Growth Processes Of High Growth Firms In The Uk-Books Pdf

Growth Processes of High Growth Firms in the UK
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Growth Processes of High Growth Firms in the UK, Alex Coad Marc Cowling Josh Siepel. SPRU University of Sussex University of Exeter SPRU University of Sussex. Aalborg University Denmark Aalborg University Denmark. Nesta Working Paper 12 10, October 2012, www nesta org uk wp12 10. We seek to complement existing research on High Growth Firms HGFs by. applying relatively advanced econometric techniques to the analysis of HGF. growth processes Structural Vector Autoregressions SVARs show that the. growth processes of firms start with employment growth then sales growth then. assets growth then profits growth while the growth processes of HGFs put more. emphasis on sales growth driving the other dimensions We then investigate the. possibility of interdependence or spillovers between the growth of HGFs and. non HGFs Peer effects econometrics dispel concerns that HGFs should be seen. as cannibals that exploit growth opportunities that would otherwise be exploited. by other firms, JEL Classification L25, Keywords High growth firms growth process SVAR rivalry firm growth. We are very much indebted to Albert Bravo Biosca Louise Marston and Alessio Moneta for many helpful comments and. participants 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 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 authors 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. 1 Introduction, 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. ten remained rather simplistic focusing on either the relative numbers of high growth. firms across countries or the sectors in which HGFs are relatively abundant or the de. terminants and characteristics of HGFs in contradistinction to non HGFs considering. variables such as size and age Henrekson and Johansson 2010 Previous work has. typically found it extremely hard to predict which firms will become HGFs and has. observed that high growth episodes are not persistent a HGF in one year need not be a. HGF in the next HGFs are found in all sectors especially the services sector but they. are not over represented in high tech sectors if anything they are under represented. here Henrekson and Johansson 2010 Mason and Brown 2010 Henrekson and Jo. hansson 2010 p 227 also observe that it is young age more than small size that is. associated with rapid growth More generally however it is difficult to predict which. firms 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 tech. niques to get new insights into the processes of high growth firms. First we investigate how growth processes of HGFs unfold by applying data driven. techniques based on Independent Component Analysis ICA for establishing causality. that exploit the non Gaussian structure of residuals to infer causal relationships In par. ticular we build upon the LiNGAM model Linear Non Gaussian Acyclic Model in. troduced in a cross sectional context by Shimizu et al 2006 and extended to a SVAR. Structural Vector Autoregression context by introducing lagged effects by Moneta. et al 2012 This VAR LiNGAM approach to obtaining causal estimates from obser. vational data is often applied in the neuroimaging literature although it has recently. been 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 opportuni. 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 On. the other hand it could be that HGFs play a more complementary role spotting opportu. nities that would otherwise remain undeveloped and generating a number of spillovers. such as knowledge spillovers productivity spillovers red queen effects boosting. economic 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 the. relevant literature Section 2 gives an introduction to firm growth processes in the UK. by 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 our. econometric methodology and then discussing the results In Section 5 we apply peer. effects econometrics to investigate whether HGFs can be seen as rivals or whether they. play a complementary role with regards to other firms The final section Section 6. contains our conclusions where we discuss policy implications of our results. 2 Preliminary findings, To begin with we present some simple vector autoregression models on firm growth. processes using the available data on sales growth and employment growth from Of. fice of National Statistics ONS data using the Business Structure Database BSD. files for more information on the BSD see Evans and Welpton 2009 BSD pro. vides a detailed record of the performance of UK firms using VAT figures collected. by HM Treasury and employment records from National Insurance Growth rates of. sales and employment are calculated by taking log differences of total sales and total. employment Table 1 looks at vector autoregression models with either 2 or 3 lags To. begin with we see plenty of evidence of negative autocorrelation in the time series of. Sales growth and Employment growth This negative autocorrelation means that ce. teris paribus Sales Employment growth in any one year is not likely to be followed by. Sales Employment growth in the following year, Another interesting result concerns the relationship between size and growth a. relationship often referred to as Gibrat s Law We proxy size by taking the lagged. natural logarithm of the number of employees With respect to sales growth we see. that lagged size has a small positive association with subsequent sales growth With. respect 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 evidence. suggests that firms with many employees are less likely than their smaller counterparts. to experience subsequent employment growth and that instead they can benefit from. growth in a different dimension sales growth, With these vector autoregression models however we are primarily interested in. the interplay of sales and employment growth The results in Table 1 show that lagged. employment helps predict sales growth and lagged sales growth helps predict subse. quent employment growth Although the results are statistically significant no doubt. bolstered by the large number of observations the magnitudes of the effects are not. especially high Moreover the R2 statistic remains low indicating that most of the vari. ation in growth rates remains unexplained The low R2 of growth rate regressions has. been observed in many other studies and has been taken as evidence that firm growth. is essentially a random walk process 1 Looking at the coefficient magnitudes it ap. pears that lagged employment growth has a slightly larger contribution to subsequent. sales 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 The. magnitude of the effects fades as the number of lags increases. Table 2 presents the results of a size disaggregation exercise These results show. that for smaller firms employment growth plays a more important role with regards. to subsequent sales growth although there are also significant effects of sales growth. on subsequent employment growth As our focus shifts towards larger firms 250. employees the co evolutionary link between sales growth and employment growth. becomes weaker, Sales growth and employment growth appear to be more mutually reinforcing in. the case of smaller firms The growth of small firms is qualitatively different from the. For a survey see e g Coad 2009 Table 7 1, growth of larger firms smaller firms must struggle through the liability of newness to.
