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Studies in Higher Education
ISSN: 0307-5079 (Print) 1470-174X (Online) Journal homepage: https://www.tandfonline.com/loi/cshe20
A societal perspective on self-employment –
Sweden as an example
Caroline Berggren & Anders Olofsson
To cite this article: Caroline Berggren & Anders Olofsson (2019): A societal perspective on self-employment – Sweden as an example, Studies in Higher Education, DOI:
10.1080/03075079.2019.1688285
To link to this article: https://doi.org/10.1080/03075079.2019.1688285
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Published online: 15 Nov 2019.
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A societal perspective on self-employment – Sweden as an
example
Caroline Berggren aand Anders Olofssonb
a
Department of Education and Special Education, University of Gothenburg, Gothenburg, Sweden;bDepartment of Education, Mid Sweden University, Sundsvall, Sweden
ABSTRACT
European Union policies emphasise the importance of entrepreneurship and self-employment to maintain economic growth, a solution influenced by the USA and research from schools of business. There are expectations of higher education graduates that they are innovative, will start businesses and will employ others. However, transferring a solution from one country to another may not be as simple as that. This study questions the recommendations made in EU policies which mainly focus on how to support individuals to become self-employed. Extensive data from the Swedish population registry of approximately 90,000 tertiary-educated people, all born in the mid-1970s, is used to analyse the extent to which this group became self-employed. Results show that societal and cultural differences need to be considered when creating such policies.
KEYWORDS
Higher education; tertiary educated; self-employment; entrepreneur; gender
Point of departure: an individual perspective
Creating new businesses and services is seen as a way to keep unemployment levels low and thus preserve or even improve living standards in European countries, as in several other countries. Earning a livelihood has increasingly become an individualised responsibility and it is assumed that individuals with the ‘right’ characteristics will take the lead in starting new businesses (Simoes, Crespo, and Moreira2016; Yendell 2001). There is a belief that university graduates will be at the frontline of creating these new employment possibilities (Gibb2011; Roman and Maxim
2017; Teixeira and & Davey2010), an objective that the European Union’s policy strongly promotes (e.g. European Commission2012a; European Commission2013). Moreover, since fewer women in most European countries are self-employed compared to men, they are seen as an‘under-exploited source’ (European Commission 2017a), a source to be subjected to policies about increased self-employment.1 European governments, including that of Sweden, have adapted similar policies to come to terms with the perceived low rate of self-employment (e.g. Ahl and Nelson2015; Europeiska kommissionen2008; Pettersson et al.2017).
Business schools have long held a leading position in research about entrepreneurship (e.g. Babson College2018). Conventional economics has been guided by the theory of‘constrained optim-isation in equilibrium’ a theory that ignores the context in which the individual makes decisions (Foster 2017). Economics generally (Gruszka, Scharbert, and Soder 2017; Foster 2017) have been
influenced by physics and mathematics and explanations based on historical development or
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http:// creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Caroline Berggren caroline.berggren@ped.gu.se Department of Education and Special Education, University of Gothenburg, P.O. 300, 405 30 Gothenburg, Sweden
STUDIES IN HIGHER EDUCATION
social and cultural structures have been overlooked. The focus onfinancial returns, the labour market and on individual traits strongly influences the field of entrepreneurship (Zachary & Mishra,2011); the variety within education and its diversefields of knowledge is often neglected when calculations of financial return from education are based on years of schooling,
Moreover, results from research conducted in one type of society or welfare state (liberal) may not easily be implemented in other kinds of societal organisation (e.g. social democratic). Push or pull
factors into self-employment such as income or unemployment do not have the same influence
on people’s decisions depending on how the welfare state and labour market is organised and how traditions and culture affect people’s perceptions (GEM 2018; Hjorth 2008). Also within the field of economics itself, the assumption of a historically, socially and culturally isolated individual who makes decisions in an abstract world is being criticised (e.g. Dopfer, Foster, and Potts 2004; Foster2017; Foster and Metcalfe2012; Gruszka, Scharbert, and Soder2017). This study contributes with empirical facts showing that‘one rule’ (Foster and Metcalfe2012, 431) does notfit all.
