Change Pays S&P Global

Venture capital continues to sideline diverse entrepreneurs and investors. Women and people of color are starting businesses at some of the highest rates across the United States and world but are receiving the least amount of investment compared to their white peers. How do we close the venture capital gap? Heather Matranga, Senior Director of Innovation at Village Capital, Melissa Bradley, Managing Director of 1863 Ventures, and Megan O’Connor, Entrepreneur in Residence at Kaplan Test Prep pinpoint the factors that cause inequality in capital allocation and find solutions to fixing the gender financing gap.

Environmental, social, and governance (ESG) factors are of dramatically increasing interest to investors who are concerned about companies adopting practices that will mitigate risk and ensure their long-term sustainability. As a result, ESG risks and opportunities are increasingly shaping the way companies do business around the globe. But how important is gender in ESG? In this episode, Sonja Gibbs, Managing Director and Head of Sustainable Finance at the Institute of International Finance, Mona Naqvi, Senior Director of ESG Product Management at S&P Dow Jones Indices, and Audrey Choi, Chief Marketing Officer and Chief Sustainability Officer at Morgan Stanley, discuss their respective interpretations of gender’s role in ESG.

What are the impacts of assigning gender to data? In this episode, Alexandra Dimitrijevic, S&P Global Ratings Head of Global Research and the founder of S&P Global’s Women’s Research Council, discusses how assigning gender to data can expand ESG research alongside Katie Darden, Head of Financial Institutions and Industry Research for S&P Global Market Intelligence, who explains how she built an algorithm in Excel that assigns gender to a database of S&P 500 CEOs. Software engineer Michaela Shtilman-Minkin and product manager Peter Licursi, both at Kensho, part of S&P Global, share how they are advancing Katie’s original tool as a potential product that can assign gender to data with a high probability of effectiveness.