The number of solo self-employed (those without employees) in Europe, and particularly the number of high-skilled solo self-employed (or independent professionals), has increased rapidly in the last two decades. Earlier research has found a low propensity among high-skilled solo self-employed to hire employees, even though many of them have growth ambitions. This low hiring propensity is worrying as it may hamper the number of newly created wage jobs in contemporary knowledge-based economies. The purpose of this paper is to shed light on the growth ambitions of different segments of solo self-employed by analyzing their varying expansion strategies. From estimating a multinomial logistic regression model on a large sample of solo self-employed workers, our main finding is that high-skilled solo self-employed with a proven track record (by operating solo for at least five years) do not plan to hire employees. Instead, insofar as they have expansion plans, they prefer to work together with other self-employed individuals in a network or outsource tasks to subcontractors. Our study highlights the crucial role of collaborative networks, particularly among high-skilled professionals operating solo. Joining such networks helps avoiding certain drawbacks of solo operations while also allowing to realize expansion ambitions by engaging in larger projects that are impossible to execute independently. An implication of our study is that high-skilled professionals need to consider collaborative networks already at the planning stage of their business operations.
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Introduction
While the share of self-employment in total employment in Europe has been relatively stable or slightly declining over the last two decades, the share of solo self-employed, i.e. operating without employees, was on the rise. In 2022, they accounted for 70% of all self-employed in the European region, whereas in some countries– Norway, United Kingdom, Czechia, Slovakia– the share of solo self-employed exceeded 80% (Bozzon, 2025, p. 16). Traditionally, this trend has been attributed to low growth potential of solo self-employed workers engaging primarily in low-skilled activities in crafts, repair, household services, and retail trade.
Yet, after 2000, a significant shift in the sectoral and occupational composition has taken place with an increasing number of solo self-employed engaging in knowledge-intensive services and other activities requiring upper secondary if not tertiary education as well as advanced skills. Based on occupational status Cieślik and Van Stel (2024) estimated the share of the upper-skilled segment of solo self-employed, labelled in the literature as independent professionals (I-Pros), to be in the range of 40% in Europe in 2017. Using a somewhat relaxed definition of I-Pros, Borghi (2025) found the respective share to be almost 62% in 2022, up 12 per cent points since 2013.
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In a contemporary knowledge-based economy advanced skills and higher education are essential prerequisites for success in business, involving larger scale operations with employees onboard. However, the rapidly growing segment of highly educated independent professionals has not translated into the formation of greater numbers of establishments with employees. In fact, the share of employer establishments in self-employment has declined, particularly after 2000 (Bozzon, 2025). This observed labour market trend, i.e. the growing number of upper-skilled solo operators and their limited propensity to shift to employership, calls for in depth-investigation. This trend is important because it can hamper the number of newly created wage jobs. Hence, the question of why the growing job creation potential of the solo self-employed population–because of their increased levels of education and skills– negatively correlates with their job creation plans becomes a highly relevant research question from theoretical, managerial and policy-making perspectives. Extant research addressing this question has focused primarily on barriers and hardships confronted by solo-self-employed workers while shifting to employer status (for an overview of this line of research, see Cowling & Wooden, 2021; Cai, 2023). This literature rests on the implicit assumption of the apparent weakness of the solo self-employed, making it very difficult to overcome barriers in taking employees onboard and running larger business organizations. So far, the implications on job creation of radical changes in the occupational composition of the solo self-employment population towards a greater share of highly-skilled and well-educated individuals has not been adequately addressed in entrepreneurship and labour economics research. The aim of this paper is to fill, at least partially, this existing research gap.
In the present paper, we are particularly interested in the potential role of (different types of) solo self-employed workers as job creators in case they were to hire employees. Thus, we view the growing pool of solo self-employed as a source of potential and actual job creation. Using Eurostat data (in particular the Self-employment Ad-hoc Module of the 2017 EU Labour Force Survey), we will create a new segmentation reflecting different potential for expansion and job creation, and we will link the segments with the actual expansion and job creation plans and strategies of the self-employed in the different segments. Such comparison is important because it provides information on the stability of the new wage jobs created by solo self-employed workers. For instance, if jobs are primarily created by self-employed with a low potential for job creation–for instance due to overconfidence–, such new wage jobs may be less stable and only temporary in nature. On the other hand, if those self-employed with high potential for job creation do not create jobs, an untapped job creation potential is implied. Given the higher vulnerability of solo businesses in the market (Fairlie et al., 2019), it is not obvious that such businesses survive, and hence that the wage jobs possibly created by such businesses last long (Cieślik et al., 2024). An investigation into the expansion and job creation plans and strategies of different types of solo self-employed is therefore of considerable policy interest. It shall be emphasized that job creation plans may not ultimately lead to future employment decisions; however the fact that such actions are contemplated increases the propensity for taking employees onboard. In turn, lack of such plans is a strong indication of considering solo operations as the ultimate business model.
Recognizing the heterogeneity among the population of solo self-employed, one may reasonably expect different job creation potential in various segments. In our analysis we examine two crucial dimensions to map the heterogeneity of the solo self-employed in terms of their job creation potential. The first one is the skill level of solo self-employed workers as reflected by their occupation. Definitely high-skilled professionals represent higher potential compared to their low-skilled counterparts. With accumulated human capital they can generate financial resources necessary for expansion. They are also better equipped to implement innovation. Finally, some skills (like human resource management and tackling complex issues) are crucial for undertaking operations on a broader scale and creating new jobs.
The second dimension of heterogeneity examined in this paper is business maturity, measured by the length of time a given business establishment is active on the market. Particularly, the early stage of operations is very turbulent and characterized by low survival rates (Morris et al., 2018). By combining both heterogeneity dimensions we create four segments of solo self-employment with divergent potential for expansion and job creation. We will then explore differences and similarities across the four created segments in terms of the personal characteristics of the solo entrepreneurs and their actual plans and strategies for scaling up to higher levels of economic activity. The extent and nature of such expansion strategies have direct consequences for the number of wage jobs created in an economy. For instance, expansion of a solo entrepreneur’s business activities (either in scale and/or scope) may be realised by hiring personnel (in which case the entrepreneur is no longer a solo entrepreneur) but also by working together with other solo self-employed individuals on a project-by-project basis.
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Our paper makes at least two contributions to the extant literature. First, although earlier segmentations of the population of solo self-employed workers have been made (see Cieślik & Dvouletý, 2019, for an overview), our paper is the first to create a segmentation that categorises solo self-employed on the basis of their potential for expansion and job creation. Second, our paper offers a broader framework for investigating growth ambitions of the solo self-employed, which includes alternative expansion strategies. While solo self-employment is often seen as a small-scale business activity that is inevitably limited by a small amount of human resources, the present study considers that solo self-employed workers actually have various expansion strategies ranging from hiring employees to cooperating with other self-employed in a network and outsourcing tasks to subcontractors. Given these contributions, we are also the first paper to confront the potential for expansion and job creation with actual expansion and job creation strategies. Such confrontation facilitates evidence-based policy responses to the increasing role of solo self-employed workers in contemporary labour markets.
