Scalers and the economy

A small group of SMEs that grow fast over a short period of time, i.e. “scalers”, provide a large part of the growth in jobs and economic value in OECD countries. This section contains an overview of the recent trends in the number of scalers in Hungary and benchmark their contributions to job and value creation with other countries.

Around one in five Hungarian SMEs is a scaler

In Hungary, about 8 300 small or medium-sized enterprises (SMEs) became scalers between 2018 and 2021, accounting for 21% of the 39 000 SMEs in the non-financial business sector. Among those, about 3 900 were scalers in employment, 7 000 were scalers in turnover, and 2 400 were scalers in both employment and turnover.

Scalers in year 2020 are defined as enterprises with 10 to 249 employees (SMEs) that increased employment or turnover by at least 10% per year, on average, over the three previous years (2017-20). This means they grow by at least 33% over the three-year period.

The number of scalers in turnover grew by 113% from 2013 to 2019, exceeding 10 500. The number of scalers in employment grew by 41% over the same period, exceeding 4 500. The upward trend reflects a period of economic expansion across the OECD in the wake of the Global Financial Crisis. The number of turnover scalers declined sharply in 2020 as the COVID-19 pandemic spread. The number of scalers in employment fell as well, but less so. The reason is that SMEs that grew in 2018 and 2019 and were on track to become scalers by 2020 were unable to continue growing and might even had to reduce output or employment. The impact on turnover was more severe than on employment as generally firms tend to try to retain staff even through a crisis and particularly during the COVID-19 pandemic, Hungary and most other OECD countries provided relief measures to support employee retention.

High-growth scalers, defined as SMEs with annual growth rates exceeding 20% over three consecutive years, may exhibit distinct trends through economic cycles. Compared to other scalers, high-growth scalers may be faster to react to economic shifts and new market opportunities, but they may also be more constrained by lack of financial resources or tight labour markets. About one in three scalers grows by more than 20% per year on average over three consecutive years, qualifying as “high-growth” scalers. In 2021, there were 1 200 high-growth scalers in employment and 3 200 high-growth scalers in turnover. The number of high-growth scalers in employment has been constant over time, starting from around 1 300 units in 2013. High-growth scalers in turnover instead trended upward until the onset of the COVID-19 pandemic, reaching a peak of 5 300 in 2019.

Scalers in employment created 92 000 jobs over three years

In Hungary, scalers in employment created 92 000 jobs over the 2017-20 period, which accounts to 8.5 jobs for every 100 workers in SMEs in 2017. The contribution of Hungarian scalers in employment is aligned with the average of all countries for which data are available.

The contribution of scalers in turnover was close to the average of other countries

The total turnover of Hungarian scalers in turnover in 2020 was EUR 17 billion larger than in 2017. The increase corresponds to 14.2% of the total turnover of Hungarian SMEs in 2017. This compares to 12% on average across countries with available data.

Scalers’ characteristics

All types of SMEs can scale up. This section describes the characteristics of scalers in terms of sector of activity, size, age, and geographical distribution. It also compares the likelihood of SMEs to scale up in Hungary and in other countries across different groups of SMEs.

Hungarian SMEs operating in construction and advanced services are more likely to scale up

Most Hungarian scalers operate in non-tradable services, construction, and other tradable services (25%, 20%, and 18%, respectively). The distribution of scalers across economic activities mirrors largely the distribution of SMEs across these activities.

However, in certain sectors scalers are overrepresented, particularly in the construction sector, which comprise 20% of scalers but 12% of all SMEs. This reflects the higher probability of SMEs to scale up in these sectors. About 41% of SMEs in the construction sector become scalers, compared to 13% in the education, social, or health services sector. Relative to other countries, similarly, Hungarian SMEs have a higher likelihood to become scalers in the construction sector and a lower likelihood in the education, social, and health services sector.

Sector groups include the following two-digit NACE sectors:

• Low and medium-low technology manufacturing and extractive industries: food, textile, paper, wood, refined petroleum, rubber, plastic, basic metal products, mining.

• Medium-high and high technology manufacturing: chemical products, pharmaceuticals, computer, electronic/electrical equipment, machinery, transport equipment.

