This article is part of a Teaching Symposium.
According to the theory of government failure, nonprofit organizations emerge when governments fail to provide goods or services to a public with heterogeneous demands. This study approaches this fundamental theory of the nonprofit sector from a pluralist political viewpoint, marrying the theory of government failure to resource-driven nonprofit arguments via updated modeling and measurement strategies. This paper proposes a new, conditional demand heterogeneity hypothesis, wherein the effect of demand heterogeneity on the nonprofit sector increases in the presence of increasing resources: nonprofit service delivery is most likely when those experiencing government failure have access to resources. This paper is the first to test supply and demand nonprofit arguments in tandem using an interactive modeling specification, and the first to operationalize demand heterogeneity using policy-based measures. I employ a finite distributed lag model with an interactive term in a time series, cross sectional analysis of public and nonprofit land conservation in the United States. I find that nonprofit land conservation increases when citizens express greater environmental concern but only in the presence of increasing disposable income. Examining nonprofit theory within the context of land conservation provides a comparable measure of government and nonprofit service provision, controls for the nature of the good provided by these institutions, and allows for a policy-driven measure of demand heterogeneity, improving upon previous studies’ employment of diversity-based proxy measures. The results advance our understanding of how to test and measure demand heterogeneity nonprofit arguments and suggest that access to resources conditions which interests find expression in nonprofit organizations.
What explains the development of the nonprofit sector? The classic theory of demand heterogeneity posits the nonprofit sector provides services for those unsatisfied by government service delivery. However, formal tests of this theory have produced conflicting results. This article revisits the theory of demand heterogeneity, refining it to include the mediating effects of government wherein increased diversity leads to lower levels of government funding, which are associated with a larger nonprofit sector. The test results do not support this refined model, and instead generally support theories of government and nonprofit interdependencies, whereby increased diversity leads to a larger nonprofit sector through larger government. We condition our results by the type of good being produced, and call for future empirical tests to identify processes by which demand heterogeneity influences the development of the nonprofit sector.
Oppenheim et al. (2015) provides the first empirical analysis of insurgent defection during armed rebellion, estimating a series of multinomial logit models of continued rebel participation using a survey of ex-combatants in Colombia. Unfortunately, many of the main results from this analysis are an artifact of separation in these data – that is, one or more of the covariates perfectly predicts the outcome. We demonstrate that this can be identified using simple cross tabulations. Furthermore, we show that Oppenheim et al.’s (2015) results are not supported when separation is explicitly accounted for. Using a generalization of Firth’s (1993) penalized-likelihood estimator – a well-known solution for separation – we are unable to reproduce any of their conditional results. While our (re-)analysis focuses on Oppenheim et al. (2015), this problem appears in other research using multinomial logit models as well. We believe that this is both because the discussion on separation in political science has primarily focused on binary-outcome models, and because software (Stata and R) does not warn researchers about separation in multinomial logit models. Therefore, we encourage researchers using multinomial logit models to be especially vigilant about separation, and discuss simple red flags to consider.