Gazelles and Their Habitats - Business Climate and the Geography of High-Growth Firms

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Using longitudinal firm-level data matched to survey measures of local business climate, we compare the determinants of start-up rates and firm scaling. Standard agglomeration and demand-side factors — population size, income, market potential, and economic structure — relate to both outcomes. In contrast, local business climate, capturing entrepreneurs’ assessments of how local authorities function in practice, is a substantially stronger predictor of HGFs than of start-ups. The effect is economically meaningful and varies with local economic structure. The findings identify a micro-level mechanism linking local framework conditions to aggregate outcomes: interactions between growth-oriented firms and local institutions during scaling. Local institutional conditions not only influence how many firms are created, but which firms are able to grow. JEL : R11, L26, M13, R30 entrepreneurship young firms firm growth agglomeration local growth startups Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Entrepreneurship is widely seen as a driver of innovation, productivity, and economic growth. Yet standard measures such as small business ownership or self-employment rates often fail to capture the dynamic, growth-oriented activities that generate most new jobs and economic dynamism (Henrekson and Sanandaji 2014; Levine and Rubinstein 2017). High-growth firms (HGFs), or “gazelles”, are rare but important because they account for a large share of net job creation and productivity gains (Haltiwanger 2015). Industries with more gazelles grow faster (Bos and Stam 2014), and only a minority of firms grow rapidly, yet these create most employment gains (Coad et al. 2014). Coad and Srhoj (2023) summarize the consensus view: HGFs make a large contribution to job creation, innovation, and economic dynamism. These findings resonate with Schumpeter’s (1933) view of entrepreneurship as a driver of innovation and structural change through creative destruction. Despite the importance of HGFs, we know relatively little about which local environments are conducive to their emergence, especially regarding local institutional or policy conditions. This paper contributes with an empirical analysis of spatial variation in the incidence of HGFs, with a focus on the role of local business climate. We investigate a mechanism through which local institutional conditions influence the geography of high-growth firms: interactions between growth-oriented firms and local business climate. While firms can start under a wide range of local conditions, scaling often entails repeated engagement with zoning, permits, infrastructure provision, and regulatory enforcement (Andersson and Henrekson 2014; Fölster et al. 2016). These interactions create locally varying transaction costs associated with expansion. A favorable local business climate, characterized by efficient and predictable local administrative practices, can lower these costs and enable firms to scale their operations locally. The ecosystem literature emphasizes that local constellations of actors, institutions, resources, and norms shape entrepreneurial outcomes (Stam 2015; Stam and Van de Ven 2018; Mason and Brown 2014; Stam and Spigel 2016; Alvedalen and Boschma 2017). The ecological perspective argues that the organizational demography of a region, especially the prevalence of small, young, and dynamic firms, shapes beliefs about entrepreneurship, learning opportunities, and access to resources, thereby influencing future entrepreneurial activity (Sorenson 2017). Regions rich in startups develop environments in which entrepreneurship becomes legitimate and supported by appropriate infrastructures. Together, these perspectives suggest that local outcomes are shaped not only by factor endowments or abstract cultural traits, but by how firms, institutions, and actors interact within a place. We argue that the local business climate is a relevant component of a city’s or region’s institutional arrangement. 1 Yet, we know of no study that explicitly assesses the role of local framework conditions, based on entrepreneurs’ perceptions, for the local incidence of HGFs. By distinguishing between start-up rates and the local incidence of HGFs, and by exploiting variations in entrepreneurs’ own assessments of the local business climate, we show that the local business climate matter especially for firm growth rather than entry. This approach complements existing research on the geography of gazelles, which shows that high-growth firms are unevenly distributed across regions and that their spatial patterns are not identical to those of general start-up activity (Guzman and Stern 2015; Li et al. 2016). It also connects with the ecological argument that regional entrepreneurial dynamics depend on the structure and evolution of local firm populations (Sorenson 2017). Research on characteristics of HGFs shows that HGFs have high productivity and productivity growth (Du and Temouri 2015), appear in all sectors (Henrekson and Stenkula 2010; Daunfeldt et al. 2016; Fairlie et al. 2023), and tend to be innovative (Sleuwaegen and Ramboer 2020). However, empirical research on local or regional settings associated with HGFs remains limited. The literature on the geography of entrepreneurship predominantly focuses on overall start-up rates and pays less attention to entrepreneurial quality (Andersson and Larsson 2020). Several scholar argue conceptually that there is a link between local contexts and HGFs (Mason and Brown 2014; Stam and Spigel 2016; Alvedalen and Boschma 2017), but empirical evidence remains limited (Coad and Srhoj 2023; Coad et al. 2025; Fritsch 2024). Existing findings suggest that the geography of entrepreneurial quality is more spatially concentrated than overall entrepreneurship (Guzman and Stern 2015), that human capital is positively associated with HGFs (Li et al. 2016; Sleuwaegen and Ramboer 2020; Motoyama 2014), and that HGFs are not confined to large metropolitan regions (Li et al. 2016; Coad et al. 2025). Yet we know less about how local institutional conditions interact with these structural factors to shape the local prevalence of high-growth firms. Our analysis employs longitudinal data for municipalities in Sweden (2010–2022) and assesses the empirical relevance of a number of local supply- and demand-side factors in explaining the number of HGFs per capita. HGFs are identified based on complete micro-level data on employment growth among young incorporated firms, which is matched to annual survey data on the perceived business climate across all Swedish municipalities. This allows us to study the interrelationship between firm characteristics and local economic outcomes. In doing so, we also contribute to the ongoing debate on the local determinants of high-growth firms (e.g., Björk et al. 2025). We test whether there are differences between the determinants of the local incidence of HGFs and the determinants of the overall rate of start-ups. The empirical analyses account for standard supply- and demand-side factors in the literature on the geography of entrepreneurship (Andersson and Larsson 2020), but also add a variable reflecting the local business climate is based on entrepreneurs’ assessments of local authorities’ attitudes and practices. Our main hypothesis is that that growing firms are more exposed than ordinary start-ups to barriers such as permits, land-use regulations, and other institutional factors that form part of the local business climate. On these grounds, a favorable local business climate can be expected to be especially important for the local incidence of HGFs. Previous research has shown that local business climate influences overall employment growth (Fölster et al. 2016) and start-up activity (Westlund et al. 2014). We document large spatial variation in the local incidence of high-growth firms across Swedish municipalities during 2010–2022. Gazelles are not confined to major metropolitan areas: while larger and richer local economies tend to host more HGFs, substantial HGF activity also appears in smaller and more peripheral places. In baseline regressions, several standard local characteristics correlate with both start-up rates and HGFs per capita, including population size, regional income, market size, and the local economic structure. This confirms that broad demand conditions and agglomeration-related factors matter for entrepreneurial activity whether one measures entrepreneurship in terms of entry or growth. At the same time, the determinants of entrepreneurial quality and quantity diverge in an important way. Local business climate stands out as a much stronger predictor of the local incidence of HGFs than of start-up rates, consistent with the view that institutional conditions matter especially for firms’ ability to scale and shape entrepreneurial outcomes (Stam 2015; Stam and Van de Ven 2018). The magnitude is economically meaningful: we find that a one–standard deviation improvement in local business climate is associated with an increase in HGFs per capita roughly equal to the sample mean. Industry-disaggregated analyses further show that this relationship varies across sectors rather than being driven by a single part of the economy, consistent with the idea that ecosystem conditions interact with local economic structures. Interaction models indicate that the effect of business climate depends on underlying regional characteristics, reinforcing the interpretation that institutional and ecosystem conditions operate primarily through growth-related constraints rather than through general entry effects (Felin et al. 2015; Sorenson 2017). The remainder of the paper proceeds as follows: Section 2 reviews the literature and develops the conceptual framework, Section 3 describes the data and empirical strategy, Section 4 presents the results and additional analyses, and Section 5 concludes. 2. THE GEOGRAPHY OF HIGH GROWTH FIRMS (HGFs) Studies of the geography of entrepreneurship has long emphasised firm formation rates as an indicator of regional dynamism. However, this approach overlooks the fact that most new firms do not grow and many exit early (Hurst and Pugsley 2011). 2 The literature has had a bias towards certain quantity-based measures, such as overall start-up rates, failing to capture the qualitative dimension of entrepreneurship (Andersson and Larsson 2020). A shift of focus toward the geography of HGFs is a way to better understand the conditions that allow firms to grow and scale and why some regions may be in a better position to foster HGFs. Policies for HGFs require an understanding of where HGFs emerge, why they do so in some regions and not others, and how bottlenecks differ between regions (Coad et al. 2025). Mason and Brown (2013) argue that governments have traditionally looked for high-growth firms in the wrong places, overemphasising high-tech sectors and ignoring the fact that HGFs are found in all industries and locations. These strands of research largely explain where entrepreneurial activity emerges, but they say less about the local conditions that determine whether firms are able to translate growth potential into realized expansion. From an entrepreneurial ecosystems perspective, many of the factors discussed below affect opportunity creation and capability development, while institutional conditions shape the transaction costs firms face when they attempt to scale. The prevalence of high-growth firms (HGFs) varies significantly across regions (Guzman and Stern 2015, Motoyama 2014, Li et al 2016), reflecting that their emergence is shaped not only by firm-level characteristics, but also by regional conditions that either support or constrain the scaling process. Theoretical explanations for why certain regions have higher incidence of HGFs build on insights from economic geography, evolutionary economics, and entrepreneurship theory and focus on factors such as human capital, industrial structure, local innovation capabilities, knowledge spillovers and agglomeration forces. Below we discuss typical arguments in the literature, and then makes that case that local business climate is one important aspect of the local conditions for HGFs. Human Capital A first widely recognised factor influencing regional HGF prevalence is human capital. Human capital enhances entrepreneurial experimentation by increasing the capacity of firms to recognise and exploit growth opportunities and to absorb external knowledge (Cohen & Levinthal, 1990; Shane, 2000). Regions with higher stocks of skilled labour are more likely to produce firms capable of scaling because growth requires access not only to founding talent but also to managerial and technical expertise during expansion (Li et al., 2016). Motoyama (2014) finds that regions in the United States with higher proportions of science and engineering graduates exhibit significantly higher numbers of high-growth firms. Similarly, Sleuwaegen and Ramboer (2020) show that in European regions, creative-class employment – an indicator of specialised human capita in occupations such as engineering and designers – strongly predicts the regional incidence of HGFs. This supports the argument that growth-oriented entrepreneurship is opportunity-driven and knowledge-based, and thus depends on local pools of advanced skills. At the aggregate regional level, Qian et al (2013) conceptualize