Entrepreneurship: Is There a Gender Gap? Francesca Lotti Bank of Italy, Research Department January 11, 2007 Abstract One of the central pillars of the Lisbon strategy is to make available more and better jobs in Europe; this goal can be attained whether by fostering firms’ size or by spurring entrepreneurship. In this perspective, female entrepreneurship can be regarded as an untapped source of potential economic growth. Nevertheless, there is still a lack of studies aimed at quantifying the economic impact of women’s entrepreneurship around the world. After a review of the relevance of this phenomenon in the OECD countries, the paper focuses on female firms in Italy. Despite the existence of a specific law (L215/92) aimed at promoting women entrepreneurship, the share of female-run firms over the period 2000-2005 has been slightly decreasing: in 2005, 25.6% of the active firm was owned (or managed, or controlled) by a woman, down from 26.2% in 2000. Moreover, the entrepreneurial gap between men and women, defined as the difference between male- and female-run firms divided by the total number of firms, increased over time: from 46.9% in 2000 to 47.8% in 2005. JEL Classification: M13, J16, J23. Keywords: Entrepreneurship, Gender, Female-run firms, Failure rates, Italy.
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Introduction
∗
Small and medium enterprises (SMEs) and entrepreneurship are commonly recognized as one of the engine of economic growth and a main source of job creation, both in industrialized countries and in emerging and developing countries. According to OECD data (OECD, 2005), SMEs are the prevailing organizational form of business, which accounted in 2003 for more than 95% of the business population and up to 99% in some countries (like in Italy). Moreover, they represent a growing share of employment in most OECD countries: in Europe only, 23 million SMEs provide employment for 66% of the workers in the private sector. If one of the central pillars of the Lisbon strategy is to make available more and better jobs in Europe (European Commission, 2005), this goal can be attained by fostering firm size and by spurring entrepreneurship. In this perspective, women’s entrepreneurship can be regarded as an untapped source of potential economic growth. Nevertheless, very little is known about the economic impact of women’s entrepreneurship. The policy rationale for the development of women’s entrepreneurship was traditionally focussed on issues like poverty alleviation, women’s equality and empowerment, and social inclusion. Only in the more recent years it became clear that female entrepreneurs create new jobs for themselves and others and “[... ] can provide society with different perspectives and approaches to management, organization and business issues” (OECD, 2004). Recent studies (GEM, 2005) estimate that the share of women owned business ranges between one third to one fourth in developed countries, and that their weight on the economy is likely to increase. Studying female entrepreneurship is important, since individual characteristics have a non negligible effect on firm performance. In fact, male and female entrepreneurs are different in terms of the industry in which they run their ∗
I would like to thank Cesare Zavaglia (Infocamere) for kindly supplying data on female-run firms in Italy. Emmanuele Somma has been a precious help for the organization of the database; Marco Chiurato and Elena Genito provided valuable research assistance. Thanks are due also to Piero Cipollone, Marco Magnani, Fabiano Schivardi and Roberto Torrini for useful discussions. Of course, the usual disclaimers apply. The views expressed are those of the author and do not necessarily reflect those of the Bank of Italy. Francesca Lotti, Economics Research Department, Bank of Italy, via Nazionale 91, 00184 Rome, Italy. Tel. +39 06 4792 2824, Fax +39 06 4792 3720. Email:
[email protected].
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business, of the products they bring to the market, of the goals they pursue, of the way they structure their business and they choose a degree of delegation (Mukhtar (2002), Verheul, Stel and Thurik (2004), and Minniti, Arenus and Langowitz (2005)). However, female entrepreneurs are still less than male entrepreneurs, which suggests that the economic potential of this group is hardly exploited. The economic impact of women is substantial, but we still lack a reliable picture describing in detail that specific impact. Recent efforts initiated by the OECD (1998) are responses to this lack of knowledge, but are mostly based on case studies and best practices analysis. This paper represents the first attempt in quantifying the relevance of female-run firms in Italy. Despite the existence of a specific law1 (L215/92) aimed at promoting women entrepreneurship, the share of female-run firms seems stable over the period 2000-2005: one firm out of four is owned (or managed, or controlled) by a woman (25.6% in 2005). The entrepreneurial gap between men and women (defined as the ratio of the difference of the number of men- and female-run to the total number of firms) increased: from 46.9% in 2000 to 47.8% in 2005. Moreover, failure rates of female firms are much lower than those of male-run firms, suggesting the existence of a pre-market selection mechanism. In line with previous studies (Franco and Winqvist, 2002), women generally start and manage firms in different industries when compared to men. The industries chosen by women are mainly retail, education and other service industries, characterized for being less capital-intensive. The remaining paper is organized as follows: Section 2 contains a review of the empirical studies concerning the role of women’s entrepreneurship in selected countries; Section 3 depicts the role of female-run firms in Italy. Section 4 contains some conclusions a poses issues for further research. 1
See the appendix for a description of the Law and for details about financed projects
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2
Previous Empirical Studies
