This chapter considers the characteristics of SMEs that scale up and classifies them into broad groups of strategic, transformational and expansionist scalers based on their investment before or during their high‑growth phase. It then assesses how policies that can support SME scaling up interact with the characteristics of scalers.
Unleashing SME Potential to Scale Up
Helping SMEs Scale Up
Abstract
SMEs that scale up come from all sizes, ages and sectors, but the probability of scaling up is greatest among young and knowledge-intensive firms. This implies a need for a policy mix that combines broad support across the whole SME population with more targeted actions.
SMEs of all sizes, ages and sectors can scale up. Both young and old SMEs can grow rapidly in employment or turnover, thereby becoming “scalers”. Overall, more than half of all scalers are mature firms that have been in business for more than 10 years. SME scalers also come from all sectors, including firms that provide non-tradable or less-knowledge-intensive services such as construction, retail and hospitality. Both large and small SMEs can scale up. However, the likelihood that an SME scales up is greatest among young firms that started their operations in the last 5 years. Across 17 OECD and accession countries, about 38% of young SMEs become scalers, compared to 22% of mature SMEs. Knowledge-intensive SMEs are also more likely to scale up. This includes SMEs in advanced tradable services, such as consultancy or information and telecommunications (ICT) services, and SMEs in high- and medium-high-tech manufacturing. Overall, most scalers are neither young nor knowledge-intensive, although the probability of scaling up is higher for SMEs in these two groups than for most others. The smaller number of young and knowledge-intensive SMEs in the SME population explains the difference.
An effective policy mix for all types of scalers combines broad-based measures that encourage scaling up across the whole SME population with more targeted measures aimed at start-ups, tech-intensive firms or those in emerging sectors. The broad-based approach establishes supportive framework conditions for SME scale-up across all sectors, sizes, age groups, and regions. This includes policies that help SMEs to overcome barriers in accessing finance, which relates to bank finance for the majority of firms, as well as barriers related to skills, digitalisation or generally access to innovation assets. The targeted approach focuses on firms with the highest likelihood to grow, including knowledge- or tech-intensive SMEs and innovative start-ups. The type of support these firms need is often very specific and related to the technologies they develop, use or sell. Their investment needs are more varied than those of non-tech SMEs and can require equity-based or hybrid modes of finance.
More than half of the policies, mapped in the OECD SME and Entrepreneurship Policy Dashboard for this report, support all SMEs without distinctions on their ages, sizes or growth performance. For example, skills development policies often target SMEs broadly rather than specific types of SMEs. However, most countries complement these broadly targeted policies with some narrowly targeted ones that are aimed specifically at high-growth young start-ups or firms with high growth potential.
Targeting specific sectors is less common but varies depending on national economic contexts and structures. Across the 38 OECD countries, only about 20% of the mapped SME and entrepreneurship policies target specific sectors. However, some countries rely more heavily on sector-targeted or technology-targeted policies. France and the United Kingdom, for example, are in line with the average, with about one in five SME and entrepreneurship policies targeting specific sectors, manufacturing or knowledge-intensive industries in most cases, whereas Hungary prioritises technology-based targeting across all sectors. Where sectors are targeted, the focus varies depending on the composition of the national SME and entrepreneurship policy mix, sectoral specialisation, and the characteristics of the business population.
Nearly all scalers invest in their workers, their capital or their technology before or during scaling up. Scalers can be classified into three broad groups: “Strategic”, “Transformational” and “Expansionist”. Strategic scalers make dedicated investments before scaling up. This can be investing in hiring a more skilled workforce, investing in machinery or the “capital stock” of the firm, or investing in the technologies and processes the firm uses to be more efficient and therefore more productive. Strategic scalers account for 53-61% of all scalers. Transformational scalers – which represent 31-39% of all scalers – do not make significant initial investments before scaling up but achieve notable gains in profits or productivity as they scale up. This can, for example, be the result of a sudden increase in market demand for their products or services that results in higher profits, or due to improvements in internal processes that become apparent only when the company starts to grow. The remaining 3-9% are Expansionist scalers that do not undergo any significant transformation in their operations or make considerable prior investments in physical or human resources. Their growth strategy relies on replicating the existing business model at a larger scale, for example, by expanding into new markets. A retail store that opens a second location in another part of the city, without significantly investing in advanced inventory management systems, is an example of an expansionist scaler.
Finance policies support investments in innovation, internal capabilities and network expansion, mainly using public loans and grants. Financial support can enable investment in innovation (R&D, digital adoption, business development), internal capabilities (human, physical and intangible assets), and network expansion (domestic market, international trade, partnerships, and digital platforms). Most policies aimed to improve access to finance for SMEs and entrepreneurs rely on public grants and loan instruments with objectives balanced across all three areas of investment. About 36% of finance policies focus on investments in innovation, 28% on network expansion, and 23% on internal capabilities.
Scalers in digital-intensive sectors invest in their workforces and intangibles rather than physical capital. SMEs that scale up in sectors that produce their goods and services with inputs that are ICT‑related (intangibles, ICT specialists or robots, ICT goods and services as inputs) or have a high share of turnover in online sales are more than 30% more likely to scale up than SMEs in other sectors. They do so with very little capital investment compared to other scalers, with a capital intensity of about one-third of that of scalers in other sectors. Instead, they rely on skilled workers, paying, on average, about 30% more per worker than other scalers.
Exports are important for the growth of scalers in manufacturing sectors. In five out of six countries with available data, scalers are more likely to be goods exporters than other SMEs. These scalers are, for the most part, manufacturing firms. For about one in four scalers that grow in turnover, high growth is mainly driven by growth in goods exports. Policy support can be valuable to tap into the potential of SMEs in tradable sectors. This may consist, for example, of support to develop the necessary expertise and networks to enter a new market targeted to firms that are successful in selling goods and services in the domestic market but have not yet made the step to sell abroad. More than 20% of mapped policies explicitly aim to support SME trade, mostly focusing on export-related activities. A broader focus that acknowledges the importance of indirect channels related to strengthening upstream or downstream (global) value chain linkages could unlock additional growth potential. Attention should also be given to service exports, since the direct and indirect contribution of services to global trade is larger than the trade in goods.
Younger and smaller SMEs are more likely to scale up but most scalers are mature firms
A young SME is more likely to scale up than a mature firm. On average, 38% of young SMEs operating for five years or less scale up in either employment or turnover across the 17 countries with available data (Figure 1.1). The likelihood of a growth spurt declines as firms mature and is, with 22%, lowest among (mature) SMEs with more than 10 years of activity. The difference in the likelihood of scaling up between young and mature SMEs ranges from 12 percentage points in Italy to 21 percentage points in Austria.
Share of young, medium-age and mature SMEs that become scalers and high-growth scalers, 2014-20
Note: Average of the share of scalers in SMEs in 2014, 2017 and 2020. Scalers are defined as SMEs with at least 10 employees that grew in employment or turnover by at least 10%, on average, per year over 3 consecutive years. High-growth scalers are defined as SMEs with at least 10 employees that grew in employment or turnover by at least 20%, on average, per year over 3 consecutive years.
Source: Calculations based on microdata sources from 17 countries. See Annex of Chapter 1 for more information.
Young firms are subject to “up or out dynamics”: successful firms grow; unsuccessful firms exit the market quickly (Jovanovic, 1982[1]). Young firms typically enter the market small and those that prove successful scale up fast. In contrast, many mature firms are more likely to scale up as a strategic move, triggered by specific circumstances, such as changes in management, a new marketing strategy or changes in production processes, and favourable market conditions. Through the creation of a new business and subsequent growth, young firms with “up” dynamics contribute a large number of new jobs to the economy, particularly in the year that the firm starts operating.1 Conversely, about half of entrants with less than 10 employees cease operations within five years, and through these “out” dynamics, young firms are also the major source of jobs lost in an economy (Calvino, Criscuolo and Menon, 2018[2]). The early “up” dynamics are important. About two-thirds of young scalers entered the market in the three years before scaling up and nearly all other young scalers (30%) were already growing by at least 10% during those years (Annex Figure 1.A.1). As SMEs mature in their operations and reach a larger size, they become less likely to scale up (Figure 1.2). Even among small SMEs, firm age remains an important factor as 32% of young and small firms scale up, compared to 20% of mature and small firms in the four countries with available data.2
Share of scalers (in employment, turnover or both) in all SMEs, 2014-2020
Note: Average of the share of scalers in SMES in 2014, 2017 and 2020. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from 17 countries. See Annex of Chapter 1 for more information.
Most scalers are mature firms despite their lower scale-up rates, as young firms account for a small share of firms. About 58% of scalers and 48% of high-growth scalers are mature SMEs, reflecting the predominance of mature businesses in the general SME population (Figure 1.3). For example, in Denmark, mature firms constitute about 80% of all SMEs, 72% of scalers and 66% of high-growth scalers.
Share of SMEs, scalers and high-growth scalers by firm age, 2014-20
Note: Average of the share of scalers in SMEs in 2014, 2017 and 2020. Scalers are defined as SMEs with at least 10 employees that grew in employment or turnover by at least 10%, on average, per year over 3 consecutive years. High-growth scalers are defined as SMEs with at least 10 employees that grew in employment or turnover by at least 20%, on average, per year over 3 consecutive years.
Source: Calculations based on microdata sources from 17 countries. See Annex of Chapter 1 for more information.
SMEs are more likely to grow in turnover than in employment and this gap grows as firms mature. SMEs that have operated for at least 10 years are 1.8 times more likely to scale up in turnover than in employment, compared to 1.3 times for young SMEs.3 One explanation is that young and small firms can exploit “economies of scale” until they reach their optimal size. They grow thanks to organisational flexibility, shorter investment horizons, and lower innovation costs that enable them to better adapt to new market opportunities. Small firms often pursue “external” innovations, i.e., they create new products and enter new markets, which can lead to significant breakthroughs and rapid growth, provided they are successful. Conversely, older and larger firms typically focus on internal, incremental innovations to achieve efficiency gains within established production processes (Akcigit and Kerr, 2018[3]). They are also more likely to grow through acquisition of (or mergers with) existing companies (Box 1.1), rather than “organically” by hiring additional employees, which remains the main way for SMEs to scale up (Nordic Innovation, 2022[4]).
