Agglomeration externalities of fast-growth firms

INTRODUCTION

Fast-growth firms have attracted increasing attention from policy-makers and academic researchers because of their disproportionate contribution to economic growth and job creation. Since the recent financial crisis, fast-growth firms have been considered a viable option to foster economic recovery and are central to the political debate on economic performance and industrial resilience (Anyadike-Danes et al., Citation2013; Bleda & Del Rio, Citation2013; Brown et al., Citation2017; Coad et al., Citation2012; Mason & Brown, Citation2013; Organisation for Economic Co-operation and Development (OECD), Citation2013; Storey & Greene, Citation2010). However, the existing literature has focused so far almost exclusively on the characteristics of fast-growth firms, seeking to identify the drivers and to predict the likelihood of fast-growth episodes (Haltiwanger et al., Citation2013; Hölzl, Citation2014; Lawless, Citation2014).

Following a different approach, recent studies have investigated the emergence of the fast-growth phenomenon at the regional level, examining if and how location-specific characteristics, industrial agglomeration and specialization could promote fast-growth (Duschl et al., Citation2015; Stam, Citation2005). The idea that region-specific characteristics affect firms’ productivity and growth is well established in economic geography and regional science, with both theoretical and empirical analysis focusing on local agglomeration externalities (Glaeser et al., Citation1992; Jacobs, Citation1969). However, empirical evidence about the ways in which agglomeration externalities occur and how they may affect firms’ growth is still mixed (Abukabarr & Mitra, Citation2017; De Groot et al., Citation2009).

The study fills this gap by analysing the impact of fast-growth firms on the economic performance of industrially related and proximately located non-fast-growth firms. We define fast-growth firms using both employment- and productivity-based definitions to capture different business population and alternative spillover channels. To the best of our knowledge, this is the first study to examine the spillover effects of fast-growth firms. By using firm-, region- and industry-level data for the UK over the period 1997–2013, we identify the main channels through which these firms indirectly affect the productivity and employment growth of other firms in the region, and investigate if these externalities spread horizontally or through forward and backward linkages.

The results show that, on the one hand, a higher proportion of fast-employment-growth firms has negative effects on the average employment growth of other firms in the same industry and region, consistent with competition-led crowding-out and labour-poaching effects. On the other hand, a higher proportion of fast-productivity-growth firms has positive spillover effects on the labour productivity of non-fast-growth firms, suggesting competition-led efficiency improvement and potential knowledge spillovers. By analysing the externalities among vertically integrated industries, we find that an increase in demand by fast-growth firms has positive market-creating spillover effects on other firms in upstream sectors. Also, a higher proportion of fast-growth suppliers results in increased productivity growth in downstream sectors, potentially because of learning and demonstration effects as a result of knowledge spillovers.

The analysis reveals the heterogeneous effects of fast-growth externalities. First, these results are particularly strong for small and old non-fast-growth firms, especially in low-tech sectors. Second, stronger negative externalities on employment growth mainly affect peripheral areas, while the most agglomerated core regions experience positive spillover effects from increased proportions of fast-growth firms, suggesting that the negative externalities on employment growth might be exacerbated by a limited supply in peripheral local labour markets. In contrast, the stronger positive externalities on labour productivity from fast-growth firms in highly agglomerated areas suggest the presence of knowledge spillovers and efficiency improvements because of increased competitive pressure and learning effects stimulated by fast-growth firms.

The paper is structured as follows. The next section reviews the main theoretical and empirical contributions in the existing literature. We then discuss the data and present the summary statistics, before explaining the methodological approach. Finally, we present and discuss the econometric results, and conclude by highlighting the policy implications and future directions for research.

LITERATURE REVIEW

The study is mainly related to two broad literatures. First is the fast-growth firms literature that has expanded exponentially in the last two decades. This strand of the literature has extensively highlighted how fast-growth firms, although forming a small fraction of the business population, generate a disproportionately high rate of new jobs (Brown et al., Citation2017; Henrekson & Johansson, Citation2010; National Endowment for Science, Technology and the Arts (NESTA), Citation2009). Moreover, the exceptional growth of fast-growth firms is not linked to cyclical economic fluctuations, as their contribution to job creation has been observed in times of both economic upturn and recession (Hart & Anyadike-Danes, Citation2015). Since the recent financial crisis, fast-growth firms have been considered as a viable option by many governments to help foster economic recovery, and have become central in the political and academic debate about economic resilience and growth (Acs et al., Citation2008; Anyadike-Danes & Hart, Citation2018; Bleda & Del Rio, Citation2013; Coad et al., Citation2014a; Mason & Brown, Citation2013; OECD, Citation2013; Storey & Greene, Citation2010).

