Unleashing SME Potential to Scale Up

1. Scalers as drivers of competitiveness

Abstract

This chapter outlines how fast-growing SMEs (scalers) contribute to competitiveness and growth in their economies. It summarises the contribution of firms of all sizes to job and economic value creation and describes the policies that can support growth. The full policy information is available in a dedicated OECD policy dashboard. A final section considers the experience of scalers during the COVID-19 pandemic.

In Brief

Scalers make an outsized contribution to job creation and economic growth, with a variety of policies supporting their success

SMEs are as important as large firms in producing economic value and more important than large firms in creating new jobs. For every 10 jobs created by large firms, new and established small and medium-sized enterprises (SMEs) with 10 to 249 employees created 16 additional jobs, on average, across 16 OECD and candidate countries with available data. SMEs accounted for 40% of employment and 38% of economic value added across the 13 countries from 2014 to 2020, compared to 29% of employment and 41% of economic value added in larger firms. Micro firms contribute the remaining 31% of employment and 21% of value added.

Most SMEs with 10 to 249 employees grow little. It is a small subset of rapid-growth SMEs, or “scalers”, that drives the SME contribution to growth. Between 2011 and 2020, half of the SMEs grew in employment at a rate of 1% or less per year, and in turnover at a rate of 3% or less per year. However, some 16% to 27% of SMEs are “scalers”, which grow much faster, at an average rate of at least 10% per year over a three-year period in either employment, turnover or both. In 2020, scalers that grew in employment made up 8-14% of all SMEs, and scalers that grew in turnover made up 12-24% of SMEs across 17 countries with available data. Employment scalers created 41% to 62% of new jobs created by growing SMEs. This was equivalent to 530 000 jobs in France and 380 000 in Italy between 2017 and 2020. Turnover scalers generated 53% to 73% of the additional turnover generated by all SMEs.

SMEs that scale up strengthen their economies’ global competitiveness by raising productivity and generating innovation spillovers in addition to creating new jobs and output. Without scalers, total multi-factor productivity of the median SME, a measure of how efficiently firms use their capital and workforce in the economy, would have been 6% lower in 2020, on average, across 11 countries with available data. The contribution of scalers combines two important channels. First, scalers are, on average, more productive than other SMEs before they start growing. They therefore contribute positively to aggregate productivity growth simply by maintaining their productivity advantage over other firms as they expand and to “allocative efficiency” of the economy as more workers and capital investment flow to already more productive firms. The second channel is an increase in the productivity of scalers as they grow. Scalers typically invest in physical capital, workforce skills, or innovations that raise their productivity. The result is that the productivity growth of scalers outpaces that of other SMEs, and the initial productivity advantage of scalers of about 20% grows to around 35% compared to other SMEs.

The total impact of scalers on job and output growth in an economy can be broken down into three factors, which represent distinct policy targets. The contribution of scalers to aggregate growth can be broken down into i) the number of scalers, ii) their growth rate, and iii) their average size prior to growth. The most prominent factor contributing to high job creation is a large share of scalers, followed by a higher growth rate among scalers, albeit the relative importance of the different factors varies across countries. For example, in the Netherlands, the contribution of scalers is driven by their large number, which compensates for a lower growth rate. The share of Dutch employment scalers is 31% higher than the average of other countries but their overall growth rate is 4% lower. In contrast, in Romania, the share of employment scalers is 8% lower than the average, but the growth rate is 10% higher, resulting in an aggregate contribution that is slightly above the cross-country average.

The OECD’s SME and Entrepreneurship Policy Dashboard profiles a wide range of policy measures supporting scalers internationally, in line with their importance and the different factors affecting their success. The Dashboard maps more than 2 500 policy interventions across five policy fields: finance, data governance, trade, innovation networks, and skills. The mix of policy measures varies by country. Financial support is the most common instrument, representing about 45% of policy interventions overall, ranging from approximately 60% to 30% according to country. The variety of policy approaches revealed by the Dashboard shows the importance of support to scalers on multiple axes and serves as a source of inspiration and learning for future policies.

A variety of institutions implement policies for scaling, implying a need for coordination. Many of the institutions (about 37%) are not SME agencies or ministries responsible for SME policies, and are often in the domains of innovation, foreign direct investment attraction, trade, and infrastructure. This highlights the cross-cutting nature of policies that can support scaling up and the need for cross-institutional coordination. National strategies and action plans are an important tool for coordination. However, they are more common in the data governance field than in the other four policy areas.

The COVID-19 pandemic showed the vulnerability of SMEs, including scalers, to shocks and the benefit of a system that supports them in times of need. The number of scalers dropped sharply in 2020 in all 16 countries with available data. Turnover scalers were more affected by the COVID-19 pandemic than employment scalers in most countries since most governments provided COVID-19 relief measures that supported workforce retention. In 11 of 12 countries with data running as far as 2021, the number of turnover scalers increased on average by 21% compared to 2020 but remained below 2019 numbers in most countries. In contrast, there was no rebound in the number of employment scalers as persistent uncertainty deterred SMEs from committing to long-term investments in expanding their workforce.

Unleashing the potential of small and medium-sized enterprises

To better understand the contribution that small and medium-sized enterprises (SMEs) make to their economies, the OECD has assembled the OECD scale-up database (Box 1.1). The database collects information on SMEs with 10 to 249 employees and focuses on a small group of fast-growing “scalers” among them. It provides aggregate information on growth performance and characteristics of growing firms for 15 OECD and 2 candidate countries: Austria, Belgium, Croatia, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, the Netherlands, Portugal, Romania, Slovenia, the Slovak Republic and Spain. The data cover a period of 5-11 years, depending on the country, with data available up to 2020 or 2021.

Box 1.1. The OECD scale-up database

The OECD scale-up database includes information on SMEs that are scaling up for Austria, Belgium, Croatia, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, the Netherlands, Portugal, Romania, the Slovak Republic, Slovenia and Spain. Depending on the country, data are available up to 2021 (12 countries), 2020 (4 countries) or 2019 (1 country).

The database contains more than 350 000 individual data points with information on

1. the contribution of firms to aggregate employment and turnover growth, broken down by firm size, age and industry classes, separating the contribution of different types of scalers from that of other SMEs;

2. information on firm counts, employment and turnover of firms of scalers and the characteristics of scalers and other SMEs (within groups according to firm size, age and industry), including on labour and multi-factor productivity, financial ratios (assets and liabilities), tangible fixed assets (capital), turnover from goods exports and ownership, with coverage of individual characteristics varying by country;

3. the growth and characteristics of scalers in the years preceding and following scaling up.

Data are aggregated from microdata sources using the same code package and a common codebook that harmonises the underlying input data sources (OECD, 2021[1]).

National statistical agencies have made substantial progress over recent decades in developing business demographic indicators that enable the analysis of “high-growth firms” and scalers. Nevertheless, the data available still do not allow systematic country comparisons and assessment of the role played by different groups of scalers. By exploiting firm-level data, the OECD database seeks to fill this gap in support of improved data for policy design.

The exploitation of firm-level data across countries is a “gold mine” for policy analysis but access is still a bottleneck. One of the reasons is that databases are collected by different administrations (e.g. customs agencies, social security agencies, etc.), and confidentially maintained within national agencies. The OECD scale-up database addresses this issue by working with national statistical offices in a coordinated way to compile common indicators from firm-level microdata sets obtained from national statistical offices and commercial data providers.1

Analysis of these data has key advantages:

• Firm-level data allow for flexible aggregation of information for individual firms along many different dimensions, which is essential to understand heterogeneity. Firms differ substantially even within sector and size classes and thus a traditional size and sector class disaggregation, while useful, rarely proves sufficient. With firm-level data, it is possible to analyse a wider array of dimensions, including age, location, detailed size, etc. On a more technical note, the analysis of a granular and large dataset (for a large OECD country, a longitudinal firm-level dataset contains several millions of observations) also enables a disentangling of the effect of variables that are strongly correlated among each other (e.g. size and age), which would be impossible to do with aggregates (Haltiwanger, Jarmin and Miranda, 2013[2]).

• With longitudinal firm-level data, it is possible to track firms over time and analyse the evolution of their growth patterns. This is important for assessing the sustainability of the scaling-up process, i.e. understanding the extent to which scaling up is a temporary or stable phenomenon. It also allows study of the transformative process that scalers undertake before, during and after their high-growth phase.

There are more jobs in SMEs with 10-249 employees than in large firms, and SMEs and large firms account for equal amounts of value added. Fewer than 10% of firms are SMEs with 10-249 employees, but these SMEs accounted for 40% of employment and 38% of economic value added in the private business economy across 13 countries from 2014 to 2020 (Figure 1.1). This is significantly more than the 29% of employment in large firms and close to the 41% of economic value produced by larger firms. The fact that SMEs and large firms produce about the same amount of value added, but SMEs provide jobs for more people, has two important implications. The first is that SMEs play a central role in providing employment opportunities for a broad range of workers with various skills. The second is that there is a gap in labour productivity between SMEs and large firms (OECD, 2024[3]). Narrowing this gap pays a double dividend by raising competitiveness in the country and providing better, higher-paying jobs.2

The role of SMEs goes beyond employment and value creation. SMEs are crucial sources of innovation, often pioneering new ideas and technologies that drive economic progress. Their smaller size also allows them to be more flexible, reactive, and responsive to changes and shocks, even if they do not benefit from the cost advantages that larger firms gain from producing at a larger scale. This agility enables SMEs to adapt quickly to market demands and emerging trends, maintaining their competitive edge. Moreover, SMEs can be the lifeblood of communities, providing essential goods and services, especially in areas too small to attract larger firms. Their presence helps to ensure that even the most remote or underserved areas have access to necessary services and products. Additionally, SMEs often have deep-rooted connections within their communities, supporting local policies and participating in social and cultural activities (OECD, 2023[4]).

Figure 1.1. SMEs contribute as much value added and more jobs to the economy as large firms

Share of firms, employment, and value added by firm size in the private business sector, average for 2014-20

Note: Unweighted average of the shares for 2014-20 across 13 countries (Czechia, Denmark, Estonia, France, Germany, Greece, Iceland, Ireland, Italy, Latvia, Poland, Spain, and Switzerland). Micro firms denote firms with 1 to 9 employees. SMEs are firms with 10 to 249 employees, and large firms are firms with 250 or more employees. Private business sector includes the total business economy; repair of computers, personal and household goods; except financial and insurance activities. Employment denotes the total persons employed, and value added is at current factor costs.

Source: Calculations based on OECD Structural and demographic business statistics

SMEs are more important for job creation than large firms

For every 10 jobs created by large firms, newly founded and established growing SMEs have created 16 additional jobs, on average, across 16 OECD and candidate countries with available data. In 14 out of 16 countries with available data,3 SMEs with 10 to 249 employees provide a larger contribution to creating new jobs than large firms with 250 or more employees (Figure 1.2). SMEs accounted for 61% of all new jobs created by non-micro firms, on average for the 2016-19 period and across the 16 countries with available data, whereas large firms contributed the remaining 39%.

Figure 1.2. SMEs create more jobs than large firms in 14 out of 16 countries

Share of new jobs created by new and existing SMEs (10-249 employees) and large firms (250+), 2016-19

Note: SMEs have 10 to 249 employees, and large firms have 250 or more employees. The chart excludes micro firms due to data limitations in some countries. New jobs are calculated based on the difference in total jobs between 2016 and 2019 for SMEs and large firms, respectively.

Source: Calculations based on microdata sources from 16 countries. See Annex Table 1.B.2 for more information.

SMEs play a crucial role in driving overall employment growth by creating more jobs than are lost when they downsize or close shop. SMEs significantly contribute to job creation but there are also job losses from shrinking or exiting SMEs. The number of jobs lost in SMEs that downsize or shut down is larger than the number of jobs shed by large firms. However, the difference between new jobs created and those lost by shrinking or exiting SMEs – the net job contribution – was positive in 15 out of 17 countries over the period 2016-19 (Figure 1.3). Moreover, the net job contribution of SMEs was larger than that of large firms in 13 out of 17 countries over the same period. This heightened churn reflects the flexibility and adaptability of SMEs, which often operate in more volatile or competitive sectors, where the ability to innovate and adjust to changing market conditions is critical.

Figure 1.3. SMEs create more jobs than they shed in 15 out of 17 countries

Share of new jobs and job losses by SMEs in total job changes (new jobs + job losses), 2016-19

Note: SMEs have 10 to 249 employees. New jobs are calculated based on the difference in total jobs between 2016 and 2019 for growing SMEs. Job losses are calculated in the same way for shrinking SMEs.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

SMEs that scale up overcome growth barriers that hold back other firms

The typical SME maintains its size over time. The OECD scale-up database provides information on the median growth rates of SMEs in each country over a three-year period, i.e., half of the SMEs in the country grow slower than this rate, and the other half grow faster. From 2011 to 2020, half of the SME population grew at a yearly growth rate of 1% or less in employment and of 3% or less in turnover. However, in most countries, the overall SME sector showed faster growth, with the average growth rate being one percentage point higher than the median value in employment, and one to seven percentage points higher in turnover. The difference between the growth rate of the median SME and the average growth of all SMEs indicates that a few high performers are driving up the average growth of the entire SME sector.

Internal capacity constraints and external factors hinder many SMEs from growing. Internally, SMEs often lack access to necessary managerial skills, technology or innovation assets, and face difficulties in accessing international markets and partners, compounded by challenges in financing expansion. Externally, they face a disproportionate regulatory burden compared to larger firms, including complex compliance requirements and navigating bureaucratic processes, which consume significant resources that could be used for development, thus stifling their ability to scale and innovate (OECD, 2023[4]). Regulation, in some cases, increases with the size of the firms, imposing additional costs on growth. For instance, estimates for France show that the cost of complying with employment regulations that apply to firms with 50 or more employees is equivalent to 2.3% of the firm’s workforce costs (Garicano, Lelarge and Van Reenen, 2016[5]). Furthermore, many business owners prioritise personal autonomy over expansion, indicating non-financial motivations such as the desire for flexible schedules and independence as their primary reasons for starting their businesses.4

Scalers are SMEs that grow rapidly over a three-year period. The most common definition of scalers, and the one used in this report, is companies that grow in employment or turnover at an average rate of 10% per year over 3 years (Box 1.2). The share of SMEs that scaled up in employment in 2020 refers to SMEs that grew in employment by at least 10% per year between 2017 and 2020, divided by the total number of SMEs active at the end of the scaling‑up period. Between 8% and 14% of all SMEs were scalers in employment, on average for the 2011-20 period across the 17 countries for which data are available. Scalers in turnover accounted for 12% to 24% of all SMEs. These relatively few SMEs combine the ambition to grow with the ability to overcome common SME growth barriers such as capacity constraints, lack of access to finance or skills, and regulatory burdens.