achieve economies of scale and the grow or die dilemma is especially acute for these. firms Smaller firms also enjoy a more flexible organizational structure and so can. respond better to new human resources to put them to work on new tasks in imaginative. ways For larger firms in constrast sales and employment growth appear to be more. random and less inter related perhaps because selection pressures are less severe for. these established firms who have reached the minimum efficient scale MES. Table 2 also contains evidence on the relationship between size and growth The. coefficients on lagged size in Table 2 indicate that larger firms tend to experience slower. growth in terms of both sales and employment a finding often referred to as Galto. nian reversion to the mean 2 This negative dependence of growth on size has been. observed in previous empirical literature see for example Sutton 1997 Caves 1998. Coad 2009 While small firms must struggle to grow to overcome their size disadvan. tage larger firms that have achieved a minimum efficient scale are under less pressure. Table 3 looks specifically at the growth processes of the firms that are growing fastest. in terms of sales or employment For the subsample of Sales HGFs we see that sales. growth has a slightly larger association with subsequent employment growth than in. the case of Employment HGFs For the subsample of Employment HGFs we see that. employment has a considerably larger association with subsequent sales growth than in. the case of Sales HGFs In other words firms with the 5 fastest employment growth. are seen to efficiently convert this employment growth into sales growth in the sense. that employment growth in these firms makes an especially visible impact on subse. quent sales growth These firms appear to be more capable of internalizing new human. resources to fuel subsequent growth of sales, These results can be broadly interpreted as follows employment growth and sales. growth are two related but distinct dimensions of firm size and growth To be sure large. firms are large in terms of both sales and employment but during their growth there may. well be stages in the growth of sales and employment where one variable has a larger. The results in Table 2 focusing on firms above a minimum size threshold appear to contrast slightly. with those in Table 1 where we looked at all firms taken together This is presumably due to the samples. containing firms of different sizes, Table 1 Vector autoregression models on ONS ABS data for sales and employment. growth for VAR models including either 2 or 3 lags Coefficients and t statistics. Sales gr t stat Empl gr t stat Sales gr t stat Empl gr t stat. 2 lags 3 lags, Sales gr lagged 0 18539 100 84 0 060222 114 03 0 20055 86 3 0 052872 87 8. Empl gr lagged 0 141156 92 94 0 03012 29 12 0 1265 75 14 0 03511 31 85. Sales gr 2nd lag 0 06156 41 52 0 034319 77 09 0 07271 35 48 0 039726 75 04. Empl gr 2nd lag 0 061671 44 64 0 02492 27 9 0 058675 36 76 0 02892 29 41. Sales gr 2nd lag 0 02967 17 76 0 03014 62 14, Empl gr 2nd lag 0 041346 25 87 0 01561 16 25. Sales Empl 8 78E 05 8 03 9 27E 06 6 70 0 000123 5 88 1 5E 05 5 47. Sales Empl 2 1 30E 10 4 68 1 38E 11 4 14 1 60E 10 6 21 1 93E 11 5 76. log Empl lagged 0 00449 10 78 0 03921 136 54 0 005476 11 54 0 02941 89 7. Constant 0 007931 5 94 0 08305 148 28 0 004 1 80 0 056948 85 79. Observations 3014995 3014995 2245566 2245566, R2 0 0399 0 0242 0 0468 0 0231.
impact on the other where one leads and the other follows We would like to know the. causal ordering of these firm growth variables not just intertemporal associations from. one year to the next but ideally see how sales growth and employment growth affect. each other in the shorter term within the same period If we focus only on lags of. one year or more in the context of reduced form vector autoregressions then we might. miss 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.

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