Skip to main content

Are high-growth firms one-hit wonders? Evidence from Sweden

Abstract

Most firms do not grow, and a small number of high-growth firms seem to create most new jobs. These firms have therefore received increasing attention among policymakers. The question is whether high-growth tends to persist? We investigate this question using firm-level data from Sweden during 1997–2008. We find that high-growth firms had declining growth rates in the previous 3-year period, and their probability of repeating high growth rates was very low. Thus, these are essentially “one-hit wonders,” and it is doubtful whether policymakers can improve economic outcomes by targeting them.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. Previous studies using quantile regression have not incorporated firm-specific fixed effects.

  2. Note that HGFs are identified over 3-year periods, which means that our analysis mainly is focused toward the 1999–2008 period.

  3. Daunfeldt et al. (2014) discuss implications of using different growth indicators to identify HGFs. Their results indicated that the results were not sensitive to whether employment or sales were used as growth indicator, but they found a clear trade-off between employment growth and productivity growth. Policies that are targeted toward promoting firms that grow fast in terms of number of employees might thus come at the cost of reduced productivity growth.

  4. We can formalize the definition by relating the set of HGFs to the probability distribution of growth rates. We define HGFs as the subset of all firms with growth rates higher than some \(x\), which correspond to growth rates with a probability of at most \(1-\tau\). The lower bound \(x\) to high growth is thus given by \(\inf \{x:F(x)\ge \tau \}=F^{-1}(\tau ) \quad {\mathrm{for}} \quad \tau \in (0,1),\) where \(F(x)=P(g\le x)\) is the cumulative distribution of growth rates \(g\). To identify the 1 % fastest growing firms we set \(\tau =0.99\), then HGFs are all firms with growth rates higher than \(F^{-1}\left( 0.99\right)\), which coincides with the 99th percentile.

  5. The expression (3) derives from the relationship between arithmetic and geometric means: \({\displaystyle {\mathrm{ln}}\left( \prod\nolimits_{i=1}^{n}E_{i}\right) ^{\frac{1}{n}}=\frac{1}{n}\sum\nolimits_{i=1}^{n}{\mathrm{ln}}\left( E_{i}\right) }\)

  6. In Sect. 7, for comparison and robustness, we also present results when growth rates have been calculated annually.

  7. All results remain qualitatively similar if we instead adopt a forward-looking or backward-looking approach when defining firm subsamples based on size. The results are available from the authors upon request.

  8. Logarithmic growth rates are good approximation of percentage growth rates in this range.

  9. Note that Hölzl (2014) only calculates the probability that a employment-HGF will remain a HGF in the next period, whereas we estimate transition probabilities for eight different growth categories and use both number of employees and sales as growth indicators.

  10. The expression \(\gamma _{k}\) follows from the two moment conditions \({\mathbb {E}}{{\left( \upsilon _{i,t}^{2}\right) }} =\upsigma _{\varepsilon }^{2}/\left( 1-\beta ^{2}\right)\) and \(\mathbb {E}{\left( \upsilon _{i,t}\upsilon _{i,t-k}\right) }= \beta ^{k}\sigma _{\varepsilon }^{2}/\left( 1-\beta ^{2}\right)\) (see Han and Phillips 2010 for further details).

  11. Because of a tent-shaped distribution in the error term, quantile regressions are often advocated over OLS (see e.g., Reichstein et al. 2010). While OLS traditionally do not account for heavier than Gaussian tails of \(\varepsilon _{i,t}\) in (7), the Han and Phillips (2010) estimator does not presuppose a Gaussian distribution, but only that the fourth moment of \(\varepsilon _{i,t}\) is finite. Even if a higher forth moment does affect the variance \((\sigma _{FDLS}^{2})\), the FDLS estimator is still consistent.

  12. For the standard OLS estimator of (7), it can be shown that the bias is inversely related to \(\beta\) and vanishes as \(\beta \rightarrow 1\). Madsen (2010) even argues that OLS can yield superior estimates even when \(\beta <1\), provided that the variation in \(\alpha _{i}\) is relatively low and that \(\sigma (\alpha _{i})<\sigma (\varepsilon _{i,t})\), which Hall and Mairesse (2005) argue are likely for short panels of firm data.

  13. We have also analyzed alternative consecutive 3-year periods during 1998–2007 and 1997–2006, and only firms that belong to a business group. All results remain qualitatively similar.

  14. In contrast to the results presented from the FDLS estimator, we cannot control for firm-specific fixed effects here. In this regard, the results from estimating the quantile autocorrelation function should be considered with more caution.