The remainder of the paper is organised as follows. In "Literature review" section, we provide a literature review on solo self-employment, including earlier efforts to map its heterogeneity. This section also reviews expansion strategies of the solo self-employed with a particular focus on highly-skilled professionals. In "Empirical analysis" section, we present the empirical analysis, from the data source and methodology right through to the results. The empirical findings are then discussed in "Discussion" section. Our discussion section also includes theoretical and practical implications, along with policy recommendations. Some final conclusions are drawn in "Conclusions, limitations, and recommendations for future research" section.
Literature review
The present literature review section consists of three subsections. "Solo self-employment in entrepreneurship and small business literature" section provides general background on the evolution of the role of self-employment in entrepreneurship and small business literature. "Earlier segmentation approaches and sources of heterogeneity" section then considers the recently emerged literature on the heterogeneity of solo self-employment, acknowledging the very different types of self-employed involved in solo operations. The third subsection considers the type of heterogeneity focused on in the present paper, namely the different expansion strategies used among the population of solo self-employed. Finally, "Our study within the broader research on solo self-employment– a summary "section positions our study within the broader literature on solo self-employment.
Solo self-employment in entrepreneurship and small business literature
For most of the 20th century, self-employment was considered a residual group of the labour market, consisting of marginal economic activity performed by lower-skilled individuals. Accordingly, academic interest in self-employment was limited. This changed in the 1990 s when an increase in the number of self-employed started to become visible in some developed economies, including the United Kingdom. Still, the focus was on the group of self-employed in general (including both self-employed with and without employees), but not so much on the latter subgroup of self-employed without employees, or solo self-employed. Only in the 21 st century was more research conducted on this ever-increasing segment of the labour market (Burke & Cowling, 2020).
Soon it became clear that the labour force category of solo self-employment forms a very heterogeneous group of workers (Knapp et al., 2021), ranging from precarious workers (Conen & Schippers, 2019) to highly skilled freelancers (Burke, 2011). For some labour force participants, solo self-employment is a last resort when individuals are unable to obtain a good wage job (necessity entrepreneurship), whereas for others, solo self-employment is a deliberate choice of highly skilled workers seeking high levels of job autonomy and freedom to conduct their work (opportunity entrepreneurship). Regarding the latter group, it shall be noted that their share in developed economies is on the rise, accounting for up to 40% of all solo self-employed (Cieślik & Van Stel, 2024). Some highly skilled professionals see the status of solo self-employment as a stepping stone to employer entrepreneurship, i.e. a lean start-up strategy (Ries, 2011) temporary stage in which to test the ‘entrepreneurial waters’, and where the entrepreneurial activity is explored and refined before investing heavily in further development of the business, including taking on employees.
For researchers and policymakers, recent developments in solo self-employment pose particular challenges, not only because of its heterogeneity but also due to the diverse roles performed in the economy and society (Cieślik & Van Stel, 2024). On one hand, solo self-employed are workforce participants and the general concerns regarding inadequate job security and social protection are very relevant here (Dvouletý & Nikulin, 2023; Gevaert, 2024). On the other hand, those operating without employees represent the majority of business owners or entrepreneurs, which calls for examination of their contribution to the economy (growth, employment, innovation). Finally, one needs to consider the non-economic dimensions affecting the quality of life and happiness of the solo self-employed (Van der Zwan & Hessels, 2019; Gevaert, 2024).
Given the heterogeneity in solo self-employment, it is essential to map this heterogeneity. In this regard, two types of studies can be identified in the existing literature. The first set of studies focuses on motivations and demographic characteristics of the category of solo self-employed workers, i.e., the personal characteristics of the entrepreneurs involved (e.g. Van Stel & Van der Zwan, 2020). A second set of studies focuses on the segmentation of the solo self-employed on the basis of various job characteristics of the entrepreneurial activity involved, including criteria such as skill and job classifications, part-time (hybrid) versus full-time self-employment, income, income security, number of customers, and so on (Cieślik & Dvouletý, 2019).
What has not been done yet is to combine the approaches of the two types of studies above, i.e., to investigate the solo self-employed in different segments of solo self-employment (segmented on the basis of job characteristics), in terms of the characteristics of the solo entrepreneurs involved in each segment, including their entrepreneurial strategies related to scaling up to higher levels of economic activity (expansion strategies). Thus, where existing segmentations of solo self-employment tend to be shaped on the basis of job characteristics, current literature does not yet focus on the different personal (demographic, motivational, and decision-making) characteristics of the solo entrepreneurs in relation to the different segments. The current paper is positioned at this intersection of solo self-employment segments and the personal characteristics of the entrepreneurs involved. We are interested in the decision-making characteristics of the solo self-employed in the different segments, particularly their expansion strategies.
Earlier segmentation approaches and sources of heterogeneity
Cieślik and Dvouletý (2019) provided a more complex approach to the segmentation of solo self-employed, and considered distinctive criteria: skill and job classification, work engagement, growth aspirations and economic dependency. Specifically, according to the International Standard Classification of Occupations 2008 (ISCO-08), the authors highlighted differences between the lower and upper segments of occupations. In their study, Graham and Bonner (2022) adopted a machine learning approach, highlighting the differences between business angels and established business owners, utilizing the tree structure towards joining entrepreneurship. McLaughlin et al. (2022), in their primary research in Ireland, adapted a latent class analysis (LCA) approach to explore different types of entrepreneurs, concerning their usage of social media for business purposes, but as they utilized only a limited sample (N = 124), their research findings were not sufficiently robust to provide an adaptable typology. Cieślik and Van Stel (2024) conclude that one of the key policy challenges is the establishment of a working definition for policy purposes that would be adapted in the directions of policy and the legislative framework, responding to the continuous shifts in the labour market and modern (digital) society.
Once the research on solo self-employment advanced, it soon became clear that this part of the labour market is highly segmented. Exploring its diversity was relevant for theory building but also for policymaking. An important dimension concerned the diverse levels of vulnerability of solo self-employed workers due to financial insecurity, lack of social protection, having a dependent status or having specific work arrangements like digital platforms (for an overview, see Cieślik & Van Stel, 2024). Bay and Koster (2023) used Dutch data to understand self-employment career types by following a cluster analysis approach, concluding on the importance of job stability, precarity and spatial distribution, among others. The major outcome of their study is the identification of several career clusters of self-employment, including wage-to-self-employment transition (short-term vs. long-term), i.e., hybrid or mixed self-employment, stable self-employment (long-term), corporate self-employment (shareholdership transition), and precarious self-employment (Bay & Koster, 2023, p. 613). This line of research helped to launch various regulatory initiatives aimed at remedying the most adverse effects (Digennaro, 2025).