Given that over the last decades women witnessed an increase in their labor market participation (see Figure 1), it would be expected that the number of female-owned and managed firms would exhibit the same path, as the former is a requisite for increasing the number of self-employed women. Despite this trend, the entrepreneurial gap between men and women is still high, even in more industrialized countries (Figure 2). The lack of reliable and up to date data is still one of the main obstacles to understanding the challenges specific to women’s entrepreneurship and their impact on economic growth. Genderdisaggregated statistics and gender-based policy analysis have become more widespread only in recent years. Only a few studies actually have been able to estimate the economic impact of women’s entrepreneurship: they roughly define female entrepreneurship as firms owned or managed by women. In Canada, the Prime Minister’s Task Force on Women Entrepreneurs (2003) has assembled statistics from Statistics Canada on women entrepreneurs. They find that between 1981 and 2001, the number of women entrepreneurs increased by 208%, compared to a 38% increase for men. However, average annual sales for women-owned firms are significantly lower. In 2000, women-owned SME were half of those sales of firms owned by men. In the United States a recent analysis from the US Census Bureau estimates that women-owned and managed firms represent 28% of the 23 million firms and they provide employment for 9.2 million people, accounting for 9% of all employees in the private sector. In Germany, there is a total of 1.03 million female-owned businesses. Women-owned and managed businesses with annual turnover of at least Euro 16,620 are 522,000, representing 18% of the total in this group and providing jobs for 2 million employees (Kay et al, 2003). Both the presence of women entrepreneurs and their economic impact is quite similar in the US and in Germany. In Sweden, the entry size for new firms differs between men and women (ITPS, 2002). Women have on average 0.6 full time employees and men have on average 1.7. Furthermore, while women-owned businesses have been smaller than their male counterparts, the difference in size seems to be diminishing. These results point 4
out that, at least for these countries, women’s entrepreneurship is an important economic factor. The Global Entrepreneurship Monitor consortium (GEM, hereinafter), evaluates, on a yearly basis, the emerging trends in entrepreneurship in several countries. In 2005, the first Report on Women and Entrepreneurship (GEM, 2005) was released: this constitutes a cross country assessment on women’s entrepreneurial activity, using standard definitions. This study includes 34 countries and is focused on three main objectives: i) to measure the level of women’s entrepreneurial activity across countries; ii) to understand why women become involved in entrepreneurial activity; iii) to suggest policies that may increase women’s involvement in entrepreneurship. The GEM computed the average level of female Total Entrepreneurial Activity rate2 (TEA) across the 34 GEM countries. In every country in the study, men are more active in entrepreneurship than women. The largest gap occurs in middle income nations where men are 75% more likely to be active entrepreneurs, compared to 33% in high-income countries and 41% in low-income countries. There are important differences between men and women entrepreneurs. For women, the lack of alternative job opportunities is a more important factor in pushing towards entrepreneurship than for men. The majority of businesses started by women employ less start-up capital as compared to men, used known technology, and targeted existing markets. This suggests that women entrepreneurs may take a more risk-averse approach to business formation, perhaps because of their higher involvement in necessity driven entrepreneurship. On average, businesses started by men used more capital than those started by women (US$65,010 vs. US$33,201 respectively). One reason for this discrepancy is the choice of the industry in which starting the business, even if also this choice is endogenous. In fact, the majority of women entrepreneurs provide all the required startup capital themselves, so it should not be surprising that women tend to select themselves in less capital-intensive industries, bearing the full cost of starting their businesses. More2
This index measures the percentage of women in the labor force that is either actively involved in starting a new business or who owns or manages a business less than 42 months old.
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over, women tend to have slower early growth trajectories. Figure 3 shows the comparative results based on the TEA index for men and women in each country. Clearly, the participation of women in entrepreneurship varies significantly across the 34 countries under exam, ranging from 39.1% in Peru, 25.5% in Uganda, and 24.4% in Ecuador, to 1.2% in Japan and 1.6% in Hong Kong and Slovenia. There is no country where women are more active than men. The gender gap is widest in France, Greece, Hong Kong and Spain. There are also a few countries where the gender gap is not statistically significant (Ecuador, Finland, Hungary, Japan, South Africa, and the United States). The distribution of the gender gap in entrepreneurship is strictly linked the the overall country’s economic conditions: the ratio of female to male entrepreneurs is higher in the case of necessity based entrepreneurship, which constitutes a high proportion of activity in the low-income countries (Ecuador, Hungary, Peru, and South Africa). For high-income countries, such as Finland and the United States, closing the gender gap may be the result of targeted programs, cultural changes, and more stress on entrepreneurial education leading to more equal opportunities for women. Studies comparing the performance of male and female owned firms show that businesses headed by women tend to be smaller than those headed by men (GEM, 2005). Normally, the smaller size is perceived as a problem and it is assumed that, if they could, women would expand their businesses as much as men. This perception has important consequences for female entrepreneurship, as women may have a harder time in obtaining external financing and, in general, credibility as business owners and managers. Also, women tend to form relatively egalitarian coalitions, while men build on relatively hierarchical coalitions (GEM, 2005). The hierarchical structure allows them to create organizations that can effectively monitor large numbers of people. On the other hand, the stronger ties of female organizations reduce the need for monitoring and for systems of explicit incentives. This analysis suggests that male and female entrepreneurs will differ in the value attached to start-up size and to business expansion. An important determinant of the level of female entrepreneurship is the availability 6
of financing. In general, the size and composition of start-up capital show that female entrepreneurs have a smaller amount of start-up capital. The issue of external financing is particularly relevant for high-income countries, where increasing numbers of women are beginning to start more technological and capital intensive businesses. Venture capitalists expect a funded venture to grow rapidly in term of sales and profits, so that the venture capital firm can exit within a few years and benefit from the risk taken. Such a strategy may not fit with women’s more conservative approach to firm growth. Some evidence, however, is starting to emerge suggesting that in high-income countries, womenowned businesses are beginning to attract venture capital in some technological intensive industries (GEM, 2004).