human capital as representing the local absorptive capacity to develop and act on business opportunities, especially in regard to knowledge-based entrepreneurial activity in US metropolitan areas. In this view, human capital is not only a factor of production but a catalyst for growth-oriented entrepreneurial strategies. While human capital enhances firms’ capabilities to recognize and exploit growth opportunities, it does not by itself address the institutional frictions encountered during expansion. Growth requires not only skilled labour, but also the ability to secure premises, permits, and infrastructure, which depend on how local institutions function in practice. Knowledge spillovers, innovation and R&D Factors such as R&D capacity, research institutions, and local incidence of knowledge spillovers are often claimed to be of importance in explaining local prevalence of HGFs. The Knowledge Spillover Theory of Entrepreneurship (KSTE) argues that knowledge created in firms and universities spills over into entrepreneurial ventures that commercialise it (Acs et al., 2009). It builds on the premise that entrepreneurial opportunities are created endogenously through knowledge investments, and that “... entrepreneurial activity will be greater where investments in new knowledge are relatively high, since start-ups will exploit spillovers from the source of knowledge production” (Acs et al., 2009, p. 17). Regions rich in R&D resources, patenting activity and university-industry collaboration can therefore be claimed to offer fertile ground for high-growth entrepreneurship because innovative firms rely on technological and scientific advances to scale. However, empirical support is mixed. Motoyama (2014) finds no direct effect of patents or academic research activity on HGF incidence, challenging simple assumptions about R&D. Coad et al (2025) also show that local prevalence of HGFs is not confined to urban agglomerations rich in knowledge resources. Guzman and Stern (2015) argue that it is not R&D quantity but entrepreneurial quality and ecosystems that mediate the conversion of knowledge into growth-oriented firms. Likewise, Fotopoulos (2022) finds that universities contribute to HGF prevalence only when they actively engage with local firms through collaborative research and entrepreneurial training. Related to innovation is the role of knowledge spillovers, which transmit ideas and practices between firms and industries. Knowledge spillovers occur through worker mobility, supplier relationships, informal networks, and co-location effects (Audretsch & Lehmann 2005). According to Fotopoulos (2022), HGFs benefit disproportionately from spillovers because they are more structurally capable of recognising and acting on external opportunities. Regions differ in their ability to exploit spillovers, partly because of differences in absorptive capacity and human capital. These mechanisms primarily explain how knowledge and innovation opportunities arise locally, but they are less informative about how firms navigate the administrative and regulatory processes that accompany physical and organizational expansion. As such, knowledge-rich regions may still differ substantially in their ability to convert innovation potential into high-growth outcomes. Industry and business structure Arguments about the role of industry and business structure partly rely on the idea that they influence the preconditions for cross-fertilization of ideas through knowledge spillovers. Local economies with a diverse industrial base are argued to offer more recombination possibilities, enabling knowledge spillovers across sectors and stimulating innovation-driven growth (Jacobs 1969, Glaeser et al. 1992). Duranton and Puga (2001) portray large and diverse cities as “nurseries” for new products and services. This type of “Jacobs externality” effect suggests that variety in the local economy promotes high-growth entrepreneurship by fostering novel business models and niche market creation. As diversity of a local economy is increasing in urban size, this argument is often interpreted as pointing to an advantage for large urban regions in fostering HGFs. Fotopoulos (2022) finds some empirical support for this line of argument, showing that economic diversity is positively associated with HGF incidence. On the other hand, Marshallian specialisation theories emphasise the growth benefits of industrial concentration and deep supply chains. Firms may scale more effectively in specialised regions where cluster effects provide access to specialist labour pools, suppliers and customers (Marshall, 1890). Li et al. (2016), for example, show that some high-growth firms thrive in specialised regional economies where industry-specific infrastructure and expertise reduce growth barriers. Another attribute of the local industry structure that is frequently discussed in the literature is average size of the firms in a region. A consistent finding in the literature on geography of entrepreneurship, at least in studies of determinats of overall rates of startup, is that regions dominated by large firms show lower entrepreneurial activity. Thus, average establishment size has been shown to negatively correlate with entrepreneurial dynamism, both for new firm formation. One reason is that local small-firm density implies there are more opportunities for workers to develop entrepreneurial human capital. For example, employees in SMEs are more likely exposed to the whole business process, making them better equipped to start a firm. They are also be more likely to be in contact with the firm’s founder(s) who could serve as role model(s) and promote entrepreneurial attitudes (Andersson and Larsson 2016). Indeed, the literature confirms that employees in small firms are more likely to switch from wage employment to be entrepreneurs (Hyytinen and Maliranta 2008, Elfenbein et al 2011). Another reason is the social interaction across local small firms may facilitate new-firm creation, which benefits from a greater density of established entrepreneurs that serve as potential role models that transmit knowledge through social networks (Minniti 2004, Sorenson and Audia 2000, Andersson and Larsson 2016). 3 Third, a local density of SMEs may indicate that the local economy has thicker input markets Chinitz (1961). In the context of HGFs, however, there are a contrasting perspective. Research on industrial dynamics and spinoffs shows that large and resourceful firms are important breeding grounds for high-growth entrepreneurs. Employees are assuned to inherit routines and know-how from their parent firms, and ‘good’ parent firms are therefore more likely to spawn ‘good’ firms that survive and grow. In U.S. lasers, for example, spinoffs traced to technological leaders exhibited higher survival and performance than other entrants (Klepper & Sleeper, 2005). Cross-industry analyses similarly argue that incumbents’ organizational capabilities transmit to founders, shaping new firms’ productivity and growth trajectories (Klepper, 2002, 2010). Andersson and Klepper (2013) show with data from Sweden that spinoffs –especially from parent firms that are large and successful – display systematically superior survival and growth compared with de novo startups, consistent with the “good firms breed good entrepreneurs” view. Li et al (2016) also finds a HGFs positive association between average establishement size in a region and HGFs. Taken together, this suggest that local density of large firms may be positive for the incidence of high-performing startups through organizational inheritance, whereas small firms may rather be beneficial for the overall startup-rate. Taken together, these arguments show that local industrial structure shapes both the supply of entrepreneurial opportunities and the quality of firms entering the market. However, they do not fully explain why firms with similar capabilities may scale successfully in one region but not in another, pointing to the importance of institutional conditions that affect the costs and feasibility of expansion. Size The sixe of a region, measured as either population or employment, is a form of ‘catch all’ argument related to both the supply- and demand-sides of a local economy. On the supply-side, it is well established that larger cities and regions offer various agglomeration benefits from ‘sharing’, ‘matching’, and ‘learning’ (Duranton and Puga 2004). In dense urban areas, firms benefit from proximity to suppliers, customers, investors and specialised services. These external economies of scale lower transaction costs and foster innovation, increasing the probability of firm scaling (Glaeser, 2011). Li et al. (2016) show that the highest concentration of HGFs in the United States is found in metropolitan areas with diverse economic bases, strong amenities and access to human capital. However, high density also brings disadvantages such as congestion, competition for skilled labour and escalating business costs, which may limit HGF formation in overly saturated regions. Coad et al. (2025) find that some developed regions do not have higher shares of HGFs, suggesting that density must interact with other conditions to leave footprints on HGFs. Regional size captures many agglomeration advantages, but it is ultimately an indirect measure of the local environment firms face when they grow. Large regions may offer deeper markets and resources, yet firms in these regions still depend on local institutional processes when expanding operations. Local business climate – a neglected aspect in research on HGFs This institutional dimension corresponds to what the entrepreneurial ecosystems literature describes as framework conditions that regulate, legitimize, and incentivize entrepreneurship (Stam 2015; Stam and Van de Ven 2018). Unlike human capital or industry structure, these conditions operate directly through firms’ interactions with public authorities during the scaling process. While previous factors explain opportunity creation and firm capabilities, institutional arguments explain why some firms grow while others are constrained by bureaucracy, regulatory complexity or weak governance. Institutions shape incentives and reduce uncertainty (North, 1990). Andersson and Henrekson (2014) argue that entrepreneurship is directed by the “rules of the game,” and whether these rules reward firm growth or protect incumbents can influence regional HGF prevalence. Institutional effects are not only national but also local. Andersson and Henrekson (2014) emphasise that even in non-federal countries, municipalities influence the business climate through how they interpret and implement regulations, public procurement, zoning, and permit systems. Local institutions can affect the direction of entrepreneurship by either facilitating or obstructing firm expansion. High-growth firms are likely to be sensitive to local business climate conditions, more so than ordinary small firms. Scaling requires rapid hiring, new facilities, planning permissions, environmental approvals and infrastructure expansion—each dependent on local government responsiveness. Local authorities control zoning, land use, business permits and expansion approvals—all of which affect firms seeking to scale. Tannenwald (1997, p. 84) stresses that regulation is not only about the written rules, but also about how regulations are enforced locally, arguing that enforcement behaviour frequently has a large impact on firms. Bertrand and Kramarz (2002) provide empirical evidence of how local regulatory enforcement affects business dynamics. Studying zoning regulation in France, they show that stricter enforcement of entry regulations by regional zoning boards reduced job creation and hindered the growth of new firms in the retail sector. Their findings illustrate how local bureaucratic discretion can create barriers to firm expansion—an issue that disproportionately affects HGFs, which are more likely than small lifestyle firms to require new facilities, expansion permits and access to commercial real estate. Zoning and land use regulations are particularly influential for firms that intend to grow. Complex or slow permit processes delay scale-up investments and can force firms to relocate to more expansion-friendly municipalities. Andersson and Henrekson (2014) argue that local variations in regulatory efficiency—measured in terms of processing speed, transparency and predictability—are a decisive part of the local business climate. Evidence from Swedish municipalities shows large regional differences in entrepreneurs’ satisfaction with local regulatory enforcement, ranging from highly business-friendly authorities to those seen as obstructive (Fölster and Peltzman, 2010). Such uneven enforcement directly impacts whether HGFs can exploit growth opportunities within a given region or are compelled to move operations elsewhere. Fölster et al. (2016) show empirically that municipalities with better business climates – measured by entrepreneurs’ perceptions of local regulatory efficiency – have significantly higher employment growth, with effects strongest for firms expanding operations. Local business climate is therefore a real enabling condition for HGFs because growth-oriented firms interact intensively with local authorities. Lead times for building permits, barriers to entry, and unfair competition are central aspects of business climate affecting firm growth. For instance, obstructive zoning boards, inconsistent regulation enforcement or weak infrastructure planning discourage firm scaling even when talent and innovation are present. In conclusion, the prevalence of high-growth firms in a region is liely to be shaped by a complex interaction of economic structure, knowledge resources and institutional conditions. Human capital, industry structure, R&D and knowledge spillovers generate growth opportunities. However, these factors alone cannot explain why some regions fail to convert entrepreneurial opportunities into high-growth outcomes. Regions that combine skilled labour, knowledge assets, networked economies and supportive local institutional frameworks are those most likely to sustain high-prevalence HGF environments. 