Box 1.2. The definition of scalers and high-growth scalers

In this report, “scalers” are small and medium-sized enterprises (SMEs) (with 10 or more employees) that achieve an average annual growth rate of at least 10% over a three-year period. This corresponds to an increase of 33.1% in either employment, turnover or both over three years. The definition builds upon the Eurostat-OECD Manual on Business Demography Statistics definition of high-growth enterprises and subsequent work that defines “medium-growth enterprises”. The Manual defines high‑growth enterprises as “all enterprises with average annualised growth greater than 20% per annum, over a three-year period, and with ten or more employees at the beginning of the observation period. Growth is thus measured by the number of employees and by turnover. The 20% growth target was set considering previous research from individual countries. The Manual also focused on so-called “medium-growth” enterprises, i.e. enterprises growing at an annualised growth rate of at least 10% for three consecutive years.

Growth is calculated using the formula:

$\sqrt[3]{\frac{{var}_{t+3}}{{var}_t}}-1$

Where ${var}_t$ represents employment or turnover at the beginning of the period, and ${var}_{t+3}$ denotes the values at the end of the three years.

In this report, “scalers in employment” are enterprises that meet the 10% yearly growth requirement in employment. “Scalers in turnover” grow at the same pace in turnover. In addition, the report identifies SMEs that scale up in both employment and turnover at the same time.

In line with the 20% growth threshold recommended in the Manual, “high-growth scalers” are defined in this report as the subset of scalers that achieve an average annual growth greater than 20% per annum over three years, which translates to a total growth of 72.8%.

The number of scalers is not exactly the same as the number of high-growth (or medium-high growth) enterprises in official statistics. There are two differences that can lead to discrepancies. First, official statistics can exclude enterprises that grow due to mergers or acquisitions. The exclusion of this type of – so-called “non-organic” – growth was not implemented in the construction of the database used for this report due to the complexity of ensuring sufficient harmonisation of its definition across countries. Chapter 2 provides a discussion on how mergers and acquisitions can impact scaling up based on evidence from a subset of countries with available data. Second, enterprises that are created in the first year of the three-year growth period are not considered among high-growth firms in official statistics, even if they meet the other criteria. This criterion was also excluded due to the limited availability of the “year of entry” variable in some countries.

Source: OECD/Eurostat (2008[6]), Eurostat – OECD Manual on Business Demography Statistics. OECD (2021[1]), Understanding Firm Growth: Helping SMEs Scale Up, OECD Studies on SMEs and Entrepreneurship, OECD Publishing, Paris. OECD (2023[7]), “Grow and Go? Retaining Scale-ups in the Nordic Countries”, OECD Regional Development Papers, No. 51, Ahmad, N. (2008[8]), “A proposed framework for business demography statistics”, OECD Statistics Working Papers 2006/03.

Scalers account for most of the SME contribution to job and turnover growth

Scalers are the main drivers of employment growth among incumbent SMEs. Scalers in employment contribute, on average, half of new jobs created by incumbent SMEs across all countries considered, over the 2017-20 period (Annex Table 1.A.1, column E). Without scalers, total SME employment would have been 7% to 18% lower in 2020 than in 2017. The significant and enduring contribution of fast-growing SMEs to job creation is persistent over time and across countries based on previous studies.5

It is easier for SMEs to scale up in turnover than employment

The contributions of scalers in turnover are larger than those of scalers in employment. Scalers in turnover account for 53% to 73% of all turnover growth of expanding SMEs (Annex Table 1.A.2, column E). This is a substantial contribution. If scalers had just followed the average growth rate instead of growing by 10% or more per year, total turnover by SMEs would have been 9% to 24% lower in 2020 than it was. Scalers in turnover outnumber scalers in employment (Figure 1.4 and Figure 1.5). This is the main reason why scalers in turnover provide a larger growth contribution, given that growth rates of scalers in turnover are only marginally higher than those of scalers in employment. On average across countries, the aggregate growth rate of turnover scalers in turnover was 86% over the 2017-20 period, which is 3 percentage points higher than the aggregate growth rate of employment scalers.

Figure 1.4. Between 8 and 14% of SMEs are scalers in employment

Share of scalers in employment in all SMEs, 2011-14; 2014-17; 2017-20

Note: Scalers in employment are firms with 10 employees or more that grow in employment by at least 10% per year, here as a share of the number of SMEs with 10-249 employees in the final year of scaling up. The sample includes scalers that ended their first 3-year scaling-up period in 2014, 2017 and 2020.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Figure 1.5. Between 12 and 24% of SMEs are scalers in turnover

Share of scalers in turnover in all SMEs, 2011-14; 2014-17; 2017-20

Note: Scalers in turnover are firms with 10 employees or more that grow in turnover by at least 10% per year, here as a share of the number of SMEs with 10-249 employees in the final year of scaling up. The sample includes scalers that ended their first 3-year scaling-up period in 2014, 2017 and 2020.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

There are several factors explaining why firms tend to scale in turnover more frequently than in employment. Firstly, turnover is a direct output of business activities, whereas employment represents just one of many inputs. Firms can increase production without necessarily expanding their workforce, e.g. by investing in capital goods like machinery or equipment. Secondly, changes in employment levels usually react only partially to variations in sales due to the fixed costs associated with hiring and laying off employees. Companies incur severance costs during downsizing and need to invest sizeable resources in recruiting. In the short term, firms might opt to meet labour demands through subcontracting or outsourcing. Thirdly, companies can outsource or subcontract some of the most labour-intensive parts of the production process or can hire temporary workers through employment agencies. Fourth, companies can grow in employment abroad by hiring workers in foreign affiliates but raising turnover at their domestic headquarters location. Finally, digital technologies enable some firms to “scale up without mass”, leveraging tools that enhance productivity and operational efficiency without the need for proportional increases in physical assets or workforce. Consequently, there are 50% more SMEs scaling up in terms of turnover than in terms of employment.

Scalers in turnover are more frequent than scalers in employment in countries with sustained economic growth and labour shortages. Romania, Estonia, Hungary and Croatia had annual average per capita growth rates of gross domestic product (GDP) ranging from 3.5% to 6.6% in real terms between 2017 and 2019, the years immediately before the COVID‑19 pandemic.6 These countries had larger shares of scalers in turnover compared to others, ranging from 20% to 24% (Figure 1.5), whereas shares of scalers in employment range from 10% to 11% (Figure 1.4). In contrast, France, Italy, and Austria, with GDP per capita growth rates of 0.7%-1.9%, had the lowest shares of turnover scalers. Romania, Estonia, Hungary and Croatia also had low unemployment rates and a competitive job market, partly reflecting significant emigration (OECD, 2023[9]). The growth potential of SMEs in these countries was curtailed by a lack of available workers, which is reflected in a higher share of turnover scalers compared to employment scalers.

Figure 1.6.  High-growth scalers account for 25% of all scalers and 66% of scalers’ contribution to new jobs and additional turnover

Share of high-growth scalers among all scalers, 2011-14; 2014-17; 2017-20

Note: High-growth scalers are firms with 10 employees or more that grow by at least 20% per year in employment or turnover. Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover, including high-growth scalers. The sample includes scalers that end their first 3-year scaling-up period between 2011 and 2014, 2014 and 2017 and 2017 and 2020 (Data for Germany does not include 2017-20 scalers).

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Among scalers, most contributions come from the “high-growth scalers”. High-growth scalers are the fastest-growing scalers, which grow in employment or turnover by 20% on average over three years (i.e., by more than 72% over the triennium, see Box 1.2 for details). Two-thirds of the contribution of scalers to job and turnover growth comes from these “high-growth scalers”. On average across 17 countries, high-growth scalers in employment account for 22%-28% of all scalers in employment but contribute 54%-75% to aggregate job creation by scalers. Similarly, high-growth scalers in turnover account for 24%-34% of all scalers in turnover and contribute 53%-72% to aggregate turnover creation by scalers (Figure 1.6).

The construction sector shows the largest discrepancy between the number of scalers in turnover and the number of scalers in employment. In this sector, turnover scalers are twice as numerous as employment scalers. SMEs in the construction sector are involved in subcontracting relationships to a greater extent (51% of SMEs in the sector) compared to those in other sectors (EIM Business & Policy Research and Ikei, 2009[10]). To mitigate the risk of “excess hiring”, many main contractors subcontract the work to specialist traders with the necessary workforce to complete tasks (Ng and Tang, 2010[11]). This is linked to evidence that in the construction sector, SMEs experience higher volatility in growth throughout business cycles, with scaling‑up episodes often followed by downturns. The higher volatility may be linked to specific characteristics of the market, such as the procurement of public works organised around large contracts, or “boom-and-bust” cycles in real estate investments. In this context, construction SMEs that are scaling up in terms of turnover may be more reluctant to undertake long-term investments in expanding their workforce (OECD, 2021[1]). In the construction sector, consequently, 21% of SMEs are scalers in terms of turnover, and only 10% are scalers in terms of employment, on average across all countries.7

The contribution of micro firms that scale up

About 90% of enterprises are micro firms with 1 to 9 employees (Figure 1.1). However, assessing their contribution to growth is complicated by gaps in administrative data in certain countries. These firms may not be fully captured due to their non-incorporated status or their registration as simplified entities, such as sole proprietorships, which have fewer reporting obligations. Consequently, administrative datasets may not provide comprehensive coverage of all micro firms, offering limited insights into their growth. For example, the analysis in this report for countries like Italy and Spain draws from balance-sheet databases that only include shareholder companies, thereby excluding a significant segment of micro firms that adopt a different legal form, such as sole proprietorship. This section presents the main findings that can be identified from the available microdata sources in selected countries with adequate coverage of micro firms.

The contribution of micro firms to job creation

Across 6 countries with adequate data coverage for micro firms, Belgium, Croatia, Denmark, Finland, Germany, and Latvia, micro firms account for 37%-51% of new jobs and 28%-49% of job losses (Figure 1.7). Like SMEs, most surviving micro firms are stable in employment over time. However, compared to SMEs, micro firms have enhanced entry and exit dynamics: entrants account for 8%-17% of new jobs and exits account for 2%-16% of job losses by micro firms. Entry rates are high because most new businesses are micro firms (Geurts and Van Biesebroeck, 2016[12]). Micro firms are also more likely than larger firms to exit the market if they experience a market downturn, as they do not have any employment or financial “buffer”.

Figure 1.7. Micro firms account for 37%-51% of new jobs and 28%-49% of job losses

Share of new jobs and job losses, by non-micro firms (i.e. large firms and SMEs), micro firms, and entrants

Note: New jobs and job losses are calculated for the period from 2016 to 2019. Micro firms are defined as those with 1 to 9 employees. Non-micro firms include SMEs, which have 10 to 249 employees, and large firms, which have 250 or more employees.

Source: Microdata from six countries.

The contribution of micro firms that “scale up”

Micro firms are technically not considered “scalers” due to the challenge of comparing their growth with larger SMEs. For example, any company with 3 employees that hires an additional employee would be a “scaler” that achieved more than 33% employment growth over three years. However, given the number of micro firms and the number of jobs in them, they also make an important contribution to job creation and warrant consideration. In a pilot exercise, Eurostat applied a new methodology that requires a minimum level of growth in absolute numbers for a micro high-growth enterprise (HGE) of 3.31 employees over a 3-year period.8 With this threshold, micro HGEs experience the same absolute growth as a high-growth enterprise that starts the high-growth period with 10 employees and grows, on average, at 10% per year. The resulting statistics show that around 15% of enterprises with 5 to 9 employees and around 5% of enterprises with 1 to 4 employees are classified as micro high-growth enterprises.

Building upon prior experimental approaches, the OECD scale-up database identifies “micro scalers” as firms with less than 10 employees that grow by at least 20% per year, on average, for 3 consecutive years. This definition is aligned with the growth rate threshold used to identify high-growth scalers. Across the six countries with adequate coverage of micro firms in the underlying data, 12% of micro firms become high-growth scalers (Figure 1.8). Micro scalers have a different profile than SMEs that scale up. They are, on average, younger and pay lower salaries. The likelihood of scaling up among young micro firms is about twice that of mature micro firms, on average, across the six countries. As a result, 50% of micro scalers are young firms with a maximum of five years of activity, compared to 35% of high-growth scalers. Average compensation per worker is, on average, 27% lower in micro scalers than in high‑growth scalers, with the greatest difference present among mature firms (31%).

Figure 1.8. Micro firms are more likely to scale up than SMEs

Share of micro firms that scale up in all micro firms; share of high-growth scalers in all SMEs by age class, 2020

Note: Micro scalers are defined as firms with 1 to 9 employees which grew in employment or turnover by at least 20% per year on average over three consecutive years. High-growth scalers are defined as SMEs that grew in employment or turnover by at least 20% per year, on average, over three consecutive years. Young firms are 0-5 years old, medium-age ones 6-10 years and mature firms are 11+ years old.

Source: Calculations based on microdata sources from six countries. See Annex Table 1.B.2 for more information.

Scaling up strengthens competitiveness through productivity growth

SMEs that scale up strengthen their economies’ global competitiveness (Box 1.3). To scale up, SMEs invest more in advanced technologies, innovation, and skilled labour, further boosting their productivity. In addition, larger-scale operations facilitate greater access to international markets through improved logistics, marketing capabilities, and compliance with global standards. The enhanced competitiveness and large scale enable scalers to be better positioned to compete internationally and can expand their market shares (see Chapter 2). The expansion into international markets allows scalers to diversify their customer base and supply chain, mitigating risks associated with dependence on local markets or having few suppliers, thereby increasing resilience to shocks affecting individual economies. This aligns with the strategic objectives of many OECD countries to enhance economic self-sufficiency and reduce vulnerabilities to market fluctuations.

