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The Geography and Disparities of Community Philanthropy: A Community Assessment Model of Needs, Resources, and Ecological Environment

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Abstract

Community foundations exemplify a growing form of place-based philanthropic efforts for addressing public problems. However, why do such efforts occur in some places, but not in others? Using an original population dataset of community foundations that combines community characteristics with service area data across 3130 US counties, the empirical analyses of hurdle negative binomial models reveal distinct patterns in explaining their locational distribution and philanthropic activities. Community foundations tend to emerge or flourish in urban communities which are White-dominant, ethnically homogeneous, and less religious but have more social and human capital. Crossing the zero hurdle, their philanthropic activities tend to bloom in ethnically diverse and unequal communities but plummet in highly educated communities with more high-income earners, whereas rural and vulnerable communities with high proportions of residents who are young, old, unemployed, and disabled exemplify potential service gaps of community foundations associated with their rarity and/or fewer philanthropic activities. This study presents a community assessment model and empirical evidence of what might promote and inhibit the occurrence of community philanthropic efforts.

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Notes

  1. The conceptual framework, hence, would not necessarily mirror those for explaining nonprofit location or density that test the merits of government failure theory and interdependence theory (Lecy and Van Slake 2013; Kim 2015; Liu 2017). In the American context, community foundations primarily rely on public donations (the public support test) from their communities but not government funding, unlike typical human service nonprofits. Archival research provides evidence that they are local innovation that was driven by self-organizing philanthropic efforts, not by government resources (Hammack, 1989; Ostrower, 2007).

  2. The final list includes community foundations with Employer Identification Number (EIN). For data handling and analysis, we excluded community foundations located in US territories and social justice funds (N = 12) since most of them are not locally based but serving across states.

  3. Robust variance estimation affects the standard errors and variance–covariance matrix of the estimators but not the estimated coefficients. Additionally, spatial regression or geographically weighted regression cannot be performed in this study since they can only deal with the clustering effect of OLS regression. Yet, the data have excessive zeros and are highly skewed, making it computationally intensive (da Costa 2016). After adding a spatial lag variable (Yan et al. 2014), the statistical model failed to converge

  4. This measure was used instead of median household income due to its high multicollinearity issue with poverty rate (VIF > 5).

  5. When selecting the social capital measure, it was aware that other county-level social capital measures were available. For example, Harvard University's 2006 Social Capital Community Benchmark Survey provides a measure of family closeness, neighborhood interactions, voluntary actions, religious attendance, and political involvement. However, as it has much less county coverage than PSU-SC Index, the PSU-SC index was adopted instead. This PSU-SC index is also weakly correlated to the primary dependent variable, r = − 0.044.

  6. Both (zero-inflated and hurdle) models can deal with the high occurrence of zeros in the observed data but have one key distinction in how they interpret and analyze zero counts. The zero-inflated model formulation assumes that the zeros and nonzeros (positives) come from the same data-generating process, whereas the hurdle model assumes two distinct decision processes, “whether or not there is a community foundation” and “how many, given yes,” which makes it more useful than other count models (Dalrymple et al. 2003; Greene 2008).

  7. The hurdle regression models were fitted via maximum likelihood using “hurdle” function available in the countreg package using R statistical software. See more: https://rdrr.io/rforge/countreg/man/hurdle.html. Alternatively, “churdle” function might be used to compute the estimation in Stata.

  8. For brevity and given the limited space, only second-part results were included for analyzing the positive values of philanthropic activities and do not list out the first-part results on the presence of these activities, because they largely mirror the results for the presence of foundations, as one would expect.

  9. Giving Indiana Funds for Tomorrow (GIFT) funded by Lilly Endowment Inc. is a case in point that funded special projects and helped community foundations in Indiana build unrestricted endowments since 1990 (See https://lillyendowment.org/our-work/community-development/strengthening-indiana/). Other private and foundation funders that have played an important role include Charles Stewart Mott Foundation which provides continuous institutional support to the community philanthropy field (see https://www.mott.org/work/civil-society/enhancing-community-philanthropy/), Ford Foundation which helped increase the community foundations’ assets (see https://www.fordfoundation.org/media/1742/2007-funding_foundations.pdf), Cleveland foundation that has inspired the community foundation movement (see https://www.clevelandfoundation100.org/foundation-of-change/invention/national-community-foundation-movement/), and The James Irvine Foundation’s Community Foundations Initiative (CFI) that supported the creation of 16 community foundations in California (see https://irvine-dot-org.s3.amazonaws.com/documents/52/attachments/Growing_Smarter.pdf?1412656307).

  10. See also https://www.legis.iowa.gov/docs/publications/SD/24090.pdf.

  11. Government variables (i.e., county government’s direct expenditure, state-level expenditure, state government payroll, and the number of local government) are found to be insignificant in the analyses.

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Acknowledgements

I am grateful to the two anonymous reviewers and scholars working in the field of community philanthropy, whom I have benefited substantially from their insightful work and comments. Much appreciation also goes to Dr. Ioana Marinescu, Dr. Chao Guo and Dr. Weiai (Wayne) Xu, and the panel participants at the 2018 ARNOVA-Asia conference and 2018 Academy of Management Conference, for providing valuable feedback to this research. Last but not least, I would like to thank important collaborators of this research project, including Dr. Laurie Paarlberg, Colton Strawser, Jin Ai, and Yue Ming, for partaking in a collaborative endeavor to collect service area data of community foundations.

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    Viviana Chiu-Sik Wu

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Wu, VS. The Geography and Disparities of Community Philanthropy: A Community Assessment Model of Needs, Resources, and Ecological Environment. Voluntas 32, 351–371 (2021). https://doi.org/10.1007/s11266-019-00180-x

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