A second dimension of heterogeneity concerned the engagement in solo self-employment by people from disadvantaged social groups: women, unemployed, immigrants, people with disabilities, etc. Also here, a better understanding of operating modalities helped implement more inclusive policies for marginalised groups (OECD/European Commission, 2023). The third dimension of apparent heterogeneity concerned the diverse approaches among solo self-employed individuals towards autonomy in work, work satisfaction, work-life balance, and well-being in general. The need to understand how different self-employment working patterns affect health, job satisfaction and well-being has been highlighted by Stephan et al. (2020, 2023). From this perspective, a study by Aguilar et al. (2013) offers insights into life satisfaction across several occupational categories of self-employed individuals, classified into self-employed professionals, business owners, farmers/fishermen, and precarious self-employed. The authors also considered in their analysis whether the individual choice was driven by necessity or opportunity.
An important conclusion from the ongoing research was that within each dimension outlined above, the distinction between “upper” and “lower” echelons of solo self-employment was very relevant. For this categorization three major criteria have been used, separately or combined. The first one is the level of education, recognizing the growing share of people with university degree entering into self-employment after 2000. An example of this research direction is Van Stel and Van der Zwan (2020), who found that the share of higher-educated women among the solo self-employed has increased sharply over the last few decades in many European countries.
The second criterion is formed by occupational differences. Based on earlier work of Kitching (2015), Cieślik and Dvouletý (2019) used the ILO International Standard Classification of Occupations 2008 (ISCO-08) to investigate differences between low-skilled and high-skilled segments of solo self-employment. The occupation criterion proved to be very useful and has been adopted in the present study.
The third criterion used for identifying the quality segment of solo self-employment is the provision of knowledge-intensive services (KIS). Knapp et al. (2021) claimed superiority of solo operators active in KIS because of the advanced nature of such services, which are typically performed by high-skilled, productive professionals. Van Stel et al. (2020) contemplated in their analysis both the level of education and the sectoral distribution. They found that the negative relationship between tertiary education and hiring plans was mediated by the choice of the KIS sector, which lends itself well to operating solo.
Researching the motivations, ambitions and expansion plans of solo self-employed
Historically, the prevailing assumption in policy-oriented entrepreneurship research was that the ultimate goal of new entrants is to expand operations, which inevitably leads to employing people. However, the accumulated empirical evidence over the last two decades (Cowling & Wooden, 2021; Dvouletý, 2022; Cai, 2023; Ramos-Poyatos et al., 2024) points to the opposite, namely that solo self-employment rarely works as a stepping stone to employership, i.e., employer entrepreneurship. Nowadays, the prevailing trend is that the overwhelming majority of new entrants consider solo operations as the ultimate format of their business activities.
So far, research on these new patterns and trends remains scarce, but several factors explaining hiring behaviors have been identified. The first factor concerns the conditions during the business formation stage. Cieślik et al. (2024) studied the diverse growth trajectories of both employer and solo start-ups during the first 5 years of operation. In line with earlier research, their study confirmed a high level of volatility among new entrants, as 2/3 of them exited the business before the end of the business formation stage. Those taking employees immediately after start-up demonstrated higher survival rates compared to those starting solo. The majority of surviving startups ended the business formation period without employees.
Second, while surviving the business formation stage and reaching initial maturity and financial stability, many solo establishments are confronted with demand fluctuations. Taking on new staff under standard employment contracts then significantly increases the fixed costs of running the business, which might be difficult to bear during downturn periods. Financial risks involved in taking on new wage employees on standard conditions call for delaying this major leap and opting for safer and more flexible options like working with part-time workers and subcontractors (Fairlie & Miranda, 2017; Rocha & Grilli, 2024). Third, Struckell et al. (2022) point to financial literacy as an important skill set for self-employment. This is arguably even more so the case for self-employment with employees. These authors also point out that financial literacy is in decline, at least in the United States. Hence, an insufficient level of financial literacy may cause a considerable proportion of solo self-employed to refrain from expanding operations and hiring employees, as this would require specific know-how, e.g. how to apply for a bank loan.
Fourth, what prompts enterprising individuals to operate solo is a notable shift in preference for intangible aspects of quality of life and well-being over pure pecuniary motives. These preferences are well demonstrated among so-called lifestyle entrepreneurs, primarily driven by the desire to pursue their own interests and passions (Cieślik, 2017). These enterprising individuals typically express a strong need for autonomy, which cannot be enjoyed within hierarchical structures typical for paid employment. However, operational autonomy can also be jeopardized by “supervising hardships” experienced by business owners managing employees (Nikolova et al., 2023). Consequently, by avoiding both upward and downward organizational hierarchy, solo entrepreneurs enjoy greater autonomy and perceived well-being not only in comparison to paid employees but also employers. In this context, the conclusions of the research conducted by Van Stel et al. (2020) are particularly relevant. They found that the desire for self-expression in work, which is strongly pronounced among solo start-ups with tertiary education, is negatively related to their hiring plans.
An essential drawback of the solo business operators who either face internal growth constraints and/or prioritize autonomy, lifestyle, and well-being, is that it results in a limited scale of operation, which in turn does not justify taking employees onboard. As a partial remedy, one can observe emerging organizational and human resource practices, allowing operating without employees when the scale of operations is growing. Here, one can mention formal cooperatives of independent solo operators (freelancers) which are active in several European countries. Based on the comparative study conducted by Mondon-Navazo et al. (2025), the core element of said arrangement is that the cooperative enters into a contractual relationship with the client as intermediary, whereas the solo operator becomes an employee of the cooperative. The primary objective was to provide the members with social protection and to help them get out of the informal economy sector. However, additional benefits are relevant as well. Shared tax and accounting services help solo operators to cope with financial illiteracy discussed above. Cooperatives organize community events, facilitating networking and presenting own services to other members. This often results in engaging in ad-hoc alliances with two or more freelancers for executing larger projects and undertakings for the clients.
Parallel with formal cooperative structures, there is growing evidence in the last two decades on informal grassroots-level collaborative initiatives. In this regard, Kroon and Paauwe (2022) have identified several configurations in small organizations emerging in the 21 st century, two of which are particularly relevant for solo operations:
Relying on subcontracted networks rather than permanent employment contracts. These include short-term employment based on commission or task contract, on-demand gig work or agency work. Despite some drawbacks (limited control over external workforce), there are clear advantages from the transaction cost perspective: avoiding dealing with the hassles of permanent employees, costly and time-consuming contract termination, and the administrative burden of handling payroll, tax and social security contributions. The popularity of the flexible forms of work is being reflected not only in shifting from hiring to subcontracting in established small organizations but also in strategic thinking among new solo entrants regarding their future employment plans.