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Female-run firms in Italy
3.1
Beyond the Obvious: how to Determine Firms’ Gender
There is no internationally recognized definition of “women entrepreneur”, nor of “femalerun firm”. Definition used by countries to disseminate data on male and female entrepreneurship, include concepts such as owners, managers, self-employed and employers (OECD 1998). The two indicators most commonly collected and used are based on the employment concept. These indicators are: 1. the number and the share of women and men employers and 2. the number and the share of self-employed women and men. These indicators are often collected by means of labor force surveys (LFS) or available in the Census data. Alternative set of indicators are based on the idea of ownership and control: this kind of statistic is mainly collected through business or enterprise registers for administrative purposes (the first) and by means of direct surveys, as the LFS (the latter). The indicators in this set are: 7
1. the number (or the percentage) of enterprises owned on managed by women and men (with territorial and industrial breakdown); 2. the number of women and men members of executive boards of large enterprises; 3. the number of women and men presidents of executive boards of large enterprises. In Italy, these indicators are collected by the National Institute of Statistics (ISTAT) and by the Chamber of Commerce. Given data availability, it is possible to clearly identify not only which firms are classified as “female” , but also the “degree of femininity”. Sole proprietorships are the simplest case, as it goes with the owner’s gender. With corporations, the gender of the equity’s owners has to be considered, while for partnerships and other legal forms, the prevailing gender of partners and of the board of directors respectively (see Table 1 for a detailed description). For our purpose, in line with the definition adopted by the Ministry of the Industry, a female-run firm has a “degree of femininity” of more than 50%.
3.2
Trend in the Presence of Female-run Firms in Italy: 20002005
Infocamere (a company controlled by the Chamber of Commerce) is the producer of gender-disaggregated statistics on firm’s demography in Italy. As all Italian firms are compelled to register at the Camber of Commerce in order to start their business, Infocamere data represent the universe of Italian firms. Table 2 describes the share of female-run firms by industry (2-digits) and time: it can be immediately noticed that, overall, this share has been remarkably stable over the six years period under exam. Some sectoral heterogeneity emerges: in line with the previous literature, also in Italy women tend to start their business in less capital-intensive industries, such as the public and domestic services, health and social works, and the tourism sectors. This result is even stronger looking at the sole proprietorship firms (Table 3), where it’s easier to identify a female-run firm. The presence of female-run firms among corporations is rarefied 8
(Table 4): the share of female firms ranges from 13.0 % of the energy and the real estate, renting & business activities, to 29.6 % of the health & social work sector. Looking at a more disaggregated industry classification (for manufacturing and retail only; Table 5), the stability of the share of female firms over time is striking, both in the retail sector and in the manufacturing. The exceptions are the basic metals and the tobacco industries, in which the presence of female firms increased, and the textile industries, where the share remarkably declined. The entrepreneurial gap between men and women, defined as the difference between the number of male- and female-run firms, divided by the total number of firms, slightly increased over the time period under exam (46.9% in 2000, 47.8% in 2005; Table 6). In 2005, this gap is nihil in the case of the clothing industry, even if in the early 2000s the gap was negative, pointing out a prevalence of female firms in this industry. On average, the gap is more marked in more capital-intensive industries. To sum up, we find that the share of female-run firms over time has slightly declined, leading to an increase in the entrepreneurial gap. Female-run firms operate in less capitalintensive industries, as they tend to provide the required start-up capital on their own. Assuming that managerial abilities are equally distributed among males and females, how can this phenomenon be explained? Compelling with data availability, the first answer concerns the likelihood of exit. Women likely-to-be entrepreneurs select themselves to operate in those industries i) where their perceived chances of survival are higher or, alternatively ii) where their managerial project are perceived to be more successful by venture capitalists or by banks, involved in providing external financing. In both cases, one should observe higher default rates for female firms operating in those industries characterized by a massive presence of male-run firms. Clearly, this is not what we observe in the data. Tables 7 - 11 report the default rates (simply computed as the number of exiting firms during the year divided by the number of active firms at the beginning of the year) by gender. Over time, Table 7, there is no significant difference in failure rates between men and women. Looking at the single industries (Tables 8 and 9), 9
in the Construction, Recycling and Trade, carry & repairing industries, female-run firms has a significant higher default rates. Concerning location, only female firms located in Molise are more likely to exit than male-run firms. Overall, with the few exceptions mentioned above, female firms exhibit failure rates significantly lower than male-run firms. This fact suggests that a pre-market selection mechanism might be at work: the presence of higher entry barriers for women likely-tobe entrepreneurs tend to select the “fittest” projects with the higher chance to become successful firms (Jovanovic, 1982).