Firms can grow in employment or turnover either by expanding their existing business (organic growth) or by acquiring other companies, i.e. mergers and acquisitions (M&A). Both events can be “domestic” – i.e., all entities that are involved are located in the same country – or “international”, if at least one entity involved in the transaction is located in a different country.
M&A may have a variety of impacts on existing businesses. Typically, enterprises that are the target of domestic or international acquisitions maintain their original legal identifier. They may experience abrupt variations in either employment or turnover as some branches of activities may be transferred to or from other enterprises in the acquiring business group. Acquired enterprises may also grow “organically” faster because of additional investments, improved managerial practices, access to new technology, and improved financial performance through economies of scale. From a measurement point of view, the “non-organic” and “organic” growth channels are difficult to disentangle. In the case of mergers, instead, at least one of the legal entities that are involved in the operation may disappear. Lacking additional information, the disappearance of the legal entity is observationally equivalent to a business closure in administrative sources. Symmetrically, the surviving legal entities resulting from the merger show an abrupt expansion in employment or turnover, as they absorb all activities from the entities that disappeared. Similar to the case of acquisitions, merging businesses may also grow “organically” in parallel due to business synergies and efficiency gains.
The definition of scaling up used in this report considers both organic and non-organic growth when calculating “high growth” in terms of employment and turnover. Evidence for a few countries for which detailed data on M&A are available shows that a minority of SMEs scale up through M&As. For instance, more than four out of five high-growth scalers in Denmark, Finland, Iceland, Norway and Sweden grow organically between 2017 and 2020. Similarly, the analysis of granular employer-employee data for Portugal shows that 89% of scaling episodes happen because of organic growth. This evidence is consistent with an experimental indicator developed in the OECD scale-up database, which identifies likely “M&A-driven” scaling-up episodes if 80% of the 3-year employment or turnover growth is concentrated in a single year. According to this approach, about 10% of scalers and 15% of high-growth scalers in either employment or turnover are M&A-driven. The indicator shows relatively small variation across countries and sectors.
Source: OECD (2021[5]), Understanding Firm Growth: Helping SMEs Scale Up, OECD Studies on SMEs and Entrepreneurship, OECD Publishing, Paris, https://doi.org/10.1787/fc60b04c.-OECD (2023[6]) Grow and Go? Retaining Scale-ups in the Nordic Countries. OECD Regional Development Papers n. 51, https://doi.org/10.1787/9be5339d-en..
Most scalers provide services rather than produce goods
Scalers are most prevalent in the sector with the largest number of SMEs in most countries. SMEs are classified into seven sectors: high and medium-high technology manufacturing; low and medium-low technology manufacturing; construction; education, social and health services; advanced tradable services; other tradable services; and non-tradable services (Annex Table 1.A.1). Non‑tradable services are provided and consumed locally, while tradable services can potentially be delivered to customers located in distant markets, either in the same country or abroad.4 The distribution of scalers across sectors mirrors to a large degree the distribution of all SMEs (Annex Figure 1.A.2). In 15 out of 17 countries, the sector with the largest number of SMEs also accounts for the largest number of scalers. For 12 out of these 15 countries, most scalers sell non-tradable services, in the remaining three – Italy, Portugal and Slovenia – the largest number of scalers are low and medium-low tech manufacturers.
The share of SMEs that become scalers is higher in advanced tradable services, high and medium-high tech manufacturing and construction than in other sectors. The likelihood to scale up in advanced tradable services is relatively similar across countries, with shares between 22% and 31% of SMEs growing in employment, turnover or both (Figure 1.4) with some greater variation in the construction sector and shares ranging from 17% to 35%. About 25% of SMEs operate in one of the three sectors but together they make up 35% of scalers. About 13% of scalers provide advanced tradable services, another 5% are manufacturers of high or medium-high tech goods and 16% are construction companies.
Share of scalers among SMEs by sector, 2014-20
Note: Average of the share of scalers in SMES in 2014, 2017 and 2020, by sector. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from 17 countries. See Annex of Chapter 1 for more information.
Scalers providing advanced tradable services are more likely to grow before scaling up than scalers in the manufacturing or construction sectors (Annex Figure 1.A.3). 63% of scalers that provide advanced tradable services grew by more than 10% in the three years before scaling up. In contrast, in construction sectors, less than half (42%) of scalers were growing before their high-growth period. Furthermore, 25% of scalers in the construction sector shrank before scaling up, which is 10 to 14 percentage points higher than in other sectors, highlighting the volatility that characterises the growth paths of SMEs in the construction sector. In manufacturing sectors, a larger share of scalers retained a stable level of employment and turnover before scaling up compared to those in other sectors. The need for investment in manufacturing means initially prioritising the allocation of their resources to augmenting physical capital rather than recruiting new talent or increasing output during this preparation period.
Digitalisation provides opportunities for scaling up without investment in physical capital
Less need for upfront investment in physical capital facilitates SME scaling up in advanced tradable services. Advanced tradable services that include legal, research and marketing services, as well as software and other ICT services, are among the most “digitally intensive” of all sectors.5 They therefore have less need to invest in physical capital, reflected in the lowest average capital intensity (measured as the ratio of the value of fixed assets over turnover) of scalers across all sectors, which ranges from 1% (Denmark) to 8% (Slovenia). The capital intensity of other sectors is, on average, 5 to 20 percentage points higher than in advanced tradable services. The reduced reliance on upfront investment in physical assets enables SMEs to be more adaptable in response to market demands. Instead of investing heavily in machinery or equipment, SMEs in advanced tradable services leverage the skills of their workforce and digitalisation to drive growth.
Opportunities to scale up by “going digital” are evident across sectors that use digital technologies intensively. SMEs in digital-intensive sectors are more than 32% more likely to scale up than other SMEs, with differences ranging between 13% and 59% across countries (Figure 1.5). These sectors are characterised by intense use of digital technologies in one or several areas and include sectors with high shares of ICT-related intangibles, a large number of ICT specialists in employment, a high share of intermediate ICT goods and services used in their production, a high share of turnover in online sales or intense use of robots in production. In Italy, France and Austria, SMEs in digital‑intensive sectors are 40%-59% more likely to become scalers. In Hungary, Latvia and Romania, the countries with the smallest differences, SMEs in digital-intensive sectors are still 13% more likely to scale up than those in other sectors. Differences in the likelihood of becoming a high-growth scaler are even larger. SMEs in digital-intensive sectors are 26% (Romania) to 100% (Austria) more likely to become high-growth scalers than those in other sectors.
Share of scalers in all SMEs, 2020
Note: Digital-intensive sectors are defined following the approach developed by Calvino et al. (2018[7]), see Annex Table 1.A.1. Scalers/High-growth scalers are defined as SMEs with at least 10 employees that grew in employment or turnover by at least 10%/20% per year over 3 consecutive years on average. Data for Germany refers to 2019.
Source: Calculations based on microdata sources from 17 countries. See Annex of Chapter 1 for more information.
Investing in human capital and securing workers with specialised skills is crucial for scalers in digital-intensive sectors. Digital technology often replaces routine tasks traditionally performed by workers with lower levels of education, shifting the composition of the workforce in favour of more skilled workers (Michaels, Natraj and Van Reenen, 2014[8]). Furthermore, digitalisation promotes an increasing demand for highly skilled workers proficient in information technology, reflecting important complementarities between investments in intangible assets and human capital (Redding, 1996[9]). In 12 out of 13 countries, scalers in digital-intensive sectors pay higher average worker compensation than scalers in other sectors, reflecting the higher level of human capital in these firms. Across all 13 countries, employment and turnover scalers in digital-intensive sectors have about 30% higher cost per worker than scalers in other sectors (Figure 1.6).
Average personnel cost, capital intensity, and labour productivity of scalers in digital-intensive sectors (scalers in other sectors = 100), 2020
Note: Digital-intensive sectors are defined as in Calvino et al. (2018[7]), see Annex Table 1.A.1. Capital intensity is defined as the amount of tangible fixed assets per unit of turnover. Labour productivity is defined as the amount of value added per employee. Personnel cost are the labour cost per worker to the firm. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from 13 countries. See Annex of Chapter 1 for more information.
Labour productivity of scalers in digital-intensive sectors is higher than in other sectors despite their lower level of investment in physical capital. Employment and turnover scalers in digital‑intensive sectors produce around 10% more value-added per employee than other scalers but have more than 60% lower capital intensity. The lower capital intensity highlights the role of human capital and investment in intangible assets, such as intellectual property, digital infrastructure, and organisational knowledge, in driving the digitalisation and efficiency of businesses.6
Most policies (60%) that can support business scale-up target either a population, a sector, a technology or a region. The OECD’s SME and Entrepreneurship policy dashboard maps policies and institutions that can support SME growth in five policy areas in the 38 OECD countries (see Chapter 1). In some countries (e.g. Costa Rica, Latvia, Korea, the United States, Lithuania, and Israel), the share of targeted policies, i.e. policies that address specific groups in the business population, can reach more than 70%. Other countries (e.g. Greece and the Slovak Republic) have a lower share of targeted policies, but even in these countries, about 30% of the policies are targeted.
Share of targeted policies, as a percentage of all policies in the OECD SME and Entrepreneurship policy dashboard
Note: Shares are calculated as a percentage of total policies in the database, based on an unweighted count. Policies can be targeted at specific firm populations such as all SMEs, as well as subpopulations of SMEs with size or performance criteria, or individuals such as entrepreneurs and business owners. Other targets include sector(s) or supply chain(s), technology(ies) or region(s) and place(s). Based on a mapping of a total of 2 578 policy elements and 517 institutions in support of SME and Entrepreneurs across OECD countries
Source: OECD SMEs and Entrepreneurship Policy Dashboard
Most OECD countries prioritise broad SME targeting. In all but two OECD countries, policies that target all SMEs without conditioning support on additional factors, such as the age of the firm, the sector in which they operate, or the firm’s performance, represent the highest share of all policies. For instance, in Costa Rica and Japan, only 6% of policies target SMEs with an additional size, age, or growth criterion, while policies targeting all SMEs are 71% and 59% of all policies, respectively (Figure 1.8). Most OECD countries follow the same pattern, with two exceptions. In Iceland, the share of policies targeting all SMEs is equal to the share of policies targeting specific subgroups. In France, the share of policies targeting SMEs with specific criteria is higher than the share of policies with broad SME targeting. As SMEs of all sizes, ages and sectors can scale up, broad targeting of SMEs and entrepreneurship policies can help countries avoid missing opportunities to raise growth and competitiveness by increasing the number of scalers. The role of narrowly targeted policies is to complement general policies, dedicating some programmes to strategic segments while continuing to offer support to all SMEs and entrepreneurs.