Forming alliances among equals– typically high-skilled professionals. The tradition of establishing professional partnerships and formal associations has evolved towards more flexible structures based on interprofessional networking and solidarity. The latter trend reflects a more general ambivalence towards institutionalisation among professionals– outside the imposition of norms, rules and control (Cross & Swart, 2021). Professional networks are crucial for surviving during periods of slowdown, finding new business opportunities and gaining legitimacy, which is particularly relevant while negotiating with clients who are reluctant to give work to “lone wolves” (Cross & Swart, 2021). They also help to overcome feelings of loneliness, facilitating self-support in personal and social matters (Maestripieri & Cucca, 2018). Potentially, professional networks allow bidding for larger projects and expanding the scope of operations.What remains unclear is whether relying on non-standard forms of employment and professional networks can be effective for more comprehensive, large-scale operations. It seems intuitive that with a certain size of operations there will be a need to revert to standard organizational patterns based on fixed employment. However, some empirical evidence indicates that this may not necessarily be the case as the advantage of subcontracting and collaborative network arrangements over taking permanent employees on board increases with the maturity and length of operations, resulting in accumulating HRM experiences and deepening network relationships (Orel et al., 2021).
Our study within the broader research on solo self-employment– a summary
Figure 1 positions our study within the broader research on the heterogeneity of solo self-employment. More specifically, it expands the line of research examining distinctive features of low-skilled versus high-skilled professionals (Cieślik & Dvouletý, 2019; Dvouletý, 2020), reflecting the growing share of the latter segment in the population of self-employed after 2000 (Cieślik & Van Stel, 2024; Borghi, 2025); see Box A of Fig. 1. The key factor behind this trend, particularly in developed market economy countries, was the increased number of people with secondary and tertiary education entering self-employment (Van Stel & Van der Zwan, 2020). At the same time, the widespread use of digital technologies has greatly facilitated the provision of intellectual services, offered by highly skilled solo professionals (Dvouletý & Postepska, 2022).
Fig. 1
Positioning our study within the extant research on the heterogeneity of solo self-employed. Source: Created by authors based on the previous research
As demonstrated in "Researching the motivations, ambitions and expansion plans of solo self-employed" sub-section, the status of low-skilled versus high-skilled self-employed significantly affects their ambitions and establishment plans (Fig. 1, Box B). Such plans have to be weighed against the hardships of running one’s own business, particularly during the business formation stage. Although the high-skilled professionals seem to be better equipped for coping with such hardships, the conclusions derived from the extant research are somewhat mixed. Financial risks identified by Fairlie and Miranda (2017) and Rocha and Grilli (2024) are most strongly felt among traditional solo business owners in crafts and household services, but at the same time, precarious work and financial insecurity are widespread among freelance professionals. Financial illiteracy (Struckell et al., 2022) is prevalent among business owners with a primary education level. At the same time, many high-skilled professionals in the creative sectors have profound difficulties in coping with taxation and accounting matters. Similarly, demand fluctuations and business volatility in general (Cieślik et al., 2024) negatively affect hiring plans in both segments.
With regard to the shift of preferences among solo self-employed towards autonomy in work, work satisfaction, and well-being in general as highlighted by Stephan et al. (2023), there are essential differences in the ways these preferences affect low- and high-skilled solo self-employed. For the first group, what primarily matters is to be their “own boss” and to avoid hierarchical structures, characteristic for paid employment (Cieślik, 2017). In the case of high-skilled and educated professionals, their preferences for autonomy and pursuing self-expression can be best realized by operating without employees, thus avoiding “supervising hardships” (Van Stel et al., 2020; Nikolova et al., 2023). This often leads to deliberate decisions to scale down operations (e.g. by limiting the number of clients), despite existing growth prospects.
Extant entrepreneurship research has demonstrated significant differences in job creation potential and decision making during early and mature stages of operations (Garcia-Martinez et al., 2023), see Box C in Fig. 1. The early stage is typically very turbulent, resulting in premature exits leading to negative job creation effects (Morris et al., 2018; Cieślik et al., 2024). Those who successfully overcome the hardships of the start-up phase experience much higher chances of survival in the following years, and demonstrate higher potential for expansion and job creation (Haltiwanger et al., 2013). In our analysis, we have combined two dimensions of heterogeneity, namely skill level and business maturity, arriving at four segments of solo self-employed with divergent expansion potential.
As outlined in "Researching the motivations, ambitions and expansion plans of solo self-employed" sub-section, in the last two decades, there is growing evidence of engagement of solo self-employed in various collaborative initiatives, both formal (Mondon-Navazo et al., 2025) and informal (Kroon & Paauwe, 2022), as shown in Box D of Fig. 1. This trend is particularly visible among highly skilled professionals. Collaborative networks are being recognized as emerging human resources practices of small professional organizations, helping to find new business opportunities (Cross & Swart, 2021) but also facilitating self-support in personal and social matters (Maestripieri & Cucca, 2018). In our research, we consider these collaborative initiatives as ways of pursuing expansion plans, an alternative to hiring staff. The role of collaborative networks in the expansion strategies of solo self-employed individuals has not been dealt with in the extant literature so far.
To sum up, our study (Fig. 1, Box O) builds on earlier work on distinctive features of low- versus high-skilled professionals (Fig. 1, Box A) and their diverging ambitions and expansion plans (Fig. 1, Box B). We contribute to the extant research by investigating solo expansion plans by combining two dimensions of heterogeneity, namely skill level and business maturity (Fig. 1, Box C), and recognizing collaborative networks as alternative ways of executing expansion plans (Fig. 1, Box D).
Empirical analysis
Data source and empirical approach
We exploit the Self-employment Ad-hoc Module of the 2017 EU Labour Force Survey, i.e., EU LFS (Eurostat, 2018), a dataset of more than 30,000 solo self-employed workers across 30 European countries who were surveyed in 2017. The EU LFS is a statistically representative survey with one of Europe’s highest numbers of individual-level responses. Our dataset includes detailed information on many characteristics of solo self-employment that are not available in other datasets of comparable sample size, such as information about the number of clients, work-related autonomy, type of self-employment and classification of the profession. Dvouletý (2020) noted that this is a very suitable dataset for studying the heterogeneity of self-employment, which has been reflected in subsequent studies on the characteristics of hybrid entrepreneurs (Dvouletý & Bögenhold, 2023) and dependent self-employment (Dvouletý & Nikulin, 2023).
Despite earlier use of the dataset in previous research, the information in this dataset has not been exploited yet to create a segmentation reflecting different potential for expansion and job creation, as in the present study. We create four segments of solo self-employment based on different job characteristics along the dimensions of Occupation (high-skill versus low-skill) and Maturity of the business (young versus established). Regarding occupation, we distinguish between high-skill and low-skill self-employment where high-skill is operationalized as the occupational ISCO categories 1–3 (Managers; Professionals; and Technicians and associate professionals).1 According to Kitching (2015), these three major occupational groups can be “argued to correspond broadly with skilled non-manual occupations and might, therefore, constitute a skill/occupation criterion demarcating freelance work from other types of own-account working” (p. 23). We follow this logic in using these three occupational groups to define high-skilled work.