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Open questions
According to an OECD study (OECD, 1998), women-owned firms (SMEs in particular), grow at faster rates than the economy as a whole in several countries. Even if female entrepreneurs are becoming a major force in most OECD countries, their contribution to economic growth could become more significant if some restrictions were removed, so that their potential could be fully exploited, both in industrialized and less developed countries. As a stylized fact, it emerges that women tend to start and to run their business in the retail, education and other service industries. Moreover, for every industry under exam, female-run firms have lower failure rates that their male counterparts. For a better understanding and to address accurate policy measures, female entrepreneurship must be examined both at the individual level (the choice of becoming self-employed) and at the firm level (looking at the dynamics of female-run firms). At the individual level, one of the main obstacles for a woman to start a new firm, as pointed out by OECD (OECD, 2004) and by the Italian Ministry of Industry (2004), is the lack of external finance for the start-up capital. For a given business opportunity and for equally capable individuals, women must secure additional resources compared to men, in order to exploit their business, as they control less capital (according to the OECD study, on average,
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women have lower personal financial assets than men). This is a reason why female-run firms can be more credit-constrained than male-run firms, as women may have a harder time getting external finance as a consequence of gender discrimination. Reviewing the empirical literature on this topic, Carter et al. (Carter et al, 2001) find rather mixed evidence on gender discrimination from lending institutions. One one hand, credit constraints can inhibit women entrepreneurship, on the other, they can can force women to start their business in less capital intensive industries, generating a sort of “segregation” between industries. Further research at the firm level is needed to understand the role of financial institutions in shaping the dynamics of female-run firms.
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Appendix The “Law 215/92” Daughters Despite the increase in labor force participation of women, and the availability of public funding to boost female entrepreneurship, the gap between men and women entrepreneurship is still large, and no catching-up seems to emerge in the period under exam (20002005). The Law 215 passed in 1992 was created with the purpose of fostering female entrepreneurship and to spur growth of female-run firms (with a total or strong presence of women, as defined in Table 1). The targeted industries are manufacturing, retail, transportation and hospitality. Subsidized activities are i) new firm creation (greenfield entry); ii) firm take-overs; iii) accomplishment of innovative managerial projects; iv) acquisition of external services aimed at increasing firm’s productivity and innovativeness. In Table 1A, the number of applications and the amount of public funding are reported. Table A1 - Applications, grants and public funding allocated by Law 215/92 (monetary aggregates are expressed in million euros). Ban
N. of applications
N. of grants
Co-funded investments
Public funding
Employment
I II III IV ∗ Total
4,109 4,852 5,301 26,951 41,213
518 917 1,311 5,669 8,415
56.6 101 154.2 474 785.8
22.5 36.9 62.8 288 410.2
3,388 5,559 7,566 30,628 47,141
Source: Ministry of Industry. ∗ : In the IV ban, co-financing by the Italian Regions is required. This explains the higher funding availability.
The financed project are mainly related to greenfield entry (more than 80%) and addressed to retail, hospitality and other services (around 70%). As employment growth is one of the main objectives of the Law, labor intensive projects are more likely to be selected (the average amount of co-financed investment per employee is 16,700 euros). The lack of data about these subsidized firms prevents a sound exercise of policy evaluation. Nevertheless, the fact that the financed project are evaluated on the criterion of the induced employment and not on efficiency, can cast some doubts on reliability of the selection procedures. Moreover, looking at the sectoral composition, there is no attempt 12
of re-balancing the presence of female-run firms in those industries, like manufacturing, where male-run firms are dominant.