Share of policies targeting SMEs in general and specific SME groups among all policies by country
Note: SMEs general targeting shares are calculated as the number of policies that target SMEs but do not target SMEs with age, size or growth criteria, divide by all policies in each country. SMEs specific targeting shares are calculated as the number of policies that target only SMEs with either age, size, or growth criteria but not SMEs general policies, divided by all policies in a country. Cumulated shares do not sum up to 100, as policies that do not target SMEs are not taken into account. Based on a mapping of a total of 2 578 policy elements and 517 institutions in support of SME and Entrepreneurs across OECD countries
Source: OECD SMEs and Entrepreneurship Policy Dashboard
Policies target population groups much more frequently than sectors or technologies. Across OECD countries, most targeted policies focus on broad firm population criteria (83%) while relatively few target specific sectors (19%) or technologies (7%), and even fewer target regions or places(4%) (Figure 1.9).7 Among population-targeted scale-up policies, most are directed to all SMEs and few focus on young or high-growth firms. When targeting firms’ population characteristics, on average, more than two-thirds of SME and entrepreneurship policies (68%) are directed to SMEs in general and only about 5% target firms based on growth criteria, i.e. high-growth firms or SMEs scaling up. The share is low in all countries but slightly higher in those where SME and entrepreneurship policy is part of the mandate of a larger share of institutions.8 The second most frequent targeting criterion is based on firms’ age (14%). An example of general SME‑targeted policies is the Australian Business Securitisation Fund, which supports lenders to all SMEs, independent of their age, turnover or growth performance. By facilitating non-bank lenders’ participation in the SME credit market, it contributes to improving lending conditions and access to finance for all SMEs.
Share of targeted policies by targeting criteria, among all policies (Panel A), and share of policies targeting specific population groups among population-targeted policies (Panel B).
Note: Shares are calculated as a percentage of all targeted and population-targeted policies, respectively, based on an unweighted count. Cumulated shares may be higher than 100% across policy areas, as policies can be targeted at several target groups and firm segments at once. SMEs with age criteria include young firms and start-ups but incumbents as well. SMEs with performance criteria include high-growth firms, scalers but also laggards. SMEs with size criteria include criteria related to both number of employees and turnover. Based on a mapping of a total of 2 578 policy elements and 517 institutions in support of SME and Entrepreneurs across OECD countries
Source: OECD SME and Entrepreneurship Policy Dashboard
There are differences in the use of policies with specific targets that reflect the structural characteristics and requirements of each policy area. Policies that target SMEs in general are the most frequent in all five policy areas captured by the policy mapping but with different intensities (Figure 1.10). Data governance is the area with the highest share of general-SME targeted policies (78%) and Skills is the policy area with the lowest share of general-SME targeted policies (59%) among the five areas. Moving to the second most frequently targeted group, individual entrepreneurs and business owners, is the second most frequently targeted among Skills and Data policy group (21% and 19% respectively), while SMEs with age criteria is the second most frequently targeted group among access to finance policies (18%).
Share of targeted policies by segments by policy area
Note: Shares are calculated as a percentage of all population-targeted policies, based on an unweighted count. Cumulated shares may be higher than 100% across policy areas, as policies can be targeted at several firm segments at once. SMEs with age criteria include young firms and start-ups but incumbents as well. SMEs with performance criteria include high-growth firms, scalers but also laggards. SMEs with size criteria include criteria related to both number of employees and turnover. Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
Measures targeting high-tech start-ups and innovation-based scalers complement general SMEs policies. The share of policies targeting high-tech start-ups is low in all countries, however, all countries have at least some policies directed at young, high-growth firms. It is common to combine policies that enhance scalers’ extensive margins (increase the number of scalers) with policies that enhance scalers’ intensive margins (enhance the scalers with the highest growth potential). Both objectives require setting framework conditions, ensuring broad access to bank finance, avoiding barriers through burdensome regulation and supporting access to and training for the type of skills that growing SMEs are looking for. In addition to supporting fast-growing young firms, countries complement these horizontal policies with specific support to high-growth potential startups. For example, equity finance takes a central role for funding startups, as does other risk finance related to developing products and services that are new and untested, e.g. in the realm of deep tech.
Almost all countries combine general SME policies with targeted high-tech policies, yet to different degrees. Korea, for instance, tends to use start-up-targeted policies almost at the same level as general SME policies, while Denmark and Spain tend to focus slightly more on general SMEs. An example of Korea’s balanced strategy is the set of policy funds (Start-up, Growth and Re-Growth stage) that provide different types of financial support to different segments of the business population. The Start-up fund provides financial assistance at low interest rates for high-tech SMEs with growth potential by combining investment and loan instruments. The Growth fund is designed to enhance the competitiveness of SMEs with innovative technology and management capabilities, while the Re-growth fund aims to create a virtuous business ecosystem by providing struggling SMEs with the funds necessary for restructuring or restarting their business. In contrast, in Belgium, the Flemish Participation Company offers financing to a wide range of companies, from the youngest to the most mature. These include the self-employed and small businesses, medium-sized and large companies, as well as start-ups and scale-ups. Its Innovation Mezzanine – a mix of debt and equity financing – is suitable for innovative and established SMEs seeking to grow their businesses. Similarly, in Spain, the Fond-ICO Pyme fund invests in established Spanish companies with expansion plans. Companies can choose or combine equity stakes with other venture capital or private equity funds. The resources invested are intended for the growth of established companies, including through asset acquisition, innovation and internationalisation activities, and the purchase of other companies.
Artificial Intelligence (AI) is an emerging focus of technology-targeted policies. Only about 7% of policies target specific technologies but among them almost 30% target specifically AI (Figure 1.11). Other digital technologies are the second most common target (13%), followed by Biotech and life science technologies (8%) and deep tech (7%) technologies. As a general-purpose technology, AI is mentioned in policies across all five policy areas but to different degrees. It is more often mentioned in data governance policy, and to a lesser extent in the trade or skills policy areas. 40% of all technology-targeted policies do not focus on one specific frontier technology, and several policies mention innovative or cutting-edge technologies in general terms. For instance, the Dutch Future Fund (DFF) invests in venture and growth capital funds that target innovative SMEs in the country, without limiting the support only to investments in start-ups that are developing specific technologies.
Share of specific technologies targeted in technology-focused policies
Note: Shares are calculated as the number of policies that mention a specific technology in its description, as a share of the subset of policies that target technologies (111 policies). Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
Scaling up is more than a period of rapid growth, it is a strategic and transformative process that firms undertake, often beginning before they scale up. The transformation can involve new activities such as selling new products or services, engaging in research or starting to export. It can also involve changes in managerial structures or even ownership. There is no single “cookie-cutter” approach.
Scalers are “strategic”, “transformational” or “expansionist”
Different scaling-up “models” co-exist. What unifies them is that most undertake some form of investment before or during scaling up. Some scalers invest in human or physical capital to develop an innovation that allows them to gain new market shares, while others grow by replicating existing processes. In some cases, scaling up may be a necessity rather than a choice, as, e.g. financing the costs for the firm’s upfront investments might require a larger scale. Finally, some scalers grow simply because they face an external and temporary increase in demand that translates into a sales windfall (OECD, 2021[5]).
Broadly, scalers can be classified into three groups depending on their growth model: “strategic", “transformational” and “expansionist” scalers. “Strategic scalers” are firms that make dedicated preparations before scaling up. These companies invest in key aspects of their business to ensure that they are prepared for the challenges and opportunities that scaling up presents. "Transformational scalers" do not make significant initial investments compared to their peers but achieve notable gains in productivity or profitability as they scale up. These gains can be due to productivity‑enhancing investments and improvements in internal processes that become apparent only when the company starts to grow or they can be the result of a sudden increase in market demand for their products or services that results in higher profits. "Expansionist scalers" do not undergo any significant transformation in their operations, nor do they make considerable prior investments in either technology, physical or human resources. Their growth strategy relies on replicating the existing business model at a larger scale to expand into new markets. A retail store that opens a second location in another part of the city, without significantly investing in advanced inventory management systems, is an example of an expansionist scaler.
Note: Scalers are separated based on their characteristics before scaling up with those among the top 25% most productive or most capital-intensive or with the highest labour cost per worker (a proxy for human-capital-intensity of production) classified as “strategic scalers”, those that do not fall among the top 25% in any of the three characteristics are then split into “transformational scalers” and “expansionist scalers” depending on whether scaling up results in higher productivity or not.
Scaling up is a strategic choice for most SMEs
Strategic scalers represent between 53% and 61% of all scalers, whereas expansionist scalers account for only 3% to 9%, across seven countries with available data (Figure 1.13). In Denmark, for example, 61% of scalers follow a strategic growth model, with the majority showing high levels of input resources, including capital and labour, before scaling up. About 33% of scalers are transformational scalers, due to faster productivity (or profitability) growth than most other SMEs. Only a small share of scalers – less than 5% – are expansionist, i.e., they do not differ from the majority of other SMEs in both preparatory investments before scaling up, or productivity growth during scaling up.
Share of scalers by growth model, 2017-20
Note: Strategic scalers are scalers that were in top 25% in terms of productivity, capital intensity and/or average personnel cost (labour costs per worker to the firm) before scaling up. Transformational scalers are scalers that are not strategic scalers but have positive productivity growth or positive profits during scaling up. Expansionists are scalers that are not neither strategic scalers nor transformational scalers. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from seven countries. See Annex of Chapter 1 for more information.