3. DATA, EMPIRICAL STRATEGY AND DESCRIPTIVE STATISTICS 3.1 Data and measures The study uses micro-level data from the Serrano database which contains yearly information on all Swedish privately held, limited liability firms for the period 2010–2022. This data is matched to local market characteristics (SCB-data). Lastly, the data is matched to the annual survey of local business climate for the duration of the period. We employ the extended Eurostat-OECD indicator for HGFs (Eurostat-OECD 2007; Henrekson and Johansson 2010 ; Coad and Srhoj 2020). The indicator is a dummy variable that takes the value 1 if a firm fullfills the following conditions: Having had 10 or more employees ( E ) in the initial period ( t = 0 ) and a geometric average of at least 20 percent growth over 3 years or having had less than 10 employees in the initial period and grown with at least 7 employees. 4 In other words, the HGF dummy takes the value 1 if the following conditions are satisfied: $$\:{\left(\frac{{E}_{t+3}}{{E}_{t=0}}\right)}^{\frac{1}{3}}-1\ge\:20\:\%$$ 1 Given that: $$\:{E}_{t=0}\ge\:10$$ 2 Alternatively, that: $$\:\left({E}_{t+3}-{E}_{t=0}>7|{E}_{t=0}<10\right)$$ 3.2 Descriptive statistics Table 1 presents descriptive statistics for all variables used in the analysis across Swedish municipalities over the period 2010–2022. There is substantial variation across municipalities in both overall start-up rates and the incidence of high-growth firms. HGFs are rare on average, but their local prevalence varies markedly, with some municipalities recording no HGFs in a given year while others display relatively high concentrations. This dispersion underscores pronounced differences in entrepreneurial quality across localities. The explanatory variables also exhibit wide heterogeneity. Population size and market size differ dramatically across municipalities, reflecting the contrast between large urban labor markets and small peripheral areas. Regional income levels and the local economic structure, captured by the service-sector share, show considerable spread. Human capital, measured as the share of the population with tertiary education, likewise varies substantially. Importantly, the local business climate index displays meaningful dispersion, indicating that entrepreneurs’ assessments of local institutional conditions differ notably across municipalities. Taken together, these patterns show that Swedish municipalities differ not only in economic scale and structure but also in institutional environments facing firms, providing relevant variation for analyzing the geography of high-growth firms. Table 1 Descriptive statistics (mean, median, min, max, standard deviation), all Swedish municipalities 2002–2022. Variables Definition Mean Median Min Max SD HGFs per capita Number of high-growth firms per capita (municipality) 0.0003 0.0003 0 0.002 0.0003 Start-ups per capita Number of firms of age \(\:\le\:\) 1 per capita (municipality) 0.006 0.005 0.0002 0.06 0.003 Population size Number of inhabitants per municipality 33,257 15,579 0 984,748 67,181 GRP per capita Gross regional product, county-level 0.30 0.3 0.0 2.1 0.1 Services share Share of turnover in municipality attributed to service industries 0.72 0.73 0.37 0.96 0.11 Local business climate Self-reported satisfaction of doing business in municipality 3.36 3.34 0 4.81 0.40 Share educated Share of population with tertiary education or higher 0.19 0.17 0.09 0.49 0.06 Market size Sum of wage sums in municipality i and neighboring municipalities, weighted by time-distance. Billion Swedish Krona (billion SEK). 17.65 4.94 0.04 508.86 40.82 Average establishment size Average number of employees per establishment, municipality i 0.31 0.30 0.09 0.88 0.11 Notes : 4. RESULTS 4.1 Baseline estimations Table 2 reports pooled OLS estimates of the relationship between local characteristics and entrepreneurial outcomes, comparing overall start-up rates with the incidence of high-growth firms. Several explanatory variables are positively and significantly associated with both outcomes. Population size, regional GDP per capita, market size, and the local share of services all display positive coefficients in both models, indicating that general demand conditions and agglomeration-related factors are relevant for entrepreneurial activity irrespective of whether it is measured in terms of entry or growth. Average establishment size is negatively associated with both start-ups and HGFs, consistent with the view that regions dominated by large establishments tend to exhibit lower levels of entrepreneurial dynamism. Table 2. Regression results (pooled OLS). Number of start-ups and HGFs per capita, 2010–2022. Swedish municipalities. No. of start-ups per capita No. of HGFs per capita (1) (2) Population size t-1 0.042*** (0.009) 0.106*** (0.021) GRP per capita t-1 0.393*** (0.022) 0.511*** (0.051) Services share of employment -1 0.801*** (0.051) 1.011*** (0.118) Local business climate t-1 0.143*** (0.041) 0.784*** (0.112) Share of population with tertiary education t-1 0.292*** (0.036) -0.034 (0.067) Market size t 0.069*** (0.008) 0.061*** (0.018) Average establishment size t-1 -0.467*** (0.023) -0.263*** (0.056) Number of obs. 3,190 2,173 Adj. R 2 0.684 0.361 Notes: All regressions control for fixed effects across local labor market regions. Despite these commonalities, there are notable differences in the strength and relevance of local determinants across the two outcomes. The most pronounced difference concerns the role of local business climate. While business climate is positively and significantly associated with start-up rates, its estimated effect on the incidence of high-growth firms is substantially larger in magnitude. This suggests that local institutional conditions play a considerably more important role for firm growth than for firm entry. By contrast, the share of the population with tertiary education is positively associated with start-up rates but is not statistically significant in the HGF model once other local characteristics are controlled for. In summary, differences indicate in several ways that the factors explaining where firms are created are not identical to those explaining where firms grow. Figure 1 provides a visual comparison of the estimated coefficients and confidence intervals across the two models. The figure highlights that although many local characteristics influence both outcomes, their relative importance differs systematically. In particular, the divergence in the estimated effect of local business climate is clearly visible, with a much stronger association for HGFs than for start-ups. The figure also shows that some variables that are central in explaining start-up rates contribute more modestly to explaining the spatial distribution of high-growth firms. Taken together, the comparison reinforces the view that the geography of entrepreneurial quality differs from the geography of entrepreneurial quantity, even when driven by a largely overlapping set of local factors. To assess the economic significance of the estimates, consider the implied magnitude of the local business climate effect on high-growth firms. The coefficient on local business climate in the HGF regression is 0.784 (Table 2), and the standard deviation of the business climate index across municipalities is 0.40 (Table 1). A one–standard deviation increase in local business climate is therefore associated with an increase of approximately 0.00031 high-growth firms per capita (0.784 × 0.40). This effect is large relative to the mean incidence of HGFs, which is 0.00030 (Table 1), implying that a one–standard deviation improvement in local business climate is associated with roughly a doubling of the average local prevalence of high-growth firms. Although HGFs remain rare in absolute terms, this calculation illustrates that variation in local institutional conditions is quantitatively important for explaining where high-growth firms emerge. More generally, while agglomeration and demand-side factors matter for both start-up activity and firm growth, differences in local business climate stand out as an economically meaningful determinant of entrepreneurial quality rather than entrepreneurial quantity. 4.2 Heterogeneity across sectors and interaction effects To better understand the mechanisms behind the baseline results, we extend the analysis along two dimensions: sectoral heterogeneity and interaction effects between local business climate and structural characteristics of municipalities. First, we explore whether the relationship between local business climate and high-growth firms varies across different parts of the local economy. Figure 2 reports industry-disaggregated regressions in which HGFs are grouped into broad sectors. The results show that the association between local business climate and HGFs is not uniform across industries. The relationship is strongest in sectors where firm expansion typically requires physical space, permits, and direct interaction with local authorities. In these sectors, scaling often entails zoning decisions, building permits, and infrastructure coordination, making firms more exposed to local administrative quality. In contrast, the association is weaker in activities that are less land- or permit-intensive, where firm growth depends more on human capital and market access than on local regulatory processes. This pattern is consistent with the interpretation that local business climate affects HGFs primarily through growth-related constraints rather than through general entrepreneurial conditions. We next examine whether the relationship between local business climate and high-growth firms depends on broader structural characteristics of municipalities. Figure 3 introduces interaction terms between business climate and key local variables in the full sample regressions. These models allow the effect of business climate on HGFs to vary with underlying economic conditions. The results reveal clear and systematic patterns rather than random heterogeneity. Most notably, the interaction between business climate and GRP per capita is positive and economically meaningful. This implies that improvements in local business climate are more strongly associated with HGF incidence in richer local economies. In other words, institutional quality and economic development appear to be complementary: favorable local institutions translate into more high-growth firms particularly where income levels and economic activity are already high. This suggests that business climate conditions may be especially important for enabling firms to exploit growth opportunities in more advanced local economies. A second pattern concerns human capital. In the baseline models, the share of highly educated residents shows little direct association with HGFs once other factors are controlled for. However, the interaction models indicate that the business-climate effect does not weaken in more highly educated municipalities. Instead, the relationship between business climate and HGFs remains stable or even slightly stronger where human capital levels are high. This points to a complementarity between local skills and institutional conditions: human capital alone does not generate more HGFs, but in places where skilled labor is abundant, a supportive business climate appears particularly important for converting capabilities into firm growth. Taken together, the interaction results suggest that local institutional quality operates as a conditioning factor rather than a standalone driver. Business climate matters most where other ingredients for growth—such as market size, income levels, and human capital—are already present. This reinforces the ecosystem interpretation of the findings. The local business climate functions as part of the institutional layer of the entrepreneurial ecosystem, influencing whether existing economic and knowledge resources are translated into high-growth outcomes 5. CONCLUSIONS Entrepreneurial ecosystems influence not only how many firms are created, but which firms are able to grow. This paper examined spatial variation in the incidence of high-growth firms (HGFs) across Swedish municipalities and compared the determinants of entrepreneurial quality with those of entrepreneurial quantity. We documented large geographic variation in HGFs over the period 2010–2022 and showed that high-growth activity is not confined to major metropolitan regions. While larger and richer local economies tend to host more HGFs, substantial HGF activity also occurs outside the largest urban areas. Standard agglomeration- and demand-side factors such as population size, income levels, market potential, and economic structure are associated with both start-up rates and HGFs, confirming that broad local economic