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References Bygrave, William D., and Stephen A. Hunt (2004) ‘2004 Financing Report.’ GEM - Global Entrepreneurship Monitor Carter, Sara, Susan Anderson, and Eleanor Shaw (2001) ‘Women’s Business Ownership: a Review of the Academic, Popular and Internet Literature.’ Small Business Services Report: UK European Commission (2005) Common Actions for Growth and Employment: The Community Lisbon Programme (Brusseles: COM(2005) 330 final) Franco, Ana, and Karin Winqvist (2002) ‘The Entrepreneurial Gap between Women and Men.’ Eurostat: Statistics in Focus Infocamere (2004) Impresa in Genere (Rome: Rapporto Nazionale sull’Imprenditoria Femminile) ITPS (2002) ‘Newly Started Enterprises in Sweden (2000 and 2001).’ ITPS: Swedish Institute for Growth Policy Studies Report Jovanovic, Boyan (1982) ‘Selection and Evolution of Industry.’ 50(3), 649–670 Kay, Rosemary, Brigitte Gunterberg, Michael Holz, and Hans-J Wolter Minniti, Maria, Pia Arenus, and Nan Langowitz (2005) ‘2004 Report on Women and Entrepreneurship.’ GEM - Global Entrepreneurship Monitor Mukhtar, Syeda-Masooda (2002) ‘Differences in Male and Female Management Characteristics: A Study of Owner-Manager Businesses.’ Small Business Economics (18), 289– 311 OECD (1998) Women Entrepreneurs in SMEs (Paris: OECD Publishing) (2004a) Promoting Entrepreneurship and Innovative SMEs in a Global Economy (Paris: OECD Publishing) (2004b) Women’s Entrepreneurship: Issues and Policies (Paris: OECD Publishing) (2005) SME and Entrepreneurship Outlook (Paris: OECD Publishing)
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Verheul, Ingrid, Andr´e Van Stel, and Roy Thurik (2004) ‘Explaining Female and Male Entrepreneurship across 29 Countries.’ Discussion Papers of Entrepreneurship, Growth and Public Policy
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16 must be female -
Total
Strong
Prevailing
Owner:
Degree of “Femininity” Sole proprietorships
> 50%
>= 60%
100%
Partners: must be females for
Partnerships
Corporations
> 50%
>= 66%
100%
Equity: must be females’ hand for
Table 1: Definition of female firm
> 50%
>= 60%
100%
Board of Directors: must be females for
Other legal forms
Table 2: Share of female-run firms on the total number of firms, by industry: all active firms, 2000-2005 (percentages). All firms
2000
2001
2002
2003
2004
2005
Agriculture Fishing Mining Manufacturing Energy Constructions Retailing HoReCa Transport, storage & communications Financial intermediation Real estate, renting & business activities Education Health & social work services Other public, social & personal services Private households with employed persons Total
26.6 13.0 16.5 24.2 9.4 9.9 28.1 38.2 12.6 18.7 34.3 37.4 43.8 46.7 49.6 26.2
25.9 13.4 16.3 23.9 9.7 9.4 32.5 39.7 13.7 19.3 28.8 36.9 45.5 44.0 47.3 26.4
26.4 14.7 16.5 24.0 10.1 9.4 31.8 35.7 15.1 19.9 34.4 37.7 44.0 47.0 44.0 27.0
27.6 13.3 16.7 23.7 9.7 9.3 28.8 41.9 14.8 17.5 27.2 36.4 45.3 42.4 41.5 25.7
26.1 14.4 16.7 23.5 10.1 8.8 30.9 37.4 15.5 22.4 25.5 37.3 45.5 47.3 41.5 25.9
27.6 14.2 16.1 23.4 12.9 9.1 28.3 43.8 17.1 21.1 24.7 36.4 44.5 47.0 47.6 25.6
Source: Author’s calculation on Infocamere data.
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Table 3: Share of female-run firms on the total number of firms, by industry: sole proprietorship active firms, 2000-2005 (percentages). Sole proprietorships
2000
2001
2002
2003
2004
2005
Agriculture Fishing Mining Manufacturing Energy Constructions Retailing HoReCa Transport, storage & communications Financial intermediation Real estate, renting & business activities Education Health & social work services Other public, social & personal services Private households with employed persons Total
28.0 11.7 10.1 21.8 14.9 2.4 25.8 41.9 6.4 21.8 34.3 41.9 42.0 62.0 37.4 24.8
27.0 12.7 10.0 23.6 14.8 2.2 31.9 41.7 5.8 21.9 28.1 42.7 43.9 54.6 37.5 25.5
27.6 16.6 10.1 27.9 15.8 2.1 29.4 35.5 6.5 21.6 28.1 43.3 46.7 58.8 34.3 25.4
28.6 12.8 10.1 21.7 16.4 2.4 27.4 41.6 7.1 17.8 28.4 43.8 49.3 52.7 34.8 24.4
26.7 15.4 10.2 23.8 16.7 2.4 30.5 43.0 7.4 20.8 33.7 44.6 51.6 59.1 33.3 25.6
28.6 14.6 10.6 24.8 17.7 2.4 28.0 45.5 8.6 23.4 32.8 44.6 57.5 58.7 20.0 25.5
Source: Author’s calculation on Infocamere data.
