# Women entrepreneurs in France

Virginie Vial, Katia Richomme Huet

► To cite this version:

Virginie Vial, Katia Richomme Huet. Women entrepreneurs in France. International Journal of Entrepreneurship and Small Business, 2017, 30 (1), pp.80. 10.1504/ijesb.2017.10001192. hal-03628415

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# Women entrepreneurs in France

## Virginie Vial*

Kedge Business School,
Domaine de Luminy, BP 921,
13288 Marseille cedex 9, France
and
Institut d'Asie Orientale,
IAO, UMR CNRS 5062, France
Email: virginie.vial@kedgebs.com
*Corresponding author

## Katia Richomme-Huet

Kedge Business School,
Domaine de Luminy, BP 921,
13288 Marseille cedex 9, France
Email: katia.richommehuet@kedgebs.com

**Abstract:** Despite supposedly supportive institutions and quasi-gender equality in employment rate, only 30% of French entrepreneurs are women. Using the 5Ms framework, we illustrate and explain the entrepreneurship gender gap in the French institutional context. To this end, we worked with a quantitative study using the large SINE database, which contains information on 48,251 new entrepreneurs. Although women are still mostly confined to traditionally gendered sectors and start smaller projects despite seemingly unconstrained access to finance, they outperform men in terms of management but fail to get outside support. However, mothers conversely struggle to get finance but innovate in sales, products and service. Our results call for a redefinition of classical concepts and measurements in entrepreneurship.

**Keywords:** women entrepreneur; market; money; management; macro and mesoenvironment; motherhood.

**Reference** to this paper should be made as follows: Vial, V. and Richomme-Huet, K. (2017) ‘Women entrepreneurs in France’, *Int. J. Entrepreneurship and Small Business*, Vol. 30, No. 1, pp.80–109.

**Biographical notes:** Virginie Vial is an Associate Professor in Development Economics at Kedge Business School, Marseilles, and an Associate Research Fellow at Lyons Institute of East Asian Studies (IAO), ENS Lyon, France. She holds a PhD in Economic History, London School of Economics. Her research deals with the measurement and determinants of economic growth and human well-being in general, focusing in particular on factors such as institutions and entrepreneurship, mostly in the context of Indonesia. This focus has led to several publications in the domain of Total Factor Productivity measurement issues, accounts of industrial dynamics, the effect of corruption on firm's performance, or the effect of entrepreneurship on economic welfare. Ongoing projects target the effect of entrepreneurship on human development, as well as women empowerment.
Katia Richomme-Huet is an Associate Professor for Entrepreneurship at Kedge Business School, Marseilles, since September 2007, where she also coordinates the cluster ‘entrepreneurship and new business models’. She received her PhD in Management Sciences at University of Montpellier, ISEM and her qualification to supervise doctoral dissertations at IAE of Brest. Her research areas and publications cover social and sustainable entrepreneurship, women entrepreneurship as well as family businesses (through the prism of succession and takeover) in the French context.

This paper is a revised and expanded version of a paper entitled ‘Les femmes entrepreneurs en France’ presented at the Journée AEI/AIREPME ‘Entrepreneuriat et PME: enjeux et perspectives’, Marseilles, France, 23 May 2014, and of a paper entitled ‘Women and Mother Entrepreneurs in France’ presented at the 60th Annual ICSB World Conference, Dubai, UAE, 6–9 June 2015.

# 1 Introduction

In spite of the worldwide emergence of theoretical and empirical research on women entrepreneurship, we know very little about the French case. A handful of studies review the general topics of motivation, business performance and management (Ballereau, 2012; Boutillier, 2008; Chabaud and Lebègue, 2013; Duchéneaut and Orhan, 2000; Fouquet, 2005; Lebègue, 2011; Orhan and Scott, 2001), whereas others concentrate more specifically on support and networking (Mione, 2006), particular business sectors (Gallioz, 2006), women business buyers or successors (Richomme-Huet and d'Andria, 2012), partnerships between spouses (Le Loarne-Lemaire, 2013; Richomme, 2000), as well as mumpreneurship (Richomme-Huet and d'Andria, 2013; Richomme-Huet et al., 2013). To the best of our knowledge, no study explores why French women entrepreneurs are under-represented, in spite of significant broad institutional advances in gender equality and a wide participation in employment (Bel, 2009; Duchéneaut and Orhan, 2000; Fouquet, 2005).

According to Brush et al. (2009, p.11), institutional contexts influence gender and provide better explanations than ‘differences in individual characteristics or pure gender discrimination’. Authors suggest reviewing specific micro-environments, motherhood characteristics (i.e., household and family context) and meso- and macro-environments (i.e., expectations of society, cultural norms, intermediate structures and institutions) to explain the men-women segmentation of economic activity. Welter (2011) insists that gender might not be a trait in itself, and its effect highly depends on the entrepreneurship context. As a first empirical review of this proposition, Adveev et al. (2011) show that, in Europe, the meso- and macro-environments, combined with men-women equality in the economic sphere, coincide with a relatively low number of children in households because of an existing inequality of the distribution of household tasks in the private sphere (micro-environment). In France, a similar phenomenon is observed, except that women are the second-most fecund in Europe with an average of 2.0 child per woman compared to a European average of 1.57 [INED, (2013), http://www.ined.fr/fr/pop_chiffres/pays_developpes/indicateurs_fecondite/]. The authors explain that supportive family policies tend to balance the distribution of tasks and
responsibilities in the private sphere. Puzzlingly, although economic equality appears in
employment figures (women represent 47.91% of the active population – INSEE, 2012),
it does not likewise appear in entrepreneurship figures (only 30% of new entrepreneurs
are women – Bel, 2009).

We aim to shed light on French women entrepreneurs’ reality and provide evidence to
explain the gender gap in a national, institutional context. To this end, we use Brush et
al.’s (2009) framework as a heuristic tool to explore empirically women entrepreneurship
through the effect of motherhood on the three core elements of entrepreneurial success
(Bates et al., 2007), namely, market, management and money (the ‘3Ms’), placed in a
French macro- and meso-environment context.

We first review the literature on women entrepreneurship and underline the particular
effects of the institutional context, including motherhood, on the ‘3Ms’, supporting and
illustrating our theoretical propositions with the particularities of the French
entrepreneurial context. We derive three propositions that enable testing for the effect of
motherhood on the ‘3Ms’ and substantiate and advance some explanations for the
existing gender gap. We rely on Moore and Buttner’s (1997, p.13) general definition of
the women entrepreneur as ‘a woman who has initiated a business, is actively involved in
management, owns 50% of the firm, and has been in operation for one year or longer’.
Second, we used the 2009 Système d’Information sur les Nouvelles Entreprises (SINE)
database, which was collected by the French National Bureau of Statistics (INSEE) and
contains information about 48,251 new entrepreneurs, to propose quantitative tests of our
propositions that enable extensive empirical investigation of gender differences among
French entrepreneurs. Our main contribution is found in the evidence and our explanation
of the difference between how women and men participate in entrepreneurship. We
review a large-scale empirical study using a wide variety of measurements for the ‘5Ms’
and test whether motherhood, within the broad macro and meso-environment, generates
significant variations in the revealed actual market in which entrepreneurs progress, their
actual management, as well as the money they need and secure. Third, we present our
results. Fourth, we discuss our results and conclude in a fifth section.

## 2 Women and entrepreneurship in the French institutional and economic sphere

Since Hisrich and Brush's (1983) pioneering article, five essential thematic areas were studied: breeding grounds, patterns, barriers, motivations and organisational and managerial methods of women entrepreneurs (Bruni et al., 2004). From the early 1970s to the 1990s, research focused on who women entrepreneurs were (Birley et al., 1987), specifically on their individual characteristics (Morris et al., 2006). These studies often used men as benchmark, trying to establish similarities and differences (Birley, 1989; Orhan, 2001), which was inspired by liberal and social feminist approaches. In the 1990s, questions about what entrepreneurs were doing (Gartner, 1988) resulted in investigations of women entrepreneurs' behaviour linked with feminism-inspired studies and women's employment choices related to economic options embedded in familial obligations and responsibilities (Minniti and Naudé, 2010). The new century began with fresh inquiries about private and public contexts, represented as an 'intersection between the reproductive life-cycle (...) and the entrepreneurial project' (Bruni et al., 2005, p.18), drawing on institutional theory (Brush et al., 2009), with gender as a new perspective.
Broken down into masculinity and femininity, it is the social construct for social practices and representations related to biological sex, a simple categorisation between men and women (Ahl, 2006). The main issue is to go beyond the radical feminist perspective, in which women's bodily experiences of motherhood induce specific characteristics (Greer and Greene, 2003), such as 'docile femaleness' as a 'new normative model of female experiences' (Bruni et al., 2005, p.23), reinforcing male dominance. On the contrary, interconnecting entrepreneurship with gender advocates a new form of determinism (Brown, 1993). A debate has taken place about the necessity of developing new theories either comparing women to a male norm or redefining classical measurements of entrepreneurship (de Bruin et al., 2007) in a 'social constructionist or poststructuralist feminist' conception of gender 'where similarities and differences are socially constructed' (Ahl, 2006, p.596). As a result, recent works emphasise the need for a focus on the influence of sets of factors and to develop an integrative multi-level research design (de Bruin et al., 2007; Jamali, 2009). The entrepreneurship literature presents the '3Ms', that is, *market, management, and money* (Bates et al., 2007), as essential to the launch and success of ventures. Although this theory has been advanced as universal, the nature or access to those '3Ms' will differ between genders (Ana et al., 2000; Marlow and Patton, 2005). This clearly represents a first step towards understanding the gender gap in entrepreneurship participation. Indeed, building on institutional theory developments, Brush et al. (2009) introduce a fourth and fifth Ms, respectively representing the *macro and meso-environment* as well as *motherhood*, both conditioning individual access to the '3Ms'. This '5Ms' framework represents an interesting tool to investigate empirically gender differences in entrepreneurship in detail, directly assessing the impact of various facets of the institutional environment on the three central pillars of entrepreneurship while still controlling for individual characteristics, thereby going beyond simple gender discrimination or individual ability explanations.

