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”


  • 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.

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