605t-statEmpl gr. t-stat5% fastest sales growth-64.21 0.061278 38.654.38-0.0761-6.84-39.41 0.043284 28.037.34-0.06156 -7.2210.05-3.2E-05 -8.47-4.421.25E-10 5.1017.30-0.01825 -7.49151.09 0.183865 40.571137690.0275Sales 6E-090.0651220.0786071634240.0808t-statEmpl gr.t-stat5% fastest empl. growth-34.59 0.04624415.1720.21-0.15564-19.65-16.25 6.192.59E-111.0823.720.0083544.3514.080.677102 198.891634240.0203cial performance of the firm, which we consider to be a better indicator than net profit,although we are aware that financial performance variables can sometimes be unreliable proxies for the underlying economic phenomena of interest (Fisher and McGowan,1983), and therefore should be treated with some caution. For a more detailed comparison of the BSD and FAME datasets, and why we use both in this analysis, see AppendixA.Turnover, Net Tangible Assets and Operating Profit are defined in terms of thousandsof GBP, and for number of employees we take the headcount of employees. With regardsto identifying industrial sectors, we use 2007 SIC Codes, and recode them at the level of3-digit, 4-digit or 5-digit industries.3 We focus on the years 2003-2011, although manyof the firms included in our analysis do not report data for the full period (that is, wehave an unbalanced panel).4In line with previous work,5 we focus on firms with 20 employees or more. Including3See ough we don’t restrict firms to be present in each year 2003-2011, we do have the restriction thatthere are no gaps in the four SVAR variables for those years where a firm does report activity for thatyear. For example, if we have observations for a firm-year for growth of sales, employment, and assets,but not operating profits, then this firm-year will be dropped.5For example, work on data from the French National Statistical Office (INSEE) which focuses onfirms above a threshold of 20 employees (see, among others, Coad (2007a, 2010).48

smaller firms would amplify difficulties of missing observations and hence selectionbias. Instead, we focus on firms with 20 employees or more, and so our results shouldbe interpreted accordingly. Therefore our analysis does not include the smallest firms inthe economy, with fewer than 20 employees.In our subsequent analysis, we sometimes split the sample into subsamples of HGFsversus non-HGFs. This is done in the following way: first we calculate a firm’s averageannual employment growth over the available time period (with a minimum of at least3 years). If we consider that a firm’s average annual employment growth rate γ can beexpressed in terms of the relationship between initial size St and final size St τ :(1 γ)τ St τSt(1)then the average annual growth rate γ can be calculated in the following way:(St τ 1)τ 1 γSt(2)HGFs are then defined as those firms that are in the top 10% of the (average annual)growth rates distribution.We choose this measure of HGFs in order to exploit the available data as best wecan, by making use of all available years (maximum duration: 2003-2011). Firms inour sample are present for different lengths of time, and so we normalize by calculatingthe average annual growth rate (with a minimum of three years). It has been observedthat high-growth events display little persistence (Coad, 2007a; Parker et al., 2010), andtherefore we do not focus on what happens after a high-growth event, but only howfirms grow during their high-growth period. Although some sectors may grow fasterthan others, we do not normalize by sector, because we argue that a high employmentgrowth rate is equally challenging (from an organizational point of view) whatever sector the firm operates in. We prefer relative growth to absolute growth, because the latteremphasizes the growth of large firms to the detriment of the growth of smaller firms(Hölzl, 2011). We also focus on the top 10% of the employment growth rates distribution to ensure that we have enough firms in the HGF category, while ensuring that weinclude as HGFs only those firms that are genuine fast growers.9

