Female Entrepreneurship – Evidence from Germany and the Baltic Sea Region (Part 1)
Analysis of women’s activity in SMEs in Poland and potential scenarios of future development (Part 2)
Dr. Christina Boll, Dr. Elisabeth Bublitz, David Heller, Dörte Nitt-Drießelmann (Part 1)
Prof. Dr. Bogusław Plawgo, Dr. Adam Tomanek, Dr. Alina Szepelska (Part 2)
Published and edited by
Baltic Sea Academy e.V.
Dr. Max A. Hogefoster
Blankeneser Landstrasse 7,
22587 Hamburg, Germany
Editorial Correspondence. editor@baltic-sea-academy.eu
© 2014 Baltic Sea Academy e.V. All rights reserved.
Printed by:
BoD-Books on Demand GmbH, Norderstedt, Germany
ISBN 9783735747365
Part-financed by the European Union (European Development Fund and European Neighbourhood and Partnership Instrument) within the QUICK IGA project. This publication does not necessarily reflect the opinion of the European Commission.
We are very grateful to the European Commission for the financial support and also to the Joint Technical Secretariat of the INTERREG IVB Programme for the support and advice.
The project "Innovative SMEs by Gender and Age (QUICK-IGA)” devoting itself to these challenges in the Baltic region and addresses the following objectives:
On four levels the project focuses on the following activities:
The outputs and results of the project were published in the Baltic Sea Academy series for the following activities:
Two investigations were carried out for the countries and regions of the Baltic Sea region as the consistent basis for all further work:
a) demographic and economic analysis in the BSR countries and regions;
b) analysis of regional education and labour markets.
The results of these investigations were published in spring 2013 as part of the Baltic Sea Academy series under the title "Economic Perspectives, Qualification and Labour Market Integration of Women in the Baltic Sea Region".
During the project, it became clear that there was a need for more in-depth, further-reaching work in some countries to the south of the Baltic Sea. The following additional activities were also carried out to cover this:
Germany: Analysis of businesswomen in Germany, including a survey.
Poland: Analysis of women's activities in SMEs in Poland and scenarios for possible future development.
The results of these two studies are published in the publication series of the Baltic Sea Academy.
The results of the analysis have been incorporated into three new education products:
a) concept and curriculum for a train the trainer programme for the permanent implementation of training courses for consultants by universities and academies;
b) concept and curricula for a training and coaching programme for consultants to enhance their advisory competences on improving work structures in SMEs in order to increase the labour participation of women and older people, as well as innovation capacities.
c) concept and curriculum for training of owners and managers of Small and Medium Enterprises to personnel management systems and human and organizational development.
The training courses have been trialed multiple times in various locations and scientifically evaluated. The curricula, lecturer slides, execution instructions and evaluation results have been published in the form of a handbook.
Analysis and preparation of ten best practice cases on the promotion of labour market participation by women and older people, especially from Denmark, Sweden, Norway and Finland and transfer to the countries south of the Baltic Sea. The specific national conditions were investigated in order to allow implementation in the recipient countries.
The analysis of the conditions for the transfer of best practices and the ten best practices have been published in the Baltic Sea Academy series of publications.
Drafting and completion of memoranda of understanding on promoting innovative SMEs through women’s entrepreneurship, and the increased employment of women and older people in Latvia, Lithuania, Belarus, North Poland and North Germany.
The memoranda containing the support activities to be implemented by the signatory institutions have been published in a manual.
Poland: organization and evaluation of a conference on "Development of the competitiveness of enterprises in the context of demographic challenges";
Poland: analysis and elaboration on the employment of women and older people and its promotion;
Lithuania: theoretical analytical study of political activities: Building the socially responsible employment policy in Baltic States.
The results of these two additional activities were published in the Baltic Sea Academy series of publications.
Strategic program to promote innovation and the labour market participation of women and older people in SMEs as well as to increase the attractiveness of regional labor markets.
