Gender stereotypes in the angel investment process

Linda F. Edelman (Department of Management, Bentley University, Waltham,Massachusetts, USA)
Róisín Donnelly (Department of Management, Bentley University, Waltham,Massachusetts, USA)
Tatiana Manolova (Department of Management, Bentley University, Waltham,Massachusetts, USA)
Candida G. Brush (Arthur M. Blank Center for Entrepreneurship, Babson College, Wellesley, Massachusetts, USA)

International Journal of Gender and Entrepreneurship

ISSN: 1756-6266

Publication date: 11 June 2018

Abstract

Purpose

Women-led companies receive less than 5 per cent of early-stage equity investment. This paper aims to explore the disparity in equity funding between men- and women-led companies, using a social identity perspective, complemented by insights from signaling theory. We argue that in the angel group context, which is male-dominated, gender stereotypes may bias angels’ interpretation of the signals sent by entrepreneurs, so that entrepreneurial ventures led by men are more favorably evaluated, thus excluding women entrepreneurs from funding. The ideas are tested on a sample of 358 entrepreneurs who applied for funding from a northeast US angel group using perceptual data from both sides of the investment dyad. Findings suggest that angel investors view women-led entrepreneurial ventures as having less legitimacy, even though we see no difference in actual legitimacy across ventures.

Design/methodology/approach

The ideas are tested on a sample of 358 entrepreneurs who applied for funding from a northeast angel group using perceptual data from both sides of the investment dyad.

Findings

The findings suggest that, in the context of angel investing, there is a subtle bias that follows from the perceived stereotype between being female and the ability to lead a legitimate new venture. Thus, this study tests the tenets of the social identity theory by finding that mostly male angel investors act in accordance to their gender prescribed roles when they evaluate businesses presented by women entrepreneurs providing some evidence of “in-group” and “out-group” effects and stereotypes.

Research limitations/implications

The findings continue the conversation about biases toward women in early-stage financing by using a social identity lens to look at the way in which adopted identities lead to particular outcomes and stereotypes. The authors have used the context of angel investing to test these ideas, finding some support for their contention that gender is pivotal when angels are making investment decisions. For researchers, this study suggests that gender should not be used solely as a control variable, but instead should be the focus of the inquiry itself.

Practical implications

For practitioners, this study reminds women seeking angel investment that they are not playing on a level field and so they should do all that they can to enhance the legitimacy of themselves and their ventures.

Originality/value

The authors contend that within an angel group that is composed of predominantly men, role stereotypes of entrepreneurs as masculine will be expected, therefore creating gender biases against women. The authors expect these biases, whether conscious or unconscious, will lead the angel investors to evaluate men entrepreneurs more favorably than women entrepreneurs as they move through the angel investment process. Therefore, for women entrepreneurs in the early stages of investment funding, the authors posit that the dearth of funding is a function of gender identity stereotypes which may be manifested in hidden and often unconscious biases on the part of the angel investor.

Keywords

Citation

Edelman, L., Donnelly, R., Manolova, T. and Brush, C. (2018), "Gender stereotypes in the angel investment process", International Journal of Gender and Entrepreneurship, Vol. 10 No. 2, pp. 134-157. https://doi.org/10.1108/IJGE-12-2017-0078

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

There is a quiet revolution in women’s entrepreneurship going on in the USA. US statistics from 2016 found that women led over 11.3 million businesses, generating over $1.6tn in revenue and employing over 9 million people (www.womenable.com). Importantly, between 2007 and 2016, the number of US women-owned firms increased by 45 per cent. That is in comparison to a 9 per cent increase among all US businesses, which means that women-owned firms in the USA have increased at a rate that is five times faster than the national average (www.forbes.com/sites/…/why-the-force-will-be-with-women-entrepreneurs-in-2016/).

The rise in the number of women-owned businesses proves that they are not a small niche market, but a major contributor to the US economy. Women-owned businesses now employ over 8 per cent of the US private sector workforce, up from 6 per cent nine years ago. However, despite their significant economic impact, women entrepreneurs continue to face challenges in obtaining growth capital, especially equity (Brush et al., 2014).

Financial capital is a critical resource for growing firms, yet there is evidence that women entrepreneurs attract fewer early-stage equity investments, both in the form of venture capital and angel investing (Sohl, 2015; Becker-Blease and Sohl, 2007). Reasons for these differences vary widely. One theory suggests that women-led ventures tend to focus on lower growth market opportunities, whereas men tend to pursue ventures across a wide variety of industries (Coleman and Robb, 2012). Another suggests that women pursue ventures in female-oriented industries such as fashion, cosmetics and cooking (Coleman and Robb, 2009, 2012). A third posits that engaging in entrepreneurship is viewed as a masculine endeavor (Ahl, 2006; Bird and Brush, 2002; Bruni et al., 2004; Marlow, 2002), and successful entrepreneurs often are described as bold, aggressive risk-takers – traits typically associated with masculinity (Baughn et al., 2006; Gupta and Turban, 2012). Thus, the image of an entrepreneur is grounded predominantly in male gender stereotypical notions of “masculinity.” In sum, biases against women-led entrepreneurial firms continue in the early-stage investment decision-making process.

One way to understand the disparities between men and women’s entrepreneurial experience is through the lens of social identity theory. The social identity theory is rooted in social psychology and explains group processes and intergroup relations (Hogg et al., 1995). The basic premise is that a social group to which one belongs provides a definition of which one is in terms of the defining characteristics of that group. Each of these social group memberships both describes and ascribes an individual’s behavior. When social identity becomes foremost, perception and conduct become stereotypical (Hogg et al., 1995; Max and Ballereau, 2013). For example, many women belong to the social groups of family and mother, and so define themselves, and are defined by others, as primarily mothers or family members based on the stereotypical characteristics of these social groupings. Furthermore, these individuals are expected to act in ways that are based on the social group to which they belong or aspire to belong.

In this study, we explore the context of entrepreneurs presenting their businesses to angel investors for possible funding. In our analysis, the entrepreneur and the angel investor form the two sides (supply and demand) of the angel investment dyad. We focus on the challenges that women face during the angel investment decision-making process and hypothesize that women entrepreneurs and their businesses will be evaluated differently from their male colleagues. Our premise is that within a predominantly male angel group, masculine stereotypes of entrepreneurs will be expected, therefore creating gendered biases against women. We expect that these biases, whether conscious or unconscious, will lead the angel investors to evaluate men entrepreneurs more favorably than women entrepreneurs, as they move through the angel investment process. In essence, we argue that the gap in women’s funding at the angel level is less about the type of business formed, and more about the gender of the entrepreneur (Gupta et al., 2009). Therefore, for women entrepreneurs in the early stages of investment funding, we posit that the dearth of funding is a function of gender identity stereotypes, which may be manifested in hidden and often unconscious biases on the part of the angel investor.

