Competitive Advantage in Nonprofit Grant Markets: Implications of Network Embeddedness and Status

ABSTRACT

This article empirically addresses the effects of network embeddedness on nonprofit organizations’ ability to access financial resources within competitive markets, with a focus in this analysis on the acquisition of foundation grants. We test theory on the role of organizational status in competitive markets using data from a network of nonprofits linked by foundation grants in metropolitan Atlanta during 2000 and 2005. We find that observable characteristics of nonprofits, including size, fundraising expenses, and financial health, explain success in grant markets. However, market status in previous time periods, operationalized as prior relationships with influential foundations in grant markets, additionally explains future grant awards. Our findings suggest that the status conferred through connections to important actors in a network can raise the profile of a nonprofit and increase the probability of grant success.

Notes

Organizational status is a concept from sociology that is distinct and separate from the concept of signaling theory in economics, which assumes actors intentionally spend effort on activities that signal their value to others. While both concepts were developed to explain behavior under conditions of uncertainty, these are two distinct theoretical perspectives. For example, a recent review of signaling theory from an economics perspective (Connelly et al. 2011) does not cover the sociological status literature. Conversely, literature reviews on status signals (Sauder, Lynn, and Podolny 2012; Piazza and Castellucci 2014) mention signaling theory in economics, but distance the sociological theories of status signals from that literature.

Network statistics are calculated using the full grant network of the foundations and before restricting the data to the Atlanta 10-county area because network statistics are sensitive to missing data (Borgatti et al. 2006).

Grants to institutions of higher education are excluded because many grants in higher education are awarded to individual researchers within the institutions instead of being based fully upon the organizations’ characteristics.

This may bias the estimates for the key variables of interest downward in the models shown because we do not observe organizations outside of the network that were denied funding. Without full grant application information (which is unavailable), self-selection is unobserved. Therefore, it is likely that estimates would be biased and lead to erroneous inferences if we were to include organizations that did not receive grants in 2005 (since we do not know whether they applied to foundations in the sample or not).

The grant networks were created through a projection of the bipartite network using the “tnet” package available for the R statistical programming environment written by Opsahl (2009).

In addition to the theoretical justification for the use of Eigenvector centrality, degree centrality measures are also perfectly collinear with the lagged dependent variable since degree centrality in this case represents the number of grants that a nonprofit has received, not taking into account the importance of the grantmaking institution.

Twenty-nine organizations were missing in the 2004 990 files. Values for two organizations came from the 2003 file and 27 organizations’ values came from 2005 data. Variables for government grants and program expenses were drawn from the Digitized 2003 (NCCS 2012b) or a custom 2005 database, because those variables are not in the 2004 file.

The effect of fundraising expenses in this study may be biased downward since many organizations that earn grants report zero fundraising expenses, which may be inaccurate. As shown and discussed in Tinkelman and Mankaney (2007), fundraising expenses have a consistent and positive impact on charitable contributions in analyses of organizations with reliable fundraising expense data.

Like the effect of fundraising expenses, the effect of debt ratio and other financial variables should be interpreted with caution in this analysis. As Tinkelman and Mankaney (2007) and Bowman (2011) discuss, it is common to restrict samples that rely on 990 data to allow for more confident inferences concerning the effects of financial variables by limiting the analysis to observations with more reliable data. However, since network statistics are sensitive to missing data and are the focus of this analysis, we use the full sample of organizations in the grant market in 2005 and discuss sensitivity analyses for financial variables of interest when their effects deviate from past studies.

We thank an anonymous reviewer for encouraging us to include direct connections to high-status funders in addition to network centrality. It is important to recognize as discussed earlier that, as with the amount of past grants, these constructs are related. And we thank Steve Kelman for pointing out that all three measures indicate an organization's status in the market in different ways. Similar to an organization's lagged grants, a past Top Ten Grant is a measure of past performance. Including the lagged Top Ten Grant measure recognizes that the top 10 foundations in the state are distinct from other funders and will have the most capacity to vet grants more carefully. Therefore, it is more competitive to win one. As a result, these grants convey more status than a grant of the same size from a smaller foundation. As a separate measure of status than eigenvector centrality, therefore, we are measuring status attained through distinct pathways with each of these variables. As shown in the analysis section, each significantly explains grant performance, even controlling for the other.

Sensitivity analysis using unadjusted standard errors and heteroskedasticity-robust (Huber-White) standard errors demonstrates consistent findings to those shown.

Sensitivity analysis demonstrates consistency between OLS regressions and the final truncated regressions, including the r² values.

For instance, Faulk, Stewart, and Boyer (2013) use data on all applications and funding decisions from a single foundation.

Additional information

Notes on contributors

Lewis Faulk

Lewis Faulk ([email protected]) is an assistant professor in the Department of Public Administration and Policy at American University's School of Public Affairs. His research focuses on competition in the nonprofit sector for financial resources, foundation grantmaking, and factors that influence nonprofit financial health. He has a doctorate in public policy with a concentration in public and nonprofit management from the joint PhD program in public policy at Georgia State University and the Georgia Institute of Technology.

Jasmine McGinnis Johnson

Jasmine McGinnis Johnson ([email protected]) is an assistant professor at The George Washington University in the Trachtenberg School of Public Policy and Public Administration. Her research interests include public participation, philanthropy, and the retention of millennial employees in public and nonprofit work. She holds a PhD in Public Policy from Georgia State University and Georgia Institute of Technology and an MPA from the University of Georgia. She worked in development and evaluation for several human service organizations prior to beginning graduate school.

Jesse D. Lecy

Jesse D. Lecy ([email protected]) is an assistant professor at The Maxwell School of Syracuse University. His research focuses on the economics of the nonprofit sector and organizational life-cycles of nonprofits: start-up, growth, and demise. Prior to graduate school, Jesse worked in the field of humanitarian relief in Kosovo and in social services with a domestic nonprofit. He holds a PhD in Social Science from Syracuse University and an MS in Public Policy and Management from Carnegie Mellon.