  15. Results are similar if we use annual growth rates, although confidence intervals are narrower due to more observations.

  16. The first number correspond to the estimated autocorrelation coefficient when number of employees is used as growth indicator (Table 12), while sales is used as growth indicator in the latter case (Table 13).

  17. We also examined whether growth persistence differed by business group member or not, since we know that much firm growth is non-organic (i.e., through acquisitions). However, all results are very similar to the ones reported in the paper and are available upon request.

References

  • Acs, Z. J. (2011). High-impact firms: Gazelles revisited. In M. Fritsch (Ed.), Handbook of research on entrepreneurship and regional development: National and regional perspectives (pp. 133–174). Cheltenham: Edward Elgar Publishing.

  • Acs, Z. J., & Mueller, P. (2008). Employment effects of business dynamics: Mice. Gazelles and Elephants. Small Business Economics, 30(1), 85–100. doi:10.1007/s11187-007-9052-3.

    Article  Google Scholar 

  • Anderson, T., & Hsiao, C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18(1), 47–82. doi:10.1016/0304-4076(82)90095-1.

    Article  Google Scholar 

  • Antonelli, C. (1997). The economics of path-dependence in industrial organization. International Journal of Industrial Organization, 15(6), 643–675. doi:10.1016/S0167-7187(97)00006-4.

    Article  Google Scholar 

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. doi:10.2307/2297968.

    Article  Google Scholar 

  • Audretsch, D., Klomp, L., Santarelli, E., & Thurik, A. (2004). Gibrat’s law: Are the services different? Review of Industrial Organization, 24(3), 301–324. doi:10.1023/B:REIO.0000038273.50622.ec.

    Article  Google Scholar 

  • Baker, D. D., & Cullen, J. B. (1993). Administrative reorganization and configurational context: The contingent effects of age, size, and change in size. Academy of Management Journal, 36(6), 1251–1277. doi:10.2307/256811.

    Article  Google Scholar 

  • Birch, D., & Medoff, J. (1994). Gazelles. In C. S. Lewis & R. L. Alec (Eds.), Labor markets, employment policy and job creation (pp. 159–167). Boulder: Westview Press.

    Google Scholar 

  • Bjuggren, C., Daunfeldt, S., & Johansson, D. (2013). Ownership and high-growth firms. Journal of Small Business and Entrepreneurship, 26, 365–385. doi:10.1080/08276331.2013.821765.

    Article  Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. doi:10.1016/S0304-4076(98)00009-8.

    Article  Google Scholar 

  • Boeri, T. (1989). Does firm size matter? Giornale degli Economisti e Annali di Economia, 48(9), 477–95.

    Google Scholar 

  • Bond, S. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1(2), 141–162. doi:10.1007/s10258-002-0009-9.

    Article  Google Scholar 

  • Bottazzi, G. (2002). Corporate growth and industrial structures: Some evidence from the Italian manufacturing industry. Industrial and Corporate Change, 11(4), 705–723. doi:10.1093/icc/11.4.705.

    Article  Google Scholar 

  • Bottazzi, G., & Secchi, A. (2003a). Common properties and sectoral specificities in the dynamics of U.S. manufacturing companies. Review of Industrial Organization, 23(3/4), 217–232. doi:10.1023/B:REIO.0000031366.28559.c1.

    Article  Google Scholar 

  • Bottazzi, G., & Secchi, A. (2003b). Why are distributions of firm growth rates tent-shaped? Economics Letters, 80(3), 415–420. doi:10.1016/S0165-1765(03)00142-3.

    Article  Google Scholar 

  • Bottazzi, G., Coad, A., Jacoby, N., & Secchi, A. (2011). Corporate growth and industrial dynamics: Evidence from french manufacturing. Applied Economics, 43(1), 103–116. doi:10.1080/00036840802400454.

    Article  Google Scholar 

  • Bravo-Biosca, A. (2010). Growth dynamics exploring business growth and contraction in Europe and the US. NESTA: Research report November.

    Google Scholar 

  • Brüderl, J., & Preisendörfer, P. (2000). Fast growing businesses: Empirical evidence from a German study. International Journal of Sociology, 30(3), 45–70.

    Google Scholar 

  • Capasso, M., Cefis, E., & Frenken, K. (2009). Do some firms persistently outperform?. Utrecht University discussion paper series, no. 09–28.

  • Chesher, A. (1979). Testing the law of proportionate effect. The Journal of Industrial Economics, 27(4), 403–411.

    Article  Google Scholar 

  • Coad, A. (2007). A closer look at serial growth rate correlation. Review of Industrial Organization, 31(1), 69–82. doi:10.1007/s11151-007-9135-y.