More than 50% of scalers grew before scaling up, one in five even scaling up for the second consecutive time. 19% of employment scalers and 20% of turnover scalers were already scalers in the three previous years (Figure 1.14). Another 38% of employment scalers and 33% of turnover scalers grew by more than 10% but less than 33% (the minimum growth of a scaler). A small share of scalers are start‑ups, founded in the previous three years (14% of employment scalers and 11% of turnover scalers). The remaining third did not grow much before scaling up or is scaling up after reducing employment or turnover by 10% or more in the previous three years.
Share of scalers (2014-20) by growth pattern during the three years before scaling up (2011-17)
Note: Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year, on average, during three consecutive years during 2014-20. Growth before the scaling-up phase is observed in the three years preceding the scaling‑up period. “Entry” are firms that only started operation within the three preceding years, “Growing” firms grew by 10% but less than 33% in the preceding three years, “Scale-up” firms grew by more than 33% in the preceding three years, “Shrinking” firms lost 10% or more of their employment or turnover during the preceding three years and “Stable” firms had their employment or turnover change by less than 10% in the preceding three years.
Source: Calculations based on microdata sources from 14 countries. See Annex of Chapter 1 for more information.
Policies trying to “select winners” are unlikely to succeed. It is difficult to predict which SMEs will become high-growth firms based on previous performance or measurable characteristics. Analyses aimed at identifying firms that are likely to grow fast in the future had very limited success (Coad and Srhoj, 2019[10]; McKenzie and Sansone, 2019[11]). Investment in physical or human capital can “hint” that an SME plans to scale up.9 There is a statistically significant association between scaling up and being in the top 25% of SMEs that invest in physical or human capital before scaling up for most of the 12 countries with available data (Annex Figure 1.A.4). An SME that is among the top 25% of SMEs that invest is about 2 percentage points more likely to scale up than an SME in the same sector, of the same age and with headquarters in the same region that is among the other 75% of SMEs. The difference is comparatively small given those between sectors and firms of different ages (see above) and results vary substantially between countries. Machine learning and Artificial Intelligence add new tools that might improve on predicting or “nowcasting” growth or growth potential. Using these new methods in combination with big- or high-frequency data is promising but still lacks precision for the majority of firms (Box 1.2).
Nowcasting is a predictive technique aimed at estimating the current state of an economic indicator without the delay associated with obtaining comprehensive data. It involves the real-time use of high‑frequency data. Experimental and scoping work by the OECD, in collaboration with ISTARI.AI, aimed to predict high growth using information retrieved from firms’ webpages. The analysis uses website embeddings and features that track companies’ digital footprints and innovation indicators, obtained from website archives for the years 2017 and 2018. These sources provide insights into company engagements in various technological and innovation domains such as artificial intelligence, mobility, sustainability, and energy. The scraped data underwent filtering based on pre-determined indicators, followed by a supervised machine learning model that classified text data into relevant categories.
These indicators were quantified into an "intensity score" representing each company’s engagement with the specific technologies. The score was then used to predict a firm’s growth or contraction above 50% over a two-year period of several firm performance measures over the 2017-19 biennium, including the number of employees, gross output, real gross output, real value added, real capital stock, labour productivity and multifactor productivity. The models were compared with estimations based on traditional metrics using firms’ financial information in 2017 as predictors.
The predictive performance of the different models is assessed using the F1-score, which evaluates the accuracy of a binary classification model by providing a balance between the model's ability to correctly identify true positives (high-growth outcomes) and its precision in labelling only relevant instances as positive. A good performance produces an F1-score above 0.8, and an acceptable performance should have an F1-score of about 0.4-0.5. To understand in which scenarios nowcasting performs better, F1-scores are calculated in different subsamples, grouping firms based on country, sector, and size class.
The accuracy of the prediction is variable. The model performs relatively better in predicting high growth in real capital stock and in value added than in employment or turnover. F1-scores are above 0.4 for firms operating in manufacturing, education, social and health services, and construction activities. Across subsamples, the highest F1-scores are found in firms in the construction sector with more than 100 employees (0.52), firms in non-tradable services with 50 to 100 employees (0.43) and firms in medium-low technology manufacturing with 20 to 50 employees (0.42). These sectors typically have a more limited digital presence compared to software or advanced tradable services. Therefore, the information available on firms' websites is more useful to identify the top performers. For instance, if a construction firm uses language related to advanced technology like artificial intelligence, it may imply significant innovation and growth potential compared to competing firms within the sector. In contrast, similar language on a software company's website might be less informative due to the higher baseline level of digital engagement in the sector.
The scoping nature of this exercise means that there is substantial room for refinement and improvement, particularly in integrating a broader array of variables beyond website text and financial metrics, and in refining the methodological approach. Still, the improvements seen in this project compared to previous attempts in the economic literature are notable. By refining these models and expanding their scope, there is potential for more robust and reliable tools to support decision-making.
Most scalers rely on traditional sources of finance
Scalers often plan to grow and rely on long-term bank loans to finance their investments. The role of bank credit as a major external source of finance for most SMEs is well-documented (OECD, 2020[12]). Access to bank credit is even more critical for scalers as the only option to fund their pre-scaling investments. Scalers have higher debt-to-assets ratios, on average, compared to other SMEs across all industries and countries, indicating a reliance on external funding to support their expansion (Figure 1.15). The fact that scalers tend to be more leveraged than their peers, with a higher debt-to-asset ratio and higher interest paid per unit of sales, signals their reliance on bank credit to expand (Bianchini, Bottazzi and Tamagni, 2017[13]). The need for finance is greater in manufacturing firms that require capital investment to increase production capacity and efficiency, with manufacturing scalers having at least 10% more debt relative to their asset size compared to other SMEs. The lower need for debt among service scalers reflects the different nature of investments required, such as human resources, training, and project financing, rather than heavy capital expenditure.10 Only at the end of the transformation phase, their debt ratio tends to decrease because, after having achieved a larger scale, they can self-finance their operations (OECD, 2021[5]), they might invest proportionally less, or they might be able to access non-credit sources of finance.
Total liabilities over total assets (non-scalers=100), 2014-20
Note: Unweighted averages across 9 countries during 2014-2020. The debt-to-total assets is total liabilities over total assets. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from nine countries. See Annex of Chapter 1 for more information.
Finance policies support investments to help scalers expand their capacity. Finance policies comprise all policies that support SMEs in financing i) innovation, ii) investments, and iii) network expansion. Among these three scale-up channels, 36% of finance policies support innovation, 28% support network expansion, and 23% support investments.11 The mix of priorities differs across countries. In New Zealand, Mexico, Colombia, Costa Rica, and Hungary, for instance, 50% or more of finance policies focus on supporting investments, but on a small total number of finance policies. In Estonia, Finland, and the Slovak Republic, few finance policies are directed to investments. In Austria, Iceland, Italy, and Switzerland, over 60% of policies focus on innovation. With some few countries (e.g., Sweden and Israel) having a roughly equal split among the three objectives.
Support to scale-up drivers in finance policies
Note: Total count, based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
Finance policies mainly support SMEs directly through loans, grants, and subsidies, rather than improving the financial market per se. Finance policies can be divided into policies that support SMEs directly and policies that improve financing through the market. About two-thirds of finance policies support SMEs directly. Among these policies, public loans and grants/subsidies are the main instruments governments use to support SMEs investments (Figure 1.17). An example is Austria’s AWS Growth Investment Initiative which supports domestic companies who want to grow and innovate by offering non-repayable grants averaging between EUR 300 000 and EUR 400 000 to be used for investments in machinery, prototypes, pilot plants, IT solutions, technical equipment, buildings, and facilities. Among policies that support SMEs indirectly through financial markets, the most common instruments are those related to loan guarantees, risk sharing mechanisms, and reduced interest rates. For instance, the Finnish Finnvera’s SME Guarantee facilitates SME access to bank loans by offering an 80% credit guarantee for loans between EUR 10 000 and EUR 120 000 and Italy’s Nuova Sabatini policy facilitates SMEs’ access to credit intended for the purchase or rental of machinery, equipment, plant, computer hardware, software, and digital technologies through credit guarantees. In this case, bank loans are linked to an SME Guarantee Fund that covers up to 80% of loans between EUR 20 000 and EUR 4 million for 5 years.
Frequency of instruments among finance policies that support investments
Note: *Alternative debt includes corporate bonds, securitised debt, covered bonds, private placements, (debt) crowdfunding, **Hybrid instruments include subordinated loans/bonds, silent participations, profit participation rights, convertible bonds, bonds with warrants, mezzanine finance. *** Equity instruments include private equity, venture capital, business angels, specialized platforms for public listing of SMEs, (equity) crowdfunding; **** Asset-based finance includes asset-based lending, factoring, purchase order finance, warehouse receipts, leasing.
Source: OECD SME and entrepreneurship policy dashboard
Venture capital-related instruments are mainly used in finance policies that support innovation and are seldom used to support general investments or network expansion. About half of the finance policies that use equity instruments are innovation-focused, accounting for 11% of all finance policies (Table 1.1). The incidence of venture capital (VC) instruments is below 5% in policies that aim at supporting investments or network expansion. Scalers supported by innovation-focused policies tend to emerge from incubators, accelerators and other facilities that promote venture-backed innovative start-ups. These firms can experience rapid growth but are few, despite the increase in equity-based finance since the 2000s. Equity-based finance tends to be a better fit for scale-up strategies based on disruptive innovation, loans tend to better fit scale-up strategies based on more traditional capacity expansion.
Instrument incidence by type of finance driver
Note: Values are computed as the count of instruments by instrument and objective, divided by the sum of all finance policies. Double counting is not taken into account.
Source: OECD SME and entrepreneurship policy dashboard
Finance policies for investment support both physical capital investment and intangibles. About 36% of investment-focused policies are directed to supporting investments in physical capital, 23% to supporting investments in human capital, and 13% to supporting investments in other intangible assets. Some SMEs require support to invest in physical capital, given the increasing importance of tradable and knowledge‑intensive services (Di Bella et al., 2023[14]), forward-looking policies may need to ensure suitable support mechanisms, e.g. loan guarantees and equity-type instruments, are in place that are more adapted to the needs of intangibles-intensive SMEs.12
Support to capital types in investments-focused finance policies
Note: Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
OECD countries have a range of policies in place that match scale-up models
SMEs that scale up typically mobilise a combination of investments and productivity‑enhancing innovations. The sequencing of investments differs, depending on a complex mix of factors related to scalers’ profiles and their overall growth model. Policies can therefore seek to promote SME scale-up through improved conditions and incentives for innovation, investments and network expansion (OECD, 2022[15]) but SMEs also need a suitable business environment as a supporting factor for growth (Box 1.3). SMEs are typically more vulnerable to deficient framework conditions, market failures and economic shocks, while inefficient infrastructure hampers their access to markets and the strategic resources they need to operate. Smaller firms also typically struggle to access finance, appropriate skills and innovation assets (e.g., technology, data, business models and organisational practices, networks etc., either in their tangible or intangible forms).