Box 1.3. What is competitiveness?

Competitiveness has increasingly become a focal point for researchers, governments, and businesses since globalisation began reshaping the roles of countries and companies in highly competitive international markets. The understanding of competitiveness has evolved over time, reflecting shifts in global dynamics. While the primary focus of competitiveness is to bolster a country’s economy and foster prosperity, its scope extends beyond individual countries to include industries, firms, and, at times, smaller geographic regions.

Competitiveness is often understood as a synonym of productivity. The elements of competitiveness have been intensively studied since Porter (1990). He argues that ‘the only meaningful definition of competitiveness is productivity’. Subsequent research has explored various dimensions of competitiveness, including exports, market shares, and technological capabilities. More recent approaches advocate that competitiveness should be sustainable over time, which involves a consideration of resilience and of social and ecological factors (such as impacts on social transfers, unemployment rates or energy intensity) in addition to more traditional cost elements. In a rapidly transforming world, no single theory or model of competitiveness remains universally applicable, but each offers valuable insights into explaining a nation’s success on the international economic stage.

As Porter (1990) and Krugman (1994) emphasise, the source of competitiveness lies within firms, as it is firms – not countries – that compete in international markets. Ultimately, the economic value generated by firms enhances industry competitiveness, which, in turn, contributes to national prosperity. Therefore, the role of the countries to create a conducive environment for firms and industries productivity. Policies and other measures need to be directed toward industries to help them capitalise on this favourable environment.

Source: M. Porter (1990). The competitive advantage of nations. London: MacMillan; A. Chikán (2008[13]). National and firm competitiveness: a general research model. Competitiveness Review: An International Business Journal; K. Aiginger, S. Bärenthaler-Sieber and J. Vogel (2013[14]). Competitiveness under New Perspectives. WWWforEurope Working Paper No. 44; P. Krugman, (1994[15]). “Competitiveness: A Dangerous Obsession”. Foreign Affairs.

Productivity is a main component of competitiveness

Productivity growth underpins competitiveness and ultimately economic well-being. In recent years, however, productivity growth in most OECD countries has been the weakest on record since 1950 (OECD, 2015[16]). Reviving productivity growth, therefore, becomes a pressing policy objective, as it is closely tied to greater economic opportunities, inclusiveness, and the resources needed to fund public goods and services. Higher productivity also translates into larger household incomes, as more productive firms in competitive labour markets tend to pay higher wages (Shapiro and Stiglitz, 1984[17]) although the wage-productivity nexus has been weakening in recent periods (Sharpe and Uguccioni, 2017[18]; Stansbury and Summers, 2018[19]). For many OECD countries experiencing depopulation and ageing populations, improvements in labour productivity are particularly crucial to sustaining current levels of economic well-being.

Productivity measures the efficiency of producing goods and services with labour, capital, knowledge, technology, and other inputs. At the firm level, productivity is typically measured through labour productivity or multi-factor productivity. Labour productivity calculates the goods and services produced per unit of labour (such as workers’ headcount or worked hours). It is a simple and intuitive measure but it does not allow for disentangling changes in productivity due to improved workforce skills or investments in complementary inputs, such as machinery or technology. Multi-factor productivity assesses how efficiently inputs, including a firm’s workers and capital assets, are used to generate output, offering a more comprehensive view of productivity. For example, if the increase in output is entirely explained by the acquisition of new machinery, labour productivity increases, while multi-factor productivity stays constant (Box 1.4).

Box 1.4. What is productivity? And how is it measured?

Productivity refers to how efficiently inputs (like labour and capital) are converted into outputs (goods or services). For a firm, higher productivity means the capability to produce more output with the same amount of inputs.

Labour productivity and multi-factor productivity

At the firm level, productivity can be measured through Labour Productivity and Multi-Factor Productivity:

• Labour Productivity (LP) measures the amount of goods and services produced per unit of labour input, commonly expressed as output per worker or output per hour worked. Labour productivity is straightforward to calculate but does not allow to disentangle changes in productivity related to improved workforce skills or investments in machinery or technology.

• Multi-Factor Productivity (MFP) considers multiple inputs used in production, not just labour. It assesses the efficiency with which a combination of inputs, such as capital, labour, and materials, is used to produce output.

Challenges in measuring firm-level productivity

Accurately measuring productivity at the firm level involves several challenges, including the availability of reliable and consistent data on all inputs and outputs. In many cases, especially for smaller firms, such data may not be readily available or systematically collected. Furthermore, variations in mark-ups, which are the differences between the cost of producing a good or a service and its selling price, can further complicate the measurement of productivity. Mark-ups can fluctuate based on market power, competition, and demand changes, obscuring the true efficiency of resource use in producing output. Addressing these issues requires robust methodologies and data on price, quality differences, and market conditions, which are not always available.

The OECD scale-up database contains both labour productivity and multi-factor productivity for comprehensive analyses. 12 countries in the database have the necessary data for labour productivity, defined as the amount of value-added per employee. Multi-factor productivity, which takes into account both capital and labour inputs, is calculated for 11 countries. Due to data constraints, a parsimonious approach – the “Solow residual” method – is used. The Solow residual method measures firm-level productivity by calculating the output that cannot be explained by the inputs of labour and capital alone. The formula is $Y=A\times K^{1-\alpha }\times L^{\alpha }$, where $A$ denotes multi-factor productivity, $Y$ is the firm’s output, $K$ represents the physical capital stock of the firms, $L$ the number workers in the firms and $1-\alpha /\alpha$ the elasticity of capital/labour and their sum the economies of scale, i.e. an increase in both capital and labour input by 1% each raises output by 1%.

Source: Elaboration based on Schreyer and Pilat, (2001[20]) and Gal (2013[21]).

Productivity growth enables firms to produce goods and services more efficiently, reducing their price and enhancing profitability. By lowering costs, firms can offer more competitive pricing, which helps them capture a larger market share. This increased market share strengthens their position domestically and provides a stronger foothold for expansion into international markets. More productive firms have a significant advantage in a global economy characterised by intense competition. They can respond faster to changing market demands, invest in innovation, and scale their operations more effectively. This heightened competitiveness allows them to enter new markets, sustain long-term growth, and fosters resilience against global economic downturns.

Larger firms benefit from productivity gains through economies of scale. Growth is especially important for smaller firms as it helps them to operate at a more efficient scale (Bartelsman and Doms, 2000[22]). This is due to the ability to spread fixed costs of production, e.g. rental cost for a building, the cost for accounting software, developing and maintaining a website, etc. over a larger number of units of output, reducing the average cost of production of their goods and services and thereby achieving greater cost efficiencies. On average across 11 countries, the median labour productivity of SMEs in the size class 50-99 is 11% higher than in the size class 10-19 (Figure 1.9).

Figure 1.9. Larger SMEs are more productive than smaller ones

Labour productivity of SMEs and scalers (SMEs with 10-19 employees = 100), 2020

Note: Unweighted averages across 12 countries. Labour productivity is defined as the amount of value-added per employee. Scalers include both scalers in employment and scalers in turnover in the 2017-20 period.

Source: Calculations based on microdata sources from 12 countries. See Annex Table 1.B.2 for more information.

Scalers contribute indirectly to aggregate productivity growth by attracting labour and capital from less productive firms. To the extent that scalers are more productive than other SMEs, simply by expanding, they contribute positively to aggregate productivity growth, even if their average productivity remains constant as they grow. Indeed, evidence for manufacturing firms in Canada and the United Kingdom finds that more than half of productivity growth is due to the expansion of the most productive firms, rather than due to the growth in productivity of the average firm (Disney, Haskel and Heden, 2003[23]; Baldwin and Gu, 2006[24]). Economists refer to this process as “allocative efficiency.”

Scalers are important for productivity and productivity growth

To scale up, SMEs typically invest in physical capital, workforce skills, and productivity enhancements either before or during the scaling-up process. Some SMEs make these investments early to position themselves for growth, while others wait until they face a surge in market demand to ramp up their resources. The timing and nature of investments vary, as growth models differ based on each SME’s competitive advantages, industry characteristics, and broader business environment (see Chapter 2). These factors collectively shape how and when SMEs take steps toward expansion.

Without scalers, median multi-factor productivity in the economy would have been 6% lower in 2020, on average, across 11 countries with available (Annex Figure 1.A.2. ). This includes the direct contribution of scalers to productivity as they become more productive and the indirect contribution as scalers grow larger, thereby raising the productivity of a larger number of workers and a larger share of the capital stock in a country.

Scalers in service sectors have higher multi-factor productivity before scaling up compared to other SMEs across 11 countries with available data (Figure 1.10). There are smaller economies of scale in the service sector than in the manufacturing sector, meaning that subsequent growth in size does not lead to significant productivity increases.9 As a result, these firms typically become highly productive first and then use that productivity as a foundation for further growth. In 2017, the productivity premium for scalers in employment in the following three years ranged from 9% in non-tradable service sectors to 30% in other (i.e., not advanced) tradable services. The pattern is very similar for SMEs that scale up in turnover, with the productivity premium for scalers ranging from 9% in advanced tradable services to 31% in other tradable services.

Figure 1.10. In service sectors, future scalers are more productive than other SMEs

Multi-factor productivity in 2017 of 2017-20 scalers, other SMEs that do not scale up in each sector=100

Note: Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-20. Multi-factor productivity is calculated as of 2017 for both scalers and other SMEs (see Box 1.4), using unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

In manufacturing and construction, scalers tend to be less productive than other SMEs before scaling up, as upfront investments are necessary to prepare to grow. For both scalers in employment and scalers in turnover, the multi-factor productivity of scalers in medium-high/tech manufacturing and construction sectors is slightly lower than that of other SMEs in the same sectors. In medium-low tech manufacturing sectors, there is a larger gap in productivity between scalers and other SMEs, with employment scalers showing 16% lower productivity and turnover scalers showing 9% lower productivity compared to other SMEs.

Scalers within the manufacturing and construction sectors require substantial investment in physical capital, such as machinery and equipment, as they prepare to scale up. The median ratio of fixed assets over turnover of prospective scalers is about 10-15 percentage points higher than in other SMEs in the same manufacturing or construction sectors, implying that prospective scalers accumulate more fixed assets compared to their peers (Figure 1.11).10

Figure 1.11. Scalers in manufacturing sectors have high capital intensity before scaling up

Capital intensity in 2017 of 2017-20 scalers and other SMEs, non-scaler SMEs in each sector=100

Note: Capital intensity is defined as the amount of tangible fixed assets per unit of turnover. Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-20. Capital intensity is calculated as of 2017 for both scalers and other SMEs, using unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

As construction and manufacturing scalers expand, they reduce their capital intensity relative to their size and eventually realise productivity gains.11 The shift results from achieving economies of scale, as well as possibly through process innovations that enhance efficiency. As the capital cost per unit of output decreases, MFP increases. This highlights the fact that for smaller firms in manufacturing and construction, scaling up may be necessary to reach a minimum efficient scale (i.e., the smallest size at which a firm can produce its goods or services, achieving the lowest possible average cost per unit). For instance, in advanced tradable service sectors, the multi-factor productivity levels of SMEs that have scaled up are 28%-38% higher than those of other SMEs. In manufacturing and construction sectors, the productivity of scalers is lower than that of other SMEs before scaling up, but the gap in multi-factor productivity with other SMEs entirely disappears once they have scaled up (Figure 1.12).12

Figure 1.12. After scaling up, scalers in service sectors are more productive than other SMEs

Median multi-factor productivity (MFP) of 2017-20 scalers as a percentage of median MFP of other SMEs in the same country and sector group, year 2020

Note: Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-2020. Multi-factor productivity is calculated as of 2020 for both scalers and other SMEs (see Box 1.4), using unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

Scalers contribute directly to aggregate productivity growth by increasing their own productivity. Productivity growth of scalers outpaces that of other SMEs across all sectors. As scalers grow, they increase output at a rate that exceeds the growth in their physical capital and labour inputs. As a result, the productivity of scalers grows faster than the productivity of other SMEs (Figure 1.13). As scalers in service sectors are more productive already before scaling up, they widen the productivity premium over non-scaler SMEs during the scaling-up period.

Figure 1.13. Scalers increase multi-factor productivity faster than other SMEs

Percentage point differences in the average growth rate of multi-factor productivity between scalers and other SMEs in the same country and sector group, 2017-20

Note: Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017‑2020. Differences at country and sector group level are calculated using median multi-factor productivity (see Box 1.4) growth rates from 2017 to 2020. Unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

Leveraging scalers’ competitiveness for aggregate growth

The contribution of scalers to aggregate job and economic value creation consists of three key components: the number of scalers, their growth rate, and their average size before growing (Box 1.5). The different combinations of scalers’ shares and growth rates determine their varying contributions across countries. While the share of scalers and their growth rates vary significantly across 17 countries, differences in their average size remain relatively minor. The share of scalers in employment ranges from 8% to 14%, and three-year growth rates range from 69% to 92% across 17 countries. In 14 out of 17 countries, the average size of scalers in employment differs by no more than 10% from the average size of all SMEs. The share of scalers in turnover ranges from 12% to 24%, and three-year growth rates range from 74% to 103% across 17 countries, and the average size in 14 out of 17 countries lies within a 20% range around the average.

Box 1.5. Breakdown of contributions to job and value creation

The ratio of job creation of scalers over the total employment of SMEs at the beginning of the period is algebraically equivalent to the product of three terms: the average size of scalers at the beginning of the period relative to all SMEs, the average growth rate of scalers, and the share of scalers in all SMEs (Figure 1.14). The same breakdown applies to scalers in turnover, whereas employment metrics are replaced with turnover metrics. The breakdown allows for a nuanced understanding (and ultimately targeted interventions) to improve the job and value creation of potential scalers across different economies. For example, if a country has a high number of scalers but a low average growth rate, efforts could be directed towards removing barriers to growth. Conversely, if there are few scalers but those that exist are growing rapidly, the focus might shift to encouraging the formation of more start‑ups that can become future scalers.

