Does equity crowdfunding democratize entrepreneurial finance?


This paper investigates whether gender, age, ethnicity, and geography affect the choice of equity crowdfunding offerings vs initial public offerings (IPO) on traditional stock markets and whether these characteristics increase the likelihood of a successful offering. Using 167 equity offerings in Crowdcube and 99 equity offerings on London’s Alternative Investment Market raising between £300,000 and £5 m, we find that companies with younger top management team (TMT) members are both more likely to launch equity crowdfunding offerings than IPOs and have higher chances to successfully complete an equity crowdfunding offering. Remotely located companies are more likely to launch equity crowdfunding offerings than IPOs and have higher chances to successfully complete an equity crowdfunding offering. On the contrary, female entrepreneurs do not have higher chances to raise funds in equity crowdfunding. Minority entrepreneurs do not have higher chances of successfully raising capital but do attract a higher number of investors. Overall, our evidence provides empirical guidance for the first time to the oft-repeated policy claim that equity crowdfunding democratizes entrepreneurial finance by providing access to funding to underrepresented groups of potential entrepreneurs.

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  1. 1.

    While the majority of recent IPOs have been offered exclusively to institutional investors, crowdfunding investors are likely to be much more diverse. Over the last two decades, three-quarters of the IPOs in Europe took place in secondary markets, such as London’s Alternative Investment Market (AIM). Most of these IPOs were offered exclusively to institutional investors (Vismara et al. 2012). Although institutional investors are being allocated the largest fraction of IPO shares (Aggarwal et al. 2002), equity crowdfunding is likely to attract a much more diverse set of investors.

  2. 2.

  3. 3.

    Brush et al. (2004) document that although women own more than 30% of US businesses, they receive less than 5% of venture capital funds distributed annually. The angel market is predominantly comprised of male investors. Only about 10% of VCs and less than 15% of business angels are women. In addition, only 15% of women-led companies were successful in raising capital, as compared with 22% for male-led companies (Stengel 2015).

  4. 4.

    Crowdcube has raised more capital than all other competing platforms (, 2015). Different sources agree on the leading role of Crowdcube. Beauhurst names Crowdcube as the leading equity investor in 2015 and the most prolific investor in the e-commerce sector. Crowdsurfer estimates Crowdcube’s share in the UK investment crowdfunding market in 2015 at 52%.

  5. 5.

    The £300,000 lower boundary has been chosen in order to drop out a 1% share of extremely small IPOs on the AIM. The £5,000,000 upper boundary has been chosen in order to drop out a less than 1% share of extremely large crowdfunding campaigns. In between, we have a sample of 167 equity crowdfunding campaigns (out of our full sample of 643 campaigns) and 99 IPOs on the AIM (out of the population of 224 IPOs), comparable in size. While we are aware that the two subsamples may not perfectly poolable, and this is why we try to control for as many variable as possible, when trying to collect such an amount of money, a venture has had the possibility to opt either for a crowdfunding campaign or for an IPO on the AIM, conditional on several variables. The goal of our first stage is indeed to try and identify how such contextual variables are correlated with the choice of financing mechanism.

  6. 6.

    Each system is a pair of equation, where the former is the selection equation and the latter the outcome equation. Following Heckman (1979), the two equations are estimated sequentially (first and second stage), in order to grant the correct estimation of the IMR’s standard errors.

  7. 7.

    Please consider that, following Heckman (1979), \( {Z}_i^{\prime } \) should grant identification by an exclusion restriction, i.e., there should be at least one parameter excluded from \( {X}_i^{\prime } \). In our setting, the exclusion restriction is given by the presence in the first stage of industry dummies.

  8. 8.

    In the case of binary dependent variable, it is common practice to use either Logit or Probit models, with preference for the one or the other often based on empirical issues. In our case, given that we need to implement a model with sample selection, we need to rely on the Heckman (1979) assumption that both error terms (in the selection and in the outcome equation) are normally distributed, in order to calculate and use the Inverse Mill’s Ratio. This is why, in line with previous literature, we opt for a Probit, rather than a Logit model, for both our equations. This choice grants estimation feasibility according to Ven and Van Pragg (1981).

  9. 9.

    The simple presence of women in the TMT is also tested in the robustness analysis, by replacing Female leadership with Female presence, a dummy variable equal to 1 for all offerings when at least one woman belongs to the TMT of the focal firm.

  10. 10.

    This variable is not available for our sample of IPOs and is therefore used only in the second-stage analysis.

  11. 11.

    In the robustness analysis, this variable is replaced with GDP per capita and Unemployment rate, both measured at the NUTS-3 level.

  12. 12.

    The Classification of Territorial Units for Statistics (NUTS; French: Nomenclature des unités territoriales statistiques) is a geocode standard for referencing the subdivisions of countries for statistical purposes, developed and regulated by the European Union. For each EU member country, a hierarchy of three NUTS levels is established by Eurostat in agreement with each member state. In the UK, the NUTS-3 level refers to upper tier authorities and groups of unitary authorities and districts: There are 93 NUT-3 areas in England, 12 in Wales, 23 in Scotland, and 5 in Northern Ireland.

  13. 13.

    We make use of nine dummies, according to the first digit (industry) of the ICB, the Industry Classification Benchmark, a taxonomy launched by Dow Jones and FTSE in 2005 and now owned solely by FTSE International. Notice that ICB is available from prospectuses for IPOs, while it has been manually identified for Crowdcube’s campaign, based on the industry description available on the platform. We are aware that the set of industry dummies is likely to potentially affect the outcome of a crowdfunding campaign. Empirically, in our setting, we tested for the excludability condition through the Hansen’s J test. The joint null hypothesis of this test is that the instruments are valid instruments, i.e., excludable from the outcome equation, and the p value states the probability that the test statistic is zero, which would imply acceptance of the null hypothesis. Given that p is much greater than 10% in our case, we have evidence supporting our choice.

  14. 14.

    A first stage is estimated for all second-stage equation presented. Given that results are qualitatively identical, and numerically extremely close, the first-stage equation is reported only once.

  15. 15.

    There are 12 NUTS-1 statistical regions in the UK: Northern Ireland, Scotland, Wales, and 9 regions for England (North East; North West; Yorkshire and the Humber; East Midlands; West Midlands; East of England; Greater London; South East and South West). Greater London is the reference case. See footnote 10 for details on the NUTS classifications.


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We owe thanks to Maksim Belitski, two anonymous reviewers, and the conference participants in Berlin at the Indiana University European Gateway in Berlin and at the University of Reading.

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Correspondence to Silvio Vismara.

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Cumming, D., Meoli, M. & Vismara, S. Does equity crowdfunding democratize entrepreneurial finance?. Small Bus Econ (2019).

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  • Crowdfunding
  • Finance democratization
  • Non-bank financial institutions
  • Entrepreneurship

JEL classifictions

  • L25
  • L26
  • G23
  • G24
  • G28