Previous large-scale research on self-employment
The extent to which higher-education graduates start businesses (not their intentions) is rarely studied. However, there are some examples with a different focus; the question of whether graduates from internationally ranked universities were more likely to become self-employed than graduates from non-ranked universities was the focus of a Swedish study by Daghbashyan and Hårsman (2014). Using Swedish registry data, they analysed outcomes of three million higher education gradu-ates from the social sciences, education, the natural sciences, technology and medicine. Their results showed that graduates from ranked universities were more likely to become self-employed, except for technology graduates for whom the type of institution did not seem to be of importance. In a Danish study by Buenstorf, Nielsen, and Timmermans (2017) the focus is on those who did not gradu-ate (drop out), if they were more likely to become self-employed than those who gradugradu-ated. They also analysed register data but reduced the student population to those who had studied the natural sciences, engineering, the social sciences, business, humanities, education and those who had studied mixedfields. Nearly 108,000 individuals were analysed of whom 28% did not complete their studies were educational drop-outs. Drop outs were more likely to register afirm, but they earned less than those who were employed. The authors criticise the idealisation of a few successful drop-outs and stress that studies conducted in the USA are not comparable to the Scandinavian context with its different welfare systems and a different perception of self-employment. Register data with information on the entire population is only available in the Nordic countries, and research-ers in other countries need to rely on other sample-based sources of information, often
question-naires. In Austria (Falk and Leoni 2009; Leoni and Falk 2010), census data (probably
questionnaires) including 366,000 higher-education graduates in a range of age groups were ana-lysed, and their likelihood of self-employment was estimated. Many influencing factors were included, and the analyses were divided by gender. The results showed that men were more likely to become self-employed than women and that the prevalence of self-employment differed depend-ing on thefield of knowledge. Gendered educational choices were an important explanation of the different outcomes in the labour market. The authors also stressed that international comparisons are crucial to become aware of institutional and other country-specific factors. A German study (Beiler
2017), also analysed survey data from questionnaires and administrative data. The author focused on how the economic conditions in a country influence the likelihood of graduates becoming self-employed. He compared graduates aged 23–32 years, from eight fields of study over an eight-year period, but did not consider gender. He found that in prosperous times, this group of graduates was more likely to start a business shortly after graduation and that the longer the period since gradu-ation, trade conditions seemed not to influence the decision. Livanos, Yalkin, and Nuñez (2009)
com-pared Greece and the UK and one reason for this comparison was the large difference in
all age groups and all levels of education. Their focus was on women and the proportions of self-employed, self-employed, and unemployed people in relation to civil status and length of education. In both countries women with less schooling were more likely to be self-employed. Self-employment was more common among married women in Greece, while single women in UK seemed to be slightly more likely to be self-employed. The authors discuss these differences in terms of economic structure and institutional characteristics, such as high unemployment in Greece and its weak social benefit system. Another example in which unemployment rates are high in Italy (OECD2015, 112). Rosti and Chelli (2009) analysed data from a national labour force survey based on recurring inter-views with the purpose of mapping theflow of people in and out of self-employment, employment
and unemployment. This study shows a substantial flow of graduates from employment to
self-employment and vice versa. Self-employed women were particularly likely to leave self-self-employment for employment. The explanations were individual, such as women were seen as less able, or because of gender discrimination. Thefield of knowledge was not considered in this study.
The Swedish case
Since the university sector was established, the aim has been to educate people for positions in administration (Collins2006). Even today this is valid; tertiary studies are designed for positions in the public sector, and for employment in the private sector. Unemployment among tertiary educated is generally low (OECD 2012; 2015, 112). Also, after the 2008 recession, tertiary-educated people largely remained in the labour force; the highest unemployment level in Sweden was 4.5 per cent in 2010 (people aged 25–64 years) (Eurostat2018). The availability of basic insurance and unemploy-ment benefits (Hjorth 2008) is also likely to influence peoples’ decisions about self-employment, implying that people are less forced to move, or to become self-employed to financially survive. Swedish unemployment benefits start from approx. 11,000 SEK per month, around 1000 EUR, but are usually augmented by insurance connected to previous employment providing 80 per cent of former salary (European Commission2017b). Moreover, it is possible that tertiary-educated people take jobs for which they are over-qualified (Archer 2003). To a large extent, women and men make traditional gendered educational choices and this gender division is further upheld by the organisation of the educational system where traditionally male-dominated trades are offered in upper secondary schools, while most of the traditionally female-dominated trades and semi-pro-fessions are housed in higher education. This organisation more or less forces most women to go into higher education. The result is a horizontal gender division within higher education by which men make up the majority in engineering programmes and women are to be found studying most other programmes (Lindberg, Riis, and Silander2011; SCB 2018, 40–41). Although Sweden is considered to be a gender-equal country (EIGE 2018), with extensive paid parental leave and state-sponsored availability of childcare (European Commission2017b; Hjorth 2008), compared to men, women are more likely to work part-time and take greater responsibility for the family (Dela-mont2001; Pettit and Hook2005; SCB2018).
