Leveraging Big Data and Network Science to understand Philanthropy
Dr. Louis Shekhtman
Associate research scientist with Albert-László Barabási at Northeastern Universit
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While philanthropic support has increased in the past decade, there is limited quantitative knowledge about the patterns that characterize it and the mechanisms that drive its distribution. Here, we collected over 3 million IRS tax forms from 685,397 non-profit organizations to create a temporal, weighted network of grants to philanthropic organizations. We begin by analyzing support for universities and research institutions finding that in volume and scope, philanthropic funding is comparable to federal research funding. Yet, distinct from government support, philanthropic funders tend to focus locally, indicating that criteria beyond research excellence play an important role in funding decisions. We further leverage the bipartite network of supporters and recipients to demonstrate the predictive power of the underlying network in foreseeing future funder-recipient relationships. Next, we compare our findings to philanthropic support of the arts, finding that grants in the arts are even more locally focused and that funders often support multiple local arts organizations offering distinct experiences e.g., both the local opera and art museum, rather than focusing on a particular area of art. We discuss the implications of our findings for philanthropic funders, groups seeking funding and quantitative understanding of philanthropy
Dr. Louis Shekhtman is an associate research scientist with Albert-László Barabási at Northeastern Universit. He received his Ph.D. in Physics in 2020 supervised by Israel-Prize winner Shlomo Havlin. He has authored over 30 peer-reviewed publications including in top journals such as Proceedings of the National Academy of Sciences, Science Translational Medicine, Nature Scientific Reports, Journal of Hepatology (IF: 25.083), and others. His research focuses on broad applications of network science and complex systems in social, biological, and technical systems.