    Article  Google Scholar 

  • Coad, A. (2009). The growth of firms: A survey of theories and empirical evidence. Cheltenham: Edward Elgar Publishing.

  • Coad, A., & Hölzl, W. (2009). On the autocorrelation of growth rates. Journal of Industry, Competition and Trade, 9(2), 139–166. doi:10.1007/s10842-009-0048-3.

    Article  Google Scholar 

  • Coad, A., Daunfeldt, S., Johansson, D., & Wennberg, K. (2014). Whom do high-growth firms hire? Industrial and Corporate Change, 23(4), 293–327. doi:10.1093/icc/dtt051.

    Article  Google Scholar 

  • Coad, A. J. L., Frankish, J., Roberts, R., & Storey, D. (2013). Growth paths and survival chances: An application of gambler’s ruin theory. Journal of Business Venturing, 28(5), 615–632. doi:10.1016/j.jbusvent.2012.06.002.

    Article  Google Scholar 

  • Contini, B., & Revelli, R. (1989). The relationship between firm growth and labor demand. Small Business Economics, 1(4), 309–314. doi:10.1007/BF00393810.

    Article  Google Scholar 

  • Daunfeldt, S., Halvarsson, D., & Johansson, D. (2013). A cautionary note on using the eurostat-oecd definition of high-growth firms. HUI Working Paper 65, Stockholm: HUI Research.Journal of Entrepreneurship and Public Policy (forthcoming).

  • Daunfeldt, S., Elert, N., & Johansson, D. (2014). Economic contribution of high-growth firms: Do policy implications depend on the choice of growth indicator? Journal of Industry, Competition and Trade, 14(3), 337-365.

  • Davidsson, P., & Henrekson, M. (2002). Determinants of the prevalance of start-ups and high-growth firms. Small Business Economics, 19(2), 81–104. doi:10.1023/A:1016264116508.

    Article  Google Scholar 

  • De Haan, J., Scholtens, B., & Shehzad, C. (2009). Growth and earnings persistence in banking firms: A dynamic panel investigation. cESifo working paper series, no. 2772.

  • Delmar, F., & Wiklund, J. (2008). The effect of small business managers’ growth motivation on firm growth: A longitudinal study. Entrepreneurship Theory and Practice, 32(3), 437–457. doi:10.1111/j.1540-6520.2008.00235.x.

    Article  Google Scholar 

  • Delmar, F., Davidsson, P., & Gartner, W. (2003). Arriving at the high-growth firm. Journal of business venturing, 18(2), 189–216. doi:10.1016/S0883-9026(02)00080-0.

    Article  Google Scholar 

  • Dosi, G., & Nelson, R. R. (2010). Technical change and industrial dynamics as evolutionary processes. In: B. H. Hall, N. Rosenberg (Eds) Handbook of The Economics of Innovation, Vol. 1 (pp. 51–127), North-Holland, Handbook of the Economics of Innovation, vol 1.

  • Dunne, J., & Hughes, A. (1994). Age, size, growth and survival: UK companies in the 1980s. Journal of Industrial Economics, 42(2), 115–40. doi:10.2307/2950485.

    Article  Google Scholar 

  • European-Commission. (2010). Europe 2020: A strategy for smart, sustainable and inclusive growth: Communication from the commission. European-Commission: Research report.

  • Eurostat-OECD. (2007). Eurostat-oecd manual on business demography statistics. Research report KS-RA-07-010-EN-NN, OECD.

  • Fotopoulos, G., & Giotopoulos, I. (2010). Gibrat’s law and persistence of growth in greek manufacturing. Small Business Economics, 35(2), 191–202. doi:10.1007/s11187-008-9163-5.

    Article  Google Scholar 

  • Garnsey, E., & Heffernan, P. (2005). Growth setbacks in new firms. Futures, 37(7), 675–697. doi:10.1016/j.futures.2004.11.011.

    Article  Google Scholar 

  • Garnsey, E., Stam, E., & Heffernan, P. (2006). New firm growth: Exploring processes and paths. Industry and Innovation, 13(1), 1–20. doi:10.1080/13662710500513367.

    Article  Google Scholar 

  • Geroski, P. (2002). The growth of firms in theory and in prattice. In: N. Foss, & V. Mahnke (Eds.), Competence, governance and entrepreneurship (Vol. 1, pp. 168–186). Oxford: Oxford University Press.

  • Geroski, P., Van Reenen, J., & Walters, C. (1997). How persistently do firms innovate? Research Policy, 26(1), 33–48. doi:10.1016/S0048-7333(96)00903-1.