In this study, the extent to which tertiary-educated people from variousfields of study become self-employed is analysed, with a particular interest in gender differences. The results are discussed in relation to contemporary research on self-employment among men and women; social structures such as parents’ resources; organisational structures such as the educational system and the labour market structure; and the influence of the type of welfare state.
Method: measuring self-employment Data and population
The data analysed in this study came from Swedish registers (Gothenburg Educational Longitudinal Database) and include information about the students’ education history, work history and income,
as well as a variety of background variables (gender, parental education, national origin, etc.). The point of departure for this study is the total population of individuals born in 1974–1976 (N = 321,612) residing in Sweden during the year in which they were 16 years old. The homogenous age group reduces influences from work experience and trade conditions. Policies about entrepre-neurship education created by European Union started to occur in mid-1990s when a majority of this population was in education and was the target for these policies. Data from two years were ana-lysed: one measure looked at 2006, when these tertiary-educated people were aged 30–32 years, which is shortly after most of them hadfinished higher education and the influence of their edu-cation could be expected to be quite strong; the second measure looked six years later in 2012 when they were aged 36–39 years. In 2006, the labour market was booming, while in 2012 it may still have been feeling the effects of the recession in 2008. From the total population, those who had completed compulsory schooling and had a grade point average were selected (N = 320,135). Moreover, the individuals also needed to have completed at least two years of higher education; that is, both those who had completed their studies with a degree, and those who had not, were included. For obvious reasons, more people have had time to reach the two-years-study limit, when analysed in 2012 (N = 106,554).‘Completed’ is defined as not studying during the years prior to and between 2006 and 2012. In addition, they needed to be active in the labour market (N = 91,245). The number of non-active individuals differs because of reasons such as studying, or being unemployed, retired or missing. The population available for analysis in 2006 numbered 71,753 tertiary-educated people, and in by 2012 their number had increased to 85,228 people.
Variables
Dependent variables
Income from employment is divided into two: main income from salaried employment or main income from self-employment. This division is used in both the descriptive and multivariate statistics. To give a more detailed description of what underlies this rough division, the population’s occu-pational status is divided into four categories: employed; hybrids (one category of hybrids has a main occupation which is employment and another has a main occupation which is self-employ-ment); and solely self-employed. (These categories are used inTable 1).
Table 1.Descriptive statistics occupation in 2006 and 2012.
Variables All HE students
n % n %
Gender Men Women
Occupation 2006
Employed 32,656 84 47,492 90
Hybrid, mostly employed 3.276 9 2.232 4
Hybrid, mostly self-empl. 471 1 318 1
Self-employed 926 2 399 1
No info/unknown 1393 4 2082 4
Occupation 2012
Employed 30,151 78 45,496 87
Hybrid, mostly employed 4502 12 3921 8
Hybrid, mostly self-empl. 642 2 475 1
Self-employed 1983 5 1024 2 No info/unknown 1444 4 1607 3 Unemployed 2005 Unemployed 845 2 1110 2 Employed/other/unknown 37,877 98 51,413 98 Unemployed 2011 Unemployed 156 0 335 1 Employed/other/unknown 38,566 100 52,188 99 Total 38,722 100 52,523 100
Independent variables
The independent variables are gender (men and women) and parents’ education. The latter is divided into two categories: students whose parents had received at least two years of higher aca-demic education, and others, including those parents whose education is unknown. Parents’ self-employment is a dichotomous variable indicating whether either of the student’s parents were self-employed as a main source of income. Field of study is divided into nine broad fields in which the students had completed most of the credit points. They are (1) humanities and theology; (2) law and social sciences; (3) education; (4) natural sciences; (5) technology; (6) agriculture and forestry; (7) medicine, odontology, and veterinary science; (8) health care; and (9)fine arts. There is also an‘others’ category, that comprises multidisciplinary studies; such as ‘law and technology’, but also comprises courses that seem to have been incorrectly categorised. This‘other’ category is included in the analysis, but the results are not shown in the tables with the multivariate analyses. Unemployed 2005 and Unemployed 2011 indicates the number of tertiary-educated people who had no statement of earnings and tax deductions the year that preceded the years assessed in this study. The variable degree indicates if the studentfinished their tertiary studies with or without a degree. Many students may not have written theirfinal essay, they lack some credit points from a course or they simply did not apply for a degree certificate. People studying at a rate comparable to half-time or more in 2005 and 2011 were deleted, since studying is perceived as their main occu-pation. Grade point average (GPA) from compulsory school is used as an indicator of ability. It is a five-grade norm-referenced scale, normally distributed, where five is the best. Finally, median monthly income is used in the descriptive analysis, but not included in the statistical analysis. It is problematic to estimate a monthly income among the self-employed; they often do not have a regular income and have the opportunity to influence the finances of their business, meaning that some of the turnovers could be used for reinvestment before being estimated for the tax authorities.
