UGR Fall Blog # 1.- Andrea Quevedo-Prince

Title: “Predictive modeling of the US Non-profit Sector: from a macro to a micro perspective”.

Our project has been running since June 2017. The student acts as research assistant, sifting through numerous academic journals and data bases to generate the inputs needed by Professor Quevedo. Our aim is to generate valuable insights to fundraisers, and to gain experience in scientific research.

Research Goals:

  1. To define a predictive model for the non-profit sector at a macro level, that is, determine the variables that dictate or moderate total revenue from donations.
  2. To define a predictive model for the non-profit sector at a segmented level, that is, determine the variables that dictate or moderate revenues from donations to specific social causes like the education, health, amateur or Olympic sports, the arts and the environment, in particular.
  3. To define a predictive model for the non-profit sector at the micro level, that is, determine the variables that dictate or moderate individual donor motivations toward a specific social cause.

Research Methodology: For the macro phase of our research (goals # 1 and 2 above), our approach combines extensive literature review and statistical methods, particularly Factor and Regression Analysis, to extract the appropriate factors that best fit the models. Indeed, we have dug into hundreds of publications, to study the matter in depth. Then we searched for data series to run the analyses. For the micro phase (goal # 3 above), we will apply this context to sample a representative group of individual donors to interview, and to determine their motivations toward a specific cause.

Review of the Literature: The nonprofit sector represents 5.4% of total GDP in the US. In 2015, the largest source of charitable giving came from individuals at $268.28 billion, or 71% of total giving; followed by foundations ($57.19 billion or 16%), bequests ($28.72 billion or 9%), and corporations ($18.46 billion or 5%). It must be noted that “giving” is just part of total nonprofit revenue. Tuition payments, in education, ticket sales, in sports, and hospital patient revenues, in the health segment, are the sector’s main sources of total income; private charitable giving represents almost 14% of that (total non-profit revenue), growing at a rate of 18.2% adjusted for inflation; government grants add another 8% and corporate donations 5%.

Figure 1

Matsunaga and Yamauchi (2004) state that the nonprofit sector has become widely recognized by researchers as having a critical and distinctive role in contemporary society; in the past, they say, it had been treated as a residual of other economic sectors, but has recently with increasing consistency, been thought of as an independent sector in its own right. The authors add that the dominant theory explaining the size variations of the non-profit sector by locality is the government failure theory.

According to List (2011), the market revolves around three major players: (1) the donors, who provide the resources to charities. These can be individuals, corporations, public institutions, and non-government organizations (NGOs). (2) Charitable organizations, which develop strategies to attract resources and allocate those resources; and (3) the government, which decides on the tax treatment of individual contributions, the level of government grants to various charities, and what public goods to provide directly by itself.

Berman, Brooks and Murphy (2006) found that percentage changes in funding from year to year are relatively stable, and are thus capable of being modeled using standard techniques. They suggest that non-profit revenues will depend on a cause’s or an organization’s public profile, networking, especially with religious organizations, and the sum of independent funding sources, including government support, that help diversify and stabilize fundraising in economic downturns.

Curry, Rodin and Carlson (2012) hypothesized that organizations that operated on transformational approaches to fundraising have fared significantly better than those which operate on a more transactional basis. They also suggested that the greater physical proximity of the donor base of an organization would positively impact fundraising. Lastly, they posited that regional economic stress patterns would impact fundraising effectiveness, with greater economic stress leading to decreases in fundraising effectiveness. This would be the only macro variable the authors explore.

As Nissan, Castaño and Carrasco (2012) suggest, some theoretical work, however, has emerged to explain the macro perspective, that is, the differences in scale, presence, composition or financing of non-profits across countries; most of them inspired by the classical argument of Government Failure, others centered in the supply side of non-profits. The authors go on to suggest a model that includes public funding, as the first variable, adding social capital (the opposite of government failure), per capita income, and entrepreneurial activity to the equation.

In trying to develop a theoretical model, McKeever (2013) states that the Situational Theory of Publics has direct application in fundraising. According to it, three independent variables—problem recognition, constraint recognition, and involvement—predict two dependent variables—information seeking and information processing. Problem recognition is similar to (public) awareness, which is a major factor in our hypotheses. “Problem recognition” is that moment when people realize that something should be done about an issue or situation and stop to think about what to do. Constraint recognition refers to people’s perceptions of obstacles in the way of acting related to the issue or situation, and involvement is defined as the extent to which people personally connect with the issue or situation. Information seeking and processing can include passive or active forms of communication.

McKeever also stated that it is not surprising that past participation would predict future support for and or participation in fundraising. All of this is unquestionably valuable for nonprofit organizations. Trying to increase awareness, participation, support, and advocacy efforts is crucial to their particular mission or cause.

Hypotheses: On a macro level, we would posit that (H1) total non-profit revenues respond for the most part to the economy and the level of public awareness regarding social issues. The null hypothesis, H0, would be stated as “non-profit revenues do not respond to the economy or to the level of public awareness regarding social issues.”

H2: cause-specific revenues respond for the most part to the DPI, and to the level of public awareness regarding those issues.

H3: individual donations will respond to the individual’s commitment to the cause, where public awareness, the economy, and personal economic considerations would act as moderators.

Our model would look as follows:

Figure 2

Data Series: Thus far, we have found the following time series most applicable to our research objectives:

  1. Total non-profit revenues in the US
  2. Non-profit revenues by subsector (social cause).
  3. Media coverage, as a measure of public awareness regarding specific social causes.
  4. Macroeconomic US data on GDP (gross national product).
  5. Macroeconomic US data on DPI (disposable personal income). 

We are have begun running the available data.

Initial Findings: Factor Analysis shows an obvious correlation between GDP and DPI. The model combining all variables, namely “Non-Profit Revenue = a + bGDP + cDPI + dPUBLICAWARENESS + Ɛ”, showed a significance level of .000. Regression Analysis showed a more significant relationship between disposable personal income, TV coverage of environmental issues, and non-profit revenues for that specific cause, thus fitting the model “Non-Profit Revenue for Environmental Causes = a + bDPI +cPUBLICAWARENESS + Ɛ”, with a R² of .874, and significance levels from .000 to  .012 (see SPSS output below).

Figure 3

Thusly, our first model would look as follows

NPR Environment = -4401.542 + 528.327(DPI) +23.121(TVCoverage) + Ɛ

 Indeed, TV continues to be the major source of information, according Bialik and Matza (2017), who cite recent Pew Research polls.

Figure 4

Initial Conclusion: Our most firm conclusion, thus far, relates to H2: non-profit revenues for environmental causes respond for the most part to the DPI, and to the level of public awareness regarding environmental issues, as reflected on TV coverage, in particular.

Still Pending: Having macroeconomic data, and non-profit revenues for different sectors, we must now secure reliable and matching data series for TV coverage of health issues, education, and the arts, if we are to test the reliability of the model in different non-profit segments.

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