A new report from the Women’s Philanthropy Institute (WPI), Women Give 2020 – New Forms of Giving in a Digital Age: Powered by Technology, Creating Community, offers new research focused on how women give more than men, even as technology disrupts philanthropy. The report encompasses research that shows broad gender differences in how women and men use the Internet and social networks, and how they give online.

Women Give 2020 finds that women are giving more gifts and a greater proportion of total donated dollars on tech platforms, women’s and girls’ causes receive substantially more online support from women donors, and technology enables women to give according to their preferences. The report, which is funded by a grant from the Bill & Melinda Gates Foundation, also finds that building community—both online and offline—and adopting a broader definition of philanthropy are key for nonprofit organizations and online platforms to better appeal to women donors.

Women Give 2020 takes a novel approach by using data and interviews from four partner organizations (GivingTuesday via Charity Navigator, GlobalGiving, Givelify, and Growfund via Global Impact) to develop case studies based on more than 3.7 million gift transactions. Three overarching themes emerged from the overall data:

  1. Women give more online gifts than men and contribute a greater proportion of online dollars overall. Across all four case studies, women give greater numbers of gifts than men (nearly two-thirds of gifts, across platforms). Women’s greater number of donations means they collectively are giving more dollars than men through each platform studied.
  2. Women give smaller online gifts than men and give to smaller charitable organizations than men.
  3. Women’s and girls’ organizations receive substantially more support—online and offline—from women donors than from men donors. Three of the four case studies examined funding for women’s and girls’ causes, with women giving between 60% and 70% of online dollars to women’s and girls’ organizations, depending on the dataset.