The existing literature on this topic has mostly looked at the characteristics and industrial distribution of fast-growth firms, highlighting their prevalence among younger (Haltiwanger et al., Citation2013), more innovative and knowledge-intensive firms (Daunfeldt et al., Citation2014), but also stressing how fast-growth phenomena are broadly spread among firms of all sizes and industry (Henrekson & Johansson, Citation2010; Lawless, Citation2014; Moreno & Coad, Citation2015; NESTA, Citation2009). However, the predictability of fast-growth episodes remains fairly limited, given their highly episodic nature (Hölzl, Citation2014; Moreno & Coad, Citation2015; Parsley & Halabisky, Citation2008) and their lack of persistence over time (Acs et al., Citation2008; Brown et al., Citation2017; Daunfeldt & Halvarsson, Citation2015; Du & Bonner, Citation2017), leaving in some respects the fast-growth literature theoretically and empirically underdeveloped (Coad, Citation2009; Coad et al., Citation2013; Denrell, Citation2004; Leitch et al., Citation2010).

Recent studies have started investigating the spatial development of fast-growth firms, although little is known about how fast-growth firms might be related to regional development (Brown & Mawson, Citation2016; Li, Citation2016). Several studies suggest that regional characteristics play an important role in affecting the development of fast-growth firms, fostered by location-specific characteristics, industrial agglomeration and specialization (Duschl et al., Citation2015; Stam, Citation2005). The main regional factors usually discussed in this literature include industrial agglomeration, human capital, transportation costs, institutional factors and the entrepreneurial ecosystem (Arauzo-Carod et al., Citation2010). While these factors have been investigated to explain the incidence of fast-growth, much less attention has been paid to the external impact of such firms’ activities and their interactions with the local economy. Given the relevant policy focus on fast growth, and the considerable resources allocated by governments to stimulate such growth, it becomes increasingly important to understand the wider economic impact of the fast-growth phenomenon in the economy.

In this regard, by linking the fast-growth literature to the main economic geography theories on agglomeration externalities, it would be possible to analyse the spillovers arising from the co-location in agglomerated regions and industries of fast-growth and other firms (Abukabarr & Mitra, Citation2017; Glaeser et al., Citation1992). In particular, following the Marshall–Arrow–Romer (MAR) theories on localization economies, the agglomeration of fast-growth firms in a given region and industry could generate both positive and negative externalities for other spatially proximate and industrially related non-fast-growth firms. Marshallian externalities linked to localization economies mainly operate through three different channels: sharing specialized suppliers, labour pooling and matching, and knowledge externalities (Glaeser et al., Citation1992; Marshall, Citation1920). First, by sharing intermediate inputs from specialized suppliers, fast-growth and non-fast-growth firms could reduce the costs of obtaining inputs or of shipping goods to customers (Fujita, Krugman, & Venables, Citation1999). In addition, fast-growth firms could also generate spillover effects for other firms in vertically integrated industries. In particular, these could be for non-fast-growth suppliers and consumers, who could internalize the externalities related to increased demand, and introduce new improved inputs, innovation and efficiency gains induced by fast-growth firms in other related sectors (Humphrey & Schmitz, Citation2002; Jabbour & Mucchielli, Citation2007). However, while firms may be able to tap into better supply chains when co-locating with fast-growth firms, they may also have to bear increased costs of production because of the negative externalities related to higher agglomeration. These negative spillovers are usually linked to high rental and transportation costs, and crowding-out effects related to the tougher competition by fast-growth firms in both the up- and downstream markets (Broersma & Van Dijk, Citation2007; Combes & Gobillon, Citation2015).