We test our ideas on a sample of entrepreneurs who applied for funding from a prominent northeast US angel group. Our sample of 358 includes both sides of the dyadic relationship, perceptual data from the entrepreneur and perceptual data from the potential angel investor. Findings indicate that angel investors view women-led entrepreneurial ventures as having less legitimacy than those led by their male colleagues, even though there were no differences in the signals of legitimacy between the men- and women-led ventures in our sample. Our study proceeds as follows. We first present our theory and hypotheses. We move to our methods, findings and discussion. Finally, we present our conclusions, future research directions and implications.

Theory and hypotheses

Gender and early-stage investment

Women are generally underrepresented in angel financing, as both equity providers (supply side) and equity seekers (demand side). In an early study based on secondary data from 20,000 portfolio companies, 34,000 executives, 120,000 company investments (on the demand side) and Pratt’s Guide to Venture Capital for 1995 and 2000 (on the supply side), Brush et al. (2002) found that in any given year between 1981 and 1987, women-led ventures represented about 4.1 per cent of all venture investments and that women comprised a very small minority of venture capital decision-makers in the USA. This study was updated in 2014, and the numbers did not improve significantly; in that 15 per cent of all businesses invested in by active venture capitalists during 2011-2013 had at least one woman on the team, but only 2.7 per cent of these companies had a woman CEO (Brush et al., 2018).

When we focus specifically on the supply side of the angel equation, research finds that with respect to the investment patterns of men and women angel investors, women investors who were active in the market differed only slightly from their male counterparts; however, women business angels were found to be slightly more likely to invest in women-owned businesses (Harrison and Mason, 2007). Sohl and Hill (2007) used a mail survey to explore the investment patterns of USA – based angel networks with significant (>25 per cent) women membership. These authors reported that “women face barriers in the private equity community as a result of investment inexperience and a lack of experience in pricing and structuring the investment deal” (Sohl and Hill, 2007, p. 220). In related research, Becker-Blease and Sohl (2008) investigated whether men and women angel investors have different levels of confidence based on participation in the angel capital market, rate of investments and stage of investments, and found evidence consistent with women angels having lower levels of confidence compared to men.

Turning to the demand side of the angel investment dyad, we see that women entrepreneurs are not actively seeking early-stage equity financing, and when they do look for funding, they face a unique set of challenges. Amatucci and Sohl (2004) conducted a qualitative study of five women entrepreneurs who were seeking angel funding. Their findings identify a set of issues for women entrepreneurs including problems with obtaining the right knowledge to proceed through a first round without future negative consequences, developing an effective management team and dealing with gender-related bias. Becker-Blease and Sohl (2007) reported that women seek angel financing at rates substantially lower than those of men, but have an equal probability of receiving investment when they do look for funding. When a women angel is involved in the investment team, women entrepreneurs are more likely to seek angel financing; however, the odds of women obtaining angel capital from women angels are not significantly higher than from their male counterparts (Sohl and Hill, 2007). A recent study using the signaling theory proposed that characteristics of the firm and entrepreneur may be gendered, and found that investors reward business characteristics of male and female entrepreneurs differently, to the disadvantage of women (Eddleston et al., 2016).

In summary, women face numerous barriers when seeking early stage angel financing. While the presence of women investors helps a little, in that it empowers the women entrepreneurs, the investment outcomes remain about the same. Women investors may need to prove themselves in a male-dominated investment world, and so may be either neutral or reluctant to invest in women-led businesses (Harrison et al., 2015). Table I provides a review of the articles on women and angel investing.

Social identity theory

Identity is a core concept that links social structure to individual behavior. The social identity theory has its roots in social psychology (Hogg et al., 1995), and is based on principles of group identification. It helps to explain group processes and intergroup relations (Tajfel and Turner, 1979). In the social identity theory, a person’s concept of self (role identity) comes from the valued groups in which that person belongs or aspires to belong. The person sees things from the group perspective, whether or not he/she has direct interaction with the group (Ashforth and Mael, 1989), and individuals have multiple identities which are associated with their respective affiliated groups (Meister et al., 2014).

In the social identity theory, there are three social processes that create an in-group versus out-group mentality, social categorization, social identification and social comparison. Social categorization is a process in which people are categorized into groups to better understand and identify them. Social identification is the process through which individuals adopt the norms and behaviors of that particular group (Turner, 1982). Social identity is the process of social comparison. After individuals are characterized and identify themselves with particular groups, they compare themselves with those groups and against other salient groups (Tajfel, 1982; Turner, 1982). When individuals perceive themselves as part of a group, that group becomes the “in-group” for them. Belonging to a group provides the member with an important source of pride and self-esteem. Conversely, other groups with which the individual does not identify are known as “out-groups.” People act differently at different times depending on the group with which they are identifying. Some negative consequences of the comparison of in-group versus out-group are prejudice and discrimination. Figure 1 presents a social identity process model that illustrates the three segments of the social identity process.

Aspects of the social identity theory have been applied to the management literature. For example, the concept of social identity has been used in concert with ideas around commitment (Mowday et al., 1979) to explain understanding and predicting employee behavior in the workplace (Meyer and Herscovitch, 2001), and in understanding issues around organizational prestige and stereotypes (Bergami and Bagozzi, 2000). Other research has used the social identity theory to explore the identification of alumni to their alma mater (Mael and Ashforth, 1992), work motivation and firm performance (Van Knippenberg, 2000), and corporate sponsorship in marketing (Madrigal, 2001). Findings from these and other studies confirm the importance of social identity in the workplace.

Comparatively less research using the social identity theory has been conducted in the entrepreneurship literature. At a macro-level, the formation of a collective social identity has been linked to new industry creation (Fiol and Romanelli, 2012). At a micro-level, Powell and Baker (2014, p. 1406) examined how founder identity can encompass multiple sub-identities that are “chronically salient” to entrepreneurs in their daily work. They argued that founder identities entail a combination of role and social identities, and that congruence (or lack thereof) between how founders see themselves, and who they aspire to be, influences their behaviors. Alternatively, Hoang and Gimeno (2010) showed how different combinations of founder role centrality, and role complexity, shape individuals’ capacity to engage in founding activities as well as their responses to negative feedback (Hoang and Gimeno, 2010). Miller and Breton-Miller (2011) looked at the link between social identity, entrepreneurial orientation and firm performance on a sample of Fortune 1000 firms. They find support for their idea that social identity leads to enhanced entrepreneurial orientation which in turn leads to greater firm performance in firms with lone founders and in firms with CEOs who embrace the entrepreneurial identity. Smith (2009) used social identity in his exploration of “divas,” finding that the entrepreneurial narrative is socially constructed. In the entrepreneurship pedagogy literature, the social identity theory has been used as one way to help students develop identities as social entrepreneurs and gain confidence in their abilities to affect positive social change (Smith and Woodworth, 2012). In the context of student entrepreneurship, Falck et al. (2012) argued that an individual’s entrepreneurial identity results from their social identity which is peer influenced. They find that peer influence is strongly influenced by the level of individualism in a country.