conditions shape entrepreneurial activity regardless of whether it is measured in terms of entry or growth. The central result of the paper concerns the role of local business climate. Measures based on entrepreneurs’ assessments of local authorities are far more strongly associated with the local incidence of HGFs than with overall start-up rates. The magnitude of this relationship is economically meaningful: differences in business climate of the size observed across municipalities correspond to changes in HGF prevalence comparable to the sample mean. Sectoral and interaction analyses further clarify this pattern. The association between business climate and HGFs varies systematically across economic structures and strengthens in more economically developed municipalities. Moreover, the relationship does not weaken in places with higher human capital. These findings indicate that institutional quality and local economic resources act as complements rather than substitutes. Taken together, the results support an interpretation in which local institutional conditions shape firm growth through mechanisms related to expansion rather than entry. Growing firms must interact repeatedly with local authorities over land use, permits, infrastructure, and other administrative processes. Where these interactions are predictable and supportive, firms appear more able to scale locally. Where they are costly or uncertain, growth may be constrained or displaced. In this sense, the local business climate constitutes part of the institutional layer of the entrepreneurial ecosystem, influencing whether existing economic and knowledge resources are translated into high-growth outcomes. The findings have implications for both research and policy. For research, they underscore the importance of distinguishing between the geography of entrepreneurial quantity and entrepreneurial quality and of linking ecosystem conditions to firm-level exposure to local institutions. For policy, they suggest that improving local administrative practices may matter less for generating start-ups than for enabling promising firms to grow. Efforts to strengthen local ecosystems should therefore pay attention not only to resources and innovation capacity, but also to how local institutions function in practice for expanding firms. Declarations Author Contribution All authors contributed equally. Data Availability Contact authors for inquiries about data availability. 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In Handbook of regional science (pp. 1-13). Springer, Berlin, Heidelberg. Andersson, M., Lavesson, N., & Partridge, M. D. (2019). Local rates of new firm formation: An empirical exploration using Swedish data. IFN Working paper. Bertrand, M., & Kramarz, F. (2002). Does entry regulation hinder job creation? Evidence from the French retail industry. the quarterly journal of economics , 117 (4), 1369-1413. Björk, P., Saarela, M., Kotavaara, O., & Muhos, M. (2026). Tracking spatial distribution and regional characteristics of gazelles. Entrepreneurship & Regional Development, 38(1-2), 164-184 Bos, J. W. B., & Stam, E. (2014). Gazelles and industry growth: A study of young high-growth firms in the Netherlands. Industrial and Corporate Change, 23(1), 145–169. Coad, A., & Srhoj, S. (2023). Entrepreneurial ecosystems and regional persistence of high-growth firms: A “broken clock” critique. Research Policy, 52(4), 104762. Coad, A., Daunfeldt, S.-O., Hölzl, W., Johansson, D., & Nightingale, P. (2014). High-growth firms: Introduction to the special section. Industrial and Corporate Change, 23(1), 91–112. Coad, A., Domnick, C., Santoleri, P., & Srhoj, S. (2025). Regional incidence and persistence of high-growth firms: Testing ideas from the entrepreneurial ecosystems literature. Regional Studies, 59(1). Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. Daunfeldt, S.-O., Elert, N., & Johansson, D. (2016). Are high-growth firms overrepresented in high-tech industries? Industrial and Corporate Change, 25(1), 1–21. Duranton, G., & Puga, D. (2004). Micro-foundations of urban agglomeration economies. In J. V. Henderson & J.-F. Thisse (Eds.), Handbook of regional and urban economics (Vol. 4, pp. 2063–2117). Elsevier. Felin, T., Foss, N. J., & Ployhart, R. E. (2015). The microfoundations movement in strategy and organization theory. Academy of Management Annals , 9 (1), 575-632. Fotopoulos, G. (2022). Knowledge spillovers, entrepreneurial ecosystems and the geography of high-growth firms. Entrepreneurship Theory and Practice, 47(5), 1877–1914. Fölster, S., & Peltzman, S. (2010). Competition, regulation and the role of local government policies in Swedish markets. In Reforming the welfare state: Recovery and beyond in Sweden (pp. 253-284). University of Chicago Press. Fölster, S., Jansson, L., & Nyrenström Gidehag, A. (2016). The effect of local business climate on employment. Journal of Entrepreneurship and Public Policy , 5 (1), 2-24 Glaeser, E. L., Kallal, H. D., Scheinkman, J. A., & Shleifer, A. (1992). Growth in cities. Journal of Political Economy, 100(6), 1126–1152. Guzman, J., & Stern, S. (2015). Nowcasting and placecasting entrepreneurial quality and performance. NBER Working Paper No. 21554. Haltiwanger, J. (2015). Job creation, job destruction, and productivity growth: The role of young businesses. American Economic Review: Papers & Proceedings, 105(5), 347–351. Henrekson, M., & Johansson, D. (2010). Gazelles as job creators: A survey and interpretation of the evidence. Small Business Economics, 35(2), 227–244. Henrekson, M., & Sanandaji, T. (2014). Small business activity does not measure entrepreneurship. Proceedings of the National Academy of Sciences, 111(5), 1760–1765. Hurst, E., & Pugsley, B. W. (2011). What do small businesses do? Brookings Papers on Economic Activity, 2011(2), 73–142. Jacobs, J. (1969). The economy of cities. Vintage Books. Levine, R., & Rubinstein, Y. (2017). Smart and illicit: who becomes an entrepreneur and do they earn more?. The Quarterly journal of economics , 132 (2), 963-1018. Li, D., Goetz, S. J., Partridge, M., & Fleming, D. A. (2016). Location determinants of high-growth firms. Entrepreneurship & Regional Development, 28(1–2), 97–125. Marshall, A. (1920). Principles of economics (8th ed.). Macmillan. (Original work published 1890) Mason, C., & Brown, R. (2013). Creating good public policy to support high-growth firms. Small Business Economics, 40(2), 211–225. Motoyama, Y. (2014). The state-level geographic analysis of high-growth companies. Economic Development Quarterly, 28(1), 60–70. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. Qian, H., Acs, Z. J., & Stough, R. R. (2013). Regional systems of entrepreneurship: The nexus of human capital, knowledge and new firm formation. Journal of Economic Geography, 13(4), 559–587. Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press. Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), 217–226. Sleuwaegen, L., & Ramboer, S. (2020). Regional competitiveness and high growth firms in the EU: the creativity premium. Applied Economics , 52 (22), 2325-2338. Sorenson, O. (2017). Regional ecologies of entrepreneurship. Journal of Economic Geography, 17(5), 959–974. Sorenson, O., & Audia, P. G. (2000). The social structure of entrepreneurial activity: Geographic concentration of footwear production in the United States, 1940–1989. American Journal of Sociology, 106(2), 424–462. Stam, E. (2015). Entrepreneurial ecosystems and regional policy: A sympathetic critique. European Planning Studies, 23(9), 1759–1769. Stam, E., & Spigel, B. (2016). Entrepreneurial ecosystems. Utrecht University Discussion Paper No. 16-13. Stam, E., & Van de Ven, A. (2018). Entrepreneurial ecosystem elements. Small Business Economics, 51(1), 173–193. Tannenwald, R. (1997). State regulatory policy and economic development. New England Economic Review , 83-98. Westlund, H., Larsson, J. P., & Olsson, A. R. (2014). Start-ups and local entrepreneurial social capital in Swedish municipalities. Regional Studies, 48(6), 974–994. Footnotes A core argument in the literature on entrepreneurial ecosystems is that institutional arrangements that regulate, legitimize, and incentivize entrepreneurship are critical (Stam 2015 ; Stam and Van de Ven 2018 ). Guzman and Stern found that 75% of all significant growth outcomes came from the top 5% of startups by “quality”. Likewise, Schoar (2010) notes a sharp divide between a “small number of transformative entrepreneurs” building high-growth ventures and the far larger number of subsistence entrepreneurs whose firms never scale. In other words, most firms are content (or forced) to remain modest in size, whereas gazelles – the rare, rapidly expanding firms – account for the lion’s share of job creation. Minniti (2005) argues that such local social networks effect are important and could imply that a city or local area develops a ‘local entrepreneurship culture’: “entrepreneurship creates a ‘culture’ of itself that influences individual behavior in its favor” (ibid, p.3). The threshold comes from calculations on the average conditions among firms with less than 10 employees (Coad and Shroj 2020). Formally, the condition states that firms need to grow with 7.28 employees to fullfil the conditions of high-growth. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9277966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621999621,"identity":"43d83a23-012c-44ee-aa4d-5aa93daf9903","order_by":0,"name":"Martin Andersson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYLCCBCSSgV+CgY2QBsYGFC2SM5iJ0IJsF4PBDQJadNvPPn/wcAeDvDl78rGPP/7Y5Bnf7j/2gOGPDU4tZmfSDRsSzzAY7ux5ljybty2t2OzOYXYDxrY03FoOpDE2JLYxMG64kWPMzNhwOHHbjWQ2CSADt5bzz8Ba7DfcyP/M+OPP4cTNM4BaGP78x63lBsSWRKAtzAw8bIcTN0iAtLAdwKPlGeOMxDaJ5A1nnhkzA/2SOOPOYTOJxLZkPA5LY/j4s83GdsPx5MdAh9kk9s9ufCbx4Y8dTi1QIIHGTyCkYRSMglEwCkYBXgAABVZYrC4c270AAAAASUVORK5CYII=","orcid":"","institution":"Blekinge Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Martin","middleName":"","lastName":"Andersson","suffix":""},{"id":621999622,"identity":"d2326490-7a7e-49a3-bbda-1695ead16b6c","order_by":1,"name":"Johan Karlsson","email":"","orcid":"","institution":"Swedish Confederation of Enterprise","correspondingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Karlsson","suffix":""},{"id":621999623,"identity":"08ffc2ff-ea89-4673-a93a-c0e8de9cdb0d","order_by":2,"name":"Johan Larsson","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Larsson","suffix":""}],"badges":[],"createdAt":"2026-03-31 09:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9277966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9277966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107152379,"identity":"4fa81f9c-8f07-4517-9754-54fb311cff22","added_by":"auto","created_at":"2026-04-17 11:12:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of local determinants of high-growth firms versus start-ups, 2010–2022. Pooled OLS estimates, Swedish municipalities\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e About 1,200 municipality-year observations contain no high-growth firms. This means that the regression estimates for high-growth firms is based on a smaller number of observations, which affects comparability of the standard errors in the two models. To account for this, we have pooled the variance-covariance matrix of both regression models, thus making their confidence intervals comparable.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9277966/v1/6b9147184bdeca00b951b9a2.png"},{"id":107152360,"identity":"80a2892a-0481-414d-b61c-4fbac99b3c2a","added_by":"auto","created_at":"2026-04-17 11:12:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIndustry-diaggregated effects of local business climate and High-Growth Firms (HGFs) and start-ups, respectively.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9277966/v1/4124dd8860de5ad3ca56611b.png"},{"id":107152355,"identity":"7e685b46-9565-4499-a28f-6953ede0b7bd","added_by":"auto","created_at":"2026-04-17 11:12:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults from regressions interacting key variables with quality of local business climate.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9277966/v1/25a8f5d59d722a637de35ec7.png"},{"id":107152444,"identity":"76725ddf-d07c-439f-9595-3a55a42a698f","added_by":"auto","created_at":"2026-04-17 11:12:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":471878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9277966/v1/57979fb6-e9e2-47a8-ad7c-33fe37f7f15c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gazelles and Their Habitats - Business Climate and the Geography of High-Growth Firms","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eEntrepreneurship is widely seen as a driver of innovation, productivity, and economic growth. Yet standard measures such as small business ownership or self-employment rates often fail to capture the dynamic, growth-oriented activities that generate most new jobs and economic dynamism (Henrekson and Sanandaji 2014; Levine and Rubinstein 2017). High-growth firms (HGFs), or \u0026ldquo;gazelles\u0026rdquo;, are rare but important because they account for a large share of net job creation and productivity gains (Haltiwanger 2015). Industries with more gazelles grow faster (Bos and Stam 2014), and only a minority of firms grow rapidly, yet these create most employment gains (Coad et al. 2014). Coad and Srhoj (2023) summarize the consensus view: HGFs make a large contribution to job creation, innovation, and economic dynamism. These findings resonate with Schumpeter\u0026rsquo;s (1933) view of entrepreneurship as a driver of innovation and structural change through creative destruction. Despite the importance of HGFs, we know relatively little about which local environments are conducive to their emergence, especially regarding local institutional or policy conditions. This paper contributes with an empirical analysis of spatial variation in the incidence of HGFs, with a focus on the role of local business climate.