Gender Gap in Business Angel Financing

1 Introduction

Despite recent progress in women’s entrepreneurial participation in the creation of new ventures, women-owned businesses remain significantly under-represented in the economy at the global level, receiving less than 3 % of the informal equity funds (Ewens and Townsend 2020; Pitchbook 2020), particularly with regards to high-growth ventures (Brush et al. 2004; Robb, Coleman, and Stangler 2014; Scott and Shu 2017). While accessing equity financing is particularly challenging for most early-stage start-ups, women-led ventures face an even lower likelihood of obtaining such resources compared to their male counterparts. This imbalance is even more pronounced for high-growth ventures (Guzman and Kacperczyk 2019; Henry et al. 2022), given that they possess fewer initial financial resources on average (Johnson and Storey 1993; Robichaud, Cachon, and McGraw 2019) and encounter greater obstacles in accessing external financing (Muravyev, Talavera, and Schäfer 2009; Roomi, Harrison, and Beaumont-Kerridge 2009).^[1]

To address these challenges and provide financial support to emerging entrepreneurial ventures, informal investors, such as Business Angels (BAs), have gained prominence, becoming the primary sources of critical seed and early-stage equity funding for start-up companies (Bessière, Stéphany, and Wirtz 2020; Brush et al. 2018; Cavich and Chinta 2021; Mason and Harrison 2000; Wu, Yuan, and Wei 2012, among others). Within this framework, the alignment of BAs’ investment preferences with the diverse characteristics of entrepreneurs is essential to facilitate the meeting of the financial needs of new ventures. Gender is one of the most relevant attributes influencing the decision-making processes of BA investors (Amatucci and Sohl 2004), impacting investment decisions and post-investment relationships from both the investor and entrepreneur perspectives. In light of these considerations, some recent studies have delved into the gender dynamics within the BA market, exploring whether perceptions of entrepreneurs’ credibility and profitability vary between men and women entrepreneurs from the demand side, as well as between men and women investors from the supply side (see Serwaah and Shneor 2021 for a review).

Regarding investment strategies, gender disparities on the supply side might result in funding biases against entrepreneurs, ultimately impacting their outcomes and performance. Although investors may possess hard information about target firms, most of their knowledge is soft, implicit, and not fully-codifiable. This type of knowledge can only be effectively recognized by individuals who are well-acquainted with the sector and the economic context in which they operate, and who engage in personal interactions (Buttner and Rosen 1988; Harrison and Mason 2007; Jiang et al. 2024; Leonelli 2024; Li and Yang 2020; Lipshitz and Shulimovitz 2007; McNamara and Bromiley 1997; Zane and DeCarolis 2024).

At the same time, the literature investigating differences in BA investment behaviors based on gender presents some contradictions, partly due to challenges in obtaining accurate information about BA investors’ characteristics, including their gender. Some studies have highlighted distinct investment preferences between women and men BAs. For instance, women BAs often select sectors and start-ups that are overlooked by men BAs (Amatucci 2016). Research suggests that women BAs generally tend to exhibit lower confidence levels than men when investing in early-stage start-ups (Becker-Blease and Sohl 2008) and tend to show higher risk aversion and lower self-confidence compared to men (Byrnes, Miller, and Schafer 1999; Croson and Gneezy 2009). Additionally, men and women may respond differently to the gender of the other party involved in transactions, potentially leading to diverse investment criteria (Becker-Blease and Sohl 2011; Bellucci, Borisov, and Zazzaro 2010; Ben-Ner, Kong, and Putterman 2004; Dufwenberg and Muren 2006; Eckel and Grossman 2001). Conversely, other studies indicate that BAs exhibit only marginal disparities based on gender (Harrison and Mason 2007). This suggests that women BAs are not less risk-averse than men, unlike certain other investor categories (Coleman and Robb 2018; Harrison, Botelho, and Mason 2020; Nygren, Olofsson, and Öhman 2020), especially when controlling for factors such as educational level or wealth (Maltby and Rutterford 2012). Furthermore, there seems to be little structural difference in financial and knowledge resources between genders, allowing BA women to invest similarly to men (Harrison and Mason 2007; Hoyt and Murphy 2016). Although recent studies indicate a convergence in investment behavior across genders, differences persist (Giudici, Guerini, and Rossi-Lamastra 2020; Hewa-Wellalage et al. 2022), thus motivating the need for further research into the gender dynamics on the supply side of the BA market.