Policies can be analysed based on their strategic objectives. A useful way to assess the strategic objectives of policies is the framework developed in the OECD SME and Entrepreneurship Outlook (OECD, 2019[16]). The conceptual framework delineates 6 pillars and highlights cross-cutting elements related to SME and entrepreneurship policy governance (the +1 pillar) in managing the interplay between broader market and framework conditions for SME performance, the conditions under which SMEs can access and make use of strategic resources, such as skills, finance, knowledge, data, technology, or networks, etc. (Figure 1.19).
Source: (OECD, 2019[16])
The policies mapped through the OECD SMEs and entrepreneurship dashboard can be categorised by their strategic objectives. The dashboard captures three potential policies’ strategic objectives: i) improve the business environment, ii) improve access to strategic resources, or iii) improve governance. The dashboard also differentiates between sub-categories of these policies. Policies whose strategic objective is improving the business environment can be separated into i.a) policies that aim at improving the institutional and regulatory framework, i.b) policies that aim at improving market conditions, and i.c) policies that aim at improving the infrastructure. Policies whose strategic objective is improving access to strategic resources can be separated into ii.a) polices that aim at improving access to finance, ii.b) policies that aim at improving access to skills, and ii.c) policies that aim at improving access to innovation assets. Policies with strategic objectives related to SME and Entrepreneurship policy governance are not broken down further.
Institutional and regulatory framework
Institutional and regulatory settings are critical for entrepreneurial activity and to ensure that businesses of all sizes compete on a level playing field. Regulation in product and labour markets, taxation, competition, insolvency regimes, legal framework and court efficiency and public governance impact entrepreneurship and SME development at all stages of the business cycle, including entry, investment and expansion, transfer and exit.
Market conditions
Prevailing and expected market conditions are important determinants that shape firms’ decision‑making – whether they scale up or down – or whether new firms are able to enter the market. Firms adapt to market conditions through a range of strategies, e.g. innovation, competition, co‑operation or collusion, which can alter market structure and the distribution of market power. Domestic markets remain the prime space where SMEs do business as they are predominantly local actors embedded in nearby markets and ecosystems. Here, public procurement offers significant opportunities for SMEs to innovate, boost competitiveness and create jobs. While only a few SMEs operate in global markets, increased public attention has been given to levelling the playing field, and improved infrastructure, especially ICT, has helped SMEs reach scale without mass, and reduce transaction costs in their activities.
Infrastructure
Efficient network (logistics, energy, Internet) and knowledge infrastructure is the foundation of a dynamic business ecosystem. A well-functioning infrastructure ensures secure and cost-efficient access to strategic resources, including data and networks. Its accessibility, reliability and affordability are particularly critical for SMEs to compete in just-in-time and knowledge-intensive production systems, to raise their business profile and to scale up internal capacity. Quality infrastructure is also critical for firms’ entry into distant markets and engagement in global value chains (GVCs).
Access to finance
Access to finance, in the form and quantity needed at each stage of their life cycle, is critical for SME creation and scale-up. Yet, SMEs face difficulties in identifying and attracting appropriate sources of finance. Barriers such as information asymmetries, high transaction costs, and low levels of financial acumen of business owners explain why small businesses and entrepreneurs often face more difficulties in accessing finance than large enterprises. These challenges are typically more pronounced among specific segments of the SME population, such as new firms, start-ups, and innovative ventures with high growth potential, with implications for aggregate productivity and growth.
Access to skills
Skilled workers are a key asset for competition in a knowledge-based economy. However, SMEs have greater difficulty in hiring and retaining skilled workers than larger firms because they lack the capacity and networks needed to identify talent and offer less attractive working conditions. Rapid digital transformation, globalisation and skills shortages worldwide are likely to put further pressure on labour markets and increase the competition for skills, placing SMEs at an even greater disadvantage.
Access to innovation assets
SMEs need to transform and innovate, and their participation in global and local knowledge and innovation networks is essential to scale-up. If access to innovation assets is critical for all firms to compete in a knowledge-based economy, the challenge is particularly acute for SMEs. SMEs face specific barriers in finding and managing the technology, data and networks that enable innovation. SMEs also tend to engage less in R&D, and while they are more dependent on external sources of knowledge, they are also less well integrated into knowledge networks.
Source: Elaboration based on OECD (2019[16])
Strategic, transformative, and expansionist scalers leverage framework conditions and access to strategic resources to different extents. Strategic scalers invest before scaling up. They need access to finance, skills, and assets, as well as infrastructure to enable their investments. Policies with strategic objectives to improve access to resources and infrastructure are particularly important to support them. Transformational scalers rely on productivity changes and adaptation to market conditions to scale. While they grow, they need access to finance and skills to accommodate their growth process. Expansionist scalers, that grow by scaling their activities without changing their business model, tend to be particularly sensitive to institutional and regulatory frameworks that may influence their capacity to expand without improving their productivity. They also need access to finance and infrastructure to enable their capacity expansion.
Strengthening firms’ internal capacity and access to strategic resources are the most common strategic objectives of SME and entrepreneurship policies. More than two-thirds of policies in the OECD SME and Entrepreneurship policy dashboard (68%) aim to strengthen SMEs’ and entrepreneurs’ internal capacity and their access to strategic resources such as access to finance, access to skills and access to innovation assets.
A smaller share of policies (23%) aims at improving the business environment, and just 8% of policies have policy governance as their strategic objective. The order of these shares is roughly similar across policy areas, yet with different intensities. Within finance policies and skills policies, objectives are overwhelmingly set to improve internal capacity (98% and 96% respectively). Among trade and network policies, strategic objectives are split between improving internal capacity (81% and 87% respectively) and improving the business environment (57% and 45% respectively). Among data governance policies, strategic objectives are balanced across improving internal capacity (59%), improving policy governance (35%), and improving the business environment (22%).
Strategic objectives of policies, by policy area
Note: Shares are calculated as a percentage of total policies, based on an unweighted count. Policies can address several dimensions and sub-dimensions of SME performance at once. Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries. A single policy can have multiple objectives.
Source: OECD SME and entrepreneurship policy dashboard
About 50% of policies that aim at improving SMEs’ internal capacity focus on access to finance, while access to skills and innovation assets are less prevalent. Policies with the strategic objective of improving internal capacity and access to resources can be broken down into policies that enhance access to finance, policies that enhance access to skills and policies that enhance access to innovation assets. 47% of policies that aim at improving SMEs’ internal capacity are about access to finance, 30% about access to skills and 23% about access to innovation assets.
Policies to help SMEs engage in innovation networks, internationalise or improve their data use and governance use various tools with different strategic objectives. Strategic objectives in the data governance space reflect the two most critical resources needed for data management: innovation assets (about 47% of data governance policies) and skills (43% of data governance policies). When it comes to trade, policy objectives are split between improving access to finance (51%), access to skills (26%) and access to innovation assets (23%). Typically, in the trade area, policies that aim at improving access to innovation assets provide data platforms and one-stop shops to deal with customs. For instance, Iceland’s Cross-Border Trade Hub policy supports SMEs’ trade by providing information and templates for customs, VAT, regulations, and cross-border employment. In the innovation network space, policy objectives are also well distributed across improving access to innovation assets (45%), access to skills (30%) and access to finance (25%). Innovation assets in this policy space are often related to platforms to connect organisations or to provide advisory services. For instance, Japan’s Yorozu Support Centres provide a one‑stop advisory service by connecting SMEs with experts such as management consultancy, IT, design, or intellectual property.
Distribution of strategic objectives among policies that aim at improving SMEs' internal capacities, by policy area
Note: Calculated as count of policies with a strategic objective of improving SMEs internal capacity and access to strategic resources. Policies can address several dimensions and sub-dimensions of SME performance at once. Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries
Source: OECD SME and entrepreneurship policy dashboard
Over 60% of policies with a strategic objective of improving the business environment focus on enhancing market conditions. Policies with the strategic objective of improving the business environment are classified as policies relating to the institutional and regulatory framework, policies that aim at improving market conditions, and policies developing and supporting relevant infrastructures (Box 2.3). Combining all policy areas, improving market conditions is the most frequent strategic objective (60%) for policies that aim at improving the business environment. Improving the infrastructure is the second most common strategic objective (31%) and improving the regulatory environment is the least common (9%). These results are driven by trade policies that are tied to business environment objectives much more often than other policies. Over 350 trade policies have a strategic target related to improving the business environment, while just about 200 innovation network policies, 100 data governance policies, 70 access to finance policies, and 25 skills policies focus on improving the business environment.
Among 350 trade policies, 82% have the strategic objective to improve market conditions, such as market access. For instance, the Netherlands Agricultural Network (LAN) supports Dutch entrepreneurs and SMEs who work abroad in finding market information, finding business partners, or dealing with foreign market access issues. Policies supporting SMEs’ engagement in innovation networks are the second most engaged in supporting the SME business environment. Improving market access (50%) and developing supporting infrastructures (46%) are almost equally common in support of innovation networks. “Market-conditions” policies include cluster-development policies such as the Japanese Industrial Cluster Policy that targets SME competitiveness through developing agglomerated areas with access to advanced transport, logistics, universities and research institutions. An example of policies providing relevant infrastructure is the Estonian Industrial Parks programme that offers manufacturing and logistics companies pre-developed modern infrastructure with the possibility to customise their site to their specific needs.