Second, by locating close to each other, fast-growth and other firms within the same industry could enjoy the benefits of ‘thick’ labour markets, having access to a large pool of suitably skilled workers (Combes & Duranton, Citation2006; Glaeser & Resseger, Citation2010). Clustering reduces the costs of managing labour needs, especially in an environment with high turnover, reducing the uncertainty about the future demand for labour and about the possible future skills required (De Groot et al., Citation2009). In addition, the clustering of firms within the same industry could facilitate the transfer of workers from unsuccessful firms to successful ones, facilitating the match between firms and workers, and result in the reinforcement of dense agglomeration (Duranton & Puga, Citation2004; Helsley & Strange, Citation1990). In this regard, the agglomeration of fast-growth firms could facilitate, on the one hand, the match between skilled workers and firms’ requirements, or the flow of labour and skills from fast-growth to other firms within the same industry. However, on the other hand, a rapid increase in the agglomeration of fast-growth firms could also create negative spillover effects related to labour poaching, where tougher competition in the local labour market could draw the limited numbers of skilled workers available towards the most successful firms in the cluster, that is, the fast-growth firms (Coad et al., Citation2014b; Combes & Duranton, Citation2006; Morris et al., Citation2019).

Finally, an increase in the agglomeration of fast-growth firms could also generate relevant knowledge externalities for other firms within the same region and industry, where fast-growth and other firms could share ideas and knowledge, mainly through informal interactions and the movement of workers (Storper & Venables, Citation2004). Firms can learn from other firms, through localized knowledge spillovers, which are mainly relevant in the case of tacit rather than codified knowledge, and intensifying the benefits of the interaction within the local labour market and with actors along the supply chain (Polanyi, Citation1967). The existing literature suggests that firms operating in regions and sectors characterized by greater levels of agglomeration experience higher rates of growth, thanks to the indirect effect of tacit or explicit externalities originated from better performing companies (Audretsch & Dohse, Citation2007; Raspe & Van Oort, Citation2007). Thus, it is reasonable to hypothesize that fast-growth firms might indirectly affect the performance of spatially proximate and industrially related non-fast-growth firms because of their dynamism, innovation, productivity growth and employment creation. In addition, horizontal externalities could arise through imitation, cooperation, competition and the movement of workers between fast- and non-fast-growth firms. Thus, spatially agglomerated fast-growth firms operating within the same industry could, on the one hand, trigger positive externalities, through demonstration effects and competition-led efficiency improvements, but, on the other hand, they could also lead to competition-led crowding out effects as a result of the tougher competition for common resources and inputs of production, such as skills and intermediate inputs (Glaeser et al., Citation1992; Porter, Citation1990).

The processes through which agglomeration externalities from fast-growth firms may occur are indeed complex, and could be mediated by a mix of different economic forces, such as the level of agglomeration and concentration in the local industry, the availability of skills and the intensity of the vertical integration. Thus, fast-growth externalities may result in differentiated effects for other co-located firms within the same industry. As such, the overall effects of fast-growth agglomeration externalities may be context dependent, based on the resources and knowledge available across regions and industries (Neffke et al., Citation2011).

DATA

Fast-growth definitions

Thanks to these detailed and comprehensive data, we can identify the incidence and distribution of fast-growth firms across the UK regions and industries in the UK using alternative measures. There is an ongoing debate over the merits and drawbacks of different definitions of fast-growth firms (Anyadike-Danes et al., Citation2015; Daunfeldt et al., Citation2014; Du & Bonner, Citation2016; Moreno & Coad, Citation2015). Recent evidence shows that employment and sales growth based measures are only modestly correlated, and different definitions produce different subsets of the business population (Daunfeldt et al., Citation2014; Du & Bonner, Citation2017; Shepherd & Wiklund, Citation2009). Hence, in this study we examine two different fast-growth definitions, based on employment and productivity growth, to capture the top performers in terms of different growth mechanisms.Footnote¹

First, the employment-based high-growth-firms (HGFs) definition captures firms with at least 10 employees that have an annual average growth in employment of 20% or more over a three-year period (Eurostat-OECD, Citation2007). Also to include firms with fewer than 10 employees, we adopt the small HGFs definition introduced by Clayton et al. (Citation2013), capturing firms with fewer than 10 employees which grow by more than eight new employees over a three-year period. Second, we follow Du and Bonner (Citation2016, Citation2017) to define productivity-based super-growth heroes (SGHs). This metric captures firms that have experienced a positive labour productivity growth over a three-year period, whereby both turnover and employment have grown relative to the base year, implying faster growth in turnover than in employment, and in addition with labour productivity levels above the SIC3 industry average. Firms are defined as fast-growth firms for the three years constituting the growth episode in order to identify precisely each of the years in which firms have registered particularly fast growth.