Figure 1.14. The contribution of scalers to growth is a function of their share, their growth rate and their average size

Increasing the number of SMEs that scale up appears to be more conducive to aggregate growth than having fewer scalers with higher growth rates. While variations in scaler growth rates occur in countries with both significant and minimal contributions from scalers to job and economic value creation, the share of scalers among SMEs consistently predicts whether their aggregate job or turnover contribution is high or low. The evidence suggests that addressing the structural and policy barriers that limit the share of scalers in these countries could lead to larger aggregate job creation.

A high share of scalers is associated with high contributions to job creation by scalers in most countries. Countries with the highest contributions by scalers to total job creation have both a large share of scalers and high growth rates among scalers. When comparing the share of scalers to their growth rates, countries with a higher share of scalers are more likely to demonstrate greater contributions than those with higher growth rates alone. Among the eight countries where the share of scalers is above the cross-country median, six have high contributions by scalers to job creation (Figure 1.15). The six countries with the lowest contribution of scalers to job creation have a share of scalers in all SMEs below the cross‑country median (Figure 1.16). In countries with the highest contributions, such as Finland, France and Spain, both the share of scalers and the growth rate of scalers is also higher than the cross‑country median. However, a high growth rate of scalers on its own is not sufficient to make a high contribution to total job creation. Although the growth rates of scalers in Belgium, Latvia and the Slovak Republic are at least four percentage points higher than the cross-country median, scalers’ contributions to job creation in those countries are lower than the cross-country median.

For turnover creation, it is also countries with a high share of scalers among all SMEs where they make the largest contributions. In the Netherlands and Romania, both high growth rates and a high share of scalers result in the largest contributions to turnover creation among the analysed countries. In Estonia and Latvia, a contribution to turnover creation above the median value is driven uniquely by a high share of scalers, as the three-year growth rates are below the cross-country median. Despite having scaler growth rates close to the median, Austria, France and Italy show the lowest contribution to turnover creation due to their economies' low share of scalers.

Figure 1.15. Scalers’ contribution to job and turnover creation varies by country

Note: Job and economic value creation (i.e. turnover) are calculated for the period 2017-20 (2016-19 for Germany). Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-2020. High/low countries are those with above/below median percentages of job or turnover creation by scalers.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Figure 1.16. Countries with high job or turnover creation by scalers have high shares of scalers

Growth rates are calculated for the period 2017-20 (2016-19 for Germany). Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-2020. Countries in blue/red have above/below median percentages of job or turnover creation by scalers (see Figure 1.15).

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Policies supporting SME scaling up span many fields

The emergence of scalers in a country depends on many interlinked factors, calling for a holistic policy approach aligned with the country’s objectives. Governments across all OECD countries use a wide set of policies to promote SME growth. These policies include, among others, measures to promote access to finance, innovation, skills, internationalisation, and leadership, as well as programmes that accompany SMEs in innovating, entering new or international markets, and expanding networks. Some of these policies are broad, aiming to create a favourable business environment, while others are targeted at a specific sub-group of SMEs, and others aim to resolve specific barriers to SME growth (e.g. reducing the cost of external financing). The co-existence of different policies in a country allows the pursuit of multiple policy objectives. For instance, policies that aim at favouring the emergence of high-growth innovation-based start-ups are deployed alongside policies that aim at boosting the growth of older, more traditional SMEs in terms of value added or employment. The balance between different types of policies greatly depends on each country’s objectives, economic structure, and general context.

The OECD’s SME and Entrepreneurship policy dashboard maps relevant national policies and implementing institutions that support SME scale-up (Box 1.6). The scope of the dashboard includes national-level policies that contribute to the growth of SMEs and entrepreneurship. Policies are defined as public actions that aim to achieve one or several policy goals, either by modifying the behaviours of actors or stakeholders that are part of (or influence) the national SME and entrepreneurship sector, or by altering the governance of the SME and entrepreneurship policy structure as a whole. The dashboard provides information on five policy areas that have been identified as important factors for SME growth. These areas are i) Access to finance, ii) Data governance, iii) Trade, iv) Innovation networks, v) Skills. The dashboard is a repository of 2 578 policy elements implemented by 517 institutions across the 38 OECD countries across these five key policy areas. The dashboard maps policy elements rather than policies because several policies contain multiple measures relevant for different policy areas. For example, one single policy can at the same time provide financial support for SMEs and advisory services for exporting SMEs.

Box 1.6. The OECD’s SME and Entrepreneurship policy dashboard

The OECD’s SME and Entrepreneurship policy dashboard maps policies and institutions in five policy areas in the 38 OECD member countries (Table 1.1). Collected data allow to assess: i) Number of policies and institutions per country and policy area; ii) core mandates of implementing institutions across policy areas; iii) main policy instruments; iv) the degree or nature of targeting across policy areas; v) synergies across policies. More details about the dashboard development process are provided in Annex 1.C and Annex 1.D.

The five policy areas are:

• Finance: The policy mapping and analysis focus on public policies that can unlock internal or external sources of finance to support SME activities related to scaling up, i.e. innovation, investment, and network expansion. The policies that governments can implement are therefore defined by the type of transformation that scalers are going through, i.e. the scale-up drivers they rely on to grow their business and capacity. Such policies can either be aimed at SMEs themselves (for unlocking internal resources, or for addressing demand-side barriers), or at the financial market and institutional actors (for unleashing external finance).

• Data governance: The policy mapping and analysis focus on public policies that can help SMEs turn data into economic value and capitalise on internal and external data to scale up capacity and grow business. As such, policy intervention for improving SME data governance falls into two categories. First, SME data policy can aim to improve SME access to external data, which is largely shaped by the degree of openness the policy environment allows for (but also the willingness of business partners to share data). Second, SME data policy can aim to incentivise and enable better exploitation and protection of data within the firm, both approaches aiming ultimately to greater business, economic, environmental, or social value for the firm.

• Skills: The policy mapping and analysis focus on public policies that can help SMEs meet changing skill needs by accessing or developing relevant “skill bundles”, with a focus on three main categories: digital skills, green skills, and transversal skills – or any combination of these. Across these three skill categories, the analysis covers four main policy approaches, i.e. strengthening SME demand for skills; activating the supply of skills to SMEs; developing relevant skills and improving skill use within SMEs, as well as facilitating SME access to skills via their ecosystem, e.g. by connecting them to relevant service providers or education institutions.

• Trade (and internationalisation): The policy mapping and analysis focus on public policies aiming to strengthen SME positioning in national and global production and supply chain networks, focusing notably on export-related support mechanisms as well as on policies for strengthening SME value chain linkages, e.g., via supplier development programmes.

• Innovation networks: The policy mapping and analysis focus on public policies aiming to strengthen SME linkages to R&D and innovation networks, including through protection of intellectual property and commercialisation of research (e.g., via company spin-offs); SME integration into clusters; and the formation of strategic partnerships involving SMEs. Across all network types, the analysis would also consider measures that promote the use of platform technologies and knowledge-intensive business services.

Policies covered by the project meet three conditions.

1. They are implemented over a set time horizon or on a continuous basis, generally excluding those put in place as one-off events or as result of emergency situations (unless they pursue a transformational objective).

2. They are implemented at the national level (although some international and regional policies may be considered when relevant).

3. They must explicitly address one (or more) strategic objectives relevant to selected policy areas. The policies repository used in this report reflects the situation as of January 2024.

The qualitative information contained in the policy dashboard is best suited for mapping countries' policies and comparing countries' approaches rather than attempting to establish quantitative relationships between policies and the incidence and growth of scalers. A sounder approach would rely on a collection of impact evaluation studies. However, only about 2% of collected policies have been evaluated, which represents a structural challenge in establishing causal relations between policies and outcomes.

Table 1.1. Policy mapping overview

Count of policy elements and institutions by policy area across all OECD countries

	OECD Total
Policy area	Policy elements count	Institutions count
Finance	846	220
Data governance	492	219
Trade	548	147
Innovation Networks	444	159
Skills	248	136
TOTAL	2 578	517

Note: The total number of institutions does not match the sum of institutions by policy area because a single institution can be assigned to multiple policy areas.

Source: OECD SMEs and Entrepreneurship Policy Dashboard

Policies for SME growth use multiple approaches and instruments

All OECD countries support SMEs and entrepreneurs through multiple policies, albeit with different modalities. All OECD governments adopt measures that cover all five aspects of SME and entrepreneurship support. The number of policy elements in place, however, varies markedly across countries, ranging from roughly 30 to 40 policies in Colombia, Costa Rica, Latvia, Mexico, or Switzerland, to over 100 policies in Ireland, Korea, Sweden and the United Kingdom (Figure 1.17) and reflecting different preferences in designing policy interventions. Some countries tend to deploy many complementary policies that address different issues, while others tend to concentrate all measures in a smaller set of broader policy instruments. All 38 OECD countries have established policies across the policy areas identified above.

Figure 1.17. The number of SME and entrepreneurship policies varies greatly across countries and policy areas

Total SME and entrepreneurship policy count by policy area and country

Note: 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

Although all countries adopt policies in the five areas, the highest share of policies are directed at supporting SMEs’ access to finance and trade. Access to finance (33%) and support to SMEs’ trade (21%) are the two most common policy areas, with large variations across countries. For example, in Czechia, Denmark, the Slovak Republic, and Sweden, between 30% and 45% of policies support SME trade, while in Canada, Chile, Finland, and Mexico, roughly 50% of policies address access to finance. There are other approaches, as, for example, in New Zealand and Portugal, where policies are almost evenly distributed across the five areas.

Complementarities and synergies cause some policies to fall under multiple policy areas. About a quarter of policies (26.8%) fall under at least two policy areas. For instance, a policy may promote access to finance for SMEs that intend to develop their activities internationally. Consequently, the same policy would be counted as both a finance policy element and a trade policy element. In Australia, for example, Export Finance Australia provides support for SMEs’ overseas investments through loans or guarantees to Australian firms. This financial support instrument provides not only access to finance but also supports the overall growth of the business, with an economic benefit for the domestic economy.

Some policies exhibit more synergies with others that share common objectives. For example, ”Trade” and “Innovation networks” frequently appear in policies associated with two or more policy areas (over 30%), while “Data governance” and “Skills” are less commonly included in multi-area policies (10%). 90% of multi-area policies involve exactly two areas, while policies addressing three or four policy areas are rare. There are no policies in the database that span all five areas.

“Trade” and “Finance” policies are the most synergistic, followed by “Trade” and “Innovation networks” policies and “Finance” and “Innovation networks” policies. Out of all policies that share exactly two common areas, 39% are “Trade” and “Finance” policies, 22% are “Trade” and “Innovation networks” policies and 15% are “Finance” and “Innovation networks” policies (Figure 1.18). Many trade facilitation policies include a financial support element such as export loans, subsidies, or vouchers. International trade often requires mobilising financial resources, leading to the combination of financial assistance with trade support. For instance, in Czechia, the SME Export Guarantees programme supports exporting SMEs via the Czech Export Bank and commercial banks, by issuing guarantees for export financing.

Efforts to connect SMEs to networks, i.e. with other firms, suppliers or clients, also have a trade dimension that aims at easing access to foreign markets or to integrating SMEs into supply chains. For instance, Korea’s Global Business Centre offers a network of 12 locations in 20 countries to help SMEs enter international markets by providing a physical presence abroad, access to local incubators/accelerators and expert advice.

Improving SMEs’ access to finance can build on public-private partnerships and networks to connect domestic and foreign investors, with policies that are part of both “Finance” and “Innovation networks” policy areas. For instance, France’s International Capital Developments policy develops long-term partnerships with sovereign funds and other international institutional investors to co-invest in SMEs located in France.

Figure 1.18. Finance and Trade is the most common policy pair among policies associated with two policy areas

Share of policy area pairs

Note: Shares a calculated as a percentage of the policies that share exactly two policy areas across all OECD countries. Based on a mapping of a total of 2 578 policy elements in support of SME and Entrepreneurs across OECD countries

Source: OECD SMEs and Entrepreneurship Policy Dashboard

“Data governance” policies are mostly combined with policies supporting skill development to accelerate SMEs’ digitalisation, and to a lesser extent with policies supporting access to finance. “Data governance” and “Skills” policies often share the goal of supporting SMEs in increasing digital capabilities. Improving SMEs' data management frequently requires upskilling employees to use specific software and raising their awareness of new data regulations. For instance, the Danish SME Digital programme supports the digital transformation of Danish SMEs through private counselling and consulting services on digitalisation. Similarly, Hungary’s Digital Workforce programme includes short-cycle IT training and a longer-term strategy to improve the capacity and content of traditional training systems. “Data governance”-related policies are also linked to Finance policies. About 7% of policies aimed at boosting SMEs’ use of digital governance tools also include financial support measures. For instance, Ireland’s Digital Process Innovation programme offers grants to SMEs to support investments that accelerate the digital transformation of businesses. Latvia’s Innovation Voucher Support Services supports technology transfer for SMEs and access to highly qualified employees who can support the development of new or significantly improved products or technologies.

SMEs and entrepreneurship policies use a range of instruments, among which financial support is the most common, yet with different approaches across countries. Policies that include a financial support instrument (e.g. a grant or a subsidy) are by far the most common (46%) across all policies (Figure 1.19), followed by non-financial instruments such as training, mentorship or advisory services (27%). Policies that include platforms and networking instruments are less common (17%), while policies that include regulations (2%) or public policy governance instruments, such as action plans or benchmarking tools (8%), are few.

Figure 1.19. Most policies mapped by the OECD SME and entrepreneurship dashboard include a financial support instrument.

Share of instruments used across all policies (%)

Note: Based on a mapping of a total of 2 578 policy elements in support of SME and Entrepreneurs across OECD countries.

Source: OECD SMEs and Entrepreneurship Policy Dashboard

The number and types of policy instruments vary significantly by country. Financial support is often used but with different intensities across countries. Some countries (e.g. Canada, Finland or Luxembourg) include financial support in about 60% of their policies, while others (e.g. Colombia, Hungary, New Zealand or Switzerland) incorporate financial support in only 20-27% of their policies.