Less well studied is the connection between social identity and women entrepreneurs. Greene and Brush (2018) examined the current literature on women entrepreneurs and social identity, considering attitudes, behaviors and confidence. They pointed out that because entrepreneurial identity is often linked to male behaviors as the ideal stereotype (Ahl, 2006; Bruni et al., 2004), women entrepreneurs may face competing identities, where they will use cultural associations of status-worthiness and competence as cues for self-definition. Because of higher status attached to masculine entrepreneurial values, women entrepreneurs might report masculine values that are similar to their male counterparts (Justo et al., 2018), reproduce masculinized representations of the normative (male) entrepreneur (Marlow and McAdam, 2015) or they may be discouraged from entrepreneurial actions, having less confidence in entrepreneurial skills because of this competing identity (Chen et al., 1998; Greene and Brush, 2018). In sum, while social identity has been used to explain social categorization in the management and entrepreneurship literatures, it is only emerging as a way to better understand the phenomenon of women and early-stage angel financing.

Below, we theorize on the effect of the entrepreneur’s social identity in the angel investors’ decision-making process. We consider the effect of the entrepreneur’s social identity on both the evaluation of the entrepreneur and the evaluation of the new venture. First, we argue that stereotypical social categories result in women entrepreneurs being evaluated more negatively than their male counterparts. Next, we complement the social identity perspective with insights from signaling theory to argue that the signals about the quality of the new venture will be evaluated differently depending on the gender of the entrepreneur.

Social categorization and signals between women entrepreneurs and angel investors.

One important component of the social identity theory is the process of social categorization. Social categorization is the cognitive basis of group behavior, and in particular, the categorization of in-group versus out-group takes away unique individuality and replaces it with stereotypical prototypes (Hogg and Terry, 2000). Stereotypical prototypes are idealized conceptualizations that in the context of group membership take the form of exemplary individuals or idealized types. A critical feature of stereotypical prototypes is that they maximize in-group similarities and out-group differences (Hogg and Terry, 2000).

In the context of angel investing, we argue that in their decision-making process, angel investors may be influenced by the widely shared beliefs about characteristics attributed to men and women, and the appropriateness of their behavior in the investment setting (Fiske, 2000). As Eagly and Karau (2002, p. 574) explained, “a potential for prejudice exists when social perceivers hold a stereotype about a social group that is incongruent with the attributes that are thought to be required for success.” Stereotyped characteristics influence classification of different occupations and jobs as masculine or feminine (Cejka and Eagly, 1999). Entrepreneurship is traditionally seen as a male preserve; the image of the entrepreneur is more often male, and men tend to own investor-preferred businesses that are larger, more profitable and faster growing (Brush et al., 2006 Ahl,2006; Bruni et al., 2004; Bird and Brush, 2002).

Research on the masculine-stereotyped behaviors of entrepreneurs suggests that the perceived incongruity between entrepreneurship and femininity creates roadblocks for women entrepreneurs (Gupta et al., 2009; Eddleston et al., 2016; Justo et al, 2018; Wheadon and Duval-Couetil, 2017). A recent study examining how governmental venture capitalists socially construct gender stereotypes when assessing the potential of male and female entrepreneurs found that women’s potential may be undermined whereas men’s potential is underpinned, because masculine gender stereotypes prevail in the context (Malmström et al., 2017). Investors, therefore, may not be biased against funding women entrepreneurs simply because they are female, but rather because they do not fit the perceived appropriate social category. In other words, based on stereotypical social categories, male “in-group” angel investors will seek to find negative aspects of a female “out-group” women entrepreneurs, which translates into women entrepreneurs being less positively perceived in the eyes of male angel investors. Formally we hypothesize that:

H1.

Female entrepreneurs receive more negative than positive comments from angel investors as compared to male entrepreneurs.

H2.

Female entrepreneurs receive more negative than positive comments from angel investors about their individual characteristics as compared to male entrepreneurs.

Early-stage angel investing is fraught with uncertainty (Wiltbank et al., 2009). To overcome information asymmetry and assuage investors’ concerns about adverse selection and moral hazard, entrepreneurs send signals about the quality of the new venture. For signals to be effective they need to be easily observable, which refers to the extent to which outsiders are able to notice the signal (Connelly et al., 2011); reliable, which denotes the ability of the signal to be trusted (Connelly et al., 2011) and costly to imitate, which proxies the value of the signal. Signals allow investors to differentiate between entrepreneurs and their ventures, discern among options and confer benefits accordingly. This occurs under conditions of uncertainty, where the signaler and recipient often have conflicting interests, and the signal recipient stands to gain from receiving accurate information.

Social grouping is an uncertainty reduction mechanism used by angel investors. In the absence of reliable market and financial data about the investment opportunity, angel investors tend to rely on less explicit social factors in the interpretation of signals sent by the entrepreneurs to form a judgment about the quality of the entrepreneur and their new venture. Because the socially accepted image of the successful entrepreneur has been grounded in male gender stereotypical notions of “masculinity,” or stereotypes of entrepreneurs as male, angel investors may develop biased social judgments of the legitimacy of the entrepreneur and, by association, the quality of the new venture (Eagly and Karau, 2002; Heilman, 2001). For example, in a series of studies including three entrepreneurial pitch competitions in the USA and two controlled experiments, Brooks et al. (2014) documented that investors prefer pitches presented by male entrepreneurs compared to pitches presented by female entrepreneurs, even when the content of the pitch is the same. Stereotypes reduce uncertainty by providing the angel investor with a form of moral support that lends credence and validation to preconceived judgments and prototype-based depersonalization.

In sum, we argue that in the investment context, social group categorization can lead angel investors to interpret the signals of a quality venture investment differently between men and women. Formally:

H3.

Angel investors interpret signals of new venture quality less favorably for female entrepreneurs than for male entrepreneurs.