Analysis method
Binary logistic regression (SPSS) and Average Marginal Effects (STATA) were used to study and
compare the influence on self-employment of the various factors, and how these influence
women’s and men’s tendency to become self-employed (Leeper2018; Mood2010).‘Employed’ is the reference category in the dependent variable, to which‘self-employed’ relate.
Building models for understanding factors that influence human behaviour in educational science is based on theory and previous research. This means the model has not been built exploratively, step-by-step, introducing the variable that best explains the residual until the modelfit no longer improves. Tests of significance were not performed. There is no ‘super-population’ against which the results could be generalised (White and Gorard2017).
Results: self-employment among tertiary educated
Table 1shows the distribution concerning occupational status among those who had studied at least two years at the tertiary level. As previously known, more women than men pursue post-secondary studies. Women comprise 58 per cent of these cohorts; this corresponds with general Swedish stat-istics (UKÄ2018) and to the mean proportion in the OECD countries (OECD2018). A large majority were employed or mostly employed. It seems as though the most common type of self-employment is to have a business on the side. Also, it is well-known, and now confirmed when looking at this group that more men than women were self-employed.
Several pieces of research have shown that the number of solo entrepreneurs without any employees are increasing (Fritsch, Kritikos, and Rusakova2012; Holmquist and Sundin2017; Yuen et al.2018). It is mainly middle-aged (or older) professionals with previous work experience in paid employment who transfer to self-employment. The economic conditions when the studentsfinish
their education are likely to influence their decision whether to become self-employed. Beiler’s (2017) results suggest that‘recent’ graduates are more likely to start a business shortly after graduation if the trade conditions are good. In this study, the proportion of self-employed in 2006 when the market was booming, was lower than anticipated. A possible explanation is that students from allfields of study were included in the present study which makes this total population less subject to influence by financial fluctuations. An explanation that seems to fit better with Swedish conditions is that professionals transfer from paid employment to self-employment when they have gained some experience. Looking at the continuity in business ownership, 34 per cent (1021 individuals) were self-employed in both 2006 and 2012. Perhaps the reasons for being self-employed vary during different life stages. A very low proportion was registered as unemployed in the year before the analyses. Generally, tertiary educated people are less exposed to unemployment than other groups (OECD2012, 119; Åberg 2003). To conclude, whether unemployment is a factor that pushes the tertiary educated to start businesses cannot be drawn because of the low numbers unem-ployed in this study.
Table 2shows some individual characteristics and how these were related to the prevalence of self-employment. From the top, those tertiary-educated students who originated from an academi-cally educated family were slightly more likely to become self-employed compared to those who ori-ginated from a less-schooled family background. To have resources, such as financial resources, education and/or social networks is likely to influence a transfer to self-employment (Tilly 2005). Since self-employment is comparatively uncommon in Sweden, a minority of the tertiary-educated students have self-employed parents with experience to share. In the descriptive statistics, a ten-dency to have a positive relationship between self-employed parents and self-employed tertiary stu-dents can be seen. (These and other associations will be further explored inTable 3). Turning tofield of study, slightly more than half of the student population were educated within thefields of social sciences and technology. In numbers, these were also the fields from which most of the self-employed originated.