\u003c/p\u003e\n\u003cp\u003eWe investigate a mechanism through which local institutional conditions influence the geography of high-growth firms: interactions between growth-oriented firms and local business climate. While firms can start under a wide range of local conditions, scaling often entails repeated engagement with zoning, permits, infrastructure provision, and regulatory enforcement (Andersson and Henrekson 2014; F\u0026ouml;lster et al. 2016). These interactions create locally varying transaction costs associated with expansion. A favorable local business climate, characterized by efficient and predictable local administrative practices, can lower these costs and enable firms to scale their operations locally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ecosystem literature emphasizes that local constellations of actors, institutions, resources, and norms shape entrepreneurial outcomes (Stam 2015; Stam and Van de Ven 2018; Mason and Brown 2014; Stam and Spigel 2016; Alvedalen and Boschma 2017). The ecological perspective argues that the organizational demography of a region, especially the prevalence of small, young, and dynamic firms, shapes beliefs about entrepreneurship, learning opportunities, and access to resources, thereby influencing future entrepreneurial activity (Sorenson 2017). Regions rich in startups develop environments in which entrepreneurship becomes legitimate and supported by appropriate infrastructures. Together, these perspectives suggest that local outcomes are shaped not only by factor endowments or abstract cultural traits, but by how firms, institutions, and actors interact within a place.\u003c/p\u003e\n\u003cp\u003eWe argue that the local business climate is a relevant component of a city\u0026rsquo;s or region\u0026rsquo;s institutional arrangement.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e Yet, we know of no study that explicitly assesses the role of local framework conditions, based on entrepreneurs\u0026rsquo; perceptions, for the local incidence of HGFs. By distinguishing between start-up rates and the local incidence of HGFs, and by exploiting variations in entrepreneurs\u0026rsquo; own assessments of the local business climate, we show that the local business climate matter especially for firm growth rather than entry. This approach complements existing research on the geography of gazelles, which shows that high-growth firms are unevenly distributed across regions and that their spatial patterns are not identical to those of general start-up activity (Guzman and Stern 2015; Li et al. 2016). It also connects with the ecological argument that regional entrepreneurial dynamics depend on the structure and evolution of local firm populations (Sorenson 2017).\u003c/p\u003e\n\u003cp\u003eResearch on characteristics of HGFs shows that HGFs have high productivity and productivity growth (Du and Temouri 2015), appear in all sectors (Henrekson and Stenkula 2010; Daunfeldt et al. 2016; Fairlie et al. 2023), and tend to be innovative (Sleuwaegen and Ramboer 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, empirical research on local or regional settings associated with HGFs remains limited. The literature on the geography of entrepreneurship predominantly focuses on overall start-up rates and pays less attention to entrepreneurial quality (Andersson and Larsson 2020). Several scholar argue conceptually that there is a link between local contexts and HGFs (Mason and Brown 2014; Stam and Spigel 2016; Alvedalen and Boschma 2017), but empirical evidence remains limited (Coad and Srhoj 2023; Coad et al. 2025; Fritsch 2024). Existing findings suggest that the geography of entrepreneurial quality is more spatially concentrated than overall entrepreneurship (Guzman and Stern 2015), that human capital is positively associated with HGFs (Li et al. 2016; Sleuwaegen and Ramboer 2020; Motoyama 2014), and that HGFs are not confined to large metropolitan regions (Li et al. 2016; Coad et al. 2025). Yet we know less about how local institutional conditions interact with these structural factors to shape the local prevalence of high-growth firms.\u003c/p\u003e\n\u003cp\u003eOur analysis employs longitudinal data for municipalities in Sweden (2010\u0026ndash;2022) and assesses the empirical relevance of a number of local supply- and demand-side factors in explaining the number of HGFs per capita. HGFs are identified based on complete micro-level data on employment growth among young incorporated firms, which is matched to annual survey data on the perceived business climate across all Swedish municipalities. This allows us to study the interrelationship between firm characteristics and local economic outcomes. In doing so, we also contribute to the ongoing debate on the local determinants of high-growth firms (e.g., Bj\u0026ouml;rk et al. 2025). We test whether there are differences between the determinants of the local incidence of HGFs and the determinants of the overall rate of start-ups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe empirical analyses account for standard supply- and demand-side factors in the literature on the geography of entrepreneurship (Andersson and Larsson 2020), but also add a variable reflecting the local business climate is based on entrepreneurs\u0026rsquo; assessments of local authorities\u0026rsquo; attitudes and practices. Our main hypothesis is that that growing firms are more exposed than ordinary start-ups to barriers such as permits, land-use regulations, and other institutional factors that form part of the local business climate. On these grounds, a favorable local business climate can be expected to be especially important for the local incidence of HGFs. Previous research has shown that local business climate influences overall employment growth (F\u0026ouml;lster et al. 2016) and start-up activity (Westlund et al. 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe document large spatial variation in the local incidence of high-growth firms across Swedish municipalities during 2010\u0026ndash;2022. Gazelles are not confined to major metropolitan areas: while larger and richer local economies tend to host more HGFs, substantial HGF activity also appears in smaller and more peripheral places. In baseline regressions, several standard local characteristics correlate with both start-up rates and HGFs per capita, including population size, regional income, market size, and the local economic structure. This confirms that broad demand conditions and agglomeration-related factors matter for entrepreneurial activity whether one measures entrepreneurship in terms of entry or growth.\u003c/p\u003e\n\u003cp\u003eAt the same time, the determinants of entrepreneurial quality and quantity diverge in an important way. Local business climate stands out as a much stronger predictor of the local incidence of HGFs than of start-up rates, consistent with the view that institutional conditions matter especially for firms\u0026rsquo; ability to scale and shape entrepreneurial outcomes (Stam 2015; Stam and Van de Ven 2018). The magnitude is economically meaningful: we find that a one\u0026ndash;standard deviation improvement in local business climate is associated with an increase in HGFs per capita roughly equal to the sample mean. Industry-disaggregated analyses further show that this relationship varies across sectors rather than being driven by a single part of the economy, consistent with the idea that ecosystem conditions interact with local economic structures. Interaction models indicate that the effect of business climate depends on underlying regional characteristics, reinforcing the interpretation that institutional and ecosystem conditions operate primarily through growth-related constraints rather than through general entry effects (Felin et al. 2015; Sorenson 2017).\u003c/p\u003e\n\u003cp\u003eThe remainder of the paper proceeds as follows: Section 2 reviews the literature and develops the conceptual framework, Section 3 describes the data and empirical strategy, Section 4 presents the results and additional analyses, and Section 5 concludes.\u003c/p\u003e"},{"header":"2. THE GEOGRAPHY OF HIGH GROWTH FIRMS (HGFs)","content":"\u003cp\u003eStudies of the geography of entrepreneurship has long emphasised firm formation rates as an indicator of regional dynamism. However, this approach overlooks the fact that most new firms do not grow and many exit early (Hurst and Pugsley 2011).\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e The literature has had a bias towards certain quantity-based measures, such as overall start-up rates, failing to capture the qualitative dimension of entrepreneurship (Andersson and Larsson 2020). A shift of focus toward the geography of HGFs is a way to better understand the conditions that allow firms to grow and scale and why some regions may be in a better position to foster HGFs. Policies for HGFs require an understanding of where HGFs emerge, why they do so in some regions and not others, and how bottlenecks differ between regions (Coad et al. 2025). Mason and Brown (2013) argue that governments have traditionally looked for high-growth firms in the wrong places, overemphasising high-tech sectors and ignoring the fact that HGFs are found in all industries and locations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese strands of research largely explain where entrepreneurial activity emerges, but they say less about the local conditions that determine whether firms are able to translate growth potential into realized expansion. From an entrepreneurial ecosystems perspective, many of the factors discussed below affect opportunity creation and capability development, while institutional conditions shape the transaction costs firms face when they attempt to scale.\u003c/p\u003e\n\u003cp\u003eThe prevalence of high-growth firms (HGFs) varies significantly across regions (Guzman and Stern 2015, Motoyama 2014, Li et al 2016), reflecting that their emergence is shaped not only by firm-level characteristics, but also by regional conditions that either support or constrain the scaling process. Theoretical explanations for why certain regions have higher incidence of HGFs build on insights from economic geography, evolutionary economics, and entrepreneurship theory and focus on factors such as human capital, industrial structure, local innovation capabilities, knowledge spillovers and agglomeration forces. Below we discuss typical arguments in the literature, and then makes that case that local business climate is one important aspect of the local conditions for HGFs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman Capital\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA first widely recognised factor influencing regional HGF prevalence is human capital. Human capital enhances entrepreneurial experimentation by increasing the capacity of firms to recognise and exploit growth opportunities and to absorb external knowledge (Cohen \u0026amp; Levinthal, 1990; Shane, 2000). Regions with higher stocks of skilled labour are more likely to produce firms capable of scaling because growth requires access not only to founding talent but also to managerial and technical expertise during expansion (Li et al., 2016). Motoyama (2014) finds that regions in the United States with higher proportions of science and engineering graduates exhibit significantly higher numbers of high-growth firms. Similarly, Sleuwaegen and Ramboer (2020) show that in European regions, creative-class employment \u0026ndash; an indicator of specialised human capita in occupations such as engineering and \u0026nbsp;designers \u0026ndash; strongly predicts the regional incidence of HGFs. This supports the argument that growth-oriented entrepreneurship is opportunity-driven and knowledge-based, and thus depends on local pools of advanced skills.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the aggregate regional level, Qian et al (2013) conceptualize human capital as representing the local absorptive capacity to develop and act on business opportunities, especially in regard to knowledge-based entrepreneurial activity in US metropolitan areas. In this view, human capital is not only a factor of production but a catalyst for growth-oriented entrepreneurial strategies.\u003c/p\u003e\n\u003cp\u003eWhile human capital enhances firms\u0026rsquo; capabilities to recognize and exploit growth opportunities, it does not by itself address the institutional frictions encountered during expansion. Growth requires not only skilled labour, but also the ability to secure premises, permits, and infrastructure, which depend on how local institutions function in practice.