The strategy program and two action plans (see below) were published as part of the Baltic Sea Academy series of publications as Volume 12 “Age, Gender and Innovation – Strategy program and action plans for the Baltic Sea Region”.
In order to involve 50 economic chambers and 16 universities in all the Baltic Sea countries in promoting the employment of women and older people in SMEs on a permanent basis, two action plans have been developed and enacted:
a) action programme for 50 SME promoters (chambers + associations) in all BSR countries on promoting higher labour market participation by women and older people and, thus, increasing innovation capacities in SMEs;
b) action programme for 16 academies/universities from 9 Baltic Sea countries on the promotion and qualification of consultants to support the labour market participation of women and older people.
The action plans and appendix were published alongside the strategy programme (see above) in the Baltic Sea Academy series of publications.
Development and publication of a manual on promoting innovation through increasing the labor market participation of women and older people and the proportion of female entrepreneurs in SMEs. The manual containing all the project results and additional tools for the management of demographic change at enterprise level.
In order to achieve the highest possible and sustainable implementation of the target project results across all the Baltic Sea Countries, in 2013 and 2014 written transfer was supported by two consultancy and transfer conferences lasting several days with representatives from all the Baltic Sea countries. All the presentations and consultancy results developed were published in the Baltic Sea Academy series of publications in the following articles:
a) Corporate Social Responsibility and Women`s Entrepreneurship around the Mare Balticum.
b) Innovative SMEs by Gender and Age around the Mare Balticum.
The book incorporates the Analysis of businesswomen in Germany and the Analysis of women’s activity in SMEs in Poland and potential scenarios of development in the future.
by Dr. Christina Boll, Dr. Elisabeth Bublitz, David Heller, Dörte Nitt-Drießelmann
Hamburgisches WeltWirtschaftsInstitut gemeinnützige GmbH (HWWI)
Hamburg Institute of International Economics (HWWI)
Early on, Schumpeter (1942) established the idea of creative destruction—a process that is led by innovative entrepreneurs. Accordingly, individuals with new ideas successfully introduce their products into markets, potentially even starting a process of structural change or a regime shift. These start-ups are expected to grow, thereby generating jobs and increasing economic welfare. Naturally, policy makers follow this idea and support entrepreneurship in various ways. Policies are in place that provide entrepreneurs with financial support (e.g., specifically designed programme for the unemployed), mentoring and network programmes (e.g., "Bundesgründerinnenagentur", National Agency for Women Start-ups Activities and Services, bga), or consultancies.
However, in recent years, research on entrepreneurship has painted a more differentiated picture than was possible several years ago. Due to more detailed data and methodological improvements, entrepreneurship per se is no longer regarded as a purely positive phenomenon. In fact, only a few small firms are truly successful while the majority fails during the first years of existence. Nightingale and Coad (2014) question whether the large amount of subsidies directed towards start-ups in the UK is justified when considering, for instance, the following: jobs created by new firms are of low quality and entrepreneurial firms are less innovative and less productive. In fact, encouraging too many market entries could lead to thin markets where profits for higher quality firms are unnecessarily reduced. The challenge is to identify and support the small number of successful firms which in comparison to the average/median firm can be regarded as atypical but which is creating jobs and producing innovations. This leads to the natural conclusion that the goal should not be to simply increase the overall entrepreneurship rate but that, with a more fine-grained distinction between different types of entrepreneurship, promising start-ups should primarily be supported. This is the type of entrepreneurship that could potentially impact economic development.
As regards Germany, we observe an increasing number of solo-entrepreneurs who, as the name indicates, stay by themselves and do not generate jobs. In addition, there is an increase in the number of part-time self-employed (Niefert and Gottschalk 2013) who want or need to make some money on the side. Neither of these groups is likely to have a positive and significant influence on economic growth. It is important to consider to what extent these individuals chose self-employment as a coping instead of an innovative business strategy.