Of the student population, 18 per cent never completed their studies with a degree (in 2010). It seems as those who became self-employed were slightly less likely to have graduated. A common explanation is that these students had started their business during their studies and did not have time to sit theirfinal exams (Buenstorf, Nielsen, and Timmermans2017).
Both self-employed men and women showed higher school ability in early schooling than their counterparts who were employed. This can be seen as a confirmation of the perception that the self-employed are able and enterprising (From 2010). Finally, income differences between men and women were large among both the employed and the self-employed. On average, self-employ-ment does not seem to contribute to high-income levels (see for example Buenstorf, Nielsen, and Timmermans 2017), particularly not within the arts (Hårsman 2012). Moreover, in 2012, Table 2
shows that self-employed women face a difficult financial situation. Employed women earned
around 25,000 SEK/month (∼2,300 EUR), while self-employed women earned around 10,000 SEK/
month (∼870 EUR). A common explanation of the low-income level among self-employed women
is that they combine self-employment with unpaid home duties (Bourne 2010; Weber and
Geneste2014). In these analyses, those whose main source of income was from self-employment were defined as ‘self-employed’; meaning that even though the income is low, it may still be their main income. Income from self-employment is difficult to estimate; self-employed people do not have as regular an income as employed people have. Another issue is the apparent reluctance of self-employed people to pay taxes (Alalehto and Larsson2012; Benedek and Lelkes 2011; Åstebro and Chen2014). A Swedish study shows that as much as 30 per cent of the total income is under-reported (Engström and Holmlund2009). In the descriptive analyses, median income was reported, since the income distribution was skewed. Among the self-employed, a majority had a low income (two per cent earned around 100,000 SEK/month∼ 8700 EUR). The income variable was not included in the multivariate analyses.
Table 2.Descriptive statistics of the whole population tertiary educated and self-employed in 2006 and 2012.
Variables All HE students Self-employed 06 Self-employed 12 Self-employed 06 Self-employed 12
n % n % n % n % n % n %
Gender Men Women Men Women
Parents’ education
Parents’ HE 18,341 47 24,693 47 822 59 1448 55 418 58 847 57
Parents’ less schooled 20,381 53 27,830 53 575 41 1177 45 299 42 652 43
Parents’ employment Parents’ employed 36,042 93 48,590 93 1285 92 2399 91 656 92 1360 91 Parents’ self-employed 2680 7 3933 8 112 8 226 9 61 9 139 9 Field of study Humanities/Theology 2062 5 4817 10 118 8 154 6 174 24 284 19 Social sci/Law 10,565 27 16,158 31 385 27 766 29 211 29 474 32 Education 3320 9 13,137 25 33 2 106 4 72 10 211 14 Natural science 2504 7 3271 6 80 6 142 5 37 5 82 5 Technology 17,290 45 6041 12 659 47 1236 47 101 14 202 13 Agriculture/Forestry 167 - 315 1 15 1 24 1 5 0 26 2 Medicine/Odontology 1075 3 1523 3 21 2 57 2 19 3 48 3 Health care 1076 3 6438 12 23 2 48 2 53 7 103 7 Fine arts 383 1 414 1 43 3 67 3 31 4 46 3 Other/unknown 206 1 337 1 20 2 25 1 14 2 22 1 Degree Degree 2004 25,922 67 39,157 75 806 58 470 66 No degree 2004 12,800 33 13,366 25 591 42 247 34 Degree 2010 30,120 78 44,808 85 1874 71 1165 78 No degree 2010 8602 22 13,366 15 751 29 334 22 Mean GPA 3.8 0.5 3.9 0.5 3.9 0.5 3.9 0.5 4.0 0.5 4.0 0.5 Median income 2006* 26,083 18,191 8325 7575 Median income 2012* 36,517 24,908 12,192 10,608 Total 38,722 100 52,523 100 1397 3.6% 2625 6.8% 717 1.4% 1499 2.9%
Notes: Per cent is calculated vertically and gender divided in relation to the total population within each category. *In 2006, 4157 individuals did not have a salaried monthly income, in 2012, 4125 individuals did not have a salaried monthly income; these were excluded when calculating median income and dispersion.
Percentages have been rounded to the nearest integer.