Methodology

Our data come from the venture proposals submitted by entrepreneurs to a large angel-financing group located in the northeast USA, between 2007 and 2016, and the angel investors’ summary sheets following the entrepreneur’s presentation to the group. Like many angel groups, the group uses a four-step decision-making process (www.angelcapitalassociation.org/entrepreneurs/faqs). Initially, the investment proposal is reviewed by an administrative committee which makes a decision to “desk reject” the proposal or to move it on to a screening committee presentation. This is the stage where the largest number of entrepreneurial proposals is removed from consideration. Some research shows that the reject rate can be as high as 73 per cent at this stage (Riding et al., 1993). A small set of proposals is successful at passing the “desk reject” stage and moving onto the screening stage of the decision-making process. Here, the new ventures receive a second review as well as some coaching about ways to present the venture to the entire angel investment group. Ventures that are successful in passing the screening presentation are invited to present their funding proposal to the larger group of angel investors. If the presentation at the larger angel investment group meeting is successful, ventures are eligible to move to the due diligence stage of the investment process. Given the extensive review that new ventures undergo prior to the due diligence process, firms that successfully pass due diligence often receive some form of angel assistance or investment (Freear et al., 1996). Figure 2 presents a model of the angel investment decision-making process.

Within this particular angel group, the membership grew from 40 to 77 during the time of this study, with the percentage of female angel investors rising from 7 to 10. At any one time, an average of 50 members attends a meeting, and of those 50 angel investors in the audience, an average three or four are women. In addition, only 2 per cent of the companies were referred to the angel group by women. Therefore, while there are women angel investors in our focal angel investment group, they represent a small percentage of the overall population of the group of angel investors, indicating the reduced role women play in the angel investment process.

Large group presentations are limited to 20 min followed by 10 min of question and answer. Following the presentations, all presenters are asked to leave the room and there is a closed discussion among the members about the strengths (positives) and weaknesses (negatives) of each of the presentations and businesses. The moderator for the group asks for the positives or strengths for the first venture. These are offered by group members and recorded in a summary sheet. Then, the moderator asks the group to list the negatives or weaknesses for the first venture, and these are also recorded in the Summary Sheet by the group administrator. The process continues for each of the venture presentations. Investors then complete a form indicating whether they would lead due diligence, are interested in investing or would like to stay informed. All these information are aggregated for the meeting and into the Summary Sheet which is distributed to all members. No single angel investor is identified in any specific comment.

We tracked angel investors’ positive and negative comments across a number of categories, related to business characteristics and individual entrepreneur characteristics. Business characteristics included: market opportunity, product quality, growth potential, legitimacy (strategic partners, endorsements and customers) and deal structure. The choice of these categories was based on earlier work by Maxwell et al. (2011), which identified critical factors in the early-stage decision-making process for angel investors. While most of the factors were firm-level, some referred to the characteristics of the individual entrepreneur. Following previous research, we added categories which captured individual attributes such as the passion/energy of the entrepreneur, managerial experience, management team and presentation/communication skills (Cardon et al., 2016). To code the comments based on these categories, we used two trained coders for inter-rater reliability, and in the case of a disagreement among coders, one of the principal investigators acted as tie-breaker.

To test our hypotheses, we implemented analysis of variance and logit (for binary outcomes) and Poisson (for count data) regression specifications with appropriate controls at the firm, and industry level using the ANOVA, LOGIT and POISSON procedures in STATA. To test H1, we examined the difference in the count of comments (total, positive and negative) that angel investors made about the presentations of male-led versus female-led entrepreneurial ventures. To test H2, we considered the likelihood of an angel investor recording a positive or negative comment about the entrepreneur’s individual characteristics. To test H3, following prior empirical work on angel investing (Maxwell et al., 2011), we tracked signals about five aspects of new venture quality: human capital, milestones, intellectual property, legitimacy and financial performance. Human capital signals are comments related to expertise, leadership skills or know how. Milestones include comments about regulatory hurdles, such as FDA clearance, and customer acquisitions. If a comment is recorded about a company having a patent or a patent pending, it indicates an intellectual property signal. Legitimacy signals come from comments about partnerships with well-known companies. Finally, financial performance signals include comments about cost advantages over competitors, high margins and high return on investment.

Keeping with the definition of signals from the signaling theory (Connelly et al., 2011), we extracted the signals sent by the entrepreneur from the “Competitive Advantage” statement in the investment proposal sheet, provided by the entrepreneur on the day of the presentation. Our decision to use the statement of competitive advantage from the investment proposal was made because we believed this would be an objective statement indicating the new venture quality. Research shows that funders look for ventures that can “keep out the competition” and compete in markets effectively (Van Osnabrugge and Robinson, 2000; Harrison et al., 2010). Then, to determine whether the signals were received by the angel investors, we examined the presentation summaries to determine if the competitive advantage factors highlighted by the entrepreneur were mentioned as either a “pro” or a “con” (e.g. positive and negative), in the qualitative investor comments.

To account for other factors that may affect our dependent variable, we used a number of control variables, such as average revenues of the firm, company age, capital sought, previous capital raised, industry sector, presentation year and the stage of company development (Harrison et al., 2010; Wiltbank et al., 2009). An overview of the data used in this paper is given in Table II.

Results

Descriptive statistics are reported in Table III. Of the 358 ventures in our overall sample, 55 of these (15.36 per cent) were women-led ventures. The companies applying for angel funding were new and small ventures, which were on average less than three years old (mean = 2.61 years) and had fewer than ten employees (mean =6.18). Over a third of the ventures (35 per cent) operated in the technology sector and close to a third (31 per cent) operated in the life sciences sector, including biotechnology, medical devices and equipment and healthcare. The remainder operated in the consumer products/services sector. We did not have correlation issues, and VIF tests indicate that our mean VIF is 1.39, with the highest VIF at 3.41, well below the rule of thumb of 10 for acceptable VIFs (Ryan, 2008).

In Table IV, we examine the gender dummy variable to see the relationship with each of the following dependent variables, which are count variables of comments recorded by the angels: total positive comments, total negative comments, total comments and the net comments (pros-cons). We see that women entrepreneurs do receive more positive, negative and total comments than men entrepreneurs. However, this difference is not statistically significant when examining both with ANOVA analysis and with Poisson analysis (Table IV). Therefore, we do not find support for H1. In robustness tests, outlined below, we also tested whether women were more likely to receive a dominance of negative comments to total comments, by calculating the proportion of comments received that were negative.

To better understand the nuances of the data, we examine the individually coded comments about personal characteristics (both negative and positive) made by the angel investors about the entrepreneur. We find that angel investors are more likely to make a positive comment about the management team surrounding the entrepreneur if the entrepreneur is female (Table V). While this result does not provide direct support for H2, it indicates interesting biases surrounding women entrepreneurs, which will be explored in the discussion section.