## 2.1 *Macro-meso environment*

The macro-environment comprises formal (e.g., official rules, state apparatuses and policies) and informal institutions (e.g., norms and culture), with the meso-environment as an intermediary (regional) level. Their effects can differ between genders, accounting for gender differences in entrepreneurship participation. The histories of women entrepreneurship and employment are indissociable. French women have actively occupied independent positions within corporations as merchants and as factory workers since the 19th century. By the mid-19th century, mechanisation led to the replacement of the skilled male by an unskilled and cheaper female workforce, introducing a labour division between sexes. In addition, women's work has been discontinuous, following the family life cycle (Battagliola, 2000). Both factors contributed to the blindness of authorities, society and scholars towards women's work, which was qualified as 'unproductive' (Richomme, 2000), leading to the concept of the 'invisible' woman (Gallioz, 2006), which we call the 'working housewife' oxymoron. This is reinforced by the legal incapacity of women and their subjection to the guardianship of their closest male parent (1804–1938). After more than a century of struggle, French women achieved substantial law reforms, such as their right to vote in 1944, the replacement of the concept of 'female wage' by 'for equal work, equal pay' in 1945 (reinforced in 1972) and the establishment, at least in theory, of the men-women equality principle in all areas of
life included in the preamble of the French Constitution in 1946. That year, French women created the first world network of women entrepreneurs (FCE – Femmes Chefs d'Entreprise). Despite those numerous advances, national institutions maintained discriminatory practices preventing societal attitudes from changing. Only gradually, programs, proposals and laws improved French women's situation, and the share of women in the workforce rose from 38.3% in 1946 to 47.91% in 2013 (Maruani and Meron, 2012). Lacking a risk-taking culture, France has long been regarded as a European laggard (Henriquez et al., 2001) in entrepreneurship. This, coupled with the 'invisible' woman phenomenon, explains the absence of historical women entrepreneurship accounts. Since 2000, however, a set of national framework measures (Table 1) has been adopted to promote and enhance the exploitation of French women entrepreneurial potential. Successive governments have attempted to strengthen this result (Chabaud and Lebègue, 2013) via networks, support and mentoring structures, as well as special conditions for creating an independent activity during parental leave (Richomme-Huet and d'Andria, 2013). Beyond classical actors, specific structures or programs directly support women entrepreneurship, such as 'Entreprendre au féminin' (female business network backing the creation of women businesses) or 'Racines-Clefe' (innovative financial support program for women entrepreneurs only). These, coupled with proactive family policies, should have boosted the rate of women entrepreneurship. However, according to Estrin and Mickiewicz (2011, p.401), women are less likely to become entrepreneurs in countries with large state sectors because resulting social structures incentivise them to stay at home. Answers to this effect are to be sought in the context affecting family organisation.

<table><caption>**Table 1** French legislation for women's rights in entrepreneurship</caption><thead><tr><td>Dates</td><td>Legislation</td></tr></thead><tbody><tr><td>1907</td><td>Ability to assert personal wages for married women.</td></tr><tr><td>1938</td><td>Repeal of civil disability and suppression of marital authority: if women can enroll in university, they need their husband's permission for working and for determining the place of residence.</td></tr><tr><td>1965</td><td>Management of their own property (bank account) and exercise of a professional activity without the consent of their husband.</td></tr><tr><td>1982</td><td>Act of 10 July 1982: Introduction of three official statuses for the assisting spouses of craft workers and trades people who work in their husband's business: spouse partner, associated spouse or salaried spouse.<br/>Introduction of maternity leave with pay for all women in non-salaried and non-agricultural professions</td></tr><tr><td>1988</td><td>Women farmers can benefit personally from access to installation aid in agriculture when they start up a business jointly with their spouse.</td></tr><tr><td>2003</td><td>The Dutreil 1 Act (law for the economic initiative):<ul><li>promoted entrepreneurship on the nation's list of priorities</li><li>created the proximity investment funds (FIP) to help finance regional SME/SMI</li><li>enabled company startup by internet (through a virtual centre for enterprises formalities), in 1 day, with 1 euro OF capital. – partially protected the assets of a “failed” entrepreneur.</li></ul></td></tr></tbody></table>
<table><caption>**Table 1** French legislation for women's rights in entrepreneurship (continued)</caption><thead><tr><td>Dates</td><td>Legislation</td></tr></thead><tbody><tr><td>2005</td><td>The Dutreil 2 Act (law for supporting small and medium-sized firms, French Small Business Act):<ul><li>improved the framework for business creation and development</li><li>expanded the spouse's rights</li><li>introduced the temporary provision of mentoring between seller and buyer in take-over</li><li>changed the relationships between producers and distributors</li><li>integrated fair trade forms as part of the national strategy for sustainable development.</li></ul></td></tr><tr><td>2006</td><td>Agricultural Orientation Act of 5 January 2006: extending social agricultural coverage to spouse of head of agricultural holding.</td></tr><tr><td>2008</td><td>Act of 4 August 2008 (Law on the modernization of the economy):<ul><li>expanded the three official statuses for the assisting spouses to the liberal professions sector, the partners bound by civil union and to the legal statutes (EURL, SARL and SARL)</li><li>enacted the status of auto-entrepreneur for smothering the transition from salaried or unemployed to self-employed.</li></ul></td></tr><tr><td>2009</td><td>New status of "auto-entrepreneur" came into effect.</td></tr><tr><td>2010</td><td>Act of 15 June 2010: creation of a new status, EIRL (limited liability sole proprietorship). Although it is similar to the single-member limited liability company -EURL-, particularly in respect of tax, the EIRL remains a sole proprietorship and not a company.</td></tr><tr><td>2012</td><td>Act of 22 March 2012: promoting simplification on business rules and reduction in firms' administrative burden. Decree of 28 December 2012: adjustment to contribution rates (for auto-entrepreneurs).</td></tr><tr><td>2013</td><td>Supplementary Budget Act: revised and adapted on an annual basis.</td></tr></tbody></table>

## 2.2 Motherhood

According to Brush et al. (2009), motherhood is used for spelling out and instilling ‘a gendered perspective at the level of the household for women entrepreneurs’ (p.10) as well as for recognising ‘the intricacies of household dynamics, including unequal division of labour and access to household resources that differentially impacts access to the standard 3Ms of markets, money, and management’ (p.11). For these authors, motherhood is defined as a metaphor representing a micro-level sub-context that affects women more than men (Jennings and McDougald, 2007), resulting from the intersection among the macro and meso-environment, family (micro-environment) and gender (social construct). Thus, if the *macro* and *meso-environment* considers women and men as equal in terms of their role in potential or actual parenthood, *motherhood* affects everyone. If it affords women more opportunities to participate in and provides more responsibilities with regard to parenthood, and identifies women without children as individuals defined by their potential motherhood role, it affects women more intensely than men. Finally, in this context, actual mothers are the most affected because they are women and because they have a child. The effect of *motherhood* and its boundaries therefore depends on the *macro and mesoenvironment*.
Since the 19th century, important public policies were developed, triggered by the deep transformation of marriage (more liberal divorce law since the Naguet Act of 1884), family life (juridical equality of the spouses since 1960) and women's formal sexual emancipation (control over pregnancy since the Neuwirth Act in 1967 for contraception and the Veil Act in 1975 for legal abortion), which directly affected the French Motherhood sub-context. It facilitated women workforce development and more childcare facilities and state support, such as national policies for maternity leave (from eight weeks unpaid in 1909 to 16 to 46 weeks paid maternity leave), parental leave (for both parents since 1984, and 11 days of paternity leave formalised in 2002) and public kindergarten. The welfare state provides some women protection through legislation such as duty of care between spouses, specific tax regimes and a limitation of the number of public infrastructures that allow children aged less than three months. In addition, French family policies continue to support long periods of parental leave (up to three years), with replacement income (early childhood benefit) so as to encourage mothers to interrupt their activities and raise their young child. Despite this, still very few women choose to become entrepreneurs. Krapf et al. (2014, p.2) advocate that 'women, whose careers require an ongoing preoccupation with the matters of their profession, benefit less from child care provisions'. Indeed, we note the co-existence of policies and institutions supportive of women's employment with subtle barriers rooted in traditional social roles and perceptions about women (Welter, 2006), which leads to formal institutions not being supportive of women who wish to engage in demanding and high-skilled economic activities. Thus, we argue that, constrained by the French macro and mesoenvironment the motherhood mostly affects women and mothers. This micro-context could hamper the full development of woman entrepreneurship through its interaction with the '3ms' factors of entrepreneurial success.