STUD IES IN HIGHER E D U CATIO N 7
InTable 3, the influence of single factors (characteristics) on self-employment, were estimated when other factors included in the analysis were the same. The importance of the various character-istics or group belongings becomes more easily discernible.
Starting with a comparison of men and women (in a total analysis not shown in Table 3), it appeared that among the self-employed in 2006 the gender difference was (0.22), meaning that men had an increased likelihood of 22 per cent of becoming self-employed compared to women. This was also the case when parents’ education and self-employment, students’ field of study, occur-rence of unemployment and graduation were the same. In 2012, that difference had increased, and men were 37 per cent more likely to become self-employed compared to women.
As can be seen fromTable 3, the choice offield of study is the most decisive factor for self-employ-ment. FromTable 2, students educated within thefields of social sciences and technology were more likely to become self-employed; however, in terms of proportions, another picture emerges. Some-what surprisingly, the technologyfield does not stand out as a field leading to new businesses. On the basis of earlier research (van Rooij2014), it might have been expected that the investments made to promote self-employment in these fields (European Commission 2012b; HSV 2009), and from technological developments accomplished during studies that could be turned into business ideas and self-employment. Maybe it is the large proportion of‘ordinary’ engineers who preferred to be employed (by for example Volvo, Ericsson) who counteracted these expectations. Similarly, thefields of social sciences and law, including business, did not appear as obvious starting points for self-employment. Many civil servants are educated within these fields and they outnumber those who start businesses as lawyers or accountants (Ahlin, Gabrielsson, and Wennberg2013). Stu-dents from thefield of education (reference group) were quite unlikely to become self-employed, as were students from thefields of health care and medicine. Education is a field in which 80 per cent of the students are female; despite this, only double the number of women as men were self-employed in 2012. Previous research has shown that men educated in low-income occupations were more likely than their female counterparts to leave their careers for another that paid better (Acker1990; Bergg-ren and Lauster2014; Kauppinen-Toropainen and Lammi1993). Teaching professions are compara-tively low-paid in Sweden. Recipients of education paths leading to professions circumscribed by
Table 3.Probability in percent to become self-employed the year 2006 and 2012, gender divided analyses.
Self-empl 2006 Self-empl 2012 Self-empl 2006 Self-empl 2012
Gender Men Women
Parents’ education
Parents’ HE 0.6 0.5 0.4 0.8
Parents’ less schooled ref ref ref ref Parents’ employment
Parents’ self-employed 0.3 1.7 0.4 0.9 Parents’ employed/other ref ref ref ref Field of study
Human/Theology 13.5 8.2 4.2 5.3
Social sci/Law 6.9 6.6 1.2 1.5
Education ref ref ref ref
Natural science 7.1 4.5 1.0 0.9
Technology 5.9 5.7 1.9 2.0
Agriculture/Forestry 24.0 18.2 too few 8.9
Medicine/Odontology 3.5 2.9 1.3 1.3
Health care 8.8 4.0 0.7 0.1
Fine arts 27.6 25.4 13.4 12.8
Unemployed year before
Unemployed 1.9 0.6 0.6 3.0
Employed/other/unknown ref ref ref ref Degree
Degree ref ref ref ref
No degree 2.5 1.3 0.5 0.5
Continuous var
public authorisation have difficulties competing within the private market. The deregulation of the state monopoly in health care and education has not facilitated self-employment within these sectors. Small businesses in health care are bought up or driven out of competition by large compa-nies (e.g. Hård, Sundin, and Tillmar2007; Sundin and Tillmar2010). Moreover, an increasing number of independent schools are run by large, international school companies (Nilsson and Johansson
2011). Most likely to become self-employed were male students educated within agriculture/forestry, followed by those who had studiedfine arts. Among women, fine arts stood out as the most likely departure for self-employment followed by agriculture/forestry and humanities/theology. Swedish forests are mostly privately owned (80%) (Skogsstyrelsen2013) and often handed down through inheritance from generation to generation. Thefine arts fields include music, dance, photography, design, crafts, and literary composition. Among students educated within thesefields, 14 per cent were self-employed (Table 2, in 2012). They are often hired for time-limited projects (e.g. actors) or, for crafts persons, as self-employed full time or part time in a workshop of their own or with others (Hårsman2012). Thefields of humanities and religion also seem to be a departure for self-employment. Unfortunately, these analyses do not include information on the labour market sector the students establish themselves in.