Although female self-employment rates differ by the data and measures employed, statistics document that worldwide less women than men are self-employed. As the overall start-up rate in Germany has been declining, in the past decade policy has discovered the potential of female entrepreneurs (Niefert and Gottschalk 2013). There is agreement that female entrepreneurs could make a significant contribution towards economic development. For instance, female and male entrepreneurs differ as regards their business and personal profile, implying that they can be expected to start different firms in different sectors with different products and goals. This leads to a more diverse market, offering consumers a larger variety of products and services which might better fulfill consumers’ preferences. Also, a larger variety in processes or forms of organizations can help entrepreneurs to learn and make improvements. Taken together, more female entrepreneurs may lead to a higher quality of entrepreneurship (Verheul et al. 2006). In developing countries, female entrepreneurship appears to play an important role for poverty reduction (Yunus, 2007). Also, the embeddedness of women into their local communities and their different networks can produce important effects on their immediate surroundings (Chamlee-Wright, 1997).
However, as mentioned earlier, self-employment should not be regarded as a coping strategy, for instance, for women who want to reconcile family and work life or who want to escape workplace discrimination (for an overview see Marlow 1997). Even though in these situations self-employment might seem advantageous from a personal—and sometimes even from a broader—perspective, it can only be regarded as a short-term treatment for symptoms but not as the cure. In fact, these start-ups can be become costly investments for the founder, potential employees, and policy makers (cf., Nightingale and Coad 2014). Women should be able to balance work and family life or receive promotions while working in paid employment. Since there is agreement (1) that women play an important role for the growth process of a country and (2) that business creation can be advantageous for economic development, it is important to understand why there are still less female than male entrepreneurs. In fact, a lack of understanding of underlying drivers may imply an underutilization of the human capital of women, leading to lower living standards and the implementation of costly policies (Langowitz and Minniti 2007).
The aim of this study is to highlight the situation of female entrepreneurs in the Baltic Sea Region (BSR). Demographic and economic structural change will affect the development of the whole region and intelligently coping with these issues is expected to shape the region’s perspectives of growth and wealth in the next decades (Biermann et al. 2013). Therefore, fully tapping women’s potentials is a vital need and boosting genders’ equal opportunities for entrepreneurship is crucial in this context. Hence, in this study we will explore the main characteristics of female entrepreneurs in contrast to their male counterparts (Section 2), female entrepreneurship rates across BSR countries and characteristics that shape country-specific profiles (Section 3). With a focus on Germany, Section 4 analyses the overall situation, drivers, and barriers of German female entrepreneurs from a life course perspective. Field data from an online survey specifically conducted for this study will be presented. Section 5 concludes with a brief summary of the core results and some policy recommendations.
Before conducting empirical analyses, this section draws a first picture of who female entrepreneurs are and what they do. We will provide a literature overview of international evidence in the field, starting with definitions of female entrepreneurship and reporting differences between male and female entrepreneurs on the firm and individual level. Before drawing some general conclusions, we will highlight potential motives and barriers of female entrepreneurs. It is important to keep in mind that this can only be a quick introduction, providing an intuition for the interpretation of the data collected for this study.
Various definitions of entrepreneurship exist. Some of them are data-driven while others rely primarily on theoretical considerations. In the spirit of Schumpeter, an entrepreneur successfully starts a firm to create something new by means of creative destruction. This implies that one can identify this something new and measure firm success. It also raises several questions such as, for instance: To what extent is firm success measured by employment growth? Do start-ups have to create jobs? How to assess the role of free-lancers? Are full-time and part-time self-employed persons comparable? The majority of definitions do not distinguish between male and female entrepreneurs. On the one hand, it appears desirable to provide a general definition that covers all types of entrepreneurship. On the other hand, different employment behavior of women might result in gender-specific entrepreneurial activities which could be identified by extended definitions of entrepreneurship. In the end, defining different types of entrepreneurship can also help us to better evaluate the quality of start-ups. In what follows, we will present different definitions of entrepreneurship.