Our second criterion is based on the maturity of the business, where we distinguish between businesses that are at most five years old (young or early-stage businesses) and businesses older than five years (established or matured businesses). Importantly, the second category may be considered to be relatively successful as they have already survived the critical business formation stage (here operationalized as five years, based on Cieślik et al., 2024), as considerable proportions of start-ups are known to exit the market within the business formation stage (Morris et al., 2018). We thus create four segments: (1) Low-skill, early-stage; (2) High-skill, early-stage; (3) Low-skill, matured; and (4) High-skill, matured. Among these four segments, the last one may be considered to represent the self-employment segment with the highest potential for expansion and job creation as these business endeavours have already survived the critical business formation stage and moreover involve high-skilled work, including skills that are crucial for undertaking operations on a broader scale and creating new jobs (e.g. human resource management and tackling complex issues).
We thus argue that, as a group, the mature (older than five years) solo self-employed have a higher potential to scale up or create (lasting) wage jobs, because it only includes survivors of the business formation stage, whereas the early-stage group inevitably also includes a weaker group of early exiters. That is, even though at the time of measurement no early-stage respondent has exited yet, we know from literature that the majority of start-ups and early-stage entrepreneurs do not survive the business formation stage (Romanelli, 1989; Nanda & Rhodes-Kropf, 2013; Fuertes-Callén et al., 2022), and hence this will also hold among our group of early-stage respondents, as they form a random sample from the population of early-stage solo entrepreneurs. Formulated still differently, in the early stage group of solo self-employed, the weaker entrepreneurs have not been filtered out of the market yet, and the more talented entrepreneurs have not had the time yet to prove they can survive the critical business formation stage. Hence, although we cannot assess the potential of any individual entrepreneur in our sample, we can say that at the aggregate level (i.e., as a group), the early-stage group is, on average, of lower potential compared to the entrepreneurs already surviving for five years in the market.
Using data from more than 30,000 solo self-employed workers, we then run a multinomial logistic regression model where several personal characteristics of the solo entrepreneurs, both demographic and strategy-related characteristics, are used as predictors of the relative probability to belong to each of the four segments. The opted empirical approach is based on the theoretical underpinning of the solo self-employment typology, explained in our literature review section. Compared to studies relying on data-driven research, such as machine learning (Graham & Bonner, 2022), cluster or latent class analysis (McLaughlin et al., 2022), our approach has the advantage of expanding the current state of knowledge by using established types of entrepreneurs and their operationalizations. Moreover, it enhances the replicability and academic rigour of the entrepreneurs’ heterogeneity research, allowing future studies to expand on the presented empirical results, by using the same operationalizations and definitions. The multinomial logistic regression model is the appropriate estimation method in our case because the dependent variable is a categorical variable with more than two (in our case, four) categories, and where the categories do not have a natural ordering.2 Our empirical work takes advantage of extracting pure solo self-employed from the EU LFS, so the analysis is not biased by the presence of multiple team members working on joint venture activities.3
Descriptive statistics
We work with the EU LFS data from 2017, supplemented by the Ad-Hoc Self-employment Module. The respective Eurostat (2018) user guides describe the data collection procedures and coding. This section describes the variables we use for the empirical analysis and presents several descriptive statistical observations.
First, as mentioned before, we divide our solo self-employed into low- and high-skilled occupations and combine the skill dimension with the duration of self-employment, i.e., early-staged vs. matured solo self-employed. Reflecting upon these four categories and the empirical approach of multinomial logistic regression, we construct our dependent variable, ranging from one (= Low-Skilled Early Self-employed) to four (= High-Skilled Matured Self-employed).
Table 1 shows summary statistics and illustrates, first, that our sample has a proportionally higher representation of matured solo self-employed (66.2%) over early-staged (33.8%). We also see that solo self-employed in low-skilled occupations are in the majority (64.1%) as compared to high-skilled occupations (35.9%). Combining these observations implies that the segment of low-skilled matured self-employment has the highest frequency (43.5%).
Table 1
Descriptive statistics (Full-time solo self-employed, 15–64 years)
Variable
Frequency (%)
N
Dependent variable: Four segments of solo self-employment
Low-Skilled Early Self-employed (= 1)
20.6
30,256
High-Skilled Early Self-employed (= 1)
13.2
30,256
Low-Skilled Matured Self-employed (= 1)
43.5
30,256
HIgh-Skilled Matured Self-employed (= 1)
22.7
30,256
Independent variables: Expansion and job creation strategies and plans
Works together with other self-employed in a network (= 1)
24.6
30,256
Does not plan to hire or subcontract (= 1)
89.2
30,256
Plans to employ only permanent employees (= 1)
0.8
30,256
Plans to employ only temporary employees (= 1)
2.1
30,256
Plans to employ both permanent and temporary employees (= 1)
0.5
30,256
Plans to only make use of subcontractors (= 1)
6.4
30,256
Plans to make use of subcontractors and employ employees (= 1)
1.0
30,256
Control variables
Female (= 1)
28.1
30,256
Nationality non-Native (= 1)
7.4
30,256
Less than Primary Education (= 1)
0.5
30,256
Primary Education (= 1)
3.8
30,256
Lower Secondary Education (= 1)
19.5
30,256
Upper Secondary Education (= 1)
43.1
30,256
Post-secondary Non-tertiary Education (= 1)
2.3
30,256
Short-cycle Tertiary Education (= 1)
5.0
30,256
Bachelor’s or Equivalent Level (= 1)
9.7
30,256
Master’s or Equivalent Level (= 1)
15.3
30,256
Doctoral or Equivalent Level (= 1)
0.8
30,256
Dependent Self-employment (= 1)
3.5
30,256
Not able to influence Contents, nor Order of Tasks (= 1)
9.6
30,256
Able to influence both contents and order of tasks (= 1)
80.2
30,256
Able to influence contents but not order of tasks (= 1)
3.7
30,256
Able to influence order but not contents of tasks (= 1)
6.5
30,256
Does not want to change professional status (= 1)
80.4
30,256
Married (= 1)
58.8
30,256
Partner/spouse living in the same household (= 1)
69.9
30,256
Cities (Densely populated area) (= 1)
38.0
30,256
Towns and suburbs (Intermediate populated area) (= 1)
30.0
30,256
Rural (Thinly populated area) (= 1)
32.0
30,256
Variable
Mean
SD
Min
Max
N
Job Satisfaction
3.3
0.7
1
4
30,256
Post-stratification weights applied
Source: Own elaboration based on the Labour Force Survey (LFS) Ad-hoc module 2017 data (Eurostat, 2018)
Second, regarding the expansion and job creation strategies and plans–our independent variables–, we see that one quarter of our sample (24.6%) works together with other self-employed in a network. Regarding the two other expansion strategies and plans, the vast majority (89.2%) does not have any plans to hire employees or make use of subcontractors to outsource tasks. Among the solo self-employed that do have such expansion strategies and plans, subcontracting is the most popular (6.4% out of 10.8%).