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKnowledge spillovers, innovation and R\u0026amp;D\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFactors such as R\u0026amp;D capacity, research institutions, and local incidence of knowledge spillovers are often claimed to be of importance in explaining local prevalence of HGFs. The Knowledge Spillover Theory of Entrepreneurship (KSTE) argues that knowledge created in firms and universities spills over into entrepreneurial ventures that commercialise it (Acs et al., 2009). It builds on the premise that entrepreneurial opportunities are created endogenously through knowledge investments, and that \u0026nbsp;\u0026ldquo;... entrepreneurial activity will be greater where investments in new knowledge are relatively high, since start-ups will exploit spillovers from the source of knowledge production\u0026rdquo; (Acs et al., 2009, p. 17). Regions rich in R\u0026amp;D resources, patenting activity and university-industry collaboration can therefore be claimed to offer fertile ground for high-growth entrepreneurship because innovative firms rely on technological and scientific advances to scale. However, empirical support is mixed. Motoyama (2014) finds no direct effect of patents or academic research activity on HGF incidence, challenging simple assumptions about R\u0026amp;D. Coad et al (2025) also show that local prevalence of HGFs is not confined to urban agglomerations rich in knowledge resources. Guzman and Stern (2015) argue that it is not R\u0026amp;D quantity but entrepreneurial quality and ecosystems that mediate the conversion of knowledge into growth-oriented firms. Likewise, Fotopoulos (2022) finds that universities contribute to HGF prevalence only when they actively engage with local firms through collaborative research and entrepreneurial training.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelated to innovation is the role of knowledge spillovers, which transmit ideas and practices between firms and industries. Knowledge spillovers occur through worker mobility, supplier relationships, informal networks, and co-location effects (Audretsch \u0026amp; Lehmann 2005). According to Fotopoulos (2022), HGFs benefit disproportionately from spillovers because they are more structurally capable of recognising and acting on external opportunities. Regions differ in their ability to exploit spillovers, partly because of differences in absorptive capacity and human capital.\u003c/p\u003e\n\u003cp\u003eThese mechanisms primarily explain how knowledge and innovation opportunities arise locally, but they are less informative about how firms navigate the administrative and regulatory processes that accompany physical and organizational expansion. As such, knowledge-rich regions may still differ substantially in their ability to convert innovation potential into high-growth outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIndustry and business structure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eArguments about the role of industry and business structure partly rely on the idea that they influence the preconditions for cross-fertilization of ideas through knowledge spillovers. Local economies with a diverse industrial base are argued to offer more recombination possibilities, enabling knowledge spillovers across sectors and stimulating innovation-driven growth (Jacobs 1969, Glaeser et al. 1992). Duranton and Puga (2001) portray large and diverse cities as \u0026ldquo;nurseries\u0026rdquo; for new products and services. This type of \u0026nbsp;\u0026ldquo;Jacobs externality\u0026rdquo; effect suggests that variety in the local economy promotes high-growth entrepreneurship by fostering novel business models and niche market creation. As diversity of a local economy is increasing in urban size, this argument is often interpreted as pointing to an advantage for large urban regions in fostering HGFs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFotopoulos (2022) finds some empirical support for this line of argument, showing that economic diversity is positively associated with HGF incidence. On the other hand, Marshallian specialisation theories emphasise the growth benefits of industrial concentration and deep supply chains. Firms may scale more effectively in specialised regions where cluster effects provide access to specialist labour pools, suppliers and customers (Marshall, 1890). Li et al. (2016), for example, show that some high-growth firms thrive in specialised regional economies where industry-specific infrastructure and expertise reduce growth barriers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother attribute of the local industry structure that is frequently discussed in the literature is average size of the firms in a region. A consistent finding in the literature on geography of entrepreneurship, at least in studies of determinats of overall rates of startup, is that regions dominated by large firms show lower entrepreneurial activity. Thus, average establishment size has been shown to negatively correlate with entrepreneurial dynamism, both for new firm formation. \u0026nbsp;One reason is that local small-firm density implies there are more opportunities for workers to develop entrepreneurial human capital. For example, employees in SMEs are more likely exposed to the whole business process, making them better equipped to start a firm. They are also be more likely to be in contact with the firm\u0026rsquo;s founder(s) who could serve as role model(s) and promote entrepreneurial attitudes (Andersson and Larsson 2016). Indeed, the literature confirms that employees in small firms are more likely to switch from wage employment to be entrepreneurs (Hyytinen and Maliranta 2008, Elfenbein et al 2011). Another reason is the social interaction across local small firms may facilitate new-firm creation, which benefits from a greater density of \u0026nbsp;established entrepreneurs that serve as potential role models that transmit knowledge through social networks (Minniti 2004, Sorenson and Audia 2000, Andersson and Larsson 2016).\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e Third, a local density of SMEs may indicate that the local economy has thicker input markets Chinitz (1961).\u003c/p\u003e\n\u003cp\u003eIn the context of HGFs, however, there are a contrasting perspective. Research on industrial dynamics and spinoffs shows that large and resourceful firms are important breeding grounds for high-growth entrepreneurs. Employees are assuned to inherit routines and know-how from their parent firms, and \u0026lsquo;good\u0026rsquo; parent firms are therefore more likely to spawn \u0026lsquo;good\u0026rsquo; firms that survive and grow. In U.S. lasers, for example, spinoffs traced to technological leaders exhibited higher survival and performance than other entrants (Klepper \u0026amp; Sleeper, 2005). Cross-industry analyses similarly argue that incumbents\u0026rsquo; organizational capabilities transmit to founders, shaping new firms\u0026rsquo; productivity and growth trajectories (Klepper, 2002, 2010). Andersson and Klepper (2013) show with data from Sweden that spinoffs \u0026ndash;especially from parent firms that are large and successful \u0026ndash; display systematically superior survival and growth compared with de novo startups, consistent with the \u0026ldquo;good firms breed good entrepreneurs\u0026rdquo; view. Li et al (2016) also finds a HGFs positive association between average establishement size in a region and HGFs. Taken together, this suggest that local density of large firms may be positive for the incidence of high-performing startups through organizational inheritance, whereas small firms may rather be beneficial for the overall startup-rate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these arguments show that local industrial structure shapes both the supply of entrepreneurial opportunities and the quality of firms entering the market. However, they do not fully explain why firms with similar capabilities may scale successfully in one region but not in another, pointing to the importance of institutional conditions that affect the costs and feasibility of expansion.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sixe of a region, measured as either population or employment, is a form of \u0026lsquo;catch all\u0026rsquo; argument related to both the supply- and demand-sides of a local economy. On the supply-side, it is well established that larger cities and regions offer various agglomeration benefits from \u0026lsquo;sharing\u0026rsquo;, \u0026lsquo;matching\u0026rsquo;, and \u0026lsquo;learning\u0026rsquo; (Duranton and Puga 2004). In dense urban areas, firms benefit from proximity to suppliers, customers, investors and specialised services. These external economies of scale lower transaction costs and foster innovation, increasing the probability of firm scaling (Glaeser, 2011). Li et al. (2016) show that the highest concentration of HGFs in the United States is found in metropolitan areas with diverse economic bases, strong amenities and access to human capital. However, high density also brings disadvantages such as congestion, competition for skilled labour and escalating business costs, which may limit HGF formation in overly saturated regions. Coad et al. (2025) find that some developed regions do not have higher shares of HGFs, suggesting that density must interact with other conditions to leave footprints on HGFs.\u003c/p\u003e\n\u003cp\u003eRegional size captures many agglomeration advantages, but it is ultimately an indirect measure of the local environment firms face when they grow. Large regions may offer deeper markets and resources, yet firms in these regions still depend on local institutional processes when expanding operations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLocal business climate \u0026ndash; a neglected aspect in research on HGFs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis institutional dimension corresponds to what the entrepreneurial ecosystems literature describes as framework conditions that regulate, legitimize, and incentivize entrepreneurship (Stam 2015; Stam and Van de Ven 2018). Unlike human capital or industry structure, these conditions operate directly through firms\u0026rsquo; interactions with public authorities during the scaling process.\u003c/p\u003e\n\u003cp\u003eWhile previous factors explain opportunity creation and firm capabilities, institutional arguments explain why some firms grow while others are constrained by bureaucracy, regulatory complexity or weak governance. Institutions shape incentives and reduce uncertainty (North, 1990). Andersson and Henrekson (2014) argue that entrepreneurship is directed by the \u0026ldquo;rules of the game,\u0026rdquo; and whether these rules reward firm growth or protect incumbents can influence regional HGF prevalence. Institutional effects are not only national but also local. Andersson and Henrekson (2014) emphasise that even in non-federal countries, municipalities influence the business climate through how they interpret and implement regulations, public procurement, zoning, and permit systems. Local institutions can affect the direction of entrepreneurship by either facilitating or obstructing firm expansion.\u003c/p\u003e\n\u003cp\u003eHigh-growth firms are likely to be sensitive to local business climate conditions, more so than ordinary small firms. Scaling requires rapid hiring, new facilities, planning permissions, environmental approvals and infrastructure expansion\u0026mdash;each dependent on local government responsiveness. Local authorities control zoning, land use, business permits and expansion approvals\u0026mdash;all of which affect firms seeking to scale. Tannenwald (1997, p. 84) stresses that regulation is not only about the written rules, but also about how regulations are enforced locally, arguing that enforcement behaviour frequently has a large impact on firms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBertrand and Kramarz (2002) provide empirical evidence of how local regulatory enforcement affects business dynamics. Studying zoning regulation in France, they show that stricter enforcement of entry regulations by regional zoning boards reduced job creation and hindered the growth of new firms in the retail sector. Their findings illustrate how local bureaucratic discretion can create barriers to firm expansion\u0026mdash;an issue that disproportionately affects HGFs, which are more likely than small lifestyle firms to require new facilities, expansion permits and access to commercial real estate.\u003c/p\u003e\n\u003cp\u003eZoning and land use regulations are particularly influential for firms that intend to grow. Complex or slow permit processes delay scale-up investments and can force firms to relocate to more expansion-friendly municipalities. Andersson and Henrekson (2014) argue that local variations in regulatory efficiency\u0026mdash;measured in terms of processing speed, transparency and predictability\u0026mdash;are a decisive part of the local business climate. Evidence from Swedish municipalities shows large regional differences in entrepreneurs\u0026rsquo; satisfaction with local regulatory enforcement, ranging from highly business-friendly authorities to those seen as obstructive (F\u0026ouml;lster and Peltzman, 2010). Such uneven enforcement directly impacts whether HGFs can exploit growth opportunities within a given region or are compelled to move operations elsewhere. F\u0026ouml;lster et al. (2016) show empirically that municipalities with better business climates \u0026ndash; measured by entrepreneurs\u0026rsquo; perceptions of local regulatory efficiency \u0026ndash; have significantly higher employment growth, with effects strongest for firms expanding operations. Local business climate is therefore a real enabling condition for HGFs because growth-oriented firms interact intensively with local authorities. Lead times for building permits, barriers to entry, and unfair competition are central aspects of business climate affecting firm growth. For instance, obstructive zoning boards, inconsistent regulation enforcement or weak infrastructure planning discourage firm scaling even when talent and innovation are present.