## 2.3 Market

First, ventures need access to *market*, which is conditioned by industrial and institutional barriers to entry, specifically, industrial structures and the regulatory environment. This access also includes the consideration of opportunity recognition that is shaped by existing markets and the profile of the entrepreneur. Women have been reported to recognise and develop opportunities, start businesses and become entrepreneurs for myriad reasons. First, 'push' factors cover the forced or necessary motivations as the best option available (Acs, 2006), linked with economic necessity, lack of employment opportunities, job security and job discrimination (Wilson et al., 2009) and an aspiration to escape from both domestic and labour market subordinations (Knörr, 2011). Second, 'pull' factors introduce more classical motives such as an advantage of a unique market opportunity (Reynolds et al., 2002), broken down into desire for independence or self-achievement, time flexibility, financial wealth and social status (Duchéneaut, 1997), with an emphasis on non-monetary aspects. Third, a combination of push and pull factors appeared together as a third force, a kind of 'unique built option/best developed opportunity/best opportunity available', in which entrepreneurship serves work-family balance synergies and nurtures satisfaction (Eddleston and Powell, 2012). Women have become entrepreneurs under a complex mix of constraints (compulsion factors) and opportunities (attraction factors), of 'external coercions and subjective aspirations' (Bruni et al., 2005, p.20). Albeit a priori nonghettoised into a single type of gendered motivation, women entrepreneurs dominate in certain businesses (service, consumer and retail
sectors) and are under-represented in others (manufacturing and construction) (Kelley et al., 2011). Since the Golden Age, French women participation in economic activities follows economic cycles: high during boom periods and low during crises (Mainguené, 2011). This explains the emergence, at the beginning of the 21st century, of a specific paradox: whereas the professionalisation of men and women appears increasingly similar, the reference model oscillates between the housewife and the ‘working housewife’ – a ‘wonderwoman’ who single-handedly manages domestic tasks as well as professional activities. Additionally, discontinuous work, mostly because of motherhood, fuels the continuing existence of an entire discriminatory system in spite of established legal frameworks designed to fight it.¹ As a result, women are clustered in traditionally gender-specific jobs, activities and industries. This is first apparent in women entrepreneurship participation rates, growing very slowly from 26% in 1998 (Orhan and Scott, 2001) to 30% in 2012 (INSEE, 2012). Women business owners account for only 13% of the total of French owner-managers with more than ten employees (INSEE, 2012), while the bulk are self-employed or micro business leaders, and very few occupy the field of high-growth or high-tech gazelle companies (Richomme-Huet and d’Andria, 2013; Le Loarne-Lemaire, 2013). Women entrepreneurs are expectedly prevalent in activities in which they traditionally dominate as employees (Vidalenc and Wolff, 2012). They cluster in the tertiary sector (Boutillier, 2008; Fouquet, 2005), specifically 69.4% of women compared to 50.8% of men, whereas the manufacturing or construction sectors are male-dominated areas (Gallioz, 2006). Focusing on micro and small firms, 20.5% are owned and operated by women in the commercial and craft sectors and 45% in liberal sectors (teaching activities, health and care, legal activities, etc.) (Bel, 2009). However, the current collection of statistical data describes visible women entrepreneurs and does not reflect women who share property with their spouse or another partner (APCE, 2007; Chabaud and Lebègue, 2013). For this hidden category, women remain statistically invisible and unproductive as non-salaried workers and minority owners (Gallioz, 2006; Richomme, 2000). This leads us to the following proposition:

P1 Because of the general French national and regional institutional contexts, women and mothers differ from other entrepreneurs in terms of *market*, that is, they tend to be polarised into traditionally gendered and/or low-tech sectors.

## 2.4 *Management*

The success of an enterprise relies on *management* in the form of human capital: the entrepreneur’s human capital and also his or her ability to mobilise other human capital. Women entrepreneurs are generally found to be risk adverse (Sexton and Bowman-Upton, 1990) and insufficiently competitive, potentially explaining their relative minority in entrepreneurship. Recent studies show, however, that it is more a question of stereotype and gender perspective and that women entrepreneurs handle their roles in different ways (Gupta et al., 2009). Women and men have similar educational achievements (Birley et al., 1987; Menzies et al., 2004) but smaller and more gendered networks (Aldrich et al., 1989). Human and social capital, especially graduate education (Carter et al., 2003), increases the ability of women to obtain equity financing, provided that their industry sector matches venture capitalist preferences (Greene et al., 2001) and helps fight negative myths about women entrepreneurs (Brush et al., 2001). Indeed, myriad challenges remain in terms of the women-environment interface. The
sociocultural context plays a major role in women's discrimination in both educational and professional career choices. Historically, French girls gained the same educational rights as boys in the 1881-1882 Ferry Act and in 1924 obtained an identical curriculum for the same generalist Baccalauréat degree. Governments regularly reaffirm the need to bridge the gender gap in French education with female-student-targeted measures,² but the result 'produces an intrinsically paradoxical picture of the situation today: an overrepresentation of girls in full-time education combined with the reproduction of gender inequalities' [Baudino, (2003), p.1]. The French education system, in promoting nongendered career paths, tends to reproduce continuous discrimination, false neutrality, as well as inequality further down the life path, with glass ceilings and walls that limit career progression and labour marketability. Thus, women have risen to the upper echelons of 20% of the French largest companies (CAC 40) for the first time above the European average (17%) but far behind Norway with 35% (McKinsey and Company, 2012). Difficulties for employed women spill over into the perception that women are or could be less efficient managers than men. In spite of women's relative higher level of education compared to men (65% hold at least a secondary school degree compared to 47% for men), their lower employment status explains their lower professional and managerial experiences (Bel, 2009; Chabaud and Lebègue, 2013; Richomme-Huet and d'Andria, 2013). According to Orhan (2001), French women entrepreneurs are increasingly educated, with a strong focus in business or technical backgrounds. Paradoxically, they still declare a lack of managerial abilities, mostly because of a poor executive managerial experience because of the glass ceiling in their previous occupation. This effect is further amplified by shorter pre-venture creation professional experience because of the slightly younger age of established French women entrepreneurs (45.5 years old for women compared to 46.5 for men - INSEE, 2012) and the time spent out of work during maternity as well as parental leaves. We therefore propose the following hypothesis:

P2 Because of the general French national and regional institutional contexts, women and mothers differ from other entrepreneurs in terms of management, that is, they tend to have more managerial difficulties.

## 2.5 *Money*

Venture creation and growth rely on access to *money*. This view of entrepreneurship rests on the premises of classical economic theory in which output is created from human and financial capital. At start-up, women use less capital and external formal financial instruments compared to men (Carter and Rosa, 1998); they also experience barriers to acquiring start-up capital as well as ongoing investments (Marlow and Patton, 2005). As later entrants in the entrepreneurial game (Brush et al., 2004), they are perceived as less credible entrepreneurs (Buttner and Rosen, 1992). Consequently, they are further disadvantaged by a smaller business size and limited growth potential and profitability (Coleman, 2002), which in return reduce the possibility of obtaining debt capital and increase size, profit and growth. Watson et al. (2006) temper this and show that women are inclined to repay their bank loans earlier than men so as to maintain control over their ventures. Furthermore, women might be led by excessive perceived risks of high-rate growth, rejection of power dilution from equity investment or desire for work-life balance and intentionally maintain the small size of their business (Cliff, 1998). Finally,
self-financing and informal finance (love money from relatives or an employed husband) play an important role in women entrepreneurial activity (Bruce, 1999). For Menzies et al. (2004), no significant differences appear between women and men in terms of start-up assistance and family support. Historically, in France, married and unmarried women were not thought of as deserving financial rights until 1907, when they were finally in a position to freely dispose of their own wages. Furthermore, until 1965, a woman could not handle her property and open a bank account without the permission of her father or husband. Wives had to wait until 1984 to benefit from the right of equal management in matrimonial properties and family finances, and until 2001 for the dual signatures required on a couple's income tax return – thereby removing the notion of the man being the sole head of household in view of French tax law. This brief historical account sheds light on the current situation of French women entrepreneurs with regards to money. Considering more recent times, Orhan (2001) reports that women's access to finance in France is not significantly different from men's. She, however, underlines the significant existence of higher requirements in terms of collateral provision. This points to a substantial hurdle, which is symptomatic of a lower confidence of investors in women entrepreneurs, more difficult access to finance in general and a possible self-selection of women entrepreneurs in higher socio-economic classes. The self-selection issue is further supported by Bernard et al. (2013), who reveal that no difference in access to financing can be found between men and women but suggest a weaker funding request when the entrepreneur is a woman because of less ambitious projects. As an indication of finance representing a key barrier to women entrepreneurship, facilitating and ensuring access to funding is one of the five main recommendations of the government-promoted Women Entrepreneurship Charter (Laboratoire de l'égalité et organismes associés, 2013). As a complement, the Guarantee Fund for Women's Initiative (Fond de Garantie à l'Initiative des Femmes) helps women to access financing in order to set up, take over or develop a business.3 The financial aspect is well backed by 'strong support networks, (...) the best microloan coverage in Western Europe and several loan guarantee programmes (...) for those excluded from mainstream finance' [Underwood and Lämmermann, (2007), p.7]. These initiatives, however, are still in their infancy and their efficiency is still to be demonstrated, resulting in our third proposition:

P3 Because of the general French national and regional institutional contexts, women and mothers differ from other entrepreneurs in terms of *money*, that is, they tend to have reduced access to formal financing.