Figure 1.20. SME and entrepreneurship policies combine a mix of instruments

Count of instruments used across all policy areas, by country

Note: Based on a mapping of a total of 2 578 policy elements in support of SME and Entrepreneurs across OECD countries

Source: OECD SMEs and Entrepreneurship Policy Dashboard

Some instruments are particularly suited to specific policy areas. To some extent, the incidence of an instrument is aligned with a policy area’s support requirements. For instance, financial support is mainly used to provide access to finance or support trade, whereas it is significantly less used to support skills development or data governance in SMEs. Similarly, platforms are frequently used to support the establishment of networks.

Data governance is the area where public policy governance instruments are used relatively more. Data governance is a new and emerging dimension in national strategies with applications to almost all domains. Governments are experimenting with different public governance instruments to improve the legal framework around data, cybersecurity, or artificial intelligence (AI). For instance, Estonia’s Kratt Strategy is a comprehensive set of actions that the government plans to introduce to advance AI adoption in both private and public sectors, including the development of a suitable legal framework. Another example is Greece’s National Cybersecurity Strategy, which coordinates the measures the government plans to take to reduce the risk of incidents that may endanger the integrity of critical infrastructure, the functioning of the State, and the security of citizens.

Figure 1.21. Instruments and policy areas align

Distribution of policy instruments across policy areas, as a share of all policies in each policy area

Note: Shares are calculated as the number of policies using an instrument in each policy area divided by the total number of policies in each policy area, based on an unweighted count. Cumulated shares may be higher than 100% across categories, as policies can leverage several policy instruments at once.

Source: OECD SMEs and Entrepreneurship Policy Dashboard

Despite bridging over multiple policy areas, policies tend to use one instrument at a time. 81% of policies mapped have at most one instrument, 17% use two instruments, and only 2% policies use 3 or more instruments simultaneously. When two instruments are used, they most frequently combine non-financial support and platform instruments (52% of the policies using exactly two instruments). A relatively large share of policies (32%) also combines financial support with non-financial instruments (32%) or financial support and platforms (11%). All other combinations of instruments account for less than 5% of policies using two instruments. An example of a policy that combines non-financial support and platforms is Poland’s Future Industry Platform Foundation. This policy consists of a digital platform that allows matching international talent and SMEs through trainee programmes and co-creation models. An example of a policy that combines financial and non-financial support is Lithuania’s Entrepreneurship Promotion Fund, a programme that offers small loans to start-ups and SMEs to improve access to finance, combined with the use of training and advice support by other entrepreneurs. Another example is iNNpulsa Colombia, which combines financial support with non-financial support, such as mentoring and acceleration programs.

Figure 1.22. Most policies use one instrument, and when they combine multiple instruments, non-financial support and platform instruments are often combined

Share of policies using multiple instruments (panel A), and most frequent combinations of instruments among policies that use two instruments (Panel B)

Note: The five instruments considered are i) Financial support, ii) Non-financial support, iii) Platforms, iv) Regulation, v) Public policy governance. Shares are calculated as a percentage of the total number of policies across the OECD. Based on a mapping of a total of 2 578 policy elements in support of SME and entrepreneurs across OECD countries.

Source: OECD SMEs and Entrepreneurship Policy Dashboard

A large number of implementing institutions can create coordination challenges

Most of the institutions mapped are autonomous government agencies (47%), or departments in ministries (30%). The total number of institutions involved in SME and entrepreneurship policies varies significantly by country, ranging between 8 in Latvia and 26 in Sweden (Figure 1.23). Countries rely on different institutional arrangements. For instance, in Ireland, the United States, Poland, the Slovak Republic, and Estonia, autonomous government agencies represent over 60% of institutions involved in policies that can support SMEs in scaling up. In contrast, in Belgium, Denmark, Greece, Iceland, Italy, Mexico, the Netherlands, Slovenia, and Switzerland, autonomous agencies represent less than 30% of institutions mapped. In Italy and Slovenia, ministry departments account for over 40% of institutions involved with SME support policies. Also in Australia, Poland, Germany, Austria, Colombia, and Latvia, more than one-third of the institutions engaged in SME and entrepreneurship policy are ministry departments.

Figure 1.23. Ministries and autonomous government agencies lead the implementation of SME and entrepreneurship policies

Number of institutions implementing SME and entrepreneurship policies by type

Note: Number of institutions implementing policies in support of SME and Entrepreneurship at national level, based on an unweighted count, based on a mapping of a total of 2 578 policy elements and 517 institutions in support of SME and Entrepreneurs across OECD countries. Data for Belgium include institutions at both the national and subnational level.

Source: OECD SMEs and Entrepreneurship Policy Dashboard

The number of policies tends to increase with the number of institutions involved (Figure 1.24). Intuitively, a higher number of institutions may suggest greater capacity to manage programmes. However, the dashboard reveals different models to assign institutions to policies. Countries such as Australia, Belgium, Denmark, Spain, and Sweden feature a larger number of institutions for a similar range of policy elements compared to most other countries with similar policies. Both approaches can be suitable, depending on the institutional context of each country. However, they may present trade-offs related to coordination, efficiency, comprehensiveness, and specialisation across policy areas.

Figure 1.24.  The number of policies increases with the number of institutions

Number of policies and number of institutions by country

Note: Number of total policies (horizontal axis) and unique institutions count (vertical axis). 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

In most OECD countries, institutions are active in a single policy area. On average, 60% of OECD countries’ institutions work only on one policy area, 20% on two policy areas, 13% on three policy areas, while the shares of institutions that work on 4 or 5 policy areas are 4% and 3% respectively (Figure 1.25). In all countries, there is at least one institution engaged in each of the five policy areas mapped by the dashboard.

Figure 1.25. Most institutions focus on one policy area, but there are overlaps.

Institutions by country and number of policy areas they are involved with.

Note: Number of policies across the OECD. Common policies refer to the fact that the same policy falls under two or more of the policy areas under analysis. 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

Policy governance differs across countries. Belgium, for instance, is the country with the most institutions (80%) focused on a single policy area. In comparison, Japan is the country with the most institutions (71%) working on at least 2 policy areas, and New Zealand is the country with the highest share of institutions working on all five policy areas (20%). All these approaches underscore a wide heterogeneity of institutional contexts in which policies that can support SMEs in scaling up are delivered.

More than half of institutions implementing measures that can support SMEs in scaling up have multiple core mandates. Across OECD countries, nearly half (47%) of mapped institutions focus on a single core mandate, while the rest handle two or more (Figure 1.26). Specifically, 31% have two mandates, 11% have three, and another 11% manage four or more mandates. Institutional approaches vary by country: Australia, Canada, and Hungary have over 70% of institutions with one core mandate, whereas France and Poland feature the highest proportions of institutions managing more than three mandates (46% and 50%, respectively).

Figure 1.26. Most institutions involved with SME and entrepreneurship policies have one or two core mandates.

Number of core mandates of institutions conducting SMEs and entrepreneurship policies by country

Note: Number of national institutions with one, two, three or more core mandates). 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

Only about one-third (37%) of institutions working on SME and Entrepreneurship have a core mandate to implement SME and entrepreneurship policy. Roughly one-third of institutions that are implementing policies relevant to SMEs and entrepreneurship have a core mandate on SME and entrepreneurship (Figure 1.27). Examples of institutions with SMEs and entrepreneurship responsibilities are Start-up Portugal, KOSEM (the Korean SMEs & Startups Agency), Start-Up Chile, Belgian Flanders Agency for Innovation and Entrepreneurship, or the Canadian Business Development Bank, whose mandate is to support new and emerging businesses through growth and transition capital, venture capital and advisory services. A significant part of the mandate of these SMEs and entrepreneurship-focused institutions often includes activities related to start-up development.

A lack of clear responsibilities and limited joint implementation across institutions highlights coordination challenges in the SME and Entrepreneurship policy space. While multiple institutions work on different aspects of SMEs and entrepreneurship policy, overlaps are frequent and joint implementation remains rare, with only 24% of policies jointly administered, on average, across OECD countries. These findings suggest potential governance issues that could be addressed to enhance policy effectiveness, ultimately improving support for SMEs and entrepreneurs, including scalers.

Figure 1.27. Just about one-third of institutions involved in SME and entrepreneurship policies have SME and entrepreneurship as a core mandate

Share of institutions with a core mandate on SME and entrepreneurship policy by country

Note: Shares are calculated as a percentage of total national institutions involved in implementing policies in support of SMEs and Entrepreneurs, based on an unweighted count. 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

Innovation policy is the most frequent mandate of the mapped institutions, followed by SME and entrepreneurship policy (Figure 1.28). Other prominent policy mandates include trade and (foreign direct) investment promotion. However, there is a significant overlap related to the cross-cutting nature of policies supporting SME scale-up, as well as the diverse composition of the institutional landscape, which calls for sound coordination across institutions to better address the specific growth challenges faced by SMEs. For instance, in Canada, the Department for Innovation, Science and Economic Development within the Ministry of Innovation, Science, and Industry, has four core mandates, including SME and entrepreneurship policy, innovation policy, foreign direct investment (FDI) and investment promotion, and trade policy. In Slovenia, Spirit Slovenia is an autonomous government agency with three core mandates spanning SME and entrepreneurship policy, innovation policy, and FDI and investment promotion.

Figure 1.28. Core mandates of institutions conducting SME and entrepreneurship policies often include innovation policies

Count of institutions by core mandates, OECD aggregate

Note: Values are computed as the sum of mentioned core mandates of institutions involved in implementing policies in support of SME and Entrepreneurs. 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

Joint implementation of policies and institutional responsibilities on SME and entrepreneurship policy vary by policy area and by country (Figure 1.29). In terms of joint responsibilities, data governance is the area where joint policy implementation across institutions is most common (37%), whereas trade is the area where very few policies are jointly implemented by multiple institutions (15%). Countries where policies tend to be jointly implemented the most include Austria and Latvia (49% of policies), while countries where joint implementation is rare include Canada and Finland (less than 10% of policies). In terms of core mandate, the highest share of institutions with a core mandate in SME and entrepreneurship is found among institutions working on Finance (55%) and the lowest share among institutions working on Skills (26%). Among countries, the highest share of institutions with an SME and entrepreneurship core mandate is found in New Zealand and Estonia (about 60% of institutions), and the lowest shares are found in Australia and Hungary (less than 10%).

Figure 1.29. Most policies are implemented by individual institutions rather than jointly with others

Number of jointly-implemented policies by policy area

Note: Based on a mapping of a total of 2 578 policy elements and 517 institutions in support of SME SMEs and Entrepreneurs across OECD countries.

Source: OECD SME and entrepreneurship policy dashboard

Improving coordination and sharpening institutional responsibilities can benefit SME growth. The presence of institutions with a clear focus and responsibilities often correlates with the incidence of scalers, especially within the framework of a well-coordinated national strategy. For instance, in Belgium and the Netherlands, two countries that have achieved high turnover growth among scalers over the 2017-20 period, about 80% of institutions are focused on a single policy area. Although it is not possible to establish a causal relationship between institutional focus and the growth rates of scalers, an institutional focus on a single policy area may indicate that responsibilities are clear, possibly leading to more decisive policy action in that area. Effective institutional coordination must avoid silo effects. In this respect, including policies in a national action plan can act as a coordination mechanism, directing all actors towards reaching a common goal.

Improved coordination can be achieved through various institutional arrangements. These include, establishing a high-level Interdepartmental Council or Advisory Committee, creating an inter‑departmental council responsible for overseeing the coordination and implementation of the strategy, establishing an SME Strategy Task Force, providing adequate resourcing to the institutions involved in implementation to ensure their operational capabilities, adopt the practice of issuing Service Level Agreements with external delivery partners of SME support programmes and services to facilitate the execution and coordination (Potter et al., 2023[25]).

Scalers during the COVID-19 pandemic

The COVID-19 pandemic triggered an unprecedented global economic downturn. Global gross domestic product (GDP) contracted by approximately 3.4% in 2020, reflecting the impact of lockdowns, reduced consumer spending and halted production across multiple sectors. The contraction was more severe for OECD economies with a drop in GDP of about 4.7% (OECD, 2021[26]). Unemployment rates soared, particularly affecting low-income workers, young people, and women who are disproportionately represented in sectors most vulnerable to lockdowns, such as retail and hospitality. The unemployment rate in the OECD area peaked at 8.8% in April 2020, up from 5.2% at the end of 2019 (OECD, 2020[27]).

SMEs faced particularly severe disruptions. Many SMEs struggled with liquidity constraints, with up to one-third in several OECD countries fearing they might not survive beyond three months without additional financial support. The impact of the crisis varied dramatically across sectors, with industries such as tourism, hospitality, and retail experiencing turnover declines exceeding 50% during peak lockdown periods, while the information and communications technology (ICT) sector benefited from increased demand for digital services (OECD, 2023[28]; OECD, 2021[29]).

Crises often act as a catalyst for “creative destruction”, while potentially leaving permanent “scars” on existing businesses. During downturns, less productive firms may exit the market, freeing up resources for growing enterprises and new ventures that capitalise on emerging opportunities, thereby supporting recovery. However, crises can also restrict access to credit and reduce market demand, jeopardising the survival of financially constrained businesses, including start-ups. The COVID-19 pandemic accelerated the transformation within the business sector, leading to lasting changes in OECD economies. It reshaped consumer demand, causing structural declines in some sectors while opening new avenues in others, particularly through the increased adoption of digital tools by businesses, which is critical for competitiveness, as well as by households. This shift has paved the way for innovative products and services and enhanced cost efficiency through advancements like cloud computing and remote work (OECD, 2021[29]).

In several OECD countries, the COVID-19 pandemic sparked a surge in entrepreneurship, which may lead to an increase in future scalers. Typically, firm entry is procyclical. It rises during booms and declines in recessions. However, during the COVID-19 pandemic, entry was countercyclical in many OECD countries, with a rising number of entrants as output fell. Evidence from the United States and the United Kingdom suggests that the surge was influenced by changes in work and lifestyle choices accelerated by the pandemic, such as increased remote work and shifts in consumer demand. Individual entrepreneurs creating companies for the first time contributed the largest share of new businesses. Geographic and sectoral analyses reveal that the increases in business applications and formations were concentrated in certain industries such as online retail and professional services, and in less central urban areas, demonstrating a spatial and economic restructuring influenced by the pandemic. The surge relates to labour market phenomena like the "Great Resignation", as increased quit rates among workers coincide with rising business formations across places (Decker and Haltiwanger, 2023[30]; Bahaj, Piton and Savagar, 2024[31]).