Finally, Table 1 also shows the descriptive statistics for a wide range of control variables included in our analysis. Control variables include individual-level characteristics such as gender (Female = 1), nationality (Nationality non-Native = 1), education attained (transformed into a set of dummy variables from Less than Primary Education to Doctoral or Equivalent Level), marital status (Married = 1), partner living situation (Partner/spouse living in the same household = 1) and area of living (transformed into a set of dummy variables from Rural to Cities). Moreover, we introduce a series of self-employment-specific variables reflecting characteristics of the self-employment occupations (next to the expansion and job creation strategies and plans). These include the ability to influence contents and order of tasks (classified into a set of dummy variables). We further use a job satisfaction indicator (measured on a scale from Not satisfied at all = 1 to Satisfied to a large extent = 4) and explore whether the self-employed want to change their professional status (Does not want to change professional status = 1). We also include a variable indicating dependent self-employment. This binary variable equals one if the respondent works full-time as a solo self-employed and works for one client only (or one client is dominating, i.e. generating 75% or more of the respondent’s income) and a (dominating) client decides his/her working hours.
Chi-square association tests
We are interested in how the studied solo self-employment segments differ in the abovementioned expansion and job creation strategies and plans, so to get a first impression of differences between segments for our variables of interest, we display tables with chi-square association tests.
Table 2 illustrates a statistically significant relationship between working with other self-employed in a network and the segment of solo self-employment. Working with other self-employed in a network is not a frequent working operandi for the solo self-employed as a whole (24.6%), as illustrated in the final column of Table 2. Nevertheless, this occurs far more often among high-skilled solo self-employed individuals (32.6% among high-skilled early self-employed (= 4.3/13.2), and 33.7% among high-skilled matured self-employed (= 7.65/22.7)), as compared to low-skilled solo self-employed.
Table 2
Association between segment of solo self-employment and working with other self-employed in a network
Working with other Self-employed in a Network/Segment of solo self-employment
Low-Skilled Early Self-employed
High-Skilled Early Self-employed
Low-Skilled Matured Self-employed
HIgh-Skilled Matured Self-employed
Total
Does not work with other Self-employed in a Network
16.2%
8.9%
35.3%
15.0%
75.4%
Working with other Self-employed in a Network
4.4%
4.3%
8.2%
7.65%
24.6%
Total
20.6%
13.2%
43.5%
22.7%
100.0%
Test of association, Chi-Square = 696.51, p-value < 0.000, Cramer’s V = 0.2. N = 30,256
Source: Own elaboration based on the Labour Force Survey (LFS) ad-hoc module 2017 data (Eurostat, 2018)
Table 3 further shows how modest the expansion plans (either by hiring or subcontracting) of solo self-employed are. Of the surveyed solo self-employed, 89% do not plan to hire or subcontract, yet the hiring plans statistically differ across the solo self-employed categories; for example, high-skilled matured solo self-employed are more often planning to use subcontractors (9.7% (= 2.2/22.6), compared to 7.4% for the whole sample).
Table 3
Association between segment of solo self-employment and hiring plans
Hiring Plans/Segment of solo self-employment
Low-Skilled Early Self-employed
High-Skilled Early Self-employed
Low-Skilled Matured Self-employed
HIgh-Skilled Matured Self-employed
Total
Plans to employ only permanent employees
0.2%
0.2%
0.2%
0.2%
0.8%
Plans to employ only temporary employees
0.6%
0.2%
1.0%
0.2%
2.0%
Plans to employ both permanent and temporary employees
0.2%
0.1%
0.2%
0.0%
0.5%
Plans to only make use of subcontractors
1.1%
1.0%
2.3%
2.0%
6.4%
Plans to make use of subcontractors and employ employees
0.2%
0.3%
0.3%
0.2%
1.0%
Does not plan to hire or subcontract
18.3%
11.4%
39.5%
20.1%
89.3
Total
20.6%
13.2%
43.5%
22.6%
100.0%
Test of association, Chi-Square = 348.12, p-value < 0.000, Cramer’s V = 0.1. N = 30,256
Source: Own elaboration based on the Labour Force Survey (LFS) ad-hoc module 2017 data (Eurostat, 2018)
Results
Table 4 shows the results of our main analysis, the multinomial logistic regression estimates. The reference group is the segment of low-skilled, early-stage solo entrepreneurs, arguably the segment with the lowest potential for expansion and job creation, and the regression coefficients display the impact of each independent and control variable on the probabilities of belonging to each of the other three segments, relative to the reference group of low-skilled, early-stage solo entrepreneurs.
Table 4
Explaining probabilities of belonging to different solo self-employment segments
Multinomial Logistic Regression Estimates (Full-time Solo self-employed only)
Reference group: Low-Skilled Early
High-Skilled Early
Low-Skilled Matured
High-Skilled Matured
Independent variables: Expansion and job creation strategies and plans
Works together with other self-employed in a network
0.327***
−0.0444
0.358***
(0.0922)
(0.0626)
(0.0862)
Plans to employ only permanent employees
0.185
−0.593*
−0.854*
(0.354)
(0.275)
(0.415)
Plans to employ only temporary employees
−0.388
−0.360**
−1.084***
(0.243)
(0.134)
(0.246)
Plans to employ both permanent and temporary employees
−0.240
−0.814+
−1.169*
(0.444)
(0.456)
(0.484)
Plans to only make use of subcontractors
0.267
0.127
0.505**
(0.176)
(0.111)
(0.165)
Plans to make use of subcontractors and employ employees
0.647+
−0.371
0.0624
(0.335)
(0.261)
(0.334)
Control variables
Female
−0.404***
−0.200***
−0.636***
(0.0950)
(0.0586)
(0.0906)
Nationality non-Native
−0.579***
−0.845***
−1.222***
(0.160)
(0.103)
(0.168)
Primary Education
0.227
0.819*
0.563
(0.825)
(0.321)
(0.715)
Lower Secondary Education
0.596
0.434
0.561
(0.764)
(0.306)
(0.697)
Upper Secondary Education
1.114
0.230
1.231+
(0.759)
(0.307)
(0.694)
Post-secondary Non-tertiary Education
1.184
0.0171
1.703*
(0.819)
(0.362)
(0.750)
Short-cycle Tertiary Education
2.263**
0.400
2.167**
(0.772)
(0.329)
(0.706)
Bachelor’s or Equivalent Level
2.303**
−0.388
2.252**
(0.770)
(0.330)
(0.706)
Master’s or Equivalent Level
2.926***
−0.245
2.936***
(0.768)
(0.336)
(0.704)
Doctoral or Equivalent Level
2.631**
−1.548*
2.529**
(0.879)
(0.721)
(0.818)
Dependent Self-employment
0.174
−0.775***
−0.360+
(0.180)
(0.134)
(0.189)
Able to influence both contents and order of tasks
0.357*
0.243**
0.542***
(0.152)
(0.0740)
(0.133)
Able to influence contents but not order of tasks
0.586*
0.525***
0.872***
(0.233)
(0.148)
(0.222)
Able to influence order but not contents of tasks
0.496*
0.226+
0.755***
(0.241)
(0.123)
(0.203)
Job Satisfaction
0.215**
−0.163***
0.0361
(0.0657)
(0.0365)
(0.0600)
Does not want to change professional status
0.0976
0.365***
0.562***
(0.110)
(0.0626)
(0.107)
Widowed, divorced or legally separated
0.203
1.136***
1.310***
(0.154)
(0.0843)
(0.125)
Married
0.0943
0.849***
0.936***
(0.115)
(0.0714)
(0.112)
Partner/spouse living in the same household
0.0114
0.103
0.155
(0.120)
(0.0742)
(0.114)
Cities (Densely populated area)
0.180+
−0.201**
−0.115
(0.108)
(0.0654)
(0.100)
Towns and suburbs (Intermediate density area)
0.151
−0.127*
−0.0849
(0.109)
(0.0611)
(0.0995)
Constant
−5.646***
0.801*
−5.355***
(0.868)
(0.361)
(0.773)
Industry dummies (NACE-2 Rev)
Yes
Yes
Yes
Country dummies
Yes
Yes
Yes
Wald chi2 (213)
8,944.31
Prob > chi2
0.00
Observations