We next investigate the likelihood of entrepreneurs sending signals and angels recording signals about the following characteristics: human capital, milestones, IP, legitimacy and financial model. In examining the results of H3 (Table VI), we find that women are less likely than men to send signals about the financial model of their business. However, angel investors do not seem to pick up on this in the presentation, as we find no significant difference in comments recorded about the new venture’s financial model. On the other hand, we do not find a statistically significant difference in the likelihood of a female or male entrepreneur sending a signal about legitimacy. However, we find that angel investors are significantly less likely to record a comment (i.e. pick the signal) about the legitimacy of female-led new ventures. Across the other categories, we do not find a significant difference.

Robustness checks

Above, we used a count variable to test whether women-led businesses receive more negative comments (H1) and a logit model to test whether the angels were more likely to record a negative comment about the individual characteristics of women entrepreneurs (H2). However, there could potentially be an effect of the weight of positive and negative comments to total comments. For instance, women may receive the same number of negative comments as men, but a higher proportion of negative to total comments. In robustness checks, we tested proportion variables of negative comments to total comments for H1 and negative comments to total comments about individual characteristics for H2. We did not find any evidence that women receive a higher proportion of negative comments.

Discussion

Early-stage gender-based funding inequities have long-term detrimental consequences for women starting and growing their businesses, in that they essentially leave women out of growth-oriented entrepreneurship (Brush et al., 2014, 2018). This paper is based on the premise that women receive less early-stage funding not because of the type of business they start, or because of their lack of ability to run a high-growth-oriented new venture, but instead they are left out because they are female. Using the social identity theory, we argue that gender differences result in stereotyping and discrimination. Therefore, we explore whether the gap in women’s funding at the angel level is less about the type of business formed, and more about the gender role stereotype of the entrepreneur.

Our findings provide some support to our ideas. While we did not see the blatant and sweeping gender biases that the low levels of women’s access to early-stage funding imply, we did find some limited support around women’s need for additional legitimacy (Aldrich and Fiol 1994) as compared to men. Our results show that while entrepreneurs think that they are conveying the message that their venture is viable and ready to be funded, as evidenced by the endorsement of strategic partners, when women are sending that message, angel investors are less likely to record it. Since there is no difference in the likelihood of sending a signal about legitimating factors between men and women entrepreneurs, why is it that signals are less likely to be recorded when the sender is a woman? One explanation is that the differences are linked to expectations about gender behavior. As noted earlier, social identity theory argues for in-group and out-group stereotypes as a mechanism for uncertainty reduction, and when it comes to receiving angel investment, women are the out-group. In other words, the signals of legitimacy sent by women may be less salient because of their social identity. Claiming entrepreneurial legitimacy requires careful identity work which takes account of the dominant male entrepreneurial discourse (Marlow and McAdam, 2015). This suggests that legitimacy is particularly problematic for women entrepreneurs who are trying to attract angel investment for their new ventures.

While being female did not have a significant direct effect on the lack of positive evaluations of women entrepreneurs, we find that angel investors commented significantly more frequently on the management team surrounding the women-led businesses. One explanation for this is that, based on the social identity theory tenets, angel investors show subtle bias by having different expectations about male and female leadership behavior. In their study on the discourses of entrepreneurial leadership, Dean and Ford (2017, p. 181) noted:

The type of behaviour thus deemed appropriate for entrepreneurs coincides with images of masculinity and centres around rationality, measurement, objectivity, control and competitiveness. Both mainstream literature and the reported practice of entrepreneurial leadership have consistently failed to question its gendered nature […] It is the masculine voice that governs entrepreneurial discourse and exchange, the worlds of business and the economy.

It is against these expectations of entrepreneurial leadership behavior that women entrepreneurs are judged. In particular, if women entrepreneurs are perceived to have inferior leadership potential, then investors would pay particular attention to the qualities of the entire top management team, expected to support the woman leader. Hence the significantly higher likelihood of entering a positive comment about the top management team for women-led new ventures.

An alternative explanation for the lack of significant differences in the negative comments about men and women-led new ventures may be that there is a social desirability not to make negative comments about women in a public setting. Because there were women investors present in the room, it would be politically correct to say something positive, or not to say something negative about women entrepreneurs, in order not to be perceived as overly critical of women.

From a theoretical perspective, our findings suggest that, in the context of angel investing, there is a subtle bias that follows from the perceived stereotype between being female and the ability to lead a legitimate new venture. Thus, our study tests the tenets of the social identity theory by finding that mostly male angel investors act in accordance to their gender prescribed roles when they evaluate businesses presented by women entrepreneurs providing evidence of “in-group” and “out-group” effects and stereotypes.

By overlaying a social identity perspective on signaling theory arguments, our study also contributes to signaling theory. Our findings strongly suggest that in addition to asking, “What signals are being sent?”, we should also be asking, “Who is sending the signal?” and just as importantly, “Who is receiving the signal,” as signal interpretation may be biased by the social identity of both the sender of the signal and the recipient of the signal.

Empirically, our findings contribute to a newly developing stream of research on gender effects in private equity financing which has documented that investors overwhelmingly prefer pitches presented by male entrepreneurs, even when the content of the pitch is the same (Brooks et al., 2014; Balachandra et al., 2017). While prior studies have tracked the outcome of the investors’ social judgment process, our data allow us to reconstruct the formation of the angel investors’ social judgment based on the investors’ own narrative.

Implications and conclusions

Our study is not without limitations, which need to be borne in mind when interpreting its results. First, the data are drawn from a series of presentations made to an east coast US angel investment group. While we are confident that other angel groups in the same geographic area have similar decision-making processes, it is quite possible that there are differences between east and west coast angels and USA versus other international angel groups. Second, our data come from statements written by entrepreneurs and comments subsequently made by angel investors, thus it is possible that intervening factors between these two sources of data may confound our findings. Finally, unlike Malmström et al. (2017) who used both survey and qualitative statements to uncover gender biases at the venture capital level, in a sample of Swedish women entrepreneurs, we focus solely on earlier stage angel investors and combine our data into distinct categories. It may be that we have missed subtle secondary biases in the data with our coding methods.

Limitations notwithstanding, our findings continue the conversation about biases toward women in early stage financing by using a social identity lens to look at the way in which adopted identities lead to particular outcomes and stereotypes. We use the context of angel investing to test these ideas, finding some support for our contention that gender is pivotal when angels are making investment decisions. For researchers, our study suggests that gender should not be used solely as a control variable, but instead should be the focus of the inquiry itself. For practitioners, our study reminds women seeking angel investment that they are not playing on a level field and so they should do all that they can to enhance the legitimacy of themselves and their ventures. Finally, given the important role played by women in entrepreneurship, we hope that others will add to this dialogue.

