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A Predictive Model for the US Non-profit Market; A Macro to a Micro Perspective

Advanced Journal of Social Science

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Field Value
 
Title A Predictive Model for the US Non-profit Market; A Macro to a Micro Perspective
 
Creator Quevedo, Francisco J
Quevedo-Prince, Andrea Katherine
 
Subject Fundraising
Environment
Social Cause
Fundraiser
Non-profit revenue
Donor
Predictive modeling
Fundraising
Non-Profit
 
Description The Non-Profit Sector contributes almost $1 trillion to the US economy, representing 5.4% of GDP, and generating over 12 million jobs in 2017. Researchers suggest that a better understanding of the factors that affect fundraising would be of great interest to policymakers and fundraisers. However, the workings of the sector are subject of much debate. Some relate its size to the Theory of Government Failure, while others propose that government funding does have a positive effect on revenues. Some have suggested they swing with Gross Domestic Product (GDP), but others contradict this view and contend that macroeconomic variables do not affect short-run dynamics. Some research found that non-profit revenues react more to economic upswings than downturns, but nationwide organizations relate the ups-and-downs to certain events, as they influence public awareness. Predictive modeling overall has focused on big-donor analytics, aimed at identifying potential sponsors. Our research set out instead to define a working model for the US Non-Profit Sector. After an exhausting search, we located complete time series for an emblematic segment, the environmental cause, Factor Analysis allowed us to pinpoint the independent variables. We found that Non-Profit Revenues (NPR) depend largely on Public Awareness, as measured by TV coverage, and on Disposable Personal Income (DPI), specifically:
NPR = -4401.542 + 528.327(DPI) +23.121(TVCoverage) + Æ
We replicated prior research, which sought out relationships between macro-economic variables and NPR. That study had discarded the correlation between GDP and NPR as obvious, but did not explore DPI as the determining factor, and stuck to single variable searches, finding a correlation between the Standard & Poors index and lagged NPR figures, with a correlation coefficient of 0.636. Our model’s Pearson's R came up to 0.935, with perfect significance levels. Confirmatory Factor Analysis reaffirmed the fit of our equation, with an R² of 0.87.
 
Publisher AIJR Publisher
 
Date 2019-01-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Article
 
Format application/pdf
 
Identifier https://journals.aijr.org/index.php/ajss/article/view/865
10.21467/ajss.5.1.1-9
 
Source Advanced Journal of Social Science; Vol. 5 No. 1 (2019); 1-9
2581-3358
10.21467/ajss.5.1.2019
 
Language eng
 
Relation https://journals.aijr.org/index.php/ajss/article/view/865/194
 
Rights Copyright (c) 2019 Francisco J Quevedo, Andrea Katherine Quevedo-Prince