• Advanced tradable services: software, telecommunications, consultancy, legal services, accounting services, architectural activities, scientific research.

• Other tradable services: travel agency, services to buildings/landscape, employment activities, veterinary, accommodation/food services, services for transportation.

• Other non-tradable services: electricity, gas and water supply, waste management, wholesale and retail trade, repair of motor vehicles/household goods, real estate activities.

• Education, social care and health services: Education, human health activities, residential care, social work.

• Construction: construction of buildings, civil engineering, specialised construction activities.

Source: Manufacturing sectors are aggregated using Eurostat’s high-technology classification of manufacturing industries. The classification of tradable and non-tradable services is based on Piton, S. (2021). Economic integration and unit labour costs. European Economic Review, 136, 103746.

SMEs across all size classes scale up

59% of Hungarian scalers have between 10 and 19 employees at the beginning of the growth period, and 30% of scalers have between 20 and 49 employees. In contrast, SMEs with 100 to 249 employees represent only 4% of scalers. The likelihood to scale up is similar across SMEs of different sizes, therefore the size distribution of scalers closely follows that of all SMEs. About 26% of SMEs in the 10-19 size class become scalers, compared to 16% of SMEs in the 100-249. Differences in the likelihood to scale up across size classes in Hungary are aligned with the cross-country averages.

Most of Hungarian scalers are mature SMEs

Most Hungarian scalers (53%) are mature SMEs that are more than 10 years old. 21% of scalers are less than 6 years old (i.e. young) and the rest (26%) are between 6 and 10 years old.

Young SMEs are 1.6 times as likely to scale up as mature SMEs. About 32% of young SMEs scale up, compared to 28% of SMEs aged 6 to 10, and 21% of mature SMEs. This results in scalers being overall younger than other SMEs. The share of young scalers in all scalers is equal to 21%, i.e., five percentage points more than the share of young SMEs in all SMEs. However, half of SMEs are mature firms in Hungary. It follows that most scalers are mature SMEs, as the lower likelihood to scale up is counterbalanced by a larger base. Similar to size, differences in the likelihood of scaling up across age classes are similar in Hungary and in the 15 other countries.

SMEs in remote areas are as likely to scale up as SMEs in metropolitan areas

Almost half of Hungarian scalers are located in (large) metropolitan regions. However, SMEs in non-metropolitan region show the same likelihood to scale up than SMEs in metropolitan regions, as the distribution of scalers and other SMEs is very similar across typologies of regions. This indicates that SME proximity to metropolitan regions does not significantly influence its scaling up potential.

The OECD metropolitan/non-metropolitan typology for small regions (TL3) helps assess differences in socio-economic trends in regions by controlling for the presence/absence of metropolitan areas and the extent to which the latter is accessible by the population living in each region. TL3 regions are classified as “metropolitan” if more than half of their population lives in a functional urban area (FUA) of at least 250 000 inhabitants and as “non-metropolitan” otherwise. A “metropolitan region” becomes a “large metropolitan region” if the FUA accounting for more than half of the regional population has over 1.5 million inhabitants. The typology further classifies “non-metropolitan” regions based on the size of the FUA that is most accessible to the regional population. More specifically, “non-metropolitan” TL3 regions are subclassified into three possible types: i) with access to a metropolitan area, if at least half of the regional population can reach an FUA of at least 250 000 inhabitants within a 60-minute car ride; ii) With access to a small/medium city, if at least half of the regional population can reach an FUA of between 50 000 and 250 000 inhabitants within a 60-minute car ride; iii) remote, if reaching the closest FUA by car takes more than 60 minutes for more than half of the regional population.

Source: Fadic, M., et al. (2019), ‘Classifying small (TL3) regions based on metropolitan population, low density and remoteness’, OECD Regional Development Working Papers, No. 2019/06, OECD Publishing, Paris, https://doi.org/10.1787/b902cc00-en

At 25%, the likelihood for Hungarian SMEs to scale up in large metropolitan regions is only marginally higher than in remote regions (24%). This indicates that SME proximity to metropolitan regions is not a strong predictor of their scaling up potential, in Hungary as well as in most other countries.