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Table 4: Share of female-run firms on the total number of firms, by industry: active corporations, 2000-2005 (percentages). Corporations
2000 2001
2002
2003
2004
2005
Agriculture Fishing Mining Manufacturing Energy Constructions Retailing HoReCa Transport, storage & communications Financial intermediation Real estate, renting & business activities Education Health & social work services Other public, social & personal services Total
18.2 10.7 14.7 18.1 8.3 16.9 24.4 24.8 17.0 12.3 28.0 28.5 27.5 16.7 20.4
17.7 12.3 14.5 16.4 9.4 15.7 26.7 25.1 19.2 13.0 26.6 26.6 28.5 18.0 20.3
19.3 12.8 14.6 19.0 8.8 15.8 18.9 26.4 17.0 10.1 21.0 26.2 28.9 20.0 18.9
18.1 13.2 14.7 19.1 9.2 10.8 19.9 28.3 18.5 20.5 19.4 27.2 31.2 20.9 19.0
20.3 12.6 13.9 16.7 13.0 15.1 21.4 26.6 20.5 14.4 13.0 27.4 29.6 21.2 17.3
Source: Author’s calculation on Infocamere data.
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17.2 11.2 14.5 17.2 8.8 14.7 19.5 24.8 18.5 12.4 19.5 26.9 28.1 18.6 18.0
Table 5: Share of female-run firms on the total number of firms: all active firms, manufacturing & retail, 2000-2005 (percentages). All firms
2000
2001
2002
2003
2004
2005
Manufacturing Food products & beverages Tobacco Textiles Clothing Leather & footwear Wood products Paper & paper products Printing & publishing Coke, refined petroleum products & nuclear fuel Chemical products Rubber & plastics products Other non-metallic mineral products Basic metals Fabricated metal products Machinery & equipment, n.e.c. Office, accounting & computing machinery Electrical machinery & apparatus, n.e.c. Radio, TV & communications equipment Medical, precision & optical instruments Motor vehicles, trailers & semi-trailers Other transports Manufacturing & furniture, n.e.c. Recycling
25.8 9.7 45.6 51.8 28.8 12.3 25.0 25.1 13.8 18.5 26.0 20.9 17.9 20.1 18.2 16.4 20.8 17.0 14.4 17.0 14.9 20.2 18.5
26.4 10.6 35.9 52.7 30.8 12.4 25.6 25.8 14.1 15.4 25.1 22.2 23.8 16.8 18.0 16.0 21.9 17.1 14.9 19.1 14.8 20.3 18.8
27.5 12.7 39.2 46.6 31.4 13.6 25.9 25.4 13.5 18.8 26.6 22.2 24.5 16.7 21.1 16.8 21.8 17.1 15.5 17.1 14.6 21.1 18.5
27.4 12.3 39.9 48.2 31.5 11.9 29.7 26.7 13.1 14.7 22.1 24.0 18.6 16.9 18.2 16.3 21.8 17.4 14.8 17.5 14.9 22.1 18.6
28.5 13.7 38.1 51.5 32.1 13.3 25.9 27.0 13.0 19.9 23.0 21.4 20.1 16.5 17.5 16.3 21.2 17.6 15.8 17.9 15.0 24.0 18.3
28.0 14.3 39.0 49.8 31.4 13.8 25.5 25.3 13.5 19.8 26.1 23.2 28.2 18.6 15.3 16.2 23.2 19.1 16.0 20.7 14.9 23.1 18.1
Retail Trade, carry repairing etc. Wholesale trade Retail trade
12.4 18.8 40.3
12.6 19.0 40.1
12.8 19.2 40.1
13.1 19.4 40.2
13.3 19.5 40.3
13.5 19.6 40.2
Source: Author’s calculation on Infocamere data.
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Table 6: Entrepreneurial gap, by industry, 2000-2005 (percentages).