Prompt interventions mitigated the adverse impact of the COVID‑19 pandemic

The COVID-19 pandemic showed the vulnerability of SMEs, including scalers, to shocks and the benefit of a system that supports them in times of need. Compared to large firms, SMEs tend to have smaller cash buffers, tighter liquidity constraints, weaker supply chains, less intense use of digital tools, and lower technology capabilities (OECD, 2021[1]).OECD member countries rolled out support measures that, on average, represented about 16.5% of GDP, combining emergency provisions and structural measures. These extensive financial efforts included furlough schemes and job support programmes, which were essential in stabilising employment and income levels. Such policies helped maintain workforce attachments to employers and provided businesses with the liquidity needed to survive the downturn. The interventions averted widespread business closures among high-potential but credit‑constrained SMEs, which could have otherwise resulted in a “lost generation” of scalers (OECD, 2021[1]).

During the COVID-19 pandemic, many countries activated existing automatic stabilisers or introduced ad-hoc measures to respond to the crisis. Policy adjustments during the crisis led countries to improve or finetune their support system to be used in future crises. These measures often include mechanisms to extend liquidity or alleviate overhead costs when a shock undermines SME operations. For instance, during the COVID-19 pandemic, according to the World Bank, 95% of the surveyed high‑income countries have deployed wage subsidies and loan guarantees, 90% reduced or deferred corporate taxes, and over 80% offered SMEs direct lending or grants. The pandemic has shown how the rapid delivery of these types of support during crises is crucial, and many governments have worked to simplify access, ensure effective digital delivery systems and accountability (Hayward et al., 2021[32]).

In addition to emergency measures, countries have introduced policies that aim to strengthen SME resilience more broadly. Governments have worked in particular on governance, digitalisation and skills. In terms of governance, it was recognised that improving policy coordination across levels of government could not only allow a more rapid and better-targeted delivery of support during a crisis but also build sounder ecosystems and more efficient policy implementation. In terms of digitalisation and training, during the pandemic, 64% of high-income countries started policies to incentivise SMEs’ use of digital tools and remote working capabilities, 56% introduced policies that aimed at increasing SMEs’ innovation, and 49% offered training and redeployment opportunities (Hayward et al., 2021[32]). These policies are aimed at improving SMEs’ structural operational capabilities, contributing to their resilience and growth potential.

Some of the structural policies initiated during the pandemic have become permanent SMEs and Entrepreneurship policies, captured by the new OECD policy dashboard.13 An example of a policy that started as a pandemic recovery measure and has evolved into a structural skills policy is Chile’s Despega Mipe policy. It is a training programme that began in 2020 to support SMEs’ recovery by enhancing their e-commerce and digital capabilities and has remained in place since then. It has evolved into an ongoing training programme for SMEs’ personnel, offering multiple courses such as digital marketing, entrepreneurship, administration, accounting, e-commerce, basic and intermediate computing, tax and labour legislation, gastronomy and catering management, social media management and electronic invoicing. In data governance, Japan’s Cyber Security Strategy (2021–2024) builds on insights from the COVID-19 digital transformation to establish long-term objectives and actions that enhance SMEs’ cybersecurity practices. In trade policy, New Zealand’s Trade Recovery Strategy, first introduced in 2020 and updated in 2022, aims to improve SMEs’ access to global markets through advisory services offered by the Regional Business Partner network. This includes guidance on applying for public funds and accessing international market intelligence.

The COVID-19 pandemic held back many potential scalers

The number of scalers in turnover in 2020 was 28% lower than in 2019 across the 16 countries with available data.14 The comparison between the 2016-19 and the 2017-20 cohorts of scalers clearly illustrates the considerable impact that a single year of the COVID-19 pandemic had on the number of scalers (Figure 1.30). The downturn was consistent across all countries, and it was particularly large in Hungary (-46%), Spain (-41%) and Italy (-37%). The countries that were less severely affected were Denmark (-16%), Belgium (-16%) and Finland (-17%). Countries with the largest percentage drop in GDP in 2020 also had the largest relative drop in the number of scalers. For instance, Spain and Italy also had the largest GDP contraction in 2020 (11% and 9% respectively), while Denmark and Finland had the lowest (2% and 3%).

Figure 1.30. As the COVID-19 pandemic hit, the number of scalers in turnover decreased sharply

Note: Scalers in turnover are firms with 10 employees or more that grow in turnover by at least 10% per year. Scalers in employment are firms with 10 employees or more that grow in jobs by at least 10% per year.

Source: Calculations based on microdata sources from 16 countries. See Annex Table 1.B.2 for more information.

Scalers in turnover were more affected by the COVID-19 pandemic than scalers in employment. In 2020, the number of scalers in employment decreased to a lesser extent than the number of scalers in turnover in 12 out of 16 countries (Figure 1.30). The impact of the crisis on scalers in employment was less severe than on scalers in turnover because COVID-19 relief measures stepped in to support workforce retention. During the initial phase of the COVID-19 pandemic, governments prioritised assisting firms and workers to manage the abrupt drop or cessation in activities due to measures aimed at controlling the spread of the virus. Many OECD governments adapted or introduced new job retention schemes, such as Germany's Kurzarbeit and France's Activité partielle, aimed to preserve employment in firms facing temporary downturns by reducing labour costs and supporting worker incomes. Job retention schemes were essential in providing firms with the liquidity needed to retain staff, thereby preserving their skills for a swift recovery.

In 2021, the number of scalers in turnover partially recovered, while the number of scalers in employment remained below pre-crisis levels. In 2021, the number of scalers in turnover increased, on average, by 21% compared to 2020 across all countries in the sample, with Latvia as the only country presenting a small reduction (2%). The general positive trend reflected a quick recovery for many SMEs, in part, supported by the large fiscal stimulus measures provided during the crisis. Conversely, the count of scalers in employment remained stable, as continued uncertainty deterred SMEs from committing to long-term investments in expanding their workforce.

Scalers in digital-intensive sectors navigated the COVID-19 pandemic better than those in less digital-intensive sectors.15 The steep acceleration in digital adoption by firms and households, induced by the COVID-19 pandemic, was a silver lining of the crisis. This, in turn, increased the market demand for SMEs providing digital goods and services. As a result, high digital-intensive sectors saw a milder decrease in both turnover and employment scalers between 2021 and 2019 than in other sectors. Scalers in turnover decreased by 16% and scalers in employment decreased by 15% in high digital-intensive sectors, compared to decreases of 23% and 27% in low digital-intensive sectors.

Annex 1.A. Additional figures and tables

Annex Figure 1.A.1. In the construction sector, scalers in turnover are twice as numerous as scalers in employment

Share of scalers in employment and scalers in turnover among all SMEs, 2011-20

Note: Scalers in employment (turnover) are firms with 10 employees or more that grow in employment (turnover) by at least 10% per year. The sample includes scalers that end their first 3-year scaling-up period in 2014, 2017 and 2020.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Annex Figure 1.A.2. Scalers contribute to increasing aggregate productivity

Median multi-factor productivity (MFP) of all SMEs compared with the median MFP of non-scalers, year 2020

Note: Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-2020. Multi-factor productivity is calculated as of 2020 using unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

Annex Figure 1.A.3. Labour productivity before scaling up

Labour productivity, non-scaler SMEs in each sector=100, 2017

Note: Labour productivity is defined as the added value per employee. Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-2020. Labour productivity is calculated as of 2017 for both scalers and other SMEs, using unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

Annex Figure 1.A.4. Capital intensity of scalers in manufacturing and construction sectors decreases after scaling up

Capital intensity after scaling up, non-scaler SMEs in each sector=100, 2020

Note: Capital intensity is defined as the amount of tangible fixed assets per unit of turnover. Scalers are firms with 10 employees or more that grow by at least 10% per year in employment or turnover during the period 2017-2020. Capital intensity is calculated as of 2020 for both scalers and other SMEs, using unweighted averages across 11 countries.

Source: Calculations based on microdata sources from 11 countries. See Annex Table 1.B.2 for more information.

Annex Table 1.A.1. Scalers contribute half of new jobs among growing SMEs

Jobs created by scalers and SMEs, 2017-20

	A	B	C	D	E	F	G
Country	Number of scalers in employment	Total SME employment in 2017	Jobs created by all growing SMEs	Jobs created by scalers	Scalers' share of jobs created by growing SMEs(D/C)	Jobs created by growing SMEs as a percentage of total SME employment (C/B)	Jobs created by scalers as a percentage of SME employment in 2017 (D/B)
Austria	2 141	892 114	132 382	54 628	41.3%	14.8%	6.1%
Belgium	3 743	1 286 889	197 642	97 324	49.2%	15.4%	7.6%
Croatia	1 371	391 193	60 734	26 373	43.4%	15.5%	6.7%
Denmark	2 864	757 292	117 014	72 163	61.7%	15.5%	9.5%
Estonia	579	190 704	27 236	11 266	41.4%	14.3%	5.9%
Finland	2 824	646 452	153 127	76 266	49.8%	23.7%	11.8%
France	20 304	5 140 090	964 180	530 766	55.0%	18.8%	10.3%
Germany	43 032	11 826 715	2 327 720	1 037 008	44.6%	19.7%	8.8%
Hungary	4 103	1 091 390	181 915	92 483	50.8%	16.7%	8.5%
Italy	14 515	4 258 332	679 764	383 368	56.4%	16.0%	9.0%
Latvia	750	274 270	39 395	21 380	54.3%	14.4%	7.8%
Netherlands	8 126	2 010 736	410 055	212 673	51.9%	20.4%	10.6%
Portugal	5 075	1 365 889	243 325	117 138	48.1%	17.8%	8.6%
Romania	5 312	1 655 324	301 675	145 735	48.3%	18.2%	8.8%
Slovak Republic	1 162	517 823	66 275	33 414	50.4%	12.8%	6.5%
Slovenia	910	216 803	37 322	18 354	49.2%	17.2%	8.5%
Spain	13 924	3 382 162	648 254	356 059	54.9%	19.2%	10.5%

Note: Column A presents the number of SMEs that grew on average above 10% during the period 2017-20. Column B presents the total employment in SMEs in 2017. Column C presents the number of gross jobs created (not considering jobs lost) by growing SMEs (scalers, non‑scalers and entrants) during the period 2017-20. Column D presents the number of jobs created during the period 2017-20 by firms that grew on average above 10% during the period. Column E presents the share of the number of jobs created by scalers in the number of jobs created by all growing SMEs during the period 2017-20. Column F presents the share of the number of jobs created by all growing SMEs during the period 2017-20 in SME employment in 2017. Column G presents the share of the number of jobs created by scalers in the period 2017-20 in total SME employment in 2017. Information for Germany is based on the period 2016-19. Owing to methodological differences, figures may differ from official statistics.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Annex Table 1.A.2. Scalers contribute 53% to 73% of value creation among growing SMEs

Turnover generated by scalers and SMEs, 2017-20

	A	B	C	D	E	F	G
Country	Number of scalers in turnover	Total SME turnover in 2017	Turnover generated by all growing SMEs	Turnover generated by scalers	Scalers' share of turnover generated by growing SMEs(D/C)	Turnover created by growing SMEs as a percentage of total SME turnover (C/B)	Turnover created by scalers as a percentage of SME turnover in 2017 (D/B)
Austria	3 087	314 665 491	55 461 220	29 770 417	54%	18%	10%
Belgium	5 909	364 905 264	67 928 528	49 611 692	73%	19%	14%
Croatia	2 642	38 650 226	8 213 482	5 572 862	68%	21%	14%
Denmark	4 173	213 111 824	43 726 356	31 848 850	73%	21%	15%
Estonia	1 221	25 852 918	5 877 654	3 672 341	63%	23%	14%
Finland	3 973	165 603 880	34 501 088	18 740 988	54%	21%	11%
France	19 972	1 397 345 856	212 861 792	113 263 872	53%	15%	8%
Germany	56 395	2 169 535 539	573 190 336	332 608 693	58%	26%	15%
Hungary	5 946	116 958 616	27 971 914	16 590 838	59%	24%	14%
Italy	16 151	1 160 681 472	163 518 368	106 216 640	65%	14%	9%
Latvia	1 750	29 303 793	6 395 902	4 554 468	71%	22%	16%
Netherlands	9 587	692 142 050	162 108 240	110 561 016	68%	23%	16%
Portugal	6 777	166 641 228	27 243 846	16 076 103	59%	16%	10%
Romania	12 855	120 810 464	39 061 156	26 984 404	69%	32%	22%
Slovak Republic	2 395	75 468 024	12 798 078	8 489 799	66%	17%	11%
Slovenia	1 262	42 544 606	6 804 063	3 971 769	58%	16%	9%
Spain	15 473	671 866 432	113 802 728	70 047 656	62%	17%	10%

Note: Column A presents the number of SMEs that grew on average above 10% during the period 2017-20. Column B presents the total turnover generated by SMEs in 2017. Column C presents the amount of turnover generated by growing SMEs during the period 2017-20. Column D presents the amount of turnover generated by scalers during the period 2010-20. Column E presents the share of the amount of turnover generated by scalers in the amount of turnover generated by all growing SMEs during the period 2017-20. Column F represents the share of the amount of turnover generated by all growing SMEs during the period 2014-20 in total SME turnover in 2017. Column G presents the share of the amount of turnover generated by scalers in the period 2017-20 in SME turnover in 2017. Information for Germany is based on the period 2016-19. Owing to methodological differences, figures may differ from official statistics.

Source: Calculations based on microdata sources from 17 countries. See Annex Table 1.B.2 for more information.

