Blog # 2 Fall 2017 – due Monday, December 11th: “A Predictive Model of the Non-Profit Sector”

Title:       “A Predictive Model of the Non-Profit Sector”

Introduction.-

  • This project reaches into the dynamics of the nonprofit sector at a macro level, fundraising for specific causes, like the environment, and individual giving at the micro level (third phase).
  • The purpose of our research is to determine what variables come into play in determining the ups and downs of nonprofit revenues, and which factors act as moderators.
  • From the macro perspective, we take into account data series such as GDP, disposable income, and public awareness regarding social causes, among others.

Progress made so far (June to December 2017).-

  1. Research Design.
  2. Review of the Literature.
  3. Securing data series. Cleaning. Trial analyses.
  4. Running clean data through SPSS.
  5. Predictive Model: Environmental segment.
  6. Replication of existing study: Macro.
  7. Corretations Matrix: Macro, Total Non-Profit Sector.

Results and findings of the research (where applicable), and provide insights and reflections on the data and/or results and findings.-

  • We replicated the research of List, John A. (2011) “The Market for Charitable Giving. Journal of Economic Perspectives, Volume 25, Number 2, Spring. ISSN: 0895-3309.
    • List (2011) sought relationships between macro-economic variables and total revenues of the non-profit sector.
    • He discarded the correlation between GDP and NPR as obvious, perhaps, but did not explore Disposable Personal Income as a macro-economic variable. And he seemed to stick to single variable searches.
    • He found a correlation between the S&P index and NPR, but not a perfect parallel. He then worked with lagged figures.
    • He found a significant correlation between prior year S&P results and NPR.
    • And he confirmed the correlation using percent changes in both, rather than the raw indices.
    • He did not, however, propose a model at the macro level, as we have done in this research study.
    • The author states the following: “Many economic facts concerning the charitable market remain unknown. The literature has begun to address some of the important issues, but a first lesson that I take from this body of research is that what we do not know dwarfs what we do know about the economics of charity. This perspective pinpoints some of the areas where economists have been able to speak to policymakers, provide theorists with empirical facts, and give practitioners useful advice, but clearly more work is necessary. I suspect that this line of research will continue to be a strong growth area. As fundraisers continue to recognize the value of experimentation, economists will increasingly be called upon to lend their services. Likewise, as economists continue to recognize the value of using naturally occurring settings as laboratories, such domains will increasingly be used to generate new data sets…
  • This clearly confirms our views below.
    • List arrived at a correlation coefficient of 0.636
  • Our model, after extracting variables, arrived at a Pearson’s R of  0.935, with almost perfect significance levels.
    • NPR Environment = -4401.542 + 528.327(DPI) +23.121(TVCoverage) + Ɛ 
  • Lastly, we ran correlations at the macro level, to find significant relationships between Google searches for “social causes” (in the absence of a macro indicator of public awareness or a sum total of press coverage for the overall sector), and DPI, as determinants of Non-Profit Revenues.

Questions raised from the data collected.-

  • There is simply no macro indicator of public awareness about social causes in general terms. Specific measures were available only for the environment, and that considering media coverage (Factor analysis suggested to disregard print media, and keep TV News Coverage only).
  • Researchers have struggled to pinpoint mathematical models of the Non-Profit Sector, without much success.
  • They have concentrated on the micro view.

Challenges and/or successes you have experienced with this project.-

  • The lack of data and analyses is overpowering, and sad.
    • But our hypothesis (H1) was proven, non-profit funding responds, for the most part, to public awareness and disposable personal income.
    • NPR=a+bDPI+cEnvironNews+e
  • We will now go on to survey donors and sponsors to tie our macro model to the micro view.

Describe what you have learned from the project.-

  • We have learned the intricacies of research, especially in regards to securing reliable data series.
  • We have learned how important the literature review is in constructing and/or consolidating hypotheses.
  • We have learned about the importance of a detailed research design and schedule.
  • We have also learned that there is much interest in our research topic.  

