She centers her research around peace leadership and issues of community development and leadership, with a particular focus on post-conflict societies. Chengxin Xu is a Ph.D. student in the School of Public Affairs and Administration (SPAA), Rutgers University-Newark. His research interests focus on nonprofit management, volunteerism, and experimental methods.
Nonprofit organizations (NPOs) rely on multiple funding sources to meet organizational needs; and, heavy reliance on any one revenue source can limit an NPO’s ability to allocate funding. As such, in this study, we examine the association between funding source and spending behavior in a national sample of NPOs from 2008 to 2012. Our sample consists of 51,812 observations from 16,035 unique NPOs. Using Tobit maximum likelihood estimation, we find that NPOs that rely on, both, restricted and nonrestricted revenue sources are more limited in their ability to spend on administrative needs, whereas donation income restricts personnel spending of compensation. Revenue diversification, though, can help NPOs overcome this limitation and can provide NPOs with greater spending flexibility. Our findings also show, however, that these results differ for NPO hospitals and universities.
Nonprofit human services organizations (HSOs) provide vital services to communities. Yet studies show that the density of these nonprofits varies from one community to the next, often with fewer quantities located in vulnerable communities. These findings have led to concerns regarding the ability of the human services subsector to meet community needs. In this article, however, we make the argument that organizational density is a limited indicator of a sector’s ability to provide services, and suggest that financial health is a more robust indicator. We model six measures of financial health as conceptualized by Bowman and examine relationships between these measures and indicators of community vulnerability. Our results indicate that variation exists in four of our six outcome measures (equity ratio, months of spending, mark up, and months of liquidity), and that contextual effects (e.g., being located in a minority or low-mobility community) partially explain these variances.
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