Table 1 presents summary statistics about the characteristics and distribution of the overall sample of manufacturing firms, HGFs and SGHs in the UK. Fast-growth firms represent around 4–5% of manufacturing firms in the sample. During this period, about 150,000 manufacturing firms have experienced at least one SGH period, while almost 120,000 have registered at least one HGF period. By comparing these two groups, we observe that during this period only 15% of these firms have experienced both an SGH and an HGF episode. Fast-growth firms are usually larger than the average firm in terms of employment and turnover, and have higher labour productivity. It appears that the average firm age of SGHs or HGFs is around 12–14 years. Both SGHs and HGFs have a higher probability of being foreign owned and of being more likely to be part of a business group. However, in our sample, the percentages of small and medium-sized enterprises (SMEs), young and high-tech firms do not differ significantly between fast-growth firms and the average firms.Footnote²

Table 1. Characteristics of UK fast-growing firms in the manufacturing sectors, 1997–2013.

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Figure 1 presents the geographical distribution of SGHs and HGFs employment as the average share of total employment for each NUTS-2 UK region during the period 1997–2013. The two maps highlight a different geographical distribution for these two groups of fast-growth firms. In fact, the SGHs incidence rate is particularly high in large cities’ regions, such as the Greater London area, Hampshire (Southampton), the East Midlands (with Nottingham and Derby), and the Leeds and Newcastle regions. These are thus mainly in highly agglomerated areas, as suggested by the positive correlation between this fast-growth metric and the Ellison and Glaeser (Citation1997) agglomeration index, and thus in turn with productivity growth as shown in the previous literature (Bertinelli & Black, Citation2004; Duranton & Puga, Citation2004). HGFs instead seem more evenly distributed both in urban and rural regions, with relatively higher incidence in Surrey, Sussex, Cheshire and County Durham, but also in more agglomerated regions, such as the Greater Manchester area and the region of Edinburgh in Scotland. This is consistent with previous evidence suggesting that employment fast growth can happen everywhere (Moreno & Coad, Citation2015).

Figure 1. Share of high-growth firms (HGFs) and super-growth heroes (SGHs) over total employment per region (NUTS-2 level).Note: Statistics are based on the Business Structure Database (BSD) between 1997 and 2013. SGHs are those firms that have experienced a positive labour productivity growth over a three-year period with both turnover and employment growth, and labour productivity is above the three-year average labour productivity of its sector at the SIC3 level. HGFs are the total employment of firms with more than 10 employees that grow by more than 20% over a three-year period, or if the total employment of firms with fewer than 10 employees grows by more than eight new employees over a three-year period.

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METHODOLOGY

We model fast-growth externalities for other non-fast-growth firms in the following way, controlling for firm, region and industry heterogeneity:(1) $\begin{array}{rl}\mathrm{\Delta }EM{P}_{irst}=& {\text{β}}_0+{\text{β}}_1\mathrm{\Delta }Horiz.HG{F}_{rst}\\ & +{\text{β}}_2\mathrm{\Delta }Backw.HG{F}_{rst}\\ & +{\text{β}}_3\mathrm{\Delta }Forw.HG{F}_{rst}+{\text{β}}_{4}FIR{M}_{it-1}\\ & +{\text{β}}_{5}EN{V}_{rst-1}+j_t+j_i+j_{rs}+{\epsilon }_{it}+{\epsilon }_{rst},\end{array}$(1) (2) $\begin{array}{rl}\mathrm{\Delta }L{P}_{irst}=& {\text{β}}_0+{\text{β}}_1\mathrm{\Delta }Horiz.SG{H}_{rst}\\ & +{\text{β}}_2\mathrm{\Delta }Backw.SG{H}_{rst}+{\text{β}}_3\mathrm{\Delta }Forw.SG{H}_{rst}\\ & +{\text{β}}_{4}FIR{M}_{it-1}+{\text{β}}_{5}EN{V}_{rst-1}+j_t+j_i\\ & +j_{rs}+{\epsilon }_{it}+{\epsilon }_{rst},\end{array}$(2) In equation (1) the dependent variable $\mathrm{\Delta }EM{P}_{irst}$ represents the year-to-year employment growth of non-HGF firms (i) at time (t), while the key explanatory variable $\mathrm{\Delta }Horiz.HG{F}_{rst}$ indicates the horizontal externalities linked to an increased incidence of employment-based HGFs in SIC2 industry (s) and NUTS-2 region (r). The potential regional vertical externalities in the backward $\mathrm{\Delta }Backw.HG{F}_{rst}$ and forward sectors $\mathrm{\Delta }Forw.HG{F}_{rst}$ are also included for the spillover effects in sector (s) and region (r) of an increased incidence of fast-growth firms in the up- and downstream industries.

