Do government activities discourage or leverage nonprofit activities? The extant literature has proposed competing lines of arguments, making the net effect ambiguous. The present study conducts a meta-analysis to synthesize extant studies concerning the relationship between the level of government activities and the level of nonprofit activities within a locality and explore potential moderating effects. Through systematically reviewing 30 extant studies, the study finds a mostly positive association between the level of government activities and the level of nonprofit activities, but this relationship is generally weak and sometimes statistically insignificant. In addition, the moderator analysis concludes that data structure, unit of analysis, and field of activity significantly moderate effect size estimates across extant studies. Overall, the net relationship between the level of government activities and the level of nonprofit activities within a locality ranges from null to slight positive. Government activities generally seem not to discourage nonprofit activities, but may slightly leverage them.
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It should be noted that our goal here is not to test government failure theory, but to demonstrate a possible relationship between government size and nonprofit sector size in a locality proposed in the nonprofit literature. A complete test of the theory needs to jointly consider the level of demand heterogeneity and the level of government expenditure. See Matsunaga and Yamauchi (2004) and Lu (2017) for more information.
According to Salamon (1987), the nonprofit sector can fail in four occasions: philanthropic insufficiency (lacking sufficient and reliable organizational resources), philanthropic particularism (focusing on particular groups of clients while ignoring others), philanthropic paternalism (serving donors’ interests instead of wider social needs), and philanthropic amateurism (inefficiently managed and operated without professional workforce).
Google Scholar provides a comprehensive coverage of scholarly literature in a variety of publishing formats such as journal articles, books, book chapters, working papers, conference papers, and dissertations. The reliance on Google Scholar in the descendant search allows us to reach a diverse set of studies.
Each of the authors independently reviewed and coded the studies that met our inclusion criteria. We then compared the coding results and reached agreements on all the coding details.
The advantage of coding multiple effect sizes from one original study, instead of averaging these effect sizes to estimate an overall study-level effect size, is that we could retain within-study variation information (e.g., effect sizes representing different nonprofit subsectors) (Ringquist 2013). Although this coding strategy might cause the concern about statistical independence among effect sizes in our meta-analysis, existing statistical evidence indicates that the lack of independence in meta-analysis has little or no adverse effect (Tracz et al. 1992; Bijmolt and Pieters 2001). Schmidt and Hunter (2015), for example, argued that the violation of independence in meta-analysis is not as serious a threat to accuracy as theoretical predictions. We thus followed existing evidence in deciding to code multiple effect sizes.
According to the common practice, a correlation coefficient below .3 is thought to represent a weak or small association (Cohen 1988).
Because of a small number of non-US studies (a total of 4 studies with 18 effect sizes in our sample), we could not estimate an average effect size for each non-US country, but only calculate an average effect size for all non-US countries as an aggregate. For example, in our sample of 151 effect sizes, there are three effect sizes on Indonesia from Okten and Osili (2004) and four effect sizes on Spain from Marcuello (1998). Again, aggregating a very small number of effect sizes from the same study could not produce a meaningful average effect size.
We cannot estimate average effect sizes for education, health, and human services separately, because a number of studies combine these fields together in their data (e.g., Ben-Ner and Hoomissen 1992; Salamon and Anheier 1998; Matsunaga et al. 2010; Van Puyvelde and Brown 2016). For example, Matsunaga et al. (2010) examined education and health nonprofits and Van Puyvelde and Brown (2016) studied health care and human services nonprofits.
It should be noted that we do not discount the significant impact of government activities on nonprofit activities in a locality. Indeed, government activities may strongly affect nonprofit activities in various ways (see the theoretical framework section of this manuscript). However, as mentioned above, we only examine the net relationship between government size and nonprofit sector size in a locality after all the positive and negative effects balance each other.
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The authors declare that they have no conflict of interest.
See Table 3.
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Lu, J., Xu, C. Complementary or Supplementary? The Relationship Between Government Size and Nonprofit Sector Size. Voluntas 29, 454–469 (2018) doi:10.1007/s11266-018-9981-2
- Government–nonprofit relations
- Nonprofit sector size
- Nonprofit sector growth