Figures

Social identity theory

Figure 1.

Social identity theory

Early-stage angel investment decision-making process

Figure 2.

Early-stage angel investment decision-making process

Review of the literature on women and angel investing

Year Author Title Journal Data Findings Supply/ Demand side
2002 Brush, Carter,
Greene, Hart and Gatewood
The role of social capital and gender in linking financial suppliers and entrepreneurial firms:
A framework for future research
Venture Capital Conceptual Develops a social capital based conceptual framework to explore women’s role in supply
and demand of equity capital
Supply and demand
2003 Carter, Brush, Greene, Gatewood and Hart Women entrepreneurs who break through to equity financing: The influence of human, social and financial capital Venture Capital Survey of 235 women business owners Findings suggest that women with a graduate education were more likely to use outside equity financing. Social capital had no direct effect on increasing likelihood of using equity Demand
2004 Amatucci and Sohl Women entrepreneurs securing business angel financing: Tales from the field Venture Capital Interview data Successful strategies of women including pre-investment processes, trust, comprehensiveness, the post-investment relationship and gender are examined Demand
2006 Orser, Riding and Manley Women Entrepreneurs and Financial Capital Entrepreneurship Theory and Practice Survey of 2,800 Canadian business owners Businesses with women holding the majority ownership were significantly less likely to seek equity capital Demand
2007 Harrison and Mason Does gender matter? Women business angels and the supply of entrepreneurial finance Entrepreneurship Theory and Practice Survey data from 40 angel investors Descriptive data on women angel investors Supply
2007 Becker Blease and Sohl Do women-owned businesses have equal access to angel capital? Journal of Business Venturing Survey of 174 angel groups Findings suggest there are no significant differences between the rates at which women-owned ventures are funded compared to men-owned ventures nor is there evidence that women entrepreneurs must surrender greater equity ownership in exchange for investment dollars than men entrepreneurs Demand
2007 Sohl and Hill Women Business Angels: Insights from
Angel Groups
Venture Capital Survey of 19 angel groups Explores the characteristics of women angel investors Supply
2008 Becker-Blease and Sohl Confidence and Angel Investors: Does Gender Matter? Frontiers of Entrepreneurship Research Survey data 2000-2005 Women angels have lower levels of confidence compared to men Supply
2009 Coleman and Robb A comparison of new firm financing by gender:
Evidence from the Kauffman Firm Survey data
Small Business Economics Kauffman Firm Survey Women start their businesses with
significantly lower levels of financial capital than
men, and women go on to raise significantly lower amounts of incremental debt and equity in years two and three
Demand
2010 Becker-Blease and Sohl The effect of gender diversity on angel group investment Entrepreneurship Theory and Practice Survey of between 33 and 47 Angel groups between 2000 and 2006 Gender is a significant predictor of group investment behavior and that the proportion of women angels in the group has a negative though nonlinear effect on investment likelihood Supply
2014 Manolova, Edelman and Brush Access to early stage financing: The case of the missing women Frontiers of Entrepreneurship Research Data from 88 ventures led by women coupled with interview data Findings indicate women-led new ventures are equally likely to pass the administrative review stage of the angel investing process. This suggests that the when it comes to angel financing, the gender of the entrepreneur is not critical. The interviews with entrepreneurs provide insights into the entrepreneurial financing decisions from the perspective of the entrepreneur Demand
2015 Harrison, Botelho and Mason Theory of women’s participation in the angel market Frontiers of Entrepreneurship Research Data from UK survey of 238 angels (28 women) and 71 angel groups Findings show that women investors less likely to invest in seed stage, made smaller investments and less likely to invest in innovative ventures. Women much less likely to become members of groups. Women do not participate as fully using knowledge and opinions, and they tend to be younger, less often having entrepreneurial backgrounds and board level experience Supply

Description of variables

Hypothesis Dependent variable
H1 Comments total pros (count) Total positive comments made by angel investors
Comments total cons (count) Total negative comments made by angel investors
Comments total number (count) Total comments made by angel investors
H2 Specific comments by angel investor about entrepreneur Positive and negative comments about CEO passion/energy, managerial experience, management team, presentation/communication
H3 Signal sent by entrepreneur 5 different signals, human capital, milestones, IP, legitimacy, finance
Signal received by angel investor 5 different signals, human capital, milestones, IP, legitimacy, finance
Independent variable
Gender dummy If entrepreneur is female
Control variables
Log of average revenues Log of average revenues of enterprise
Log of capital seeking Amount sought from angel investors
Log of previous capital Current funding
Employees Number of employees
Company age Company age
Industry sector Industry sector: 0 is consumer products, 1 is technology and 2 is life sciences
Stage Dummy variable for stage of development: stage 0 is product in development, stage 1 is prototype ready, stage 2 is product ready, stage 3 is up to 500k in TTM revenues, stage 4 is up to 1m in TTM revenues, stage 5 is up to 3m in TTM revenues and stage 6 is up to 5m in TTM revenues
Presentation year Year presentation was made

Summary statistics (mean and standard deviation) for variables used in analysis, and disaggregated by male and female entrepreneurs

Variables Full sample Male entrepreneurs Female entrepreneurs
Mean SD Mean SD Mean SD
Log of average revenues 15.93 1.87 15.97 1.75 15.68 2.42
Log of capital seeking 13.28 2.61 13.32 2.47 13.07 3.27
Log of previous capital 12.50 3.27 12.56 3.19 12.21 3.69
Employees 6.18 5.29 6.17 5.40 6.24 4.75
Company age 2.69 2.61 2.72 2.71 2.60 2.17
Industry sector 0.99 0.80 0.97 0.80 1.11 0.81
Stage 1.93 1.25 1.95 1.28 1.85 1.07
Total positive comments 4.39 1.61 4.36 1.62 4.53 1.54
Total negative comments 3.72 1.64 3.69 1.65 3.85 1.54
Total comments 8.10 2.54 8.05 2.57 8.38 2.43
Positive comment about CEO passion/energy 0.22 0.42 0.22 0.42 0.24 0.43
Positive comment about managerial experience 0.20 0.40 0.20 0.40 0.20 0.40
Positive comment about mgmt. team 0.15 0.35 0.13 0.34 0.24 0.43
Positive comment about presentation/communication 0.14 0.35 0.14 0.35 0.16 0.37
Negative comment about CEO passion/energy 0.05 0.21 0.04 0.20 0.07 0.26
Negative comment about managerial experience 0.08 0.27 0.08 0.28 0.07 0.26
Negative comment about lack of mgmt. team 0.06 0.25 0.07 0.26 0.04 0.19
Negative comment about presentation/communication 0.13 0.34 0.13 0.34 0.15 0.36
Signal recorded by angels about human capital 0.71 0.70 0.69 0.68 0.84 0.79
Signal recorded by angels about legitimacy 0.18 0.40 0.19 0.41 0.11 0.31
Signal recorded by angels about IP 0.08 0.27 0.07 0.26 0.09 0.29
Signal recorded by angels about finance 0.28 0.52 0.27 0.49 0.35 0.64
Signal recorded by angels about milestones 0.46 0.62 0.46 0.63 0.45 0.57
Signal sent by entrepreneurs about human capital 0.14 0.35 0.13 0.34 0.18 0.39
Signal sent by entrepreneurs about milestones 0.16 0.37 0.16 0.37 0.16 0.37
Signal sent by entrepreneurs about IP 0.66 0.47 0.66 0.47 0.67 0.47
Signal sent by entrepreneurs about legitimacy 0.16 0.37 0.15 0.36 0.18 0.39
Signal sent by entrepreneurs about finance 0.23 0.42 0.24 0.43 0.13 0.34
Observations 358 298 55

