Section three puts forth a classification model of philanthropic organizations based on funding structure and funding strategy, creating four organization types. Following the assignment process, the classification model is vetted to assess what organizational characteristics distinguish categories, and then applied to estimate how organization type affects grantmaking practices.
3.4.1 Vetting the System
In the first part of the analysis, a multinomial logit model is estimated by regressing the categories on a set of organizational and financial characteristics to vet the system by nonprofit i
(Equation 4.1). The multinomial logit model assesses probabilities of assignment to the non-ordered categorical outcome of classification category as a function of characteristics of the nonprofit, the decision-maker. The marginal effects of the model do not depend on the other alternatives, assuming the Independence from Irrelevant Alternatives (IIA) holds.7 Just as with a binary logit
model, marginal effects describe how a change in the decision-maker’s characteristics changes the estimated probability of being in a specific category.
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Nonprofit characteristics include whether the organization provides an impact statement, if they require an application form for potential grants, if they have a single state geographical focus in their giving, if they are members to regional grantmaking associations, or to affinity groups, and if their fields of interest include arts and culture, health or science research, the environment, or health care. The IRS ruling year is also included to capture nonprofit age. Financial characteristics include the nonprofits total revenue and share of expenditures on salaries in the first year of the dataset.
The model is also run on the binary outcome of the financial distinction (donation-based versus endowed) to compare on what characteristics funding source distinguishes nonprofits versus the 2x2 model. For comparison, the same multinomial model is also run using each the Guidestar Exchange Level ranking and the Charity Navigator ranked status as alternate outcomes to assess what organizational variation they capture.
3.4.2 Applying the System
Given that there are meaningful distinctions between these organizations, the next question is how these distinctions affect behavior: How does a grantmaker’s funding source and strategy affect their
grantmaking portfolios? An OLS model is estimated by regressing a series of logged outcome variables
on the classification system, lagged time-varying financial statistics, and lagged grantmaking behavior, clustering by the nonprofit (Equation 4.2).
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The outcomes estimated are grouped into two levels of observation: year and grant. At the year level, data are aggregated by nonprofit to their annual total. Models are run using annual logged outcomes of total giving, median grant size, total giving to universities, total giving through socially innovative practices, total giving for research, and total giving for health. Total giving to universities
innovation is calculated based on the grant description including program/mission related investment/loan,
micro-credit/enterprise/finance/entrepreneurship, social entrepreneur/enterprise, or social innovation. Total giving
for research is also determined by the grant description including any of a series of words that describe the research process including research, clinical trial, proof-of-concept, analyze, case study, experiment, as well as others. Total giving for health is based off of descriptions including any of a series of words related to health care, specific conditions, or diseases (See Appendix Table 3.1 for full list).
At the grant level, the outcome variable is the logged grant size. The model is run on the full sample of grants as well as sub-samples of grants supporting research, arts and culture, the
environment, health, health-disease related only, and health-care related only. As in the year level analysis, these fields are based on grant descriptions including sets of words (Appendix 3.1).
Control variables include lagged financial and grantmaking behavior. At the year level analysis, controls include the lagged proportion of grantmaker-recipient state match (how many of the grants went to in-state recipients), lobbying activities, proportion of expenditures to salaries, proportion of expenditures to fundraising, and the outcome. In the grant level analyses, controls also include the lagged lobbying activities, proportion of expenditures to salaries, proportion of
expenditures to fundraising, and the outcome. In addition, whether the grant is going in-state is included, as well as the lagged logged average grant size and lagged logged total giving for the outcome area (total giving for full sample, giving to research for research sub-sample). For the full sample, lagged logged giving to research and lagged proportions of giving to universities, schools, and hospitals are each included. The nonprofit’s age based on IRS ruling year and lagged assets are controlled for in every model.
Temporal variation is also examined to assess if grantmakers change their strategies in response to economic recession. The analysis is rerun with samples during and post the Great Recession to identify how organizations vary behavior in changing economic conditions. The full
sample includes data from 2007 to 2011. Since the Great Recession began in December of 2007 and ended in June 2009, data from 2008 and 2009 are treated as the recession and 2010 and 2011 are considered post-recession.
As in the first part of the analysis with the multinomial logit model, comparison models are run using the same independent variables but changing the classification system. For the year and grant level analyses, IRS tax status, Guidestar Exchange Level, and Charity Navigator ranked status are used as alternate categorical systems to assess how they explain grantmaking behavior.