## 3 Data and methodology

We gathered data through the SINE database (Système d'Information sur les Nouvelles Entreprises) collected by the French National Bureau of Statistics (INSEE). This is one of the rare databases that enables the study of entrepreneurship by individual and gender (OECD, 2012). Micro-data surveys indeed provide a necessary additional analysis framework to macro-level studies (Klyver, 2008). SINE's latest cohort dates from 2006 cover a representative sample of the population of new firms from regional, industrial and employment points of view. Surviving enterprises were surveyed a second time in 2009. The 2009 data cover a total of 48,251 entrepreneurs and provide information on the
entrepreneur's profile and initial conditions; evolution of the enterprise's activity; type of customers; cooperation with other firms; evolution of employment, investments and modes of financing; training and consulting after start-up; difficulties faced by the entrepreneur; the firm's strategy as well as the conditions of its development. Variables provided in the SINE database are collected at the individual entrepreneur level and have been constructed in accordance with European definitions and standards. The raw collected variables are directly used in this paper without transformation, reconstruction, aggregation or clustering, thereby using the most detailed level of information available and providing the deepest analysis possible.

Our core concern is showing and explaining the difference between women and men participation in entrepreneurship. To this end, we have used Brush et al.'s (2009) theoretical framework to build three propositions about the effect of the motherhood context on the three pillars of entrepreneurship, controlling for the general institutional environment. our strategy rests on exploring these propositions and testing whether motherhood, within the broad macro and meso-environment, leads to significant differences in the revealed actual market in which entrepreneurs operate and develop, their actual management, as well as the money they need and have access to.

Our model writes:

$$Mi = a. Motherhood_i + b. Macro and meso - Environment_i + d. Controls_i + c + e_i \quad (1)$$

where i is a subscript representing an individual entrepreneur, M stands for each of the '3Ms', motherhood stands for the motherhood context, macro and meso-environment stands for the macro and meso-environment. We add a set of standard control variables (controls) to account for other individual and regional characteristics. Finally, c is a constant and e an error term.

In order to explore our three propositions, we have to rely on at least three different measurements of M, each respectively representing the market, management, and money. additionally, because no standard and comprehensive measure of each of the

'3Ms' exists, and to account for their potentially gender-varying nature (Ana et al., 2000; Marlow and Patton, 2005), we use different measurements for each of the three Ms and thereby capture some of their different facets. This also provides some sensitivity analysis to our study. We choose to test P1 using two sets of dummies characterising, first, the activity of the market, and, second, the innovativeness of the market. the first set includes nine dummies that each equal 1 if the enterprise belongs to the given sector, 0 otherwise. We use the standard classification as follows: manufacturing, extractive and other industries; construction; wholesale and retail sales, transports, catering; information and communication; finance and insurance; real estate; scientific and technical activities, administrative and support services; public administration, teaching, health and social support; and other services. The second set includes four dummy variables accounting for the innovativeness and type of innovation in markets in which firms operate. The data enable distinguishing the general innovative sector (equals 1 if the enterprise belongs to the innovative sector field of study, 0 otherwise), innovation in sales (equals 1 if the
enterprise has developed an innovative sales concept in the market, 0 otherwise),
*innovation in the production process* (equals 1 if the enterprise has introduced new
production methods or processes, 0 otherwise) and *innovation in the product or service*
(equals 1 if the enterprise has introduced new products or services on the market,
0 otherwise). Innovative sectors are defined by INSEE (2008), as information and
communication technology (ICT), pharmaceutical products, biotechnology and new
materials sectors. The other variables enable taking into account the innovation of
enterprises in all sectors, covering technological (*innovation in the production process
and innovation in the product or service*) and organisational (*innovation in sales*)
innovation, following the standard European classification of the community innovation
survey.

In order to test P2, the *management* area is measured using two types of variables. First, we create a variable indicating the *use of external paying services at start up* – an indication of the entrepreneur’s self-awareness of her or his weaknesses and ability to surround him- or herself with experts. Indeed, social capital and networks, as well as the ability to surround oneself with the appropriate professionals, are central to the success of new ventures (Baron and Markman, 2003; Davidsson and Honig, 2003). The data allow distinguishing whether the entrepreneur used the following services: accounting; general management such as IT and personal assistants; transports, storage and logistics; cleaning; sales; advertising and external communication and other services; and the use of each type of service is represented by a dummy variable (equals 1 if the entrepreneur used the service, 0 otherwise). We add up all of the seven available dummies to obtain a discrete variable ranging from 0 to 7, indicating whether the entrepreneur used zero to seven types of external paying services at start-up. Second, we use four dummies indicating whether or not the entrepreneur faced managerial difficulties during the entrepreneurial process (each equalling 1 if the entrepreneur faced this difficulty, 0 otherwise). The types of difficulties covered are difficulty being alone as an entrepreneur; difficulty hiring qualified labour; difficulty setting the price of goods and services; and difficulty getting in contact with customers and reflect managerial difficulties generally faced by entrepreneurs (Heilbrunn, 2004; Maxwell and Yaw, 2008; Wu and Young, 2002).

Finally, the *money* area is measured using three types of variables. Firstly, a discrete
variable indicates the *necessary funds to start up* on a scale ranging from 1 to 8 (1: less
than 2,000 Euros; 2: 2,000 to less than 4,000 Euros; 3: 4,000 to less than 8,000 Euros; 4:
8,000 to less than 16,000 Euros; 5: 16,000 to less than 40,000 Euros; 6: 40,000 to less
than 80,000 Euros; 7: 80,000 to less than 160,000 Euros; and 8: more than 160,000
Euros). This variable provides information about the size and ambition of the
entrepreneurial project in financial terms (Bernard et al., 2013). Second, a continuous
variable indicates the *share of the bank loan in financing a project* on a scale ranging
from 0% to 100% and accounts for the access to the formal financing of the venture.
finally, three dummy variables directly indicate eventual financial difficulties faced by
the entrepreneur in terms of access to finance (equalling 1 if the entrepreneur faced that
difficulty, 0 otherwise): difficulty getting financing; difficulty getting an overdraft and
difficulty opening a bank account. These are also in line with the general difficulties
faced by entrepreneurs (Heilbrunn, 2004; Wu and Young, 2002).
We include two measures of the sub-context of motherhood: first, the woman dummy takes the value 1 if the entrepreneur is a woman, 0 otherwise. This is a broad account of the context of motherhood, including all women considering the perspective of their potential motherhood (actual or hypothetical). Second, we include a dummy variable mother that equals 1 if the entrepreneur is a woman with a child living in the household, and 0 otherwise. It is a direct measure of the revealed current motherhood of women. By construction the variable mother is a subgroup of the group defined by the variable woman. more precisely, it is the interaction of two variables woman and child, the latter indicating whether there are children living in the entrepreneur's household. in order to avoid the classical omitted variable bias in our estimation, we also include the variable child.⁴

Regarding the macro- and meso-environment, we introduce two measures of individual-specific institutional support capturing the macro-environment defined through different segments of the population as well as a set of geographical variables accounting for general local institutions. The first measure of individual-specific macroinstitutional support is a dummy indicating the allocation of the minimum social revenue (equals 1 if the entrepreneur receives this support, 0 otherwise), accounting for the effect of a macro-institutional policy over different socio-economic groups. the second dummy variable accounts for direct macro-institutional support to entrepreneurs and reveals whether the entrepreneur received financial government support for starting up (equals 1 if the entrepreneur receives this support, 0 otherwise). Third, we use a set of 26 dummies indicating the Region of enterprise location to account for any geographical specificities in terms of institutions and industrial focus.⁵

Finally, we use four control variables for entrepreneurs' individual characteristics: level of education, professional experience, and age, as well as its quadratic form (age²). the entrepreneur's initial level of education is measured with the highest degree obtained, ranging from 1 to 10 (1: no degree; 2: generalist degree, nine years of schooling⁶ – CEP, BEPC, Brevet élémentaire, Brevet des collèges; 3: professional secondary school degree, ten years of schooling – CAP/BEP; 4: technical or professional degree, 12 years of schooling – BAC technique or professionnel; 5: generalist degree, 12 years of schooling – BAC généraliste; 6: technical bachelor degree, 14 years of schooling – BTS, DEUST, DUT and so on; 7: university or generalist bachelor degree, healthcare degrees, 14 years of schooling; 8: university graduates, 16 years of schooling; 9: university master degrees, 17 years of schooling; and 10: engineering degree from Grandes Ecoles, 17 years of schooling).

The entrepreneur's professional experience in the activity of the current business is measured on a scale from 0 to 3 (0: no experience; 1: less than three years; 2: three to ten years; and 3: over ten years). Both variables are used to control for the general level of human capital that positively influences entrepreneurship participation and success (Cuervo, 2005; Davidsson and Honig, 2003). We account for the age of the entrepreneur on a scale ranging from 1 to 7 (1: less than 25; 2: 25 to less than 30; 3: 30 to less than 35; 4: 35 to less than 40; 5: 40 to less than 45; 6: 45 to less than 50; and 7: more than 50 years old). We introduce the square of the age reflecting the potential quadratic form of the relationship between the entrepreneur's age and measures of the 3Ms. Indeed, although entrepreneurship participation and success are more likely to increase with age, they can also decline after a certain age, especially when approaching retirement (Davidsson and Honig, 2003; Klyver et al., 2008). Finally, c is a constant and ei represents the error term.
The model writes:

$$ 
\begin{aligned}
Mi = & a1. Woman_i + a2. Mother_i + a3. Childi + b1. \text{Minimum Social revenue}_i \\
& + b2. \text{Financial government support}_i + d1. \text{Regions}_i \\
& + d2. \text{Level of education}_i + d3. \text{Professional experience}_i \\
& + d4. Age_i + d5. Age_i^2 + c + e_i
\end{aligned} 
\quad (2) $$

The estimation methods differ depending on the form of the dependent variable. Models with a dependent variable in the form of a dummy are estimated using a probit model. Models using a scale as the dependent variable are estimated with an ordered logit model, and models using a continuous dependent variable are estimated using a simple OLS regression.⁷ Estimates of the coefficients $a_1$ and $a_2$ can be interpreted as the net effect of the context of ‘Motherhood’ on each of the 3Ms once the specificities of the environment and personal characteristics (control variables) have been taken into account. We therefore mostly concentrate on commenting and discussing results regarding these two coefficients.