Distribution of strategic objective among policies that aim at improving SMEs’ business environment, by policy area
Note: Calculated as count of policies with a strategic objective of improving SMEs’ business environment. Policies can address several dimensions and sub-dimensions of SME performance at once. Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
SMEs that export goods are more likely to scale up than non-exporters
International trade and participation in global value chains (GVCs) support SME performance and growth. Firms that internationalise can access foreign know-how, technology and diverse supply-chain finance mechanisms (OECD, 2023[17]; OECD, 2019[16]; OECD, 2008[18]). In many countries, half of SMEs’ contribution to exports is indirect as part of a (global) value chain, supplying inputs to larger firms that export rather than as exporters themselves. Overall, SMEs engaged in international activities are more profitable and innovative than their domestic peers and more often engaged in various business collaborations (St-Pierre, 2003[19]). While there are several ways for SMEs to seize growth opportunities by internationalising their activities (i.e., via exports, imports, and as suppliers to larger exporting firms), selling to foreign markets can be particularly important for scaling up. Beyond having the opportunity to sell their products or services to more consumers, SMEs can also “learn from exporting”, which helps them to improve products by adopting higher quality standards, or optimise their sourcing strategies (OECD, 2021[5]). Moreover, the sharing of resources and knowledge via horizontal relationships and information channels (e.g. in the context of export consortia) can enable firms to cope with the risks associated with foreign-market entry (Musteen, Francis and Datta, 2010[20]; Boehe, 2013[21]).
Export-driven scalers play a crucial role in boosting the innovation and resilience of SMEs. Export-driven scalers are defined as scalers in turnover that achieve more than half of their turnover growth from increased exports during their scaling-up period. A recent example is evidence on exporting scalers available for Croatia that shows that scalers that rely on exports for growth expand rapidly, introducing new products and entering new markets. They diversify their sales portfolios, reduce reliance on individual products and export markets, and increase unit prices, becoming competitive in sophisticated markets like the EU (Srhoj, Coad and Walde, 2024[22]).
SMEs offering services and those producing goods benefit from internationalisation, yet limited data on trade in services constrains understanding in this area. Nearly two-thirds of global trade is now driven by services, either directly as exports or indirectly as services embodied in goods, e.g. the energy required to produce a mobile phone, or through establishing a commercial presence abroad, e.g. a French restaurant opening a location in Luxembourg (Cernat, 2024[23]). Measuring service trade remains challenging, as there is less trade from direct cross-border service sales than trade from services exported via foreign subsidiaries or those embedded in goods. Addressing these data gaps requires linking various (confidential) data sources, which often makes comprehensive analysis difficult. As a result, most available insights focus on goods-exporting firms, particularly those in manufacturing.13
In five out of six countries with available data, scalers are more likely to be exporters of goods than other SMEs (Figure 1.23). In the Netherlands, 45% of scalers are exporters, i.e. they directly sell at least some of their goods to customers abroad, compared to 33% among other SMEs. Due to data confidentiality restrictions, a more detailed breakdown of the available data is not possible. However, aggregate figures reveal that the majority of exporters are manufacturing SMEs or operate within the wholesale and retail trade sectors. Approximately two-thirds of goods exports originate from firms in the industrial sector, with the vast majority of this share coming from firms in the manufacturing sector. Most of the remaining part comes from wholesale and retail trade.14 In Latvia, Denmark, and Portugal, the share of exporters among scalers is around 5-10 percentage points higher than among other SMEs. In Finland, around 15% of SMEs export goods abroad, indicating that Finnish SMEs are less export-oriented, with a similar share of exporters among scalers and non-scaler SMEs.
Share of Scalers and other SMEs that export goods, 2014-20
Note: Unweighted averages across six countries during 2014-20. Exporters are SMEs with goods export values exceeding EUR 1 000 at the end of the scale-up period considered. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from six countries. See Annex of Chapter 1 for more information.
Goods exports help scalers to sustain growth in the years following their initial expansion. In 2019, goods exporting scalers experienced faster employment growth after scaling up over the 2015-18 period than SMEs that scaled up in the same period but did not export.15 Access to foreign markets can provide a less volatile business environment, which allows scalers to undertake long-term investments in expanding their workforce. Among employment scalers, growth rates after scaling up are one to four percentage points higher in exporters than non-exporters, in the six countries with available data (Figure 1.24). Similarly, for turnover scalers, employment growth rates are one to two percentage points higher among exporters than among non-exporters, while turnover growth rates are similar.
Growth rates as of 2019 for scalers in employment that scaled up from 2015 to 2018
Note: Unweighted averages. Exporters are scalers with goods export values exceeding EUR 1 000 as of the end of their scaling-up period. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from six countries. See Annex of Chapter 1 for more information.
Goods exports drive growth in about a quarter of scalers in turnover
For about a quarter of scalers in turnover, more than half of the increase in sales comes from an increase in exports of goods (Figure 1.25). In Latvia, 42% of turnover scalers are “export-driven scalers”,16 highlighting the critical role of international trade in the expansion of SMEs in the country. In other countries, 21% to 30% of scalers are “export-driven scalers”. The Netherlands has the highest share of exporters but the second-lowest share of export-driven scalers among the six countries with available data. This implies that Dutch scalers grow by simultaneously expanding in domestic and foreign markets.
Share of goods-export-driven scalers in all scalers in turnover
Note: Unweighted averages. Export-driven scales are defined as scalers in turnover that achieve more than half of their turnover growth from increased goods exports during their scaling-up period. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from five countries. See Annex of Chapter 1 for more information.
The typical scaler that grows through an increase in goods exports is older and larger than other scalers (Figure 1.26). Before scaling up, the median age of goods-export-driven scalers was three to five years higher than that of other scalers, except in Denmark, where there is no difference. The median export-driven scaler is significantly larger, with employee numbers ranging from 23 to 28, compared to 16 to 18 employees for all scalers in turnover. This disparity in size and age highlights the additional resources and experience that export-driven scalers require for investment in tools and machinery for goods production, workers with the right skills and to establish and maintain their trade networks.
Export-driven scalers are more productive and offer higher worker compensation compared to non-exporter scalers (Figure 1.27). The labour productivity of scalers that grow by increasing goods exports is 9% to 12% higher than that of all scalers in all four countries with available data, reflecting the productivity benefits from investments in machinery and skilled workers. Compensation per employee is 17% to 42% higher in scalers where goods exports drive growth than in other scalers. Another channel that cannot be measured with the available data is learning by exporting, which helps firms become more efficient (Srhoj, Coad and Walde, 2024[22]; Atkin, Khandelwal and Osman, 2017[24]).
Firm age and size, export-driven scalers vs. all scalers in turnover, 2014-20
Note: Unweighted averages. Export-driven scales are defined as scalers in turnover that achieve more than half of their turnover growth from increased goods exports during their scaling-up period. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from six countries. See Annex of Chapter 1 for more information.
Labour productivity and worker compensations, all scalers in turnover=100, 2014-2020
Note: Unweighted averages. Labour productivity and workers’ compensations are measured at the end of the scaling-up period. Export-driven scalers are defined as scalers in turnover that achieve more than half of their turnover growth from increased goods exports during their scaling-up period. Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year on average over three consecutive years.
Source: Calculations based on microdata sources from four countries. See Annex of Chapter 1 for more information.
Trade policies can play a key role in facilitating access to foreign markets and supporting SME scale-up
Policies supporting SME internationalisation focus on improving internal capabilities. Exploiting global trade opportunities is an important channel for some SMEs to scale up, yet in practice, the majority of SMEs are constrained by limited internal capabilities, such as a lack of managerial skills, technology, capital, or innovation assets. In addition, external barriers, such as access to trade finance or intellectual property protection in potential partner countries, can further hinder SMEs’ exports (OECD, 2023[25]). An example of a policy that aims at removing barriers to SME participation in GVCs is the Korean Export Factoring policy. It is a trade finance facility through which Korea’s Eximbank purchases exporters' receivables arising from open-account export transactions, easing SMEs’ trade‑related financial constraints. Another example is Canada’s EDC X FITT Lite Learning Series, which aims at improving access to skills in exporting activities. The policy provides SMEs with export-related learning resources regarding market entry strategies, mastering digital marketing, or managing cash flows.
Export-driven open economies tend to dedicate a relatively larger share of their SME and entrepreneurship policies to trade (Figure 1.28). In most cases, these are small open economies with a strong reliance on exports for national growth. In the Slovak Republic, Czechia, and Sweden, the share of trade policies among policies in the OECD SME and Entrepreneurship policy dashboard is between 32 and 45%, well above the OECD average. For instance, in the Slovak Republic, policies cover the full range of challenges that SMEs can encounter in attempting to participate in GVC, including multiple policies that improve access to finance or trade finance, an export academy, networking events, advisory services, assistance in Business-to-Business (B2B) negotiations with foreign companies, databases, and business portals.
Note: Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard and OECD Data Explorer [National Accounts]
Most policies that support SME internationalisation provide broad support rather than support for a single channel. Over 60% of all trade-related policies in OECD countries do not focus on a specific channel to support SME trade networks. In Spain, for example, the SME-Invest policy supports Spanish SMEs in investing abroad to improve their competitiveness and set up operations in foreign markets, and the Working Credit policy offers Spanish SMEs finance to purchase raw materials, pay suppliers, or support one or more internationalisation contracts. Among policies that use a specific channel, 26% focus on exports, 8% support internationalisation through professional networks, 4% through (global) value chain integration, and 2% through public procurement. Sweden uses the export channel more proactively in its trade-related policies by targeting exporting companies. For instance, the small export credits policy offers a 95% guarantee from the Swedish Export Credit Agency on export credits of goods-exporting SMEs. Similarly, the Shipping Manual policy provides country-specific export regulations for over 190 countries. There are also examples of policies that simplify regulatory and administrative compliance. Sweden’s Export Wizard, for example, provides a guide, a checklist, tips, and advice on how to prepare export ventures to meet international supply chain standards.
Distribution of integration channel among SMEs and entrepreneurship trade policies
Note: Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
Digital platforms are used by some countries to connect SMEs to international markets. Among all trade-related policies, 13% include a digital platform to promote network expansion (Figure 1.30). Other policies rely on non-digital platforms or networking infrastructures. For example, Czechia’s Long-Term Export Assistance helps SMEs identify local representatives in potential export destinations and supports compliance with customs regulations, registration or certification processes. Another example is Colombia’s FuturExpo Fair, which brings together communities, entrepreneurs and businesses aiming to internationalise. It provides workshops and opportunities to create strategic alliances. None of these programmes provides a digital platform to manage contacts or communications.