Concerning degrees, those who became self-employed, were slightly less likely to have graduated, it applies particularly to men. Six years later, the difference in start-up propensity between graduates and non-graduates had declined (among men). A possible explanation of this equalisation is that pro-fessionals start a business after getting experience and building their reputation (Fritsch, Kritikos, and Rusakova2012; Holmquist and Sundin2017; Yuen et al.2018).
Men with higher grades from school are more likely to start a business. Technically, the results should be interpreted such as each grade increase on thefive-grade scale increased the likelihood of self-employment by 1.5–1.7 times (150–170%) among men, and by 20–90 per cent among women. As mentioned previously, because few tertiary-educated people were unemployed the year before the analysis year, the results only lend themselves to a starting point for further research.
Discussion
Neither tertiary-educated Swedish men or women were likely to become self-employed; less than two per cent were registered as having their main income from self-employment in 2006, and 4.5 per cent in 2012 (Table 2). The low prevalence of self-employment in Swedish society is likely to be a result of influences from different societal structures acting both separately and together.
The welfare regime in Sweden is universal (Esping-Andersen1990), meaning that the state ensures a certain living standard for its population including in the case of unemployment and ill health. In this system, incentives for self-employment (Buenstorf, Nielsen, and Timmermans2017; GEM2011; Ulmestig2013) decrease; people do not immediately have to move or become self-employed to avoid poverty.
A higher education degree is designed for employment within large institutions which further explains some of the extremely low prevalence of self-employment. A large proportion of students educated in technologicalfields (a large majority of men) find employment within Swedish multi-national companies, graduates from social science and lawfields are directed towards positions in governmental institutions, and graduates from thefields of health care and education are directed towards positions in the public sector which correspond precisely or well with their degree.
People start businesses infields in which they are knowledgeable (Hård, Sundin, and Tillmar2007). Despite the image of Sweden as a gender-equal country, men and women choose different fields of study, in line with men and women in most western European countries (UNESCO2012, 81). This indi-vidual‘choice’ about education places people in different positions in various sectors of the labour market and influences their likelihood of starting businesses. Hård, Sundin, and Tillmar (2007) suggest that the profiles of the small business owners are even more gender-segregated than the overall labour market. Women were highly unlikely to become self-employed; partly because of
the indirect effect, the gendered educational choices, and partly because of the direct effect of gender discrimination.
Starting a business needs resources, capital and a network (Tillväxtverket2007; Tilly2005). When resources are lacking, the dearth of self-employment may be a result of structural obstacles rather than unwillingness. In Sweden, the self-employed may face difficulties in getting credit, and they also may have problems buying an apartment or a house because mortgages will not usually be given to people who cannot prove they have a reliable and steady income.
The contribution of this study
To understand the‘reluctance’ to become self-employed, the individual and the individual character-istics need to be complemented by studies that consider contextual and historical influences (Ahl
2006,2008; Calás, Smircich, and Bourne2009; Dopfer, Foster, and Potts2004; Foster2017; Holmquist and Sundin2002).
Individuals can be inspired by entrepreneurship education, but societal organisation and expec-tations of family and friends set frames for what is or feels possible to choose and do (Dopfer, Foster, and Potts 2004; Foster 2017). There are cultural differences between Europe and North America (Welter and Lasch2008), but there are also variations within the Nordic countries (Hjorth
2008), thus, international comparisons are needed (Leoni and Falk2010) to become aware of how various societal organisations limit or facilitate self-employment.
The data in this study were based on the total population of students born in 1974, 1975 and 1976 who pursued higher education studies during the 1990s and early 2000s. This means that the stu-dents went through the same educational system and made their choice of higher education and labour market careers during the same period of life. The large data set without selection bias means the results are reliable and that they provide an overview. Such studies are needed to aid com-parisons between the employed and self-employed, between men and women, between the various studyfields and labour market sectors, to yield insight into which groups of people who actually start businesses and to serve as a base for improving policies.
Note
1. In this article, we use self-employment, owning a business and entrepreneurship as exchangeable concepts.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by the Swedish Research Council [grant number 2008-4760].
ORCID
Caroline Berggren http://orcid.org/0000-0001-9328-9680
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