Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark
This research developed a machine-learning classifier that reliably automates the coding process using the National Taxonomy of Exempt Entities as a schema and remapped the U.S. nonprofit sector.
Computational Social Science for Nonprofit Studies: Developing a Toolbox and Knowledge Base for the Field
How can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods from a research design perspective, and highlights key applications in our field. We define CSS as a set of computationally intensive empirical methods for data management, concept representation, data analysis, and visualization.
This study considers the effects of government funding to nonprofits from a network perspective. By analyzing a novel, 12-year panel dataset from the People's Republic of China, I find no evidence that government funding to a nonprofit crowds out private donations to the same organization. However, I find a substantial crosswise crowding-in effect at the ego network level: an increase of one Chinese Yuan in government funding to a nonprofit's neighbor organizations in board interlocking network can increase the private giving to the nonprofit by 0.4 Chinese Yuan.
What new knowledge has been generated through the academic study of nonprofit organizations? This study examines how research in the field of nonprofit studies has developed and what ideas have had significant resonance and cohesion, in particular, ideas related to theories of volunteering, as well as social capital and civic engagement.