Trade policies that include a digital platform
Note: Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries
Source: OECD SME and entrepreneurship policy dashboard
When used, digital platforms are primarily employed for E-commerce or marketing, advertising, and branding purposes. About 28% are used for creating e-commerce channels, 25% for marketing purposes, 17% for service delivery and 13% to set up communication channels (Figure 1.31). Inspired by Iceland is an example of a platform used for marketing purposes. A public-private communications and marketing organisation, it assembles top national brands to promote Iceland’s tourism, services, products, and culture abroad.
Digital platform applications among trade policies that include a digital platform
Note: Total count among trade policies that use digital platforms. Based on a mapping of a total of 2 578 policy elements and 517 institutions across OECD countries.
Source: OECD SME and entrepreneurship policy dashboard
E-commerce-related policies either support SMEs in improving their e-commerce capabilities or offer an e-commerce infrastructure. An example of the former is Ireland’s Trading Online Voucher Scheme, which helps SMEs increase their online presence, improve sales, and expand their customer base. An example of the latter is Greece’s Strategic partnerships with international e-commerce platforms. This policy offers Greek SMEs direct access to e-sales on international e-commerce platforms. Enterprise Greece has signed partnership agreements with the platforms to get access for Greek exporters and provides training and technical support to SMEs for their supply of products and services via the internet. An example of a digital platform policy used for service delivery is Italy’s Export Tips, a multimedia training programme of 15 videos to train SMEs on internationalisation processes. An example of a digital platform used for communication is Hungary’s Supplier Database, which collects information on Hungarian suppliers and consulting services companies to simplify matching between them. It is also possible to combine multiple activities in one platform, as demonstrated by Denmark’s Digital Sales, which is used both for e-commerce and marketing. The policy connects Danish SMEs with a network of global digital sales consultants who help SMEs export their products and expand their online sales channels.
The sector group classification used in the report builds upon the Eurostat classification17 of less and more knowledge-intensive services and low-, medium- and high-tech manufacturing, integrated with an additional classification of service sectors based on their tradability. As a first step, three-digit NACE sectors are grouped into manufacturing, services, construction and education, social care and health services. Manufacturing is then subdivided according to Eurostat technological intensity classification (low and middle-low; medium-high and high). Services are further classified into “tradable” and “non-tradable” according to their exposure to international trade (Piton, 2021[26]). A sector is considered tradable if the sum of import and export flows is greater than 10% of the total value of the sector’s production. Finally, tradable services are split into “advanced tradable services”, which include information and communication technologies (ICT) services and professional services, and a residual group of “other tradable services”. Sectors include the following (see the Annex in Chapter 1 for the full list of two-digit NACE codes):
Low and medium-low technology manufacturing and extractive industries: food, textile, paper, wood, refined petroleum, rubber, plastic, basic metal products, mining.
Medium-high and high technology manufacturing: chemical products, pharmaceuticals, computer, electronic/electrical equipment, machinery, transport equipment.
Advanced tradable services: software, telecommunications, consultancy, legal services, accounting services, architectural activities, scientific research.
Other tradable services: travel agency, services to buildings/landscape, employment activities, veterinary, accommodation/food services, services for transportation.
Other non-tradable services: electricity, gas and water supply, waste management, wholesale and retail trade, repair of motor vehicles/household goods, real estate activities.
Education, social care and health services: Education, human health activities, residential care, social work.
Construction: construction of buildings, civil engineering, specialised construction activities.
Digital-intensive sectors are cross-cutting and include advanced tradable services (with the exception of “software”, Manufacture of transport equipment; Veterinary activities; Employment activities; Travel agency, tour operator, reservation service and related activities; Services to buildings and landscape activities; Office administrative, office support and other business support activities and Repair of computers and personal and household goods.
Note: i) The manufacturing sectors are classified using Eurostat’s high‑technology classification of manufacturing industries (https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:High-tech_classification_of_manufacturing_industries).
ii) The advanced service sectors are identified using Eurostat’s definition of knowledge-intensive and less knowledge-intensive services.
iii) The classification of tradable sectors comes from Piton (2021), which identifies tradable sectors using data from 21 European countries in the period 1995-2015. Piton (2021) calculates the ratio of total trade (imports + exports) to total production, and a sector is considered tradable if its openness ratio is greater than 10%.
iv) NACE division codes are extracted from https://ec.europa.eu/competition/mergers/cases/index/nace_all.html.
v) Digital‑intensive sectors are defined according to the methodology established by (Calvino et al., 2018[7]). Indicators to identify digital‑intensive sectors include share of ICT tangible and intangible (i.e. software) investment; share of purchases of intermediate ICT goods and services; stock of robots per hundreds of employees; share of ICT specialists in total employment; and the share of turnover from online sales.
Younger scalers are more likely to grow before scaling up than mature scalers (Annex Figure 1.A.1). 70% of medium-aged scalers (i.e. scalers with 6-10 years of age) were growing before scaling up, compared to about 45% of mature scalers (i.e. scalers with more than 10 years of age). Moreover, 23% of mature scalers were shrinking before scaling up, while about 13% of medium-aged scalers were shrinking before scaling up. Among young firms with three to five years of activity at the beginning of the scaling-up period (i.e., excluding new firms that were created less than three years before their high growth phase), 85% of them were growing before their scaling-up period.
Share of scalers by growth path during three years before scaling up, 2011-17
Note: Scalers are SMEs with at least 10 employees that grew in employment or turnover by at least 10% per year, on average, during three consecutive years during 2014-20. Growth before the scaling-up phase is observed in the three years preceding the scaling‑up period. “Entry” are firms that only started operation within the three preceding years, “Growing” firms grew by 10% but less than 33% in the preceding three years, “Scale-up” firms grew by more than 33% in the preceding three years, “Shrinking” firms lost 10% or more of their employment or turnover during the preceding three years and “Stable” firms had their employment or turnover change by less than 10% in the preceding three years.
Source: Calculations based on microdata sources from 14 countries. See Annex of Chapter 1 for more information.
Distribution of SMEs, scalers and high-growth scalers across sectors, 2020
Note: Data for 2020 (2019 for Germany). Scalers are defined as SMEs that grew in employment or turnover by at least 10%, on average, per year over 3 consecutive years. High-growth scalers are defined as SMEs that grew in employment or turnover by at least 20%, on average, per year over 3 consecutive years.
Source: Calculations based on microdata sources from 17 countries. See Annex of Chapter 1 for more information.
Share of scalers by growth path during three years before scaling up, 2011-17
Note: Scalers include both scalers in employment and scalers in turnover. Scalers are SMEs that scaled up during 2014-20. Growth before the scaling-up phase is observed in the three years preceding scaling up. Growth before the scaling-up phase is observed in the three years preceding the scaling‑up period. “Entry” are firms that only started operation within the three preceding years, “Growing” firms grew by 10% but less than 33% in the preceding three years, “Scale-up” firms grew by more than 33% in the preceding three years, “Shrinking” firms lost 10% or more of their employment or turnover during the preceding three years and “Stable” firms had their employment or turnover change by less than 10% in the preceding three years.
Source: Calculations based on microdata sources from 14 countries. See Annex of Chapter 1 for more information.
Prospective scalers invest more in physical and human capital than other SMEs. In the year preceding their scaling-up period, the top 25% of SMEs in terms of capital intensity (i.e. investments in physical capital relative to their total turnover) or average workers’ compensation (a measure of skill-intensity of the firm) are around 2 percentage points more likely to become a scaler after three years (Annex Figure 1.A.4) than firms with lower levels of investment (the “bottom” 75%). This tendency is consistent across most countries in the sample but with high statistical uncertainty. It is also far from a “golden bullet” that hits potential scalers as it partly reflects differences in the likelihood of scaling up across sectors (digital-intensive services that rely on human capital; manufacturing sectors that rely on physical capital).
Marginal effect of ranking among the top 25% SMEs for average workers’ compensation or physical capital intensity on the probability to be a scaler after three years
Note: Coefficients show regression results where the dependent variable is a dummy variable taking 1 if a firm becomes a scaler after three years and 0 otherwise. Firm size, firm age, and TL2 region where a firm operates are included as control variables. The independent variables include a dummy variable set to 1 if a firm ranks in the top 25% in terms of workers' compensation or capital intensity. Dots indicate point estimates, and bars denote 95% confidence intervals.
Source: Estimations based on microdata sources from 12 countries. See Annex of Chapter 1 for more information.
Institutions that have a core mandate on SMEs and entrepreneurship tend to target high-growth firms relatively more. The share of policies targeting high-growth firms to all target policies tends to increase with the percentage of institutions that have SME and Entrepreneurship policy’s core mandate (Annex Figure 1.A.5. ).
Share of policies targeted toward high-growth firms (HGF) and share of institutions with SME and Entrepreneurship (SMEE) as core mandate
Note: The percentage of institutions with SME and Entrepreneurship as a core mandate is defined as the number institutions that mention SME as at least one of its core mandates divided by all institutions in a country. The percentage of policies targeted at HGF is the number of policies targeting specifically SMEs with growth or performance criteria (high-growth, scalers), divided by all targeted policies. Source: Based on a mapping of a total of 2 578 policy elements and 517 institutions in support of SME scale-up across OECD countries
Source: OECD SME and entrepreneurship policy dashboard.
References
[3] Akcigit, U. and W. Kerr (2018), Growth through Heterogeneous Innovations, http://www.journals.uchicago.edu/t-and-c.
[24] Atkin, D., A. Khandelwal and A. Osman (2017), “Exporting and Firm Performance: Evidence from a Randomized Experiment*”, The Quarterly Journal of Economics, Vol. 132/2, pp. 551-615, https://doi.org/10.1093/qje/qjx002.
[13] Bianchini, S., G. Bottazzi and F. Tamagni (2017), “What does (not) characterize persistent corporate high-growth?”, Small Business Economics, Vol. 48/3, https://doi.org/10.1007/s11187-016-9790-1.