Impact this project has had on us and any future plans we may have related to this research.-

  • We thank the Division of Student Success of Pace University for its support.
  • We are also most excited about the support offered by other foundations, to make their donor networks available for polling in the micro stage (Spring 2018).
  • We are most interested in pursuing other research projects, particularly one, relating to the application of Neuroscience in Marketing.

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.

Blog post # 2.- 08/11/17. “A Predictive Model of Non-Profit revenues in the US”

Blog post # 2.- due Monday, August 14, 2017.

  • Title:       “A Predictive Model of Non-Profit revenues in the US: From a macro to a micro perspective”
  • Introduction.-
    • This project reaches into the dynamics of the nonprofit sector at a macro level, fundraising for specific causes like education, health, the environment or amateur sports, and individual giving at the micro level.
    • The purpose of our research is to determine what variables come into play in determining the ups and downs of nonprofit revenues, and which factors act as moderators.
    • From the macro perspective, we will take into account data series such as GDP, disposable income, and public awareness regarding social causes, among others.
  • Progress made so far (08/08/2017).-
    • We have advanced the Research Design, and the Review of the Literature, gathering over 100 possible references and citations, exploring positions that support and oppose our hypothesis.
    • While we continue to scan the literature, we are presently advancing to secure reliable data series to run the statistical analyses.
  • Results and findings of the research (where applicable), and provide insights and reflections on the data and/or results and findings.-
    • According to the National Philanthropic Trust, this sector contributes almost $1.0 trillion to the US economy, representing 5.4% of GDP.
    • 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 residual of other economic sectors, but recently, and with increasing consistency, it has come to be regarded as an independent sector in its own right. The authors add that the dominant theory explaining the size variations of the nonprofit sector by locality is the government failure theory, whereby NGO’s provide what the public sector cannot.
    • List (2011) suggests that this market evolves and revolves around three major players: (1) the donors, whom 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 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 nonprofit 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.
    • Research has focused more on the micro than the macro view. Curry, Rodin and Carlson (2012), for instance, 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 nonprofits across countries; most of them inspired by the classical argument of Government Failure, others centered in the supply side of nonprofits. 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 awareness, which is a major factor in our hypotheses, and has been defined as the 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. The author 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.
  • Questions raised from the data collected.-
    • We see little or no research on predictive modeling at the macro level, and even at the segmented level, for amateur sports, it seems inexistent. Indeed, Wallace (2016) points to the fact that predictive modeling has focused big-donor analytics, micro, that is, largely aimed at the identification of potential donors.
    • We feel it may be misleading to run a partial model, with available data series only. Thusly, we will wait until a complete set of variables is secured.
  • Challenges and/or successes you have experienced with this project.-
    • We are ever more confident about the correct orientation of our hypotheses:
      • On a macro level, we would posit that (H1) funding responds, for the most part, to public awareness.
        • We propose that the more people know of a specific cause (Karate in the Olympics, for instance, global warming, hunger in Africa, cancer, etc.), the more money that will flow toward these. Extraordinary events, such as natural disasters would boost public awareness.
    • We worry about the abstract nature of the concept of public awareness, especially at the macro level (awareness about what? that is…), and feel we will have to work with cause-specific polling data, and the equivalent data series for cause-related nonprofit revenues.
    • Securing data series has proven to be a challenge.
  • Describe what you have learned from the project.-
    • We have learned the intricacies of research, especially in regards to securing reliable data series.
    • We have learned how important the literature review is in constructing and/or consolidating hypotheses.
    • We have learned about the importance of a detailed research design and schedule.
    • We have also learned that there is much interest in our research topic. Indeed, the US Department of Agriculture (Mena Report, 2017) is sponsoring a bidding process to build a predictive model of the nonprofit sector. 
  • Impact this project has had on us and any future plans we may have related to this research.-
    • We were thrilled to learn that the Division of Student Success extended our funding through May 2018, as it is clear that the three stages of our research, from macro to micro, will require more time.
    • We are most excited about the support offered by foundations, which have offered their donor networks for polling in the micro stage.

We are most interested in pursuing other research interests, particularly one, relating to the application of Neuroscience in Marketing.

