Leading cryptocurrency exchange and Web3 company Bitget has disclosed a comprehensive study that exposes alarming gender disparities in the funding landscape of blockchain startups.
To shed light on critical issues of gender inequality within the crypto industry, the company has released a comprehensive two-year analysis that delves into the investment patterns affecting male and female-led startups.
Per the analysis report, the industry has seen a 70.1% decline in fundraising for blockchain startups from Q1 2022 to Q3 2023. Shockingly, female-led blockchain startups secured only 6.34% of the total funding, amounting to $1.77 billion.
The study highlights a 45.2% decrease in female-led blockchain startups since the beginning of 2022. Despite this decline, there is a glimmer of hope with a modest increase in the share of female-led startups from 8.3% in 2022 to 8.6% by Q3 2023. However, female-led projects are shown to be more sensitive to adverse changes but less affected by bullish trends.
Furthermore, the report delves into the root causes of this gender-based funding disparity, pinpointing factors such as general investment trends, the overall crypto market situation, investor sentiment, and a substantial bias favoring male-led projects. With over 90% of the funding directed towards male-led startups, Bitget emphasizes the need for incubators to foster an inclusive environment for female-founded startups.
In addition to this, the study also highlights the correlation between founder gender and investment volumes, raising questions about the prevalence of bias in the crypto industry. In a market that prides itself on inclusivity and a global approach, the study underlines the urgency of addressing gender bias to ensure equal opportunities for everyone within the transformative blockchain sector.
Bitget’s Managing Director, Gracy Chen, acknowledged the industry’s responsibility to address these disparities, emphasizing the commitment to creating an ecosystem where talent and potential are the sole criteria, free from gender bias.