In our SVAR analysis in Section 4.1.3, we include all firms, whereas in our peereffects estimates in Section 5 we focus only on larger firms (with a mean size of either200 , 250 or 300 employees), because we consider that larger firms are more likelyto be engaged in direct competition whereas small firms can escape direct competitionby specializing in niche markets or regions.4SVAR models of firm growthIn this section, we seek to unravel the processes of firm growth by considering differentfacets of the growth process: sales growth, employment growth, growth of assets, andgrowth of profit margins. We therefore contribute to the literature that considers howfirms grow in terms of sales and profits (Cowling, 2004). To this end, we apply Structural Vector Autoregressions (SVARs) – to be precise, we apply a Linear non-GaussianAcyclic SVAR model (VAR-LiNGAM) that is identified through Independent Component Analysis (ICA). We being by presenting our methodology in non-technical termsbefore applying it to our FAME data (Section 4.1). Technical details on our methodology, the intuition behind ICA, our identification strategy, and the assumptions on whichthe estimator is built are presented in Appendix B.Our reduced-form VAR models presented earlier in Section 2 were interesting indescribing the intertemporal associations between two dimensions of firm growth – salesand employment. Although intertemporal associations can describe the evolution offirms over time, they do not identify which variable is driving the other. Correlationdoes not imply causality – or in everyday language ‘you can’t get an ‘ought’ from an‘is.” Knowledge of the causal relations (as opposed to mere associations) is essentialas soon as one wishes to consider how to intervene in the system being observed. Awell-placed intervention will target one particular variable to have predictable effectson other variables as the ‘shock’ propagates throughout the system. However, withoutknowledge of the causal relations, a misplaced intervention might have no effect (oreven perverse effects) if the variable targeted has no causal effect (or unexpected effects)on the other variables.One analogy relates to sailing – we observe a boat that is sailing due west, although10

Table 4: Matrix of correlation coefficients for the VAR series. 150’920 observations.All correlations are statistically significantly different from zero at the 5% level.Sales gr.Empl. gr.Tot. Ass. gr.Op. Prof. gr.Sales gr.10.53250.19540.3412Empl. gr.Tot. Ass. gr.Op. Prof. gr.10.12470.122410.17391the rudder is pointing northwest to take into account a wind that blows south. If therudder was aimed due west (in the desired direction of motion), the boat would not endup in the desired location because of the wind. Simply observing intertemporal dynamics does not provide enough information on which to base an intervention. Instead,knowledge of the underlying causal relations is essential.In the following SVAR models, we are interested in the causal relations that underpinthe growth process as described in reduced-form VARs. Does sales growth have a causalimpact on employment growth, or vice versa? Can job creation be stimulated by firstboosting firm profits (which will then be subsequently reinvested in the firm)? If a firmseeks growth, should it focus on seeking new employees, or boosting sales, or investingin fixed assets, or striving to improve its financial performance? If a policymaker seeksto craft a new policy aimed at encouraging firms to create jobs, should he/she aim toallow firms to first earn high profits, or perhaps enjoy sales growth before subsequentlyseeking new employment? Our SVAR results will shed light on these issues.4.1SVAR AnalysisWe begin with some simple correlations, before applying reduced-form VAR and structural VAR models. We follow Coad (2010) and Moneta et al. (2012) and focus on 1-lagmodels, which give a parsimonious and fairly accurate representation of the underlyingrelationships.11

4.1.1CorrelationsTable 4 contains a correlation matrix of the four VAR series. Growth of sales is highlycorrelated with growth of employment, with a correlation coefficient of 0.5325. All ofthe four variables - growth of sales, employment, total assets, and operating profits are positively correlated with each other. These correlations give a preliminary viewand serve as an introduction to our VAR and SVAR results. Another interesting feature is that the correlations are all below the frequently-cited threshold value of 0.70,which suggests that we do not need to be overly concerned about multicollinearity inour particular context (especially considering that we have a relatively large number ofobservations, which should also help in identification).4.1.2Reduced-form VAR resultsTable 5 contains the reduced-form VAR results, which are similar in spirit to those inCoad (2010). These intertemporal associations are helpful in describing the time seriesevolution of the VAR series, but they do not allow any causal interpretation.We begin by looking at the results for the full sample (top panel of Table 5). Firstof all, along the diagonal we can see the autocorrelation coefficients. Sales and employment growth display positive autocorrelation over time, while the growth of operatingprofits displays strong negative autocorrelation.6 Sales growth is followed by positivechanges in employment and total assets, while employment growth is followed by positive changes in sales and total assets.Comparing the results for the full sample (top panel of Table 5) with results for thesubsample of HGFs, the results are generally quite similar, although a few differencescan be mentioned. For HGFs, we observe a weaker association of employment growthwith growth of the other variables – sales, assets and operating profits. Nevertheless, forHGFs the association between assets growth and subsequent growth of sales, employment and operating profits is stronger. Another interesting finding is that, for HGFs,6The attentive reader will recall that, for the ONS data in Tables 1 – 3, we saw that Sales and Employment displayed negative autocorrelation. Presumably this discrepancy is due to our FAME data consistingof larger-sized firms – indeed, previous work has shown that growth rate autocorrelation is negative forsmaller firms and positive for larger firms (Coad, 2007a).12