Agriculture Fishing Mining Manufacturing Food products & beverages Tobacco Textiles Clothing Leather & footwear Wood products Paper & paper products Printing & publishing Coke, refined petroleum products & nuclear fuel Chemical products Rubber & plastics products Other non-metallic mineral products Basic metals Fabricated metal products Machinery & equipment, n.e.c. Office, accounting & computing machinery Electrical machinery & apparatus, n.e.c. Radio, TV & communications equipment Medical, precision & optical instruments Motor vehicles, trailers & semi-trailers Other transports Manufacturing & furniture, n.e.c. Recycling Energy Constructions Retail Trade, carry repairing etc. Wholesale trade Retail trade Hotel & restaurants Transport, storage & communications Financial intermediation Real estate renting & business activities Education Health & social work services Other public, social & personal services Private households with employed persons N.e.c. Total
2000
2001
2002
2003
2004
2005
46.8 74.1 67.0 51.8 48.3 80.7 8.8 -3.6 42.4 75.4 49.9 49.9 72.5 63.1 48.0 58.2 64.2 59.9 63.6 67.2 58.5 65.9 71.2 66.1 70.3 59.5 62.9 81.1 80.2 41.5 75.2 62.4 19.4 23.6 74.9 62.6 31.4 25.3 12.5 6.5 0.8 47.6 46.9
48.2 73.3 67.4 53.4 47.2 78.8 28.2 -5.4 38.4 75.3 48.9 48.5 71.8 69.3 49.9 55.5 52.3 66.5 64.1 68.0 56.2 65.7 70.2 61.7 70.3 59.4 62.4 80.6 81.3 41.5 74.9 61.9 19.8 20.6 72.6 61.3 42.4 26.3 9.0 12.0 5.4 50.3 48.7
47.1 70.5 66.9 52.4 44.9 74.6 21.7 6.7 37.3 72.9 48.2 49.3 73.0 62.3 46.8 55.5 51.0 66.6 57.8 66.4 56.5 65.8 68.9 65.9 70.7 57.8 63.1 79.8 81.2 41.2 74.3 61.5 19.7 28.5 69.7 60.2 31.1 24.6 11.9 6.0 12.0 45.6 47.1
47.7 71.2 66.7 52.2 43.0 72.6 23.9 -3.1 35.8 73.5 48.2 46.0 74.0 60.2 54.0 57.3 59.8 67.0 65.0 67.4 57.5 64.9 68.4 64.2 70.0 52.0 63.3 79.8 82.5 40.7 73.4 61.1 19.4 25.2 68.9 55.2 49.0 25.5 8.9 5.3 17.0 38.5 48.6
47.7 71.2 66.7 52.2 43.0 72.6 23.9 -3.1 35.8 73.5 48.2 46.0 74.0 60.2 54.0 57.3 59.8 67.0 65.0 67.4 57.5 64.9 68.4 64.2 70.0 52.0 63.3 79.8 82.5 40.7 73.4 61.1 19.4 25.2 68.9 55.2 49.0 25.5 8.9 5.3 17.0 38.5 48.6
44.8 71.6 67.9 51.4 43.9 71.4 22.0 0.4 37.2 72.5 48.9 49.4 73.1 60.3 47.7 53.5 43.7 62.7 69.4 67.7 53.5 61.8 68.0 58.6 70.1 53.9 63.8 74.2 81.9 40.6 73.1 60.9 19.6 12.4 65.7 57.7 50.5 27.2 10.9 6.0 4.8 45.9 47.8
Source: Author’s calculation on Infocamere data. The entrepreneurial gap is defined as the difference between the number of male and female-run firms divided by the total number of firms.
21
Table 7: Failure rates by gender and year. Years δF δM δF − δM H0 : δF − δM =0 >0 2000 2001 2002 2003 2004 2005
4.110 4.350 4.386 4.503 4.799 5.004
4.740 4.590 4.814 5.039 5.209 5.660
-0.630 -0.240 -0.428 -0.536 -0.410 -0.656
0.040 0.442 0.169 0.096 0.215 0.064
0.980 0.779 0.915 0.952 0.892 0.968
All period
4.520
5.000
-0.480
0.000
1.000
Note: Failure rates are computed as the ratio of the number of bankruptcies and the total number of firms. In the last two columns p-values are reported.
Industry
Table 8: Failure rates by gender and economic activity. δF δM δF − δM H0 : δF − δM =0 >0
Agricultural, hunting & forestry Fishing Mining & quarrying Manufacturing Electricity, gas & water Construction Wholesale & retail trade Hotel & restaurants Transport, storage & communications Financial intermediation Real estate, renting & business activities Education Health & social work services Other public, social & personal services Private households with employed persons N.e.c.
2.638 4.108 8.361 7.696 2.013 7.596 6.944 4.466 5.359 2.747 3.117 2.206 1.728 2.345 3.606 7.091
3.298 4.081 7.232 8.615 2.093 6.556 7.921 5.810 5.367 2.577 3.696 2.399 2.423 3.596 6.610 8.785
-0.660 0.027 1.128 -0.919 -0.081 1.040 -0.977 -1.344 -0.008 0.170 -0.579 -0.193 -0.695 -1.251 -3.004 -1.694
0.008 0.970 0.264 0.073 0.880 0.005 0.035 0.000 0.980 0.477 0.065 0.400 0.000 0.000 0.097 0.016
0.996 0.485 0.132 0.964 0.560 0.002 0.982 1.000 0.510 0.238 0.968 0.800 1.000 1.000 0.951 0.992
Note: Averages over the period 2000-2005. Failure rates are computed as the ratio of the number of bankruptcies and the total number of firms. In the last two columns p-values are reported.