Similarly, equation (2) models the spillover effects of a higher proportion of productivity-based SGHs on the labour productivity growth of non-SGH firms. In this case, the dependent variable $\mathrm{\Delta }L{P}_{irst}$ measures the year-to-year labour productivity growth of non-SGHs in region (r) and industry (s) at time (t) measured as total revenue per employee.Footnote⁴ The key explanatory variables include $\mathrm{\Delta }Horiz.SG{H}_{rst}$, an increase of the proportion of SGHs over total employment in region r and industry s, and the vertical externalities linked to an increase in proportion of SGHs in backward ($\mathrm{\Delta }Backw.SG{H}_{rst}$) and forward sectors $(\mathrm{\Delta }Forw.SG{H}_{rst})$.Footnote⁵

Both equations control for variables at different levels. First, the vector $FIR{M}_{it-1}$ includes several firm-level characteristics, including lagged levels of employment, labour productivity, firm age, foreign and group ownership. Second, $EN{V}_{rst-1}$, represents the different control variables at the industry and region level. We control for the overall performance of sectors and regions where each firm operates at the NUTS-2 and SIC2 level, including lagged levels of employment, labour productivity growth and the net entry rate, in order to take into account the level of competition and the dynamism of the local industries.Footnote⁶ In addition, we control for the region and industry R&D intensity and for the agglomeration index at the region–industry level, using the Ellison and Glaeser (Citation1997) definition.Footnote⁷ We also interact each externality metric with the agglomeration index in order to estimate the relationship between fast-growth incidence rate and other firms’ employment and productivity growth, while controlling for the self-selection of fast-growth and highly performing firms in industries and regions characterized by high levels of spatial agglomeration and industrial concentration (Autor et al., Citation2017; Combes & Gobillon, Citation2015).

By using an ordinary least squares (OLS) first-difference fixed-effects model, we can identify the relationship between fast-growth externalities and the economic performance of non-fast-growth firms. In particular, the first-difference model allows one to measure the effect of a standard deviation increase in the externalities of fast-growth incidence rates on other firms’ employment and productivity growth. In this way, we can identify the dynamic evolution of fast-growth firms and of their related spillovers, given their definition being based on rapid growth and of a volatile nature. In particular, we are interested in understanding how the variation in fast-growth firms agglomeration affects employment and productivity growth for other firms clustered within the same industry.Footnote⁸ In addition, the firm, industry and regional control variables, together with firm-level ($j_i$), year ($j_t$) and region–industry fixed-effects ($j_{rs}$), improve the estimation precision by accounting for a range of firm heterogeneities and controlling for time-invariant firm, industry and region specific effects. Standard errors are clustered at the region, industry and year level.

RESULTS

Spatial and industrial heterogeneity

Based on the previous estimates, Figures 2 and 3 analyse the spatial and industrial heterogeneity of horizontal fast-growth externalities. At the regional level, Figure 2 shows that the negative externalities on the employment growth of non-fast-growth firms are stronger in peripheral areas, as the South West, Scotland and Cumbria, where local labour markets are less developed, and the costs of labour poaching and concentration are higher because of the particularly fierce competition of fast-growth firms (Sena et al., Citation2013). In contrast, the positive spillover effect of fast-productivity growth is more evenly scattered across the UK, with particularly strong positive externalities for productivity growth in the Greater Birmingham area, East Midlands, Yorkshire, Newcastle and South of Scotland, especially in highly agglomerated areas in line with previous evidence (Coad et al., Citation2014a; Stam, Citation2005).

Figure 2. Average marginal effect of horizontal fast-growth spillovers on employment and productivity growth per region (NUTS-2 level).

Note: The marginal effects of the impact of horizontal fast-growth industry–region spillovers aggregated at the NUTS-2 level were estimated using an ordinary least squares (OLS) first-difference fixed-effects model. Fast-growth firms are defined as super-growth heroes (SGHs) for the productivity growth models and as high-growth firms (HGFs) for the employment growth models.