    Article  Google Scholar 

  • Gibrat, R. (1931). Les inégalités économiques. Recueil Sirey.

  • Goddard, J., McKillop, D., & Wilson, J. (2002a). The growth of us credit unions. Journal of banking & finance, 26(12), 2327–2356. doi:10.1016/S0378-4266(01)00203-5.

    Article  Google Scholar 

  • Goddard, J., Wilson, J., & Blandon, P. (2002b). Panel tests of Gibrat’s law for Japanese manufacturing. International Journal of Industrial Organization, 20(3), 415–433. doi:10.1016/S0167-7187(00)00085-0.

    Article  Google Scholar 

  • Goddard, J., Molyneux, P., & Wilson, J. (2004). Dynamics of growth and profitability in banking. Journal of Money, Credit and Banking, 36(6), 1069–1090.

    Article  Google Scholar 

  • Halabisky, D., Dreessen, E., & Parsley, C. (2006). Growth firms in Canada, 1985–1999. Journal of Small Business and Entrepreneurship, 19(3), 255. doi:10.1080/08276331.2006.10593370.

    Article  Google Scholar 

  • Hall, B., & Mairesse, J. (2005). Testing for unit roots in panel data: An exploration using real and simulated data. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and inference for econometric models: Essays in honor of Thomas Rothenberg (pp. 451–479). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Han, C., & Phillips, P. (2010). Gmm estimation for dynamic panels with fixed effects and strong instruments at unity. Econometric theory, 12(1), 119. doi:10.1017/S026646660909063X.

    Article  Google Scholar 

  • Headd, B., & Kirchhoff, B. (2009). The growth, decline and survival of small businesses: An exploratory study of life cycles. Journal of Small Business Management, 47(4), 531–550. doi:10.1111/j.1540-627X.2009.00282.x.

    Article  Google Scholar 

  • Henrekson, M., & Johansson, D. (2010). Gazelles as job creators: A survey and interpretation of the evidence. Small Business Economics, 35(2), 227–244. doi:10.1007/s11187-009-9172-z.

    Article  Google Scholar 

  • Hoffman, A. N., & Junge, M. (2006). Documenting data on high-growth firms and entrepreneurs across 17 countries. Working paper Mimeo, Fora.

  • Hölzl, W. (2014). Persistence, survival and growth: A closer look at 20 years of high growth firms and firm dynamics in austria. Industrial and Corporate Change, 23(1), 199–231. doi:10.1093/icc/dtt054.

    Article  Google Scholar 

  • Ijiri, Y., & Simon, H. (1967). A model of business firm growth. Econometrica, 35(2), 348–355. doi:10.2307/1909116.

    Article  Google Scholar 

  • Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46(1), 33–50. doi:10.2307/1913643.

  • Kumar, M. (1985). Growth, acquisition activity and firm size: Evidence from the united kingdom. The Journal of Industrial Economics, 33(3), 327–338. doi:10.2307/2098540.

    Article  Google Scholar 

  • Lee, N. (2014). What holds back high-growth firms? Evidence from UK SMEs. Small Business Economics, 43(1), 183–195. doi:10.1007/s11187-013-9525-5.

    Article  Google Scholar 

  • Li, G., Li, Y., & Tsay, C. (2012). Quantile correlations and quantile autoregressive modeling. sSRN working paper. http://dx.doi.org/10.2139/ssrn.2153697

  • Littunen, H., & Tohmo, T. (2003). The high growth in new metal-based manufacturing and business service firms in Finland. Small Business Economics, 21(2), 187–200. doi:10.1023/A:1025014427294.

    Article  Google Scholar 

  • Lockett, A., Wiklund, J., Davidsson, P., & Girma, S. (2011). Organic and acquisitive growth: Re-examining, testing and extending penrose’s growth theory. Journal of Management Studies, 48(1), 48–74. doi:10.1111/j.1467-6486.2009.00879.x.

    Article  Google Scholar 

  • Lopez-Garcia, P., & Puente, S. (2012). What makes a high-growth firm? A dynamic probit analysis using Spanish firm-level data. Small Business Economics, 39(4), 1029–1041. doi:10.1007/s11187-011-9321-z.

    Article  Google Scholar 

  • Lotti, F., Santarelli, E., & Vivarelli, M. (2003). Does gibrat’s law hold among young, small firms? Journal of Evolutionary Economics, 13(3), 213–235. doi:10.1007/s00191-003-0153-0.