On the demand side, businesses owned by women appear to face greater obstacles in securing funding from BAs compared to those owned by men, which generally receive more favorable consideration (Edelman et al. 2018). Moreover, when women-owned firms secure funding, the amounts tend to be smaller, and a larger share of equity is exchanged in return (Poczter and Shapsis 2018). Although the start-up ecosystem faces gender-related challenges, with evidence indicating a predominant allocation of equity investments toward men-led companies, the success rate of women-led start-ups seeking funding has been observed to be comparable to that of funded men-led start-ups (Aernoudt and de San José 2020).

Examining the gender of both the BA investor and the entrepreneur in isolation is valuable for exploring the behaviors of different participants in the BA market. However, considering gender matching is crucial for understanding investment patterns, particularly in light of the relevance of homophily in entrepreneurial relationships (Becker 1971).^[2] Hence, it remains crucial to investigate how gender differences between investors and investees contribute to BA funding outcomes, as the existing literature has primarily focused on either the supply or demand side of the market.

This study aims to address this research gap by exploring the impact of the interaction between the gender of Business Angels and the entrepreneurs in the BA market. Specifically, in this paper, we test the hypothesis that the gender of the BA investors plays a role in determining the average value of BA investments received by start-ups also depending on the gender of the entrepreneur. We, therefore, compare the four possible interactions between BA (women or men) and entrepreneur (women or men) to understand in detail whether and when any gender bias emerges in line with taste-based discrimination theory.^[3]

To achieve this, we use a unique dataset that includes information on Business Angel investors, target companies, and entrepreneur characteristics worldwide spanning from 2018 to 2020. First, we focus on the demand side by assessing whether businesses owned by women exhibit a comparable likelihood, alongside those owned by men, of securing BA investments of similar average amounts. Second, we examine the supply side by determining whether, on average, women BAs exhibit investment patterns similar to their male counterparts. Third, we explore the intersectionality between the gender of BA investors and investees, aiming to test for the presence of any potential heterogenous behaviors regarding the average invested amounts by men (women) Business Angels towards women-(men-) owned target companies.

Using a set of Probit estimations, our findings reveal that, on average, women entrepreneurs receive smaller investments compared to their male counterparts, whereas this difference is not significant when looking at the investors’ side. When examining the supply side, we do not observe systematically different behaviors in investment strategies between women and men BAs. However, when considering all possible gender combinations between BA investors and target companies, women-owned businesses exhibit a reduced likelihood of raising a larger investment from men investors compared to men-owned companies. Notably, such a bias does not emerge when assessing investments made by women BAs towards men entrepreneurs, thereby corroborating the view that the amount of funding received by women entrepreneurs is influenced by the gender of the BA investor.

We then explore heterogeneous effects based on several companies’ characteristics to gain insights into the mechanisms and channels that may contribute to distinct behavior between men and women Business Angels, as well as the importance of gender pairing. Specifically, we test whether the lower average funds invested by men BAs in women-owned companies are driven by: (i) limited information; (ii) men BAs’ beliefs about women’s entrepreneurial skills and capabilities in competitively producing and selling goods and services on the market; (iii) investors’ preferences for syndicated risk sharing; (iv) the proximity between investor and investee which could facilitate information exchange and acquisition. Our findings indicate that the bias of men BAs towards women-owned firms persists even as uncertainty in the decision-making diminishes, suggesting support for the taste-based discrimination theory. More specifically, our results indicate that gender prejudice holds regardless of factors such as age and turnover of the target company, syndication among investors, and geographical distance between investors and investees, revealing that the bias is driven by men investors’ behaviors and preferences rather than informational factors.

Lastly, our findings remain robust and consistent even when accounting for potential effects in severely uncertain contexts, such as the COVID-19 pandemic. Additionally, we consider the contextual presence of possible confounding macro-factors, such as countries’ attitudes towards (in-)equal treatment for all individuals and income inequality, thereby confirming the existence of gender bias in the BA market.

The remainder of the paper is organized as follows. Section 2 presents the data and variables underlying the analysis, while Section 3 outlines the empirical strategy. Section 4 reports the main findings, the underlying mechanisms of which are explored in Section 5. Section 6 provides robustness tests. Lastly, we conclude in Section 7.

2 Data

To explore the investment decision process of Business Angels and the possible funding gap in early-stage financing of women-owned companies, we adopt a dataset that includes detailed information at the BA’s transaction level. We use all BA funding deals that took place between January 2018 and July 2020 at the worldwide level available on Zephyr, a Bureau van Dijk database. This database provides information on the characteristics of BA deals (e.g. the invested amount, transaction date, and the deal description and rationale), the BA investors (e.g. the name, the gender, and the country of origin of the investor), the BA-backed companies (e.g. name, place of origin, industry), and their owners (including their gender).^[4]

We focus on one outcome variable, i.e. the invested amount. In particular, we build an indicator variable LargeBA that measures whether the firm raises a larger-than-median amount of BA financing. In analytical terms, we first compute the median value of all BA transactions in our time-frame period and then build this indicator so that it takes the value of 1 if the firm i raises a larger amount of the BA financing than the median at time t, and zero otherwise. In this way, we proxy the relevance of BA investments, by categorizing them into larger and smaller BA investments, to measure whether the likelihood of receiving a larger amount of financing is the same for men or women entrepreneurs, all else equal.

In our exercise, we capture gender from both the demand and supply sides. First, we consider the gender of the owner of the target company raising the BA financing. Therefore, we construct a binary indicator, GenderTarget, that accounts for the gender of the BA-backed entrepreneur. GenderTarget takes the value of 1 if the firm is owned by a woman entrepreneur, and zero if by a man. In our sample, approximately 6.3 % of the firms are women-owned. Second, we look at the gender of the Business Angel investor. In particular, we construct an indicator, GenderBA, that equals to 1 if the Business Angel is a woman investor, and zero if it is a man. On average, nearly 5.6 % of BA transactions in our sample are financed by women BA investors.^[5]

To account for any possible unobserved heterogeneity across firms, we consider a set of control variables that could have an impact on both the probability for a firm to raise a larger BA investment and the likelihood for it to be owned by a woman entrepreneur. In particular, we include the variable Assets to control for the firm’s size. The expected impact of Assets is to facilitate firms’ access to finance by lowering the likelihood of experiencing credit tightness. Then, we include the variable Age defined as the number of years from the incorporation of the observed firms at the time of financing. In the analysis, we take a logarithmic transformation of all these variables. To account for potential shocks occurring in different timings and shared by all firms of the sample, we also include year fixed effects, φ _t . Moreover, to consider cross-sectional heterogeneity across BA markets, we also add a set of country, φ _c , and sector, φ _s , fixed effects.

Overall, our sample includes information on 756 Business Angel investments observed from 2018 to 2020. For each transaction, we have information on the gender of the BA and of the entrepreneur, as well as on the amount of the investment and the balance sheet information of the BA-backed companies.^[6] The investment transactions included in our sample involve companies based in 45 different countries – though most of them are concentrated in a limited number of markets, such as the US (22 %), the UK (21 %), and India (17 %). Angel investors from 40 countries participated in these transactions, with the same three countries accounting for more than 50 % of the total. Looking at the industry of the BA-backed companies, they are mostly concentrated in sectors ‘Information and Communication’ (approximately 70 % of the total) and ‘Manufacturing’ (14 %), based on the NACE Rev.2 Classification (European Commission 2008). Within the ICT macro-sector, the most prevalent activities are related to data processing and hosting (50 %), and software publishing (16 %). The information available in our dataset related to the geographical and sectoral distribution of the BA market is in substantial alignment with previous studies investigating the characteristics of BA investments and BA-backed companies worldwide (e.g. Cumming and Zhang 2019).

Our dataset also includes information on whether angel investments are completed in syndication among several investors. In our sample, most of the investments (78 %) are completed by single angel investors, while approximately 10 % are co-investments among two or more angels, and 12 % are syndicated with Corporate Venture Capitalists (CVCs). In our empirical analysis, in the case of a syndicate of angels, we attribute the gender based on the angel signaled as the first ‘Acquiror’ in the Zephyr database.

3 Empirical Strategy

We first investigate whether the probability of securing an investment larger than the median from Business Angels varies based on the gender of both BA investors and the entrepreneurs of target companies. In particular, we study whether the gender of the investor influences the size of the BA transaction and whether businesses owned by women encounter discrimination in the Business Angel market.

More formally, we run two models estimating: (i) the likelihood of the BA financing exceeding the median amount for target companies owned by men versus women entrepreneurs, and (ii) the likelihood of the target company receiving financing exceeding median amounts from women versus men BA investors. To estimate the probability of the binary outcomes in our models, we employ Probit model specifications. Probit models are particularly well suited to this task, as they are designed to analyze data where the dependent variables are binary indicators. Specifically, we estimate the following specification:

In Equation (1), Pr(LargeBA) is a binary indicator that is equal to 1 if firm i raises larger-than-median BA financing at time t, and zero otherwise. GenderTarget is a binary indicator that equals 1 when the firm is owned by a woman entrepreneur, and zero otherwise. To account for any possible unobserved heterogeneity across firms, we include a set of control variables that could have an impact on both the firm probability to raise a larger BA investment and the likelihood for it to be owned by a woman entrepreneur. In particular, the vector Controls includes two variables related to the size (Assets) and seniority (Age) of the observed firms. To account for potential shocks occurring in different timings and shared by all firms of the sample, in some of the specifications, we also include year fixed effects, φ _t . Moreover, to consider cross-sectional heterogeneity across BA markets, we also add a set of country, φ _c , and sector, φ _s , fixed effects. Lastly, ε _it , is the error term clustered at the BA investment level.

In Equation (1), the coefficient β, together with the related marginal effect, is the focus of our interest, since it measures our estimate of the effect of the gender of the target companies’ owner on the probability of raising a larger-than-median BA investment.

While we are interested in understanding how differently target companies are BA-backed based on the gender of the entrepreneur, our investigation also considers the investor side. In particular, we want to test whether Business Angel investors show heterogeneous behaviors in terms of invested amounts depending on their own gender. Accordingly, we estimate the following specification of the Probit model:

This model is equivalent to that of Equation (1), with the notable exception of the main independent variable, GenderBA, which is an indicator that equals 1 when the Business Angel is a woman investor, and zero otherwise. In Equation (2), the coefficient β represents the effect of the gender of the BA investors on the probability of a firm raising a larger-than-median BA investment.

In order to capture any potential presence of a gender bias in the context of a BA investment, we also consider a specification of the model that compares the probability of obtaining a larger-than-median BA investment based on the gender of both the Business Angel and the entrepreneur. Specifically, we interact the GenderBA and GenderTarget indicators to identify the motives that may drive Business Angels of different genders to behave differently towards women versus men entrepreneurs. As a result, we estimate the following model:

Looking at the gender-pairing between investors and investees allows us to understand whether the behaviors of investors are driven by a sort of affinity or solidarity towards entrepreneurs of the same gender or, differently, whether they are affected by gender biases. This estimation also allows us to understand: (i) whether or not gender bias – if present – is exhibited both by men investors toward women entrepreneurs, and by women investors towards men entrepreneurs; (ii) whether men and women behave differently in their BA relationships with the other gender.

4 Results

5 Mechanisms and Channels

We now examine heterogeneous effects based on several firms’ characteristics to offer some insights into possible mechanisms that may drive BA investors to behave differently from their female counterparts.

Smaller-than-median investments made by men BAs in women-owned enterprises could be linked to a lower perception that men BAs have towards women entrepreneurs, on average, compared to their male counterparts, for instance in terms of lower managerial skills (Amatucci and Sohl 2004) and a lower level of legitimacy (Edelman et al. 2018). A similar result would be consistent with investor discrimination, where the discrimination of men BAs could be attributed to radically distinct mechanisms driven by their beliefs or preferences. In the case of beliefs, discrimination would stem from insufficient information about the quality of women’s enterprises (statistical discrimination), while in the latter, it would result from the behavioral taste of the BAs called to finance the transaction (taste discrimination).

To investigate possible rationales that may drive men BAs to invest differently from their female counterparts, we generate several interaction terms to identify whether the lower average funds that women entrepreneurs receive from men BA investors are driven by the lack of sufficient information on the quality of women-owned enterprises. Specifically, we interact both the gender of the BA investors and BA-backed entrepreneurs with a set of indicators able to capture different degrees of information about the invested firms that might be assessed by the BA investor at the time of the transaction. In particular, we explore four relevant channels: age and turnover of the target company, syndication of the BA transaction with other financial operators, and geographical proximity between investor and investee.^[7] Therefore, we augment Equation (3) with the triple interaction indicator, Gender BA × GenderTarget × Channels, as follows:

where Channels _i,t represents, alternatively, the four indicators underlying the channels, i.e. Age of the target company, Turnover, Syndication, and Geographical proximity. The estimated coefficient β should be interpreted as per Equation (3), while the coefficient δ of the triple interaction term reflects, alternatively, the effects for (i) more established and (ii) active-in-sales BA-backed companies, (iii) with BA investors operating in syndication (iv) or geographically farther to the target company. The results of these analyses are reported in Table 4 and discussed in the following.

6 Robustness Tests

7 Conclusions

In this work, we have investigated the importance of the gender of the contracting parties involved in equity early-stage financing. Using a unique dataset that includes information on Business Angel investors, target companies, and entrepreneur characteristics worldwide between 2018 and 2020, we have explored the relevance of a possible gender gap by looking at both sides of the BA transactions.

Using a set of Probit estimations, we find that women entrepreneurs receive smaller investments on average than their male counterparts, while this difference is not significant when looking at the investor side. Women-owned companies face about a 20 % lower likelihood of receiving a larger-than-median BA investment compared to men-owned firms when they are funded by men Business Angels. When examining the supply side to determine whether women Business Angels invest similarly to their male counterparts, we observe no systematic differences in behavior between men and women investors. Analyzing all gender combinations between BA investors and target companies, we find that women-owned companies are 20 % less likely to raise larger BA investments from men investors than men-owned firms. Interestingly, such bias does not emerge when looking at investments made by women BA investors towards men-owned start-ups, suggesting that the gender bias may not be generally related to a different attitude towards the other gender, but rather appears to be specific to men investors. Furthermore, we find no significant differences in the investment attitudes between men and women investors towards target firms owned by entrepreneurs of the same gender.

We also examine potential factors that may influence investment decisions, including the age and turnover of target companies, investment syndication, and the geographical proximity between investors and companies. Our findings indicate that gender bias persists irrespective of the target company’s age and turnover or the presence of syndication in the investment deal. This suggests that the bias is driven by the behavior and preferences of male investors rather than by informational factors. Furthermore, our results are robust to high uncertainty resulting from the pandemic, and after excluding countries with high levels of socio-economic inequality and income disparities from the analysis.

Overall, our findings support the view that the amount of funding received by women entrepreneurs is influenced by the gender of the BA investor. More specifically, women-owned firms do not raise lower average funds from women BA investors, thus rejecting the ‘prejudice’ hypothesis among women investors and entrepreneurs. Conversely, women entrepreneurs receive lower funds only when the BA investors are men, suggesting that men investors seem to exhibit ‘taste discrimination’ against women entrepreneurs. The ‘cultural affinity’ towards the same gender is not observed for both men and women BAs. To offer insights into some of the mechanisms that may be driving this result, we examine the heterogeneous effects underlying the average estimates. We show that men’s bias towards women-owned firms does not diminish as uncertainty in decision-making decreases, supporting the hypothesis of taste-based discrimination.

These findings come with some limitations. First, although we observe that women-owned start-ups receive on average smaller amounts of BA funding, we cannot fully exclude that this result is at least partially influenced by the fact that women entrepreneurs may request smaller amounts compared to men, as we cannot observe the demand for equity with our data. At the same time, we note that the bias against women-owned start-ups only occurs when the BA investors are men, which could indicate that the demand for finance from women entrepreneurs may not differ from that of men entrepreneurs, but rather, it could be the men BAs who recognize fewer resources for women-owned businesses. One question that remains unexplored, and that we leave for future research, is whether women entrepreneurs demand less finance from men BAs than from women BAs.

Second, our dataset does not include information at the investor and entrepreneur level on other possible characteristics that may drive homophily and/or stereotyping in equity financing other than gender, such as ethnicity, education, occupational background, income, and neighborhood (e.g. Choi 2023; Gornall and Strebulaev 2020; Qin, Mickiewicz, and Estrin 2022). While in this paper we have attempted to account for these factors using macro (country) level indicators, future research could focus on enriching this database to include these and other factors in our model to isolate the specific effect of gender bias.