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Figure 3. Average marginal effect of horizontal fast-growth spillovers on employment and productivity growth per industry (SIC2 level).

Note: The marginal effects of the impact of fast-growth industry–region spillovers aggregated at the industry SIC2 level were estimated using an ordinary least squares (OLS) first-difference fixed-effects model. Fast-growth firms are defined as super-growth heroes (SGHs) for the productivity growth models and as high-growth firms (HGFs) for the employment growth models.

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Turning to the industrial distribution, Figure 3 shows that the industries mostly negatively affected in terms of employment growth by fast-growth spillovers are the fuels, apparel, mineral products and food-processing sectors. On the contrary, the automotive industry, the sector producing medical and precision instruments, and electric machinery industries seem to benefit the most in terms of employment spillovers. These findings suggest an overall negative spillover effect, especially on employment growth in medium-low-tech sectors, highlighting a concentration of the labour share in these sectors to a smaller number of fast-growth companies (Autor et al., Citation2017). On the contrary, medium-high-tech manufacturing sectors seem to experience positive externalities from fast-growth firms in terms of employment growth, in line with previous predictions (Grillitsch & Nilsson, Citation2019; Moreno & Coad, Citation2015). Productivity spillovers seem to follow a similar pattern, with strong positive effects for the machinery, metals, computers and information technology equipment industries, and a negative effect for the medium-low-tech sectors of leather, apparel and oil. These findings suggest that positive fast-growth externalities are more relevant in high-tech sectors, propagated through external collaborations between firms, and strongly relying on the internal absorptive capacity of non-fast-growth firms needed to internalize the spillovers (De Silva & McComb, Citation2012; Sena et al., Citation2013).

CONCLUSIONS

This is the first study to evaluate the agglomeration externalities of fast-growth firms. Overall, we find that a higher incidence of fast-employment growth has negative spillover effects on employment growth at the horizontal level, while a higher incidence of fast-productivity growth generates positive spillover effects in terms of labour productivity.

Furthermore, we find positive inter-industry externalities: high-employment-growth firms in the downstream sectors have positive demand-driven spillover effects on employment in upstream sectors, while backwards productivity externalities from fast-growth firms in upstream sectors are mainly relevant to non-fast-growth customers. These findings are more pronounced among small and old firms, in both low- and high-tech sectors. In addition, agglomeration plays an important role in mediating these externalities, with spatially heterogeneous effects driven by stronger negative externalities on employment growth in peripheral regions, and with positive spillover effects, in terms of both employment and productivity growth, in densely agglomerated regions and industries.

The results highlight several policy implications. First, the policy goals of job creation and productivity growth might not always be complementary. While national and subnational policies might be designed to promote fast growth, it is important to understand the indirect implications for the employment and productivity growths of non-fast-growth firms.

Second, having more fast-productivity-growth firms in a region is beneficial for the productivity growth of other firms overall. However, more fast-employment-growth firms may put a strain on other firms’ abilities to attract workers and to upscale. This points to the skills and labour competition between employers in places experiencing skill shortages, skill mismatches and the local labour market-sorting problems. The imbalance between skill supply and skill demand is a longstanding challenge faced by the UK. Reforms and investment have been put in place by all previous governments to address these issues, but with limited success (Payne & Keep, Citation2011; Sissons & Jones, Citation2016). More promising policy designs would lean towards subnational approaches that emphasize place-based strategies to diagnose local labour market imbalances (Green, Citation2012), emphasize demand-led skills development (Froy, Citation2013), and improve local skills utilization and integration (Sissons & Jones, Citation2016).

Third, the externalities of the fast-growth phenomenon are highly heterogeneous across industries, positions in the supply chains, firms and regional characteristics. This variability should be considered when designing specific policy instruments. This lends clear support to linking industrial strategy with skills policy, aiming to drive regional economic growth (Payne & Keep, Citation2011). However, demand-driven skills development is crucial in responding to specific needs of skills for workplace and fast-pacing technological changes.

Research on fast-growth firms needs to go beyond the within-firm growth analysis to understand better the overall welfare effect of the fast-growth phenomenon on the economy. Future research needs to analyse further the specific mechanisms through which the agglomeration externalities related to fast-growth firms operate, both within and across industries and regions, given their increasing relevance in the economy. This will then help to design appropriate policies to promote long-term and balanced growth.