22
Industry
Table 9: Failure rates by gender and industry. δF δM δF − δM
H0 : δ F − δ M =0 >0
Manufacturing Food products & beverages 5.034 6.307 Tobacco 8.627 8.022 Textiles 6.339 12.829 Clothing 10.222 18.535 Leather & footwear 13.526 15.737 Wood products 8.052 8.526 Paper & paper products 8.091 8.786 Printing & publishing 3.924 5.703 Coke, refined petroleum products & nuclear fuel 5.893 9.149 Chemical products 10.454 9.442 Rubber & plastics products 8.728 10.823 Other non-metallic mineral products 7.337 10.627 Basic metals 8.868 9.106 Fabricated metal products 7.445 7.415 Machinery & equipment, n.e.c. 6.944 6.855 Office, accounting & computing machinery 5.056 5.969 Electrical machinery & apparatus, n.e.c. 5.332 7.136 Radio, TV & communications equipment 6.089 6.192 Medical, precision & optical instruments 3.699 4.066 Motor vehicles, trailers & semi-trailers 8.821 8.791 Other transports 7.371 6.850 Manufacturing & furniture, n.e.c. 6.194 8.229 Recycling 4.301 3.201
-1.272 0.605 -6.490 -8.313 -2.211 -0.473 -0.695 -1.779 -3.256 1.012 -2.096 -3.291 -0.238 0.030 0.089 -0.913 -1.804 -0.102 -0.367 0.030 0.522 -2.035 1.101
0.001 0.808 0.000 0.000 0.024 0.429 0.426 0.000 0.025 0.249 0.023 0.000 0.784 0.951 0.868 0.315 0.000 0.872 0.397 0.978 0.467 0.000 0.097
1.000 0.404 1.000 1.000 0.988 0.786 0.787 1.000 0.987 0.125 0.989 1.000 0.608 0.475 0.434 0.842 1.000 0.564 0.802 0.489 0.234 1.000 0.048
Retail Trade, carry & repairing Wholesale trade Retail trade
1.359 -0.267 -1.943
0.001 0.404 0.000
0.001 0.798 1.000
5.804 7.577 5.212
4.445 7.844 7.155
Note: Averages over the period 2000-2005. Failure rates are computed as the ratio of the number of bankruptcies and the total number of firms. In the last two columns p-values are reported.
23
Table 10: Failure rates by gender and region. Regions δF δM δF − δM H0 : δF − δM =0 >0 Abruzzo Basilicata Calabria Campania Emilia-Romagna Friuli Venezia Giulia Lazio Liguria Lombardia Marche Molise Piemonte Puglia Sardegna Sicilia Toscana Trentino Alto-Adige Umbria Valle d’Aosta Veneto
4.631 4.256 3.454 4.687 3.583 4.276 3.348 4.864 5.070 3.680 5.665 3.328 5.569 5.582 5.789 6.324 2.237 4.232 4.004 5.629
4.172 5.730 3.892 6.192 3.371 6.178 5.706 4.518 4.495 4.357 4.416 3.287 6.295 4.908 7.247 5.469 3.337 4.430 5.838 5.999
0.459 -1.474 -0.439 -1.504 0.212 -1.901 -2.359 0.346 0.575 -0.677 1.249 0.040 -0.726 0.674 -1.458 0.855 -1.100 -0.198 -1.834 -0.369
0.368 0.023 0.184 0.001 0.664 0.000 0.000 0.415 0.239 0.111 0.094 0.883 0.183 0.426 0.054 0.280 0.003 0.409 0.059 0.600
0.184 0.989 0.908 1.000 0.332 1.000 1.000 0.208 0.119 0.944 0.047 0.441 0.909 0.213 0.973 0.140 0.999 0.796 0.970 0.700
Note: Averages over the period 2000-2005. Failure rates are computed as the ratio of the number of bankruptcies and the total number of firms. In the last two columns p-values are reported.
Table 11: Failure rates by gender and legal form. Legal form δF δM δF − δM H0 : δF − δM =0 >0 Corporations 7.304 8.067 Sole proprietorships 3.624 3.531 Partnerships 3.678 4.133 Other legal forms 3.512 4.346
-0.763 0.093 -0.455 -0.834
0.002 0.769 0.003 0.003
0.999 0.385 0.999 0.998
Note: Averages over the period 2000-2005. Failure rates are computed as the ratio of the number of bankruptcies and the total number of firms. In the last two columns p-values are reported.
24
Figure 1: Labor force participation rates for women (1995, 2000 & 2004).
# $
7 5
1(
6( $
# *
3 $(
-
/( 2/
2/ 1
'
% , -(.
0/"
/" )(&
*
)(&
& "
($ '
& %
!
Source: OECD database on Labor Force Statistics (permanent URL http://dx.doi.org/10.1787/077514107464). Countries are sorted according to the participation rate in 2004.
"
, -(. +
. ($ 4(5
25
% /
" &
"
!
Source: OECD database on Labor Force Statistics.
4
,
"! *
3
2 0! 1
2 0! 1
0 & ".
, $ ( )* +
(
'
Figure 2: Women and men self-employed in selected OECD countries, 2002
$ % # !
!!0 # !
& ! +
26 3 !% )
!
/
012
"
+ )
( #
&
)
( %
"
# $!
!
Source: GEM, Global Entrepreneurship Monitor. Countries are sorted according to the values of the TEA index.
&
01& +
( '
, + *
( + -
! -
/
.
)
Figure 3: Total Entrepreneurship Activity (TEA) Index, males and females (2004).
% '
27