# 4 Results

## 4.1 Descriptive statistics

Table 2 present the summary statistics of the variables used.

In 2009, in terms of ‘markets’, only 4% of enterprises were classified in the general innovative sector, but between 10% and 32% of firms presented at least one form of milder innovation, primarily in terms of product or services. the bulk of enterprises operated in the ‘wholesale and retail sales, transports, catering’ sectors with 39% of firms, followed by ‘construction’ (17%) and ‘scientific and technical activities, administrative and support services’ (14%). On average, in terms of ‘management’, entrepreneurs used 1.41 external paying services at start-up, only 9% had difficulties hiring qualified labour, 12% felt the difficulty of being alone as an entrepreneur and 14% had difficulties setting prices and getting in contact with customers. In terms of ‘Money’, necessary funds to start up are situated in the middle of the scale, corresponding to about 7,000 Euros, with an average share of bank loans to finance it at 26.86%. Only 6% of entrepreneurs faced difficulties opening a bank account, 8% had difficulties negotiating an overdraft, and a good proportion – 19% – actually faced difficulties getting financed. Women represent only 31% of entrepreneurs, and although 57% of entrepreneurs have children, actual mother entrepreneurs represent only 18% of the entrepreneurial sample. A large 43% of entrepreneurs receive government support and 12% benefit from the minimum social revenue. Entrepreneurs are middle-aged persons with average education and average professional experience. Entrepreneurs are fairly distributed across regions, with, however, a large concentration in Ile-de-France (21% in the Paris region).⁸
<table><caption>**Table 2** Summary statistics</caption><thead><tr><th>Variable</th><th></th><th>Obs</th><th>Mean</th><th>Std dev</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="7"><strong>Market</strong></td></tr><tr><td rowspan="4">Innovativeness and type of innovation</td><td>Innovative sector</td><td>48,251</td><td>0.04</td><td>0.19</td><td>0</td><td>1</td></tr><tr><td>Innovation in sales</td><td>48,251</td><td>0.15</td><td>0.36</td><td>0</td><td>1</td></tr><tr><td>Innovation in production process</td><td>48,251</td><td>0.10</td><td>0.30</td><td>0</td><td>1</td></tr><tr><td>Innovation in product or service</td><td>48,251</td><td>0.32</td><td>0.47</td><td>0</td><td>1</td></tr><tr><td rowspan="10">Sectors</td><td>Manufacturing, extractive and other industries</td><td>48,251</td><td>0.08</td><td>0.27</td><td>0</td><td>1</td></tr><tr><td>Construction</td><td>48,251</td><td>0.17</td><td>0.37</td><td>0</td><td>1</td></tr><tr><td>Whole- and retail sales, transports, catering</td><td>48,251</td><td>0.39</td><td>0.49</td><td>0</td><td>1</td></tr><tr><td>Information and communication</td><td>48,251</td><td>0.04</td><td>0.19</td><td>0</td><td>1</td></tr><tr><td>Finance and insurance</td><td>48,251</td><td>0.02</td><td>0.13</td><td>0</td><td>1</td></tr><tr><td>Real estate</td><td>48,251</td><td>0.04</td><td>0.19</td><td>0</td><td>1</td></tr><tr><td>Scientific/technical activities, admin/support services</td><td>48,251</td><td>0.14</td><td>0.34</td><td>0</td><td>1</td></tr><tr><td>Public admin, teaching, health and social support</td><td>48,251</td><td>0.05</td><td>0.22</td><td>0</td><td>1</td></tr><tr><td>Other services</td><td>48,251</td><td>0.08</td><td>0.27</td><td>0</td><td>1</td></tr><tr><td colspan="7"><strong>Management</strong></td></tr><tr><td>Use of experts</td><td>Use of external paying services at start up</td><td>48,251</td><td>1.41</td><td>0.70</td><td>1</td><td>7</td></tr><tr><td rowspan="4">Potential difficulties</td><td>Difficulty being alone as entrepreneur</td><td>48,251</td><td>0.12</td><td>0.32</td><td>0</td><td>1</td></tr><tr><td>Difficulty to hire qualified labour</td><td>48,251</td><td>0.09</td><td>0.29</td><td>0</td><td>1</td></tr><tr><td>Difficulty to set the price of goods and services</td><td>48,251</td><td>0.14</td><td>0.35</td><td>0</td><td>1</td></tr><tr><td>Difficulty to get in contact with customers</td><td>48,251</td><td>0.14</td><td>0.34</td><td>0</td><td>1</td></tr><tr><td colspan="7"><strong>Money</strong></td></tr><tr><td rowspan="3">Potential difficulties</td><td>Difficulty to get financing</td><td>48,251</td><td>0.19</td><td>0.40</td><td>0</td><td>1</td></tr><tr><td>Difficulty to get an overdraft</td><td>48,251</td><td>0.08</td><td>0.27</td><td>0</td><td>1</td></tr><tr><td>Difficulty to open a bank account</td><td>48,251</td><td>0.06</td><td>0.23</td><td>0</td><td>1</td></tr><tr><td>Bank Loan</td><td>Share of bank loan in financing of project</td><td>48,251</td><td>26.86</td><td>35.01</td><td>0</td><td>100</td></tr><tr><td>Start up</td><td>Necessary funds to start up</td><td>48,251</td><td>3.73</td><td>2.07</td><td>1</td><td>8</td></tr><tr><td rowspan="3">Motherhood</td><td>Woman</td><td>48,251</td><td>0.31</td><td>0.46</td><td>0</td><td>1</td></tr><tr><td>Child in household</td><td>48,251</td><td>0.57</td><td>0.50</td><td>0</td><td>1</td></tr><tr><td>Mother</td><td>48,251</td><td>0.18</td><td>0.38</td><td>0</td><td>1</td></tr><tr><td rowspan="2">Macro-meso environment</td><td>Financial government support</td><td>48,251</td><td>0.43</td><td>0.50</td><td>0</td><td>1</td></tr><tr><td>Minimum social revenue</td><td>48,251</td><td>0.12</td><td>0.33</td><td>0</td><td>1</td></tr><tr><td rowspan="3">Control variables</td><td>Age</td><td>48,251</td><td>4.33</td><td>1.83</td><td>1</td><td>7</td></tr><tr><td>Highest degree obtained</td><td>48,251</td><td>4.42</td><td>2.63</td><td>1</td><td>10</td></tr><tr><td>Experience in core activity</td><td>48,251</td><td>1.26</td><td>1.29</td><td>0</td><td>3</td></tr></tbody></table>
<table><caption>**Table 3(a)** Effect of motherhood context on markets in terms of sectors¹⁰</caption><thead><tr><th>Dependent variables</th><th>Manufacturing, extractive and other industries</th><th>Construction</th><th>Whole- and retail sales, transports, catering</th><th>Information and communication</th><th>Finance and insurance</th><th>Real estate</th><th>Scientific and technical activities, admin and support services</th><th>Public admin, teaching, health and social support</th><th>Other services</th></tr></thead><tbody><tr><th scope="row">Independent variables</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th scope="row">Woman</th><td>-0.148***</td><td>-1.207***</td><td>0.079***</td><td>-0.837***</td><td>-0.456***</td><td>-0.178**</td><td>0.059*</td><td>0.921***</td><td>1.081***</td></tr><tr><th scope="row">Child in household</th><td>0.044</td><td>0.199***</td><td>-0.067***</td><td>-0.242***</td><td>0.19</td><td>-0.048</td><td>-0.055**</td><td>-0.066</td><td>-0.311**</td></tr><tr><th scope="row">Mother</th><td>-0.05</td><td>-0.018</td><td>0.092***</td><td>0.139</td><td>-0.139</td><td>0.003</td><td>-0.083**</td><td>0.131*</td><td>0.244***</td></tr><tr><th scope="row">Financial government support</th><td>0.279***</td><td>0.024</td><td>0.021*</td><td>0.091</td><td>-1.151***</td><td>-0.945***</td><td>-0.02</td><td>-0.393***</td><td>0.376***</td></tr><tr><th scope="row">Minimum social revenue</th><td>-0.045</td><td>-0.095***</td><td>0.059***</td><td>0.210***</td><td>-0.804***</td><td>-0.237***</td><td>-0.027</td><td>-0.009</td><td>0.013</td></tr><tr><th scope="row">Highest degree obtained</th><td>-0.438***</td><td>-1.012***</td><td>-0.452***</td><td>1.699***</td><td>1.541***</td><td>0.827***</td><td>1.120***</td><td>2.087***</td><td>-0.424***</td></tr><tr><th scope="row">Experience in core activity</th><td>0.258***</td><td>0.460***</td><td>-0.343***</td><td>0.187***</td><td>-0.442***</td><td>-1.029***</td><td>0.070***</td><td>0.364***</td><td>0.180***</td></tr><tr><th scope="row">Age</th><td>0.303*</td><td>0.364***</td><td>-0.125</td><td>0.246</td><td>1.133*</td><td>-0.059</td><td>-0.021</td><td>0.532**</td><td>-0.516***</td></tr><tr><th scope="row">Age squared</th><td>-0.338--</td><td>-0.629***</td><td>0.179***</td><td>-0.475</td><td>-0.816</td><td>0.526*</td><td>0.328***</td><td>-0.481*</td><td>0.135</td></tr><tr><th scope="row">Constant</th><td>-1.361</td><td>-0.317***</td><td>0.229***</td><td>-2.163***</td><td>-2.498***</td><td>-2.138***</td><td>-2.070***</td><td>-2.701***</td><td>-1.306***</td></tr><tr><th scope="row">Observations</th><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td></tr><tr><th scope="row">Pseudo-R<sup>2</sup></th><td>0.0222</td><td>0.1794</td><td>0.0406</td><td>0.1021</td><td>0.0705</td><td>0.0736</td><td>0.0949</td><td>0.1583</td><td>0.1026</td></tr><tr><th scope="row">Shapairo-Wilk W (test for normality of residuals)</th><td>0.5995</td><td>0.5216</td><td>0.7811</td><td>0.1987</td><td>0.11479</td><td>0.2007</td><td>0.4979</td><td>0.2702</td><td>0.3450</td></tr></tbody></table>

Notes: *p < 0.10, **p < 0.05, ***p < 0.01; NB: results on regional dummies not reported but available upon request.
## 4.2 Model results

Table 3(a)⁹ displays the results found regarding the effect of the general context of Motherhood on Market in terms of sectors.

Women are more likely to operate in wholesale and retail sales, transports, catering, sectors; scientific and technical activities, administrative and support services, 'public administration, teaching, health and social support' sectors; as well as 'other services', ; but they are significantly less likely to operate in 'manufacturing, extractive and other industries', 'construction', 'information and communication', 'finance and insurance', or 'real estate', thereby hinting at a polarisation of women into historically femaledominated activities. interestingly, for a woman, actually having children (being a mother) does further increase the orientation in the most historically female sectors ('wholesale and retail sales, transports, catering', 'public administration, teaching, health and social support', as well as 'other services') but pulls out women from a potentially more innovative sector ('scientific and technical activities, administrative and support services'). This supports our proposition 1: the context of motherhood tends to cluster women into traditionally gendered sectors, and even more so for mothers than women.

Government supports entrepreneurship in 'manufacturing, extractive and other industries', 'wholesale and retail sales, transports, catering', and 'other services', while discouraging it for 'finance and insurance', 'real estate', or 'public administration, teaching, health and social support'. entrepreneurs receiving a minimum revenue are encouraged into 'wholesale and retail sales, transports, catering' as well as 'information and communication', while discouraged from 'construction', 'finance and insurance', and 'real estate'. Not surprisingly, higher education gears entrepreneurs towards sectors needing higher skills such as 'information and communication', 'finance and insurance', 'real estate', 'scientific and technical activities, administrative and support services', and 'public administration, teaching, health and social support', while driving them away from other sectors. professional experience is needed in all sectors but three: 'wholesale and retail sales, transports, catering', 'finance and insurance', and 'real estate'. age does display an inverted u-shaped relationship with participation in the four following sectors: 'manufacturing, extractive and other industries', 'construction', and 'public administration, teaching, health and social support'. Meanwhile, participation in 'wholesale and retail sales, transports, catering' decreases with age.

Table 3(b)¹¹ displays the results found regarding the effect of the general context of 'motherhood' on the '...' in terms of innovation.

The isolated effect of being a woman negatively and significantly affects the firm operating in an innovative sector, innovating in sales or production process. This means that women are indeed less innovative than men. However, women strongly and positively stand out in terms of innovative products or services. The additional effect of having a child for a woman (being a mother) is nonexistent for participation in the innovative sector or innovating in the production process. Being a mother, however, strengthens innovation in products or services and reverses the negative effect on sales innovation – mothers are more innovative than both women and men in that area. This is in line with our proposition 1 and shows that the national macro-environment does have a negative effect on women and mother entrepreneurs' being part of the innovative sector. Decomposing innovation into technological and non-technological areas for all sectors allows refining the analysis and partially invalidate our P1 regarding some types of innovations.
<table><caption>**Table 3(b)** Effect of motherhood context on markets in terms of innovation</caption><thead><tr><th rowspan="2">Independent variables</th><th colspan="2">Dependent variables</th><th rowspan="2">Innovation in<br>production<br>process</th><th rowspan="2">Innovation in<br>product or<br>service</th></tr><tr><th>Innovative<br>in sector</th><th>Innovation<br>sales</th></tr></thead><tbody><tr><td>Woman</td><td>-1.121***</td><td>-0.106***</td><td>-0.180***</td><td>0.054***</td></tr><tr><td>Child in household</td><td>-0.192***</td><td>-0.014</td><td>0.03</td><td>-0.048***</td></tr><tr><td>Mother</td><td>0.102</td><td>0.065**</td><td>-0.024</td><td>0.073***</td></tr><tr><td>Financial government support</td><td>0.371***</td><td>0.182***</td><td>0.151***</td><td>0.331***</td></tr><tr><td>Minimum social revenue</td><td>0.169***</td><td>0.062***</td><td>0.124***</td><td>0.02</td></tr><tr><td>Highest degree obtained</td><td>1.638***</td><td>0.203***</td><td>0.047*</td><td>0.171***</td></tr><tr><td>Experience in core activity</td><td>0.137**</td><td>-0.239***</td><td>0.098***</td><td>-0.185***</td></tr><tr><td>Age</td><td>0.773**</td><td>0.170*</td><td>-0.251*</td><td>0.189***</td></tr><tr><td>Age squared</td><td>-1.172***</td><td>-0.200*</td><td>0.065</td><td>-0.243***</td></tr><tr><td>Constant</td><td>-2.742***</td><td>-1.171***</td><td>-1.106***</td><td>-0.746***</td></tr><tr><td>Observations</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.1031</td><td>0.0124</td><td>0.0089</td><td>0.0271</td></tr><tr><td>Shapiro-Wilk W (test for the<br>normality of residuals)</td><td>0.2064</td><td>0.4599</td><td>0.3616</td><td>0.7020</td></tr></tbody></table>

Notes: *p < 0.10, **p < 0.05, ***p < 0.01; NB: results on regional dummies not reported but available upon request.

Government support strengthens innovation in all areas, while minimum revenue allocation has a similar but reduced effect except on innovation in product and service. Not surprisingly, education has a strong positive effect on all types of innovation, while professional experience has only a positive effect on being part of the innovative sector and innovation in the production process. It has a negative effect on the two other types of innovation. In general, innovation increases with the age of the entrepreneur, to decline after a certain age. The effect is however reversed in terms of production process innovation.

Table 4 displays the results found regarding the effect of the general context of motherhood on management.

Very interestingly, women systematically display fewer managerial difficulties than men entrepreneurs, while using significantly less external paying services than men. The additional effect of having a child for a woman (being a mother) is not significant. These results partially depart from our proposition 2 in terms of the four classical selfperceived managerial difficulties, and partially validate it in terms of externally observed managerial efficacy.

Government support corresponds to increased managerial difficulties, except for the difficulty of hiring qualified labour, which is alleviated. Entrepreneurs receiving government support use the same number of external paying services as others. Entrepreneurs receiving minimum social revenue tend to be less likely to use external paying services, face significantly more difficulties being alone as an entrepreneur and get in contact with customers. However, they face fewer managerial difficulties in terms of hiring qualified labour. A higher degree pushes the use of more paying external services. All highly educated entrepreneurs find it difficult to be alone as an entrepreneur,
and get in contact with customers, but they have fewer difficulties hiring qualified labour. Professional experience tends to lower all sorts of managerial difficulties except for hiring qualified labour. It has no effect on the use of external services. Age bears a significant, inverted U-shaped relationship with the use of external paying services: the older the entrepreneurs, the more likely they are to use them, with a decreasing propensity to do so after a certain age. Age also bears a significant, inverted U-shaped relationship with two managerial difficulties (the difficulty of being alone as an entrepreneur and the difficulty of hiring qualified labour), and its effect is not significant for the other managerial dimensions.

<table><caption>**Table 4** Effect of motherhood context on management</caption><thead><tr><th>Independent variables</th><th>Use of external paying services at start up</th><th>Difficulty being alone as entrepreneur</th><th>Difficulty to hire qualified labour</th><th>Difficulty to set the price of goods and services</th><th>Difficulty to get in contact with customers</th></tr></thead><tbody><tr><td>Woman</td><td>-0.111***</td><td>-0.196***</td><td>-0.357***</td><td>-0.079**</td><td>0.045</td></tr><tr><td>Child in household</td><td>0.088***</td><td>-0.176***</td><td>0.183***</td><td>0.017</td><td>-0.033</td></tr><tr><td>Mother</td><td>-0.018</td><td>0.048</td><td>-0.015</td><td>0.028</td><td>-0.006</td></tr><tr><td>Financial government support</td><td>0.012</td><td>0.284***</td><td>-0.386***</td><td>0.514***</td><td>0.401***</td></tr><tr><td>Minimum social revenue</td><td>-0.204***</td><td>0.195***</td><td>-0.227***</td><td>-0.002</td><td>0.075***</td></tr><tr><td>Highest degree obtained</td><td>0.105***</td><td>0.335***</td><td>-0.140***</td><td>-0.031</td><td>0.264***</td></tr><tr><td>Experience in core activity</td><td>0.002</td><td>-0.141***</td><td>0.164***</td><td>-0.085***</td><td>-0.247***</td></tr><tr><td>Age</td><td>0.323***</td><td>0.529***</td><td>0.478***</td><td>0.089</td><td>-0.031</td></tr><tr><td>Age squared</td><td>-0.413***</td><td>-0.555***</td><td>-0.685***</td><td>-0.453***</td><td>-0.086</td></tr><tr><td>Constant</td><td>Cut constant not reported</td><td>-1.564***</td><td>-1.229***</td><td>-1.110***</td><td>-1.234***</td></tr><tr><td>Observations</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.0076</td><td>0.0227</td><td>0.0242</td><td>0.0325</td><td>0.0246</td></tr><tr><td>Shapiro-Wilk W (test for the normality of residuals)</td><td>0.7072</td><td>0.4105</td><td>0.3493</td><td>0.4617</td><td>0.4486</td></tr></tbody></table>

Notes: *p < 0.10, **p < 0.05, ***p < 0.01; NB: results on regional dummies not reported but available upon request.

Table 5¹² displays the results found regarding the effect of the general context of motherhood on money.

Women systematically face fewer difficulties than men entrepreneurs getting financing, an overdraft or opening a bank account. However, they do not significantly borrow a larger share of the initial investment to start up and require significantly less funds to start up. In sharp contrast, actual mothers do face significantly more difficulties getting financed, negotiate an overdraft and open a bank account, but are not significantly
different in terms of initial capital requirements or shares of bank loans in financing projects. Our results therefore depart from our proposition 3 for women but validate it for mothers in terms of self-perceived difficulty. We validate our P3 for women in terms of external indicators for capital requirements.

<table><caption>**Table 5** Effect of motherhood context on money</caption><thead><tr><th>Independent variables</th><th>Difficulty to get financing</th><th>Difficulty to get an overdraft</th><th>Difficulty to open a bank account</th><th>Share of bank loan in financing of project</th><th>Necessary funds to start up</th></tr></thead><tbody><tr><td>Woman</td><td>-0.158***</td><td>-0.264***</td><td>-0.283***</td><td>0.01</td><td>-0.042***</td></tr><tr><td>Child in household</td><td>-0.027</td><td>-0.008</td><td>-0.151***</td><td>0.051***</td><td>0.048***</td></tr><tr><td>Mother</td><td>0.106***</td><td>0.114**</td><td>0.141**</td><td>-0.011</td><td>0</td></tr><tr><td>Financial government support</td><td>0.160***</td><td>-0.252***</td><td>-0.229***</td><td>0.040***</td><td>0.042***</td></tr><tr><td>Minimum social revenue</td><td>0.206***</td><td>0.260***</td><td>0.357***</td><td>-0.081***</td><td>-0.085***</td></tr><tr><td>Highest degree obtained</td><td>-0.284***</td><td>-0.258***</td><td>-0.285***</td><td>-0.052***</td><td>0</td></tr><tr><td>Experience in core activity</td><td>-0.140***</td><td>-0.109***</td><td>-0.070*</td><td>0.051***</td><td>-0.002</td></tr><tr><td>Age</td><td>0.015</td><td>-0.03</td><td>0.118</td><td>0.181***</td><td>0.195***</td></tr><tr><td>Age squared</td><td>-0.162*</td><td>0.167</td><td>-0.212</td><td>-0.269***</td><td>-0.206***</td></tr><tr><td>Constant</td><td>-0.140***</td><td>-1.173***</td><td>-1.117***</td><td>11.855***</td><td>Cut constant not reported</td></tr><tr><td>Observations</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td><td>48,251</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.0229</td><td>0.0154</td><td>0.0258</td><td>0.0551</td><td>0.0089</td></tr><tr><td>Shapiro-Wilk W (test for the normality of residuals)</td><td>0.5422</td><td>0.3062</td><td>0.2604</td><td>0.8716</td><td>0.9328</td></tr></tbody></table>

Notes: *p < 0.10, **p < 0.05, ***p < 0.01; NB: results on regional dummies not reported but available upon request

Government support seems to target projects with a higher need for funds at start-up. For government-supported entrepreneurs, the difficulty to find financing, however, is higher, contrasting with an associated higher share of bank loans in the financing of the project. Government support seems to ease access to bank account openings and negotiations of an overdraft. Entrepreneurs with minimum social revenue generally face more problems in terms of access to finance; however, they also require a lot less funds and loans to start up. A higher level of education removes barriers to financing for all entrepreneurs while having no effect on the need for funds, but reduces the use of bank loans to finance projects. Professional experience plays a similar but less marked role for all entrepreneurs, except for the higher use of bank loans to finance the project. Age has only a positive and significant effect on the necessary funds to start up and on the use of bank loans. This effect declines after a certain age.
# 5 Discussion

Although France has benefited from an institutional environment supportive of women, no gender equality appears in entrepreneurship in which women are still a minority, representing just under a third of the population.

## 5.1 *Contribution to theory*

The context of *motherhood* and the broad *macro-* and *meso-environment* could have different effects in different settings. French state policies tend to encourage large families through different measures (long paid parental leave, generous social subsidies and fiscal advantages for child care). The different policy measures tend to pull the engagement of women who participate in economic activities in different directions, depending on their needs, preferences and alternative opportunities. Overall, however, one can confidently argue that the French environments promote a model of the family in which the woman still plays the role of the housewife, including the one of the ‘working housewife’. Although the specificity of the context of *motherhood* in French society does not appear to impinge on overall women participation in the active population, the neutrality of *motherhood* towards women entrepreneurship is far less evident. Some reasons stemming from the environment can be put forward. Thus, support for women is geared towards employees rather than entrepreneurs in terms of means testing and condition of access. But, women entrepreneurs do not have the same needs regarding most of the family policy measures. Women entrepreneurs need more free time from home, more flexible times away from work and structures that are more adapted (matching not only with nine-to-five jobs, but providing larger timeslots).

The French case seems typical in that it funnels women into traditionally female dominated industries. Women tend to be clustered in the tertiary sector (55%) and in public administration, education and health and social action (66%) (Bel, 2009). Our results show that female entrepreneurs follow a similar clustering in spite of half of them actually not having any previous experience in the activity of their venture. This clearly shows a process of self-selection by which women themselves choose to stay in traditionally female-dominated sectors, supporting the hypothesis of internalisation of certain gender stereotypes (Fouquet, 2005; Gallioz, 2006). Women tend to engage in known areas, showing a form of prudential management of their entrepreneurial career, which can also be interpreted as a form of higher risk aversion, which could make them less innovative. Our results confirm that conformity and risk aversion for new sectors is further amplified by actual motherhood. Although the bulk of women cluster in traditionally gendered sectors, significant exceptions exist such as in the ‘Scientific and technical activities, administrative and support services’ sector, showing that mentalities are evolving and women are now making increased use of their higher educational levels.

However, actually having children pushes women to go massively back to their historically allocated sectors.

Although there is some hope for sector openings for women in general, at least before they give birth, the high-tech sector is definitely dominated by men, as is innovation in terms of production process in all industries. This can be explained by two French specific phenomena. First, the share of high-tech companies is very small (4%), and although there are significantly more men in that sector, there are still very few. This is, however, an interesting indication as to how it could develop, because men will again
benefit from first-mover advantage in this area and shape it using a male norm. Second, although men and women average similar years of schooling, differences may emerge from the type of specialisation in terms of training and skills. Traditional gender stereotypes and norms as well as socialisation within families and educational institutions still influence women's perceptions of themselves, interfering with their choices. Indeed, women tend to be under-represented in engineering schools, which is directly reflected in the high-tech sector participation figures. Women prefer humanities, where they are in contact with practice-oriented and heuristic-creative approaches. This is why women strongly stand out in terms of innovative products or services and, even more interestingly, why mothers excel in the latter, coupled with innovation in sales. This result is totally in line with the emerging literature in mumpreneurship (Richomme-Huet et al., 2013), which defines their specificity and success in those terms exactly. Therefore, it seems that being diverted from the male-defined innovative sector – covering improvements in terms of productivity confined in the economic sphere – women turn to a female-defined innovative sector – covering improvements overlapping the various spheres of life.