Annex 1.B. Methodology and data sources

Industry classification

Annex Table 1.B.1. Sectoral groups with corresponding NACE sector divisions

Label	NACE Rev. 2 Divisions	Sector titles	Digital-intensive sector
Low-tech and medium-low-technology manufacturing & extractive industries	5-9	Mining and quarrying
	10-12	Manufacture of food products, beverages and tobacco products
	13-15	Manufacture of textiles, apparel, leather and related products
	16-18	Manufacture of wood and paper products, and printing
	19	Manufacture of coke and refined petroleum products
	22, 23	Manufacture of rubber and plastics products, and other non-metallic mineral products
	24, 25	Manufacture of basic metals and fabricated metal products, except machinery and equipment
	31-33	Other manufacturing, and repair and installation of machinery and equipment
Medium-high and high-technology manufacturing	20	Manufacture of chemicals and chemical products
	21	Manufacture of pharmaceuticals, medicinal chemicals and botanical products
	26	Manufacture of computer, electronic and optical products
	27	Manufacture of electrical equipment
	28	Manufacture of machinery and equipment
	29, 30	Manufacture of transport equipment	yes
Advanced services	582	Software publishing
	61-63	Telecommunications; Computer programming, consultancy and related activities; Information service activities	yes
	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	yes
Other services-tradable	49	Land transport and transport via pipelines
	52-53	Warehousing and support activities for transportation; Postal and courier activities
	55-56	Accommodation and food service activities
	75	Veterinary activities	yes
	78	Employment activities	yes
	79	Travel agency, tour operator, reservation service and related activities	yes
	81-82	Services to buildings and landscape activities; Office administrative, office support and other business support activities	yes
Other services- non tradable	35-39	Electricity, gas, steam and air conditioning supply; Water supply, sewerage, waste management and remediation activities
	45-47	Wholesale and retail trade and repair of motor vehicles and motorcycles
	68	Real estate activities
	95	Repair of computers and personal and household goods	yes
Education, social care and health services	85-88	Education; Human health and social work activities
Construction	41-43	Construction

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.

a. Digital‑intensive sectors are defined according to the methodology established by (Calvino et al., 2018[33]). 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.

Microdata sources

Most analyses in this report require access to firm-level data. 17 countries are covered through one of three primary methods: 1) reaching dedicated legal agreements to share the relevant data with the OECD or the use of commercial data; 2) reaching dedicated legal agreements that allow the OECD direct access to the data via secure servers or similar secure technological arrangements; 3) executing the OECD-provided code on their systems and sharing the outputs. This flexibility has been instrumental in enabling a diverse range of countries to participate, adapting to their legal frameworks and technological infrastructures.16 Annex Table 1.B.2 shows the full list of countries and participating institutions. The OECD remains committed to expanding the network and is open to facilitating the participation of additional countries, should the opportunity arise.

Annex Table 1.B.2. Microdata sources

Country	Data access partner
Austria	IHS
Belgium	OECD
Croatia	University of Split
Denmark	Statistics Denmark
Estonia	OECD (Commercial dataset)
Finland	Statistics Finland
France	University Paris Dauphine
Germany	DIW
Hungary	OECD (remote access)
Italy	Bank of Italy
Latvia	OECD (remote access)
Netherlands	CBS
Portugal	OECD
Romania	OECD (Commercial dataset)
Slovak Republic	Ministry of Finance
Slovenia	OECD (Commercial dataset)
Spain	OECD (Commercial dataset)

Annex 1.C. Mapping policies in support of SME and entrepreneurship through a new policy dashboard

To better understand national approaches in supporting scalers, the OECD has mapped institutions and policies that can support SMEs and entrepreneurs to develop and scale up. The policy work and the related mapping undertaken in this project aim to determine more precisely which areas matter for different purposes and in different contexts, and how they could overlap (OECD, 2022[34]).

A key feature of the database is that it seeks to capture the set of policy rationales, governance arrangements and policy instruments that promote SME scaling up, as well as the interactions that can take place between these elements. In practical terms, the approach requires i) identifying the components of the policy mix (relevant policies in place), their characteristics and relative balance, and ii) specifying the areas where these components might interact (Meissner and Kergroach, 2021[35]). Interactions may take the form of complementarities, reinforcing the effectiveness of other policies in the mix, trade-offs attenuating the impact of each policy but can also be neutral and may occur within or across different dimensions such as policy domains, policy objectives, targets, or policy instruments (Rogge and Reichardt, 2016[36]; Borrás and Edquist, 2013[37]; Flanagan, Uyarra and Laranja, 2011[38]).

The dashboard was developed in two phases. In Phase I, the five policy areas were defined, and a pilot was conducted to collect policies in two of these five areas (1. Access to finance and 2. Data governance). In Phase II, the pilot repository was expanded and updated with the collection of additional policies and the removal of discontinued policies in the two ‘pilot’ policy areas. The repository was also expanded with the collection of policies in three other areas (3. Skills for the twin transition, 4. SME integration into innovation networks, and 5. SME trade).

Policy selection criteria. Policies covered by the project meet three conditions: i) They are implemented over a set time horizon or on a continuous basis, generally excluding those put in place as one-off events or as a result of emergency situations (unless they pursue a transformational objective); ii) they are implemented at the national level (although some international and regional policies may be considered when relevant); and iii) they must explicitly address one (or more) strategic objectives relevant to selected policy area. The policies repository used in this report reflects the situation as of January 2024.

A particular focus has been placed on the nature, benefits, and conditions of different connections that SMEs can develop with relevant actors, such as academia, large (multinational) firms, other SMEs, (local) governments, or business associations, chambers of commerce and other stakeholders. This includes measures for reinforcing SMEs’ positioning and performance within intersecting and overlapping transnational (European or OECD-wide) value chains; measures for connecting SMEs to national or international innovation networks, including technology or finance partnerships, as well as measures for facilitating SME access to skills and competencies via their wider ecosystem (e.g. by connecting SMEs to relevant service providers or education institutions).

Data collection methodology. The mapping of national policies is carried out through desk research, drawing on official national sources (such as national strategies, action plans, websites of relevant Ministries and agencies, etc.), as well as on previous OECD reports and publications. Information is collected at the institutional level: first, the relevant institutions are identified and encoded; second, the policies they implement are subsequently identified and encoded.

Key indicators. Collected data allow to assess: i) Number of policies and institutions per country and policy area; ii) Core mandates of implementing institutions across policy areas, iii) Main policy instruments; iv) Degree or nature of targeting across policy areas; v) Synergies across policies.

Possible uses of the database. The database can be used to understand the priority given to different areas, drivers, or mechanisms of SME growth. It is also possible to assess the characteristics of national policy mixes and the balance between targeted and generic approaches to deliver support to SMEs or specific segments of the SME population, as well as the institutional arrangements in place to support policy design and implementation across different policy areas.

Core research questions that can be answered using the repository include: How much government attention goes to the specific dimension in a given policy area and in which form? What patterns emerge in terms of policy intervention? Where are blind spots? How far is the policy mix or targeted at specific objectives or populations (especially SMEs or start-ups)? Which institution(s) oversee the implementation? How is policy coordination organised?

Annex Table 1.C.1. Number of policies and implementing institutions across policy areas, by country

Country	Access to finance		Data governance		Trade		Innovation networks		Skills		Total
	Inst.	Pol.	Inst.	Pol.	Inst.	Pol.	Inst.	Pol.	Inst.	Pol.	Institutions	Policies
Australia	8	27	9	21	6	6	5	12	4	5	20	71
Austria	3	29	7	14	4	19	7	24	4	8	13	94
Belgium	14	34	7	23	3	6	3	16	6	14	25	93
Canada	6	36	4	6	6	11	5	14	2	4	12	71
Chile	9	23	5	10	1	7	2	2	3	5	13	47
Colombia	5	13	4	13	1	4	3	6	2	2	10	38
Costa Rica	6	13	2	5	2	2	5	7	2	4	9	31
Czechia	6	20	4	11	4	26	4	5	2	2	10	64
Denmark	5	14	9	13	3	19	5	11	4	6	19	63
Estonia	2	14	5	12	3	17	4	12	2	4	10	59
Finland	5	33	7	9	2	21	2	6	2	4	9	73
France	3	31	9	19	2	7	2	8	4	10	16	75
Germany	8	29	4	16	6	13	4	20	2	6	13	84
Greece	6	14	7	15	2	11	3	3	4	5	15	48
Hungary	5	9	4	19	4	9	4	5	3	9	13	51
Iceland	2	15	7	7	4	6	3	15	3	4	11	47
Ireland	9	37	7	19	5	18	7	23	6	15	15	112
Israel	5	11	6	15	3	5	3	6	4	7	15	44
Italy	5	35	6	16	6	20	5	16	3	4	13	91
Japan	7	23	8	14	6	13	7	30	2	4	14	84
Korea	7	39	5	15	6	29	5	12	6	10	16	105
Latvia	3	14	4	7	3	6	2	7	3	7	8	41
Lithuania	5	19	6	10	4	6	2	9	3	5	12	49
Luxembourg	3	21	5	8	3	12	3	10	3	3	12	54
Mexico	5	22	3	4	3	8	2	2	6	8	14	44
Netherlands	5	21	4	9	5	17	2	9	5	11	13	67
New Zealand	5	15	5	10	5	10	2	7	3	9	10	51
Norway	4	18	10	19	3	18	4	10	5	5	14	70
Poland	6	17	5	13	5	17	3	8	1	2	13	57
Portugal	4	21	5	16	6	21	5	16	5	18	13	92
Slovak Republic	4	22	3	4	3	32	3	7	4	6	9	71
Slovenia	7	19	5	11	3	13	4	6	2	4	11	53
Spain	11	23	7	19	6	24	3	6	4	8	20	80
Sweden	8	25	9	17	7	34	10	24	5	5	26	105
Switzerland	5	9	4	8	3	9	6	12	3	4	12	42
Türkiye	5	21	6	17	3	15	4	13	4	7	12	73
United Kingdom	6	33	5	20	4	21	4	32	3	6	14	112
United States	4	27	6	8	3	16	5	13	5	8	13	72
Total	216	846	218	492	148	548	152	444	134	248	517	2 578

Source: OECD SME and entrepreneurship policy dashboard

Annex 1.D. Concepts, analytical frameworks and typologies for policy mapping work

The policy work consists of a cross-country analysis of relevant national institutions and policies implemented in OECD countries to create the conditions and incentives for SMEs to scale up. More specifically, the policy work aims to understand what shape policies in support of SME scale-up take in countries, as well as to identify and characterise typologies of policy practices at the national level, while paying attention to synergies and trade-offs across policy measures by placing a focus on coordination and governance mechanisms.

Based on a harmonised framework to monitor and benchmark policy actions, the policy mapping work also creates the base of a new database of policy actions and institutions, and their relationship to the wider policy mix. This database enables policymakers to compare their own approaches with those of peers, identify potential bottlenecks and gaps and learn about innovative ideas in supporting SME scale-up across other countries.

Concepts and operational definitions

An overview of underlying key concepts and operational definitions, adapted from (Meissner and Kergroach, 2019[39]), is provided below, with a more detailed discussion in Chapter 1 of the report Financing Growth and Turning Data into Business (OECD, 2022[40]).

Policy domain. A policy domain refers to the space (or area) where a variety of policy sub-systems for promoting the performance and business conditions of SMEs interact. Each sub-system is characterised by different sets of norms, actors and institutions, focuses on distinct policy issues (such as employment, productivity, industrial transition, local development, etc.), and administers specific policies on these issues. A major governance challenge consists of breaking ‘in silos’ thinking and ensuring different policy sub-systems interact positively within the same policy domain (e.g. entrepreneurship policy domain).

SME&E strategic objectives. Governments seek to achieve specific and diverse objectives, including, for instance, strengthening SME capacity to perform R&D and innovate, or to export, etc. Strategic objectives typically address particular issues of the SME&E policy domain (e.g. easing business entry and exit), specific actors or groups of actors (e.g. small firms, start-ups, entrepreneurs, etc.), or specific processes (e.g. knowledge exchange, innovation diffusion, digital adoption, etc.). In some cases, strategic objectives are translated into concrete and measurable targets, usually bound to a specific time horizon (e.g. ensuring 100% SMEs are connected to high-speed broadband by 2020).

Policy target. Policies are targeted at specific target groups, e.g., at one (or several) firm populations (e.g., SMEs, start-ups, micro firms, etc.) or one (or several) groups of individuals (e.g., venture capitalists, entrepreneurs, women, etc.). They can also be targeted at specific economic sectors, technologies or geographic areas. In fact, many policies cumulate such targets in their design and implementation.

Policy instrument. Policy instruments are identifiable techniques for public action and the means for accomplishing the objectives they are designed for. By combining policy instruments, policymakers aim to cumulate – or multiply – the positive externalities that each instrument taken separately could bring. A more diverse policy toolbox adds, however, to the complexity of managing (sometimes negative) interactions and evaluating impact, especially since there is a wide consensus among policy and academic communities that policy instruments are context- and time-specific and should thus be customised to the nature of the problem they intend to address. Toolkits in use include the following typologies of instruments (Kuhlmann and Smits, 2004[41]; Vedung, 1998[42]; OECD, 2008[43]):

• Financial support: Economic and financial instruments (“carrots”), such as grants, subsidies or tax concessions, are pecuniary incentives.

• Regulation: Regulatory instruments (“sticks”) are legal tools that set ‘the rules of the game’. They include, for example, laws and binding regulations.

• Non-financial support: Non-financial and “soft” instruments (“sermons”) are voluntary and non-coercive tools, such as information and awareness campaigns, guidelines and diagnostic tools, or technical norms. This type of instrument transforms the role of governments from a regulator and support provider into a coordinator and facilitator.

• Platforms & networking infrastructure: “Systemic” or system-enabling instruments such as interfaces, platforms, infrastructures or networking facilities that enable interactions and facilitate knowledge flows and exchange. System-enabling instruments also support public governance through e.g. policy learning, experimentation and debate.

• Policy governance: Meta instruments, i.e. national strategies or action plans but also benchmarking, scoreboard, technology foresight, impact assessment or peer reviews, etc., which provide strategic intelligence to policymakers. They differ from other instruments in their reflexive function and because they do not aim to change actors’ behaviours but rather to inform and structure the policy process.

Policy/ institutional governance. This refers to the institutional and governance structures and arrangements that underpin policy making, from design to implementation to evaluation. These governance arrangements are very country-specific. In practice, the design and governance of policies may cut across several governance levels and policy domains that fall under the responsibility of different Ministries and agencies, raising the question of horizontal and vertical policy coordination. This is particularly likely for scale-up policies that are diverse and cross-cutting by nature.

