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Category Archives: L31

Nonprofit Location, Survival, and Success: A Case Study of El Sistema USA

By Andie Carroll  

As nonprofits work to serve their communities, they must choose a place to locate that best suits their needs and the needs of the population they aim to serve. Locational characteristics such as median income and population density have been shown to impact how many nonprofits choose to locate in a given area. However, few studies have examined the impact of locational characteristics on how nonprofits survive and thrive. This study examines the impact of geographic and demographic factors on nonprofit survival and success through a case study of El Sistema USA (ESUSA), a nationwide network of music education programs with the goal of helping underserved youth. The study analyzes panel survey data from 131 El Sistema-inspired programs in the U.S. from 2005 to 2018 along with demographic data from the American Community Survey, charitable giving data from the IRS, and GIS data compiled through a review of ESUSA program websites. By using regression models of ESUSA program survival and success (defined by more students served and higher program budgets), this study found that ESUSA programs in areas of more need are more likely to survive and thrive.

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Advisors: Professor Lorrie Schmid, Professor Michelle Connolly | JEL Codes: L3, L31, D23

The Impact of State and Local Government Spending on Charitable Giving in the United States

By Lynn Vandendriessche

This paper seeks to further understand how government spending impacts private giving to charitable organizations. It considers giving and spending in the United States in 2008 with a focus on government spending on education, welfare, healthcare, and hospitals. Government spending is looked at at the state and local levels. The results indicate that the impact of government spending depends not only on the category of spending, but also on the income level of the giver. Increased welfare spending is shown to cause incomplete crowding-out across all income groups. Results consistently show education spending to cause crowding-out as well. The impact of both healthcare and hospital spending is more ambiguous, with differing results for different government levels (state and local) and income brackets.

Honors Thesis

Advisor: Michelle Connolly, Peter Arcidiacon | JEL Codes: L3, L31, L38 | Tagged: Altruism/Philanthropy, and Education, Charitable Giving, Health, Non-profit Institutions, Welfare

Federal and Industrial Funded Research Expenditures and University Technology Transfer licensing

By Trent Chiang

In this paper I relate the numbers of university licenses and options to both university research characteristics and research expenditures from federal government or industrial sources. I apply the polynomial distributed lag model for unbalanced panel data to understand the effects of research expenditures from different sources on licensing activity. We find evidence suggesting both federal and industrial funded research expenditures take 2-3 years from lab to licenses while federal expenditures have higher long-term dynamic effect. Break down licenses by different types of partners, we found that federal expenditures have highest effect with small companies and licenses generating high income. Further research is necessary to analyze the reason for such difference.

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Advisor: David Ridley, Henry Grabowski | JEL Codes: I23, L31, O31, O32, O38 | Tagged: Innovation, Research Expenditures, Science Policy, Technology Transfer

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu