Our methodology involved four phases of work:
Phase 1
Scoping, literature review, refine hypothesis
Phase 2
Fieldwork and analysis of secondary data
Phase 3
Triangulation & validation of evidence
Phase 4
Reporting & dissemination
Developing, refining, and testing actions
We identified a shortlist of 14 actions that were supported by existing literature and initial scoping interviews as having the potential to generate impact in increasing diversity in venture capital investment. When selecting the actions we took into account the amount, strength and robustness of the evidence (e.g. opinion versus causal analysis). There was a review of over 40 documents, identified by the study team (including the British Business Bank, SQW and the Expert Panel) and sourced from bottom-up online research. Based on the findings, a long list of 28 actions was developed and subsequently refined into a shortlist of 14 actions to be tested with venture capital firms and entrepreneurs.
The actions were categorised into three ‘focus’ areas for increasing diversity: connecting and information sharing between venture capital firms and entrepreneurs (7 actions); decision making processes at venture capital firms (4 actions); and availability of data and accountability (3 actions).
| Action |
|---|
| Connecting and information sharing between venture capital firms and entrepreneurs: |
| Venture capital firms should clearly communicate their investment strategies and commitment to diversity via their website and social media |
| Venture capital firms should actively monitor social media to identify strong propositions from a diverse pool of entrepreneurs |
| Entrepreneurs should actively engage on social media to raise awareness of their business/proposition and connect with venture capital firms |
| Venture capital firms should use incubators as a referral mechanism to identify and support diverse entrepreneurs |
| Venture capital firms should use accelerators as a referral mechanism to identify and support diverse entrepreneurs |
| Venture capital firms should use ‘office hours’ to network with and provide support to diverse entrepreneurs |
| Venture capital firms should use scouts to access diverse networks and identify quality propositions |
| Decision making processes for firms: |
| Venture capital firms should provide constructive feedback on the quality of propositions and reasoning behind investment decisions |
| Venture capital firms and other intermediaries should encourage cross-referrals to other funds that may be interested |
| Venture capital firms should take steps to increase the diversity among those involved in the identification of potential propositions |
| Venture capital firms should ensure senior decision makers, including Investment Committees, are made up of people from a diverse set of backgrounds |
| Availability of data and accountability: |
| Venture capital firms should participate in industry-wide surveys and make D&I data on their investments public |
| Limited Partners should encourage venture capital firms to monitor and report progress in supporting diverse entrepreneurs |
| Limited Partners and venture capital firms should design funds that are targeted specifically at diverse entrepreneurs |
Actions were tested through interviews with 40 venture capital firms.
The sample was drawn from those with a high proportion of deals with entrepreneurs from underserved communities – identified using Beauhurst data – as well as firms with high levels of engagement with the Investing in Women Code1 . The venture capital firms interviewed are themselves more diverse than the wider market – the average and median representation of women among key contacts listed on Beauhurst is c.30%, compared to 20% for the wider market2 . This adds weight to the interview findings, as they are based on the experiences of those with a strong track record in this area and can inform development of best practice across the industry.
Interviews were held via video call and used Q-sort methodology. Venture capital firm consultees were asked to consider the effectiveness of the 14 actions identified through our review of existing evidence. Our findings from this strand of research are based on the experiences and observations of consultees, and so reflect the perceived relative effectiveness of different actions. We combined formal statistical and qualitative approaches to the analysis of collected data, which allowed us to identify patterns and combinations of actions that, in venture capital firms’ experience, are effective for increasing diversity among venture capital backed entrepreneurs. The evidence collected through interviews with venture capital firms was analysed in three stages:
-
Stage 1
Descriptive analysis of the data on the effectiveness of the 14 actions (the ranking by the average score, graphical analysis of the distributions of scores given to each action).
-
Stage 2
Identification of groups of responses with common patterns using factor analysis techniques – statistical methods of ‘grouping’ variables (in this case views of consultees).
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Stage 3
Qualitative analysis of the reasoning provided by consultees to gain a deeper understanding of the narrative for each of the groups.
- 1
Of the 40 Venture Capital firms interviewed, 29 (73%) have a strong track record in investing in underserved entrepreneurs, as proxied by Beauhurst data on their share of deals to female founders.
- 2
Key people tend to be C-suite individuals and department heads. Beauhurst source these individuals from a range of sources, including company websites, LinkedIn, press articles, and using directorship information from Companies House. These individuals are selected because they are key decision-makers at the company who are core to its operations. This information is maintained by a team of analysts. The analysis of data on the composition of Investment Committees and Investment Teams for IWC signatories that participated in in-depth interviews revealed that the gender mix of key people from Beauhurst correlated with both Investment Committees and Teams, but the relationship is stronger with Investment Teams.
The Q-sort methodology puts emphasis on ensuring objective data collection, despite the inherently subjective nature of the research question, and allows for a direct comparison of experiences and observations across research participants.
This was achieved by asking consultees to sort the 14 actions into five categories that reflected the relative effectiveness of those actions. There were limits on the number of actions that could be placed in each category. These limits were derived from a quasinormal distribution.
This approach ensured that all interview participants treated the one to five effectiveness scale the same way, considered all actions on the list (rather than focus on a single experience, e.g. the most recent one or ‘most vivid’) and lowered any social pressure to give ‘the right’ answer (e.g. to say that an action is effective because it is on trend).
The factor analysis of responses was implemented in specialised statistical software Stata using the qfactor module3 . The analysis was executed iteratively by varying the number of factors (that determined the number of pathways discussed in Key Findings), the type of factor rotation (orthogonal vs oblique to allow a correlation between factors given the context of this analysis) and the criteria for determining differentiating statements – i.e. actions that ‘define’ each pathway relative to other pathways. This tactic allowed us to identify a factor model that fit the data the best.
- 3
Akhtar-Danesh, N., 2021. QFACTOR: Stata module to perform Q-analysis on Q-sorts using different factor extraction and factor rotation techniques.
| 1 - not very effective | 2 | 3 | 4 | 5 - action highly effective |
|---|---|---|---|---|
124 entrepreneurs were surveyed (via telephone and online)
Comprising 86 venture capital-backed and 38 venture capital-ready entrepreneurs4 . The sample was identified from Beauhurst data, with founders from underserved communities (women and Ethnic Minorities) prioritised. Education as a proxy for socioeconomic status couldn’t be identified ex-ante; ex-post, responses were compared for those who attended ‘elite and extended elite’5 , Russell Group6 , or Top 100 universities, against those from other universities or no university (the latter group making up the greater share of the sample). The sample was not completely weighted toward gender and ethnic diversity, in an effort to capture a sufficient sample of education levels as a proxy for socio-economic status.
Entrepreneurs were asked to rank the effectiveness of 14 actions identified7 . The majority of firms were already venture capital-backed, with the remaining share venture capital-ready, meaning they had attracted some equity finance, such as angel investment, but were yet to attract venture capital investment. Due to the sample size, all subgroup analyses (e.g. by respondents’ age, ethnicity, and education) were carried out descriptively rather than using formal statistical tests.
As part of the venture capital firm interviews and the entrepreneur survey, participants were asked if there were any additional effective actions which were not included in the list of 14. Their suggestions broadly fit the three focus areas and generally took a slightly different stance on the existing actions, validating the scope of the study.
The figures to the right summarise key characteristics of the venture capital firm and entrepreneur samples.
Analysis of secondary data
From Beauhurst, Extend Ventures, and the Investing in Women Code was conducted to provide additional insight and/or evidence of correlations to support the actions tested. The data on deals covered the 2018-2022 period and venture capital and private equity investments only. Findings from analysis of Beauhurst data support some actions, although should be considered in context of the depth of insight from the qualitative surveys. Triangulation of all these research strands were consolidated into this final report in consultation with a Steering Group and SQW Expert Panel (see Acknowledgements).
Sample of venture capital firms
- 4
Venture Capital-backed refers to a private, independent UK company that has been involved in any announced equity investment deals involving Venture Capital or PE investors. Venture Capital-ready refers to a private, independent UK company that has received between £50k - £1m in total equity investment – and has not been involved in any announced equity investment deals involving Venture Capital or PE or investors. The sample of entrepreneurs comprised of 60% Male and 40% Female. This partly reflected the need to include education (proxy for socio-economic status) as a diversity characteristic which was not possible to ascertain a priori, to include entrepreneurs who had not attended university or had not attended a Russell Group/elite or extended elite university.
- 5
Elite includes Oxford, Cambridge, Harvard, or Stanford Universities. Extended elite includes Imperial College London and University College London.
- 6
- 7
The margin of error of this survey is estimated to be between 7.5 and 9 percentage points. This means that, for example, if 75% of respondents suggested a particular action to be effective, we can be 95% certain that the true proportion that we would have observed had we been able to survey all Venture Capital-backed and Venture Capital-ready companies in the UK lies between 66% and 84%.
40 firms interviewed
- 35 IWC signatories
- 65% seed stage
- 70% venture
- 43% growth
- 77% head office in London/SE
- 23% head office outside London/SE (East of England, Yorkshire, WM, NW, NE, Scotland, Wales
44 individuals interviewed
- 59% female
- 36% male
- 5% did not answer
- 81% white
- 14% ethnic minority
- 5% did not answer
Sample of entrepreneurs
Population
Total sample of 5,037 firms:
- 1,484 VC ready firms (29%)
- 3,553 VC backed firms (71%)
Primary key person in Beauhurst data:
- 1,482 female (29%)
- 3,459 male (69%)
- 96 unknown (2%)
Sample
Response were collected from 124 firms:
- 38 VC ready firms (31%)
- 86 VC backed firms (69%)
Survey respondents (124):
- 49 female (40%)
- 75 male (60%)
- 90 White (73%)
- 33 Ethnic Minorities (26%)
- 1 did not answer (1%)
- 24 elite & extended elite (19%)
- 3 Top 100 (2%)
- 26 Russell Group (21%)
- 60 other (48%)
- 11 did not answer (9%)