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the prevalence of high-growth firms in a region is liely to be shaped by a complex interaction of economic structure, knowledge resources and institutional conditions. Human capital, industry structure, R\u0026amp;D and knowledge spillovers generate growth opportunities. However, these factors alone cannot explain why some regions fail to convert entrepreneurial opportunities into high-growth outcomes. Regions that combine skilled labour, knowledge assets, networked economies and supportive local institutional frameworks are those most likely to sustain high-prevalence HGF environments.\u003c/p\u003e"},{"header":"3. DATA, EMPIRICAL STRATEGY AND DESCRIPTIVE STATISTICS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data and measures\u003c/h2\u003e\n \u003cp\u003eThe study uses micro-level data from the Serrano database which contains yearly information on all Swedish privately held, limited liability firms for the period 2010\u0026ndash;2022. This data is matched to local market characteristics (SCB-data). Lastly, the data is matched to the annual survey of local business climate for the duration of the period.\u003c/p\u003e\n \u003cp\u003eWe employ the extended Eurostat-OECD indicator for HGFs (Eurostat-OECD 2007; Henrekson and Johansson \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Coad and Srhoj 2020). The indicator is a dummy variable that takes the value 1 if a firm fullfills the following conditions: Having had 10 or more employees (\u003cem\u003eE\u003c/em\u003e) in the initial period (\u003cem\u003et\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e) and a geometric average of at least 20 percent growth over 3 years \u003cem\u003eor\u003c/em\u003e having had less than 10 employees in the initial period and grown with at least 7 employees.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e \u003csup\u003e4\u003c/sup\u003e In other words, the HGF dummy takes the value 1 if the following conditions are satisfied:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\left(\\frac{{E}_{t+3}}{{E}_{t=0}}\\right)}^{\\frac{1}{3}}-1\\ge\\:20\\:\\%$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eGiven that:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{E}_{t=0}\\ge\\:10$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAlternatively, that:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\left({E}_{t+3}-{E}_{t=0}\u0026gt;7|{E}_{t=0}\u0026lt;10\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Descriptive statistics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for all variables used in the analysis across Swedish municipalities over the period 2010\u0026ndash;2022. There is substantial variation across municipalities in both overall start-up rates and the incidence of high-growth firms. HGFs are rare on average, but their local prevalence varies markedly, with some municipalities recording no HGFs in a given year while others display relatively high concentrations. This dispersion underscores pronounced differences in entrepreneurial quality across localities.\u003c/p\u003e\n \u003cp\u003eThe explanatory variables also exhibit wide heterogeneity. Population size and market size differ dramatically across municipalities, reflecting the contrast between large urban labor markets and small peripheral areas. Regional income levels and the local economic structure, captured by the service-sector share, show considerable spread.\u003c/p\u003e\n \u003cp\u003eHuman capital, measured as the share of the population with tertiary education, likewise varies substantially. Importantly, the local business climate index displays meaningful dispersion, indicating that entrepreneurs\u0026rsquo; assessments of local institutional conditions differ notably across municipalities. Taken together, these patterns show that Swedish municipalities differ not only in economic scale and structure but also in institutional environments facing firms, providing relevant variation for analyzing the geography of high-growth firms.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics (mean, median, min, max, standard deviation), all Swedish municipalities 2002\u0026ndash;2022.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHGFs per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNumber of high-growth firms per capita (municipality)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStart-ups per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNumber of firms of age \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 1 per capita (municipality)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePopulation size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNumber of inhabitants per municipality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e33,257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15,579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e984,748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e67,181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGRP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGross regional product, county-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eServices share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eShare of turnover in municipality attributed to service industries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLocal business climate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSelf-reported satisfaction of doing business in municipality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShare educated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eShare of population with tertiary education or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarket size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSum of wage sums in municipality \u003cem\u003ei\u003c/em\u003e and neighboring municipalities,\u003c/p\u003e\n \u003cp\u003eweighted by time-distance. Billion Swedish Krona (billion SEK).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e508.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e40.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAverage establishment size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAverage number of employees per establishment, municipality \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes\u003c/em\u003e:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cp\u003e4.1 Baseline estimations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 reports pooled OLS estimates of the relationship between local characteristics and entrepreneurial outcomes, comparing overall start-up rates with the incidence of high-growth firms. Several explanatory variables are positively and significantly associated with both outcomes. Population size, regional GDP per capita, market size, and the local share of services all display positive coefficients in both models, indicating that general demand conditions and agglomeration-related factors are relevant for entrepreneurial activity irrespective of whether it is measured in terms of entry or growth. Average establishment size is negatively associated with both start-ups and HGFs, consistent with the view that regions dominated by large establishments tend to exhibit lower levels of entrepreneurial dynamism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Regression results (pooled OLS). Number of start-ups and HGFs per capita, 2010\u0026ndash;2022. Swedish municipalities.\u0026nbsp;\u003c/p\u003e\n\u003ctable style=\"width: 100%;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo. of start-ups per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo. of HGFs per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePopulation size\u003csub\u003et-1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.042***\u003c/p\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.106***\u003c/p\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGRP per capita\u003csub\u003et-1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.393***\u003c/p\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.511***\u003c/p\u003e\n \u003cp\u003e(0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eServices share of employment\u003csub\u003e-1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.801***\u003c/p\u003e\n \u003cp\u003e(0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.011***\u003c/p\u003e\n \u003cp\u003e(0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLocal business climate\u003csub\u003et-1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.143***\u003c/p\u003e\n \u003cp\u003e(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.784***\u003c/p\u003e\n \u003cp\u003e(0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eShare of population with tertiary education\u003csub\u003et-1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.292***\u003c/p\u003e\n \u003cp\u003e(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003cp\u003e(0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarket size\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.069***\u003c/p\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061***\u003c/p\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage establishment size\u003csub\u003et-1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.467***\u003c/p\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.263***\u003c/p\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNumber of obs.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNotes:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eAll regressions control for fixed effects across local labor market regions.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDespite these commonalities, there are notable differences in the strength and relevance of local determinants across the two outcomes. The most pronounced difference concerns the role of local business climate. While business climate is positively and significantly associated with start-up rates, its estimated effect on the incidence of high-growth firms is substantially larger in magnitude. This suggests that local institutional conditions play a considerably more important role for firm growth than for firm entry. By contrast, the share of the population with tertiary education is positively associated with start-up rates but is not statistically significant in the HGF model once other local characteristics are controlled for. In summary, differences indicate in several ways that the factors explaining where firms are created are not identical to those explaining where firms grow.\u003c/p\u003e\n\u003cp\u003eFigure 1 provides a visual comparison of the estimated coefficients and confidence intervals across the two models. The figure highlights that although many local characteristics influence both outcomes, their relative importance differs systematically. In particular, the divergence in the estimated effect of local business climate is clearly visible, with a much stronger association for HGFs than for start-ups. The figure also shows that some variables that are central in explaining start-up rates contribute more modestly to explaining the spatial distribution of high-growth firms. Taken together, the comparison reinforces the view that the geography of entrepreneurial quality differs from the geography of entrepreneurial quantity, even when driven by a largely overlapping set of local factors.\u003c/p\u003e\n\u003cp\u003eTo assess the economic significance of the estimates, consider the implied magnitude of the local business climate effect on high-growth firms. The coefficient on local business climate in the HGF regression is 0.784 (Table 2), and the standard deviation of the business climate index across municipalities is 0.40 (Table 1). A one\u0026ndash;standard deviation increase in local business climate is therefore associated with an increase of approximately 0.00031 high-growth firms per capita (0.784 \u0026times; 0.40). This effect is large relative to the mean incidence of HGFs, which is 0.00030 (Table 1), implying that a one\u0026ndash;standard deviation improvement in local business climate is associated with roughly a doubling of the average local prevalence of high-growth firms. Although HGFs remain rare in absolute terms, this calculation illustrates that variation in local institutional conditions is quantitatively important for explaining where high-growth firms emerge. More generally, while agglomeration and demand-side factors matter for both start-up activity and firm growth, differences in local business climate stand out as an economically meaningful determinant of entrepreneurial quality rather than entrepreneurial quantity.\u003c/p\u003e\n\u003cp\u003e4.2 Heterogeneity across sectors and interaction effects\u003c/p\u003e\n\u003cp\u003eTo better understand the mechanisms behind the baseline results, we extend the analysis along two dimensions: sectoral heterogeneity and interaction effects between local business climate and structural characteristics of municipalities.