Models examining the effect of entrepreneur gender on the number of comments made by the angel investors about the entrepreneurs

Dependent variable Comments total pros Comments total cons Comments total no. Net comments (Pros-Cons)
Gender dummy - 1 if female 0.01 (0.05) 0.04 (0.06) 0.03 (0.04) −0.10 (0.30)
Industry sector = 1 −0.12* (0.05) 0.04 (0.05) −0.05 (0.04) −0.66* (0.28)
Industry sector = 2 −0.09+ (0.06) −0.07 (0.06) −0.08+ (0.05) −0.18 (0.32)
Log of average revenues −0.00 (0.01) 0.03* (0.01) 0.01 (0.01) −0.12* (0.05)
Log of capital seeking 0.03* (0.01) 0.01 (0.02) 0.02+ (0.01) 0.07+ (0.04)
Log of previous capital −0.01 (0.01) −0.00 (0.01) −0.01 (0.01) −0.05 (0.03)
Employees −0.00 (0.00) 0.00 (0.01) 0.00 (0.00) −0.01 (0.03)
Company age 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) −0.01 (0.05)
Stage = 1 0.08 (0.07) 0.01 (0.08) 0.05 (0.06) 0.29 (0.39)
Stage = 2 0.10 (0.07) −0.01 (0.08) 0.05 (0.06) 0.46 (0.40)
Stage = 3 0.10 (0.08) 0.03 (0.09) 0.07 (0.07) 0.30 (0.43)
Stage = 4 0.14 (0.09) −0.10 (0.11) 0.03 (0.08) 1.02+ (0.52)
Stage = 5 0.26+ (0.14) −0.12 (0.30) 0.09 (0.17) 1.58+ (0.87)
Stage = 6 −0.24 (0.20) 0.05 (0.23) −0.08 (0.18) −1.20 (1.07)
Constant 1.08** (0.33) 0.44 (0.35) 1.50*** (0.31) 2.16+ (1.15)
Observations 324 324 324 324

Model examining the effect of entrepreneur gender on comments made about personal characteristics

Dependent variables: Positive comment Negative comment Net comments
CEO passion/ energy Managerial experience Mgmt. team Presentation/ communication CEO passion/ energy Managerial experience Mgmt. team Presentation/ communication CEO passion/ energy Managerial experience Mgmt. team Presentation/ communication
Gender dummy - 1 if female 0.20 (0.26) 0.02 (0.30) 0.53**** (0.29) 0.03 (0.32) 0.48 (0.70) 0.33 (0.51) −0.49 (0.82) −0.15 (0.35) 0.03 (0.08) −0.01 (0.07) 0.11 (0.07) 0.04 (0.07)
Industry sector = 1 −0.29 (0.25) 0.08 (0.26) 0.07 (0.35) 0.23 (0.29) −0.98 (0.76) 0.63 (0.54) 0.95 (0.68) 0.36 (0.37) −0.02 (0.07) −0.02 (0.07) −0.05 (0.06) −0.02 (0.07)
Industry sector = 2 −0.76* (0.34) −0.41 (0.33) 0.39 (0.33) −0.53 (0.46) −0.17 (0.75) 0.49 (0.57) 0.51 (0.80) −0.05 (0.43) −0.15**** (0.08) −0.11 (0.07) 0.02 (0.07) −0.05 (0.07)
Log of average revenues 0.03 (0.04) 0.08 (0.12) −0.06 (0.07) 0.14 (0.10) −0.01 (0.09) 0.04 (0.09) 0.15 (0.12) 0.03 (0.09) 0.01 (0.01) 0.01 (0.02) −0.01 (0.02) 0.01 (0.01)
Log of capital seeking 0.08**** (0.05) 0.13 (0.10) −0.06 (0.05) 0.06 (0.06) −0.12* (0.06) 0.12* (0.06) −0.04 (0.25) 0.02 (0.07) 0.03** (0.01) 0.01 (0.01) −0.01 (0.01) 0.00 (0.01)
Log of previous capital 0.01 (0.03) 0.03 (0.06) 0.00 (0.05) −0.10** (0.03) 0.57* (0.23) −0.02 (0.05) −0.15** (0.05) −0.02 (0.07) −0.01 (0.01) 0.01 (0.01) 0.02 (0.01) −0.01 (0.01)
Employees 0.00 (0.03) −0.00 (0.02) 0.03 (0.03) 0.01 (0.03) −0.17* (0.07) −0.04 (0.06) 0.04 (0.05) −0.00 (0.03) 0.01 (0.01) 0.00 (0.01) 0.00 (0.01) 0.00 (0.01)
Company age 0.04 (0.03) −0.01 (0.04) −0.04 (0.08) −0.15* (0.07) 0.11**** (0.06) −0.02 (0.08) 0.17* (0.08) 0.05 (0.06) 0.00 (0.01) 0.00 (0.01) −0.02 (0.01) −0.02 (0.01)
Stage = 1 −0.41 (0.40) −0.05 (0.42) 0.49 (0.52) 0.05 (0.68) −0.04 (1.07) 0.28 (0.72) 0.88 (0.66) 0.47 (0.73) −0.05 (0.10) −0.03 (0.10) −0.01 (0.09) −0.06 (0.09)
Stage = 2 −0.40 (0.41) −0.20 (0.43) 0.06 (0.51) 0.75 (0.58) 0.42 (1.08) −0.26 (0.82) 0.23 (0.80) 0.85 (0.71) −0.08 (0.10) −0.02 (0.10) −0.05 (0.09) −0.03 (0.09)
Stage = 3 −0.28 (0.39) −0.24 (0.47) −0.10 (0.61) 1.17**** (0.64) 0.48 (1.06) 0.02 (0.84) 0.17 (1.12) 0.49 (0.72) −0.05 (0.12) −0.05 (0.12) −0.07 (0.10) 0.05 (0.10)
Stage = 4 −0.24 (0.46) −0.24 (0.59) −1.11 (1.13) 1.64* (0.72) −0.04 (1.22) 0.27 (0.90) 0.30 (1.26) −0.89 (1.15) −0.07 (0.16) −0.07 (0.15) −0.14 (0.10) 0.26* (0.11)
Stage = 5 −0.31 (0.65) −13.54*** (1.22) 1.93 (1.26) 2.41* (1.02) −13.94*** (1.96) −11.58*** (1.58) −16.64*** (1.92) −11.93*** (1.27) 0.01 (0.31) −0.11 (0.21) 0.41 (0.38) 0.46 (0.35)
Stage = 6 −1.12 −0.46 0.85 1.53 −11.39*** 1.75 −1.02 −12.45*** −0.27 −0.24 0.12 0.20
Constant −1.76 (1.10) −19.36*** (2.33) −1.46 (1.86) −4.52* (1.90) −7.61* (3.44) −4.03* (1.81) −3.68 (4.73) −3.14**** (1.85) 0.08 (0.31) −0.49 (0.34) 0.21 (0.33) 0.09 (0.27)
Observations 324 324 324 324 324 324 324 324 324 324 324 324
Notes:

Robust standard errors in parentheses;

***

p < 0.001;

**

p < 0.01;

*

p < 0.05;

****

p < 0.1; year dummies are also included but not displayed

Models examining the effect of entrepreneur gender on the likelihood of both sending and recording a signal about human capital, milestones, IP and legitimacy and financial model

Dependent variables: Signal recorded by angels about human capital Signal sent by entrepreneurs about human capital Signal recorded by angels about milestones Signal sent by entrepreneurs about milestones Signal recorded by Angels about IP Signal sent by entrepreneurs about IP Signal recorded by angels about legitimacy Signal sent by entrepreneurs about legitimacy Signal recorded by angels about finance Signal sent by entrepreneur. about finance
Gender dummy - 1 if female 0.25 (0.35) 0.34 (0.43) 0.26 (0.34) 0.08 (0.47) −0.14 (0.62) −0.27 (0.35) −0.92* (0.48) 0.25 (0.41) 0.01 (0.38) −0.88**** (0.48)
Log of average revenues 0.06 (0.06) 0.06 (0.09) −0.03 (0.06) −0.01 (0.09) −0.02 (0.07) 0.13**** (0.07) −0.07 (0.07) 0.12 (0.10) −0.01 (0.07) −0.03 (0.06)
Log of capital seeking 0.07 (0.06) 0.07 (0.09) 0.12 (0.10) 0.26**** (0.14) −0.20 (0.12) −0.02 (0.06) −0.14 (0.09) −0.01 (0.09) 0.02 (0.08) −0.01 (0.07)
Log of previous capital 0.01 (0.04) −0.07 (0.05) −0.03 (0.05) 0.07 (0.08) 0.01 (0.07) 0.06 (0.05) −0.05 (0.05) −0.04 (0.05) −0.05 (0.05) −0.09* (0.04)
Employees 0.05**** (0.03) −0.02 (0.05) −0.01 (0.03) −0.02 (0.04) −0.05 (0.11) −0.03 (0.03) 0.04 (0.03) 0.021 (0.04) 0.02 (0.04) 0.05 (0.03)
Company age −0.03 (0.05) −0.16**** (0.08) −0.01 (0.05) 0.02 (0.06) 0.12 (0.08) −0.00 (0.05) 0.05 (0.06) −0.04 (0.08) −0.03 (0.08) 0.03 (0.06)
Industry sector = 1 −0.20 (0.30) 0.07 (0.46) −0.85** (0.31) −0.30 (0.44) −0.11 (0.58) 0.24 (0.32) −0.19 (0.38) 0.08 (0.40) −0.28 (0.36) −0.69* (0.35)
Industry sector = 2 −0.94** (0.35) −0.26 (0.53) −0.69**** (0.36) −0.23 (0.52) −0.38 (0.79) 0.83* (0.40) −0.16 (0.43) −0.48 (0.53) 0.30 (0.43) −0.39 (0.41)
Stage = 1 −0.22 (0.43) 0.20 (0.62) 0.96**** (0.54) 0.70 (0.73) −0.99 (0.62) 0.06 (0.51) −0.08 (0.50) 0.69 (0.71) 0.88 (0.56) 0.19 (0.50)
Stage = 2 −0.17 (0.45) −0.42 (0.65) 0.99**** (0.55) 0.47 (0.73) −1.88* (0.89) 0.34 (0.53) −0.56 (0.52) 0.45 (0.74) 1.18* (0.59) 0.25 (0.55)
Stage = 3 −0.07 (0.50) 0.037 (0.73) 1.65** (0.61) 0.79 (0.84) −1.47**** (0.84) −0.11 (0.55) −0.27 (0.56) 0.84 (0.77) 1.00 (0.69) 0.49 (0.59)
Stage = 4 −0.52 (0.58) 0.06 (1.14) 1.78* (0.71) 0.96 (0.85) −1.99**** (1.10) 0.30 (0.70) −0.55 (0.86) 0.14 (1.06) 1.29 (0.91) −0.52 (0.84)
Stage = 5 1.90 (1.54) 0.79 (1.51) 0.50 (1.73)
Stage = 6 −1.54**** (0.93) 1.51 (1.36) 1.49 (1.10) 2.12**** (1.27) 1.39 (1.29) −1.16 (1.34) 0.68 (1.42) 0.87 (1.59) −1.22 (1.51)
Constant −0.55 (1.52) −2.94 (2.45) −2.76 (1.77) −6.23* (2.85) 1.94 (2.40) −2.18 (1.65) 1.18 (2.10) −3.27 (2.63) −0.80 (1.71) 0.311 (1.72)
Observations 322 322 322 324 259 322 324 322 322 324
Notes:

Robust standard errors in parentheses; ***p < 0.001;

**

p < 0.01;

*

p < 0.05;

****

p < 0.1; year dummies are also included but not displayed

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Acknowledgements

The authors gratefully acknowledge Marsden Ashley and Joe Haftel for their help with data collection for this project and the Ewing Marion Kauffman Foundation for their generous financial support.

Corresponding author

Linda F. Edelman can be contacted at: ledelman@bentley.edu