Good starting points are the reasons for becoming self-employed which are manifold. One trigger might, for instance, be a life-changing event such as a large inheritance (Bergmann and Sternberg 2007). On the other hand, for members of minorities, entrepreneurship offers an alternative employment path for personal advancement when traditional jobs are difficult to get (for an overview, Cromie and Hayes, 1988). In the case of female entrepreneurship, however, the evidence suggests that women might face even greater discrimination in entrepreneurship than they do in the labour market (Weiler and Bernasek 2001). A basic distinction can be made by dividing reasons for self-employment into pull and push factors. As the names suggest, pull factors represent all those instances where an individual freely decides to enter the market, for instance, because of identifying a market niche whereas push factors force this decision on the individual, for instance, due to unemployment. These groups are also labeled as opportunity (pull) and necessity (push) entrepreneurs. As entrepreneurship represents a big challenge, not everyone might be suited to successfully tackle it. Therefore, it is necessary to identify promising ventures and these are more likely to be found among opportunity entrepreneurs. A simple identification strategy is to separate solo-entrepreneurs (necessity) from entrepreneurs with employees (opportunities). This builds on the assumption that a successful start-up is able to grow and to generate jobs.
Nonetheless, simply looking at start-up rates or employment growth could tamper results. In general, there are fewer female than male entrepreneurs. But when looking at the transition rates from nascent to active entrepreneurs, there are almost no differences between the two groups. Thus, although there are less female than male entrepreneurs, the lower startup propensity of women is unlikely to result from lower chances of success in the founding process (Koellinger et al., 2013). This is an interesting result because studies have shown that starting a business is far more complex for women than for men. For instance, Minniti and Nardone (2007) suggest that the choice for entrepreneurship is driven by the need for independence to accommodate family needs and child rearing which might simultaneously complicate the founding process. This finding might be related to the fact that we see that the number of women in part-time entrepreneurship is above average (Bundesministerium für Wirtschaft und Energie, 26.11.2013; Niefert and Gottschalk 2013). Taken together, this provides further evidence that a classification of female entrepreneurs from a life course perspective could be a fruitful approach.
A promising classification of female entrepreneurs might take into account their current living and family situation. Goffee and Scase (1985) suggest a four-way classification that considers the individuals’ attachment to entrepreneurial values and conventional female values. This returns the following groups: (1) Conventionals who have a high commitment to both entrepreneurship and domestic roles, (2) Radicals who show a low commitment to entrepreneurship and domesticity, (3) Innovators who are primarily concerned with entrepreneurial values and (4) Domestics who are primarily dedicated to their domestic role. While an interesting approach due to the contrasting of family and work values it is difficult to implement with the available data sets. An alternative classification was developed by Cromie and Hayes (1988) whose approach evolves around women and their children: (1) Innovators who have no or only grown up children, (2) Dualists who have children and work while being a mother and (3) Returners who have older children and who lack other attractive job prospects. In general, it appears a promising approach to capture family commitments (e.g., by the number and/or age of children) in an analysis of working conditions. In the data analysis in Section 4, we therefore decided to build on this conceptualization by classifying women according to their family situation, proxied by the degree of support (with or without partner) and the degree of responsibility (children and/or adult care duties). We therefore aim to extend previous definitions and, simultaneously, to construct a measure that is more easily implemented in existing data sets.
When defining entrepreneurship, it is most important to avoid stereotypes. When this is not done in practice, so called stereotype threats might kick in. Stereotype threats describe situations where individuals could confirm a negative stereotype of their group. This might in return change their behavior, for instance, impair their performance on tests. It has been shown by Gupta, Goktan, and Gunay (2014) that women (men) evaluate entrepreneurial opportunities worse than men (women) when the opportunities are described using male (female) attributes. However, when using gender-neutral attributes, the difference in opportunity perception is no longer statistically significant. Since the identification of business opportunities is the first step to become an (opportunity) entrepreneur, it shows that to wipe out gender differences in entrepreneurship rates, one might start by changing the perception of entrepreneurship in society. In fact, Welter (2004) documents that in most western countries, particularly in Germany, entrepreneurship is primarily attributed with male characteristics. This brings up the role that policy can play. We will return to this point in the end of our study in Section 5 when it comes to the political inferences that can be derived from the reported results.