Issues of across-the-board coordination are typically of high relevance when SME&E policy is thought of as a combination of targeted and mainstreamed policies.

• Targeted policies identify explicitly SMEs as beneficiaries, e.g. as recipients of financial or non-financial support, targets of new regulation, or main beneficiaries of networking facilities. Targeted policies can be formulated and administered by an organisation other than the main Department/ Ministry/ Agency in charge of SME&E policies (e.g. eco-innovation programmes by the Department in charge of environmental affairs promoting eco-innovation in SMEs)

• Mainstreamed policies aim to influence SME&E performance and business conditions and are designed and delivered by Departments/ Ministries or Agencies that do not have SMEs and entrepreneurship as their prime (or even partial) focus (for instance, urban transport policies that aim to improve smart mobility infrastructure and that are likely to improve the SME&E ecosystem). Mainstreamed policies can also intend to shape broader framework conditions, applying equally to all firms or stakeholders – albeit often with a differential impact on SMEs

Framework for mapping policies in the pilot phase

The analysis in the pilot phase builds upon a systematic policy mapping in two areas identified through the measurement work as drivers of scaling up, namely SME access to ‘scale-up’ finance and SME data governance. The respective analytical frameworks were developed based on state-of-the-art knowledge in each field, and then refined after several iterations of “test mapping” in a selected number of countries. The analytical scope is intentionally broad, so as to capture the “ecosystem of policies” which shape the conditions of SME scaling up in the two selected domains. The scope of the policy analysis goes, therefore, beyond venture capital for financing SME growth or beyond the use of big data analytics for improving SME data governance.

Annex Table 1.D.1 provides a schematic overview of what the exercise entailed. The analytical frameworks and notably strategic policy objectives have been aligned with broader OECD work in the respective area, notably the G20/OECD High Level Principles on SME Financing (for access to finance) (OECD, 2015[44]), and the Going Digital III - OECD Horizontal Project on Data Governance for Growth and Well-being (for data governance) (OECD, 2022[45]).

Annex Table 1.D.1. Schematic overview of the policy mapping scope (Phase I)

What it is	What it is not
SME access to finance
Traditional financing instruments designed to explicitly support firm growth Bank loans, credit guarantees Alternative financing instruments designed to explicitly support firm growth Alternative debt (corporate bonds, securitised debt, private placements, (debt) crowdfunding, etc.) Hybrid instruments (subordinated loans/bonds, silent participations, convertible bonds, etc.) Equity instruments (private equity, venture capital, business angels, (equity) crowdfunding, etc.) Financing instruments designed to explicitly support scale up drivers Innovation: R&D&I tax incentives, technology funds, regulatory sandboxes, etc. Investment: Funds to improve provisions of asset finance and leasing, public loans, grants, etc. Network expansion: Export guarantees, trade credit insurance, trade finance, etc. Finance for long-term or structural corporate investments Platforms, networking infrastructures, facilities for crowdfunding or improving transparency on financial markets etc. Policies to raise SME financial literacy and investment readiness, e.g., training vouchers, mentoring programmes, business acceleration services etc.	× Bank loans or credit guarantees that do not pursue growth objectives (to the extent it is made explicit) × Short-term loans and other financing instruments that cover cash flow or operating costs needs × COVID-19 emergency measures in support of liquidity shortages × Microloans, travel vouchers (e.g., to attend international fairs) × Business formalisation support, incubators, firm creation finance support × Support to SME public procurement
SME data governance
Policies to help SMEs create (economic) value with data SME access/ uptake of data-intensive technologies (e.g., data analytics, artificial intelligence) Reinforcing digital security practices in SMEs Related reskilling and raising awareness efforts (e.g., guides and other information material/ campaigns as well as targeted training programmes and incentives) Policies enabling data openness and sharing Data (sharing) infrastructures (e.g., open data portals, supercomputers, data centres) Data laws/ Directives (e.g. on data privacy, sharing, portability) Technical standards Comprehensive approaches through National Data Strategies and Action Plans but also relevant pillars in national Digital Agendas, Innovation Plans, SME Strategies, and Smart Specialisation/ AI/ Industry 4.0 Strategies etc.…)	× Basic (SME) digitalisation support (e.g., related to e-commerce or building website activities) × Generic R&D/ innovation support × Innovation clusters/ networks/ platforms without an explicit tech/ data dimension × e-Government policies

Source: OECD (2022[40])

Extended framework in the roll-out phase

The rollout phase places a particular focus on policies that support SMEs networking and interconnectedness, i.e. ways to link with industrial ecosystems through either trade or (joint) innovation activities. As the European Commission’s Industrial Strategy highlights, SME performance depends not only on their own assets and position in a specific value chain but also, and maybe more importantly, on their integration in their industrial ecosystem. On that basis, the following three policy areas will be examined more closely as part of the workstream’s policy pillar:

• Equipping SMEs with the skills to navigate the twin transition and grow.

• Supporting SME trade and internationalisation in a context of reconfiguring supply chains.

• Networks and internationalisation – focus on scaling up through SME integration into (global) innovation networks.

In addition, the mapping included an update of the policy information collected as part of the pilot phase to reflect the most recent policy developments in these areas. Annex Table 1.D.2 provides a schematic overview of what the exercise entailed.

Annex Table 1.D.2. Schematic overview of the policy mapping scope (Phase II)

What it is	What it is not
SME skills for the twin transition
The mapping will investigate policies to help SMEs access or develop relevant “skill bundles” needed to navigate the twin transition and grow, with a focus on Policies for strengthening digital skills in SMEs Policies for strengthening green skills in SMEs Policies for strengthening transversal skills in SMEs for developing frameworks and structures to make effective internal changes at leadership level Any combination of the above The following channels for delivering or developing these skills to/ within SMEs will be considered ✓ Strengthen SME demand for skills through awareness raising on the importance of training, provision of information material and diagnostic or other guidance tools ✓ Activating the supply of skills to SMEs by facilitating labour mobility and recruitment Policies that encourage labour mobility from foreign firms to the domestic entrepreneurial ecosystem, e. g. through detachments or open-ended contracts Job information and matching programmes to lower search costs in domestic labour and product markets ✓ Developing relevant skills and improving skill use within SMEs via upskilling and reskilling measures Financial support, e.g. in the form of grants, tax incentives; vouchers, training subsidies or levies; etc. Support for the creation of SME/ employer networks to bundle training efforts within similar companies/ industries or facilitate job rotation schemes ✓ Facilitating SME access to skills and competencies via the wider ecosystem Technology and knowledge transfer through incubators/ accelerators/ test beds Infrastructure and support to facilitate collaboration with MNEs/ large firms or knowledge institutions/ universities on tech or greening projects Facilitating access to knowledge-intensive business services (KIBS) ✓ Sectoral approaches for reskilling in “brown” sectors/ industries or supply chains E.g. Sector-based training funds linking SMEs in a particular sector with pre-determined green skills providers ✓ Comprehensive approaches for creating conducive framework or enabling conditions through National Skill Strategies and Action Plans but also but also relevant pillars in national Digital Agendas, Innovation Plans, SME Strategies, Greening Strategies, etc…)	× Generic training/ upskilling/ reskilling support without a specific reference to the development of digital/ green/ transversal skills × Broader labour market laws and regulations × Regulatory/ statutory mechanisms such as rights to training leave × Green public procurement × Environmental standards and regulations, × Development of new markets through carbon pricing × Greening VET systems
SME trade and internationalisation
✓ Policies to strengthen SME participation and positioning in GVCs Provision of information, guidelines and expertise for linking SME to international markets (e.g. supplier databases, export market research) Financial support in the form of grants, loans and loan guarantees, etc., including to mitigate costs and risks of trading and international expansion support SME participation in internationals expositions & trade fairs incentivise SMEs to diversify their supply chain, production locations, and/ or source more intermediate outputs from abroad Training and capacity building programmes to prepare SMEs for international expansion, including pre-expansion advice for established SMEs in the form business audits and diagnostics training courses and programmes for successful internationalisation accelerators preparing start-ups for international expansion and connecting them with relevant business partners Facilitating the development of export-oriented SME consortia or clusters Policies to support SME participation in public procurement bids with a cooperation component (with other business actors) and international dimension Policies that promote the adoption of international standards and certifications ✓ Policies to support SMEs in expanding their professional networks Channelling business or market intelligence to SMEs via connections to other relevant ecosystem players (e.g. industry experts, sectoral networks, entrepreneurship networks, networks to scale up business, etc.) Supplier development programmes (SDPs) that focus explicitly on strengthening the linkages in supply chains, in particular between SMEs and large firms ✓ Policies to strengthen SME internationalisation via digital platforms Digitalisation policies that explicitly aim to help SMEs use/ establish a presence on digital platforms Digital matchmaking services connecting SMEs with foreign clients, sellers, and other (innovation) partners Programmes developing collaboration with (large) providers of digital platforms	× Labour mobility policies × (Broader) Policies related to raising SME internal capacity that could facilitate their ability expand domestic and/ or international networks (e.g. generic upskilling policies) × Basic (SME) digitalisation support (e.g., related to e-commerce or building website activities) × Policies associated with reshoring strategies (e.g. subsidies, tariffs, local content requirements, competition policy measures)
SME innovation networks
✓ Policies to help SMEs connect to (global) knowledge and innovation networks and cooperate with relevant partners in their innovation ecosystem on R&D, applications, technology transfer and commercialisation, including Innovation advisory and matchmaking services (intermediaries) Connecting SMEs to providers of knowledge-intensive business services (KIBS) Transformation of research results into marketable products and services, e.g. via company spin-offs Facilitate access to and protection of shared intellectual property (IP) Financial support (e.g. vouchers, grants, R&D funding programmes, etc.) for SMEs to connect to national and international innovation networks identify relevant partners and develop joint innovation projects ✓ Policies that support the development of strategic partnerships to link SMEs with business partners through contractual agreements (e.g. equity partnerships), joint ventures, or consortia ✓ Establishment of clusters, industrial parks, special economic zones, etc. to facilitate collaborative innovation projects between SMEs and relevant partners (e.g. higher education institutions -HEIs-, research institutes, and technology-providing firms) ✓ Policies to strengthen SME international networks and interconnectedness via digital platforms Digitalisation policies that explicitly aim to help SMEs use/ establish a presence on digital platforms Digital matchmaking services connecting SMEs with foreign clients, sellers, and other (innovation) partners Programmes developing collaboration with (large) providers of digital platforms Comprehensive approaches through National Strategies and Action Plans but also relevant innovation/ networking components within national Innovation Plans, SME Strategies, or Digital Agendas, etc.…)	× Generic R&D/ innovation support without an explicit dimension related to cooperation with other partners (e.g. financial or other support for in-house R&D or innovation activities) × (Broader) Policies related to increasing territorial attractiveness to facilitate the development of regional clusters/ networks × Joint public research programmes/ research infrastructure without an explicit dimension related to cooperation with other partners × (Broader) Policies related to raising SME internal capacity that could facilitate their ability expand domestic and/ or international networks (e.g. generic upskilling policies) × Basic (SME) digitalisation support (e.g., related to e-commerce or building website activities)

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Notes

← 1.  Data for Austria, Belgium, Croatia, Denmark, Finland, France, Germany, Hungary, Italy, Latvia, the Netherlands, Portugal, and Slovak Republic come from national statistical offices. Data for Estonia, Romania, Slovenia and Spain come from commercial data providers.

← 2.  See, for example, Criscuolo et al. (2021[52]) for evidence on the link between firm-level productivity and workers’ wages.

← 3.  Germany is excluded as data confidentiality requires suppression of some information on large firms.

← 4.  Despite the well-documented examples of highly driven and ambitious entrepreneurs, data from representative surveys paint a different picture for the majority. For instance, over half of new business founders in the US cited non-financial motivations as their primary drivers for starting their businesses, such as the desire for flexible schedules or the independence of being their own boss (Hurst and Pugsley, 2011[46]). This trend is echoed in the European Union, where recent survey data from 27 countries show that 51% of companies have no plans to expand in the next three years. Moreover, only a small fraction, about 6%, aim for growth exceeding 20% annually (European Commission - Kantar, 2020[47]).

← 5.  See Bravo-Biosca (2010[50]), OECD (2021[1]) and Grover Goswami, Medvedev and Olafsen (2019[51]).

← 6.  Based on National accounts, OECD database

← 7.  Refer to Annex Figure 1.A.1. for shares of scalers in employment and in turnover by industry.

← 8.  Based on Eurostat, high-growth enterprises development project.

← 9.  See also Berlingieri, Calligaris and Criscuolo (2018[53]) on the relationship between productivity and firm size in manufacturing and service sectors.

← 10.  Investments do not produce immediate productivity gains. If the cost of these investments is not included in the productivity calculation (which is the case of labour productivity estimates), scalers in construction and manufacturing exhibit productivity levels that are on par with their counterparts in other sectors. If their cost is included, e.g. by estimating multi-factor productivity, scalers in construction and manufacturing have lower productivity levels compared to the service sectors.

← 11.  See Figure 1.11 for capital intensity before and Annex Figure 1.A.4 for capital intensity after scaling up.

← 12.  The results are consistent with evidence from the United Kingdom and the United States, which show that (young) scalers contribute significantly to productivity growth, with disproportionate contributions in high-tech and energy-related industries (Haltiwanger et al., 2017[49]; Du and Temouri, 2015[48]).

← 13.  Making temporary measures permanent can improve SME resilience in the long term but can be challenging in the aftermath of a crisis, as it also requires sustainable financing.

← 14.  Germany is excluded from this analysis because information for Germany is only available until 2019.

← 15.  The digital-intensive sectors are defined following the approach developed by (Calvino et al., 2018[33]). The 38-industry groups proposed in the System of National Accounts are classified into quartiles. Low digital-intensive sectors fall into the lowest quartile and high digital-intensive sectors fall into the highest quartile.

← 16.  Full compliance with the confidentiality rules governing access to the data, as well as with the best practices in data protection and security adopted by the OECD, has always been a key priority in the process.