30,190
Pseudo R2
0.336
Akaike information criterion
21,401.4
Bayesian information criterion
23,147.6
Robust multinomial logistic regression estimates. Pooled sample of the following countries: Austria, Belgium, Bulgaria, Switzerland, Cyprus, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Norway, Poland, Portugal, Romania, Sweden, Slovenia, United Kingdom. Post-stratification weights applied. Robust standard errors are in parentheses; statistical significance is reported as follows:
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference groups for dummy variables: Does not work together with other self-employed in a network;Does not plan to hire or subcontract;Male; Native of own Country; Less than Primary Education; Not dependent self-employed; Not able to influence Contents nor Order of Tasks; Is Self-employed but wishes to work as an Employee; Single; Living without a partner; Rural area (Thinly populated area)
Regarding strategic behaviour related to expansion and job creation, we see that working together in a network with other self-employed is strongly associated with high-skilled occupations, as both the early and matured high-skilled segments have highly significant, and clearly positive coefficients. This does not hold for the low-skilled matured segment, hence, in terms of working together in a network, this is very much a strategy of high-skilled self-employed (as opposed to low-skilled).
Furthermore, considering the five “Plans to…” variables, these results clearly emphasise that the matured high-skilled solo self-employed are deliberately working on their own, as the coefficients for the three variables involving plans to employ employees are all significantly negative, and with a large magnitude. However, this group does have ambitions and makes use of external workers, but through subcontracting (see the large positive and significant coefficient for “Plans to only make use of subcontractors”). This shows that hiring people is seen as a hassle or risk by high-skilled solo self-employed. They seem to experience this hassle in the first five years of the business as a shift is visible from planning to work with employees to working with subcontractors when comparing early-stage to mature segments (please note the positive coefficient for the high-skilled early segment for “Plans to make use of subcontractors and employ employees”).
Here we will also point at some of the more interesting results among our control variables. Regarding demographic characteristics, we note that women and foreigners have a smaller chance to belong to each of the three segments (as the signs are significantly negative) as compared to the reference group. Stated differently, female and non-native solo self-employed are overrepresented in the low-quality segment of low-skilled, early-stage self-employment. For the higher education levels we see they are more often in the high-skill categories, as expected.
The strong and highly significant negative sign of dependent self-employment for low-skilled matured (−0.775) shows that dependent self-employment (where self-employed depend on a single customer) tends to be a temporary status. That is, dependent self-employed workers are much less likely to belong to the matured stage (implying business survival for at least five years) as compared to the (low-skilled) early-stage.
The results for the three “Able to…” variables suggest that the solo self-employed in the high-quality job segment (high-skilled matured) have the highest level of autonomy in their jobs, although the differences with other segments (except the low-quality reference group) are not that large.
Regarding job satisfaction, somewhat remarkably, both for the low-skilled and high-skilled segments, we find that solo self-employed in the matured stages have lower job satisfaction than their counterparts in the early stages.4 The pattern suggests that job satisfaction of solo self-employed decreases over time, even if surviving the business formation stage. Possibly, this reflects a better understanding over time of the pros and cons of running one’s own business, gained through accumulated experiences.
Discussion
Our study has largely confirmed earlier findings (Kraaij & Elbers, 2016; Fairlie & Miranda, 2017; Dvouletý, 2022; Cai, 2023) that across the board, only a small minority of solo self-employed (below 5 per cent) have any plans to hire employees, be it on a permanent or temporary basis. The segmentation approach, adopted in our research, helped to gain deeper insights revealing that solo self-employed in the segment with the highest job creation potential (those in highly skilled occupations who already survived the business formation stage) are deliberately working on their own, rather than planning to hire employees. Compared with other segments, the propensity to hire employees was found to be lowest in the High-Skilled Matured segment and decreased with the length of business experience.
On the surface, this can be interpreted as a mismatch between the segment with the highest job creation potential (i.e. the High-Skilled Matured segment) and the actual job creation plans of the solo self-employed belonging to this segment. With higher skills and levels of education, the solo self-employed in this segment may generate high levels of value-added and implement innovations among their customer firms (Burke, 2011); however, the traditional contribution of entrepreneurs to socio-economic development in the form of job creation seems to be largely missing. Surprisingly, the low hiring propensity of the high-potential segment coincided with a high propensity to engage in collaborative networks and work with sub-contractors. It may imply that high-skilled mature freelancers do not generally give up business expansion plans, but when contemplating such expansion, they rather opt to engage in networks with their peers to augment the skills and resources necessary for the execution of larger projects. This finding bears important theoretical and practical implications, which are discussed in the following section.
Further conclusions from our study point to another interesting contradiction. Compared with other segments, freelancers in the high-skilled matured segment demonstrate the highest autonomy in work, reflecting their priorities for intangible aspects of the quality of life, discussed in "Researching the motivations, ambitions and expansion plans of solo self-employed" sub-section. Unfortunately, this does not correspond with their job satisfaction, which decreases with maturity — a trend which is characteristic for all segments. This finding points to the risk that the expectations of freelancers of well-being benefits associated with solo operations may not be achieved and diminish over time.
Theoretical implications
We found that the solo self-employed in the most promising segment (high-skilled matured) contribute to socio-economic development primarily through networking and collaboration with other self-employed individuals rather than directly creating new wage jobs. Overall, our analysis contributes to a better understanding of the heterogeneity of the group of solo self-employed workers, particularly in terms of their expansion and job creation strategies. Indeed, our paper offers a broader framework for investigating the growth ambitions of the solo self-employed, which includes alternative expansion strategies. The present study considers that solo self-employed workers have various expansion strategies, ranging from hiring employees to cooperating with other self-employed workers in a network and outsourcing tasks to subcontractors. This is a novelty of our paper.