\u003c/p\u003e\n\u003cp\u003eFirst, we explore whether the relationship between local business climate and high-growth firms varies across different parts of the local economy. Figure 2 reports industry-disaggregated regressions in which HGFs are grouped into broad sectors. The results show that the association between local business climate and HGFs is not uniform across industries. The relationship is strongest in sectors where firm expansion typically requires physical space, permits, and direct interaction with local authorities. In these sectors, scaling often entails zoning decisions, building permits, and infrastructure coordination, making firms more exposed to local administrative quality. In contrast, the association is weaker in activities that are less land- or permit-intensive, where firm growth depends more on human capital and market access than on local regulatory processes. This pattern is consistent with the interpretation that local business climate affects HGFs primarily through growth-related constraints rather than through general entrepreneurial conditions.\u003c/p\u003e\n\u003cp\u003eWe next examine whether the relationship between local business climate and high-growth firms depends on broader structural characteristics of municipalities. Figure 3 introduces interaction terms between business climate and key local variables in the full sample regressions. These models allow the effect of business climate on HGFs to vary with underlying economic conditions.\u003c/p\u003e\n\u003cp\u003eThe results reveal clear and systematic patterns rather than random heterogeneity. Most notably, the interaction between business climate and GRP per capita is positive and economically meaningful. This implies that improvements in local business climate are more strongly associated with HGF incidence in richer local economies. In other words, institutional quality and economic development appear to be complementary: favorable local institutions translate into more high-growth firms particularly where income levels and economic activity are already high. This suggests that business climate conditions may be especially important for enabling firms to exploit growth opportunities in more advanced local economies.\u003c/p\u003e\n\u003cp\u003eA second pattern concerns human capital. In the baseline models, the share of highly educated residents shows little direct association with HGFs once other factors are controlled for. However, the interaction models indicate that the business-climate effect does not weaken in more highly educated municipalities. Instead, the relationship between business climate and HGFs remains stable or even slightly stronger where human capital levels are high. This points to a complementarity between local skills and institutional conditions: human capital alone does not generate more HGFs, but in places where skilled labor is abundant, a supportive business climate appears particularly important for converting capabilities into firm growth.\u003c/p\u003e\n\u003cp\u003eTaken together, the interaction results suggest that local institutional quality operates as a conditioning factor rather than a standalone driver. Business climate matters most where other ingredients for growth\u0026mdash;such as market size, income levels, and human capital\u0026mdash;are already present. This reinforces the ecosystem interpretation of the findings. The local business climate functions as part of the institutional layer of the entrepreneurial ecosystem, influencing whether existing economic and knowledge resources are translated into high-growth outcomes\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eEntrepreneurial ecosystems influence not only how many firms are created, but which firms are able to grow. This paper examined spatial variation in the incidence of high-growth firms (HGFs) across Swedish municipalities and compared the determinants of entrepreneurial quality with those of entrepreneurial quantity. We documented large geographic variation in HGFs over the period 2010\u0026ndash;2022 and showed that high-growth activity is not confined to major metropolitan regions. While larger and richer local economies tend to host more HGFs, substantial HGF activity also occurs outside the largest urban areas. Standard agglomeration- and demand-side factors such as population size, income levels, market potential, and economic structure are associated with both start-up rates and HGFs, confirming that broad local economic conditions shape entrepreneurial activity regardless of whether it is measured in terms of entry or growth.\u003c/p\u003e \u003cp\u003eThe central result of the paper concerns the role of local business climate. Measures based on entrepreneurs\u0026rsquo; assessments of local authorities are far more strongly associated with the local incidence of HGFs than with overall start-up rates. The magnitude of this relationship is economically meaningful: differences in business climate of the size observed across municipalities correspond to changes in HGF prevalence comparable to the sample mean. Sectoral and interaction analyses further clarify this pattern. The association between business climate and HGFs varies systematically across economic structures and strengthens in more economically developed municipalities. Moreover, the relationship does not weaken in places with higher human capital. These findings indicate that institutional quality and local economic resources act as complements rather than substitutes.\u003c/p\u003e \u003cp\u003eTaken together, the results support an interpretation in which local institutional conditions shape firm growth through mechanisms related to expansion rather than entry. Growing firms must interact repeatedly with local authorities over land use, permits, infrastructure, and other administrative processes. Where these interactions are predictable and supportive, firms appear more able to scale locally. Where they are costly or uncertain, growth may be constrained or displaced. In this sense, the local business climate constitutes part of the institutional layer of the entrepreneurial ecosystem, influencing whether existing economic and knowledge resources are translated into high-growth outcomes.\u003c/p\u003e \u003cp\u003eThe findings have implications for both research and policy. For research, they underscore the importance of distinguishing between the geography of entrepreneurial quantity and entrepreneurial quality and of linking ecosystem conditions to firm-level exposure to local institutions. For policy, they suggest that improving local administrative practices may matter less for generating start-ups than for enabling promising firms to grow. Efforts to strengthen local ecosystems should therefore pay attention not only to resources and innovation capacity, but also to how local institutions function in practice for expanding firms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eContact authors for inquiries about data availability.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlvedalen, J., \u0026amp; Boschma, R. (2017). A critical review of entrepreneurial ecosystems research: Towards a future research agenda. \u003cem\u003eEuropean planning studies\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(6), 887-903.\u003c/li\u003e\n\u003cli\u003eAndersson, M., \u0026amp; Henrekson, M. (2014). Local competitiveness fostered through local institutions for entrepreneurship. In D. B. Audretsch, A. N. Link, \u0026amp; M. 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NBER Working Paper No. 21554.\u003c/li\u003e\n\u003cli\u003eHaltiwanger, J. (2015). Job creation, job destruction, and productivity growth: The role of young businesses. American Economic Review: Papers \u0026amp; Proceedings, 105(5), 347\u0026ndash;351.\u003c/li\u003e\n\u003cli\u003eHenrekson, M., \u0026amp; Johansson, D. (2010). Gazelles as job creators: A survey and interpretation of the evidence. Small Business Economics, 35(2), 227\u0026ndash;244.\u003c/li\u003e\n\u003cli\u003eHenrekson, M., \u0026amp; Sanandaji, T. (2014). Small business activity does not measure entrepreneurship. Proceedings of the National Academy of Sciences, 111(5), 1760\u0026ndash;1765.\u003c/li\u003e\n\u003cli\u003eHurst, E., \u0026amp; Pugsley, B. W. (2011). What do small businesses do? Brookings Papers on Economic Activity, 2011(2), 73\u0026ndash;142.\u003c/li\u003e\n\u003cli\u003eJacobs, J. (1969). The economy of cities. Vintage Books.\u003c/li\u003e\n\u003cli\u003eLevine, R., \u0026amp; Rubinstein, Y. 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Journal of Economic Geography, 17(5), 959\u0026ndash;974.\u003c/li\u003e\n\u003cli\u003eSorenson, O., \u0026amp; Audia, P. G. (2000). The social structure of entrepreneurial activity: Geographic concentration of footwear production in the United States, 1940\u0026ndash;1989. American Journal of Sociology, 106(2), 424\u0026ndash;462.\u003c/li\u003e\n\u003cli\u003eStam, E. (2015). Entrepreneurial ecosystems and regional policy: A sympathetic critique. European Planning Studies, 23(9), 1759\u0026ndash;1769.\u003c/li\u003e\n\u003cli\u003eStam, E., \u0026amp; Spigel, B. (2016). Entrepreneurial ecosystems. Utrecht University Discussion Paper No. 16-13.\u003c/li\u003e\n\u003cli\u003eStam, E., \u0026amp; Van de Ven, A. (2018). Entrepreneurial ecosystem elements. Small Business Economics, 51(1), 173\u0026ndash;193.\u003c/li\u003e\n\u003cli\u003eTannenwald, R. (1997). State regulatory policy and economic development. \u003cem\u003eNew England Economic Review\u003c/em\u003e, 83-98.\u003c/li\u003e\n\u003cli\u003eWestlund, H., Larsson, J. P., \u0026amp; Olsson, A. R. (2014). Start-ups and local entrepreneurial social capital in Swedish municipalities. Regional Studies, 48(6), 974\u0026ndash;994.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eA core argument in the literature on entrepreneurial ecosystems is that institutional arrangements that regulate, legitimize, and incentivize entrepreneurship are critical (Stam \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stam and Van de Ven \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuzman and Stern found that 75% of all significant growth outcomes came from the top 5% of startups by \u0026ldquo;quality\u0026rdquo;. Likewise, Schoar (2010) notes a sharp divide between a \u0026ldquo;small number of transformative entrepreneurs\u0026rdquo; building high-growth ventures and the far larger number of subsistence entrepreneurs whose firms never scale. In other words, most firms are content (or forced) to remain modest in size, whereas gazelles \u0026ndash; the rare, rapidly expanding firms \u0026ndash; account for the lion\u0026rsquo;s share of job creation.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinniti (2005) argues that such local social networks effect are important and could imply that a city or local area develops a \u0026lsquo;local entrepreneurship culture\u0026rsquo;: \u0026ldquo;entrepreneurship creates a \u0026lsquo;culture\u0026rsquo; of itself that influences individual behavior in its favor\u0026rdquo; (ibid, p.3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The threshold comes from calculations on the average conditions among firms with less than 10 employees (Coad and Shroj 2020). Formally, the condition states that firms need to grow with 7.28 employees to fullfil the conditions of high-growth.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"entrepreneurship, young firms, firm growth, agglomeration, local growth, startups","lastPublishedDoi":"10.21203/rs.3.rs-9277966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9277966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe examine how the geography of entrepreneurial \u003cem\u003equality\u003c/em\u003e differs from the geography of entrepreneurial \u003cem\u003equantity\u003c/em\u003e by analyzing spatial variation in high-growth firms (HGFs) across Swedish municipalities during 2010–2022. Using longitudinal firm-level data matched to survey measures of local business climate, we compare the determinants of start-up rates and firm scaling. Standard agglomeration and demand-side factors — population size, income, market potential, and economic structure — relate to both outcomes. In contrast, local business climate, capturing entrepreneurs’ assessments of how local authorities function in practice, is a substantially stronger predictor of HGFs than of start-ups. The effect is economically meaningful and varies with local economic structure. The findings identify a micro-level mechanism linking local framework conditions to aggregate outcomes: interactions between growth-oriented firms and local institutions during scaling. Local institutional conditions not only influence how many firms are created, but which firms are able to grow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL\u003c/strong\u003e: R11, L26, M13, R30\u003c/p\u003e","manuscriptTitle":"Gazelles and Their Habitats - Business Climate and the Geography of High-Growth Firms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 11:10:17","doi":"10.21203/rs.3.rs-9277966/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3791ec73-f388-4fad-b9fd-22bd926a9635","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T11:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 11:10:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9277966","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9277966","identity":"rs-9277966","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}