High-growth firms (HCF) represent a highly desirable subset of firms, which provide disproportionate economic gains, and greater insight into their determinants which is of interest to policymakers, scholars and business owners. We contribute to the literature on HGFs, which is largely absent of cross-national institutional studies, by examining the institutional conditions driving HGFs in 26 transition countries over a long period comprising three panels between 1998 and 2009. Using an institutional hierarchy approach, we test for the influence of formal and informal institutions on HGF prevalence in countries. Our analysis relies first on a principal component analysis to identify institutional factors. Second, we use GLS estimation to test the influence of these three factors on HGF prevalence in a country, followed by a robustness check. Our results show that interaction effects, rather than direct effects, are useful in explaining systematic variations in HGFs prevalence in transition economies. We find that the interaction between formal and informal institutions positively influences HGFs. Further, we find that in fast-reforming transition economies, more burdensome formal institutions discourage HGFs but in slow-reforming transition economies, informal institutions encourage HGFs.
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The question of HGFs is distinct from two parallel but relevant streams of research. One stream is an established literature on antecedents and drivers of firm growth, broadly speaking (Peev 2015; Baum and Locke 2004; Baum et al. 2001) and not specifically on rapidly growing firms. HGFs are special because they generate disproportionate economic gains (Moreno and Coad 2015). The second stream is on growth aspirations of entrepreneurs (Estrin et al. 2013; Tominc and Rebernik 2007) or managers (Wiklund et al. 2003). This nascent stream in the entrepreneurship literature has recently examined the drivers of cross-national differences in entrepreneurial growth aspirations, e.g., institutional drivers like intellectual property rights (Autio and Acs 2010), corruption and property rights (Estrin et al. 2013), or cultural and personal factors (Tominc and Rebernik 2007). This question asks what shapes intentions to grow, but it does not apply to real growth (Autio and Acs 2010). As noted by Wiklund and Shepherd (2003), aspiring to grow does reflect actual growth. Growth aspirations are a “best guess” and likely optimistic (Autio and Acs 2010). In other words, just because a firm wants to grow does not mean it will grow. Studying HGFs has the advantage that these firms have already proven they can achieve growth.
The exception is the control variable logarithm GDP growth, for economic growth. This variable should be interpreted with caution. All other variables did not change statistical significance and sign.
Finding a way to measure “growth” is conceptually challenging (Penrose 1959). There is no standard definition or measurement of “high-growth” firms (Delmar et al. 2003), contributing to a lack of research on the subject (Henrekson and Johansson 2010). Measures of firm growth have focused on an increase in volume of a certain firm dynamic, typically sales or employment.
Zeger and Liang (1986) suggest a random effect equivalent GEE model has an advantage over fixed-effect model because estimates for variance of regression coefficients are consistent, even though assumed correlations are not correctly specified. The panel component for the 3-year period is short and would not allow fixed-effect model, as losing within-country variation would be rather imprecise (Allison 2005; Sine et al. 2006). There was especially little variation of institutional variables; in fact, inclusion of institutional variables was critical in rejecting fixed effect using Hausman test. Random model captures more cross-national variation; GEE is particularly germane here.
Taylor (1980) showed that for T ≥ 3 and (N − K) ≥ 9, where T is the number of time series data, N is the number of cross sectional units and K is the number of regressors, the statement holds.
We used normal distribution and skewness test (sktest command in STATA 11) to test for normality of our dependent and independent variables. High p values (0.19 for our dependent variables >0.05) are insignificant; therefore, we cannot reject the hypothesis of normal distribution.
Coefficients in random effect models are estimated for a hypothetical unit (individual) where random effect is zero, but the population-average model presents coefficients averaged for the full sample. The models make different assumptions about underlying distribution of random effects and are oriented to different research aims. Random (and mixed) effects models like GLS are more appropriate for describing how effects of level 1 predictors vary across level 2 units. Population-averaged models, in contrast, give answers to population-averaged questions; in other words, they are more appropriate for predicting about the whole population. Population-averaged inferences are based on fewer assumptions than random effect models and more robust if erroneous assumptions about random effects in the model. Both GLS and GEE allow for the inclusion of time-invariant variables.
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Besnik Krasniqi thanks the US State Department Fulbright program for support in Fall 2014 for time at Indiana University, as well as staff at the Institute for Development Strategies. We thank David Audretsch and Diemo Urbig for helpful discussion.
See Table 6.
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Krasniqi, B.A., Desai, S. Institutional drivers of high-growth firms: country-level evidence from 26 transition economies. Small Bus Econ 47, 1075–1094 (2016). https://doi.org/10.1007/s11187-016-9736-7
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