It seems important to distinguish between the number and the share of female entrepreneurs as the latter is a measure of diversity (Verheul, 2006). Female entrepreneurship can thus be investigated as the share of the population and as the share of the total number of entrepreneurs, yielding data that requires different interpretations. The share of female entrepreneurs in Germany differs, depending on the data that is used (cf., Niefert and Gottschalk 2013). While their number increases, there are, nonetheless, on average less female than male entrepreneurs (see for instance, Niefert and Gottschalk 2013, Wagner 2007).
As regards differences in firm success between male and female entrepreneurs, the picture has changed over the years. Naturally, one might conclude that female entrepreneurs have caught up with their male counterparts. The empirical evidence draws, in fact, a more sophisticated picture. In 1996, Rosa, Carter and Hamilton point out that, although they find marked sex differences in performance indicators, the evidence continues to be inconsistent, suggesting that a product of factors and not simply gender differences drive the results. This can be taken exemplarily for previous research where no clear pictures emerges as regards performance differences (for an overview see, e.g., Niefert and Gottschalk 2013; Bruin et al. 2007).
For instance, evidence from OECD countries for 2009 suggests that birth rates as well as death rates of female-owned enterprises are mostly higher than those of their male counterparts1. There is no clear evidence that female-owned enterprises fail at a faster rate than male-owned firms. Moreover, female firm birth rates turned out to be less affected by the economic crisis than male firm birth rates. As to sole-proprietor enterprises of women and men, both groups show similar survival rates three years after start-up. Furthermore, women- and men-owned enterprises perform rather similarly as regards job creation in the first three years). In Poland, women exhibited an even higher employment growth rate than men in 2009. However, women mostly differ from men in the size of business operations, measured in sales, profit or value-added. For instance, in 2009 female-owned firms held only a 4 %-share on the top decile of German firms ranked by the value of assets and ranked by shareholder capital, respectively. Among the top decile of the employment distribution, women-owned firms were not present. The situation was more favourable in Poland where the corresponding shares of women-owned firms amounted to 7 % (value of assets), 9 % (shareholder capital), and 1 % (employment), and much more in favour for women in Norway with shares of 14, 16 and 13 %. Despite the fact that apparently, nowhere in Europe women hit the jackpot, the particular small deal of German female entrepreneurs in terms of core economic indicators is conspicuous. Which firm-level characteristics answer for the gender performance gap? Blinder-Oaxaca decompositions show that the lower capital intensity, the lower average firm size, and the sector of activity answer for large parts of the gender performance gap. If female-owned firms exhibited the same characteristics in the named three aspects than enterprises owned by men2, the gender productivity gap (value added per employee) would be reduced by 51 % and the profit gap by 92 %.
Other studies also go one step further arguing that gender differences in performance indicators vanish once sectors of activities and key characteristics of business owners other than gender are controlled for (Fairlie and Robb 2009; Niefert and Gottschalk 2011). The question arises if different outcomes of women and men follow from deliberate choices signaling different preferences (or even abilities), or if they result from different opportunity structures of women and men, especially in the start-up phase and when growing the company. It is a priori not clear whether women intentionally select themselves in certain industries, nor whether they are particularly affine to small-sized firms and comparably low but flexible weekly workloads. For instance, the findings of Gatewood et al. (2009) point to a substantial part of female entrepreneurs with aspirations for growth.