    Article  Google Scholar 

  • Lotti, F., Santarelli, E., & Vivarelli, M. (2009). Defending gibrat’s law as a long-run regularity. Small Business Economics, 32(1), 31–44. doi:10.1007/s11187-007-9071-0.

    Article  Google Scholar 

  • Madsen, E. (2010). Unit root inference in panel data models where the time-series dimension is fixed: A comparison of different tests. Econometrics Journal, 13(1), 63–94. doi:10.1111/j.1368-423X.2009.00302.x.

    Article  Google Scholar 

  • Mansfield, E. (1962). Entry, gibrat’s law, innovation, and the growth of firms. The American Economic Review, 52(5), 1023–1051.

    Google Scholar 

  • Oliveira, B., & Fortunato, A. (2006). Testing Gibrat’s law : Empirical evidence from a panel. International Journal of the Economics of Business, 13(1), 65–81. doi:10.1080/13571510500519996.

    Article  Google Scholar 

  • Oliveira, B., & Fortunato, A. (2008). The dynamics of the growth of firms: Evidence from the services sector. Empirica, 35(3), 293–312. doi:10.1007/s10663-008-9065-4.

    Article  Google Scholar 

  • Parker, S., Storey, D., & van Witteloostuijn, A. (2010). What happens to gazelles? the importance of dynamic management strategy. Small Business Economics, 35(2), 203–226. doi:10.1007/s11187-009-9250-2.

    Article  Google Scholar 

  • Penrose, E. T. (1959). The theory of the growth ofthe firm. New York: Sharpe.

    Google Scholar 

  • Reichstein, T., Dahl, M. S., Ebersberger, B., & Jensen, M. B. (2010). The devil dwells in the tails. Journal of Evolutionary Economics, 20(2), 219–231. doi:10.1007/s00191-009-0152-x.

    Article  Google Scholar 

  • Schreyer, P. (2000). High-growth firms and employment. Research report 72, OECD Science, Technology and Industry Working Papers.

  • Segarra, A., & Teruel, M. (2014). High-growth firms and innovation: An empirical analysis for Spanish firms. Small Business Economics, forthcomming,. doi:10.1007/s11187-014-9563-7.

    Google Scholar 

  • Singh, A., & Whittington, G. (1975). The size and growth of firms. Review of Economic Studies, 42(438), 15–26.

    Article  Google Scholar 

  • Stanley, M., Amaral, L., Buldyrev, S., Havlin, S., Leschhorn, H., Maass, P., et al. (1996). Scaling behaviour in the growth of companies. Nature, 379(6568), 804–806. doi:10.1038/379804a0.

    Article  Google Scholar 

  • Sutton, J. (1997). Gibrat’s legacy. Journal of economic Literature, 35(1), 40–59.

  • Teruel-Carrizosa, M. (2006). Firm growth, persistence and multiplicity of equilibria: An analysis of spanish manufacturing and service industries. PhD thesis, Universitat Rovira i Virgili.

  • Tschoegl, A. (1983). Size, growth, and transnationality among the world’s largest banks. The Journal of Business, 56(2), 187–201.

    Article  Google Scholar 

  • Vander Vennet, R. (2001). The law of proportionate effect and OECD bank sectors. Applied Economics, 33(4), 539–546. doi:10.1080/00036840122263.

    Article  Google Scholar 

  • Wagner, J. (1992). Firm size, firm growth, and persistence of chance—Testing Gibrat’s law with establishment data from the lower Saxony, 1978–1989. Small Business Economics, 4(2), 125–131. doi:10.1007/BF00389853.

    Article  Google Scholar 

  • Weiss, C. (1998). Size, growth, and survival in the upper austrian farm sector. Small Business Economics, 10(4), 305–312. doi:10.1023/A:1007972518380.

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank Mats Bergman, Pontus Braunerhjelm, Alex Coad, Hans Lööf, Björn Falkenhall, Rick Wicks, seminar participants at KTH Royal Institute of Technology, Ratio, Tillväxtanalys, and Umeå University, as well as two anonymous referees for valuable comments and suggestions. Ragnar Söderbergs Stiftelse is gratefully acknowledged for financial support, and Tillväxtanalys for providing data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sven-Olov Daunfeldt.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Daunfeldt, SO., Halvarsson, D. Are high-growth firms one-hit wonders? Evidence from Sweden. Small Bus Econ 44, 361–383 (2015). https://doi.org/10.1007/s11187-014-9599-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11187-014-9599-8

Keywords

  • Gazelles
  • High-growth firms
  • Growth persistence
  • Autocorrelation
  • Transition probabilities

JEL Classification

  • L11
  • L25
  • L26