Measuring the non-profit sector in the US economy: conceptual framework and methodology

Author(s):  
Virginia A. Hodgkinson ◽  
Murray S. Weitzman
2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Francisco J Quevedo ◽  
Andrea Katherine Quevedo-Prince

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) + Ɛ 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.


Author(s):  
Gilles Duruflé ◽  
Thomas Hellmann ◽  
Karen Wilson

This chapter examines the challenge for entrepreneurial companies of going beyond the start-up phase and growing into large successful companies. We examine the long-term financing of these so-called scale-up companies, focusing on the United States, Europe, and Canada. The chapter first provides a conceptual framework for understanding the challenges of financing scale-ups. It emphasizes the need for investors with deep pockets, for smart money, for investor networks, and for patient money. It then shows some data about the various aspects of financing scale-ups in the United States, Europe, and Canada, showing how Europe and Canada are lagging behind the US relatively more at the scale-up than the start-up stage. Finally, the chapter raises the question of long-term public policies for supporting the creation of a better scale-up environment.


2021 ◽  
pp. 048661342098262
Author(s):  
Tyler Saxon

In the United States, the military is the primary channel through which many are able to obtain supports traditionally provided by the welfare state, such as access to higher education, job training, employment, health care, and so on. However, due to the nature of the military as a highly gendered institution, these social welfare functions are not as accessible for women as they are for men. This amounts to a highly gender-biased state spending pattern that subsidizes substantially more human capital development for men than for women, effectively reinforcing women’s subordinate status in the US economy. JEL classification: B54, B52, Z13


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