Blog post #1: “A Predictive Model of the Non-Profit Sector’s revenues in the US”

Blog post #1.- Andrea Katherine Quevedo-Prince Graphic Model of the Non-Profit Sector-2m7o3ix

Please describe the title and purpose of your project as well as the goals and objectives of your research.

Title: “A Predictive Model of the Non-Profit Sector’s revenues in the US: From a macro to a micro perspective”

General description: Our research aims to define predictive models of the non-profit sector, from a macro to a micro perspective. Accordingly, it comprises three stages: (1) A macro phase, focused on the non-profit sector’s total revenues, as the dependent variable; these are close to one trillion dollars a year, (2) a segmented phase, focusing on fundraising for amateur sports, health and/or education, among other social causes, and (3) a micro phase that would look into individual donor motivations.

Research goals:

1.            To define a predictive model that integrates the variables, and identifies moderating factors, that come into play to determine the ups and downs of the non-profit sector, as measured by total revenues, from the macro perspective.

2.            To define a predictive model that integrates the variables, and identifies moderating factors that come into play to determine the ups and downs of fundraising for amateur sports, and other segments of the non-profit market as health or education, as measured by social cause specific revenues.

3.            To define a predictive model that integrates the variables, and identifies any moderating factors that come into play to determine individual donor motivations for amateur sports, and other segments of the non-profit market as health or education.

Our hypothesis: On a macro or segmented, social cause-specific level, is that total non-profit revenues will respond more greatly to public awareness, among other variables that may include the country’s per capita disposable income, tax incentives, and the legal framework. See enclosed graph of our model.

On a micro level, we posit that as public awareness cascades down into the individual donor’s own awareness of a specific social cause, his or her relationship the cause and non-profit organization would greatly influence his commitment to its cause, again, among other factors. 

Research objectives:

1.            To obtain a clear understanding of the theory and dynamics of the non-profit sector.

2.            To clearly identify the variables and moderating factors that determine non-profit revenues.

3.            To secure reliable statistical sources.

4.            To select the appropriate tests and statistical techniques to determine the weights and interactions of the determining variables and moderating factors.

5.     To select the appropriate scale and questionnaire to determinate individual donor motivations.

6.     To apply all of the above to test our research hypotheses.

Caveats: We worry about the abstract nature of “public awareness” at the macro level. We feel this concept may drive us into a segment-specific metric (ex: concern about health, education, or sports), but the review of the literature and statistical sources should provide early orientations.

Also, we understand total revenues are a combination of individual, corporate, government and NGO donations, sales and fees, plus other fundraising sources. Though we would take all into account, our focus will be on donations, which stand close to $300 billion per year in the US alone.

 

Highlight what you expect to achieve or learn from this project.

We should get a clear understanding of the dynamics of the non-profit sector, its variables, sources of funds, and its players.

We believe that the combination of these predictive models would provide great insight and guidelines for non-profit organizations to properly adjust their fundraising strategies and processes to the dynamics of their sector, segment, and target donor.

 

Explain what methods you will use to answer your research questions.

Our work schedule entails the following. Points 4.1., 4.2., and 5.1. to 5.4. address the question:

1.            Theoretical Grounding, involving the review of close to 100 academic articles.

2.            Research Design, properly sustained hypotheses, research questions, and methodology.

3.            Statistical sources.

4.            Statistical tests, tools and methods.

4.1.         Pace University’s SPSS license

4.2.         Factor Analysis to sort out the variables that determine total revenues, specify their weight and statistical significance; this on the macro phase of the project. 

5.            On a micro level, individual interviews and or surveys among donors of NGOs would be utilized. This would require:

5.1.         Sampling

5.2.         Questionnaire design

5.3.         Appropriate scale

5.4.         Pre-test, and other steps.

Currently, we are deep into the review of the literature. We have sorted out over 100 academic articles and statistical sources, and cited over 25 already, in the development of our theoretical grounding.

Gaps in the literature: We are finding an absence of macro models for the non-profit sector. An overwhelming majority of the articles refer to donor motivations. The amateur sports’ segment is rarely reported on.

We would expect our second post to include a detailed review of the literature, an analysis of non-profit sector statistics, and a clear orientation of our research.