Looking at survival rates, some studies have and some have not found differences between men and women. To disentangle these contradicting results, Kalnins and Williams (2013) suggest that differences in outcomes vary by industry and geographical location, a result that they can actually confirm. Female-owned businesses out-survived men in several industries and vice versa. At the same time, female-owned businesses out-survived male-owned businesses in the largest cities while male-owned businesses fared better elsewhere. The basic idea is that women are more likely to start their business in industries that have, e.g., fewer employees and lower survival rates. The regional environment is also of high importance in the start-up process (Bergmann and Sternberg 2007). Also, some industries require less financial (start-up) capital than others which results in higher failures rates in these industries because less is at stake. If women are overrepresented in such industries, overall failure rates of female enterprises will also be higher (Watson 2003). Koellinger, Minniti, and Schade (2013) find that the significant difference between the number of start-ups of men and women disappears when controlling for other factors, for instance, firm size and sectoral distribution. As alternative explanation for gender differences, they suggest that women lack confidence in their skills because of (1) different perceptions of own skills, (2) different perception of opportunities, and (3) different skills and circumstances between men and women. Alternatively, female-owned businesses might underperform because (1) their businesses may be younger, (2) due to family commitments they may lack working time, (3) they may lack access to finances, (4) they may show lower education and experience levels, (5) they may be more risk averse, and (6) they may be less interested in financial rewards (cf., Watson 2003). In what follows, we will look in more details at several of these individual characteristics.
It is well established that there exists an inverted U-shaped relationship between age and entrepreneurial propensity (e.g., Bergmann and Sternberg 2007). The likelihood to become self-employed increases with age and peaks between 35 and 40 years before it starts to decrease again. Previous experience in the industry, in self-employment, or in a management position further increases the entrepreneurial propensity. Nitschke (2010) finds that in older age cohorts there are fewer female entrepreneurs, indicating that either the entrepreneurial propensity of women has increased in recent years or that more female entrepreneurs have exited the market in the older age cohorts. Female entrepreneurs in Germany are on average 40 years old (Dautzenberg et al. 2013; HypoVereinsbank 2013). Overall, however, there appear to be no differences between the average age of male and female entrepreneurs at the time of start-up (Dautzenberg et al. 2013).
Similar to experience, entrepreneurial propensity also increases with higher education levels (Davidsson and Honig 2003; Robinson and Sexton 1994). In addition, higher education levels improve performance (for overview see Coleman 2007). This can be explained with the high level of task complexity during the start-up process. In addition, many entrepreneurs who start a business are alone and thus need to fulfill all required functions themselves. They therefore need to be jack-of-all-trades or have balanced skills (Lazear, 2004, 2005). Indeed, most likely due to their different work experience, women show on average lower levels of balanced skills (Bublitz and Noseleit 2014). This might, however, partly result from a different perception of own skills because the study used self-reported data (Koellinger et al. 2013). Self-employed women in Germany are on average higher qualified than their dependently employed counterparts (Dautzenberg et al. 2013).
Of particular interest in the case of female entrepreneurs is the role of the network and the family. As mentioned above, due to child-rearing and other care commitments, women might not be as flexible as men in different stages of their life. Instead of being pulled into entrepreneurship, women might be forced due a lack of reconcilability of children and work in dependent employment or due to workplace discrimination in form of a “glass ceiling” (for an overview see Marlow 1997). This is in line with the finding that having children increases the propensity to become self-employed but being self-employed does not increase fertility as such. Instead, according to the findings of Noseleit (2014), younger self-employed women show a significant decrease and older self-employed women a significant increase in fertility, suggesting that self-employment influences the postponement of childbirth. In the same vein falls the argument of a lack of childcare facilities, impeding flexible working hours for the mother (Welter 2004). Other evidence suggests that women’s transitions to entrepreneurship are mainly driven by their desire for independence (Gerlach and Damhus 2010). A review of the international empirical literature on fertility concludes that fertility decisions – as far as they are economically motivated – are the result of many socio-economic determinants which interact with country specific meta data (e. g. institutional childcare facilities and prevailing gender stereotypes) and moreover affect different fertility indicators in different ways (Boll et al. 2013).
Whereas the causality in the relationship between self-employment and fertility is not straightforward, as regards networks, it is known that having self-employed parents or personally knowing an entrepreneur increases the likelihood of becoming self-employed (e.g., Bosma et al., 2012). Weiler and Bernasek (2001) find that networks may indeed be barriers for women’s success in traditional and entrepreneurial labour markets, possibly because they are dominated by a subset of non-supportive agents. Also, women, compared to men, are less likely to know entrepreneurs (Koellinger, Minniti, and Schade, 2013).
So far, the focus was on traditionally measured characteristics. More recently, the analysis has been extended to include behavioral approaches, looking for instance at psychological variables and perceptions. Wagner (2007) shows that women have a higher fear of failure then men, which lowers the entrepreneurial propensity because self-employment is a risky employment choice. The evidence on different risk attitudes of men and women is still inconclusive (for an overview, Minniti, 2010). Interestingly, risk taking behavior can be influenced by the surrounding environment. After exposing female students to a single-sex environment over several weeks, women became less risk averse than their counterparts in co-educational groups. This result was not found for men (Booth et al. 2014). The authors do not suggest that inherent traits do not exist but that they can be altered in different environments through social learning. Langowitz and Minniti (2007) find that women tend to perceive themselves as well as their firm environment less favorably than men do. This is an important finding because it relates to psychological factors such as self-confidence or fear of failure which are important explanations for entrepreneurial propensity. Using a unique approach, Minniti (Minniti 2010) finds that if women were exactly like men in terms of their socio-demographic variables, the self-employment rates between both groups would still differ. Women appear to be differently affected by macro-economic conditions and perceptual variables. Koellinger, Minniti, and Schade (2013) confirm this result, showing that, for instance, age, education, and work status may influence start-up decisions through their influence on perceptions. Correspondingly, women show a higher fear of failure, lower exposure to entrepreneurs, and lower self-efficacy which all reduce entrepreneurial propensity. Throughout, the gender differences in these perceptual variables are persistently high and cannot be explained with the socio-economic differences or self-selection into sectors.
Naturally, due to their family duties, women need to reconcile work and children. Marlow (1997) documents in a qualitative study that, as motivating factors for entering self-employment, women when compared to men are more interested in combining waged and domestic labour and in developing a hobby. Men mention independence and financial gain more often while there are no differences as regards career frustration. As regards current trading difficulties, women mention most often domestic and firm clash, suitable staff, and finance issues. Men are mostly concerned with finance issues, credit control, or report to have no difficulties. It is interesting to see that in the study more than one-third of the men did not expect women to face particular problems. Only a minority of women agreed with this notion. A general conception appears to be that men and women differ in the factors that drive their self-employment choices. Correspondingly, men are expected to focus more on standard economic factors such as, for instance, access to credit and economic opportunity. In contrast, women are expected to attach more importance to social factors such as, for instance, work-life balance and parenthood. As a consequence, more women than men start their business out of a position of non-employment indicating that self-employment serves as a re-entry option for women (Döbler 1998). Moreover, women who have to cope with restricted time schedules due to family tasks might consider self-employment as an intelligible and durable strategy to reconcile family and work (Jung 2011). However, Saridikis, Marlow, and Storey (2014) show using official, as opposed to self-reported, data that macro-economic factors powerfully explain self-employment behavior of men and women in the short and long-run. Indeed, a respondent in the pretest of our survey (see Section 4) pointed this out when commenting on our questionnaire design, emphasizing that, for instance, the most important barriers would be the same for women as for men and that one should avoid focusing too much on soft factors. The literature reviewed here indicates that although the individual circumstances, motives, and barriers of women and men may differ in the start-up phase, both genders face similar challenges in maintaining and growing their business in the later career.