[21] Boehe, D. (2013), “Collaborate at Home to Win Abroad: How Does Access to Local Network Resources Influence Export Behavior?”, Journal of Small Business Management, Vol. 51/2, pp. 167-182, https://doi.org/10.1111/jsbm.12013.
[7] Calvino, F. et al. (2018), “A taxonomy of digital intensive sectors”, OECD Science, Technology and Industry Working Papers, No. 2018/14, OECD Publishing, Paris, https://doi.org/10.1787/f404736a-en.
[2] Calvino, F., C. Criscuolo and C. Menon (2018), “Cross-country evidence on start-up dynamics”, Industrial and Corporate Change, Vol. 27/4, pp. 677–698, https://doi.org/10.1093/icc/dty006.
[29] Calvino, F. et al. (2022), “Closing the Italian digital gap: The role of skills, intangibles and policies”, OECD Science, Technology and Industry Policy Papers, No. 126, OECD Publishing, Paris, https://doi.org/10.1787/e33c281e-en.
[23] Cernat, L. (2024), “What Mode of Supply Will Matter the Most for the Future of Services Trade?”, Policy Brief, No. 16/2024, ECIPE.
[10] Coad, A. and S. Srhoj (2019), “Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms”, Small Business Economics, Vol. 55/3, pp. 541-565, https://doi.org/10.1007/s11187-019-00203-3.
[30] Demmou, L. and G. Franco (2021), “Mind the financing gap: Enhancing the contribution of intangible assets to productivity”, Oecd 1681.
[14] Di Bella, L. et al. (2023), Annual report on European SMEs 2022/2023.
[28] Grover Goswami, A., D. Medvedev and E. Olafsen (2019), High-growth firms: Facts, fiction, and policy options for emerging economies, The World Bank.
[27] Haltiwanger, J., R. Jarmin and J. Miranda (2011), “Who Creates Jobs? Small vs. Large vs. Young*”, No. Working Paper 16300, NBER, http://econweb.umd.edu/~haltiwan/size_age_paper_R&R_Aug_16_2011.pdf (accessed on 11 October 2019).
[1] Jovanovic, B. (1982), “Selection and the Evolution of Industry”, Econometrica, Vol. 50/3, https://doi.org/10.2307/1912606.
[11] McKenzie, D. and D. Sansone (2019), “Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria”, Journal of Development Economics, Vol. 141, p. 102369, https://doi.org/10.1016/j.jdeveco.2019.07.002.
[8] Michaels, G., A. Natraj and J. Van Reenen (2014), “Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years”, The Review of Economics and Statistics, Vol. 96/1, pp. 60-77, https://doi.org/10.1162/rest_a_00366.
[20] Musteen, M., J. Francis and D. Datta (2010), “The influence of international networks on internationalization speed and performance: A study of Czech SMEs”, Journal of World Business, Vol. 45/3, pp. 197-205, https://doi.org/10.1016/j.jwb.2009.12.003.
[4] Nordic Innovation (2022), Scale-ups in the Nordics 2020.
[6] OECD (2023), “Grow and Go? Retaining Scale-ups in the Nordic Countries”, OECD Regional Development Papers, No. 51, https://doi.org/10.1787/9be5339d-en.
[25] OECD (2023), OECD SME and Entrepreneurship Outlook 2023, OECD Publishing, Paris.
[17] OECD (2023), Policy Toolkit for Strengthening FDI and SME Linkages, OECD Publishing, Paris, https://doi.org/10.1787/688bde9a-en.
[15] OECD (2022), “Financing Growth and Turning Data into Business: Helping SMEs Scale Up”, OECD Studies on SMEs and Entrepreneurship, OECD Publishing, Paris, https://www.oecd-ilibrary.org/industry-and-services/financing-growth-and-turning-data-into-business_6962c7a9-en (accessed on 11 October 2022).
[5] OECD (2021), Understanding Firm Growth: Helping SMEs Scale Up, OECD Studies on SMEs and Entrepreneurship, OECD Publishing, Paris, https://doi.org/10.1787/fc60b04c-en.
[12] OECD (2020), “Financing SMEs and Entrepreneurs: An OECD Scoreboard”, OECD Special edition: The impact of COVID-19.
[31] OECD (2019), “Fostering the use of Intangibles to strengthen SME access to finance”, in Financing SMEs and Entrepreneurs 2019: An OECD Scoreboard, OECD Publishing, Paris, https://doi.org/10.1787/155dc1a2-en.
[16] OECD (2019), OECD SME and Entrepreneurship Outlook 2019, OECD Publishing, Paris, https://doi.org/10.1787/34907e9c-en.
[18] OECD (2008), Enhancing the Role of SMEs in Global Value Chains, OECD Publishing, Paris, https://doi.org/10.1787/9789264051034-en.
[26] Piton, S. (2021), “Economic integration and unit labour costs”, European Economic Review, Vol. 136, https://doi.org/10.1016/j.euroecorev.2021.103746.
[32] Publishing., O. (ed.) (2014), “The Dynamics of Employment Growth: New Evidence from 18 Countries”, OECD Science, Technology and Industry Policy Papers, No. 14., https://doi.org/10.1787/5jz417hj6hg6-en.
[9] Redding, S. (1996), “The Low-Skill, Low-Quality Trap: Strategic Complementarities between Human Capital and R & D”, The Economic Journal, Vol. 106/435, pp. 458-470, https://doi.org/10.2307/2235260.
[22] Srhoj, S., A. Coad and J. Walde (2024), “HGX: the anatomy of high growth exporters”, Small Business Economics, https://doi.org/10.1007/s11187-024-00884-5.
[19] St-Pierre, J. (2003), “Relations entre l‟exportation, le développement organisationnel et la situation financière des PME canadiennes”, Revue Internationale PME, Vol. 16 (2), pp. pp. 61–82, https://doi.org/10.7202/1008439ar.
← 1. The empirical evidence of the disproportionate contribution to employment growth of young small firms is extensive and covers the United States (Haltiwanger, Jarmin and Miranda, 2011[27]), several OECD countries (Criscuolo, Gal and Menon, 2014[32]), as well as emerging economies (Grover Goswami, Medvedev and Olafsen, 2019[28]).
← 2. The combination of size and firm age is available for France, Portugal, Latvia and the Netherlands.
← 3. 18% of mature firms scale up in turnover, while 10% scale up in employment. Among young firms, 32% scale up in turnover, and 24% scale up in employment.
← 4. Non-tradable services typically include governmental services, education, health care, the construction sector and retail. A growing range of business and technical services is becoming increasingly tradable, but a large percentage remains local. Marketing or public relations agencies have a global reach, lawyers much less so and cleaning services are clearly a locally provided and non-tradable service.
← 5. Digital‑intensive sectors are defined according to the methodology established by (Calvino et al., 2018[7]). Indicators to identify digital-intensive sectors include the share of ICT tangible and intangible (i.e. software) investment; share of purchases of intermediate ICT goods and services; stock of robots per hundreds of employees; share of ICT specialists in total employment; and the share of turnover from online sales. Sectors classified as “digital‑intensive” are (Nace 29, 30) Manufacture of transport equipment; (61-63) Telecommunications; Computer programming, consultancy and related activities; Information service activities; (69-74) Legal and accounting activities; Activities of head offices; management consultancy activities; Architectural and engineering activities; technical testing and analysis; Scientific research and development; Advertising and market research; Other professional, scientific and technical activities; (75) Veterinary activities; (78) Employment activities; (79) Travel agency, tour operator, reservation service and related activities; (81-82) Services to buildings and landscape activities; Office administrative, office support and other business support activities(95) Repair of computers and personal and household goods. See the Annex of Chapter 1 for the full list of sectors including less digital-intensive sectors.
← 6. See also Calvino et al. (2022[29])
← 7. The shares do not add to 100% as policies can have multiple targets.
← 8. See the Annex Figure 1.A.5.
← 9. Through econometric analysis, it is possible to assess whether the association between the probability to scale up and the preparatory investments that define strategic scalers is statistically significant, once other firm characteristics – such as age, size, location and detailed sector of activity – are taken into account. The analysis relies on a linear probability model (LPM) estimated on the full sample of SMEs in the country, in which the dependent variable is a binary variable indicating if the firm is a scaler in the given period, and the independent variables are binary variables indicating whether the SME is among the top 25% SMEs for investments in human capital or in physical capital, respectively. The model also includes size, age and large region (TL2) dummies.
← 10. This finding aligns with the results in Chapter 1, showing that scalers in manufacturing sectors increase their capital intensity more than other SMEs in the same industry before entering the scaling-up period. In contrast, scalers in service sectors focus on increasing productivity both before and during the scaling-up period, but their reliance on physical capital remains lower than that of other SMEs in the same industry.
← 11. 13% of policies do not focus on any of these three scale up channels
← 12. See Demmou and Franco (2021[30]) for a discussion of easing access to finance constraints for intangible-intensive firms in the context of the COVID-19 pandemic. See also (OECD, 2019[31]) on leveraging intangible assets to enhance access to finance.
← 13. About 62% of goods exporters are firms in manufacturing (see the following endnote).
← 14. Manufacturing accounts for 62% of goods exports, mining and quarrying for 4%, wholesale and retail trade; repair of motor vehicles and motorcycles for 22% and the remaining sectors for the missing 12%. Calculations based on Trade in goods by enterprise characteristics by activity sectors (OECD Database) for 2022. The average includes countries with available data (AUT, BEL, CAN, CRI, CZE, DNK, EST, FIN, FRA, DEU, GRC, HUN, ISL, IRL, ISR, ITA, LVA, LTU, LUX, NLD, NOR, POL, PRT, SVK, SVN, ESP, SWE, TUR and USA).
← 15. In the case of Latvia scalers did not grow in employment in 2019 but exporting scalers reduced employment less than non-exporting scalers.
← 16. Export-driven scalers are defined as scalers with more than 50% of the increase in turnover coming from an increase in goods exports. Data on service exports are not available (see text).
← 17. Eurostat’s high‑technology classification of manufacturing industries (https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:High-tech_classification_of_manufacturing_industries); Eurostat’s definition of knowledge-intensive and less knowledge-intensive services (https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Knowledge-intensive_services_(KIS)