Table 5: Reduced-form VAR estimates and t-statistics, estimated using Least AbsoluteDeviation regressions (as opposed to conventional OLS). A constant term is included inthe estimations but not reported in the tables.Sales grFull sampleSales gr0.0050.0019Empl. gr0.06290.0013Tot. Ass. gr0.04910.0023Op. Prof. gr0.19130.0072HGFs subsampleSales gr0.00020.011Empl. gr0.04960.0104Tot. Ass. gr0.030.0122Op. Prof. gr0.18410.0182Non-HGFs subsampleSales gr0.00930.0027Empl. gr0.06740.002Tot. Ass. gr0.06950.0038Op. Prof. gr0.20340.0093Empl. grAˆ1Tot. Ass. grOp. Prof. 88020.0185588020.0092588020.02265880213

growth of operating profit has no significant effect on subsequent growth of either of theother variables.To investigate the robustness of these estimates, we repeated the analysis in Table 5including a full set of 3-digit industry dummies. The results obtained were very similar.These reduced-form regression results give us a first insight into the intertemporalassociations between the variables, although they are merely associations and not causaleffects.4.1.3Structural VAR resultsBefore applying our SVAR model to our data, we first check that the residuals are nonGaussian, which is one of the model requirements. Figure C.3 in the Appendix presentsqq-plots (quantile-quantile plots) of the 1-lag VAR residuals, and shows that these residuals are indeed non-Gaussian. Non-Gaussianity is observed to be highly statisticallysignificant when formal tests are applied.7 Similar qq-plots are obtained for the HGFand non-HGF subsamples (not shown here for space limitations). This indicates that ourSVAR identification strategy that applies ICA is an appropriate technique for our data.Figure C.3 in the Appendix presents qq-plots of the 1-lag VAR residuals, and showsthat these residuals are indeed non-Gaussian.Following on from the reduced-form VAR results, we now focus on the structuralVAR results that incorporate insights into causal relations and instantaneous effects(i.e. effects that occur within one period of observation, which in our case is one year).SVAR estimates of B0 and B1 are presented in Table 6.We begin by looking at the results for the full sample, shown in the top panel of Table 6. Our SVAR estimates suggest the following causal ordering: employment growthappears to ‘kick-start’ the growth process, having a positive effect on sales growth,as well as a negative effect on growth of operating profit (the effect of employmentgrowth on assets growth is not significant). Taken together, these results show that employment growth is a direct cost (hence having a negative direct effect on operatingprofits) although there is an important indirect channel according to which employmentgrowth boosts sales, and sales growth will boost profits. Following on from employ7Shapiro-Wilk and Shapiro-Francia tests are applied, and the p-values are all smaller than 1 10 40 .14

ment growth, sales growth has a positive causa

Interest in high-growth firms (HGFs) has exploded in recent years, once the job-creating prowess of a minority of fast-growing firms became recognized - roughly 4% of firms can be expected to generate 50% of jobs (Storey, 1994, p. 117). Research into high-growth firms has itself undergone high-growth. However, the level of analysis has of-

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