Our findings regarding the systematic superiority of women in terms of self-defined managerial qualities can be at first surprising. It seems that women are able to counterbalance shorter and shallower professional experiences as well as their status of 'working housewives' by a general higher academic level. Also, extending Ettl and Welter's (2010) view, managerial difficulties are self-perceived, and women may have different definitions and perceptions with regard to these. Besides, we find that women use significantly fewer external paying services than men. First, women benefit from smaller (if any), less numerous and gendered networks, translating in a generally less supportive environment (Aldrich et al., 1989). The less frequent use of external services could reflect exactly this and jeopardise entrepreneurial success. Second, however, it could also reflect the general risk-averse profile of women entrepreneurs, who prefer to allocate money in other areas (Cliff, 1998). As a matter of fact, part of this risk aversion transpires into the systematically lower size of initial funds requirements. Although women systematically have less difficulty than men accessing any form of finance, they use a lower share of external financing for projects. However, scholars (Orhan, 2001) and recent reports, such as the 'Baromètre des femmes entrepreneurs' carried out by a French bank (La Caisse d'Epargne, 2013), relate that, in terms of funding, women and men have similar behaviours. More precisely, regarding three of the main financing needs, women finance equipment with self-financing (55% against 59% for men) rather than through a loan or leasing; use conventional bank financing to support their business development (86% against 81% for men) and solicit their bank (47% against 45% for men) or use their personal savings (43% against 40% for men) to cope with a need for cash. The overall picture could therefore be a perceived easier access to external finance mostly because of low demand for it. In effect, mothers, who do not differ from other entrepreneurs in terms of initial capital requirements or share of bank loans to finance the project, do face significantly more difficulties accessing external funds, which indicates that there is still a long way to go in terms of achieving gender equality in this area.

Our findings confirm that the context of motherhood in France influences women's and mothers' entrepreneurship and the '3Ms', thereby contributing to describe and explain the exacerbated gender gap. Women entrepreneurs invest in traditionally female sectors, with small and micro projects requiring limited resources and technological
innovation, but compensate traditional sector clustering by innovating in sales, product and service. Women outperform men in terms of management but rely very little on external support services and seem to benefit from an unconstrained access to finance. Actual motherhood induces similar effects, in some instances even more acutely, except for the access to finance for which they struggle. These results, using recent representative national statistics on French entrepreneurs, provide a new detailed account of women's and mother's entrepreneurial performances that could prove useful to design policies clearly supporting women and mother entrepreneurs in the areas identified as potentially impinging on their development. Moreover, these elements call for a redefinition of classical concepts and measurements in entrepreneurship.

Additionally, through our empirical analysis, we show that the Brush et al. (2009) model provides a useful framework for exploration and analysis in that it extracts entrepreneurship from its vacuum and places it into a broad institutional context, thereby moving away from the focus on individual characteristics and embracing the idea of entrepreneurship as being part of a system. In this system, entrepreneurship attributes stem from the interaction between individuals' characteristics and institutions, providing a richer insight into entrepreneurs' behaviours and can be used in a variety of contexts to study a wide array of cases. With regards to female entrepreneurship, the model enables going beyond the classical gender-as-a-trait approach and moves to the study of environmentally constrained behaviours already advocated by Gartner as recently as 1988.

## 5.2 Contributions to practice

Government intervention seems to have advanced a step in promoting women entrepreneurship but more policies should be developed. This will require the complex coordination among policies supporting equality in employment so as to reduce the existing gap between qualifications and experience for women, policies gearing towards successful entrepreneurship, including a subtle mix between project soundness (more prudent) and innovativeness (riskier) and family policies that would back the family – avoiding the self-reinforcement of the gender bias in the provision of domestic functions such as child care and household chores. Such family policies would entail, for example, more equal sharing of such tasks.

This seems essential for the development of women entrepreneurship, which, beyond its intrinsic interest, could benefit the rest of society. Indeed, as attested by our findings women entrepreneurs seem to display higher management skills than men, especially in terms of marketing, sales and human resources, and feel less the burden of the lone entrepreneur syndrome. Betting on those qualities could surely benefit the entire entrepreneurship sector. But again, although women are less inclined than men to use external services for their enterprises, all entrepreneurs with children tend to use more of those services, first, because of their longer experience (they are older) but also because they delegate tasks so as to be able to manage other family-related tasks. However, within the group of entrepreneurs with children, women do not significantly use more external services in spite of heavier responsibilities. This is again a sign of the internalisation of gender role differences that encourages women to seek less support as well as the sign that such support might not be accessible, either because of institutional or financial barriers.
# 6 Conclusions and implications

The topic of French women entrepreneurship is of particular importance because of the scarcity of studies and because of an apparent gender gap. Additionally, research on entrepreneurship for the most vulnerable segments of the population is of central importance in terms of policy making in times of higher and increasing unemployment. Using a framework designed for the specific study of women entrepreneurship, we empirically study the effect of ‘motherhood’ on ‘market’, ‘management’ and ‘money’ placed in the wider institutional context of the French ‘macro- and meso-environment’. What clearly stands out in our study is the revealed impact of ‘Motherhood’. Women entrepreneurs are still clustered in traditionally female-dominated sectors but tend to move slowly outside of these, however, without colonising more high-tech ones. Women revert to an even smaller number of female-dominated sectors as mothers, and focus on sales and product and service innovation, as has been evidenced elsewhere in mumpreneurship studies (Richomme-Huet et al., 2013). This result represents a first step in acknowledging the difference of views between women and men about entrepreneurship (Reavley and Lituchy, 2008) and might call for a redefinition of ‘classical measurements’ as suggested by de Bruin et al. (2007). Indeed, innovation is generally understood as a technological one, a form that is geared mostly towards productivity improvements in the economic sphere. Alternatively, women innovation tends to concentrate on product, service and organisational innovation geared towards the customers, which might privilege improvements in all areas of life. This calls for a refined definition of innovation in terms of what matters to both women and men and why.

Similarly, the bulk of the literature advances logical arguments as well as indirect empirical evidence as to why women should be less able in terms of management in spite of their higher education: they have lower managerial positions in employment on average because of shorter and cyclical employment experience. Our results show clearly that women’s self-perception of managerial abilities are far higher than men’s, suggesting that we need to work towards a redefinition of the factors that boost managerial quality as well as managerial quality itself. It could well be the case that constant struggle against discriminations, combined with a status of the ‘working housewife’ does increase managerial abilities in ways that are specific to women. This redefinition will need to encompass a wider range of realities, particularly women’s realities.

Finally, the evident lower financial size of women’s projects combined with a clear disadvantage of mothers in terms of access to finance calls for the need for deep changes in the broad world of entrepreneurship. Given women’s higher academic achievements, higher managerial abilities, as well as more marked innovativeness in terms of product, service and/or sales, systematic gender bias cannot be attributed solely to the lower quality or scalability of projects. Women need to gain confidence and not restrain themselves in terms of project size. This will only be made possible if the financial sector starts to remove artificial barriers to start-up funds for women.

To conclude, we note that women are less likely to choose entrepreneurship because of existing barriers as well as a lack of recognition of their qualities. Returning to the classification of the ‘5Ms’ in Brush et al. (2009), the ‘motherhood’ dimension and the institutional context keep women in projects of small financial scale in traditionally gendered and poorly innovative sectors. These characteristics do not reflect a positive
image of the business woman who succeeds. Nevertheless, it appears that ‘working housewives’ feel they have the same management skills as men, and they innovate in products, services and sales. The combination of a stereotyped image of women entrepreneurs and a lack of knowledge of their realities do not favour the desire of potential candidates, and instead continues a status quo over the years.

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## Notes

1. For instance, the Roudy Act, which introduced gender equality in employment in 1983, was reinforced in 2001 by the Genisson Act and in 2014 by the Minister for Women's Rights, Najat Vallaud-Belkacem.

2. For instance, in 2003, a framework agreement was established for the participation of women and young girls in scientific and technical fields.

3. The level of maximum guarantee is 70% of the loan amount, which should exceed 5,000 euros.

4. The omitted variable bias occurs when a variable, here *child*, significantly explains the dependent variable (here *markets, management and money*) and is also highly correlated with at least one of the explanatory variables, in our case, by construction, *mother*.
5 The Regions included in the study are the following: Ile-de-France, ChampagneArdenne, Picardie, Haute-Normandie, Centre, Basse-Normandie, Bourgogne, Nord-Pasde-Calais, Lorraine, Alsace, Franche-Comté, Pays de la Loire, Bretagne, PoitouCharentes, Aquitaine, Midi-Pyrénées, Limousin, Rhône-Alpes, Auvergne, LanguedocRoussillon, Provence-Alpes-Côte d'Azur, Corse, Guadeloupe, Martinique, and Guyane.

6 Years of schooling are counted starting in the first year of primary school at age six. We test for potential multicollinearity between independent variables using variance inflation factors (VIFs). The average VIF for all variables in each model is 7.4 and below the critical value of 10, indicating a low multicollinearity (Kutner et al., 2004).

7 Statistics and results for the 26 regions are not reported in the paper but are available on request.

8 We do not report estimated coefficients on the region dummies; these are available from the authors on request.

9 For transparency purposes we report the $R^2$ of our estimations, but these are not to be interpreted as the overall explanatory power of our models. Indeed, offering a set of guidelines regarding the use of logistic regression techniques, Peng et al. (2002) recommend exercising caution when analysing $R^2$. 'In linear regression, $R^2$ has a clear definition: it is the proportion of the variation in the dependent variable that can be explained by predictors in the model. Attempts have been devised to yield an equivalent of this concept for the logistic model. None, however, renders the meaning of variance explained (Long, 1997, pp.104–109; Menard, 2000). Furthermore, none corresponds to predictive efficiency or can be tested in an inferential framework (Menard, 2000)' [Peng et al., (2002), p.6]. In line with this argument, Hosmer and Lemeshow (2000) advise to avoid 'routine publishing of $R^2$ values from fitted logistic regression models' (p.167).

10 Same note applies for Tables 3(a), 3(b), 4 and 5.

11 We do not report estimated coefficients on the region dummies; these are available from the authors on request.

12 We do not report estimated coefficients on the region dummies; these are available from the authors on request.