One might add that the provided time flexibility of self-employed women does not necessarily correspond to an overall reduced workload of women in the private sphere. Within couples in many European countries, women uptake the major part of housework even when they are full-time employed. At the turn of the millennium, the gender gap in daily housework time in dual earner couples (both partners working full-time) amounted to 46 minutes for Sweden, 69 minutes for Finland, 77 minutes for Norway, and 64 minutes for Germany (Boll, Leppin, and Reich 2012). Whereas the gender gap in the unpaid workload also applies to dependently employed women, self-employed women are additionally burdened with the business risk (Gerlach and Damhus 2010).
1 The evidence presented in this paragraph refers to OECD (2012). Births refer to the creation of new enterprises with employees or to transitions of existing enterprises from 0 to 1 or more employees. Analogously, death rates refer to the dissolution of enterprises or to transitions to no employees.
2 Enterprises are defined as women (men)-owned if one or more women (men) won more than 50 % of the shares.
The literature overview illustrated that male and female entrepreneurs differ. In addition, a cross-country comparison can provide us with a richer picture of female entrepreneurship. In this Chapter, the labour market involvement of women in general and the importance and characteristics of female entrepreneurship in particular are portrayed for the countries bordering the Baltic Sea: Finland, Sweden, Denmark, Germany, Poland and the Baltic States Estonia, Lithuania and Latvia. The data for Chapters 3.2 through 3.6 come from the Labour Force Survey (LFS) and the 'Global Entrepreneurship Monitor' (GEM), respectively, and for Chapter 3.7 from the 'Global Entrepreneurship and Development Index' (GEDI).
The LFS is the largest household sample survey in Europe.3 GEM is an international research pool and has been active for 15 years. It collects information on startup activities and attitudes of entrepreneurs and compares them on an international scale (comprising 69 countries).4 The GEDI index measures the entrepreneurial char acter of 118 nations.5 Detailed data source information is provided in the figures and tables and (for Eurostat data) in Tables A1a and A1b, respectively in the Appendix. However, for improve readability, sources will not always be explicitly mentioned within the text.
The duration of women's working lives in BSR countries differs by more than ten years, ranging from 28,9 years in Poland to 38,5 years in Sweden (figures for 2010, see Biermann et al. 2013). To circumvent biases caused by retirees’ self-employment, our analyses of individuals at working age henceforth refer to those aged 15 to 64 years and to 20 to 64 years, respectively (depending on data availability).
Germany, with 24.7 million women has the largest absolute number of female persons at working age (20 to 64 year old) of the analysed countries (see Figure 1). Thus, the German female workforce is greater than the aggregate workforce of the remaining eight countries bordering the Baltic Sea (21.2 million women). Moreover, the aggregate female workforce living in the three northernmost federal states, namely Schleswig-Holstein (1.7 million), Hamburg (1.1 million), and Mecklenburg-Vorpommern (1.0 million) equals the respective total of Denmark, Norway and Latvia (3.649 million women). This highlights the particular importance of German labour market policy for growth and employment perspectives in the Baltic Sea region.
Figure 1
With 11.8 million, Poland has less than half the number of women at working age when compared to Germany. The third highest female population at working age is found in Sweden. Totaling 2.7 million women, the Swedish female working age population is roughly a quarter of the size of the Polish, or one-ninth of the German population respectively.
In turn, as depicted in Figure 1, the remaining Scandinavian countries of Denmark, Finland, and Norway count about half of the Swedish female population at working age, each summing up to about 1.5 million women. The Baltic States have a total of 2.0 million female inhabitants at working age, while almost half of them live in Lithuania (0.9 million). Latvia (0.6 million) and Estonia (0.4 million) account for the smallest female populations at working age of all countries in our sample.
Within the entire female population at working age, on average 5.8 % are self-employed. This rate is highest for Poland (8.0 %), followed by Finland (6.0 %). The two countries with the lowest share of female self-employed at working age are Norway (3.1 %) which is directly followed by Estonia (3.2 %). For a more detailed count of the absolute amount of female self-employed, see Figure 1.