Implications for the solo self-employed
Our study has exemplified the crucial role of collaborative networks, particularly among high-skilled professionals operating solo. Joining such networks can help avoid certain drawbacks of solo operations while also allowing to realize expansion ambitions by engaging in larger projects that are impossible to execute independently. Therefore, high-skilled professionals need to consider collaborative networks already at the planning stage of their business operations. Parallel with formalized cooperatives and informal grass-root level collaborations, discussed in sub-Sect. 2.3, solo self-employed workers should be aware of the growing number of collective representation initiatives after 2000. The latter trend has been particularly visible in several European countries (for an overview, see Mezihorák et al., 2025) and has also led to transnational alliances (Borghi et al., 2025).
Implication for policymakers
The findings of our study bear important implications with regard to policies promoting entrepreneurship. They cast additional doubts as to the rationale of general policies promoting new business formation in the expectation that this will translate into the generation of sizeable numbers of new jobs (Shane, 2008). More specifically, this does not seem to work in the case of highly skilled professionals due to their preferences for flexible network arrangements. Instead, support measures addressed to this group should focus on building cooperation and networking capabilities. Respective instruments might include specialized counselling and networking facilitation as well as broadening support for local and regional clusters so that small business establishments operating without employees will also be eligible.
Reuter and Conen (2024) point out that “public policy with regards to self-employment is as diverse as self-employment itself” (p. 488). Among other categorizations of public policy they distinguish between “policies that support self-employment mainly for its potentially positive effect on job creation and economic growth from policies that emphasise and seek to boost the extent to which new businesses can act as engines of innovation to enhance a national economy’s productivity and international competitiveness” (p. 488). Our findings suggest that, in order to optimize the socio-economic contribution of high-skilled solo self-employed, this dichotomy can be avoided by recognizing the contribution of high-skilled professionals to employment understood in a broad sense, i.e. not only by hiring new employees but also by expanding collaborative networks of high-skilled professionals which allow for larger projects performed by incidental teams of I-Pros.
Key takeaways
We complete this Discussion section with the key takeaways from our study. The major patterns, contradictions, and challenges of the evolution of solo self-employment in the first quarter of the 21 st century which need to be addressed by self-employed workers, policymakers, and by researchers in future research, are summarized in Table 5.
Table 5
Key takeaways from our study
#
Key takeaway
1
Increasing numbers of people with higher levels of education and skills engage in self-employment.
2
The higher potential of new entrants does not translate into employer entrepreneurship. The percentage of employers among self-employed declines.
3
Overcoming the hardships of the startup stage and reaching initial maturity does not increase the propensity to hire staff. Operating solo is considered as an ultimate business model.
4
The key factor behind the reluctance to employership by mature high-skilled solo self-employed is their preference for autonomy in work and well-being.
5
High-skilled solo self-employed who are dissatisfied with the status quo and do not wish to give up expansion plans, opt for engaging in collaborative networks to execute larger projects.
Conclusions, limitations, and recommendations for future research
Concluding remarks
The number of solo self-employed (those without employees) is increasing rapidly in many European countries. Recent research has shown that the solo self-employed make up a very heterogeneous group of workers, ranging from precarious workers to highly skilled freelancers (Dvouletý, 2020; Van Stel et al., 2023). In the present paper, we have exploited the Self-employment Ad-hoc Module of the 2017 EU Labour Force Survey to shed further light on this heterogeneity. We were particularly interested in the potential role of solo self-employed as job creators in case they were to hire employees. Thus, we viewed the pool of solo self-employed as a source of potential and actual job creation. Concretely, we created four segments of solo self-employment associated with different potential for expansion and job creation, and we linked them with the actual expansion and job creation plans and strategies of the self-employed in the different segments. The self-employment segments were based on the two dimensions Occupation (high-skilled versus low-skilled) and Maturity of the business. Using multinomial logistic regression analysis, we then compared the four segments on the key personal characteristics of the solo self-employed belonging to the different segments, including their expansion strategies. Our main finding was that high-skilled solo self-employed with a proven track record (by operating solo for at least five years) do not plan to hire employees. Instead, insofar as they have expansion plans, they prefer to work together with other self-employed in a network or outsource tasks to subcontractors.
Limitations
A limitation of our study is that, to some extent, our results may have been influenced by potential self-selection bias in the sample. Specifically, solo self-employed workers who actually do hire workers at some point in time, will have a smaller probability of being included in our sample, simply because they are no longer part of the population of solo self-employed at the time of the survey. Concretely, self-employed workers who started out solo and who hired one or more employees before the time of our survey (2017), will have had no chance of being included in our sample (because they were no longer solo self-employed), whereas the probability of being included would have been non-zero in case they would still have been solo at the time of the survey. Such self-selection bias may cause the proportion of solo self-employed with hiring plans to be underrepresented in our sample.
In this regard, Cieślik et al. (2024) investigated the growth dynamics of solo and employer start-ups during the business formation stage. They show that switches from solo to employer (i.e. the hiring of an employee) occur most often in the first two years after start-up and then quickly die out over time. This implies that the ‘missing’ solos due to potential self-selection bias are most likely to be in the early-stage segments. In other words, the number of solos with hiring plans is most strongly underestimated in the early-stage segments, which include the reference group. Conversely, relative to the early-stage segments, the number of solos with hiring plans in the matured stages is overestimated, even though their numbers are already substantially lower. This would imply that, if anything, the ‘true’ coefficients for the variables representing “Plans to employ permanent and/or temporary employees” on the probability of belonging to the Matured segments (both Low-skilled and High-skilled) are even more negative than reported in Table 4. Hence, reassuringly, if we had been able to account for the (potential) self-selection bias, our conclusions regarding the hiring plans of High-skilled matured freelancers would probably have been supported even more strongly than suggested by the evidence in Table 4.
Recommendations for future research
Our analysis was based on Eurostat statistics covering 30 European countries with comprehensive cross-sectional data collected during 2017. Since then, new developments instigated by the COVID-19 pandemic and technological advancements have further reinforced the trends and patterns outlined in this paper. First, distant home-working has become a widespread phenomenon among both paid employees and self-employed workers, indirectly diminishing the stigma of working from home, which is typical for solo self-employment. The widespread availability and decreased cost of advanced communication technologies have helped solo entrepreneurs to engage in online sales of goods and provision of personal, professional and technical services via the Internet (Cieślik & Van Stel, 2024). Similarly, easy access to modern communication technologies, particularly videoconferencing, greatly facilitated participation in professional networks used for undertaking more complex projects. In view of these radical changes in the operating environment after 2019, it would be worthwhile to replicate our study to evaluate the impact of these changes.
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If we would have had two categories for the dependent variable, a binary logistic regression model would have been in order. Moreover, if the categories would have had a natural ordering (e.g., poor, fair, good, excellent), an ordered logistic regression model would have been in order.
In terms of our database we excluded observations where respondents answered confirmative on the items: “Works together with a co-owner” or “Works together with co-owner and self-employed”.
For the two high-skilled segments, this finding for job satisfaction is confirmed in a robustness test in which the high-skilled early segment is now the reference group. We find a coefficient of -0.179 for the high-skilled matured segment which is significant at the 1% level.
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